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Sample records for cell network function

  1. Quantitation, network and function of protein phosphorylation in plant cell

    Directory of Open Access Journals (Sweden)

    Lin eZHU

    2013-01-01

    Full Text Available Protein phosphorylation is one of the most important post-translational modifications (PTMs as it participates in regulating various cellular processes and biological functions. It is therefore crucial to identify phosphorylated proteins to construct a phosphor-relay network, and eventually to understand the underlying molecular regulatory mechanism in response to both internal and external stimuli. The changes in phosphorylation status at these novel phosphosites can be accurately measured using a 15N-stable isotopic labeling in Arabidopsis (SILIA quantitative proteomic approach in a high-throughput manner. One of the unique characteristics of the SILIA quantitative phosphoproteomic approach is the preservation of native PTM status on protein during the entire peptide preparation procedure. Evolved from SILIA is another quantitative PTM proteomic approach, AQUIP (absolute quantitation of isoforms of post-translationally modified proteins, which was developed by combining the advantages of targeted proteomics with SILIA. Bioinformatics-based phosphorylation site prediction coupled with an MS-based in vitro kinase assay is an additional way to extend the capability of phosphosite identification from the total cellular protein. The combined use of SILIA and AQUIP provides a novel strategy for molecular systems biological study and for investigation of in vivo biological functions of these phosphoprotein isoforms and combinatorial codes of PTMs.

  2. Niche-dependent development of functional neuronal networks from embryonic stem cell-derived neural populations

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    Siebler Mario

    2009-08-01

    Full Text Available Abstract Background The present work was performed to investigate the ability of two different embryonic stem (ES cell-derived neural precursor populations to generate functional neuronal networks in vitro. The first ES cell-derived neural precursor population was cultivated as free-floating neural aggregates which are known to form a developmental niche comprising different types of neural cells, including neural precursor cells (NPCs, progenitor cells and even further matured cells. This niche provides by itself a variety of different growth factors and extracellular matrix proteins that influence the proliferation and differentiation of neural precursor and progenitor cells. The second population was cultivated adherently in monolayer cultures to control most stringently the extracellular environment. This population comprises highly homogeneous NPCs which are supposed to represent an attractive way to provide well-defined neuronal progeny. However, the ability of these different ES cell-derived immature neural cell populations to generate functional neuronal networks has not been assessed so far. Results While both precursor populations were shown to differentiate into sufficient quantities of mature NeuN+ neurons that also express GABA or vesicular-glutamate-transporter-2 (vGlut2, only aggregate-derived neuronal populations exhibited a synchronously oscillating network activity 2–4 weeks after initiating the differentiation as detected by the microelectrode array technology. Neurons derived from homogeneous NPCs within monolayer cultures did merely show uncorrelated spiking activity even when differentiated for up to 12 weeks. We demonstrated that these neurons exhibited sparsely ramified neurites and an embryonic vGlut2 distribution suggesting an inhibited terminal neuronal maturation. In comparison, neurons derived from heterogeneous populations within neural aggregates appeared as fully mature with a dense neurite network and punctuated

  3. Secretome analysis of multiple pancreatic cancer cell lines reveals perturbations of key functional networks.

    Science.gov (United States)

    Schiarea, Silvia; Solinas, Graziella; Allavena, Paola; Scigliuolo, Graziana Maria; Bagnati, Renzo; Fanelli, Roberto; Chiabrando, Chiara

    2010-09-01

    The cancer secretome is a rich repository in which to mine useful information for both cancer biology and clinical oncology. To help understand the mechanisms underlying the progression of pancreatic cancer, we characterized the secretomes of four human pancreatic ductal adenocarcinoma (PDAC) cell lines versus a normal counterpart. To this end, we used a proteomic workflow based on high-confidence protein identification by mass spectrometry, semiquantitation by a label-free approach, and network enrichment analysis by a system biology tool. Functional networks significantly enriched with PDAC-dysregulated proteins included not only expected alterations within key mechanisms known to be relevant for tumor progression (e.g., cell-cell/cell-matrix adhesion, extracellular matrix remodeling, and cytoskeleton rearrangement), but also other extensive, coordinated perturbations never observed in pancreatic cancer. In particular, we highlighted perturbations possibly favoring tumor progression through immune escape (i.e., inhibition of the complement system, deficiency of selected proteasome components within the antigen-presentation machinery, and inhibition of T cell cytoxicity), and a defective protein folding machinery. Among the proteins found concordantly oversecreted in all of our PDAC cell lines, many are reportedly overexpressed in pancreatic cancer (e.g., CD9 and Vimentin), while others (PLOD3, SH3L3, PCBP1, and SFRS1) represent novel PDAC-secreted proteins that may be worth investigating. PMID:20687567

  4. Generalized Network Externality Function

    OpenAIRE

    A. Paothong; G.S. Ladde

    2012-01-01

    In this work, we focus on the development of mathematical modeling of network externality processes. The introduction of the generalized network externality function provides a unified source of a tool for developing and analyzing the planning, policy and performance of the network externality process and network goods/services in a systematic way. This leads to fulfill all existing network externality assumptions as special cases. We study its properties and applications. This study provides...

  5. Topological Small-World Organization of the Fibroblastic Reticular Cell Network Determines Lymph Node Functionality

    Science.gov (United States)

    Abe, Jun; Bomze, David; Cremasco, Viviana; Scandella, Elke; Stein, Jens V.; Turley, Shannon J.; Ludewig, Burkhard

    2016-01-01

    Fibroblastic reticular cells (FRCs) form the cellular scaffold of lymph nodes (LNs) and establish distinct microenvironmental niches to provide key molecules that drive innate and adaptive immune responses and control immune regulatory processes. Here, we have used a graph theory-based systems biology approach to determine topological properties and robustness of the LN FRC network in mice. We found that the FRC network exhibits an imprinted small-world topology that is fully regenerated within 4 wk after complete FRC ablation. Moreover, in silico perturbation analysis and in vivo validation revealed that LNs can tolerate a loss of approximately 50% of their FRCs without substantial impairment of immune cell recruitment, intranodal T cell migration, and dendritic cell-mediated activation of antiviral CD8+ T cells. Overall, our study reveals the high topological robustness of the FRC network and the critical role of the network integrity for the activation of adaptive immune responses. PMID:27415420

  6. Stem Cell Networks

    OpenAIRE

    Werner, Eric

    2016-01-01

    We present a general computational theory of stem cell networks and their developmental dynamics. Stem cell networks are special cases of developmental control networks. Our theory generates a natural classification of all possible stem cell networks based on their network architecture. Each stem cell network has a unique topology and semantics and developmental dynamics that result in distinct phenotypes. We show that the ideal growth dynamics of multicellular systems generated by stem cell ...

  7. Boolean networks with veto functions

    Science.gov (United States)

    Ebadi, Haleh; Klemm, Konstantin

    2014-08-01

    Boolean networks are discrete dynamical systems for modeling regulation and signaling in living cells. We investigate a particular class of Boolean functions with inhibiting inputs exerting a veto (forced zero) on the output. We give analytical expressions for the sensitivity of these functions and provide evidence for their role in natural systems. In an intracellular signal transduction network [Helikar et al., Proc. Natl. Acad. Sci. USA 105, 1913 (2008), 10.1073/pnas.0705088105], the functions with veto are over-represented by a factor exceeding the over-representation of threshold functions and canalyzing functions in the same system. In Boolean networks for control of the yeast cell cycle [Li et al., Proc. Natl. Acad. Sci. USA 101, 4781 (2004), 10.1073/pnas.0305937101; Davidich et al., PLoS ONE 3, e1672 (2008), 10.1371/journal.pone.0001672], no or minimal changes to the wiring diagrams are necessary to formulate their dynamics in terms of the veto functions introduced here.

  8. Networks in Cell Biology

    Science.gov (United States)

    Buchanan, Mark; Caldarelli, Guido; De Los Rios, Paolo; Rao, Francesco; Vendruscolo, Michele

    2010-05-01

    Introduction; 1. Network views of the cell Paolo De Los Rios and Michele Vendruscolo; 2. Transcriptional regulatory networks Sarath Chandra Janga and M. Madan Babu; 3. Transcription factors and gene regulatory networks Matteo Brilli, Elissa Calistri and Pietro Lió; 4. Experimental methods for protein interaction identification Peter Uetz, Björn Titz, Seesandra V. Rajagopala and Gerard Cagney; 5. Modeling protein interaction networks Francesco Rao; 6. Dynamics and evolution of metabolic networks Daniel Segré; 7. Hierarchical modularity in biological networks: the case of metabolic networks Erzsébet Ravasz Regan; 8. Signalling networks Gian Paolo Rossini; Appendix 1. Complex networks: from local to global properties D. Garlaschelli and G. Caldarelli; Appendix 2. Modelling the local structure of networks D. Garlaschelli and G. Caldarelli; Appendix 3. Higher-order topological properties S. Ahnert, T. Fink and G. Caldarelli; Appendix 4. Elementary mathematical concepts A. Gabrielli and G. Caldarelli; References.

  9. Sinc-function based Network

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1999-01-01

    The purpose of this paper is to describe a neural network (SNN), that is based on Shannons ideas of reconstruction of a real continuous function from its samples. The basic function, used in this network, is the Sinc-function. Two learning algorithms are described. A simple one called IM...

  10. Sinc-function based Network

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1998-01-01

    The purpose of this paper is to describe a neural network (SNN), that is based on Shannons ideas of reconstruction of a real continuous function from its samples. The basic function, used in this network, is the Sinc-function. Two learning algorithms are described. A simple one called IM...

  11. Identification and functional characterization of the miRNA-gene regulatory network in chronic myeloid leukemia lineage negative cells

    Science.gov (United States)

    Agatheeswaran, S.; Pattnayak, N. C.; Chakraborty, S.

    2016-09-01

    Chronic myeloid leukemia (CML) is maintained by leukemic stem cells (LSCs) which are resistant to the existing TKI therapy. Hence a better understanding of the CML LSCs is necessary to eradicate these cells and achieve complete cure. Using the miRNA-gene interaction networks from the CML lin(-) cells we identified a set of up/down-regulated miRNAs and corresponding target genes. Association studies (Pearson correlation) from the miRNA and gene expression data showed that miR-1469 and miR-1972 have significantly higher number of target genes, 75 and 50 respectively. We observed that miR-1972 induces G2-M cell cycle arrest and miR-1469 moderately arrested G1 cell cycle when overexpressed in KCL22 cells. We have earlier shown that a combination of imatinib and JAK inhibitor I can significantly bring down the proliferation of CML lineage negative cells. Here we observed that imatinib and JAK inhibitor I combination restored the expression pattern of the down-regulated miRNAs in primary CML lin(-) cells. Thus effective manipulation of the deregulated miRNAs can restore the miRNA-mRNA networks that can efficiently inhibit CML stem and progenitor cells and alleviate the disease.

  12. From networks of protein interactions to networks of functional dependencies

    Directory of Open Access Journals (Sweden)

    Luciani Davide

    2012-05-01

    Full Text Available Abstract Background As protein-protein interactions connect proteins that participate in either the same or different functions, networks of interacting and functionally annotated proteins can be converted into process graphs of inter-dependent function nodes (each node corresponding to interacting proteins with the same functional annotation. However, as proteins have multiple annotations, the process graph is non-redundant, if only proteins participating directly in a given function are included in the related function node. Results Reasoning that topological features (e.g., clusters of highly inter-connected proteins might help approaching structured and non-redundant understanding of molecular function, an algorithm was developed that prioritizes inclusion of proteins into the function nodes that best overlap protein clusters. Specifically, the algorithm identifies function nodes (and their mutual relations, based on the topological analysis of a protein interaction network, which can be related to various biological domains, such as cellular components (e.g., peroxisome and cellular bud or biological processes (e.g., cell budding of the model organism S. cerevisiae. Conclusions The method we have described allows converting a protein interaction network into a non-redundant process graph of inter-dependent function nodes. The examples we have described show that the resulting graph allows researchers to formulate testable hypotheses about dependencies among functions and the underlying mechanisms.

  13. Functional Aspects of Biological Networks

    Science.gov (United States)

    Sneppen, Kim

    2007-03-01

    We discuss biological networks with respect to 1) relative positioning and importance of high degree nodes, 2) function and signaling, 3) logic and dynamics of regulation. Visually the soft modularity of many real world networks can be characterized in terms of number of high and low degrees nodes positioned relative to each other in a landscape analogue with mountains (high-degree nodes) and valleys (low-degree nodes). In these terms biological networks looks like rugged landscapes with separated peaks, hub proteins, which each are roughly as essential as any of the individual proteins on the periphery of the hub. Within each sup-domain of a molecular network one can often identify dynamical feedback mechanisms that falls into combinations of positive and negative feedback circuits. We will illustrate this with examples taken from phage regulation and bacterial uptake and regulation of small molecules. In particular we find that a double negative regulation often are replaced by a single positive link in unrelated organisms with same functional requirements. Overall we argue that network topology primarily reflects functional constraints. References: S. Maslov and K. Sneppen. ``Computational architecture of the yeast regulatory network." Phys. Biol. 2:94 (2005) A. Trusina et al. ``Functional alignment of regulatory networks: A study of temerate phages". Plos Computational Biology 1:7 (2005). J.B. Axelsen et al. ``Degree Landscapes in Scale-Free Networks" physics/0512075 (2005). A. Trusina et al. ``Hierarchy and Anti-Hierarchy in Real and Scale Free networks." PRL 92:178702 (2004) S. Semsey et al. ``Genetic Regulation of Fluxes: Iron Homeostasis of Escherichia coli". (2006) q-bio.MN/0609042

  14. Functional network inference of the suprachiasmatic nucleus.

    Science.gov (United States)

    Abel, John H; Meeker, Kirsten; Granados-Fuentes, Daniel; St John, Peter C; Wang, Thomas J; Bales, Benjamin B; Doyle, Francis J; Herzog, Erik D; Petzold, Linda R

    2016-04-19

    In the mammalian suprachiasmatic nucleus (SCN), noisy cellular oscillators communicate within a neuronal network to generate precise system-wide circadian rhythms. Although the intracellular genetic oscillator and intercellular biochemical coupling mechanisms have been examined previously, the network topology driving synchronization of the SCN has not been elucidated. This network has been particularly challenging to probe, due to its oscillatory components and slow coupling timescale. In this work, we investigated the SCN network at a single-cell resolution through a chemically induced desynchronization. We then inferred functional connections in the SCN by applying the maximal information coefficient statistic to bioluminescence reporter data from individual neurons while they resynchronized their circadian cycling. Our results demonstrate that the functional network of circadian cells associated with resynchronization has small-world characteristics, with a node degree distribution that is exponential. We show that hubs of this small-world network are preferentially located in the central SCN, with sparsely connected shells surrounding these cores. Finally, we used two computational models of circadian neurons to validate our predictions of network structure.

  15. Functional network inference of the suprachiasmatic nucleus

    Energy Technology Data Exchange (ETDEWEB)

    Abel, John H.; Meeker, Kirsten; Granados-Fuentes, Daniel; St. John, Peter C.; Wang, Thomas J.; Bales, Benjamin B.; Doyle, Francis J.; Herzog, Erik D.; Petzold, Linda R.

    2016-04-04

    In the mammalian suprachiasmatic nucleus (SCN), noisy cellular oscillators communicate within a neuronal network to generate precise system-wide circadian rhythms. Although the intracellular genetic oscillator and intercellular biochemical coupling mechanisms have been examined previously, the network topology driving synchronization of the SCN has not been elucidated. This network has been particularly challenging to probe, due to its oscillatory components and slow coupling timescale. In this work, we investigated the SCN network at a single-cell resolution through a chemically induced desynchronization. We then inferred functional connections in the SCN by applying the maximal information coefficient statistic to bioluminescence reporter data from individual neurons while they resynchronized their circadian cycling. Our results demonstrate that the functional network of circadian cells associated with resynchronization has small-world characteristics, with a node degree distribution that is exponential. We show that hubs of this small-world network are preferentially located in the central SCN, with sparsely connected shells surrounding these cores. Finally, we used two computational models of circadian neurons to validate our predictions of network structure.

  16. Functional network inference of the suprachiasmatic nucleus.

    Science.gov (United States)

    Abel, John H; Meeker, Kirsten; Granados-Fuentes, Daniel; St John, Peter C; Wang, Thomas J; Bales, Benjamin B; Doyle, Francis J; Herzog, Erik D; Petzold, Linda R

    2016-04-19

    In the mammalian suprachiasmatic nucleus (SCN), noisy cellular oscillators communicate within a neuronal network to generate precise system-wide circadian rhythms. Although the intracellular genetic oscillator and intercellular biochemical coupling mechanisms have been examined previously, the network topology driving synchronization of the SCN has not been elucidated. This network has been particularly challenging to probe, due to its oscillatory components and slow coupling timescale. In this work, we investigated the SCN network at a single-cell resolution through a chemically induced desynchronization. We then inferred functional connections in the SCN by applying the maximal information coefficient statistic to bioluminescence reporter data from individual neurons while they resynchronized their circadian cycling. Our results demonstrate that the functional network of circadian cells associated with resynchronization has small-world characteristics, with a node degree distribution that is exponential. We show that hubs of this small-world network are preferentially located in the central SCN, with sparsely connected shells surrounding these cores. Finally, we used two computational models of circadian neurons to validate our predictions of network structure. PMID:27044085

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

    Directory of Open Access Journals (Sweden)

    Steffen Sass

    2015-12-01

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

  18. Functional characterization of FLT3 receptor signaling deregulation in acute myeloid leukemia by single cell network profiling (SCNP.

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    David B Rosen

    Full Text Available BACKGROUND: Molecular characterization of the FMS-like tyrosine kinase 3 receptor (FLT3 in cytogenetically normal acute myeloid leukemia (AML has recently been incorporated into clinical guidelines based on correlations between FLT3 internal tandem duplications (FLT3-ITD and decreased disease-free and overall survival. These mutations result in constitutive activation of FLT3, and FLT3 inhibitors are currently undergoing trials in AML patients selected on FLT3 molecular status. However, the transient and partial responses observed suggest that FLT3 mutational status alone does not provide complete information on FLT3 biological activity at the individual patient level. Examination of variation in cellular responsiveness to signaling modulation may be more informative. METHODOLOGY/PRINCIPAL FINDINGS: Using single cell network profiling (SCNP, cells were treated with extracellular modulators and their functional responses were quantified by multiparametric flow cytometry. Intracellular signaling responses were compared between healthy bone marrow myeloblasts (BMMb and AML leukemic blasts characterized as FLT3 wild type (FLT3-WT or FLT3-ITD. Compared to healthy BMMb, FLT3-WT leukemic blasts demonstrated a wide range of signaling responses to FLT3 ligand (FLT3L, including elevated and sustained PI3K and Ras/Raf/Erk signaling. Distinct signaling and apoptosis profiles were observed in FLT3-WT and FLT3-ITD AML samples, with more uniform signaling observed in FLT3-ITD AML samples. Specifically, increased basal p-Stat5 levels, decreased FLT3L induced activation of the PI3K and Ras/Raf/Erk pathways, decreased IL-27 induced activation of the Jak/Stat pathway, and heightened apoptotic responses to agents inducing DNA damage were observed in FLT3-ITD AML samples. Preliminary analysis correlating these findings with clinical outcomes suggests that classification of patient samples based on signaling profiles may more accurately reflect FLT3 signaling

  19. SATWG networked quality function deployment

    Science.gov (United States)

    Brown, Don

    1992-01-01

    The initiative of this work is to develop a cooperative process for continual evolution of an integrated, time phased avionics technology plan that involves customers, technologists, developers, and managers. This will be accomplished by demonstrating a computer network technology to augment the Quality Function Deployment (QFD). All results are presented in viewgraph format.

  20. Caffeine Modulates Attention Network Function

    Science.gov (United States)

    Brunye, Tad T.; Mahoney, Caroline R.; Lieberman, Harris R.; Taylor, Holly A.

    2010-01-01

    The present work investigated the effects of caffeine (0 mg, 100 mg, 200 mg, 400 mg) on a flanker task designed to test Posner's three visual attention network functions: alerting, orienting, and executive control [Posner, M. I. (2004). "Cognitive neuroscience of attention". New York, NY: Guilford Press]. In a placebo-controlled, double-blind…

  1. Networks in Cell Biology = Modelling cell biology with networks

    OpenAIRE

    Buchanan, Mark; Caldarelli, Guido; De Los Rios, Paolo; Rao, Francesco; Vendruscolo, M.

    2010-01-01

    The science of complex biological networks is transforming research in areas ranging from evolutionary biology to medicine. This is the first book on the subject, providing a comprehensive introduction to complex network science and its biological applications. With contributions from key leaders in both network theory and modern cell biology, this book discusses the network science that is increasingly foundational for systems biology and the quantitative understanding of living systems. It ...

  2. APPROXIMATION MULTIDIMENSION FUCTION WITH FUNCTIONAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    Li Weibin; Liu Fang; Jiao Licheng; Zhang Shuling; Li Zongling

    2006-01-01

    The functional network was introduced by E.Catillo, which extended the neural network. Not only can it solve the problems solved, but also it can formulate the ones that cannot be solved by traditional network.This paper applies functional network to approximate the multidimension function under the ridgelet theory.The method performs more stable and faster than the traditional neural network. The numerical examples demonstrate the performance.

  3. Functional Conservation of the Glide/Gcm Regulatory Network Controlling Glia, Hemocyte, and Tendon Cell Differentiation in Drosophila.

    Science.gov (United States)

    Cattenoz, Pierre B; Popkova, Anna; Southall, Tony D; Aiello, Giuseppe; Brand, Andrea H; Giangrande, Angela

    2016-01-01

    High-throughput screens allow us to understand how transcription factors trigger developmental processes, including cell specification. A major challenge is identification of their binding sites because feedback loops and homeostatic interactions may mask the direct impact of those factors in transcriptome analyses. Moreover, this approach dissects the downstream signaling cascades and facilitates identification of conserved transcriptional programs. Here we show the results and the validation of a DNA adenine methyltransferase identification (DamID) genome-wide screen that identifies the direct targets of Glide/Gcm, a potent transcription factor that controls glia, hemocyte, and tendon cell differentiation in Drosophila. The screen identifies many genes that had not been previously associated with Glide/Gcm and highlights three major signaling pathways interacting with Glide/Gcm: Notch, Hedgehog, and JAK/STAT, which all involve feedback loops. Furthermore, the screen identifies effector molecules that are necessary for cell-cell interactions during late developmental processes and/or in ontogeny. Typically, immunoglobulin (Ig) domain-containing proteins control cell adhesion and axonal navigation. This shows that early and transiently expressed fate determinants not only control other transcription factors that, in turn, implement a specific developmental program but also directly affect late developmental events and cell function. Finally, while the mammalian genome contains two orthologous Gcm genes, their function has been demonstrated in vertebrate-specific tissues, placenta, and parathyroid glands, begging questions on the evolutionary conservation of the Gcm cascade in higher organisms. Here we provide the first evidence for the conservation of Gcm direct targets in humans. In sum, this work uncovers novel aspects of cell specification and sets the basis for further understanding of the role of conserved Gcm gene regulatory cascades. PMID:26567182

  4. Functional optimization of the arterial network

    CERN Document Server

    Mauroy, Benjamin

    2014-01-01

    We build an evolutionary scenario that explains how some crucial physiological constraints in the arterial network of mammals - i.e. hematocrit, vessels diameters and arterial pressure drops - could have been selected by evolution. We propose that the arterial network evolved while being constrained by its function as an organ. To support this hypothesis, we focus our study on one of the main function of blood network: oxygen supply to the organs. We consider an idealized organ with a given oxygen need and we optimize blood network geometry and hematocrit with the constraint that it must fulfill the organ oxygen need. Our model accounts for the non-Newtonian behavior of blood, its maintenance cost and F\\aa hr\\ae us effects (decrease in average concentration of red blood cells as the vessel diameters decrease). We show that the mean shear rates (relative velocities of fluid layers) in the tree vessels follow a scaling law related to the multi-scale property of the tree network, and we show that this scaling la...

  5. Generalized multiscale radial basis function networks.

    Science.gov (United States)

    Billings, Stephen A; Wei, Hua-Liang; Balikhin, Michael A

    2007-12-01

    A novel modelling framework is proposed for constructing parsimonious and flexible multiscale radial basis function networks (RBF). Unlike a conventional standard single scale RBF network, where all the basis functions have a common kernel width, the new network structure adopts multiscale Gaussian functions as the bases, where each selected centre has multiple kernel widths, to provide more flexible representations with better generalization properties for general nonlinear dynamical systems. As a direct extension of the traditional single scale Gaussian networks, the new multiscale network is easy to implement and is quick to learn using standard learning algorithms. A k-means clustering algorithm and an improved orthogonal least squares (OLS) algorithm are used to determine the unknown parameters in the network model including the centres and widths of the basis functions, and the weights between the basis functions. It is demonstrated that the new network can lead to a parsimonious model with much better generalization property compared with the traditional single width RBF networks.

  6. Scale-Free Brain Functional Networks

    Science.gov (United States)

    Eguíluz, Victor M.; Chialvo, Dante R.; Cecchi, Guillermo A.; Baliki, Marwan; Apkarian, A. Vania

    2005-01-01

    Functional magnetic resonance imaging is used to extract functional networks connecting correlated human brain sites. Analysis of the resulting networks in different tasks shows that (a)the distribution of functional connections, and the probability of finding a link versus distance are both scale-free, (b)the characteristic path length is small and comparable with those of equivalent random networks, and (c)the clustering coefficient is orders of magnitude larger than those of equivalent random networks. All these properties, typical of scale-free small-world networks, reflect important functional information about brain states.

  7. Network stratification analysis for identifying function-specific network layers.

    Science.gov (United States)

    Zhang, Chuanchao; Wang, Jiguang; Zhang, Chao; Liu, Juan; Xu, Dong; Chen, Luonan

    2016-04-22

    A major challenge of systems biology is to capture the rewiring of biological functions (e.g. signaling pathways) in a molecular network. To address this problem, we proposed a novel computational framework, namely network stratification analysis (NetSA), to stratify the whole biological network into various function-specific network layers corresponding to particular functions (e.g. KEGG pathways), which transform the network analysis from the gene level to the functional level by integrating expression data, the gene/protein network and gene ontology information altogether. The application of NetSA in yeast and its comparison with a traditional network-partition both suggest that NetSA can more effectively reveal functional implications of network rewiring and extract significant phenotype-related biological processes. Furthermore, for time-series or stage-wise data, the function-specific network layer obtained by NetSA is also shown to be able to characterize the disease progression in a dynamic manner. In particular, when applying NetSA to hepatocellular carcinoma and type 1 diabetes, we can derive functional spectra regarding the progression of the disease, and capture active biological functions (i.e. active pathways) in different disease stages. The additional comparison between NetSA and SPIA illustrates again that NetSA could discover more complete biological functions during disease progression. Overall, NetSA provides a general framework to stratify a network into various layers of function-specific sub-networks, which can not only analyze a biological network on the functional level but also investigate gene rewiring patterns in biological processes. PMID:26879865

  8. A Method to Design Synthetic Cell-Cycle Networks

    Institute of Scientific and Technical Information of China (English)

    MIAO Ke-Ke

    2009-01-01

    The interactions among proteins, DNA and RNA in an organism form elaborate cell-cycle networks which govern cell growth and proliferation. Understanding the common structure of ce11-cycle networks will be of great benefit to science research. Here, inspired by the importance of the cell-cycle regulatory network of yeast which has been studied intensively, we focus on small networks with 11 nodes, equivalent to that of the cell-cycle regulatory network used by Li et al. [Proc. Natl. Acad. Sci. USA 101(2004)4781] Using a Boolean model, we study the correlation between structure and function, and a possible common structure. It is found that cascade-like networks with a great number of interactions between nodes are stable. Based on these findings, we are able to construct synthetic networks that have the same functions as the cell-cycle regulatory network.

  9. Computational network design from functional specifications

    KAUST Repository

    Peng, Chi Han

    2016-07-11

    Connectivity and layout of underlying networks largely determine agent behavior and usage in many environments. For example, transportation networks determine the flow of traffic in a neighborhood, whereas building floorplans determine the flow of people in a workspace. Designing such networks from scratch is challenging as even local network changes can have large global effects. We investigate how to computationally create networks starting from only high-level functional specifications. Such specifications can be in the form of network density, travel time versus network length, traffic type, destination location, etc. We propose an integer programming-based approach that guarantees that the resultant networks are valid by fulfilling all the specified hard constraints and that they score favorably in terms of the objective function. We evaluate our algorithm in two different design settings, street layout and floorplans to demonstrate that diverse networks can emerge purely from high-level functional specifications.

  10. Network Physiology reveals relations between network topology and physiological function

    CERN Document Server

    Bashan, Amir; Kantelhardt, Jan W; Havlin, Shlomo; Ivanov, Plamen Ch; 10.1038/ncomms1705

    2012-01-01

    The human organism is an integrated network where complex physiologic systems, each with its own regulatory mechanisms, continuously interact, and where failure of one system can trigger a breakdown of the entire network. Identifying and quantifying dynamical networks of diverse systems with different types of interactions is a challenge. Here, we develop a framework to probe interactions among diverse systems, and we identify a physiologic network. We find that each physiologic state is characterized by a specific network structure, demonstrating a robust interplay between network topology and function. Across physiologic states the network undergoes topological transitions associated with fast reorganization of physiologic interactions on time scales of a few minutes, indicating high network flexibility in response to perturbations. The proposed system-wide integrative approach may facilitate the development of a new field, Network Physiology.

  11. Cell outage compensation in LTE networks: Algorithms and performance assessment

    NARCIS (Netherlands)

    Amirijoo, M.; Jorguseski, L.; Litjens, R.; Schmelz, L.C.

    2011-01-01

    Cell outage compensation is a self-healing function and as such part of the Self-Organising Networks concept for mobile wireless networks. It aims at mitigating the degradation of coverage, capacity and service quality caused by a cell or site level outage. Upon detection of such an outage, cell out

  12. Nested Canalizing Functions and Their Networks

    CERN Document Server

    Kadelka, Claus; Adeyeye, John O; Laubenbacher, Reinhard

    2014-01-01

    The concept of a nested canalizing Boolean function has been studied over the last decade in the context of understanding the regulatory logic of molecular interaction networks, such as gene regulatory networks. Such networks are predominantly governed by nested canalizing functions. Derrida values are frequently used to analyze the robustness of a Boolean network to perturbations. This paper introduces closed formulas for the calculation of Derrida values of networks governed by Boolean nested canalizing functions, which previously required extensive simulations. Recently, the concept of nested canalizing functions has been generalized to include multistate functions, and a recursive formula has been derived for their number, as a function of the number of variables. This paper contains a detailed analysis of the class of nested canalizing functions over an arbitrary finite field. In addition, the concept of nested canalization is further generalized and closed formulas for the number of such generalized fun...

  13. Comprehensive Expression Profiling and Functional Network Analysis of p53-Regulated MicroRNAs in HepG2 Cells Treated with Doxorubicin.

    Directory of Open Access Journals (Sweden)

    Yalan Yang

    Full Text Available Acting as a sequence-specific transcription factor, p53 tumor suppressor involves in a variety of biological processes after being activated by cellular stresses such as DNA damage. In recent years, microRNAs (miRNAs have been confirmed to be regulated by p53 in several cancer types. However, it is still unclear how miRNAs orchestrate their regulation and function in p53 network after p53 activation in hepatocellular carcinoma (HCC. In this study, we used small RNA sequencing and systematic bioinformatic analysis to characterize the regulatory networks of differentially expressed miRNAs after the p53 activation in HepG2. Here, 33 miRNAs significantly regulated by p53 (12 up-regulated and 21 down-regulated were detected between the doxorubicin-treated and untreated HepG2 cells in two biological replicates for small RNA sequencing and 8 miRNAs have been reported previously to be associated with HCC. Gene ontology (GO and KEGG pathway enrichment analysis showed that 87.9% (29 out of 33 and 90.9% (30 out of 33 p53-regulated miRNAs were involved in p53-related biological processes and pathways with significantly low p-value, respectively. Remarkably, 18 out of 33 p53-regulated miRNAs were identified to contain p53 binding sites around their transcription start sites (TSSs. Finally, comprehensive p53-miRNA regulatory networks were constructed and analyzed. These observations provide a new insight into p53-miRNA co-regulatory network in the context of HCC.

  14. Understanding biological functions through molecular networks

    Institute of Scientific and Technical Information of China (English)

    Jing-Dong Jackie Han

    2008-01-01

    The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.

  15. Alu elements in ANRIL non-coding RNA at chromosome 9p21 modulate atherogenic cell functions through trans-regulation of gene networks.

    Directory of Open Access Journals (Sweden)

    Lesca M Holdt

    Full Text Available The chromosome 9p21 (Chr9p21 locus of coronary artery disease has been identified in the first surge of genome-wide association and is the strongest genetic factor of atherosclerosis known today. Chr9p21 encodes the long non-coding RNA (ncRNA antisense non-coding RNA in the INK4 locus (ANRIL. ANRIL expression is associated with the Chr9p21 genotype and correlated with atherosclerosis severity. Here, we report on the molecular mechanisms through which ANRIL regulates target-genes in trans, leading to increased cell proliferation, increased cell adhesion and decreased apoptosis, which are all essential mechanisms of atherogenesis. Importantly, trans-regulation was dependent on Alu motifs, which marked the promoters of ANRIL target genes and were mirrored in ANRIL RNA transcripts. ANRIL bound Polycomb group proteins that were highly enriched in the proximity of Alu motifs across the genome and were recruited to promoters of target genes upon ANRIL over-expression. The functional relevance of Alu motifs in ANRIL was confirmed by deletion and mutagenesis, reversing trans-regulation and atherogenic cell functions. ANRIL-regulated networks were confirmed in 2280 individuals with and without coronary artery disease and functionally validated in primary cells from patients carrying the Chr9p21 risk allele. Our study provides a molecular mechanism for pro-atherogenic effects of ANRIL at Chr9p21 and suggests a novel role for Alu elements in epigenetic gene regulation by long ncRNAs.

  16. Framework for Ethernet Network Functionality Testing

    Directory of Open Access Journals (Sweden)

    Mirza Aamir Mehmood

    2011-11-01

    Full Text Available Computer networks and telecommunication systems use a wide range of applications. Therefore, the power and complexity of computer networks are increasing every day which enhances the possibilities of the end user, but also makes harder the work of those who have to design, maintain and make a network efficient, optimized and secure. Ethernet functionality testing as a generic term used for checking connectivity, throughput and capability to transfer packets over the network. Especially in the packet-switch environment, Ethernet testing has become an essential part for deploying a reliable network. A platform and vendor independent framework is required to verify and test the functionality of the Ethernet network and to verify the functionality and performance of the TCP/IP stack. NetBurst is developed for Ethernet functionality testing

  17. Arabidopsis gene co-expression network and its functional modules

    Directory of Open Access Journals (Sweden)

    Dash Sudhansu

    2009-10-01

    Full Text Available Abstract Background Biological networks characterize the interactions of biomolecules at a systems-level. One important property of biological networks is the modular structure, in which nodes are densely connected with each other, but between which there are only sparse connections. In this report, we attempted to find the relationship between the network topology and formation of modular structure by comparing gene co-expression networks with random networks. The organization of gene functional modules was also investigated. Results We constructed a genome-wide Arabidopsis gene co-expression network (AGCN by using 1094 microarrays. We then analyzed the topological properties of AGCN and partitioned the network into modules by using an efficient graph clustering algorithm. In the AGCN, 382 hub genes formed a clique, and they were densely connected only to a small subset of the network. At the module level, the network clustering results provide a systems-level understanding of the gene modules that coordinate multiple biological processes to carry out specific biological functions. For instance, the photosynthesis module in AGCN involves a very large number (> 1000 of genes which participate in various biological processes including photosynthesis, electron transport, pigment metabolism, chloroplast organization and biogenesis, cofactor metabolism, protein biosynthesis, and vitamin metabolism. The cell cycle module orchestrated the coordinated expression of hundreds of genes involved in cell cycle, DNA metabolism, and cytoskeleton organization and biogenesis. We also compared the AGCN constructed in this study with a graphical Gaussian model (GGM based Arabidopsis gene network. The photosynthesis, protein biosynthesis, and cell cycle modules identified from the GGM network had much smaller module sizes compared with the modules found in the AGCN, respectively. Conclusion This study reveals new insight into the topological properties of

  18. The functional consequences of mutualistic network architecture.

    Directory of Open Access Journals (Sweden)

    José M Gómez

    Full Text Available The architecture and properties of many complex networks play a significant role in the functioning of the systems they describe. Recently, complex network theory has been applied to ecological entities, like food webs or mutualistic plant-animal interactions. Unfortunately, we still lack an accurate view of the relationship between the architecture and functioning of ecological networks. In this study we explore this link by building individual-based pollination networks from eight Erysimum mediohispanicum (Brassicaceae populations. In these individual-based networks, each individual plant in a population was considered a node, and was connected by means of undirected links to conspecifics sharing pollinators. The architecture of these unipartite networks was described by means of nestedness, connectivity and transitivity. Network functioning was estimated by quantifying the performance of the population described by each network as the number of per-capita juvenile plants produced per population. We found a consistent relationship between the topology of the networks and their functioning, since variation across populations in the average per-capita production of juvenile plants was positively and significantly related with network nestedness, connectivity and clustering. Subtle changes in the composition of diverse pollinator assemblages can drive major consequences for plant population performance and local persistence through modifications in the structure of the inter-plant pollination networks.

  19. Network architecture functional description and design

    Energy Technology Data Exchange (ETDEWEB)

    Stans, L.; Bencoe, M.; Brown, D.; Kelly, S.; Pierson, L.; Schaldach, C.

    1989-05-25

    This report provides a top level functional description and design for the development and implementation of the central network to support the next generation of SNL, Albuquerque supercomputer in a UNIX{reg sign} environment. It describes the network functions and provides an architecture and topology.

  20. Functional clustering in hippocampal cultures: relating network structure and dynamics

    International Nuclear Information System (INIS)

    In this work we investigate the relationship between gross anatomic structural network properties, neuronal dynamics and the resultant functional structure in dissociated rat hippocampal cultures. Specifically, we studied cultures as they developed under two conditions: the first supporting glial cell growth (high glial group), and the second one inhibiting it (low glial group). We then compared structural network properties and the spatio-temporal activity patterns of the neurons. Differences in dynamics between the two groups could be linked to the impact of the glial network on the neuronal network as the cultures developed. We also implemented a recently developed algorithm called the functional clustering algorithm (FCA) to obtain the resulting functional network structure. We show that this new algorithm is useful for capturing changes in functional network structure as the networks evolve over time. The FCA detects changes in functional structure that are consistent with expected dynamical differences due to the impact of the glial network. Cultures in the high glial group show an increase in global synchronization as the cultures age, while those in the low glial group remain locally synchronized. We additionally use the FCA to quantify the amount of synchronization present in the cultures and show that the total level of synchronization in the high glial group is stronger than in the low glial group. These results indicate an interdependence between the glial and neuronal networks present in dissociated cultures

  1. Hierarchical modularity in human brain functional networks

    CERN Document Server

    Meunier, D; Fornito, A; Ersche, K D; Bullmore, E T; 10.3389/neuro.11.037.2009

    2010-01-01

    The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or "modules-within-modules") decomposition of human brain functional networks, measured using functional magnetic resonance imaging (fMRI) in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I=0.63. The largest 5 modules at ...

  2. Density-dependence of functional spiking networks in vitro

    Energy Technology Data Exchange (ETDEWEB)

    Ham, Michael I [Los Alamos National Laboratory; Gintautuas, Vadas [Los Alamos National Laboratory; Rodriguez, Marko A [Los Alamos National Laboratory; Bettencourt, Luis M A [Los Alamos National Laboratory; Bennett, Ryan [UNIV OF NORTH TEXAS; Santa Maria, Cara L [UNIV OF NORTH TEXAS

    2008-01-01

    During development, the mammalian brain differentiates into specialized regions with unique functional abilities. While many factors contribute to this functional specialization, we explore the effect neuronal density can have on neuronal interactions. Two types of networks, dense (50,000 neurons and glia support cells) and sparse (12,000 neurons and glia support cells), are studied. A competitive first response model is applied to construct activation graphs that represent pairwise neuronal interactions. By observing the evolution of these graphs during development in vitro we observe that dense networks form activation connections earlier than sparse networks, and that link-!llltropy analysis of the resulting dense activation graphs reveals that balanced directional connections dominate. Information theoretic measures reveal in addition that early functional information interactions (of order 3) are synergetic in both dense and sparse networks. However, during development in vitro, such interactions become redundant in dense, but not sparse networks. Large values of activation graph link-entropy correlate strongly with redundant ensembles observed in the dense networks. Results demonstrate differences between dense and sparse networks in terms of informational groups, pairwise relationships, and activation graphs. These differences suggest that variations in cell density may result in different functional specialization of nervous system tissue also in vivo.

  3. Generating fracture networks using iterated function systems

    Science.gov (United States)

    Mohrlok, U.; Liedl, R.

    1996-03-01

    In order to model flow and transport in fractured rocks it is important to know the geometry of the fracture network. A stochastic approach is commonly used to generate a synthetic fracture network from the statistics measured at a natural fracture network. The approach presented herein is able to incorporate the structures found in a natural fracture network into the synthetic fracture network. These synthetic fracture networks are the images generated by Iterated Function Systems (IFS) as introduced by Barnsley (1988). The conditions these IFS have to fulfil to determine images resembling fracture networks and the effects of their parameters on the images are discussed. It is possible to define the parameters of the IFS in order to generate some properties of a fracture network. The image of an IFS consists of many single points and has to be suitably processed for further use.

  4. Multiprotocol label-switching network functional description

    Science.gov (United States)

    Owens, Kenneth R.; Kroculick, Joseph

    1999-11-01

    This paper integrates a functional transport and control layer network architecture for MPLS emphasizing Traffic Engineering concepts such as the specification and provisioning of end-to-end QoS service layer agreements. MPLS transport networks are provisioned considering administrator-defined policies on bandwidth allocation, security, and accounting techniques. The MPLS architecture consists of the transport and control layer networks. The transport layer network is concerned with configuration, packet forwarding, signaling, adaptation to higher layers, and support of higher layers. The control layer network is concerned with policy configuration, management, distribution, definitions, schemas, elements, settings, and enforcement.

  5. An Adaptive Complex Network Model for Brain Functional Networks

    OpenAIRE

    Gomez Portillo, Ignacio J.; Gleiser, Pablo M.

    2009-01-01

    Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show diffe...

  6. Simple models of human brain functional networks.

    Science.gov (United States)

    Vértes, Petra E; Alexander-Bloch, Aaron F; Gogtay, Nitin; Giedd, Jay N; Rapoport, Judith L; Bullmore, Edward T

    2012-04-10

    Human brain functional networks are embedded in anatomical space and have topological properties--small-worldness, modularity, fat-tailed degree distributions--that are comparable to many other complex networks. Although a sophisticated set of measures is available to describe the topology of brain networks, the selection pressures that drive their formation remain largely unknown. Here we consider generative models for the probability of a functional connection (an edge) between two cortical regions (nodes) separated by some Euclidean distance in anatomical space. In particular, we propose a model in which the embedded topology of brain networks emerges from two competing factors: a distance penalty based on the cost of maintaining long-range connections; and a topological term that favors links between regions sharing similar input. We show that, together, these two biologically plausible factors are sufficient to capture an impressive range of topological properties of functional brain networks. Model parameters estimated in one set of functional MRI (fMRI) data on normal volunteers provided a good fit to networks estimated in a second independent sample of fMRI data. Furthermore, slightly detuned model parameters also generated a reasonable simulation of the abnormal properties of brain functional networks in people with schizophrenia. We therefore anticipate that many aspects of brain network organization, in health and disease, may be parsimoniously explained by an economical clustering rule for the probability of functional connectivity between different brain areas.

  7. Network Assemblies in the Functional Brain

    Science.gov (United States)

    Sepulcre, Jorge; Sabuncu, Mert R.; Johnson, Keith A.

    2012-01-01

    Purpose of review This review focuses on recent advances in functional connectivity MRI and renewed interest in knowing the large-scale functional network assemblies in the brain. We also consider some methodological aspects of graph theoretical analysis. Recent findings Network science applied to neuroscience is quickly growing in recent years. The characterization of the functional connectomes in normal and pathological brain conditions is now a priority for researchers in the neuropsychiatric field and current findings have provided new insights regarding the pivotal role of network epicenters and specific configurations of the functional networks in the brain. Summary Functional connectivity and its analytical tools are providing organization of the functional brain that will be key for the understanding of pathologies in neurology. PMID:22766721

  8. Encoding network states by striatal cell assemblies.

    Science.gov (United States)

    Carrillo-Reid, Luis; Tecuapetla, Fatuel; Tapia, Dagoberto; Hernández-Cruz, Arturo; Galarraga, Elvira; Drucker-Colin, René; Bargas, José

    2008-03-01

    Correlated activity in cortico-basal ganglia circuits plays a key role in the encoding of movement, associative learning and procedural memory. How correlated activity is assembled by striatal microcircuits is not understood. Calcium imaging of striatal neuronal populations, with single-cell resolution, reveals sporadic and asynchronous activity under control conditions. However, N-methyl-d-aspartate (NMDA) application induces bistability and correlated activity in striatal neurons. Widespread neurons within the field of observation present burst firing. Sets of neurons exhibit episodes of recurrent and synchronized bursting. Dimensionality reduction of network dynamics reveals functional states defined by cell assemblies that alternate their activity and display spatiotemporal pattern generation. Recurrent synchronous activity travels from one cell assembly to the other often returning to the original assembly; suggesting a robust structure. An initial search into the factors that sustain correlated activity of neuronal assemblies showed a critical dependence on both intrinsic and synaptic mechanisms: blockage of fast glutamatergic transmission annihilates all correlated firing, whereas blockage of GABAergic transmission locked the network into a single dominant state that eliminates assembly diversity. Reduction of L-type Ca(2+)-current restrains synchronization. Each cell assembly comprised different cells, but a small set of neurons was shared by different assemblies. A great proportion of the shared neurons was local interneurons with pacemaking properties. The network dynamics set into action by NMDA in the striatal network may reveal important properties of striatal microcircuits under normal and pathological conditions. PMID:18184883

  9. Delay estimation for CMOS functional cells

    DEFF Research Database (Denmark)

    Madsen, Jan

    1991-01-01

    Presents a new RC tree network model for delay estimation of CMOS functional cells. The model is able to reflect topological changes within a cell, which is of particular interest when doing performance driven layout synthesis. Further, a set of algorithms to perform worst case analysis on arbitr...... arbitrary CMOS functional cells using the proposed delay model, is presented. Both model and algorithms have been implemented as a part of a cell compiler (CELLO) working in an experimental silicon compiler environment.......Presents a new RC tree network model for delay estimation of CMOS functional cells. The model is able to reflect topological changes within a cell, which is of particular interest when doing performance driven layout synthesis. Further, a set of algorithms to perform worst case analysis on...

  10. Radial basis function networks and complexity regularization in function learning.

    Science.gov (United States)

    Krzyzak, A; Linder, T

    1998-01-01

    In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function network. Our approach differs from previous complexity regularization neural-network function learning schemes in that we operate with random covering numbers and l(1) metric entropy, making it possible to consider much broader families of activation functions, namely functions of bounded variation. Some constraints previously imposed on the network parameters are also eliminated this way. The network is trained by means of complexity regularization involving empirical risk minimization. Bounds on the expected risk in terms of the sample size are obtained for a large class of loss functions. Rates of convergence to the optimal loss are also derived.

  11. A universal formula for network functions

    DEFF Research Database (Denmark)

    Skelboe, Stig

    1975-01-01

    A linear electrical network can be described in a convenient way by means of the node equations. This letter presents a universal formula which expresses any network function as the quotient of two determinants. The determinants belong to matrices derived from the indefinite nodal admittance...

  12. Fuzzy Functional Dependencies and Bayesian Networks

    Institute of Scientific and Technical Information of China (English)

    LIU WeiYi(刘惟一); SONG Ning(宋宁)

    2003-01-01

    Bayesian networks have become a popular technique for representing and reasoning with probabilistic information. The fuzzy functional dependency is an important kind of data dependencies in relational databases with fuzzy values. The purpose of this paper is to set up a connection between these data dependencies and Bayesian networks. The connection is done through a set of methods that enable people to obtain the most information of independent conditions from fuzzy functional dependencies.

  13. Origin of cells and network information

    Institute of Scientific and Technical Information of China (English)

    Shihori Tanabe

    2015-01-01

    All cells are derived from one cell, and the origin ofdifferent cell types is a subject of curiosity. Cells constructlife through appropriately timed networks at each stageof development. Communication among cells andintracellular signaling are essential for cell differentiationand for life processes. Cellular molecular networksestablish cell diversity and life. The investigation ofthe regulation of each gene in the genome within thecellular network is therefore of interest. Stem cellsproduce various cells that are suitable for specificpurposes. The dynamics of the information in thecellular network changes as the status of cells isaltered. The components of each cell are subject toinvestigation.

  14. Visualizing and Clustering Protein Similarity Networks: Sequences, Structures, and Functions.

    Science.gov (United States)

    Mai, Te-Lun; Hu, Geng-Ming; Chen, Chi-Ming

    2016-07-01

    Research in the recent decade has demonstrated the usefulness of protein network knowledge in furthering the study of molecular evolution of proteins, understanding the robustness of cells to perturbation, and annotating new protein functions. In this study, we aimed to provide a general clustering approach to visualize the sequence-structure-function relationship of protein networks, and investigate possible causes for inconsistency in the protein classifications based on sequences, structures, and functions. Such visualization of protein networks could facilitate our understanding of the overall relationship among proteins and help researchers comprehend various protein databases. As a demonstration, we clustered 1437 enzymes by their sequences and structures using the minimum span clustering (MSC) method. The general structure of this protein network was delineated at two clustering resolutions, and the second level MSC clustering was found to be highly similar to existing enzyme classifications. The clustering of these enzymes based on sequence, structure, and function information is consistent with each other. For proteases, the Jaccard's similarity coefficient is 0.86 between sequence and function classifications, 0.82 between sequence and structure classifications, and 0.78 between structure and function classifications. From our clustering results, we discussed possible examples of divergent evolution and convergent evolution of enzymes. Our clustering approach provides a panoramic view of the sequence-structure-function network of proteins, helps visualize the relation between related proteins intuitively, and is useful in predicting the structure and function of newly determined protein sequences. PMID:27267620

  15. Suprachiasmatic Nucleus: Cell Autonomy and Network Properties

    Science.gov (United States)

    Welsh, David K.; Takahashi, Joseph S.; Kay, Steve A.

    2013-01-01

    The suprachiasmatic nucleus (SCN) is the primary circadian pacemaker in mammals. Individual SCN neurons in dispersed culture can generate independent circadian oscillations of clock gene expression and neuronal firing. However, SCN rhythmicity depends on sufficient membrane depolarization and levels of intracellular calcium and cAMP. In the intact SCN, cellular oscillations are synchronized and reinforced by rhythmic synaptic input from other cells, resulting in a reproducible topographic pattern of distinct phases and amplitudes specified by SCN circuit organization. The SCN network synchronizes its component cellular oscillators, reinforces their oscillations, responds to light input by altering their phase distribution, increases their robustness to genetic perturbations, and enhances their precision. Thus, even though individual SCN neurons can be cell-autonomous circadian oscillators, neuronal network properties are integral to normal function of the SCN. PMID:20148688

  16. Establishing and maintaining the Langerhans cell network.

    Science.gov (United States)

    Chopin, Michaël; Nutt, Stephen L

    2015-05-01

    Langerhans cells (LCs) are the unique antigen-presenting cell of the epidermis. LCs have long been depicted in textbooks as the archetypical dendritic cell that alerts the immune system upon pathogen induced skin barrier breakage, however recent findings argue instead for a more tolerogenic function. While the LCs that populate the epidermis in steady-state arise from progenitors that seed the skin during embryogenesis, it is now apparent that a second pathway generating LCs from a bone marrow derived progenitor is active in inflammatory settings. This review emphasizes the determinants underpinning the establishment of the LC network in steady-state and under inflammatory conditions, as well as the transcriptional machinery governing their differentiation. The dual origin of LCs raises important questions about the functional differences between these subsets in balancing the epidermal immune response between immunity and tolerance.

  17. Deciphering living networks : Perturbation strategies for functional genomics

    NARCIS (Netherlands)

    Fuente van Bentem, A. de la

    2006-01-01

    Thesis: Deciphering living networks: Perturbation strategies for functional genomics Alberto de la Fuente alf@crs4.it Molecular Cell Physiology Free University Amsterdam Advisors: Prof.Dr. H.V. Westerhoff Prof.Dr. J.L. Snoep Supervisor: Dr. P.J. Mendes Using modern experimental techn

  18. Representation of Functional Data in Neural Networks

    CERN Document Server

    Rossi, Fabrice; Conan-Guez, Brieuc; Verleysen, Michel

    2005-01-01

    Functional Data Analysis (FDA) is an extension of traditional data analysis to functional data, for example spectra, temporal series, spatio-temporal images, gesture recognition data, etc. Functional data are rarely known in practice; usually a regular or irregular sampling is known. For this reason, some processing is needed in order to benefit from the smooth character of functional data in the analysis methods. This paper shows how to extend the Radial-Basis Function Networks (RBFN) and Multi-Layer Perceptron (MLP) models to functional data inputs, in particular when the latter are known through lists of input-output pairs. Various possibilities for functional processing are discussed, including the projection on smooth bases, Functional Principal Component Analysis, functional centering and reduction, and the use of differential operators. It is shown how to incorporate these functional processing into the RBFN and MLP models. The functional approach is illustrated on a benchmark of spectrometric data ana...

  19. Small Cell Network Topology Comparison

    Directory of Open Access Journals (Sweden)

    Jan Oppolzer

    2013-01-01

    Full Text Available One of the essential problems in a mobile network with small cells is that there is only a limited number of (PCIs available. Due to this fact, operators face the inevitable need for reusing (PCIs. In our contribution, we are dealing with a (PCI assignment to FAPs in three different topologies. The first model places FAPs randomly within the network while respecting overlapping defined. The second model places FAPs in a grid without other restrictions. The third model forms a grid as well, although buildings and roads are taken into account and (FAPs are always inside buildings. The proposed models are compared and a conclusion is made based on simulation results.

  20. Functional brain networks in schizophrenia: a review

    Directory of Open Access Journals (Sweden)

    Vince D Calhoun

    2009-08-01

    Full Text Available Functional magnetic resonance imaging (fMRI has become a major technique for studying cognitive function and its disruption in mental illness, including schizophrenia. The major proportion of imaging studies focused primarily upon identifying regions which hemodynamic response amplitudes covary with particular stimuli and differentiate between patient and control groups. In addition to such amplitude based comparisons, one can estimate temporal correlations and compute maps of functional connectivity between regions which include the variance associated with event related responses as well as intrinsic fluctuations of hemodynamic activity. Functional connectivity maps can be computed by correlating all voxels with a seed region when a spatial prior is available. An alternative are multivariate decompositions such as independent component analysis (ICA which extract multiple components, each of which is a spatially distinct map of voxels with a common time course. Recent work has shown that these networks are pervasive in relaxed resting and during task performance and hence provide robust measures of intact and disturbed brain activity. This in turn bears the prospect of yielding biomarkers for schizophrenia, which can be described both in terms of disrupted local processing as well as altered global connectivity between large scale networks. In this review we will summarize functional connectivity measures with a focus upon work with ICA and discuss the meaning of intrinsic fluctuations. In addition, examples of how brain networks have been used for classification of disease will be shown. We present work with functional network connectivity, an approach that enables the evaluation of the interplay between multiple networks and how they are affected in disease. We conclude by discussing new variants of ICA for extracting maximally group discriminative networks from data. In summary, it is clear that identification of brain networks and their

  1. Cell cycle-dependent gene networks relevant to cancer

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The analysis of sophisticated interplays between cell cycle-dependent genes in a disease condition is one of the largely unexplored areas in modern tumor biology research. Many cell cycle-dependent genes are either oncogenes or suppressor genes, or are closely asso- ciated with the transition of a cell cycle. However, it is unclear how the complicated relationships between these cell cycle-dependent genes are, especially in cancers. Here, we sought to identify significant expression relationships between cell cycle-dependent genes by analyzing a HeLa microarray dataset using a local alignment algorithm and constructed a gene transcriptional network specific to the cancer by assembling these newly identified gene-gene relationships. We further characterized this global network by partitioning the whole network into several cell cycle phase-specific sub-networks. All generated networks exhibited the power-law node-degree dis- tribution, and the average clustering coefficients of these networks were remarkably higher than those of pure scale-free networks, indi- cating a property of hierarchical modularity. Based on the known protein-protein interactions and Gene Ontology annotation data, the proteins encoded by cell cycle-dependent interacting genes tended to share the same biological functions or to be involved in the same biological processes, rather than interacting by physical means. Finally, we identified the hub genes related to cancer based on the topo- logical importance that maintain the basic structure of cell cycle-dependent gene networks.

  2. Dynamical Adaptation in Terrorist Cells/Networks

    DEFF Research Database (Denmark)

    Hussain, Dil Muhammad Akbar; Ahmed, Zaki

    2010-01-01

    Typical terrorist cells/networks have dynamical structure as they evolve or adapt to changes which may occur due to capturing or killing of a member of the cell/network. Analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long...... history of their successful use in revealing the importance of various members of the network. However, modeling of covert, terrorist or criminal networks through social graph dose not really provide the hierarchical structure which exist in these networks as these networks are composed of leaders...

  3. Enzymatic regulation of functional vascular networks using gelatin hydrogels.

    Science.gov (United States)

    Chuang, Chia-Hui; Lin, Ruei-Zeng; Tien, Han-Wen; Chu, Ya-Chun; Li, Yen-Cheng; Melero-Martin, Juan M; Chen, Ying-Chieh

    2015-06-01

    To manufacture tissue engineering-based functional tissues, scaffold materials that can be sufficiently vascularized to mimic the functionality and complexity of native tissues are needed. Currently, vascular network bioengineering is largely carried out using natural hydrogels as embedding scaffolds, but most natural hydrogels have poor mechanical stability and durability, factors that critically limit their widespread use. In this study, we examined the suitability of gelatin-phenolic hydroxyl (gelatin-Ph) hydrogels that can be enzymatically crosslinked, allowing tuning of the storage modulus and the proteolytic degradation rate, for use as injectable hydrogels to support the human progenitor cell-based formation of a stable and mature vascular network. Porcine gelatin-Ph hydrogels were found to be cytocompatible with human blood-derived endothelial colony-forming cells and white adipose tissue-derived mesenchymal stem cells, resulting in >87% viability, and cell proliferation and spreading could be modulated by using hydrogels with different proteolytic degradability and stiffness. In addition, gelatin was extracted from mouse dermis and murine gelatin-Ph hydrogels were prepared. Importantly, implantation of human cell-laden porcine or murine gelatin-Ph hydrogels into immunodeficient mice resulted in the rapid formation of functional anastomoses between the bioengineered human vascular network and the mouse vasculature. Furthermore, the degree of enzymatic crosslinking of the gelatin-Ph hydrogels could be used to modulate cell behavior and the extent of vascular network formation in vivo. Our report details a technique for the synthesis of gelatin-Ph hydrogels from allogeneic or xenogeneic dermal skin and suggests that these hydrogels can be used for biomedical applications that require the formation of microvascular networks, including the development of complex engineered tissues.

  4. Scalable Semisupervised Functional Neurocartography Reveals Canonical Neurons in Behavioral Networks.

    Science.gov (United States)

    Frady, E Paxon; Kapoor, Ashish; Horvitz, Eric; Kristan, William B

    2016-08-01

    Large-scale data collection efforts to map the brain are underway at multiple spatial and temporal scales, but all face fundamental problems posed by high-dimensional data and intersubject variability. Even seemingly simple problems, such as identifying a neuron/brain region across animals/subjects, become exponentially more difficult in high dimensions, such as recognizing dozens of neurons/brain regions simultaneously. We present a framework and tools for functional neurocartography-the large-scale mapping of neural activity during behavioral states. Using a voltage-sensitive dye (VSD), we imaged the multifunctional responses of hundreds of leech neurons during several behaviors to identify and functionally map homologous neurons. We extracted simple features from each of these behaviors and combined them with anatomical features to create a rich medium-dimensional feature space. This enabled us to use machine learning techniques and visualizations to characterize and account for intersubject variability, piece together a canonical atlas of neural activity, and identify two behavioral networks. We identified 39 neurons (18 pairs, 3 unpaired) as part of a canonical swim network and 17 neurons (8 pairs, 1 unpaired) involved in a partially overlapping preparatory network. All neurons in the preparatory network rapidly depolarized at the onsets of each behavior, suggesting that it is part of a dedicated rapid-response network. This network is likely mediated by the S cell, and we referenced VSD recordings to an activity atlas to identify multiple cells of interest simultaneously in real time for further experiments. We targeted and electrophysiologically verified several neurons in the swim network and further showed that the S cell is presynaptic to multiple neurons in the preparatory network. This study illustrates the basic framework to map neural activity in high dimensions with large-scale recordings and how to extract the rich information necessary to perform

  5. Additive functions in boolean models of gene regulatory network modules.

    Science.gov (United States)

    Darabos, Christian; Di Cunto, Ferdinando; Tomassini, Marco; Moore, Jason H; Provero, Paolo; Giacobini, Mario

    2011-01-01

    Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity

  6. Generalization performance of radial basis function networks.

    Science.gov (United States)

    Lei, Yunwen; Ding, Lixin; Zhang, Wensheng

    2015-03-01

    This paper studies the generalization performance of radial basis function (RBF) networks using local Rademacher complexities. We propose a general result on controlling local Rademacher complexities with the L1 -metric capacity. We then apply this result to estimate the RBF networks' complexities, based on which a novel estimation error bound is obtained. An effective approximation error bound is also derived by carefully investigating the Hölder continuity of the lp loss function's derivative. Furthermore, it is demonstrated that the RBF network minimizing an appropriately constructed structural risk admits a significantly better learning rate when compared with the existing results. An empirical study is also performed to justify the application of our structural risk in model selection.

  7. Schizophrenia classification using functional network features

    Science.gov (United States)

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

    2012-03-01

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

  8. Distributed Function Calculation over Noisy Networks

    Directory of Open Access Journals (Sweden)

    Zhidun Zeng

    2016-01-01

    Full Text Available Considering any connected network with unknown initial states for all nodes, the nearest-neighbor rule is utilized for each node to update its own state at every discrete-time step. Distributed function calculation problem is defined for one node to compute some function of the initial values of all the nodes based on its own observations. In this paper, taking into account uncertainties in the network and observations, an algorithm is proposed to compute and explicitly characterize the value of the function in question when the number of successive observations is large enough. While the number of successive observations is not large enough, we provide an approach to obtain the tightest possible bounds on such function by using linear programing optimization techniques. Simulations are provided to demonstrate the theoretical results.

  9. Deep networks for motor control functions

    Directory of Open Access Journals (Sweden)

    Max eBerniker

    2015-03-01

    Full Text Available The motor system generates time-varying commands to move our limbs and body. Conventional descriptions of motor control and learning rely on dynamical representations of our body’s state (forward and inverse models, and control policies that must be integrated forward to generate feedforward time-varying commands; thus these are representations across space, but not time. Here we examine a new approach that directly represents both time-varying commands and the resulting state trajectories with a function; a representation across space and time. Since the output of this function includes time, it necessarily requires more parameters than a typical dynamical model. To avoid the problems of local minima these extra parameters introduce, we exploit recent advances in machine learning to build our function using a stacked autoencoder, or deep network. With initial and target states as inputs, this deep network can be trained to output an accurate temporal profile of the optimal command and state trajectory for a point-to-point reach of a nonlinear limb model, even when influenced by varying force fields. In a manner that mirrors motor babble, the network can also teach itself to learn through trial and error. Lastly, we demonstrate how this network can learn to optimize a cost objective. This functional approach to motor control is a sharp departure from the standard dynamical approach, and may offer new insights into the neural implementation of motor control.

  10. Deep networks for motor control functions.

    Science.gov (United States)

    Berniker, Max; Kording, Konrad P

    2015-01-01

    The motor system generates time-varying commands to move our limbs and body. Conventional descriptions of motor control and learning rely on dynamical representations of our body's state (forward and inverse models), and control policies that must be integrated forward to generate feedforward time-varying commands; thus these are representations across space, but not time. Here we examine a new approach that directly represents both time-varying commands and the resulting state trajectories with a function; a representation across space and time. Since the output of this function includes time, it necessarily requires more parameters than a typical dynamical model. To avoid the problems of local minima these extra parameters introduce, we exploit recent advances in machine learning to build our function using a stacked autoencoder, or deep network. With initial and target states as inputs, this deep network can be trained to output an accurate temporal profile of the optimal command and state trajectory for a point-to-point reach of a non-linear limb model, even when influenced by varying force fields. In a manner that mirrors motor babble, the network can also teach itself to learn through trial and error. Lastly, we demonstrate how this network can learn to optimize a cost objective. This functional approach to motor control is a sharp departure from the standard dynamical approach, and may offer new insights into the neural implementation of motor control. PMID:25852530

  11. Chain functions and scoring functions in genetic networks.

    Science.gov (United States)

    Gat-Viks, I; Shamir, R

    2003-01-01

    One of the grand challenges of system biology is to reconstruct the network of regulatory control among genes and proteins. High throughput data, particularly from expression experiments, may gradually make this possible in the future. Here we address two key ingredients in any such 'reverse engineering' effort: The choice of a biologically relevant, yet restricted, set of potential regulation functions, and the appropriate score to evaluate candidate regulatory relations. We propose a set of regulation functions which we call chain functions, and argue for their ubiquity in biological networks. We analyze their complexity and show that their number is exponentially smaller than all boolean functions of the same dimension. We define two new scores: one evaluating the fitness of a candidate set of regulators of a particular gene, and the other evaluating a candidate function. Both scores use established statistical methods. Finally, we test our methods on experimental gene expression data from the yeast galactose pathway. We show the utility of using chain functions and the improved inference using our scores in comparison to several extant scores. We demonstrate that the combined use of the two scores gives an extra advantage. We expect both chain functions and the new scores to be helpful in future attempts to infer regulatory networks. PMID:12855446

  12. Sweet Taste Receptor Signaling Network: Possible Implication for Cognitive Functioning

    Directory of Open Access Journals (Sweden)

    Menizibeya O. Welcome

    2015-01-01

    Full Text Available Sweet taste receptors are transmembrane protein network specialized in the transmission of information from special “sweet” molecules into the intracellular domain. These receptors can sense the taste of a range of molecules and transmit the information downstream to several acceptors, modulate cell specific functions and metabolism, and mediate cell-to-cell coupling through paracrine mechanism. Recent reports indicate that sweet taste receptors are widely distributed in the body and serves specific function relative to their localization. Due to their pleiotropic signaling properties and multisubstrate ligand affinity, sweet taste receptors are able to cooperatively bind multiple substances and mediate signaling by other receptors. Based on increasing evidence about the role of these receptors in the initiation and control of absorption and metabolism, and the pivotal role of metabolic (glucose regulation in the central nervous system functioning, we propose a possible implication of sweet taste receptor signaling in modulating cognitive functioning.

  13. Influence of the fibroblastic reticular network on cell-cell interactions in lymphoid organs.

    Directory of Open Access Journals (Sweden)

    Frederik Graw

    Full Text Available Secondary lymphoid organs (SLO, such as lymph nodes and the spleen, display a complex micro-architecture. In the T cell zone the micro-architecture is provided by a network of fibroblastic reticular cells (FRC and their filaments. The FRC network is thought to enhance the interaction between immune cells and their cognate antigen. However, the effect of the FRC network on cell interaction cannot be quantified to date because of limitations in immunological methodology. We use computational models to study the influence of different densities of FRC networks on the probability that two cells meet. We developed a 3D cellular automaton model to simulate cell movements and interactions along the FRC network inside lymphatic tissue. We show that the FRC network density has only a small effect on the probability of a cell to come into contact with a static or motile target. However, damage caused by a disruption of the FRC network is greatest at FRC densities corresponding to densities observed in the spleen of naïve mice. Our analysis suggests that the FRC network as a guiding structure for moving T cells has only a minor effect on the probability to find a corresponding dendritic cell. We propose alternative hypotheses by which the FRC network might influence the functionality of immune responses in a more significant way.

  14. Network Coding Capacity Regions via Entropy Functions

    CERN Document Server

    Chan, Terence H

    2012-01-01

    In this paper, we use entropy functions to characterise the set of rate-capacity tuples achievable with either zero decoding error, or vanishing decoding error, for general network coding problems. We show that when sources are colocated, the outer bound obtained by Yeung, A First Course in Information Theory, Section 15.5 (2002) is tight and the sets of zero-error achievable and vanishing-error achievable rate-capacity tuples are the same. We also characterise the set of zero-error and vanishing-error achievable rate capacity tuples for network coding problems subject to linear encoding constraints, routing constraints (where some or all nodes can only perform routing) and secrecy constraints. Finally, we show that even for apparently simple networks, design of optimal codes may be difficult. In particular, we prove that for the incremental multicast problem and for the single-source secure network coding problem, characterisation of the achievable set is very hard and linear network codes may not be optimal...

  15. Arithmetic functions in torus and tree networks

    Science.gov (United States)

    Bhanot, Gyan; Blumrich, Matthias A.; Chen, Dong; Gara, Alan G.; Giampapa, Mark E.; Heidelberger, Philip; Steinmacher-Burow, Burkhard D.; Vranas, Pavlos M.

    2007-12-25

    Methods and systems for performing arithmetic functions. In accordance with a first aspect of the invention, methods and apparatus are provided, working in conjunction of software algorithms and hardware implementation of class network routing, to achieve a very significant reduction in the time required for global arithmetic operation on the torus. Therefore, it leads to greater scalability of applications running on large parallel machines. The invention involves three steps in improving the efficiency and accuracy of global operations: (1) Ensuring, when necessary, that all the nodes do the global operation on the data in the same order and so obtain a unique answer, independent of roundoff error; (2) Using the topology of the torus to minimize the number of hops and the bidirectional capabilities of the network to reduce the number of time steps in the data transfer operation to an absolute minimum; and (3) Using class function routing to reduce latency in the data transfer. With the method of this invention, every single element is injected into the network only once and it will be stored and forwarded without any further software overhead. In accordance with a second aspect of the invention, methods and systems are provided to efficiently implement global arithmetic operations on a network that supports the global combining operations. The latency of doing such global operations are greatly reduced by using these methods.

  16. Function Analyses of Geographic Information System on Rural Distribution Network

    Institute of Scientific and Technical Information of China (English)

    FANG Junlong; FAN Yongcun; ZHANG Chunmei; GU Shumin

    2006-01-01

    With the actuality and characteristic and requirement of rural power enterprise distribution network management, this article introduced the function of geographic information system on the framework of distribution network, in order to develop rural distribution network.

  17. Application of network construction to estimate functional changes to insulin receptor substrates 1 and 2 in Huh7 cells following infection with the hepatitis C virus.

    Science.gov (United States)

    Liu, Jingkun; Wang, Linbang; Wang, Wenjun; Li, Yaping; Jia, Xiaoli; Zhai, Song; Shi, Juan; Dang, Shuangsuo

    2016-09-01

    Hepatitis C virus (HCV) is closely associated with insulin resistance (IS), acting primarily by interfering with insulin signaling pathways, increasing cytokine-mediated (tumor necrosis factor α, interleukin 6) inflammatory responses and enhancing oxidative stress. In the insulin signaling pathways, the insulin receptor substrate (IRS) is one of the key regulatory factors. The present study constructed gene regulatory sub‑networks specific for IRS1 and IRS2 in Huh7 cells and HCV‑infected Huh7 (HCV‑Huh7) cells using linear programming and a decomposition algorithm, and investigated the possible mechanisms underlying the function of IRS1/2 in HCV‑induced IS in Huh7 cells. All data were obtained from GSE20948 of the Gene Expression Omnibus database from the National Center for Biotechnology Information. Genes which were significantly differentially expressed between Huh7 and HCV‑Huh7 cells were analyzed using the significance analysis of microarray algorithm. The top 50 genes, including IRS1/2, were used as target genes to determine the gene regulatory networks and next the sub‑networks of IRS1 and IRS2 in HCV‑Huh7 and Huh7 cells using Gene Regulatory Network Inference Tool, an algorithm based on linear programming and the decomposition process. The IRS1/2 sub‑networks were divided into upstream/downstream groups and activation/suppression clusters, and were further analyzed using Molecule Annotation System 3.0 and Database for Annotation, Visualization, and Integrated Discovery software, two online platforms for enrichment and clustering analysis and visualization. The results indicated that in Huh7 cells, the downstream network of IRS2 is more complex than that of IRS1, indicating that the insulin metabolism in Huh7 cells may be primarily mediated by IRS2. In HCV‑Huh7 cells, the downstream pathway of IRS2 is blocked, suggesting that this may be the underlying mechanism in HCV infection that leads to insulin resistance. The present

  18. Systems analysis of biological networks in skeletal muscle function.

    Science.gov (United States)

    Smith, Lucas R; Meyer, Gretchen; Lieber, Richard L

    2013-01-01

    Skeletal muscle function depends on the efficient coordination among subcellular systems. These systems are composed of proteins encoded by a subset of genes, all of which are tightly regulated. In the cases where regulation is altered because of disease or injury, dysfunction occurs. To enable objective analysis of muscle gene expression profiles, we have defined nine biological networks whose coordination is critical to muscle function. We begin by describing the expression of proteins necessary for optimal neuromuscular junction function that results in the muscle cell action potential. That action potential is transmitted to proteins involved in excitation-contraction coupling enabling Ca(2+) release. Ca(2+) then activates contractile proteins supporting actin and myosin cross-bridge cycling. Force generated by cross-bridges is transmitted via cytoskeletal proteins through the sarcolemma and out to critical proteins that support the muscle extracellular matrix. Muscle contraction is fueled through many proteins that regulate energy metabolism. Inflammation is a common response to injury that can result in alteration of many pathways within muscle. Muscle also has multiple pathways that regulate size through atrophy or hypertrophy. Finally, the isoforms associated with fast muscle fibers and their corresponding isoforms in slow muscle fibers are delineated. These nine networks represent important biological systems that affect skeletal muscle function. Combining high-throughput systems analysis with advanced networking software will allow researchers to use these networks to objectively study skeletal muscle systems. PMID:23188744

  19. On Functional Module Detection in Metabolic Networks

    Directory of Open Access Journals (Sweden)

    Ina Koch

    2013-08-01

    Full Text Available Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models.

  20. On functional module detection in metabolic networks.

    Science.gov (United States)

    Koch, Ina; Ackermann, Jörg

    2013-01-01

    Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models. PMID:24958145

  1. The architecture of functional interaction networks in the retina.

    Science.gov (United States)

    Ganmor, Elad; Segev, Ronen; Schneidman, Elad

    2011-02-23

    Sensory information is represented in the brain by the joint activity of large groups of neurons. Recent studies have shown that, although the number of possible activity patterns and underlying interactions is exponentially large, pairwise-based models give a surprisingly accurate description of neural population activity patterns. We explored the architecture of maximum entropy models of the functional interaction networks underlying the response of large populations of retinal ganglion cells, in adult tiger salamander retina, responding to natural and artificial stimuli. We found that we can further simplify these pairwise models by neglecting weak interaction terms or by relying on a small set of interaction strengths. Comparing network interactions under different visual stimuli, we show the existence of local network motifs in the interaction map of the retina. Our results demonstrate that the underlying interaction map of the retina is sparse and dominated by local overlapping interaction modules.

  2. Complex network perspective on structure and function of Staphylococcus aureus metabolic network

    Indian Academy of Sciences (India)

    L Ying; D W Ding

    2013-02-01

    With remarkable advances in reconstruction of genome-scale metabolic networks, uncovering complex network structure and function from these networks is becoming one of the most important topics in system biology. This work aims at studying the structure and function of Staphylococcus aureus (S. aureus) metabolic network by complex network methods. We first generated a metabolite graph from the recently reconstructed high-quality S. aureus metabolic network model. Then, based on `bow tie' structure character, we explain and discuss the global structure of S. aureus metabolic network. The functional significance, global structural properties, modularity and centrality analysis of giant strong component in S. aureus metabolic networks are studied.

  3. Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach.

    Science.gov (United States)

    Li, Jun; Zhao, Patrick X

    2016-01-01

    Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/. PMID:27446133

  4. Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach.

    Science.gov (United States)

    Li, Jun; Zhao, Patrick X

    2016-01-01

    Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/.

  5. Allocating resources between network nodes for providing a network node function

    NARCIS (Netherlands)

    Strijkers, R.J.; Meulenhoff, P.J.

    2014-01-01

    The invention provides a method wherein a first network node advertises available resources that a second network node may use to offload network node functions transparently to the first network node. Examples of the first network node are a client device (e.g. PC, notebook, tablet, smart phone), a

  6. Non-Invasive Green Small Cell Network

    OpenAIRE

    Mawlawi, Baher; Bastug, Ejder; Nerguizian, Chahé; Azarian, Sylvain; Debbah, Mérouane

    2011-01-01

    Future low cost wireless networks are expected to provide high data rates with low power consumption. A dense deployment of distributed small-cells, within the existing network infrastructure, is one of the candidate solutions to achieve this goal. Unfortunately, the aggregate signal resulting from the transmission of these multiples small cells can be considered as an electromagnetic (EM) pollution for passive users who do not carry wireless devices. These users are victim of primary electro...

  7. Deterministic Function Computation with Chemical Reaction Networks

    CERN Document Server

    Chen, Ho-Lin; Soloveichik, David

    2012-01-01

    We study the deterministic computation of functions on tuples of natural numbers by chemical reaction networks (CRNs). CRNs have been shown to be efficiently Turing-universal when allowing for a small probability of error. CRNs that are guaranteed to converge on a correct answer, on the other hand, have been shown to decide only the semilinear predicates. We introduce the notion of function, rather than predicate, computation by representing the output of a function f:N^k --> N^l by a count of some molecular species, i.e., if the CRN starts with n_1,...,n_k molecules of some "input" species X_1,...,X_k, the CRN is guaranteed to converge to having f(n_1,...,n_k) molecules of the "output" species Y_1,...,Y_l. We show that a function f:N^k --> N^l is deterministically computed by a CRN if and only if its graph {(x,y) \\in N^k x N^l | f(x) = y} is a semilinear set. Finally, we show that each semilinear function f can be computed on input x in expected time O(polylog |x|).

  8. The known unknowns of the human dendritic cell network

    Directory of Open Access Journals (Sweden)

    Mélanie eDurand

    2015-03-01

    Full Text Available Dendritic cells (DC initiate and orient immune responses and comprise several subsets that display distinct phenotypes and properties. Most of our knowledge of DC subsets biology is based on mouse studies. In the past few years, the alignment of the human DC network with the mouse DC network has been the focus of much attention. Although comparative phenotypic and transcriptomic analysis have shown a high level of homology between mouse and human DC subsets, significant differences in phenotype and function have also been evidenced. Here we review recent advances in our understanding of the human DC network and discuss some remaining gaps and future challenges of the human DC field.

  9. Nano-guided cell networks as conveyors of molecular communication.

    Science.gov (United States)

    Terrell, Jessica L; Wu, Hsuan-Chen; Tsao, Chen-Yu; Barber, Nathan B; Servinsky, Matthew D; Payne, Gregory F; Bentley, William E

    2015-01-01

    Advances in nanotechnology have provided unprecedented physical means to sample molecular space. Living cells provide additional capability in that they identify molecules within complex environments and actuate function. We have merged cells with nanotechnology for an integrated molecular processing network. Here we show that an engineered cell consortium autonomously generates feedback to chemical cues. Moreover, abiotic components are readily assembled onto cells, enabling amplified and 'binned' responses. Specifically, engineered cell populations are triggered by a quorum sensing (QS) signal molecule, autoinducer-2, to express surface-displayed fusions consisting of a fluorescent marker and an affinity peptide. The latter provides means for attaching magnetic nanoparticles to fluorescently activated subpopulations for coalescence into colour-indexed output. The resultant nano-guided cell network assesses QS activity and conveys molecular information as a 'bio-litmus' in a manner read by simple optical means.

  10. Quantifying the connectivity of a network: The network correlation function method

    CERN Document Server

    Barzel, Baruch; 10.1103/PhysRevE.80.046104

    2009-01-01

    Networks are useful for describing systems of interacting objects, where the nodes represent the objects and the edges represent the interactions between them. The applications include chemical and metabolic systems, food webs as well as social networks. Lately, it was found that many of these networks display some common topological features, such as high clustering, small average path length (small world networks) and a power-law degree distribution (scale free networks). The topological features of a network are commonly related to the network's functionality. However, the topology alone does not account for the nature of the interactions in the network and their strength. Here we introduce a method for evaluating the correlations between pairs of nodes in the network. These correlations depend both on the topology and on the functionality of the network. A network with high connectivity displays strong correlations between its interacting nodes and thus features small-world functionality. We quantify the ...

  11. ``Backpack'' Functionalized Living Immune Cells

    Science.gov (United States)

    Swiston, Albert; Um, Soong Ho; Irvine, Darrell; Cohen, Robert; Rubner, Michael

    2009-03-01

    We demonstrate that functional polymeric ``backpacks'' built from polyelectrolyte multilayers (PEMs) can be attached to a fraction of the surface area of living, individual lymphocytes. Backpacks containing fluorescent polymers, superparamagnetic nanoparticles, and commercially available quantum dots have been attached to B and T-cells, which may be spatially manipulated using a magnetic field. Since the backpack does not occlude the entire cellular surface from the environment, this technique allows functional synthetic payloads to be attached to a cell that is free to perform its native functions, thereby synergistically utilizing both biological and synthetic functionalities. For instance, we have shown that backpack-modified T-cells are able to migrate on surfaces for several hours following backpack attachment. Possible payloads within the PEM backpack include drugs, vaccine antigens, thermally responsive polymers, nanoparticles, and imaging agents. We will discuss how this approach has broad potential for applications in bioimaging, single-cell functionalization, immune system and tissue engineering, and cell-based therapeutics where cell-environment interactions are critical.

  12. Human embryonic stem cell-derived neuronal cells form spontaneously active neuronal networks in vitro.

    Science.gov (United States)

    Heikkilä, Teemu J; Ylä-Outinen, Laura; Tanskanen, Jarno M A; Lappalainen, Riikka S; Skottman, Heli; Suuronen, Riitta; Mikkonen, Jarno E; Hyttinen, Jari A K; Narkilahti, Susanna

    2009-07-01

    The production of functional human embryonic stem cell (hESC)-derived neuronal cells is critical for the application of hESCs in treating neurodegenerative disorders. To study the potential functionality of hESC-derived neurons, we cultured and monitored the development of hESC-derived neuronal networks on microelectrode arrays. Immunocytochemical studies revealed that these networks were positive for the neuronal marker proteins beta-tubulin(III) and microtubule-associated protein 2 (MAP-2). The hESC-derived neuronal networks were spontaneously active and exhibited a multitude of electrical impulse firing patterns. Synchronous bursts of electrical activity similar to those reported for hippocampal neurons and rodent embryonic stem cell-derived neuronal networks were recorded from the differentiated cultures until up to 4 months. The dependence of the observed neuronal network activity on sodium ion channels was examined using tetrodotoxin (TTX). Antagonists for the glutamate receptors NMDA [D(-)-2-amino-5-phosphonopentanoic acid] and AMPA/kainate [6-cyano-7-nitroquinoxaline-2,3-dione], and for GABAA receptors [(-)-bicuculline methiodide] modulated the spontaneous electrical activity, indicating that pharmacologically susceptible neuronal networks with functional synapses had been generated. The findings indicate that hESC-derived neuronal cells can generate spontaneously active networks with synchronous communication in vitro, and are therefore suitable for use in developmental and drug screening studies, as well as for regenerative medicine.

  13. Constructive feedforward neural networks using hermite polynomial activation functions.

    Science.gov (United States)

    Ma, Liying; Khorasani, K

    2005-07-01

    In this paper, a constructive one-hidden-layer network is introduced where each hidden unit employs a polynomial function for its activation function that is different from other units. Specifically, both a structure level as well as a function level adaptation methodologies are utilized in constructing the network. The functional level adaptation scheme ensures that the "growing" or constructive network has different activation functions for each neuron such that the network may be able to capture the underlying input-output map more effectively. The activation functions considered consist of orthonormal Hermite polynomials. It is shown through extensive simulations that the proposed network yields improved performance when compared to networks having identical sigmoidal activation functions.

  14. Improving the Network Structure can lead to Functional Failures

    CERN Document Server

    Pade, Jan Philipp

    2014-01-01

    In many real-world networks the ability to synchronize is a key property for its performance. Examples include power-grid, sensor, and neuron networks as well as consensus formation. Recent work on undirected networks with diffusive interaction revealed that improvements in the network connectivity such as making the network more connected and homogeneous enhances synchronization. However, real-world networks have directed and weighted connections. In such directed networks, understanding the impact of structural changes on the network performance remains a major challenge. Here, we show that improving the structure of a directed network can lead to a failure in the network function. For instance, introducing new links to reduce the minimum distance between nodes can lead to instabilities in the synchronized motion. This counter-intuitive effect only occurs in directed networks. Our results allow to identify the dynamical importance of a link and thereby have a major impact on the design and control of direct...

  15. From Boolean Network Model to Continuous Model Helps in Design of Functional Circuits

    OpenAIRE

    Bin Shao; Xiang Liu; Dongliang Zhang; Jiayi Wu; Qi Ouyang

    2015-01-01

    Computational circuit design with desired functions in a living cell is a challenging task in synthetic biology. To achieve this task, numerous methods that either focus on small scale networks or use evolutionary algorithms have been developed. Here, we propose a two-step approach to facilitate the design of functional circuits. In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model. In the second step, ...

  16. Connectivity in the yeast cell cycle transcription network: inferences from neural networks.

    Directory of Open Access Journals (Sweden)

    Christopher E Hart

    2006-12-01

    Full Text Available A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico "mutation" to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that "network-local discrimination" occurs when regulatory connections (here between MBF and target genes are explicitly disfavored in one network module (G2, relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of

  17. Identification of Resting State Networks Involved in Executive Function.

    Science.gov (United States)

    Connolly, Joanna; McNulty, Jonathan P; Boran, Lorraine; Roche, Richard A P; Delany, David; Bokde, Arun L W

    2016-06-01

    The structural networks in the human brain are consistent across subjects, and this is reflected also in that functional networks across subjects are relatively consistent. These findings are not only present during performance of a goal oriented task but there are also consistent functional networks during resting state. It suggests that goal oriented activation patterns may be a function of component networks identified using resting state. The current study examines the relationship between resting state networks measured and patterns of neural activation elicited during a Stroop task. The association between the Stroop-activated networks and the resting state networks was quantified using spatial linear regression. In addition, we investigated if the degree of spatial association of resting state networks with the Stroop task may predict performance on the Stroop task. The results of this investigation demonstrated that the Stroop activated network can be decomposed into a number of resting state networks, which were primarily associated with attention, executive function, visual perception, and the default mode network. The close spatial correspondence between the functional organization of the resting brain and task-evoked patterns supports the relevance of resting state networks in cognitive function. PMID:26935902

  18. Phase resetting reveals network dynamics underlying a bacterial cell cycle.

    Directory of Open Access Journals (Sweden)

    Yihan Lin

    Full Text Available Genomic and proteomic methods yield networks of biological regulatory interactions but do not provide direct insight into how those interactions are organized into functional modules, or how information flows from one module to another. In this work we introduce an approach that provides this complementary information and apply it to the bacterium Caulobacter crescentus, a paradigm for cell-cycle control. Operationally, we use an inducible promoter to express the essential transcriptional regulatory gene ctrA in a periodic, pulsed fashion. This chemical perturbation causes the population of cells to divide synchronously, and we use the resulting advance or delay of the division times of single cells to construct a phase resetting curve. We find that delay is strongly favored over advance. This finding is surprising since it does not follow from the temporal expression profile of CtrA and, in turn, simulations of existing network models. We propose a phenomenological model that suggests that the cell-cycle network comprises two distinct functional modules that oscillate autonomously and couple in a highly asymmetric fashion. These features collectively provide a new mechanism for tight temporal control of the cell cycle in C. crescentus. We discuss how the procedure can serve as the basis for a general approach for probing network dynamics, which we term chemical perturbation spectroscopy (CPS.

  19. Advanced functional network analysis in the geosciences: The pyunicorn package

    Science.gov (United States)

    Donges, Jonathan F.; Heitzig, Jobst; Runge, Jakob; Schultz, Hanna C. H.; Wiedermann, Marc; Zech, Alraune; Feldhoff, Jan; Rheinwalt, Aljoscha; Kutza, Hannes; Radebach, Alexander; Marwan, Norbert; Kurths, Jürgen

    2013-04-01

    Functional networks are a powerful tool for analyzing large geoscientific datasets such as global fields of climate time series originating from observations or model simulations. pyunicorn (pythonic unified complex network and recurrence analysis toolbox) is an open-source, fully object-oriented and easily parallelizable package written in the language Python. It allows for constructing functional networks (aka climate networks) representing the structure of statistical interrelationships in large datasets and, subsequently, investigating this structure using advanced methods of complex network theory such as measures for networks of interacting networks, node-weighted statistics or network surrogates. Additionally, pyunicorn allows to study the complex dynamics of geoscientific systems as recorded by time series by means of recurrence networks and visibility graphs. The range of possible applications of the package is outlined drawing on several examples from climatology.

  20. Functional alignment of regulatory networks: a study of temperate phages.

    Directory of Open Access Journals (Sweden)

    Ala Trusina

    2005-12-01

    Full Text Available The relationship between the design and functionality of molecular networks is now a key issue in biology. Comparison of regulatory networks performing similar tasks can provide insights into how network architecture is constrained by the functions it directs. Here, we discuss methods of network comparison based on network architecture and signaling logic. Introducing local and global signaling scores for the difference between two networks, we quantify similarities between evolutionarily closely and distantly related bacteriophages. Despite the large evolutionary separation between phage lambda and 186, their networks are found to be similar when difference is measured in terms of global signaling. We finally discuss how network alignment can be used to pinpoint protein similarities viewed from the network perspective.

  1. Nonequilibrium functional bosonization of quantum wire networks

    Energy Technology Data Exchange (ETDEWEB)

    Ngo Dinh, Stephane, E-mail: stephane.ngodinh@kit.edu [Institut fuer Theorie der Kondensierten Materie, Karlsruhe Institute of Technology, 76128 Karlsruhe (Germany); DFG Center for Functional Nanostructures, Karlsruhe Institute of Technology, 76128 Karlsruhe (Germany); Bagrets, Dmitry A. [Institut fuer Theoretische Physik, Universitaet zu Koeln, Zuelpicher Str. 77, 50937 Koeln (Germany); Mirlin, Alexander D. [Institut fuer Theorie der Kondensierten Materie, Karlsruhe Institute of Technology, 76128 Karlsruhe (Germany); Institut fuer Nanotechnologie, Karlsruhe Institute of Technology, 76021 Karlsruhe (Germany); DFG Center for Functional Nanostructures, Karlsruhe Institute of Technology, 76128 Karlsruhe (Germany); Petersburg Nuclear Physics Institute, 188300 St. Petersburg (Russian Federation)

    2012-11-15

    We develop a general approach to nonequilibrium nanostructures formed by one-dimensional channels coupled by tunnel junctions and/or by impurity scattering. The formalism is based on nonequilibrium version of functional bosonization. A central role in this approach is played by the Keldysh action that has a form reminiscent of the theory of full counting statistics. To proceed with evaluation of physical observables, we assume the weak-tunneling regime and develop a real-time instanton method. A detailed exposition of the formalism is supplemented by two important applications: (i) tunneling into a biased Luttinger liquid with an impurity, and (ii) quantum Hall Fabry-Perot interferometry. - Highlights: Black-Right-Pointing-Pointer A nonequilibrium functional bosonization framework for quantum wire networks is developed Black-Right-Pointing-Pointer For the study of observables in the weak tunneling regime a real-time instanton method is elaborated. Black-Right-Pointing-Pointer We consider tunneling into a biased Luttinger liquid with an impurity. Black-Right-Pointing-Pointer We analyze electronic Fabry-Perot interferometers in the integer quantum Hall regime.

  2. Analysis of Neural Networks through Base Functions

    NARCIS (Netherlands)

    Zwaag, van der B.J.; Slump, C.H.; Spaanenburg, L.

    2002-01-01

    Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more

  3. The Endoplasmic Reticulum: A Social Network in Plant Cells

    Institute of Scientific and Technical Information of China (English)

    Jun Chen; Caitlin Doyle; Xingyun Qi; Huanquan Zheng

    2012-01-01

    The endoplasmic reticulum (ER) is an interconnected network comprised of ribosome-studded sheets and smooth tubules.The ER plays crucial roles in the biosynthesis and transport of proteins and lipids,and in calcium (Ca2+) regulation in compartmentalized eukaryotic cells including plant cells.To support its well-segregated functions,the shape of the ER undergoes notable changes in response to both developmental cues and outside influences.In this review,we will discuss recent findings on molecular mechanisms underlying the unique morphology and dynamics of the ER,and the importance of the interconnected ER network in cell polarity.In animal and yeast cells,two family proteins,the reticulons and DP1/Yop1,are required for shaping high-curvature ER tubules,while members of the atlastin family of dynamin-like GTPases are involved in the fusion of ER tubules to make an interconnected ER network.In plant cells,recent data also indicate that the reticulons are involved in shaping ER tubules,while RHD3,a plant member of the atlastin GTPases,is required for the generation of an interconnected ER network.We will also summarize the current knowledge on how the ER interacts with other membrane-bound organelles,with a focus on how the ER and Golgi interplay in plant cells.

  4. Sox17-Mediated XEN Cell Conversion Identifies Dynamic Networks Controlling Cell-Fate Decisions in Embryo-Derived Stem Cells

    Directory of Open Access Journals (Sweden)

    Angela C.H. McDonald

    2014-10-01

    Full Text Available Little is known about the gene regulatory networks (GRNs distinguishing extraembryonic endoderm (ExEn stem (XEN cells from those that maintain the extensively characterized embryonic stem cell (ESC. An intriguing network candidate is Sox17, an essential transcription factor for XEN derivation and self-renewal. Here, we show that forced Sox17 expression drives ESCs toward ExEn, generating XEN cells that contribute to ExEn when placed back into early mouse embryos. Transient Sox17 expression is sufficient to drive this fate change during which time cells transit through distinct intermediate states prior to the generation of functional XEN-like cells. To orchestrate this conversion process, Sox17 acts in autoregulatory and feedforward network motifs, regulating dynamic GRNs directing cell fate. Sox17-mediated XEN conversion helps to explain the regulation of cell-fate changes and reveals GRNs regulating lineage decisions in the mouse embryo.

  5. A Functional Complexity Framework for the Analysis of Telecommunication Networks

    CERN Document Server

    Dzaferagic, Merim; Macaluso, Irene; Marchetti, Nicola

    2016-01-01

    The rapid evolution of network services demands new paradigms for studying and designing networks. In order to understand the underlying mechanisms that provide network functions, we propose a framework which enables the functional analysis of telecommunication networks. This framework allows us to isolate and analyse a network function as a complex system. We propose functional topologies to visualise the relationships between system entities and enable the systematic study of interactions between them. We also define a complexity metric $C_F$ (functional complexity) which quantifies the variety of structural patterns and roles of nodes in the topology. This complexity metric provides a wholly new approach to study the operation of telecommunication networks. We study the relationship between $C_F$ and different graph structures by analysing graph theory metrics in order to recognize complex organisations. $C_F$ is equal to zero for both a full mesh topology and a disconnected topology. We show that complexi...

  6. Hierarchical feedback modules and reaction hubs in cell signaling networks.

    Directory of Open Access Journals (Sweden)

    Jianfeng Xu

    Full Text Available Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.

  7. Differential Protein Network Analysis of the Immune Cell Lineage

    Directory of Open Access Journals (Sweden)

    Trevor Clancy

    2014-01-01

    Full Text Available Recently, the Immunological Genome Project (ImmGen completed the first phase of the goal to understand the molecular circuitry underlying the immune cell lineage in mice. That milestone resulted in the creation of the most comprehensive collection of gene expression profiles in the immune cell lineage in any model organism of human disease. There is now a requisite to examine this resource using bioinformatics integration with other molecular information, with the aim of gaining deeper insights into the underlying processes that characterize this immune cell lineage. We present here a bioinformatics approach to study differential protein interaction mechanisms across the entire immune cell lineage, achieved using affinity propagation applied to a protein interaction network similarity matrix. We demonstrate that the integration of protein interaction networks with the most comprehensive database of gene expression profiles of the immune cells can be used to generate hypotheses into the underlying mechanisms governing the differentiation and the differential functional activity across the immune cell lineage. This approach may not only serve as a hypothesis engine to derive understanding of differentiation and mechanisms across the immune cell lineage, but also help identify possible immune lineage specific and common lineage mechanism in the cells protein networks.

  8. Changes in cognitive state alter human functional brain networks

    Directory of Open Access Journals (Sweden)

    Malaak Nasser Moussa

    2011-08-01

    Full Text Available The study of the brain as a whole system can be accomplished using network theory principles. Research has shown that human functional brain networks during a resting state exhibit small-world properties and high degree nodes, or hubs, localized to brain areas consistent with the default mode network (DMN. However, the study of brain networks across different tasks and or cognitive states has been inconclusive. Research in this field is important because the underpinnings of behavioral output are inherently dependent on whether or not brain networks are dynamic. This is the first comprehensive study to evaluate multiple network metrics at a voxel-wise resolution in the human brain at both the whole brain and regional level under various conditions: resting state, visual stimulation, and multisensory (auditory and visual stimulation. Our results show that despite global network stability, functional brain networks exhibit considerable task-induced changes in connectivity, efficiency, and community structure at the regional level.

  9. Novel Sequential Neural Network Learning Algorithm for Function Approximation

    Institute of Scientific and Technical Information of China (English)

    KANG Huai-qi; SHI Cai-cheng; HE Pei-kun; LI Xiao-qiong

    2007-01-01

    A novel sequential neural network learning algorithm for function approximation is presented.The multi-step-ahead output predictor of the stochastic time series is introduced to the growing and pruning network for constructing network structure.And the network parameters are adjusted by the proportional differential filter (PDF) rather than EKF when the network growing criteria are not met.Experimental results show that the proposed algorithm can obtain a more compact network along with a smaller error in mean square sense than other typical sequential learning algorithms.

  10. Diabetes and Stem Cell Function

    Directory of Open Access Journals (Sweden)

    Shin Fujimaki

    2015-01-01

    Full Text Available Diabetes mellitus is one of the most common serious metabolic diseases that results in hyperglycemia due to defects of insulin secretion or insulin action or both. The present review focuses on the alterations to the diabetic neuronal tissues and skeletal muscle, including stem cells in both tissues, and the preventive effects of physical activity on diabetes. Diabetes is associated with various nervous disorders, such as cognitive deficits, depression, and Alzheimer’s disease, and that may be caused by neural stem cell dysfunction. Additionally, diabetes induces skeletal muscle atrophy, the impairment of energy metabolism, and muscle weakness. Similar to neural stem cells, the proliferation and differentiation are attenuated in skeletal muscle stem cells, termed satellite cells. However, physical activity is very useful for preventing the diabetic alteration to the neuronal tissues and skeletal muscle. Physical activity improves neurogenic capacity of neural stem cells and the proliferative and differentiative abilities of satellite cells. The present review proposes physical activity as a useful measure for the patients in diabetes to improve the physiological functions and to maintain their quality of life. It further discusses the use of stem cell-based approaches in the context of diabetes treatment.

  11. Diabetes and stem cell function.

    Science.gov (United States)

    Fujimaki, Shin; Wakabayashi, Tamami; Takemasa, Tohru; Asashima, Makoto; Kuwabara, Tomoko

    2015-01-01

    Diabetes mellitus is one of the most common serious metabolic diseases that results in hyperglycemia due to defects of insulin secretion or insulin action or both. The present review focuses on the alterations to the diabetic neuronal tissues and skeletal muscle, including stem cells in both tissues, and the preventive effects of physical activity on diabetes. Diabetes is associated with various nervous disorders, such as cognitive deficits, depression, and Alzheimer's disease, and that may be caused by neural stem cell dysfunction. Additionally, diabetes induces skeletal muscle atrophy, the impairment of energy metabolism, and muscle weakness. Similar to neural stem cells, the proliferation and differentiation are attenuated in skeletal muscle stem cells, termed satellite cells. However, physical activity is very useful for preventing the diabetic alteration to the neuronal tissues and skeletal muscle. Physical activity improves neurogenic capacity of neural stem cells and the proliferative and differentiative abilities of satellite cells. The present review proposes physical activity as a useful measure for the patients in diabetes to improve the physiological functions and to maintain their quality of life. It further discusses the use of stem cell-based approaches in the context of diabetes treatment.

  12. Estimation of spatiotemporal neural activity using radial basis function networks.

    Science.gov (United States)

    Anderson, R W; Das, S; Keller, E L

    1998-12-01

    We report a method using radial basis function (RBF) networks to estimate the time evolution of population activity in topologically organized neural structures from single-neuron recordings. This is an important problem in neuroscience research, as such estimates may provide insights into systems-level function of these structures. Since single-unit neural data tends to be unevenly sampled and highly variable under similar behavioral conditions, obtaining such estimates is a difficult task. In particular, a class of cells in the superior colliculus called buildup neurons can have very narrow regions of saccade vectors for which they discharge at high rates but very large surround regions over which they discharge at low, but not zero, levels. Estimating the dynamic movement fields for these cells for two spatial dimensions at closely spaced timed intervals is a difficult problem, and no general method has been described that can be applied to all buildup cells. Estimation of individual collicular cells' spatiotemporal movement fields is a prerequisite for obtaining reliable two-dimensional estimates of the population activity on the collicular motor map during saccades. Therefore, we have developed several computational-geometry-based algorithms that regularize the data before computing a surface estimation using RBF networks. The method is then expanded to the problem of estimating simultaneous spatiotemporal activity occurring across the superior colliculus during a single movement (the inverse problem). In principle, this methodology could be applied to any neural structure with a regular, two-dimensional organization, provided a sufficient spatial distribution of sampled neurons is available.

  13. Ultrananocrystalline diamond thin films functionalized with therapeutically active collagen networks.

    Energy Technology Data Exchange (ETDEWEB)

    Huang, H.; Chen, M.; Bruno, P.; Lam, R.; Robinson, E.; Gruen, D.; Ho, D.; Materials Science Division; Northwestern Univ.

    2009-01-01

    The fabrication of biologically amenable interfaces in medicine bridges translational technologies with their surrounding biological environment. Functionalized nanomaterials catalyze this coalescence through the creation of biomimetic and active substrates upon which a spectrum of therapeutic elements can be delivered to adherent cells to address biomolecular processes in cancer, inflammation, etc. Here, we demonstrate the robust functionalization of ultrananocrystalline diamond (UNCD) with type I collagen and dexamethasone (Dex), an anti-inflammatory drug, to fabricate a hybrid therapeutically active substrate for localized drug delivery. UNCD oxidation coupled with a pH-mediated collagen adsorption process generated a comprehensive interface between the two materials, and subsequent Dex integration, activity, and elution were confirmed through inflammatory gene expression assays. These studies confer a translational relevance to the biofunctionalized UNCD in its role as an active therapeutic network for potent regulation of cellular activity toward applications in nanomedicine.

  14. Fuzzy adaptive learning control network with sigmoid membership function

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    To get simpler operation in modified fuzzy adaptive learning control network (FALCON) in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing (SA) learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived;and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.

  15. Enhancing the functional content of eukaryotic protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Gaurav Pandey

    Full Text Available Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over 100 GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the HC.cont measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks.

  16. Cognitive fitness of cost-efficient brain functional networks

    OpenAIRE

    Bassett, Danielle S; Bullmore, Edward T.; Meyer-Lindenberg, Andreas; Apud, José A; Weinberger, Daniel R.; Coppola, Richard

    2009-01-01

    The human brain's capacity for cognitive function is thought to depend on coordinated activity in sparsely connected, complex networks organized over many scales of space and time. Recent work has demonstrated that human brain networks constructed from neuroimaging data have economical small-world properties that confer high efficiency of information processing at relatively low connection cost. However, it has been unclear how the architecture of complex brain networks functioning at differe...

  17. Functional brain networks associated with eating behaviors in obesity

    OpenAIRE

    Bo-yong Park; Jongbum Seo; Hyunjin Park

    2016-01-01

    Obesity causes critical health problems including diabetes and hypertension that affect billions of people worldwide. Obesity and eating behaviors are believed to be closely linked but their relationship through brain networks has not been fully explored. We identified functional brain networks associated with obesity and examined how the networks were related to eating behaviors. Resting state functional magnetic resonance imaging (MRI) scans were obtained for 82 participants. Data were from...

  18. Radial basis function network design for chaotic time series prediction

    Energy Technology Data Exchange (ETDEWEB)

    Shin, Chang Yong; Kim, Taek Soo; Park, Sang Hui [Yonsei University, Seoul (Korea, Republic of); Choi, Yoon Ho [Kyonggi University, Suwon (Korea, Republic of)

    1996-04-01

    In this paper, radial basis function networks with two hidden layers, which employ the K-means clustering method and the hierarchical training, are proposed for improving the short-term predictability of chaotic time series. Furthermore the recursive training method of radial basis function network using the recursive modified Gram-Schmidt algorithm is proposed for the purpose. In addition, the radial basis function networks trained by the proposed training methods are compared with the X.D. He A Lapedes`s model and the radial basis function network by non-recursive training method. Through this comparison, an improved radial basis function network for predicting chaotic time series is presented. (author). 17 refs., 8 figs., 3 tabs.

  19. Rational design of functional and tunable oscillating enzymatic networks

    Science.gov (United States)

    Semenov, Sergey N.; Wong, Albert S. Y.; van der Made, R. Martijn; Postma, Sjoerd G. J.; Groen, Joost; van Roekel, Hendrik W. H.; de Greef, Tom F. A.; Huck, Wilhelm T. S.

    2015-02-01

    Life is sustained by complex systems operating far from equilibrium and consisting of a multitude of enzymatic reaction networks. The operating principles of biology's regulatory networks are known, but the in vitro assembly of out-of-equilibrium enzymatic reaction networks has proved challenging, limiting the development of synthetic systems showing autonomous behaviour. Here, we present a strategy for the rational design of programmable functional reaction networks that exhibit dynamic behaviour. We demonstrate that a network built around autoactivation and delayed negative feedback of the enzyme trypsin is capable of producing sustained oscillating concentrations of active trypsin for over 65 h. Other functions, such as amplification, analog-to-digital conversion and periodic control over equilibrium systems, are obtained by linking multiple network modules in microfluidic flow reactors. The methodology developed here provides a general framework to construct dissipative, tunable and robust (bio)chemical reaction networks.

  20. Transcriptional Regulatory Network for the Development of Innate Lymphoid Cells

    Directory of Open Access Journals (Sweden)

    Chao Zhong

    2015-01-01

    Full Text Available Recent studies on innate lymphoid cells (ILCs have expanded our knowledge about the innate arm of the immune system. Helper-like ILCs share both the “innate” feature of conventional natural killer (cNK cells and the “helper” feature of CD4+ T helper (Th cells. With this combination, helper-like ILCs are capable of initiating early immune responses similar to cNK cells, but via secretion of a set of effector cytokines similar to those produced by Th cells. Although many studies have revealed the functional similarity between helper-like ILCs and Th cells, some aspects of ILCs including the development of this lineage remain elusive. It is intriguing that the majority of transcription factors involved in multiple stages of T cell development, differentiation, and function also play critical roles during ILC development. Regulators such as Id2, GATA-3, Nfil3, TOX, and TCF-1 are expressed and function at various stages of ILC development. In this review, we will summarize the expression and functions of these transcription factors shared by ILCs and Th cells. We will also propose a complex transcriptional regulatory network for the lineage commitment of ILCs.

  1. Emotion-Induced Topological Changes in Functional Brain Networks.

    Science.gov (United States)

    Park, Chang-Hyun; Lee, Hae-Kook; Kweon, Yong-Sil; Lee, Chung Tai; Kim, Ki-Tae; Kim, Young-Joo; Lee, Kyoung-Uk

    2016-01-01

    In facial expression perception, a distributed network is activated according to stimulus context. We proposed that an interaction between brain activation and stimulus context in response to facial expressions could signify a pattern of interactivity across the whole brain network beyond the face processing network. Functional magnetic resonance imaging data were acquired for 19 young healthy subjects who were exposed to either emotionally neutral or negative facial expressions. We constructed group-wise functional brain networks for 12 face processing areas [bilateral inferior occipital gyri (IOG), fusiform gyri (FG), superior temporal sulci (STS), amygdalae (AMG), inferior frontal gyri (IFG), and orbitofrontal cortices (OFC)] and for 73 whole brain areas, based on partial correlation of mean activation across subjects. We compared the topological properties of the networks with respect to functional distance-based measures, global and local efficiency, between the two types of face stimulus. In both face processing and whole brain networks, global efficiency was lower and local efficiency was higher for negative faces relative to neutral faces, indicating that network topology differed according to stimulus context. Particularly in the face processing network, emotion-induced changes in network topology were attributable to interactions between core (bilateral IOG, FG, and STS) and extended (bilateral AMG, IFG, and OFC) systems. These results suggest that changes in brain activation patterns in response to emotional face stimuli could be revealed as changes in the topological properties of functional brain networks for the whole brain as well as for face processing areas.

  2. Dissecting Germ Cell Metabolism through Network Modeling.

    Directory of Open Access Journals (Sweden)

    Leanne S Whitmore

    Full Text Available Metabolic pathways are increasingly postulated to be vital in programming cell fate, including stemness, differentiation, proliferation, and apoptosis. The commitment to meiosis is a critical fate decision for mammalian germ cells, and requires a metabolic derivative of vitamin A, retinoic acid (RA. Recent evidence showed that a pulse of RA is generated in the testis of male mice thereby triggering meiotic commitment. However, enzymes and reactions that regulate this RA pulse have yet to be identified. We developed a mouse germ cell-specific metabolic network with a curated vitamin A pathway. Using this network, we implemented flux balance analysis throughout the initial wave of spermatogenesis to elucidate important reactions and enzymes for the generation and degradation of RA. Our results indicate that primary RA sources in the germ cell include RA import from the extracellular region, release of RA from binding proteins, and metabolism of retinal to RA. Further, in silico knockouts of genes and reactions in the vitamin A pathway predict that deletion of Lipe, hormone-sensitive lipase, disrupts the RA pulse thereby causing spermatogenic defects. Examination of other metabolic pathways reveals that the citric acid cycle is the most active pathway. In addition, we discover that fatty acid synthesis/oxidation are the primary energy sources in the germ cell. In summary, this study predicts enzymes, reactions, and pathways important for germ cell commitment to meiosis. These findings enhance our understanding of the metabolic control of germ cell differentiation and will help guide future experiments to improve reproductive health.

  3. Dimensionality reduction in conic section function neural network

    Indian Academy of Sciences (India)

    Tulay Yildirim; Lale Ozyilmaz

    2002-12-01

    This paper details how dimensionality can be reduced in conic section function neural networks (CSFNN). This is particularly important for hardware implementation of networks. One of the main problems to be solved when considering the hardware design is the high connectivity requirement. If the effect that each of the network inputs has on the network output after training a neural network is known, then some inputs can be removed from the network. Consequently, the dimensionality of the network, and hence, the connectivity and the training time can be reduced. Sensitivity analysis, which extracts the cause and effect relationship between the inputs and outputs of the network, has been proposed as a method to achieve this and is investigated for Iris plant, thyroid disease and ionosphere databases. Simulations demonstrate the validity of the method used.

  4. On the Inference of Functional Circadian Networks Using Granger Causality.

    Science.gov (United States)

    Pourzanjani, Arya; Herzog, Erik D; Petzold, Linda R

    2015-01-01

    Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals.

  5. On the Inference of Functional Circadian Networks Using Granger Causality

    Science.gov (United States)

    Pourzanjani, Arya; Herzog, Erik D.; Petzold, Linda R.

    2015-01-01

    Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals. PMID:26413748

  6. Network-Level Structure-Function Relationships in Human Neocortex.

    Science.gov (United States)

    Mišić, Bratislav; Betzel, Richard F; de Reus, Marcel A; van den Heuvel, Martijn P; Berman, Marc G; McIntosh, Anthony R; Sporns, Olaf

    2016-07-01

    The dynamics of spontaneous fluctuations in neural activity are shaped by underlying patterns of anatomical connectivity. While numerous studies have demonstrated edge-wise correspondence between structural and functional connections, much less is known about how large-scale coherent functional network patterns emerge from the topology of structural networks. In the present study, we deploy a multivariate statistical technique, partial least squares, to investigate the association between spatially extended structural networks and functional networks. We find multiple statistically robust patterns, reflecting reliable combinations of structural and functional subnetworks that are optimally associated with one another. Importantly, these patterns generally do not show a one-to-one correspondence between structural and functional edges, but are instead distributed and heterogeneous, with many functional relationships arising from nonoverlapping sets of anatomical connections. We also find that structural connections between high-degree hubs are disproportionately represented, suggesting that these connections are particularly important in establishing coherent functional networks. Altogether, these results demonstrate that the network organization of the cerebral cortex supports the emergence of diverse functional network configurations that often diverge from the underlying anatomical substrate. PMID:27102654

  7. Network Function Virtualization Technology:Progress and Standardization

    Institute of Scientific and Technical Information of China (English)

    Huiling Zhao; Yunpeng Xie; Fan Shi

    2014-01-01

    Network innovation and business transformation are both necessary for telecom operators to adapt to new situations, but operators face challenges in terms of network bearer complexity, business centralization, and IT/CT integration. Network function virtualiza-tion (NFV) may inspire new development ideas, but many doubts still exist within industry, especially about how to introduce NFV into an operator ’s network. This article describes the latest progress in NFV standardization, NFV requirements and hot technology issues, and typical NFV applications in an operator networks.

  8. Density functional and neural network analysis

    DEFF Research Database (Denmark)

    Jalkanen, K. J.; Suhai, S.; Bohr, Henrik

    1997-01-01

    dichroism (VCD) intensities. The large changes due to hydration on the structures, relative stability of conformers, and in the VA and VCD spectra observed experimentally are reproduced by the DFT calculations. Furthermore a neural network was constructed for reproducing the inverse scattering data (infer...... the structural coordinates from spectroscopic data) that the DFT method could produce. Finally the neural network performances are used to monitor a sensitivity or dependence analysis of the importance of secondary structures....

  9. Systematic Functional Annotation and Visualization of Biological Networks.

    Science.gov (United States)

    Baryshnikova, Anastasia

    2016-06-22

    Large-scale biological networks represent relationships between genes, but our understanding of how networks are functionally organized is limited. Here, I describe spatial analysis of functional enrichment (SAFE), a systematic method for annotating biological networks and examining their functional organization. SAFE visualizes the network in 2D space and measures the continuous distribution of functional enrichment across local neighborhoods, producing a list of the associated functions and a map of their relative positioning. I applied SAFE to annotate the Saccharomyces cerevisiae genetic interaction similarity network and protein-protein interaction network with gene ontology terms. SAFE annotations of the genetic network matched manually derived annotations, while taking less than 1% of the time, and proved robust to noise and sensitive to biological signal. Integration of genetic interaction and chemical genomics data using SAFE revealed a link between vesicle-mediate transport and resistance to the anti-cancer drug bortezomib. These results demonstrate the utility of SAFE for examining biological networks and understanding their functional organization. PMID:27237738

  10. Using Radial-basis Function Network for CLV

    Institute of Scientific and Technical Information of China (English)

    李纯青; 郑建国

    2002-01-01

    Analysis and comparing with three existing and popularly used forcasting customer lifetime value (CLV) methods, which are the Dwyer method, customer event-method and fitting method, and using performances of the existent artificial neural network, we apply the Radial-basis Function(RBF) network to forecast the CLV, the RBF network can approach the objective function partially. To every input/output pairs, it only needs adjust the weight a little and learn quickly which is very important to the forecast precision. Simulation and experimental results on the customers' data of a company in Shaanxi Province reveal the effectiveness and feasibility of the RBF network.

  11. Genes2FANs: connecting genes through functional association networks

    Directory of Open Access Journals (Sweden)

    Dannenfelser Ruth

    2012-07-01

    Full Text Available Abstract Background Protein-protein, cell signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs, researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent. Results Genes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI network to build subnetworks that connect lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user’s PubMed query. As a case study, we applied Genes2FANs to connect disease genes from 90 well-studied disorders. We find an inverse correlation between the counts of links connecting disease genes through PPI and links connecting diseases genes through FANs, separating diseases into two categories. Conclusions Genes2FANs is a useful tool for interpreting the relationships between gene/protein lists in the context of their various functions and networks. Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation. Our

  12. Microtubule networks for plant cell division.

    Science.gov (United States)

    de Keijzer, Jeroen; Mulder, Bela M; Janson, Marcel E

    2014-09-01

    During cytokinesis the cytoplasm of a cell is divided to form two daughter cells. In animal cells, the existing plasma membrane is first constricted and then abscised to generate two individual plasma membranes. Plant cells on the other hand divide by forming an interior dividing wall, the so-called cell plate, which is constructed by localized deposition of membrane and cell wall material. Construction starts in the centre of the cell at the locus of the mitotic spindle and continues radially towards the existing plasma membrane. Finally the membrane of the cell plate and plasma membrane fuse to form two individual plasma membranes. Two microtubule-based cytoskeletal networks, the phragmoplast and the pre-prophase band (PPB), jointly control cytokinesis in plants. The bipolar microtubule array of the phragmoplast regulates cell plate deposition towards a cortical position that is templated by the ring-shaped microtubule array of the PPB. In contrast to most animal cells, plants do not use centrosomes as foci of microtubule growth initiation. Instead, plant microtubule networks are striking examples of self-organizing systems that emerge from physically constrained interactions of dispersed microtubules. Here we will discuss how microtubule-based activities including growth, shrinkage, severing, sliding, nucleation and bundling interrelate to jointly generate the required ordered structures. Evidence mounts that adapter proteins sense the local geometry of microtubules to locally modulate the activity of proteins involved in microtubule growth regulation and severing. Many of the proteins and mechanisms involved have roles in other microtubule assemblies as well, bestowing broader relevance to insights gained from plants. PMID:25136380

  13. Response functions for electrically coupled neuronal network: a method of local point matching and its applications.

    Science.gov (United States)

    Yihe, Lu; Timofeeva, Yulia

    2016-06-01

    Neuronal networks connected by electrical synapses, also referred to as gap junctions, are present throughout the entire central nervous system. Many instances of gap-junctional coupling are formed between dendritic arbours of individual cells, and these dendro-dendritic gap junctions are known to play an important role in mediating various brain rhythms in both normal and pathological states. The dynamics of such neuronal networks modelled by passive or quasi-active (resonant) membranes can be described by the Green's function which provides the fundamental input-output relationships of the entire network. One of the methods for calculating this response function is the so-called 'sum-over-trips' framework which enables the construction of the Green's function for an arbitrary network as a convergent infinite series solution. Here we propose an alternative and computationally efficient approach for constructing the Green's functions on dendro-dendritic gap junction-coupled neuronal networks which avoids any infinite terms in the solutions. Instead, the Green's function is constructed from the solution of a system of linear algebraic equations. We apply this new method to a number of systems including a simple single cell model and two-cell neuronal networks. We also demonstrate that the application of this novel approach allows one to reduce a model with complex dendritic formations to an equivalent model with a much simpler morphological structure. PMID:26994016

  14. Changes in brain functional network connectivity after stroke

    Institute of Scientific and Technical Information of China (English)

    Wei Li; Yapeng Li; Wenzhen Zhu; Xi Chen

    2014-01-01

    Studies have shown that functional network connection models can be used to study brain net-work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their ifrst ever stroke. Using independent component analysis, six spatially independent components highly correlat-ed to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our ifndings suggest that functional network connectivity in stroke patients is more complex than that in hea-lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke.

  15. Cell Multiprocessor Communication Network: Built for Speed

    International Nuclear Information System (INIS)

    The existence of major obstacles to the traditional path to processor performance improvement has led chip manufacturers to consider multi-core designs. These architectural solutions promise a variety of power/performance and area/performance benefits. But additional care must be taken to ensure that these benefits are not lost due to inadequate design of the on-chip communication network. This paper presents the design challenges of the on-chip network of the Cell Broadband Engine (Cell BE) processor, and describes in detail its architectural design and the network, communication and synchronization protocols. In the experimental evaluation, performed on an early prototype, we analyze the communication characteristics of the Cell BE processor, using a series of microbenchmarks involving various DMA traffic patterns and synchronization protocols. We find that the on-chip communication subsystem is well matched to the to computational capacity of the processor. A Synergistic Processing Element (SPE) can issue an internal direct memory access (DMA) operation in less than 4 nanoseconds, and a DMA of a single cache line can be executed in less the than 100 nanoseconds. SPEs can achieve the optimal bandwidth of 25.6 GB/second in point to point communication with surprisingly small messages ?only a few KB, using batches of non-blocking DMAs. The aggregate network behavior under heavy load is also remarkably efficient, reaching almost 200 GB/second with collective patterns and optimal contention resolution under hot-spot traffic. Additionally, we demonstrate the consistency of these hardware results with identical experiments carried out using the Mambo simulator software for Cell BE

  16. Transcriptional regulatory networks for CD4 T cell differentiation.

    Science.gov (United States)

    Christie, Darah; Zhu, Jinfang

    2014-01-01

    CD4(+) T cells play a central role in controlling the adaptive immune response by secreting cytokines to activate target cells. Naïve CD4(+) T cells differentiate into at least four subsets, Th1Th1 , Th2Th2 , Th17Th17 , and inducible regulatory T cellsregulatory T cells , each with unique functions for pathogen elimination. The differentiation of these subsets is induced in response to cytokine stimulation, which is translated into Stat activation, followed by induction of master regulator transcription factorstranscription factors . In addition to these factors, multiple other transcription factors, both subset specific and shared, are also involved in promoting subset differentiation. This review will focus on the network of transcription factors that control CD4(+) T cell differentiation.

  17. A stochastic multi-scale model of electrical function in normal and depleted ICC networks.

    Science.gov (United States)

    Gao, Jerry; Du, Peng; Archer, Rosalind; O'Grady, Greg; Gibbons, Simon J; Farrugia, Gianrico; Cheng, Leo K; Pullan, Andrew J

    2011-12-01

    Multi-scale modeling has become a productive strategy for quantifying interstitial cells of Cajal (ICC) network structure-function relationships, but the lack of large-scale ICC network imaging data currently limits modeling progress. The single normal equation simulation (SNESIM) algorithm was utilized to generate realistic virtual images of small real wild-type (WT) and 5-HT(2B)-receptor knockout (Htr2b(-/-)) mice ICC networks. Two metrics were developed to validate the performance of the algorithm: 1) network density, which is the proportion of ICC in the tissue; and 2) connectivity, which reflects the degree of connectivity of the ICC network. Following validation, the SNESIM algorithm was modified to allow variation in the degree of ICC network depletion. ICC networks from a range of depletion severities were generated, and the electrical activity over these networks was simulated. The virtual ICC networks generated by the original SNESIM algorithm were similar to that of their real counterparts. The electrical activity simulations showed that the maximum current density magnitude increased as the network density increased. In conclusion, the SNESIM algorithm is an effective tool for generating realistic virtual ICC networks. The modified SNESIM algorithm can be used with simulation techniques to quantify the physiological consequences of ICC network depletion at various physical scales.

  18. Emotional cuing to test attentional network functioning in trait anxiety

    OpenAIRE

    Gómez Íñiguez, Consolación; Luis J Fuentes; Martínez-Sánchez, Francisco; Campoy, Guillermo; Montoro, Pedro J.; Palmero Cantero, Francisco

    2014-01-01

    The Attention Networks Test (ANT) has been widely used to assess the three attentional networks proposed by Posner and his collaborators. Here we present a version of the ANT that uses emotionally laden words as cues to evaluate the functioning of the attention networks and their interactions. University students participated in the task and the results replicated those found in previous studies with the original version of the test. Then, those with extreme scores on a trai...

  19. The Efficiency of a Small-World Functional Brain Network

    Institute of Scientific and Technical Information of China (English)

    ZHAO Qing-Bai; ZHANG Xiao-Fei; SUI Dan-Ni; ZHOU Zhi-Jin; CHEN Qi-Cai; TANG Yi-Yuan

    2012-01-01

    We investigate whether the small-world topology of a functional brain network means high information processing efficiency by calculating the correlation between the small-world measures of a functional brain network and behavioral reaction during an imagery task.Functional brain networks are constructed by multichannel eventrelated potential data,in which the electrodes are the nodes and the functional connectivities between them are the edges.The results show that the correlation between small-world measures and reaction time is task-specific,such that in global imagery,there is a positive correlation between the clustering coefficient and reaction time,while in local imagery the average path length is positively correlated with the reaction time.This suggests that the efficiency of a functional brain network is task-dependent.%We investigate whether the small-world topology of a functional brain network means high information processing efficiency by calculating the correlation between the small-world measures of a functional brain network and behavioral reaction during an imagery task. Functional brain networks are constructed by multichannel event-related potential data, in which the electrodes are the nodes and the functional connectivities between them are the edges. The results show that the correlation between small-world measures and reaction time is task-specific, such that in global imagery, there is a positive correlation between the clustering coefficient and reaction time, while in local imagery the average path length is positively correlated with the reaction time. This suggests that the efficiency of a functional brain network is task-dependent.

  20. Binary higher order neural networks for realizing Boolean functions.

    Science.gov (United States)

    Zhang, Chao; Yang, Jie; Wu, Wei

    2011-05-01

    In order to more efficiently realize Boolean functions by using neural networks, we propose a binary product-unit neural network (BPUNN) and a binary π-ς neural network (BPSNN). The network weights can be determined by one-step training. It is shown that the addition " σ," the multiplication " π," and two kinds of special weighting operations in BPUNN and BPSNN can implement the logical operators " ∨," " ∧," and " ¬" on Boolean algebra 〈Z(2),∨,∧,¬,0,1〉 (Z(2)={0,1}), respectively. The proposed two neural networks enjoy the following advantages over the existing networks: 1) for a complete truth table of N variables with both truth and false assignments, the corresponding Boolean function can be realized by accordingly choosing a BPUNN or a BPSNN such that at most 2(N-1) hidden nodes are needed, while O(2(N)), precisely 2(N) or at most 2(N), hidden nodes are needed by existing networks; 2) a new network BPUPS based on a collaboration of BPUNN and BPSNN can be defined to deal with incomplete truth tables, while the existing networks can only deal with complete truth tables; and 3) the values of the weights are all simply -1 or 1, while the weights of all the existing networks are real numbers. Supporting numerical experiments are provided as well. Finally, we present the risk bounds of BPUNN, BPSNN, and BPUPS, and then analyze their probably approximately correct learnability. PMID:21427020

  1. Advanced Functionalities for Highly Reliable Optical Networks

    DEFF Research Database (Denmark)

    An, Yi

    to increase the availability of highly reliable optical networks. A cost-effective transmitter based on a directly modulated laser (DML) using a silicon micro-ring resonator (MRR) to enhance its modulation speed is proposed, analysed and experimentally demonstrated. A modulation speed enhancement from 10 Gbit...

  2. Distribution of Cell in Mobile Network

    Directory of Open Access Journals (Sweden)

    Robert Bestak

    2015-01-01

    Full Text Available Femtocell concept has emerged as a cost-effective solution to manage indoor environment coverage and increasing capacity requirements. Compare to the conventional control macrocell deployment, femtocells are spread in the uncontrolled manner as they are deployed in network by customers themselves. This paper discusses multi-distance spatial analysis, Ripley's K function, to describe distribution of femtocells in a macrocell. In our study, we investigate various femtocell distributions and various numbers of femtocells in the macrocell.

  3. On the classification enhancement of radial basis function networks

    NARCIS (Netherlands)

    Ciftcioglu, O.; Durmisevic, S.; Sariyildiz, I.S.

    2001-01-01

    Artificial neural networks are powerfultools for analysing information expressed as data sets, which contain complex nonlinear relationships to be identified and classified. In particular radial basis function (RBF) neural networks have outstanding features for this. However, due to far reaching imp

  4. The Union of Shortest Path Trees of Functional Brain Networks

    NARCIS (Netherlands)

    Meier, J.; Tewarie, P.; Van Mieghem, P.

    2015-01-01

    Communication between brain regions is still insufficiently understood. Applying concepts from network science has shown to be successful in gaining insight in the functioning of the brain. Recent work has implicated that especially shortest paths in the structural brain network seem to play a major

  5. Universal approximation by radial basis function networks of Delsarte translates.

    Science.gov (United States)

    Arteaga, Cristian; Marrero, Isabel

    2013-10-01

    We prove that, under certain mild conditions on the kernel function (or activation function), the family of radial basis function neural networks obtained by replacing the usual translation with the Delsarte one, and taking the same smoothing factor in all kernel nodes, has the universal approximation property.

  6. A network function-based definition of communities in complex networks

    Science.gov (United States)

    Chauhan, Sanjeev; Girvan, Michelle; Ott, Edward

    2012-09-01

    We consider an alternate definition of community structure that is functionally motivated. We define network community structure based on the function the network system is intended to perform. In particular, as a specific example of this approach, we consider communities whose function is enhanced by the ability to synchronize and/or by resilience to node failures. Previous work has shown that, in many cases, the largest eigenvalue of the network's adjacency matrix controls the onset of both synchronization and percolation processes. Thus, for networks whose functional performance is dependent on these processes, we propose a method that divides a given network into communities based on maximizing a function of the largest eigenvalues of the adjacency matrices of the resulting communities. We also explore the differences between the partitions obtained by our method and the modularity approach (which is based solely on consideration of network structure). We do this for several different classes of networks. We find that, in many cases, modularity-based partitions do almost as well as our function-based method in finding functional communities, even though modularity does not specifically incorporate consideration of function.

  7. Joint Modelling of Structural and Functional Brain Networks

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther; Herlau, Tue; Mørup, Morten;

    Functional and structural magnetic resonance imaging have become the most important noninvasive windows to the human brain. A major challenge in the analysis of brain networks is to establish the similarities and dissimilarities between functional and structural connectivity. We formulate a non......-parametric Bayesian network model which allows for joint modelling and integration of multiple networks. We demonstrate the model’s ability to detect vertices that share structure across networks jointly in functional MRI (fMRI) and diffusion MRI (dMRI) data. Using two fMRI and dMRI scans per subject, we establish...... significant structures that are consistently shared across subjects and data splits. This provides an unsupervised approach for modeling of structure-function relations in the brain and provides a general framework for multimodal integration....

  8. The Function Biomedical Informatics Research Network Data Repository

    OpenAIRE

    Keator, DB; van Erp, TGM; Turner, JA; Glover, GH; Mueller, BA; Liu, TT; Voyvodic, JT; Rasmussen, J.; Calhoun, VD; Lee, HJ.; Toga, AW; McEwen, S.; Ford, JM; Mathalon, DH; Diaz, M

    2016-01-01

    © 2015 Elsevier Inc. The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associa...

  9. Nodal centrality of functional network in the differentiation of schizophrenia.

    Science.gov (United States)

    Cheng, Hu; Newman, Sharlene; Goñi, Joaquín; Kent, Jerillyn S; Howell, Josselyn; Bolbecker, Amanda; Puce, Aina; O'Donnell, Brian F; Hetrick, William P

    2015-10-01

    A disturbance in the integration of information during mental processing has been implicated in schizophrenia, possibly due to faulty communication within and between brain regions. Graph theoretic measures allow quantification of functional brain networks. Functional networks are derived from correlations between time courses of brain regions. Group differences between SZ and control groups have been reported for functional network properties, but the potential of such measures to classify individual cases has been little explored. We tested whether the network measure of betweenness centrality could classify persons with schizophrenia and normal controls. Functional networks were constructed for 19 schizophrenic patients and 29 non-psychiatric controls based on resting state functional MRI scans. The betweenness centrality of each node, or fraction of shortest-paths that pass through it, was calculated in order to characterize the centrality of the different regions. The nodes with high betweenness centrality agreed well with hub nodes reported in previous studies of structural and functional networks. Using a linear support vector machine algorithm, the schizophrenia group was differentiated from non-psychiatric controls using the ten nodes with the highest betweenness centrality. The classification accuracy was around 80%, and stable against connectivity thresholding. Better performance was achieved when using the ranks as feature space as opposed to the actual values of betweenness centrality. Overall, our findings suggest that changes in functional hubs are associated with schizophrenia, reflecting a variation of the underlying functional network and neuronal communications. In addition, a specific network property, betweenness centrality, can classify persons with SZ with a high level of accuracy. PMID:26299706

  10. Selectively disrupted functional connectivity networks in type 2 diabetes mellitus

    Directory of Open Access Journals (Sweden)

    Yaojing eChen

    2015-12-01

    Full Text Available Background: The high prevalence of type 2 diabetes mellitus (T2DM in individuals over 65 years old and cognitive deficits caused by T2DM have attracted broad attention. The pathophysiological mechanism of T2DM induced cognitive impairments, however, remains poorly understood. Previous studies have suggested that the cognitive impairments can be attributed not merely to local functional and structural abnormalities but also to specific brain networks. Thus, we aimed to investigate the changes of global networks selectively affected by T2DM. Methods: A resting state functional network analysis was conducted to investigate the intrinsic functional connectivity in 37 patients with diabetes and 40 healthy controls which were recruited from local communities in Beijing, China. Results: We found that patients with T2DM exhibited cognitive function declines and functional connectivity disruptions within the default mode network, left frontal parietal network, and sensorimotor network. More importantly, the fasting glucose level was correlated with abnormal functional connectivity.Conclusions: These findings could help to understand the neural mechanisms of cognitive impairments in T2DM and provide potential neuroimaging biomarkers that may be used for early diagnosis and intervention in cognitive decline.

  11. Functional brain networks associated with eating behaviors in obesity.

    Science.gov (United States)

    Park, Bo-Yong; Seo, Jongbum; Park, Hyunjin

    2016-01-01

    Obesity causes critical health problems including diabetes and hypertension that affect billions of people worldwide. Obesity and eating behaviors are believed to be closely linked but their relationship through brain networks has not been fully explored. We identified functional brain networks associated with obesity and examined how the networks were related to eating behaviors. Resting state functional magnetic resonance imaging (MRI) scans were obtained for 82 participants. Data were from an equal number of people of healthy weight (HW) and non-healthy weight (non-HW). Connectivity matrices were computed with spatial maps derived using a group independent component analysis approach. Brain networks and associated connectivity parameters with significant group-wise differences were identified and correlated with scores on a three-factor eating questionnaire (TFEQ) describing restraint, disinhibition, and hunger eating behaviors. Frontoparietal and cerebellum networks showed group-wise differences between HW and non-HW groups. Frontoparietal network showed a high correlation with TFEQ disinhibition scores. Both frontoparietal and cerebellum networks showed a high correlation with body mass index (BMI) scores. Brain networks with significant group-wise differences between HW and non-HW groups were identified. Parts of the identified networks showed a high correlation with eating behavior scores. PMID:27030024

  12. A framework for interpreting functional networks in schizophrenia

    Directory of Open Access Journals (Sweden)

    Peter eWilliamson

    2012-06-01

    Full Text Available Some promising genetic correlates of schizophrenia have emerged in recent years but none explain more than a small fraction of cases. The challenge of our time is to characterize the neuronal networks underlying schizophrenia and other neuropsychiatric illnesses. It has been proposed that schizophrenia arises from a uniquely human brain network associated with directed effort including the dorsal anterior and posterior cingulate cortex, auditory cortex, and hippocampus and while mood disorders arise from a different brain network associated with emotional encoding including the ventral anterior cingulate cortex, orbital frontal cortex, and amygdala. Both interact with a representation network including the frontal and temporal poles and the fronto-insular cortex, allowing the representation of the thoughts, feelings and actions of self and others. This paper reviews recent morphological and functional literature in light of the proposed networks underlying these disorders. It is suggested that there is considerable support for the involvement of the directed effort network in schizophrenia from studies of brain structure with voxel-based morphometry (VBM and diffusion tensor imaging (DTI. While early studies of resting brain networks are inconclusive, functional magnetic resonance imaging imaging (fMRI studies of task-related networks clearly implicate these regions. In keeping with the model, functional deficits in regions associated with directed effort and self-monitoring are associated with structural anomalies in action-related regions in schizophrenic patients. VBM, DTI, fMRI studies of mood disordered patients support the involvement of a different network associated with emotional encoding. The distinction between disorders is enhanced by combining structural and functional data. It is concluded that brain networks associated with directed effort are particularly vulnerable to failure in the human brain leading to the symptoms of

  13. Assortative mixing in functional brain networks during epileptic seizures

    CERN Document Server

    Bialonski, Stephan

    2013-01-01

    We investigate assortativity of functional brain networks before, during, and after one-hundred epileptic seizures with different anatomical onset locations. We construct binary functional networks from multi-channel electroencephalographic data recorded from 60 epilepsy patients, and from time-resolved estimates of the assortativity coefficient we conclude that positive degree-degree correlations are inherent to seizure dynamics. While seizures evolve, an increasing assortativity indicates a segregation of the underlying functional network into groups of brain regions that are only sparsely interconnected, if at all. Interestingly, assortativity decreases already prior to seizure end. Together with previous observations of characteristic temporal evolutions of global statistical properties and synchronizability of epileptic brain networks, our findings may help to gain deeper insights into the complicated dynamics underlying generation, propagation, and termination of seizures.

  14. Orthogonal least squares learning algorithm for radial basis function networks

    Energy Technology Data Exchange (ETDEWEB)

    Chen, S.; Cowan, C.F.N.; Grant, P.M. (Dept. of Electrical Engineering, Univ. of Edinburgh, Mayfield Road, Edinburgh EH9 3JL, Scotland (GB))

    1991-03-01

    The radial basis function network offers a viable alternative to the two-layer neural network in many applications of signal processing. A common learning algorithm for radial basis function networks is based on first choosing randomly some data points as radial basis function centers and then using singular value decomposition to solve for the weights of the network. Such a procedure has several drawbacks and, in particular, an arbitrary selection of centers is clearly unsatisfactory. The paper proposes an alternative learning procedure based on the orthogonal least squares method. The procedure choose radial basis function centers one by one in a rational way until an adequate network has been constructed. The algorithm has the property that each selected center maximizes the increment to the explained variance or energy of the desired output and does not suffer numerical ill-conditioning problems. The orthogonal least squares learning strategy provides a simple and efficient means for fitting radial basis function networks, and this is illustrated using examples taken from two different signal processing applications.

  15. Orthogonal least squares learning algorithm for radial basis function networks.

    Science.gov (United States)

    Chen, S; Cowan, C N; Grant, P M

    1991-01-01

    The radial basis function network offers a viable alternative to the two-layer neural network in many applications of signal processing. A common learning algorithm for radial basis function networks is based on first choosing randomly some data points as radial basis function centers and then using singular-value decomposition to solve for the weights of the network. Such a procedure has several drawbacks, and, in particular, an arbitrary selection of centers is clearly unsatisfactory. The authors propose an alternative learning procedure based on the orthogonal least-squares method. The procedure chooses radial basis function centers one by one in a rational way until an adequate network has been constructed. In the algorithm, each selected center maximizes the increment to the explained variance or energy of the desired output and does not suffer numerical ill-conditioning problems. The orthogonal least-squares learning strategy provides a simple and efficient means for fitting radial basis function networks. This is illustrated using examples taken from two different signal processing applications.

  16. Uncovering Biological Network Function via Graphlet Degree Signatures

    Directory of Open Access Journals (Sweden)

    Nataša Pržulj

    2008-01-01

    Full Text Available Motivation: Proteins are essential macromolecules of life and thus understanding their function is of great importance. The number of functionally unclassified proteins is large even for simple and well studied organisms such as baker’s yeast. Methods for determining protein function have shifted their focus from targeting specific proteins based solely on sequence homology to analyses of the entire proteome based on protein-protein interaction (PPI networks. Since proteins interact to perform a certain function, analyzing structural properties of PPI networks may provide useful clues about the biological function of individual proteins, protein complexes they participate in, and even larger subcellular machines.Results: We design a sensitive graph theoretic method for comparing local structures of node neighborhoods that demonstrates that in PPI networks, biological function of a node and its local network structure are closely related. The method summarizes a protein’s local topology in a PPI network into the vector of graphlet degrees called the signature of the protein and computes the signature similarities between all protein pairs. We group topologically similar proteins under this measure in a PPI network and show that these protein groups belong to the same protein complexes, perform the same biological functions, are localized in the same subcellular compartments, and have the same tissue expressions. Moreover, we apply our technique on a proteome-scale network data and infer biological function of yet unclassified proteins demonstrating that our method can provide valuable guidelines for future experimental research such as disease protein prediction.Availability: Data is available upon request.

  17. A functional and regulatory network associated with PIP expression in human breast cancer.

    Directory of Open Access Journals (Sweden)

    Marie-Anne Debily

    Full Text Available BACKGROUND: The PIP (prolactin-inducible protein gene has been shown to be expressed in breast cancers, with contradictory results concerning its implication. As both the physiological role and the molecular pathways in which PIP is involved are poorly understood, we conducted combined gene expression profiling and network analysis studies on selected breast cancer cell lines presenting distinct PIP expression levels and hormonal receptor status, to explore the functional and regulatory network of PIP co-modulated genes. PRINCIPAL FINDINGS: Microarray analysis allowed identification of genes co-modulated with PIP independently of modulations resulting from hormonal treatment or cell line heterogeneity. Relevant clusters of genes that can discriminate between [PIP+] and [PIP-] cells were identified. Functional and regulatory network analyses based on a knowledge database revealed a master network of PIP co-modulated genes, including many interconnecting oncogenes and tumor suppressor genes, half of which were detected as differentially expressed through high-precision measurements. The network identified appears associated with an inhibition of proliferation coupled with an increase of apoptosis and an enhancement of cell adhesion in breast cancer cell lines, and contains many genes with a STAT5 regulatory motif in their promoters. CONCLUSIONS: Our global exploratory approach identified biological pathways modulated along with PIP expression, providing further support for its good prognostic value of disease-free survival in breast cancer. Moreover, our data pointed to the importance of a regulatory subnetwork associated with PIP expression in which STAT5 appears as a potential transcriptional regulator.

  18. Control and Communication Network in Hybrid Fuel Cell Vehicles

    Institute of Scientific and Technical Information of China (English)

    朱元; 吴昊; 田光宇; 阳宪惠; 赵立安; 周伟波

    2004-01-01

    This paper describes the control and communication network in fuel cell vehicles, including both the protocol and the hardware.Based on the current protocol (ISO-11898 and SAE J1939), a new practical protocol is proposed and implemented for the control and communication network in fuel cell vehicles.To improve the reliability of data communication and to unify the network management, a new network system based on dual-port RAM is also implemented.

  19. Mapping Multiplex Hubs in Human Functional Brain Networks.

    Science.gov (United States)

    De Domenico, Manlio; Sasai, Shuntaro; Arenas, Alex

    2016-01-01

    Typical brain networks consist of many peripheral regions and a few highly central ones, i.e., hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. First, we show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. We then demonstrate that hubs in the multiplex network, in general different from those ones obtained after discarding or aggregating the measured signals as usual, provide a more accurate map of brain's most important functional regions, allowing to distinguish between healthy and schizophrenic populations better than conventional network approaches. PMID:27471443

  20. EEG-based research on brain functional networks in cognition.

    Science.gov (United States)

    Wang, Niannian; Zhang, Li; Liu, Guozhong

    2015-01-01

    Recently, exploring the cognitive functions of the brain by establishing a network model to understand the working mechanism of the brain has become a popular research topic in the field of neuroscience. In this study, electroencephalography (EEG) was used to collect data from subjects given four different mathematical cognitive tasks: recite numbers clockwise and counter-clockwise, and letters clockwise and counter-clockwise to build a complex brain function network (BFN). By studying the connectivity features and parameters of those brain functional networks, it was found that the average clustering coefficient is much larger than its corresponding random network and the average shortest path length is similar to the corresponding random networks, which clearly shows the characteristics of the small-world network. The brain regions stimulated during the experiment are consistent with traditional cognitive science regarding learning, memory, comprehension, and other rational judgment results. The new method of complex networking involves studying the mathematical cognitive process of reciting, providing an effective research foundation for exploring the relationship between brain cognition and human learning skills and memory. This could help detect memory deficits early in young and mentally handicapped children, and help scientists understand the causes of cognitive brain disorders. PMID:26405867

  1. Development of large-scale functional brain networks in children.

    Directory of Open Access Journals (Sweden)

    Kaustubh Supekar

    2009-07-01

    Full Text Available The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7-9 y and 22 young-adults (ages 19-22 y. Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar "small-world" organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism.

  2. Development of large-scale functional brain networks in children.

    Science.gov (United States)

    Supekar, Kaustubh; Musen, Mark; Menon, Vinod

    2009-07-01

    The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7-9 y) and 22 young-adults (ages 19-22 y). Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar "small-world" organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism. PMID:19621066

  3. Reorganization of functional networks in mild cognitive impairment.

    Directory of Open Access Journals (Sweden)

    Javier M Buldú

    Full Text Available Whether the balance between integration and segregation of information in the brain is damaged in Mild Cognitive Impairment (MCI subjects is still a matter of debate. Here we characterize the functional network architecture of MCI subjects by means of complex networks analysis. Magnetoencephalograms (MEG time series obtained during a memory task were evaluated by synchronization likelihood (SL, to quantify the statistical dependence between MEG signals and to obtain the functional networks. Graphs from MCI subjects show an enhancement of the strength of connections, together with an increase in the outreach parameter, suggesting that memory processing in MCI subjects is associated with higher energy expenditure and a tendency toward random structure, which breaks the balance between integration and segregation. All features are reproduced by an evolutionary network model that simulates the degenerative process of a healthy functional network to that associated with MCI. Due to the high rate of conversion from MCI to Alzheimer Disease (AD, these results show that the analysis of functional networks could be an appropriate tool for the early detection of both MCI and AD.

  4. Integration of macromolecular diffraction data using radial basis function networks.

    Science.gov (United States)

    Pokrić, B; Allinson, N M; Helliwell, J R

    2000-11-01

    This paper presents a novel approach for intensity calculation of X-ray diffraction spots based on a two-stage radial basis function (RBF) network. The first stage uses pre-determined reference profiles from a database as basis functions in order to locate the diffraction spots and identify any overlapping regions. The second-stage RBF network employs narrow basis functions capable of local modifications of the reference profiles leading to a more accurate observed diffraction spot approximation and therefore accurate determination of spot positions and integrated intensities.

  5. Regulation of satellite cell function in sarcopenia

    OpenAIRE

    Alway, Stephen E.; Myers, Matthew J.; Mohamed, Junaith S.

    2014-01-01

    The mechanisms contributing to sarcopenia include reduced satellite cell (myogenic stem cell) function that is impacted by the environment (niche) of these cells. Satellite cell function is affected by oxidative stress, which is elevated in aged muscles, and this along with changes in largely unknown systemic factors, likely contribute to the manner in which satellite cells respond to stressors such as exercise, disuse or rehabilitation in sarcopenic muscles. Nutritional intervention provides...

  6. Regulation of Satellite Cell Function in Sarcopenia

    OpenAIRE

    Alway, Stephen E.; Myers, Matthew J.; Mohamed, Junaith S.

    2014-01-01

    The mechanisms contributing to sarcopenia include reduced satellite cell (myogenic stem cell) function that is impacted by the environment (niche) of these cells. Satellite cell function is affected by oxidative stress, which is elevated in aged muscles, and this along with changes in largely unknown systemic factors, likely contribute to the manner in which satellite cells respond to stressors such as exercise, disuse, or rehabilitation in sarcopenic muscles. Nutritional intervention provide...

  7. Functional-Friction Networks: New Insights on the Laboratory Earthquakes

    CERN Document Server

    Ghaffari, H O

    2013-01-01

    We formulate the universality of regular precursor rupture fronts in functional network parameter space, in light of recent analysis of acoustics emissions-coupled friction experimental results. Furthermore, using a phenomenological approach based on friction networks, we propose that the energy of the ruptures can be extended in terms of networks motifs and the transition from regular rupture to slow deformation can have a third production from the critical rupture class, comparable with the direct observations of this phenomena in the transparent samples . Based on this model, the transition from slow ruptures (i.e., creep pulse) to the critical speeds of ruptures is possible. In addition, the evolution of arrested rupture fronts is inspected through a statistical-network modelling which sheds light on the communities evolution. We propose a phase diagram for the friction networks which depends on the scaling coefficients of scalar parameters and can show a transition towards the capturing of the links by a...

  8. Controlled stochastic networks in heavy traffic: Convergence of value functions

    CERN Document Server

    Budhiraja, Amarjit; 10.1214/11-AAP784

    2012-01-01

    Scheduling control problems for a family of unitary networks under heavy traffic with general interarrival and service times, probabilistic routing and an infinite horizon discounted linear holding cost are studied. Diffusion control problems, that have been proposed as approximate models for the study of these critically loaded controlled stochastic networks, can be regarded as formal scaling limits of such stochastic systems. However, to date, a rigorous limit theory that justifies the use of such approximations for a general family of controlled networks has been lacking. It is shown that, under broad conditions, the value function of the suitably scaled network control problem converges to that of the associated diffusion control problem. This scaling limit result, in addition to giving a precise mathematical basis for the above approximation approach, suggests a general strategy for constructing near optimal controls for the physical stochastic networks by solving the associated diffusion control problem...

  9. Learning without local minima in radial basis function networks.

    Science.gov (United States)

    Bianchini, M; Frasconi, P; Gori, M

    1995-01-01

    Learning from examples plays a central role in artificial neural networks. The success of many learning schemes is not guaranteed, however, since algorithms like backpropagation may get stuck in local minima, thus providing suboptimal solutions. For feedforward networks, optimal learning can be achieved provided that certain conditions on the network and the learning environment are met. This principle is investigated for the case of networks using radial basis functions (RBF). It is assumed that the patterns of the learning environment are separable by hyperspheres. In that case, we prove that the attached cost function is local minima free with respect to all the weights. This provides us with some theoretical foundations for a massive application of RBF in pattern recognition.

  10. Self-Organization in Disaster Resilient Heterogeneous Small Cell Networks

    OpenAIRE

    Zhang, Haijun; Jiang, Chunxiao; Hu, Rose Qingyang; Qian, Yi

    2015-01-01

    Heterogeneous small cell networks with overlay femtocells and macrocell is a promising solution for future heterogeneous wireless cellular communications. However, great resilience is needed in heterogeneous small cells in case of accidents, attacks and natural disasters. In this article, we first describe the network architecture of disaster resilient heterogeneous small cell networks (DRHSCNs), where several self-organization inspired approaches are applied. Based on the proposed resilient ...

  11. Developmental self-construction and -configuration of functional neocortical neuronal networks.

    Science.gov (United States)

    Bauer, Roman; Zubler, Frédéric; Pfister, Sabina; Hauri, Andreas; Pfeiffer, Michael; Muir, Dylan R; Douglas, Rodney J

    2014-12-01

    The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors. Although knowledge of various genetic and cellular aspects of development is accumulating rapidly, there is less systematic understanding of how these various processes play together in order to construct such functional networks. Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative ('winner-take-all', WTA) network architecture can arise by development from a single precursor cell. This precursor is granted a simplified gene regulatory network that directs cell mitosis, differentiation, migration, neurite outgrowth and synaptogenesis. Once initial axonal connection patterns are established, their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns. We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks, and compare our simulation results with biological data. PMID:25474693

  12. Developmental self-construction and -configuration of functional neocortical neuronal networks.

    Directory of Open Access Journals (Sweden)

    Roman Bauer

    2014-12-01

    Full Text Available The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors. Although knowledge of various genetic and cellular aspects of development is accumulating rapidly, there is less systematic understanding of how these various processes play together in order to construct such functional networks. Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative ('winner-take-all', WTA network architecture can arise by development from a single precursor cell. This precursor is granted a simplified gene regulatory network that directs cell mitosis, differentiation, migration, neurite outgrowth and synaptogenesis. Once initial axonal connection patterns are established, their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns. We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks, and compare our simulation results with biological data.

  13. How pathogens use linear motifs to perturb host cell networks

    KAUST Repository

    Via, Allegra

    2015-01-01

    Molecular mimicry is one of the powerful stratagems that pathogens employ to colonise their hosts and take advantage of host cell functions to guarantee their replication and dissemination. In particular, several viruses have evolved the ability to interact with host cell components through protein short linear motifs (SLiMs) that mimic host SLiMs, thus facilitating their internalisation and the manipulation of a wide range of cellular networks. Here we present convincing evidence from the literature that motif mimicry also represents an effective, widespread hijacking strategy in prokaryotic and eukaryotic parasites. Further insights into host motif mimicry would be of great help in the elucidation of the molecular mechanisms behind host cell invasion and the development of anti-infective therapeutic strategies.

  14. The Edinburgh human metabolic network reconstruction and its functional analysis

    OpenAIRE

    Ma, Hongwu; Sorokin, Anatoly; Mazein, Alexander; Selkov, Alex; Selkov, Evgeni; Demin, Oleg; Goryanin, Igor

    2007-01-01

    A better understanding of human metabolism and its relationship with diseases is an important task in human systems biology studies. In this paper, we present a high-quality human metabolic network manually reconstructed by integrating genome annotation information from different databases and metabolic reaction information from literature. The network contains nearly 3000 metabolic reactions, which were reorganized into about 70 human-specific metabolic pathways according to their functional...

  15. Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function

    Science.gov (United States)

    Martin, O. C.; Krzywicki, A.; Zagorski, M.

    2016-07-01

    Living cells can maintain their internal states, react to changing environments, grow, differentiate, divide, etc. All these processes are tightly controlled by what can be called a regulatory program. The logic of the underlying control can sometimes be guessed at by examining the network of influences amongst genetic components. Some associated gene regulatory networks have been studied in prokaryotes and eukaryotes, unveiling various structural features ranging from broad distributions of out-degrees to recurrent "motifs", that is small subgraphs having a specific pattern of interactions. To understand what factors may be driving such structuring, a number of groups have introduced frameworks to model the dynamics of gene regulatory networks. In that context, we review here such in silico approaches and show how selection for phenotypes, i.e., network function, can shape network structure.

  16. Random matrix analysis for gene interaction networks in cancer cells

    CERN Document Server

    Kikkawa, Ayumi

    2016-01-01

    Motivation: The investigation of topological modifications of the gene interaction networks in cancer cells is essential for understanding the desease. We study gene interaction networks in various human cancer cells with the random matrix theory. This study is based on the Cancer Network Galaxy (TCNG) database which is the repository of huge gene interactions inferred by Bayesian network algorithms from 256 microarray experimental data downloaded from NCBI GEO. The original GEO data are provided by the high-throughput microarray expression experiments on various human cancer cells. We apply the random matrix theory to the computationally inferred gene interaction networks in TCNG in order to detect the universality in the topology of the gene interaction networks in cancer cells. Results: We found the universal behavior in almost one half of the 256 gene interaction networks in TCNG. The distribution of nearest neighbor level spacing of the gene interaction matrix becomes the Wigner distribution when the net...

  17. Functional Extinctions of Species in Ecological Networks

    OpenAIRE

    Säterberg, Torbjörn

    2016-01-01

    Current rates of extinctions are estimated to be around 1000 times higher than background rates that would occur without anthropogenic impacts. These extinction rates refer to the traditional view of extinctions, i.e. numerical extinctions. This thesis is about another type of extinctions: functional extinctions. Those occur when the abundance of a species is too small to uphold the species’ ecologically interactive role. I have taken a theoretical approach and used dynamical models to invest...

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

    Directory of Open Access Journals (Sweden)

    Damien A Fair

    2009-05-01

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

  19. Functional deficits of the attentional networks in autism

    OpenAIRE

    Fan, Jin; Bernardi, Silvia; Van Dam, Nicholas T.; Anagnostou, Evdokia; Gu, Xiaosi; Martin, Laura; Park, Yunsoo; Liu, Xun; Kolevzon, Alexander; Soorya, Latha; Grodberg, David; Hollander, Eric; Hof, Patrick R.

    2012-01-01

    Attentional dysfunction is among the most consistent observations of autism spectrum disorders (ASD). However, the neural nature of this deficit in ASD is still unclear. In this study, we aimed to identify the neurobehavioral correlates of attentional dysfunction in ASD. We used the Attention Network Test-Revised and functional magnetic resonance imaging to examine alerting, orienting, and executive control functions, as well as the neural substrates underlying these attentional functions in ...

  20. Characterizing and prototyping genetic networks with cell-free transcription-translation reactions.

    Science.gov (United States)

    Takahashi, Melissa K; Hayes, Clarmyra A; Chappell, James; Sun, Zachary Z; Murray, Richard M; Noireaux, Vincent; Lucks, Julius B

    2015-09-15

    A central goal of synthetic biology is to engineer cellular behavior by engineering synthetic gene networks for a variety of biotechnology and medical applications. The process of engineering gene networks often involves an iterative 'design-build-test' cycle, whereby the parts and connections that make up the network are built, characterized and varied until the desired network function is reached. Many advances have been made in the design and build portions of this cycle. However, the slow process of in vivo characterization of network function often limits the timescale of the testing step. Cell-free transcription-translation (TX-TL) systems offer a simple and fast alternative to performing these characterizations in cells. Here we provide an overview of a cell-free TX-TL system that utilizes the native Escherichia coli TX-TL machinery, thereby allowing a large repertoire of parts and networks to be characterized. As a way to demonstrate the utility of cell-free TX-TL, we illustrate the characterization of two genetic networks: an RNA transcriptional cascade and a protein regulated incoherent feed-forward loop. We also provide guidelines for designing TX-TL experiments to characterize new genetic networks. We end with a discussion of current and emerging applications of cell free systems.

  1. Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression

    KAUST Repository

    Onesto, Valentina

    2016-05-10

    Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect.

  2. Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression

    Science.gov (United States)

    Onesto, Valentina; Cosentino, Carlo; Di Fabrizio, Enzo; Cesarelli, Mario; Amato, Francesco; Gentile, Francesco

    2016-01-01

    Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect.

  3. Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression

    Directory of Open Access Journals (Sweden)

    Valentina Onesto

    2016-01-01

    Full Text Available Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect.

  4. Information in a Network of Neuronal Cells: Effect of Cell Density and Short-Term Depression.

    Science.gov (United States)

    Onesto, Valentina; Cosentino, Carlo; Di Fabrizio, Enzo; Cesarelli, Mario; Amato, Francesco; Gentile, Francesco

    2016-01-01

    Neurons are specialized, electrically excitable cells which use electrical to chemical signals to transmit and elaborate information. Understanding how the cooperation of a great many of neurons in a grid may modify and perhaps improve the information quality, in contrast to few neurons in isolation, is critical for the rational design of cell-materials interfaces for applications in regenerative medicine, tissue engineering, and personalized lab-on-a-chips. In the present paper, we couple an integrate-and-fire model with information theory variables to analyse the extent of information in a network of nerve cells. We provide an estimate of the information in the network in bits as a function of cell density and short-term depression time. In the model, neurons are connected through a Delaunay triangulation of not-intersecting edges; in doing so, the number of connecting synapses per neuron is approximately constant to reproduce the early time of network development in planar neural cell cultures. In simulations where the number of nodes is varied, we observe an optimal value of cell density for which information in the grid is maximized. In simulations in which the posttransmission latency time is varied, we observe that information increases as the latency time decreases and, for specific configurations of the grid, it is largely enhanced in a resonance effect. PMID:27403421

  5. One-way hash function based on hyper-chaotic cellular neural network

    Institute of Scientific and Technical Information of China (English)

    Yang Qun-Ting; Gao Tie-Gang

    2008-01-01

    The design of an efficient one-way hash function with good performance is a hot spot in modern cryptography researches. In this paper, a hash function construction method based on cell neural network with hyper-chaos characteristics is proposed. First, the chaos sequence is gotten by iterating cellular neural network with Runge-Kutta algorithm, and then the chaos sequence is iterated with the message. The hash code is obtained through the corresponding transform of the latter chaos sequence. Simulation and analysis demonstrate that the new method has the merit of convenience, high sensitivity to initial values, good hash performance, especially the strong stability.

  6. Personal networking function of the Internet

    OpenAIRE

    Petrović Dalibor

    2009-01-01

    The aim of the paper is to understand the role of Internet in creating new forms of sociability in the modern society. In the first part the history of social studies of Internet is reviewed, and the conclusion put forward that the anti-social role of the Internet cannot be proved. In the theoretical part of the paper the author presents his idea of two basic roles of Internet as interpersonal interaction tool: transmissional and procreative. These two Internet functions are very important me...

  7. Multimodal functional network connectivity: an EEG-fMRI fusion in network space.

    Directory of Open Access Journals (Sweden)

    Xu Lei

    Full Text Available EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs are extracted using spatial independent component analysis (ICA in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA. Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI. Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.

  8. Characteristic functions and process identification by neural networks

    CERN Document Server

    Dente, J A

    1997-01-01

    Principal component analysis (PCA) algorithms use neural networks to extract the eigenvectors of the correlation matrix from the data. However, if the process is non-Gaussian, PCA algorithms or their higher order generalisations provide only incomplete or misleading information on the statistical properties of the data. To handle such situations we propose neural network algorithms, with an hybrid (supervised and unsupervised) learning scheme, which constructs the characteristic function of the probability distribution and the transition functions of the stochastic process. Illustrative examples are presented, which include Cauchy and Levy-type processes

  9. Optimal Computation of Symmetric Boolean Functions in Collocated Networks

    CERN Document Server

    Kowshik, Hemant

    2011-01-01

    We consider collocated wireless sensor networks, where each node has a Boolean measurement and the goal is to compute a given Boolean function of these measurements. We first consider the worst case setting and study optimal block computation strategies for computing symmetric Boolean functions. We study three classes of functions: threshold functions, delta functions and interval functions. We provide exactly optimal strategies for the first two classes, and a scaling law order-optimal strategy with optimal preconstant for interval functions. We also extend the results to the case of integer measurements and certain integer-valued functions. We use lower bounds from communication complexity theory, and provide an achievable scheme using information theoretic tools. Next, we consider the case where nodes measurements are random and drawn from independent Bernoulli distributions. We address the problem of optimal function computation so as to minimize the expected total number of bits that are transmitted. In ...

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

    Energy Technology Data Exchange (ETDEWEB)

    Shim, Sung-Mi; Jung, So-Young; Nam, Hye-Young; Kim, Hye-Ryun; Lee, Mee-Hee; Kim, Jun-Woo; Han, Bok-Ghee [National Biobank of Korea, Center for Genome Science, Korea National Institute of Health, Osong 363-951 (Korea, Republic of); Jeon, Jae-Pil, E-mail: jaepiljeon@hanmail.net [Division of Brain Diseases, Center for Biomedical Science, Korea National Institute of Health, Osong 363-951 (Korea, Republic of)

    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.

  11. Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity.

    Directory of Open Access Journals (Sweden)

    Sadra Sadeh

    2015-06-01

    Full Text Available In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational

  12. Mutual connectivity analysis (MCA) using generalized radial basis function neural networks for nonlinear functional connectivity network recovery in resting-state functional MRI

    Science.gov (United States)

    D'Souza, Adora M.; Abidin, Anas Zainul; Nagarajan, Mahesh B.; Wismüller, Axel

    2016-03-01

    We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 +/- 0.037) as well as the underlying network structure (Rand index = 0.87 +/- 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.

  13. A growing and pruning method for radial basis function networks.

    Science.gov (United States)

    Bortman, M; Aladjem, M

    2009-06-01

    A recently published generalized growing and pruning (GGAP) training algorithm for radial basis function (RBF) neural networks is studied and modified. GGAP is a resource-allocating network (RAN) algorithm, which means that a created network unit that consistently makes little contribution to the network's performance can be removed during the training. GGAP states a formula for computing the significance of the network units, which requires a d-fold numerical integration for arbitrary probability density function p(x) of the input data x (x in R(d)) . In this work, the GGAP formula is approximated using a Gaussian mixture model (GMM) for p(x) and an analytical solution of the approximated unit significance is derived. This makes it possible to employ the modified GGAP for input data having complex and high-dimensional p(x), which was not possible in the original GGAP. The results of an extensive experimental study show that the modified algorithm outperforms the original GGAP achieving both a lower prediction error and reduced complexity of the trained network.

  14. Modeling Marine Electromagnetic Survey with Radial Basis Function Networks

    Directory of Open Access Journals (Sweden)

    Agus Arif

    2011-08-01

    Full Text Available A marine electromagnetic survey is an engineering endeavour to discover the location and dimension of a hydrocarbon layer under an ocean floor. In this kind of survey, an array of electric and magnetic receivers are located on the sea floor and record the scattered, refracted and reflected electromagnetic wave, which has been transmitted by an electric dipole antenna towed by a vessel. The data recorded in receivers must be processed and further analysed to estimate the hydrocarbon location and dimension. To conduct those analyses successfuly, a radial basis function (RBF network could be employed to become a forward model of the input-output relationship of the data from a marine electromagnetic survey. This type of neural networks is working based on distances between its inputs and predetermined centres of some basis functions. A previous research had been conducted to model the same marine electromagnetic survey using another type of neural networks, which is a multi layer perceptron (MLP network. By comparing their validation and training performances (mean-squared errors and correlation coefficients, it is concluded that, in this case, the MLP network is comparatively better than the RBF network

  15. A Pair Correlation Function Characterizing the Anisotropy of Force Networks

    Institute of Scientific and Technical Information of China (English)

    SUN Qi-Cheng; JI Shun-Ying

    2011-01-01

    Force networks may underlie the constitutive relations among granular solids and granular flows and inter-state transitions. However, it is difficult to effectively describe the anisotropy of force networks. We propose a new pair correlation Function g(r, 0) to describe the characteristic lengths and orientations of force chains that are composed of particles with contact forces greater than the threshold values. A formulation g(r,0) ? A(r)+b(r) cos 2(0 -n/2) is used to fit the g(r, 0) data. The characteristic lengths and orientations of force networks are then elucidated.%@@ Force networks may underlie the constitutive relations among granular solids and granular flows and inter-state transitions.However, it is difficult to effectively describe the anisotropy of force networks.We propose a new pair correlation function g(r,θ) to describe the characteristic lengths and orientations of force chains that are composed of particles with contact forces greater than the threshold values.A formulation g(r,θ)≈a(r) + b( r ) cos 2(θ-π/2) is used to fit the g(r,θ) data.The characteristic lengths and orientations of force networks are then elucidated.

  16. Primary Cilia, Signaling Networks and Cell Migration

    DEFF Research Database (Denmark)

    Veland, Iben Rønn

    Primary cilia are microtubule-based, sensory organelles that emerge from the centrosomal mother centriole to project from the surface of most quiescent cells in the human body. Ciliary entry is a tightly controlled process, involving diffusion barriers and gating complexes that maintain a unique...... this controls directional cell migration as a physiological response. The ciliary pocket is a membrane invagination with elevated activity of clathrin-dependent endocytosis (CDE). In paper I, we show that the primary cilium regulates TGF-β signaling and the ciliary pocket is a compartment for CDE...... on formation of the primary cilium and CDE at the pocket region. The ciliary protein Inversin functions as a molecular switch between canonical and non-canonical Wnt signaling. In paper II, we show that Inversin and the primary cilium control Wnt signaling and are required for polarization and cell migration...

  17. An incremental design of radial basis function networks.

    Science.gov (United States)

    Yu, Hao; Reiner, Philip D; Xie, Tiantian; Bartczak, Tomasz; Wilamowski, Bogdan M

    2014-10-01

    This paper proposes an offline algorithm for incrementally constructing and training radial basis function (RBF) networks. In each iteration of the error correction (ErrCor) algorithm, one RBF unit is added to fit and then eliminate the highest peak (or lowest valley) in the error surface. This process is repeated until a desired error level is reached. Experimental results on real world data sets show that the ErrCor algorithm designs very compact RBF networks compared with the other investigated algorithms. Several benchmark tests such as the duplicate patterns test and the two spiral problem were applied to show the robustness of the ErrCor algorithm. The proposed ErrCor algorithm generates very compact networks. This compactness leads to greatly reduced computation times of trained networks.

  18. Radial basis function networks for fast contingency ranking

    Energy Technology Data Exchange (ETDEWEB)

    Devaraj, D.; Ramar, K. [Indian Inst. of Technology, Madras (India). Dept. of Electrical Engineering; Yegnanarayana, B. [Indian Inst. of Technology, Madras (India). Dept. of Computer Science and Engineering

    2002-06-01

    This paper presents an artificial neural network-based approach for static-security assessment. The proposed approach uses radial basis function (RBF) networks to predict the system severity level following a given list of contingencies. The RBF networks are trained off-line to capture the nonlinear relationship between the pre-contingency line flows and the post-contingency severity index. A method based on mutual information is proposed for selecting the input features of the networks. Mutual information has the advantage of measuring the general relationship between the independent variables and the dependent variables as against the linear relationship measured by the correlation-based methods. The performance of the proposed approach is demonstrated through contingency ranking in IEEE 30-bus test system. (author)

  19. Fabric Quality Optimization by Using Desirability Function and Neural Networks

    Directory of Open Access Journals (Sweden)

    Hajer Souid

    2012-01-01

    Full Text Available The present paper presents a new method to estimate objective reflection of Denim fabric quality by using desirability function and neural networks. The global fabric quality was defined through one index belonging to the closed interval [0, 1]. For this reason, we have created a first algorithm that is modified when the definition of fabric quality is changed. This prediction would allow fabric producer to estimate customer’s quality satisfaction level. The present approach has conferred a good evaluation and prediction of the all-encompassing denim fabric quality. In the second stage of the study, we developed a model to predict global fabric quality from fiber, yarn, weaving parameters and finishing characteristics by using neural networks. The neural network model is accomplished by using a second algorithm based on back-propagation concept. The results have shown that the neuronal networks could predict global fabric quality of the untrained fabrics with better precision

  20. Spectra: Robust Estimation of Distribution Functions in Networks

    CERN Document Server

    Borges, Miguel; Baquero, Carlos; Almeida, Paulo Sérgio

    2012-01-01

    Distributed aggregation allows the derivation of a given global aggregate property from many individual local values in nodes of an interconnected network system. Simple aggregates such as minima/maxima, counts, sums and averages have been thoroughly studied in the past and are important tools for distributed algorithms and network coordination. Nonetheless, this kind of aggregates may not be comprehensive enough to characterize biased data distributions or when in presence of outliers, making the case for richer estimates of the values on the network. This work presents Spectra, a distributed algorithm for the estimation of distribution functions over large scale networks. The estimate is available at all nodes and the technique depicts important properties, namely: robust when exposed to high levels of message loss, fast convergence speed and fine precision in the estimate. It can also dynamically cope with changes of the sampled local property, not requiring algorithm restarts, and is highly resilient to n...

  1. Functional analysis of prognostic gene expression network genes in metastatic breast cancer models.

    Directory of Open Access Journals (Sweden)

    Thomas R Geiger

    Full Text Available Identification of conserved co-expression networks is a useful tool for clustering groups of genes enriched for common molecular or cellular functions [1]. The relative importance of genes within networks can frequently be inferred by the degree of connectivity, with those displaying high connectivity being significantly more likely to be associated with specific molecular functions [2]. Previously we utilized cross-species network analysis to identify two network modules that were significantly associated with distant metastasis free survival in breast cancer. Here, we validate one of the highly connected genes as a metastasis associated gene. Tpx2, the most highly connected gene within a proliferation network specifically prognostic for estrogen receptor positive (ER+ breast cancers, enhances metastatic disease, but in a tumor autonomous, proliferation-independent manner. Histologic analysis suggests instead that variation of TPX2 levels within disseminated tumor cells may influence the transition between dormant to actively proliferating cells in the secondary site. These results support the co-expression network approach for identification of new metastasis-associated genes to provide new information regarding the etiology of breast cancer progression and metastatic disease.

  2. Functional Reorganizations of Brain Network in Prelingually Deaf Adolescents

    Science.gov (United States)

    Li, Wenjing; Li, Jianhong; Wang, Jieqiong; Zhou, Peng; Wang, Zhenchang; Xian, Junfang; He, Huiguang

    2016-01-01

    Previous neuroimaging studies suggested structural or functional brain reorganizations occurred in prelingually deaf subjects. However, little is known about the reorganizations of brain network architectures in prelingually deaf adolescents. The present study aims to investigate alterations of whole-brain functional network using resting-state fMRI and graph theory analysis. We recruited 16 prelingually deaf adolescents (10~18 years) and 16 normal controls matched in age and gender. Brain networks were constructed from mean time courses of 90 regions. Widely distributed network was observed in deaf subjects, with increased connectivity between the limbic system and regions involved in visual and language processing, suggesting reinforcement of the processing for the visual and verbal information in deaf adolescents. Decreased connectivity was detected between the visual regions and language regions possibly due to inferior reading or speaking skills in deaf subjects. Using graph theory analysis, we demonstrated small-worldness property did not change in prelingually deaf adolescents relative to normal controls. However, compared with healthy adolescents, eight regions involved in visual, language, and auditory processing were identified as hubs only present in prelingually deaf adolescents. These findings revealed reorganization of brain functional networks occurred in prelingually deaf adolescents to adapt to deficient auditory input. PMID:26819781

  3. Graphical Features of Functional Genes in Human Protein Interaction Network.

    Science.gov (United States)

    Wang, Pei; Chen, Yao; Lu, Jinhu; Wang, Qingyun; Yu, Xinghuo

    2016-06-01

    With the completion of the human genome project, it is feasible to investigate large-scale human protein interaction network (HPIN) with complex networks theory. Proteins are encoded by genes. Essential, viable, disease, conserved, housekeeping (HK) and tissue-enriched (TE) genes are functional genes, which are organized and functioned via interaction networks. Based on up-to-date data from various databases or literature, two large-scale HPINs and six subnetworks are constructed. We illustrate that the HPINs and most of the subnetworks are sparse, small-world, scale-free, disassortative and with hierarchical modularity. Among the six subnetworks, essential, disease and HK subnetworks are more densely connected than the others. Statistical analysis on the topological structures of the HPIN reveals that the lethal, the conserved, the HK and the TE genes are with hallmark graphical features. Receiver operating characteristic (ROC) curves indicate that the essential genes can be distinguished from the viable ones with accuracy as high as almost 70%. Closeness, semi-local and eigenvector centralities can distinguish the HK genes from the TE ones with accuracy around 82%. Furthermore, the Venn diagram, cluster dendgrams and classifications of disease genes reveal that some classes of disease genes are with hallmark graphical features, especially for cancer genes, HK disease genes and TE disease genes. The findings facilitate the identification of some functional genes via topological structures. The investigations shed some light on the characteristics of the compete interactome, which have potential implications in networked medicine and biological network control. PMID:26841412

  4. Functional Reorganizations of Brain Network in Prelingually Deaf Adolescents.

    Science.gov (United States)

    Li, Wenjing; Li, Jianhong; Wang, Jieqiong; Zhou, Peng; Wang, Zhenchang; Xian, Junfang; He, Huiguang

    2016-01-01

    Previous neuroimaging studies suggested structural or functional brain reorganizations occurred in prelingually deaf subjects. However, little is known about the reorganizations of brain network architectures in prelingually deaf adolescents. The present study aims to investigate alterations of whole-brain functional network using resting-state fMRI and graph theory analysis. We recruited 16 prelingually deaf adolescents (10~18 years) and 16 normal controls matched in age and gender. Brain networks were constructed from mean time courses of 90 regions. Widely distributed network was observed in deaf subjects, with increased connectivity between the limbic system and regions involved in visual and language processing, suggesting reinforcement of the processing for the visual and verbal information in deaf adolescents. Decreased connectivity was detected between the visual regions and language regions possibly due to inferior reading or speaking skills in deaf subjects. Using graph theory analysis, we demonstrated small-worldness property did not change in prelingually deaf adolescents relative to normal controls. However, compared with healthy adolescents, eight regions involved in visual, language, and auditory processing were identified as hubs only present in prelingually deaf adolescents. These findings revealed reorganization of brain functional networks occurred in prelingually deaf adolescents to adapt to deficient auditory input. PMID:26819781

  5. Functional Reorganizations of Brain Network in Prelingually Deaf Adolescents.

    Science.gov (United States)

    Li, Wenjing; Li, Jianhong; Wang, Jieqiong; Zhou, Peng; Wang, Zhenchang; Xian, Junfang; He, Huiguang

    2016-01-01

    Previous neuroimaging studies suggested structural or functional brain reorganizations occurred in prelingually deaf subjects. However, little is known about the reorganizations of brain network architectures in prelingually deaf adolescents. The present study aims to investigate alterations of whole-brain functional network using resting-state fMRI and graph theory analysis. We recruited 16 prelingually deaf adolescents (10~18 years) and 16 normal controls matched in age and gender. Brain networks were constructed from mean time courses of 90 regions. Widely distributed network was observed in deaf subjects, with increased connectivity between the limbic system and regions involved in visual and language processing, suggesting reinforcement of the processing for the visual and verbal information in deaf adolescents. Decreased connectivity was detected between the visual regions and language regions possibly due to inferior reading or speaking skills in deaf subjects. Using graph theory analysis, we demonstrated small-worldness property did not change in prelingually deaf adolescents relative to normal controls. However, compared with healthy adolescents, eight regions involved in visual, language, and auditory processing were identified as hubs only present in prelingually deaf adolescents. These findings revealed reorganization of brain functional networks occurred in prelingually deaf adolescents to adapt to deficient auditory input.

  6. GPCRs in Stem Cell Function

    OpenAIRE

    Doze, Van A.; PEREZ, DIANNE M.

    2013-01-01

    Many tissues of the body cannot only repair themselves, but also self-renew, a property mainly due to stem cells and the various mechanisms that regulate their behavior. Stem cell biology is a relatively new field. While advances are slowly being realized, stem cells possess huge potential to ameliorate disease and counteract the aging process, causing its speculation as the next panacea. Amidst public pressure to advance rapidly to clinical trials, there is a need to understand the biology o...

  7. Reactor Network Synthesis Based on Instantaneous Objective Function Characteristic Curves

    Institute of Scientific and Technical Information of China (English)

    张治山; 赵文; 王艳丽; 周传光; 袁希钢

    2003-01-01

    It is believed that whether the instantaneous objective function curves of plug-flow-reactor (PFR) and continuous-stirred-tank-reactor (CSTR) overlap or not, they have a consistent changing trend for complex reactions(steady state, isothermal and constant volume). As a result of the relation of the objective functions (selectivity or yield) to the instantaneous objective functions (instantaneous selectivity or instantaneous reaction rate), the optimal reactor network configuration can be determined according to the changing trend of the instantaneous objective function curves. Further, a recent partition strategy for the reactor network synthesis based on the instantaneous objective function characteristic curves is proposed by extending the attainable region partition strategy from the concentration space to the instantaneous objective function-unreacted fraction of key reactant space. In this paper,the instantaneous objective function is closed to be the instantaneous selectivity and several samples axe examined to illustrate the proposed method. The comparison with the previous work indicates it is a very convenient and practical systematic tool of the reactor network synthesis and seems also promising for overcoming the dimension limit of the attainable region partition strategy in the concentration space.

  8. Extending the functional equivalence of radial basis function networks and fuzzy inference systems.

    Science.gov (United States)

    Hunt, K J; Haas, R; Murray-Smith, R

    1996-01-01

    We establish the functional equivalence of a generalized class of Gaussian radial basis function (RBFs) networks and the full Takagi-Sugeno model (1983) of fuzzy inference. This generalizes an existing result which applies to the standard Gaussian RBF network and a restricted form of the Takagi-Sugeno fuzzy system. The more general framework allows the removal of some of the restrictive conditions of the previous result.

  9. Side effects of normalising radial basis function networks.

    Science.gov (United States)

    Shorten, R; Murray-Smith, R

    1996-05-01

    Normalisation of the basis function activations in a Radial Basis Function (RBF) network is a common way of achieving the partition of unity often desired for modelling applications. It results in the basis functions covering the whole of the input space to the same degree. However, normalisation of the basis functions can lead to other effects which are sometimes less desirable for modelling applications. This paper describes some side effects of normalisation which fundamentally alter properties of the basis functions, e.g. the shape is no longer uniform, maxima of basis functions can be shifted from their centres, and the basis functions are no longer guaranteed to decrease monotonically as distance from their centre increases--in many cases basis functions can 'reactivate', i.e. re-appear far from the basis function centre. This paper examines how these phenomena occur, discusses their relevance for non-linear function approximation and examines the effect of normalisation on the network condition number and weights.

  10. Function estimation by feedforward sigmoidal networks with bounded weights

    Energy Technology Data Exchange (ETDEWEB)

    Rao, N.S.V.; Protopoescu, V. [Oak Ridge National Lab., TN (United States). Center for Engineering Systems Advanced Research; Qiao, H. [Fort Valley State Coll., GA (United States). Dept. of Mathematics and Physics

    1996-05-01

    The authors address the problem of PAC (probably and approximately correct) learning functions f : [0, 1]{sup d} {r_arrow} [{minus}K, K] based on iid (independently and identically distributed) sample generated according to an unknown distribution, by using feedforward sigmoidal networks. They use two basic properties of the neural networks with bounded weights, namely: (a) they form a Euclidean class, and (b) for hidden units of the form tanh ({gamma}z) they are Lipschitz functions. Either property yields sample sizes for PAC function learning under any Lipschitz cost function. The sample size based on the first property is tighter compared to the known bounds based on VC-dimension. The second estimate yields a sample size that can be conveniently adjusted by a single parameter, {gamma}, related to the hidden nodes.

  11. A Service-Oriented Approach for Dynamic Chaining of Virtual Network Functions over Multi-Provider Software-Defined Networks

    Directory of Open Access Journals (Sweden)

    Barbara Martini

    2016-06-01

    Full Text Available Emerging technologies such as Software-Defined Networks (SDN and Network Function Virtualization (NFV promise to address cost reduction and flexibility in network operation while enabling innovative network service delivery models. However, operational network service delivery solutions still need to be developed that actually exploit these technologies, especially at the multi-provider level. Indeed, the implementation of network functions as software running over a virtualized infrastructure and provisioned on a service basis let one envisage an ecosystem of network services that are dynamically and flexibly assembled by orchestrating Virtual Network Functions even across different provider domains, thereby coping with changeable user and service requirements and context conditions. In this paper we propose an approach that adopts Service-Oriented Architecture (SOA technology-agnostic architectural guidelines in the design of a solution for orchestrating and dynamically chaining Virtual Network Functions. We discuss how SOA, NFV, and SDN may complement each other in realizing dynamic network function chaining through service composition specification, service selection, service delivery, and placement tasks. Then, we describe the architecture of a SOA-inspired NFV orchestrator, which leverages SDN-based network control capabilities to address an effective delivery of elastic chains of Virtual Network Functions. Preliminary results of prototype implementation and testing activities are also presented. The benefits for Network Service Providers are also described that derive from the adaptive network service provisioning in a multi-provider environment through the orchestration of computing and networking services to provide end users with an enhanced service experience.

  12. Bayesian network models in brain functional connectivity analysis

    OpenAIRE

    Ide, Jaime S.; Zhang, Sheng; Chiang-shan R. Li

    2013-01-01

    Much effort has been made to better understand the complex integration of distinct parts of the human brain using functional magnetic resonance imaging (fMRI). Altered functional connectivity between brain regions is associated with many neurological and mental illnesses, such as Alzheimer and Parkinson diseases, addiction, and depression. In computational science, Bayesian networks (BN) have been used in a broad range of studies to model complex data set in the presence of uncertainty and wh...

  13. Radial basis function neural networks applied to NASA SSME data

    Science.gov (United States)

    Wheeler, Kevin R.; Dhawan, Atam P.

    1993-01-01

    This paper presents a brief report on the application of Radial Basis Function Neural Networks (RBFNN) to the prediction of sensor values for fault detection and diagnosis of the Space Shuttle's Main Engines (SSME). The location of the Radial Basis Function (RBF) node centers was determined with a K-means clustering algorithm. A neighborhood operation about these center points was used to determine the variances of the individual processing notes.

  14. Functional clustering algorithm for the analysis of dynamic network data

    OpenAIRE

    Feldt, S.; Waddell, J; Hetrick, V. L.; Berke, J. D.; Żochowski, M

    2009-01-01

    We formulate a technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulat...

  15. Implementation of Radial Basis Function Neural Network for Image Steganalysis

    OpenAIRE

    Sambasiva Rao Baragada; S. Ramakrishna; M.S. Rao; S. Purushothaman

    2008-01-01

    Steganographic tools and techniques are becoming more potential and widespread. Illegal use of steganography poses serious challenges to the law enforcement agencies. Limited work has been carried out on supervised steganalysis using neural network as a classifier. We present a combined method of identifying the presence of covert information in a carrier image using fisher’s linear discriminant (FLD) function followed by the radial basis function (RBF). Experiments show promising resu...

  16. Age-related changes in task related functional network connectivity.

    Directory of Open Access Journals (Sweden)

    Jason Steffener

    Full Text Available Aging has a multi-faceted impact on brain structure, brain function and cognitive task performance, but the interaction of these different age-related changes is largely unexplored. We hypothesize that age-related structural changes alter the functional connectivity within the brain, resulting in altered task performance during cognitive challenges. In this neuroimaging study, we used independent components analysis to identify spatial patterns of coordinated functional activity involved in the performance of a verbal delayed item recognition task from 75 healthy young and 37 healthy old adults. Strength of functional connectivity between spatial components was assessed for age group differences and related to speeded task performance. We then assessed whether age-related differences in global brain volume were associated with age-related differences in functional network connectivity. Both age groups used a series of spatial components during the verbal working memory task and the strength and distribution of functional network connectivity between these components differed across the age groups. Poorer task performance, i.e. slower speed with increasing memory load, in the old adults was associated with decreases in functional network connectivity between components comprised of the supplementary motor area and the middle cingulate and between the precuneus and the middle/superior frontal cortex. Advancing age also led to decreased brain volume; however, there was no evidence to support the hypothesis that age-related alterations in functional network connectivity were the result of global brain volume changes. These results suggest that age-related differences in the coordination of neural activity between brain regions partially underlie differences in cognitive performance.

  17. Observation and inverse problems in coupled cell networks

    International Nuclear Information System (INIS)

    A coupled cell network is a model for many situations such as food webs in ecosystems, cellular metabolism and economic networks. It consists in a directed graph G, each node (or cell) representing an agent of the network and each directed arrow representing which agent acts on which. It yields a system of differential equations .x(t)=f(x(t)), where the component i of f depends only on the cells xj(t) for which the arrow j → i exists in G. In this paper, we investigate the observation problems in coupled cell networks: can one deduce the behaviour of the whole network (oscillations, stabilization, etc) by observing only one of the cells? We show that the natural observation properties hold for almost all the interactions f

  18. Virtual Networking Performance in OpenStack Platform for Network Function Virtualization

    Directory of Open Access Journals (Sweden)

    Franco Callegati

    2016-01-01

    Full Text Available The emerging Network Function Virtualization (NFV paradigm, coupled with the highly flexible and programmatic control of network devices offered by Software Defined Networking solutions, enables unprecedented levels of network virtualization that will definitely change the shape of future network architectures, where legacy telco central offices will be replaced by cloud data centers located at the edge. On the one hand, this software-centric evolution of telecommunications will allow network operators to take advantage of the increased flexibility and reduced deployment costs typical of cloud computing. On the other hand, it will pose a number of challenges in terms of virtual network performance and customer isolation. This paper intends to provide some insights on how an open-source cloud computing platform such as OpenStack implements multitenant network virtualization and how it can be used to deploy NFV, focusing in particular on packet forwarding performance issues. To this purpose, a set of experiments is presented that refer to a number of scenarios inspired by the cloud computing and NFV paradigms, considering both single tenant and multitenant scenarios. From the results of the evaluation it is possible to highlight potentials and limitations of running NFV on OpenStack.

  19. Impaired Leydig cell function in infertile men

    DEFF Research Database (Denmark)

    Andersson, A-M; Jørgensen, N; Frydelund-Larsen, L;

    2004-01-01

    of the fertile levels.Thus, the group of infertile men showed significant signs of impaired Leydig cell function in parallel to their impaired spermatogenesis. The association of decreased spermatogenesis and impaired Leydig cell function might reflect a disturbed paracrine communication between the seminiferous...

  20. Intrinsic network activity in tinnitus investigated using functional MRI.

    Science.gov (United States)

    Leaver, Amber M; Turesky, Ted K; Seydell-Greenwald, Anna; Morgan, Susan; Kim, Hung J; Rauschecker, Josef P

    2016-08-01

    Tinnitus is an increasingly common disorder in which patients experience phantom auditory sensations, usually ringing or buzzing in the ear. Tinnitus pathophysiology has been repeatedly shown to involve both auditory and non-auditory brain structures, making network-level studies of tinnitus critical. In this magnetic resonance imaging (MRI) study, two resting-state functional connectivity (RSFC) approaches were used to better understand functional network disturbances in tinnitus. First, we demonstrated tinnitus-related reductions in RSFC between specific brain regions and resting-state networks (RSNs), defined by independent components analysis (ICA) and chosen for their overlap with structures known to be affected in tinnitus. Then, we restricted ICA to data from tinnitus patients, and identified one RSN not apparent in control data. This tinnitus RSN included auditory-sensory regions like inferior colliculus and medial Heschl's gyrus, as well as classically non-auditory regions like the mediodorsal nucleus of the thalamus, striatum, lateral prefrontal, and orbitofrontal cortex. Notably, patients' reported tinnitus loudness was positively correlated with RSFC between the mediodorsal nucleus and the tinnitus RSN, indicating that this network may underlie the auditory-sensory experience of tinnitus. These data support the idea that tinnitus involves network dysfunction, and further stress the importance of communication between auditory-sensory and fronto-striatal circuits in tinnitus pathophysiology. Hum Brain Mapp 37:2717-2735, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. PMID:27091485

  1. Dynamics of the cell-cycle network under genome-rewiring perturbations

    Science.gov (United States)

    Katzir, Yair; Elhanati, Yuval; Averbukh, Inna; Braun, Erez

    2013-12-01

    The cell-cycle progression is regulated by a specific network enabling its ordered dynamics. Recent experiments supported by computational models have shown that a core of genes ensures this robust cycle dynamics. However, much less is known about the direct interaction of the cell-cycle regulators with genes outside of the cell-cycle network, in particular those of the metabolic system. Following our recent experimental work, we present here a model focusing on the dynamics of the cell-cycle core network under rewiring perturbations. Rewiring is achieved by placing an essential metabolic gene exclusively under the regulation of a cell-cycle's promoter, forcing the cell-cycle network to function under a multitasking challenging condition; operating in parallel the cell-cycle progression and a metabolic essential gene. Our model relies on simple rate equations that capture the dynamics of the relevant protein-DNA and protein-protein interactions, while making a clear distinction between these two different types of processes. In particular, we treat the cell-cycle transcription factors as limited ‘resources’ and focus on the redistribution of resources in the network during its dynamics. This elucidates the sensitivity of its various nodes to rewiring interactions. The basic model produces the correct cycle dynamics for a wide range of parameters. The simplicity of the model enables us to study the interface between the cell-cycle regulation and other cellular processes. Rewiring a promoter of the network to regulate a foreign gene, forces a multitasking regulatory load. The higher the load on the promoter, the longer is the cell-cycle period. Moreover, in agreement with our experimental results, the model shows that different nodes of the network exhibit variable susceptibilities to the rewiring perturbations. Our model suggests that the topology of the cell-cycle core network ensures its plasticity and flexible interface with other cellular processes, without

  2. Regulation of satellite cell function in sarcopenia

    Directory of Open Access Journals (Sweden)

    Stephen E Alway

    2014-09-01

    Full Text Available The mechanisms contributing to sarcopenia include reduced satellite cell (myogenic stem cell function that is impacted by the environment (niche of these cells. Satellite cell function is affected by oxidative stress, which is elevated in aged muscles, and this along with changes in largely unknown systemic factors, likely contribute to the manner in which satellite cells respond to stressors such as exercise, disuse or rehabilitation in sarcopenic muscles. Nutritional intervention provides one therapeutic strategy to improve the satellite cell niche and systemic factors, with the goal of improving satellite cell function in aging muscles. Although many elderly persons consume various nutraceuticals with the hope of improving health, most of these compounds have not been thoroughly tested, and the impacts that they might have on sarcopenia, and satellite cell function are not clear. This review discusses data pertaining to the satellite cell responses and function in aging skeletal muscle, and the impact that three compounds: resveratrol, green tea catechins and β-Hydroxy-β-methylbutyrate have on regulating satellite cell function and therefore contributing to reducing sarcopenia or improving muscle mass after disuse in aging. The data suggest that these nutraceutical compounds improve satellite cell function during rehabilitative loading in animal models of aging after disuse (i.e., muscle regeneration. While these compounds have not been rigorously tested in humans, the data from animal models of aging provide a strong basis for conducting additional focused work to determine if these or other nutraceuticals can offset the muscle losses, or improve regeneration in sarcopenic muscles of older humans via improving satellite cell function.

  3. Regulation of satellite cell function in sarcopenia.

    Science.gov (United States)

    Alway, Stephen E; Myers, Matthew J; Mohamed, Junaith S

    2014-01-01

    The mechanisms contributing to sarcopenia include reduced satellite cell (myogenic stem cell) function that is impacted by the environment (niche) of these cells. Satellite cell function is affected by oxidative stress, which is elevated in aged muscles, and this along with changes in largely unknown systemic factors, likely contribute to the manner in which satellite cells respond to stressors such as exercise, disuse, or rehabilitation in sarcopenic muscles. Nutritional intervention provides one therapeutic strategy to improve the satellite cell niche and systemic factors, with the goal of improving satellite cell function in aging muscles. Although many elderly persons consume various nutraceuticals with the hope of improving health, most of these compounds have not been thoroughly tested, and the impacts that they might have on sarcopenia and satellite cell function are not clear. This review discusses data pertaining to the satellite cell responses and function in aging skeletal muscle, and the impact that three compounds: resveratrol, green tea catechins, and β-Hydroxy-β-methylbutyrate have on regulating satellite cell function and therefore contributing to reducing sarcopenia or improving muscle mass after disuse in aging. The data suggest that these nutraceutical compounds improve satellite cell function during rehabilitative loading in animal models of aging after disuse (i.e., muscle regeneration). While these compounds have not been rigorously tested in humans, the data from animal models of aging provide a strong basis for conducting additional focused work to determine if these or other nutraceuticals can offset the muscle losses, or improve regeneration in sarcopenic muscles of older humans via improving satellite cell function. PMID:25295003

  4. Master stability functions reveal diffusion-driven instabilities in multi-layer networks

    CERN Document Server

    Brechtel, Andreas; Ritterskamp, Daniel; Drossel, Barbara; Gross, Thilo

    2016-01-01

    Many systems in science and technology can be described as multilayer networks, which are known to exhibit phenomena such as catastrophic failure cascades and pattern-forming instabilities. A particular class of multilayer networks describes systems where different interacting copies of a local network exist in different spatial locations, including for instance regulatory and metabolic networks of identical cells and interacting habitats of ecological populations. Here, we show that such systems can be analyzed by a master stability function (MSF) approach, which reveals conditions for diffusion-driven instabilities (DDIs). We demonstrate the methodology on the example of state-of-the-art meta-foodweb models, where it reveals diffusion-driven instabilities that lead to localized dynamics and spatial patterns. This type of approach can be applied to a variety of systems from nature, science and engineering to aid the understanding and design of complex self-organizing systems.

  5. Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates.

    Science.gov (United States)

    Dann, Benjamin; Michaels, Jonathan A; Schaffelhofer, Stefan; Scherberger, Hansjörg

    2016-08-15

    The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks.

  6. Functional equivalence between radial basis function networks and fuzzy inference systems.

    Science.gov (United States)

    Jang, J R; Sun, C T

    1993-01-01

    It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent. This functional equivalence makes it possible to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is of interest to observe that two models stemming from different origins turn out to be functionally equivalent.

  7. Optimal computation of symmetric Boolean functions in Tree networks

    CERN Document Server

    Kowshik, Hemant

    2010-01-01

    In this paper, we address the scenario where nodes with sensor data are connected in a tree network, and every node wants to compute a given symmetric Boolean function of the sensor data. We first consider the problem of computing a function of two nodes with integer measurements. We allow for block computation to enhance data fusion efficiency, and determine the minimum worst-case total number of bits to be exchanged to perform the desired computation. We establish lower bounds using fooling sets, and provide a novel scheme which attains the lower bounds, using information theoretic tools. For a class of functions called sum-threshold functions, this scheme is shown to be optimal. We then turn to tree networks and derive a lower bound for the number of bits exchanged on each link by viewing it as a two node problem. We show that the protocol of recursive innetwork aggregation achieves this lower bound in the case of sumthreshold functions. Thus we have provided a communication and in-network computation stra...

  8. Relationship between topology and functions in metabolic network evolution

    Institute of Scientific and Technical Information of China (English)

    WANG Zhuo; CHEN Qi; LIU Lei

    2009-01-01

    What is the relationship between the topological connections among enzymes and their functions during metabolic network evolution? Does this relationship show similarity among closely related or-ganisms? Here we investigated the relationship between enzyme connectivity and functions in meta-bolic networks of chloroplast and its endosymbiotic ancestor, cyanobacteria (Synechococcus sp. WH8102). Also several other species, including E. coil, Arabidopsis thaliana and Cyanidioschyzon merolae, were used for the comparison. We found that the average connectivity among different func-tional pathways and enzyme classifications (EC) was different in all the species examined. However, the average connectivity of enzymes in the same functional classification was quite similar between chloroplast and one representative of cyanobacteria, syw. In addition, the enzymes in the highly con-served modules between chloroplast and syw, such as amino acid metabolism, were highly connected compared with other modules. We also discovered that the isozymes of chloroplast and syw often had higher connectivity, corresponded to primary metabolism and also existed in conserved module. In conclusion, despite the drastic re-organization of metabolism in chloroplast during endosymbiosis, the relationship between network topology and functions is very similar between chloroplast and its pre-cursor cyanobacteria, which demonstrates that the relationship may be used as an indicator of the closeness in evolution.

  9. Functional Connectivity Hubs and Networks in the Awake Marmoset Brain

    Directory of Open Access Journals (Sweden)

    Annabelle Marie Belcher

    2016-03-01

    Full Text Available In combination with advances in analytical methods, resting-state fMRI is allowing unprecedented access to achieve a better understanding of the network organization of the brain. Increasing evidence suggests that this architecture may incorporate highly functionally connected nodes, or hubs, and we have recently proposed local functional connectivity density (lFCD mapping to identify highly-connected nodes in the human brain. Here we imaged awake nonhuman primates to test whether, like the human brain, the marmoset brain contains functional connectivity hubs. Ten adult common marmosets (Callithrix jacchus were acclimated to mild, comfortable restraint using individualized helmets. Following restraint training, resting BOLD data were acquired during eight consecutive 10 min scans for each subject. lFCD revealed prominent cortical and subcortical hubs of connectivity across the marmoset brain; specifically, in primary and secondary visual cortices (V1/V2, higher-order visual association areas (A19M/V6[DM], posterior parietal and posterior cingulate areas (PGM and A23b/A31, thalamus, dorsal and ventral striatal areas (caudate, putamen, lateral septal nucleus, and anterior cingulate cortex (A24a. lFCD hubs were highly connected to widespread areas of the brain, and further revealed significant network-network interactions. These data provide a baseline platform for future investigations in a nonhuman primate model of the brain’s network topology.

  10. Breakdown of cell-collagen networks through collagen remodeling

    OpenAIRE

    Iordan, Andreea; Duperray, Alain; Gérard, Anaïs; Grichine, Alexei; Verdier, Claude

    2010-01-01

    International audience Collagen model tissues are analyzed, which consist of cells embedded in a collagen matrix at different concentrations (of cells and collagen). Rheological properties are measured and complementary confocal microscopy analyses are carried out. An important feature is observed, corresponding to the breakdown of the collagen network (i.e. decrease in network elasticity) for high collagen concentrations, due to the presence of cells. Thanks to confocal microscopy, we sho...

  11. A New Cell Association Scheme In Heterogeneous Networks

    OpenAIRE

    Yang, Bin; Mao, Guoqiang; Ge, Xiaohu; Han, Tao

    2015-01-01

    Cell association scheme determines which base station (BS) and mobile user (MU) should be associated with and also plays a significant role in determining the average data rate a MU can achieve in heterogeneous networks. However, the explosion of digital devices and the scarcity of spectra collectively force us to carefully re-design cell association scheme which was kind of taken for granted before. To address this, we develop a new cell association scheme in heterogeneous networks based on ...

  12. Neural network parametrization of deep-inelastic structure functions

    International Nuclear Information System (INIS)

    We construct a parametrization of deep-inelastic structure functions which retains information on experimental errors and correlations, and which does not introduce any theoretical bias while interpolating between existing data points. We generate a Monte Carlo sample of pseudo-data configurations and we train an ensemble of neural networks on them. This effectively provides us with a probability measure in the space of structure functions, within the whole kinematic region where data are available. This measure can then be used to determine the value of the structure function, its error, point-to-point correlations and generally the value and uncertainty of any function of the structure function itself. We apply this technique to the determination of the structure function F2 of the proton and deuteron, and a precision determination of the isotriplet combination F2[p-d]. We discuss in detail these results, check their stability and accuracy, and make them available in various formats for applications. (author)

  13. Topological and functional properties of the small GTPases protein interaction network.

    Directory of Open Access Journals (Sweden)

    Anna Delprato

    Full Text Available Small GTP binding proteins of the Ras superfamily (Ras, Rho, Rab, Arf, and Ran regulate key cellular processes such as signal transduction, cell proliferation, cell motility, and vesicle transport. A great deal of experimental evidence supports the existence of signaling cascades and feedback loops within and among the small GTPase subfamilies suggesting that these proteins function in a coordinated and cooperative manner. The interplay occurs largely through association with bi-partite regulatory and effector proteins but can also occur through the active form of the small GTPases themselves. In order to understand the connectivity of the small GTPases signaling routes, a systems-level approach that analyzes data describing direct and indirect interactions was used to construct the small GTPases protein interaction network. The data were curated from the Search Tool for the Retrieval of Interacting Genes (STRING database and include only experimentally validated interactions. The network method enables the conceptualization of the overall structure as well as the underlying organization of the protein-protein interactions. The interaction network described here is comprised of 778 nodes and 1943 edges and has a scale-free topology. Rac1, Cdc42, RhoA, and HRas are identified as the hubs. Ten sub-network motifs are also identified in this study with themes in apoptosis, cell growth/proliferation, vesicle traffic, cell adhesion/junction dynamics, the nicotinamide adenine dinucleotide phosphate (NADPH oxidase response, transcription regulation, receptor-mediated endocytosis, gene silencing, and growth factor signaling. Bottleneck proteins that bridge signaling paths and proteins that overlap in multiple small GTPase networks are described along with the functional annotation of all proteins in the network.

  14. Discovering cancer genes by integrating network and functional properties

    Directory of Open Access Journals (Sweden)

    Davis David P

    2009-09-01

    Full Text Available Abstract Background Identification of novel cancer-causing genes is one of the main goals in cancer research. The rapid accumulation of genome-wide protein-protein interaction (PPI data in humans has provided a new basis for studying the topological features of cancer genes in cellular networks. It is important to integrate multiple genomic data sources, including PPI networks, protein domains and Gene Ontology (GO annotations, to facilitate the identification of cancer genes. Methods Topological features of the PPI network, as well as protein domain compositions, enrichment of gene ontology categories, sequence and evolutionary conservation features were extracted and compared between cancer genes and other genes. The predictive power of various classifiers for identification of cancer genes was evaluated by cross validation. Experimental validation of a subset of the prediction results was conducted using siRNA knockdown and viability assays in human colon cancer cell line DLD-1. Results Cross validation demonstrated advantageous performance of classifiers based on support vector machines (SVMs with the inclusion of the topological features from the PPI network, protein domain compositions and GO annotations. We then applied the trained SVM classifier to human genes to prioritize putative cancer genes. siRNA knock-down of several SVM predicted cancer genes displayed greatly reduced cell viability in human colon cancer cell line DLD-1. Conclusion Topological features of PPI networks, protein domain compositions and GO annotations are good predictors of cancer genes. The SVM classifier integrates multiple features and as such is useful for prioritizing candidate cancer genes for experimental validations.

  15. Learn the effective connectivity pattern of attention networks: a resting functional MRI and Bayesian network study

    Science.gov (United States)

    Li, Juan; Li, Rui; Yao, Li; Wu, Xia

    2011-03-01

    Task-based neuroimaging studies revealed that different attention operations were carried out by the functional interaction and cooperation between two attention systems: the dorsal attention network (DAN) and the ventral attention network (VAN), which were respectively involved in the "top-down" endogenous attention orienting and the "bottomup" exogenous attention reorienting process. Recent focused resting functional MRI (fMRI) studies found the two attention systems were inherently organized in the human brain regardless of whether or not the attention process were required, but how the two attention systems interact with each other in the absence of task is yet to be investigated. In this study, we first separated the DAN and VAN by applying the group independent component analysis (ICA) to the resting fMRI data acquired from 12 healthy young subjects, then used Gaussian Bayesian network (BN) learning approach to explore the plausible effective connectivity pattern of the two attention systems. It was found regions from the same attention network were strongly intra-dependent, and all the connections were located in the information flow from VAN to DAN, which suggested that an orderly functional interactions and information exchanges between the two attention networks existed in the intrinsic spontaneous brain activity, and the inherent connections might benefit the efficient cognitive process between DAN and VAN, such as the "top-down" and "bottom-up" reciprocal interaction when attention-related tasks were involved.

  16. DNA damage activates a spatially distinct late cytoplasmic cell-cycle checkpoint network controlled by MK2-mediated RNA stabilization

    DEFF Research Database (Denmark)

    Reinhardt, H Christian; Hasskamp, Pia; Schmedding, Ingolf;

    2010-01-01

    Following genotoxic stress, cells activate a complex kinase-based signaling network to arrest the cell cycle and initiate DNA repair. p53-defective tumor cells rewire their checkpoint response and become dependent on the p38/MK2 pathway for survival after DNA damage, despite a functional ATR-Chk1...... expression as part of the DNA damage response in cancer cells....

  17. Aberrant structural and functional connectivity in the salience network and central executive network circuit in schizophrenia.

    Science.gov (United States)

    Chen, Quan; Chen, Xingui; He, Xiaoxuan; Wang, Lu; Wang, Kai; Qiu, Bensheng

    2016-08-01

    Consistent structural and functional abnormities have been detected in the salience network (SN) and the central-executive network (CEN) in schizophrenia. SN, known for its critical role in switching CEN and default-mode network (DMN) during cognitively demanding tasks, is proved to show aberrant regulation on the interaction between DMN and CEN in schizophrenia. However, it has not been elucidated whether there is a direct alteration of structural and functional connectivity between SN and CEN. 22 schizophrenia patients and 21 healthy controls were recruited for functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) in present study. The results show that schizophrenia patients had lower fractional anisotropy (FA) in right inferior long fasciculus (ILF), left inferior fronto-occipital fasciculus (IFOF) and callosal body than healthy controls. Significantly reduced functional connectivity was also found between right fronto-insular cortex (rFIC) and right posterior parietal cortex (rPPC). FA in right ILF was positively correlated with the functional connectivity of rFIC-rPPC. Therefore, we proposed a disruption of structural and functional connectivity and a positive anatomo-functional relationship in SN-CEN circuit, which might account for a core feature of schizophrenia. PMID:27233217

  18. Structure network analysis to gain insights into GPCR function.

    Science.gov (United States)

    Fanelli, Francesca; Felline, Angelo; Raimondi, Francesco; Seeber, Michele

    2016-04-15

    G protein coupled receptors (GPCRs) are allosteric proteins whose functioning fundamentals are the communication between the two poles of the helix bundle. Protein structure network (PSN) analysis is one of the graph theory-based approaches currently used to investigate the structural communication in biomolecular systems. Information on system's dynamics can be provided by atomistic molecular dynamics (MD) simulations or coarse grained elastic network models paired with normal mode analysis (ENM-NMA). The present review article describes the application of PSN analysis to uncover the structural communication in G protein coupled receptors (GPCRs). Strategies to highlight changes in structural communication upon misfolding, dimerization and activation are described. Focus is put on the ENM-NMA-based strategy applied to the crystallographic structures of rhodopsin in its inactive (dark) and signalling active (meta II (MII)) states, highlighting changes in structure network and centrality of the retinal chromophore in differentiating the inactive and active states of the receptor. PMID:27068978

  19. Reliable distribution networks design with nonlinear fortification function

    Science.gov (United States)

    Li, Qingwei; Savachkin, Alex

    2016-03-01

    Distribution networks have been facing an increased exposure to the risk of unpredicted disruptions causing significant economic losses. The current literature features a limited number of studies considering fortification of network facilities. In this paper, we develop a reliable uncapacitated fixed-charge location model with fortification to support the design of distribution networks. The model considers heterogeneous facility failure probabilities, one layer of supplier backup, and facility fortification within a finite budget. Facility reliability improvement is modelled as a nonlinear function of fortification investment. The problem is formulated as a nonlinear mixed integer programming model proven to be ?-hard. A Lagrangian relaxation-based heuristic algorithm is developed and its computational efficiency for solving large-scale problems is demonstrated.

  20. EIGENVALUE FUNCTIONS IN EXCITATORY-INHIBITORY NEURONAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    Zhang Linghai

    2004-01-01

    We study the exponential stability of traveling wave solutions of nonlinear systems of integral differential equations arising from nonlinear, nonlocal, synaptically coupled, excitatory-inhibitory neuronal networks. We have proved that exponential stability of traveling waves is equivalent to linear stability. Moreover, if the real parts of nonzero spectrum of an associated linear differential operator have a uniform negative upper bound, namely, max{Reλ: λ∈σ(L), λ≠ 0} ≤ -D, for some positive constant D, and λ = 0 is an algebraically simple eigenvalue of , then the linear stability follows, where is the linear differential operator obtained by linearizing the nonlinear system about its traveling wave and σ(L) denotes the spectrum of . The main aim of this paper is to construct complex analytic functions (also called eigenvalue or Evans functions) for exploring eigenvalues of linear differential operators to study the exponential stability of traveling waves. The zeros of the eigenvalue functions coincide with the eigenvalues of(L) .When studying multipulse solutions, some components of the traveling waves cross their thresholds for many times. These crossings cause great difficulty in the construction of the eigenvalue functions. In particular, we have to solve an over-determined system to construct the eigenvalue functions. By investigating asymptotic behaviors as z → -co of candidates for eigenfunctions, we find a way to construct the eigenvalue functions.By analyzing the zeros of the eigenvalue functions, we can establish the exponential stability of traveling waves arising from neuronal networks.

  1. Default mode, executive function, and language functional connectivity networks are compromised in mild Alzheimer's disease.

    Science.gov (United States)

    Weiler, Marina; Fukuda, Aya; Massabki, Lilian H P; Lopes, Tatila M; Franco, Alexandre R; Damasceno, Benito P; Cendes, Fernando; Balthazar, Marcio L F

    2014-03-01

    Alzheimer's disease (AD) is characterized by mental and cognitive problems, particularly with memory, language, visuospatial skills (VS), and executive functions (EF). Advances in the neuroimaging of AD have highlighted dysfunctions in functional connectivity networks (FCNs), especially in the memory related default mode network (DMN). However, little is known about the integrity and clinical significance of FNCs that process other cognitive functions than memory. We evaluated 22 patients with mild AD and 26 healthy controls through a resting state functional MRI scan. We aimed to identify different FCNs: the DMN, language, EF, and VS. Seed-based functional connectivity was calculated by placing a seed in the DMN (posterior cingulate cortex), language (Broca's and Wernicke's areas), EF (right and left dorsolateral prefrontal cortex), and VS networks (right and left associative visual cortex). We also performed regression analyses between individual connectivity maps for the different FCNs and the scores on cognitive tests. We found areas with significant decreases in functional connectivity in patients with mild AD in the DMN and Wernicke's area compared with controls. Increased connectivity in patients was observed in the EF network. Regarding multiple linear regression analyses, a significant correlation was only observed between the connectivity of the DMN and episodic memory (delayed recall) scores. In conclusion, functional connectivity alterations in mild AD are not restricted to the DMN. Other FCNs related to language and EF may be altered. However, we only found significant correlations between cognition and functional connectivity in the DMN and episodic memory performance.

  2. Functional Brain Network Changes Associated with Maintenance of Cognitive Function in Multiple Sclerosis

    Directory of Open Access Journals (Sweden)

    Santosh A Helekar

    2010-11-01

    Full Text Available In multiple sclerosis (MS functional changes in connectivity due to cortical reorganization could lead to cognitive impairment (CI, or reflect a re-adjustment to reduce the clinical effects of widespread tissue damage. Such alterations in connectivity could result in changes in neural activation as assayed by executive function tasks. We examined cognitive function in MS patients with mild to moderate cognitive impairment and age-matched controls. We evaluated brain activity using functional magnetic resonance imaging (fMRI during the successful performance of the Wisconsin-card sorting (WCS task by MS patients, showing compensatory maintenance of normal function, as measured by response latency and error rate. To assess changes in functional connectivity throughout the brain, we performed a global functional brain network analysis by computing voxel by voxel correlations on the fMRI time series data and carrying out a hierarchical cluster analysis. We found that during the WCS task there is a significant reduction in the number of smaller size brain functional networks, and a change in the brain areas representing the nodes of these networks in MS patients compared to age-matched controls. There is also a concomitant increase in the strength of functional connections between brain loci separated at intermediate scale distances in these patients. These functional alterations might reflect compensatory neuroplastic reorganization underlying maintenance of relatively normal cognitive function in the face of white matter lesions and cortical atrophy produced by MS.

  3. Disrupted Brain Functional Network Architecture in Chronic Tinnitus Patients

    Science.gov (United States)

    Chen, Yu-Chen; Feng, Yuan; Xu, Jin-Jing; Mao, Cun-Nan; Xia, Wenqing; Ren, Jun; Yin, Xindao

    2016-01-01

    Purpose: Resting-state functional magnetic resonance imaging (fMRI) studies have demonstrated the disruptions of multiple brain networks in tinnitus patients. Nonetheless, several studies found no differences in network processing between tinnitus patients and healthy controls (HCs). Its neural bases are poorly understood. To identify aberrant brain network architecture involved in chronic tinnitus, we compared the resting-state fMRI (rs-fMRI) patterns of tinnitus patients and HCs. Materials and Methods: Chronic tinnitus patients (n = 24) with normal hearing thresholds and age-, sex-, education- and hearing threshold-matched HCs (n = 22) participated in the current study and underwent the rs-fMRI scanning. We used degree centrality (DC) to investigate functional connectivity (FC) strength of the whole-brain network and Granger causality to analyze effective connectivity in order to explore directional aspects involved in tinnitus. Results: Compared to HCs, we found significantly increased network centrality in bilateral superior frontal gyrus (SFG). Unidirectionally, the left SFG revealed increased effective connectivity to the left middle orbitofrontal cortex (OFC), left posterior lobe of cerebellum (PLC), left postcentral gyrus, and right middle occipital gyrus (MOG) while the right SFG exhibited enhanced effective connectivity to the right supplementary motor area (SMA). In addition, the effective connectivity from the bilateral SFG to the OFC and SMA showed positive correlations with tinnitus distress. Conclusions: Rs-fMRI provides a new and novel method for identifying aberrant brain network architecture. Chronic tinnitus patients have disrupted FC strength and causal connectivity mostly in non-auditory regions, especially the prefrontal cortex (PFC). The current findings will provide a new perspective for understanding the neuropathophysiological mechanisms in chronic tinnitus. PMID:27458377

  4. Membrane elastic properties and cell function.

    Directory of Open Access Journals (Sweden)

    Bruno Pontes

    Full Text Available Recent studies indicate that the cell membrane, interacting with its attached cytoskeleton, is an important regulator of cell function, exerting and responding to forces. We investigate this relationship by looking for connections between cell membrane elastic properties, especially surface tension and bending modulus, and cell function. Those properties are measured by pulling tethers from the cell membrane with optical tweezers. Their values are determined for all major cell types of the central nervous system, as well as for macrophage. Astrocytes and glioblastoma cells, which are considerably more dynamic than neurons, have substantially larger surface tensions. Resting microglia, which continually scan their environment through motility and protrusions, have the highest elastic constants, with values similar to those for resting macrophage. For both microglia and macrophage, we find a sharp softening of bending modulus between their resting and activated forms, which is very advantageous for their acquisition of phagocytic functions upon activation. We also determine the elastic constants of pure cell membrane, with no attached cytoskeleton. For all cell types, the presence of F-actin within tethers, contrary to conventional wisdom, is confirmed. Our findings suggest the existence of a close connection between membrane elastic constants and cell function.

  5. Ecological interaction and phylogeny, studying functionality on composed networks

    Science.gov (United States)

    Cruz, Claudia P. T.; Fonseca, Carlos Roberto; Corso, Gilberto

    2012-02-01

    We study a class of composed networks that are formed by two tree networks, TP and TA, whose end points touch each other through a bipartite network BPA. We explore this network using a functional approach. We are interested in how much the topology, or the structure, of TX (X=A or P) determines the links of BPA. This composed structure is a useful model in evolutionary biology, where TP and TA are the phylogenetic trees of plants and animals that interact in an ecological community. We make use of ecological networks of dispersion of fruits, which are formed by frugivorous animals and plants with fruits; the animals, usually birds, eat fruits and disperse their seeds. We analyse how the phylogeny of TX determines or is correlated with BPA using a Monte Carlo approach. We use the phylogenetic distance among elements that interact with a given species to construct an index κ that quantifies the influence of TX over BPA. The algorithm is based on the assumption that interaction matrices that follows a phylogeny of TX have a total phylogenetic distance smaller than the average distance of an ensemble of Monte Carlo realisations. We find that the effect of phylogeny of animal species is more pronounced in the ecological matrix than plant phylogeny.

  6. Population spikes in cortical networks during different functional states.

    Directory of Open Access Journals (Sweden)

    Shirley eMark

    2012-07-01

    Full Text Available Brain computational challenges vary between behavioral states. Engaged animals react according to incoming sensory information, while in relaxed and sleeping states consolidation of the learned information is believed to take place. Different states are characterized by different forms of cortical activity. We study a possible neuronal mechanism for generating these diverse dynamics and suggest their possible functional significance. Previous studies demonstrated that brief synchronized increase in a neural firing (Population Spikes can be generated in homogenous recurrent neural networks with short-term synaptic depression. Here we consider more realistic networks with clustered architecture. We show that the level of synchronization in neural activity can be controlled smoothly by network parameters. The network shifts from asynchronous activity to a regime in which clusters synchronized separately, then, the synchronization between the clusters increases gradually to fully synchronized state. We examine the effects of different synchrony levels on the transmission of information by the network. We find that the regime of intermediate synchronization is preferential for the flow of information between sparsely connected areas. Based on these results, we suggest that the regime of intermediate synchronization corresponds to engaged behavioral state of the animal, while global synchronization is exhibited during relaxed and sleeping states.

  7. Diabetes and Stem Cell Function

    OpenAIRE

    Shin Fujimaki; Tamami Wakabayashi; Tohru Takemasa; Makoto Asashima; Tomoko Kuwabara

    2015-01-01

    Diabetes mellitus is one of the most common serious metabolic diseases that results in hyperglycemia due to defects of insulin secretion or insulin action or both. The present review focuses on the alterations to the diabetic neuronal tissues and skeletal muscle, including stem cells in both tissues, and the preventive effects of physical activity on diabetes. Diabetes is associated with various nervous disorders, such as cognitive deficits, depression, and Alzheimer’s disease, and that may b...

  8. Generalized Virtual Networking: an enabler for Service Centric Networking and Network Function Virtualization

    OpenAIRE

    Salsano, Stefano; Blefari-Melazzi, Nicola; Presti, Francesco Lo; Siracusano, Giuseppe; Ventre, Pier Luigi

    2014-01-01

    In this paper we introduce the Generalized Virtual Networking (GVN) concept. GVN provides a framework to influence the routing of packets based on service level information that is carried in the packets. It is based on a protocol header inserted between the Network and Transport layers, therefore it can be seen as a layer 3.5 solution. Technically, GVN is proposed as a new transport layer protocol in the TCP/IP protocol suite. An IP router that is not GVN capable will simply process the IP d...

  9. Targeting single neuronal networks for gene expression and cell labeling in vivo.

    Science.gov (United States)

    Marshel, James H; Mori, Takuma; Nielsen, Kristina J; Callaway, Edward M

    2010-08-26

    To understand fine-scale structure and function of single mammalian neuronal networks, we developed and validated a strategy to genetically target and trace monosynaptic inputs to a single neuron in vitro and in vivo. The strategy independently targets a neuron and its presynaptic network for specific gene expression and fine-scale labeling, using single-cell electroporation of DNA to target infection and monosynaptic retrograde spread of a genetically modifiable rabies virus. The technique is highly reliable, with transsynaptic labeling occurring in every electroporated neuron infected by the virus. Targeting single neocortical neuronal networks in vivo, we found clusters of both spiny and aspiny neurons surrounding the electroporated neuron in each case, in addition to intricately labeled distal cortical and subcortical inputs. This technique, broadly applicable for probing and manipulating single neuronal networks with single-cell resolution in vivo, may help shed new light on fundamental mechanisms underlying circuit development and information processing by neuronal networks throughout the brain.

  10. Construction of a chloroplast protein interaction network and functional mining of photosynthetic proteins in Arabidopsis thaliana

    Institute of Scientific and Technical Information of China (English)

    Qing-Bo Yu; Yong-Lan Cui; Kang Chong; Yi-Xue Li; Yu-Hua Li; Zhongming Zhao; Tie-Liu Shi; Zhong-Nan Yang; Guang Li; Guan Wang; Jing-Chun Sun; Peng-Cheng Wang; Chen Wang; Hua-Ling Mi; Wei-Min Ma; Jian Cui

    2008-01-01

    Chloroplast is a typical plant cell organeUe where photosynthesis takes place.In this study,a total of 1 808 chloroplast core proteins in Arabidopsis thaliana were reliably identified by combining the results of previously published studies and our own predictions.We then constructed a chloroplast protein interaction network primarily based on these core protein interactions.The network had 22 925 protein interaction pairs which involved 2 214 proteins.A total of 160 previously uncharacterized proteins were annotated in this network.The subunits of the photosynthetic complexes were modularized,and the functional relationships among photosystem Ⅰ (PSI),photosystem Ⅱ (PSII),light harvesting complex of photosystem Ⅰ (LHC Ⅰ) and light harvesting complex of photosystem Ⅰ (LHC Ⅱ) could be deduced from the predicted protein interactions in this network.We further confirmed an interaction between an unknown protein AT1G52220 and a photosynthetic subunit PSI-D2 by yeast two-hybrid analysis.Our chloroplast protein interaction network should be useful for functional mining of photosynthetic proteins and investigation of chloroplast-related functions at the systems biology level in Arabidopsis.

  11. Direction-dependent learning approach for radial basis function networks.

    Science.gov (United States)

    Singla, Puneet; Subbarao, Kamesh; Junkins, John L

    2007-01-01

    Direction-dependent scaling, shaping, and rotation of Gaussian basis functions are introduced for maximal trend sensing with minimal parameter representations for input output approximation. It is shown that shaping and rotation of the radial basis functions helps in reducing the total number of function units required to approximate any given input-output data, while improving accuracy. Several alternate formulations that enforce minimal parameterization of the most general radial basis functions are presented. A novel "directed graph" based algorithm is introduced to facilitate intelligent direction based learning and adaptation of the parameters appearing in the radial basis function network. Further, a parameter estimation algorithm is incorporated to establish starting estimates for the model parameters using multiple windows of the input-output data. The efficacy of direction-dependent shaping and rotation in function approximation is evaluated by modifying the minimal resource allocating network and considering different test examples. The examples are drawn from recent literature to benchmark the new algorithm versus existing methods.

  12. Lukasiewicz-Topos Models of Neural Networks, Cell Genome and Interactome Nonlinear Dynamic Models

    CERN Document Server

    Baianu, I C

    2004-01-01

    A categorical and Lukasiewicz-Topos framework for Lukasiewicz Algebraic Logic models of nonlinear dynamics in complex functional systems such as neural networks, genomes and cell interactomes is proposed. Lukasiewicz Algebraic Logic models of genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable 'next-state functions' is extended to a Lukasiewicz Topos with an n-valued Lukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis.

  13. Enhanced Inter-Cell Interference Coordination Challenges in Heterogeneous Networks

    CERN Document Server

    Lopez-Perez, David; de la Roche, Guillaume; Kountouris, Marios; Quek, Tony Q S; Zhang, Jie; 10.1109/MWC.2011.5876497

    2011-01-01

    3GPP LTE-Advanced has started a new study item to investigate Heterogeneous Network (HetNet) deployments as a cost effective way to deal with the unrelenting traffic demand. HetNets consist of a mix of macrocells, remote radio heads, and low-power nodes such as picocells, femtocells, and relays. Leveraging network topology, increasing the proximity between the access network and the end-users, has the potential to provide the next significant performance leap in wireless networks, improving spatial spectrum reuse and enhancing indoor coverage. Nevertheless, deployment of a large number of small cells overlaying the macrocells is not without new technical challenges. In this article, we present the concept of heterogeneous networks and also describe the major technical challenges associated with such network architecture. We focus in particular on the standardization activities within the 3GPP related to enhanced inter-cell interference coordination.

  14. Nuclear charge radii: Density functional theory meets Bayesian neural networks

    CERN Document Server

    Utama, Raditya; Piekarewicz, Jorge

    2016-01-01

    The distribution of electric charge in atomic nuclei is fundamental to our understanding of the complex nuclear dynamics and a quintessential observable to validate nuclear structure models. We explore a novel approach that combines sophisticated models of nuclear structure with Bayesian neural networks (BNN) to generate predictions for the charge radii of thousands of nuclei throughout the nuclear chart. A class of relativistic energy density functionals is used to provide robust predictions for nuclear charge radii. In turn, these predictions are refined through Bayesian learning for a neural network that is trained using residuals between theoretical predictions and the experimental data. Although predictions obtained with density functional theory provide a fairly good description of experiment, our results show significant improvement (better than 40%) after BNN refinement. Moreover, these improved results for nuclear charge radii are supplemented with theoretical error bars. We have successfully demonst...

  15. The AII Amacrine Cell Connectome: A Dense Network Hub

    OpenAIRE

    Marc, Robert E.; James Russell Anderson; Bryan William Jones; Crystal Lynn Sigulinsky; James Scott Lauritzen

    2014-01-01

    The mammalian AII retinal amacrine cell is a narrow-field, multistratified glycinergic neuron best known for its role in collecting scotopic signals from rod bipolar cells and distributing them to ON and OFF cone pathways in a crossover network via a combination of inhibitory synapses and heterocellular AII::ON cone bipolar cell gap junctions. Long considered a simple cell, a full connectomics analysis shows that AII cells possess the most complex interaction repertoire of any known vertebrat...

  16. Combining regression trees and radial basis function networks.

    Science.gov (United States)

    Orr, M; Hallam, J; Takezawa, K; Murra, A; Ninomiya, S; Oide, M; Leonard, T

    2000-12-01

    We describe a method for non-parametric regression which combines regression trees with radial basis function networks. The method is similar to that of Kubat, who was first to suggest such a combination, but has some significant improvements. We demonstrate the features of the new method, compare its performance with other methods on DELVE data sets and apply it to a real world problem involving the classification of soybean plants from digital images.

  17. Platelet Serotonin Transporter Function Predicts Default-Mode Network Activity

    OpenAIRE

    Christian Scharinger; Ulrich Rabl; Christian H. Kasess; Meyer, Bernhard M.; Tina Hofmaier; Kersten Diers; Lucie Bartova; Gerald Pail; Wolfgang Huf; Zeljko Uzelac; Beate Hartinger; Klaudius Kalcher; Thomas Perkmann; Helmuth Haslacher; Andreas Meyer-Lindenberg

    2014-01-01

    Background The serotonin transporter (5-HTT) is abundantly expressed in humans by the serotonin transporter gene SLC6A4 and removes serotonin (5-HT) from extracellular space. A blood-brain relationship between platelet and synaptosomal 5-HT reuptake has been suggested, but it is unknown today, if platelet 5-HT uptake can predict neural activation of human brain networks that are known to be under serotonergic influence. Methods A functional magnetic resonance study was performed in 48 healthy...

  18. Exploring functional connectivity networks with multichannel brain array coils.

    Science.gov (United States)

    Anteraper, Sheeba Arnold; Whitfield-Gabrieli, Susan; Keil, Boris; Shannon, Steven; Gabrieli, John D; Triantafyllou, Christina

    2013-01-01

    The use of multichannel array head coils in functional and structural magnetic resonance imaging (MRI) provides increased signal-to-noise ratio (SNR), higher sensitivity, and parallel imaging capabilities. However, their benefits remain to be systematically explored in the context of resting-state functional connectivity MRI (fcMRI). In this study, we compare signal detectability within and between commercially available multichannel brain coils, a 32-Channel (32Ch), and a 12-Channel (12Ch) at 3T, in a high-resolution regime to accurately map resting-state networks. We investigate whether the 32Ch coil can extract and map fcMRI more efficiently and robustly than the 12Ch coil using seed-based and graph-theory-based analyses. Our findings demonstrate that although the 12Ch coil can be used to reveal resting-state connectivity maps, the 32Ch coil provides increased detailed functional connectivity maps (using seed-based analysis) as well as increased global and local efficiency, and cost (using graph-theory-based analysis), in a number of widely reported resting-state networks. The exploration of subcortical networks, which are scarcely reported due to limitations in spatial-resolution and coil sensitivity, also proved beneficial with the 32Ch coil. Further, comparisons regarding the data acquisition time required to successfully map these networks indicated that scan time can be significantly reduced by 50% when a coil with increased number of channels (i.e., 32Ch) is used. Switching to multichannel arrays in resting-state fcMRI could, therefore, provide both detailed functional connectivity maps and acquisition time reductions, which could further benefit imaging special subject populations, such as patients or pediatrics who have less tolerance in lengthy imaging sessions. PMID:23510203

  19. Neural network modeling and control of proton exchange membrane fuel cell

    Institute of Scientific and Technical Information of China (English)

    CHEN Yue-hua; CAO Guang-yi; ZHU Xin-jian

    2007-01-01

    A neural network model and fuzzy neural network controller was designed to control the inner impedance of a proton exchange membrane fuel cell(PEMFC)stack. A radial basis function(RBF)neural network model was trained by the input-output data of impedance. A fuzzy neural network controller Was designed to control the impedance response.The RBF neural network model was used to test the fuzzy neural network controller.The results show that the RBF model output Can imitate actual output well, themaximal errorisnotbeyond 20 mΩ, thetrainingtime is about 1 s by using 20 neurons, and the mean squared errors is 141.9 mΩ2.The impedance of the PEMFC stack is controlled within the optimum range when the load changes, and the adjustive time is ahnllt 3 rain.

  20. Comments on "Functional equivalence between radial basis function networks and fuzzy inference systems".

    Science.gov (United States)

    Anderson, H C; Lotfi, A; Westphal, L C; Jang, J R

    1998-01-01

    The above paper claims that under a set of minor restrictions radial basis function networks and fuzzy inference systems are functionally equivalent. The purpose of this letter is to show that this set of restrictions is incomplete and that, when it is completed, the said functional equivalence applies only to a small range of fuzzy inference systems. In addition, a modified set of restrictions is proposed which is applicable for a much wider range of fuzzy inference systems.

  1. SoyFN: a knowledge database of soybean functional networks.

    Science.gov (United States)

    Xu, Yungang; Guo, Maozu; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

    2014-01-01

    Many databases for soybean genomic analysis have been built and made publicly available, but few of them contain knowledge specifically targeting the omics-level gene-gene, gene-microRNA (miRNA) and miRNA-miRNA interactions. Here, we present SoyFN, a knowledge database of soybean functional gene networks and miRNA functional networks. SoyFN provides user-friendly interfaces to retrieve, visualize, analyze and download the functional networks of soybean genes and miRNAs. In addition, it incorporates much information about KEGG pathways, gene ontology annotations and 3'-UTR sequences as well as many useful tools including SoySearch, ID mapping, Genome Browser, eFP Browser and promoter motif scan. SoyFN is a schema-free database that can be accessed as a Web service from any modern programming language using a simple Hypertext Transfer Protocol call. The Web site is implemented in Java, JavaScript, PHP, HTML and Apache, with all major browsers supported. We anticipate that this database will be useful for members of research communities both in soybean experimental science and bioinformatics. Database URL: http://nclab.hit.edu.cn/SoyFN.

  2. Unitary Networks from the Exact Renormalization of Wave Functionals

    CERN Document Server

    Fliss, Jackson R; Parrikar, Onkar

    2016-01-01

    The exact renormalization group (ERG) for $O(N)$ vector models (at large $N$) on flat Euclidean space can be interpreted as the bulk dynamics corresponding to a holographically dual higher spin gauge theory on $AdS_{d+1}$. This was established in the sense that at large $N$ the generating functional of correlation functions of single trace operators is reproduced by the on-shell action of the bulk higher spin theory, which is most simply presented in a first-order (phase space) formalism. In this paper, we extend the ERG formalism to the wave functionals of arbitrary states of the $O(N)$ vector model at the free fixed point. We find that the ERG flow of the ground state and a specific class of excited states is implemented by the action of unitary operators which can be chosen to be local. Consequently, the ERG equations provide a continuum notion of a tensor network. We compare this tensor network with the entanglement renormalization networks, MERA, and its continuum version, cMERA, which have appeared rece...

  3. SoyFN: a knowledge database of soybean functional networks.

    Science.gov (United States)

    Xu, Yungang; Guo, Maozu; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

    2014-01-01

    Many databases for soybean genomic analysis have been built and made publicly available, but few of them contain knowledge specifically targeting the omics-level gene-gene, gene-microRNA (miRNA) and miRNA-miRNA interactions. Here, we present SoyFN, a knowledge database of soybean functional gene networks and miRNA functional networks. SoyFN provides user-friendly interfaces to retrieve, visualize, analyze and download the functional networks of soybean genes and miRNAs. In addition, it incorporates much information about KEGG pathways, gene ontology annotations and 3'-UTR sequences as well as many useful tools including SoySearch, ID mapping, Genome Browser, eFP Browser and promoter motif scan. SoyFN is a schema-free database that can be accessed as a Web service from any modern programming language using a simple Hypertext Transfer Protocol call. The Web site is implemented in Java, JavaScript, PHP, HTML and Apache, with all major browsers supported. We anticipate that this database will be useful for members of research communities both in soybean experimental science and bioinformatics. Database URL: http://nclab.hit.edu.cn/SoyFN. PMID:24618044

  4. Reduced synaptic activity in neuronal networks derived from embryonic stem cells of murine Rett syndrome model

    OpenAIRE

    Lydia eBarth; Rosmarie eSütterlin; Markus eNenniger; Kaspar Emanuel Vogt

    2014-01-01

    Neurodevelopmental diseases such as the Rett syndrome have received renewed attention, since the mechanisms involved may underlie a broad range of neuropsychiatric disorders such as schizophrenia and autism. In vertebrates early stages in the functional development of neurons and neuronal networks are difficult to study. Embryonic stem cell-derived neurons provide an easily accessible tool to investigate neuronal differentiation and early network formation. We used in vitro cultures of neuron...

  5. Cell cycle networks link gene expression dysregulation, mutation, and brain maldevelopment in autistic toddlers.

    Science.gov (United States)

    Pramparo, Tiziano; Lombardo, Michael V; Campbell, Kathleen; Barnes, Cynthia Carter; Marinero, Steven; Solso, Stephanie; Young, Julia; Mayo, Maisi; Dale, Anders; Ahrens-Barbeau, Clelia; Murray, Sarah S; Lopez, Linda; Lewis, Nathan; Pierce, Karen; Courchesne, Eric

    2015-12-01

    Genetic mechanisms underlying abnormal early neural development in toddlers with Autism Spectrum Disorder (ASD) remain uncertain due to the impossibility of direct brain gene expression measurement during critical periods of early development. Recent findings from a multi-tissue study demonstrated high expression of many of the same gene networks between blood and brain tissues, in particular with cell cycle functions. We explored relationships between blood gene expression and total brain volume (TBV) in 142 ASD and control male toddlers. In control toddlers, TBV variation significantly correlated with cell cycle and protein folding gene networks, potentially impacting neuron number and synapse development. In ASD toddlers, their correlations with brain size were lost as a result of considerable changes in network organization, while cell adhesion gene networks significantly correlated with TBV variation. Cell cycle networks detected in blood are highly preserved in the human brain and are upregulated during prenatal states of development. Overall, alterations were more pronounced in bigger brains. We identified 23 candidate genes for brain maldevelopment linked to 32 genes frequently mutated in ASD. The integrated network includes genes that are dysregulated in leukocyte and/or postmortem brain tissue of ASD subjects and belong to signaling pathways regulating cell cycle G1/S and G2/M phase transition. Finally, analyses of the CHD8 subnetwork and altered transcript levels from an independent study of CHD8 suppression further confirmed the central role of genes regulating neurogenesis and cell adhesion processes in ASD brain maldevelopment. PMID:26668231

  6. Cell cycle networks link gene expression dysregulation, mutation, and brain maldevelopment in autistic toddlers.

    Science.gov (United States)

    Pramparo, Tiziano; Lombardo, Michael V; Campbell, Kathleen; Barnes, Cynthia Carter; Marinero, Steven; Solso, Stephanie; Young, Julia; Mayo, Maisi; Dale, Anders; Ahrens-Barbeau, Clelia; Murray, Sarah S; Lopez, Linda; Lewis, Nathan; Pierce, Karen; Courchesne, Eric

    2015-12-14

    Genetic mechanisms underlying abnormal early neural development in toddlers with Autism Spectrum Disorder (ASD) remain uncertain due to the impossibility of direct brain gene expression measurement during critical periods of early development. Recent findings from a multi-tissue study demonstrated high expression of many of the same gene networks between blood and brain tissues, in particular with cell cycle functions. We explored relationships between blood gene expression and total brain volume (TBV) in 142 ASD and control male toddlers. In control toddlers, TBV variation significantly correlated with cell cycle and protein folding gene networks, potentially impacting neuron number and synapse development. In ASD toddlers, their correlations with brain size were lost as a result of considerable changes in network organization, while cell adhesion gene networks significantly correlated with TBV variation. Cell cycle networks detected in blood are highly preserved in the human brain and are upregulated during prenatal states of development. Overall, alterations were more pronounced in bigger brains. We identified 23 candidate genes for brain maldevelopment linked to 32 genes frequently mutated in ASD. The integrated network includes genes that are dysregulated in leukocyte and/or postmortem brain tissue of ASD subjects and belong to signaling pathways regulating cell cycle G1/S and G2/M phase transition. Finally, analyses of the CHD8 subnetwork and altered transcript levels from an independent study of CHD8 suppression further confirmed the central role of genes regulating neurogenesis and cell adhesion processes in ASD brain maldevelopment.

  7. Implementation of Radial Basis Function Neural Network for Image Steganalysis

    Directory of Open Access Journals (Sweden)

    Sambasiva Rao Baragada

    2008-09-01

    Full Text Available Steganographic tools and techniques are becoming more potential and widespread. Illegal use of steganography poses serious challenges to the law enforcement agencies. Limited work has been carried out on supervised steganalysis using neural network as a classifier. We present a combined method of identifying the presence of covert information in a carrier image using fisher’s linear discriminant (FLD function followed by the radial basis function (RBF. Experiments show promising results when compared to the existing supervised steganalysis methods, but arranging the retrieved information is still a challenging problem.

  8. Dynamic changes in protein functional linkage networks revealed by integration with gene expression data.

    Directory of Open Access Journals (Sweden)

    Shubhada R Hegde

    2008-11-01

    Full Text Available Response of cells to changing environmental conditions is governed by the dynamics of intricate biomolecular interactions. It may be reasonable to assume, proteins being the dominant macromolecules that carry out routine cellular functions, that understanding the dynamics of protein:protein interactions might yield useful insights into the cellular responses. The large-scale protein interaction data sets are, however, unable to capture the changes in the profile of protein:protein interactions. In order to understand how these interactions change dynamically, we have constructed conditional protein linkages for Escherichia coli by integrating functional linkages and gene expression information. As a case study, we have chosen to analyze UV exposure in wild-type and SOS deficient E. coli at 20 minutes post irradiation. The conditional networks exhibit similar topological properties. Although the global topological properties of the networks are similar, many subtle local changes are observed, which are suggestive of the cellular response to the perturbations. Some such changes correspond to differences in the path lengths among the nodes of carbohydrate metabolism correlating with its loss in efficiency in the UV treated cells. Similarly, expression of hubs under unique conditions reflects the importance of these genes. Various centrality measures applied to the networks indicate increased importance for replication, repair, and other stress proteins for the cells under UV treatment, as anticipated. We thus propose a novel approach for studying an organism at the systems level by integrating genome-wide functional linkages and the gene expression data.

  9. Standard—Cell Placement from Functional Descriptions

    Institute of Scientific and Technical Information of China (English)

    KlausBuchenrieder

    1991-01-01

    This paper presents a functional language for the unambiguous description of digital circuits,a method and algorithms to obtain a standard-cell layout,and a comparative evaluation of the developed functional standard-cell placement technique.The presented placement scheme is different from traditional methods because the complete layout grometry is specified and constructed automatically from a functional description.The construction relies on a translation that combines the simplicity of standard-cells with the elegance of functional programming.An evaluation of the method introduced shows that the quality of the resulting placement is close to the results achieved with simulated annealing while the computation time is significantly less.Furthermore,the evaluation suggests to employ the functional placement method in conjunction with low-temperature simulated annealing for running-time reduction and improvedresults.

  10. ASYNCHRONOUS MEASUREMENT OF CDV IN ATM NETWORKS USING OAM CELLS

    Directory of Open Access Journals (Sweden)

    J. GARCÍA-HERNÁNDEZ

    2003-09-01

    Full Text Available The synchronisation between source and destination clocks at network nodes to a common reference time of day is important in order to achieve accurate measurements of cells delays in ATM networks. Otherwise, direct measurement of the cell transfer delay and cell delay variation using time-stamped OAM cells would be inaccurate. In this work, a method for the accurate measurement of cell delay variation using OAM cells, which does not rely on synchronised clocks at the measurement points is presented. This is achieved by assuming that each ATM switch has the capability to process and to modify the information contained in the OAM cells. The system presented here was modelled by means of a block-oriented network simulator (BONeS, which is a graphically-oriented, general-purpose simulation language for modelling and simulating communications networks, including ATM networks. The measurements of the cell delay variation were carried out on a VBR video source generated by a real MPEG-2 encoder with a fixed quantizer step size. The simulation results obtained show that measurements of the cumulative cell delay variation carried out without synchronised clocks at the measurement points were very closed to those obtained using synchronised clocks at the measurement points.

  11. Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks

    Directory of Open Access Journals (Sweden)

    Lindsay eRutter

    2013-07-01

    Full Text Available Complex networks have been observed to comprise small-world properties, believed to represent an optimal organization of local specialization and global integration of information processing at reduced wiring cost. Here, we applied magnitude squared coherence to resting magnetoencephalographic time series in reconstructed source space, acquired from controls and patients with schizophrenia, and generated frequency-dependent adjacency matrices modeling functional connectivity between virtual channels. After configuring undirected binary and weighted graphs, we found that all human networks demonstrated highly localized clustering and short characteristic path lengths. The most conservatively thresholded networks showed efficient wiring, with topographical distance between connected vertices amounting to one-third as observed in surrogate randomized topologies. Nodal degrees of the human networks conformed to a heavy-tailed exponentially truncated power-law, compatible with the existence of hubs, which included theta and alpha bilateral cerebellar tonsil, beta and gamma bilateral posterior cingulate, and bilateral thalamus across all frequencies. We conclude that all networks showed small-worldness, minimal physical connection distance, and skewed degree distributions characteristic of physically-embedded networks, and that these calculations derived from graph theoretical mathematics did not quantifiably distinguish between subject populations, independent of bandwidth. However, post-hoc measurements of edge computations at the scale of the individual vertex revealed trends of reduced gamma connectivity across the posterior medial parietal cortex in patients, an observation consistent with our prior resting activation study that found significant reduction of synthetic aperture magnetometry gamma power across similar regions. The basis of these small differences remains unclear.

  12. Radial Basis Function Networks for Conversion of Sound Spectra

    Directory of Open Access Journals (Sweden)

    Carlo Drioli

    2001-03-01

    Full Text Available In many advanced signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN is proposed for the modeling of the spectral changes (or conversions related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. Moreover, a singular value decomposition (SVD approach is used to reduce the dimension of conversion functions. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency.

  13. Quetiapine modulates functional connectivity in brain aggression networks.

    Science.gov (United States)

    Klasen, Martin; Zvyagintsev, Mikhail; Schwenzer, Michael; Mathiak, Krystyna A; Sarkheil, Pegah; Weber, René; Mathiak, Klaus

    2013-07-15

    Aggressive behavior is associated with dysfunctions in an affective regulation network encompassing amygdala and prefrontal areas such as orbitofrontal (OFC), anterior cingulate (ACC), and dorsolateral prefrontal cortex (DLPFC). In particular, prefrontal regions have been postulated to control amygdala activity by inhibitory projections, and this process may be disrupted in aggressive individuals. The atypical antipsychotic quetiapine successfully attenuates aggressive behavior in various disorders; the underlying neural processes, however, are unknown. A strengthened functional coupling in the prefrontal-amygdala system may account for these anti-aggressive effects. An inhibition of this network has been reported for virtual aggression in violent video games as well. However, there have been so far no in-vivo observations of pharmacological influences on corticolimbic projections during human aggressive behavior. In a double-blind, placebo-controlled study, quetiapine and placebo were administered for three successive days prior to an fMRI experiment. In this experiment, functional brain connectivity was assessed during virtual aggressive behavior in a violent video game and an aggression-free control task in a non-violent modification. Quetiapine increased the functional connectivity of ACC and DLPFC with the amygdala during virtual aggression, whereas OFC-amygdala coupling was attenuated. These effects were observed neither for placebo nor for the non-violent control. These results demonstrate for the first time a pharmacological modification of aggression-related human brain networks in a naturalistic setting. The violence-specific modulation of prefrontal-amygdala networks appears to control aggressive behavior and provides a neurobiological model for the anti-aggressive effects of quetiapine. PMID:23501053

  14. Colored dual-functional photovoltaic cells

    Science.gov (United States)

    Lee, Kyu-Tae; Lee, Jae Yong; Xu, Ting; Park, Hui Joon; Guo, L. Jay

    2016-06-01

    In this article, we review our recent efforts on multi-functional photovoltaic (PV) cells that can produce desired reflective, transmissive, or neutral colors, by controlling light interaction with semiconductors and electrode structures in a desired manner. The PV cells integrated with plasmonic color filtering schemes using subwavelength gratings, and other approaches exploiting photonic resonances in an optical nanocavity consisting of highly absorbing semiconductor media are described. For further enhancement of optical and electrical performance characteristics of the multi-functional PV cells, possible difficulties and the outlook for future work are discussed.

  15. Unconditioned responses and functional fear networks in human classical conditioning.

    Science.gov (United States)

    Linnman, Clas; Rougemont-Bücking, Ansgar; Beucke, Jan Carl; Zeffiro, Thomas A; Milad, Mohammed R

    2011-08-01

    Human imaging studies examining fear conditioning have mainly focused on the neural responses to conditioned cues. In contrast, the neural basis of the unconditioned response and the mechanisms by which fear modulates inter-regional functional coupling have received limited attention. We examined the neural responses to an unconditioned stimulus using a partial-reinforcement fear conditioning paradigm and functional MRI. The analysis focused on: (1) the effects of an unconditioned stimulus (an electric shock) that was either expected and actually delivered, or expected but not delivered, and (2) on how related brain activity changed across conditioning trials, and (3) how shock expectation influenced inter-regional coupling within the fear network. We found that: (1) the delivery of the shock engaged the red nucleus, amygdale, dorsal striatum, insula, somatosensory and cingulate cortices, (2) when the shock was expected but not delivered, only the red nucleus, the anterior insular and dorsal anterior cingulate cortices showed activity increases that were sustained across trials, and (3) psycho-physiological interaction analysis demonstrated that fear led to increased red nucleus coupling to insula but decreased hippocampus coupling to the red nucleus, thalamus and cerebellum. The hippocampus and the anterior insula may serve as hubs facilitating the switch between engagement of a defensive immediate fear network and a resting network. PMID:21377494

  16. Functional Genomics Assistant (FUGA: a toolbox for the analysis of complex biological networks

    Directory of Open Access Journals (Sweden)

    Ouzounis Christos A

    2011-10-01

    Full Text Available Abstract Background Cellular constituents such as proteins, DNA, and RNA form a complex web of interactions that regulate biochemical homeostasis and determine the dynamic cellular response to external stimuli. It follows that detailed understanding of these patterns is critical for the assessment of fundamental processes in cell biology and pathology. Representation and analysis of cellular constituents through network principles is a promising and popular analytical avenue towards a deeper understanding of molecular mechanisms in a system-wide context. Findings We present Functional Genomics Assistant (FUGA - an extensible and portable MATLAB toolbox for the inference of biological relationships, graph topology analysis, random network simulation, network clustering, and functional enrichment statistics. In contrast to conventional differential expression analysis of individual genes, FUGA offers a framework for the study of system-wide properties of biological networks and highlights putative molecular targets using concepts of systems biology. Conclusion FUGA offers a simple and customizable framework for network analysis in a variety of systems biology applications. It is freely available for individual or academic use at http://code.google.com/p/fuga.

  17. A functional genomic analysis of cell morphology using RNA interference

    Directory of Open Access Journals (Sweden)

    Jones MR

    2003-10-01

    Full Text Available Abstract Background The diversity of metazoan cell shapes is influenced by the dynamic cytoskeletal network. With the advent of RNA-interference (RNAi technology, it is now possible to screen systematically for genes controlling specific cell-biological processes, including those required to generate distinct morphologies. Results We adapted existing RNAi technology in Drosophila cell culture for use in high-throughput screens to enable a comprehensive genetic dissection of cell morphogenesis. To identify genes responsible for the characteristic shape of two morphologically distinct cell lines, we performed RNAi screens in each line with a set of double-stranded RNAs (dsRNAs targeting 994 predicted cell shape regulators. Using automated fluorescence microscopy to visualize actin filaments, microtubules and DNA, we detected morphological phenotypes for 160 genes, one-third of which have not been previously characterized in vivo. Genes with similar phenotypes corresponded to known components of pathways controlling cytoskeletal organization and cell shape, leading us to propose similar functions for previously uncharacterized genes. Furthermore, we were able to uncover genes acting within a specific pathway using a co-RNAi screen to identify dsRNA suppressors of a cell shape change induced by Pten dsRNA. Conclusions Using RNAi, we identified genes that influence cytoskeletal organization and morphology in two distinct cell types. Some genes exhibited similar RNAi phenotypes in both cell types, while others appeared to have cell-type-specific functions, in part reflecting the different mechanisms used to generate a round or a flat cell morphology.

  18. A unified algorithm for mobility load balancing in 3GPP LTE multi-cell networks

    Institute of Scientific and Technical Information of China (English)

    WANG Hao; LIU Nan; LI ZhiHang; WU Ping; PAN ZhiWen; YOU XiaoHu

    2013-01-01

    3GPP long term evolution (LTE) is a promising candidate for the next-generation wireless network, which is expected to achieve high spectrum efficiency by using advanced physical layer techniques and flat network structures. However, the LTE network still faces the problem of load imbalance as in GSM/WCDMA networks, and this may cause significant deterioration of system performance. To deal with this problem, mobility load balancing (MLB) has been proposed as an important use case in 3GPP self-organizing network (SON), in which the serving cell of a user can be selected to achieve load balancing rather than act as the cell with the maximum received power. Furthermore, the LTE network aims to serve users with different quality-of-service (QoS) requirements, and the network-wide objective function for load balancing is distinct for different kinds of users. Thus, in this paper, a unified algorithm is proposed for MLB in the LTE network. The load balancing problem is first formulated as an optimization problem with the optimizing variables being cell-user connections. Then the complexity and overhead of the optimal solution is analyzed and a practical and distributed algorithm is given. After that, the proposed algorithm is evaluated for users with different kinds of QoS requirements, i.e., guaranteed bit rate (GBR) users with the objective function of load balance index and non-GBR (nGBR) users with the objective function of total utility, respectively. Simulation results show that the proposed algorithm leads to significantly balanced load distribution for GBR users to decrease the new call blocking rate, and for nGBR users to improve the cell-edge throughput at the cost of only slight deterioration of total throughput.

  19. Adipose tissue: cell heterogeneity and functional diversity.

    Science.gov (United States)

    Esteve Ràfols, Montserrat

    2014-02-01

    There are two types of adipose tissue in the body whose function appears to be clearly differentiated. White adipose tissue stores energy reserves as fat, whereas the metabolic function of brown adipose tissue is lipid oxidation to produce heat. A good balance between them is important to maintain energy homeostasis. The concept of white adipose tissue has radically changed in the past decades, and is now considered as an endocrine organ that secretes many factors with autocrine, paracrine, and endocrine functions. In addition, we can no longer consider white adipose tissue as a single tissue, because it shows different metabolic profiles in its different locations, with also different implications. Although the characteristic cell of adipose tissue is the adipocyte, this is not the only cell type present in adipose tissue, neither the most abundant. Other cell types in adipose tissue described include stem cells, preadipocytes, macrophages, neutrophils, lymphocytes, and endothelial cells. The balance between these different cell types and their expression profile is closely related to maintenance of energy homeostasis. Increases in adipocyte size, number and type of lymphocytes, and infiltrated macrophages are closely related to the metabolic syndrome diseases. The study of regulation of proliferation and differentiation of preadipocytes and stem cells, and understanding of the interrelationship between the different cell types will provide new targets for action against these diseases.

  20. A Geometric Method for Model Reduction of Biochemical Networks with Polynomial Rate Functions.

    Science.gov (United States)

    Samal, Satya Swarup; Grigoriev, Dima; Fröhlich, Holger; Weber, Andreas; Radulescu, Ovidiu

    2015-12-01

    Model reduction of biochemical networks relies on the knowledge of slow and fast variables. We provide a geometric method, based on the Newton polytope, to identify slow variables of a biochemical network with polynomial rate functions. The gist of the method is the notion of tropical equilibration that provides approximate descriptions of slow invariant manifolds. Compared to extant numerical algorithms such as the intrinsic low-dimensional manifold method, our approach is symbolic and utilizes orders of magnitude instead of precise values of the model parameters. Application of this method to a large collection of biochemical network models supports the idea that the number of dynamical variables in minimal models of cell physiology can be small, in spite of the large number of molecular regulatory actors.

  1. Constrained Synaptic Connectivity in Functional Mammalian Neuronal Networks Grown on Patterned Surfaces

    Science.gov (United States)

    Bourdieu, Laurent; Wyart, Claire; Ybert, Christophe; Herr, Catherine; Chatenay, Didier

    2002-03-01

    The use of ordered neuronal networks in vitro is a promising approach to study the development and the activity of neuronal assemblies. However in previous attempts, sufficient growth control and physiological maturation of neurons could not be achieved. We describe an original protocol in which polylysine patterns confine the adhesion of cellular bodies to prescribed spots and the neuritic growth to thin lines. Hippocampal neurons are maintained healthy in serum free medium up to five weeks in vitro. Electrophysiology and immunochemistry show that neurons exhibit mature excitatory and inhibitory synapses and calcium imaging reveals spontaneous bursting activity of neurons in isolated networks. Neurons in these geometrical networks form functional synapses preferentially to their first neighbors. We have therefore established a simple and robust protocol to constrain both the location of neuronal cell bodies and their pattern of connectivity.

  2. A cascade reaction network mimicking the basic functional steps of adaptive immune response

    Science.gov (United States)

    Han, Da; Wu, Cuichen; You, Mingxu; Zhang, Tao; Wan, Shuo; Chen, Tao; Qiu, Liping; Zheng, Zheng; Liang, Hao; Tan, Weihong

    2015-10-01

    Biological systems use complex ‘information-processing cores’ composed of molecular networks to coordinate their external environment and internal states. An example of this is the acquired, or adaptive, immune system (AIS), which is composed of both humoral and cell-mediated components. Here we report the step-by-step construction of a prototype mimic of the AIS that we call an adaptive immune response simulator (AIRS). DNA and enzymes are used as simple artificial analogues of the components of the AIS to create a system that responds to specific molecular stimuli in vitro. We show that this network of reactions can function in a manner that is superficially similar to the most basic responses of the vertebrate AIS, including reaction sequences that mimic both humoral and cellular responses. As such, AIRS provides guidelines for the design and engineering of artificial reaction networks and molecular devices.

  3. Functional dynamics of cell surface membrane proteins

    Science.gov (United States)

    Nishida, Noritaka; Osawa, Masanori; Takeuchi, Koh; Imai, Shunsuke; Stampoulis, Pavlos; Kofuku, Yutaka; Ueda, Takumi; Shimada, Ichio

    2014-04-01

    Cell surface receptors are integral membrane proteins that receive external stimuli, and transmit signals across plasma membranes. In the conventional view of receptor activation, ligand binding to the extracellular side of the receptor induces conformational changes, which convert the structure of the receptor into an active conformation. However, recent NMR studies of cell surface membrane proteins have revealed that their structures are more dynamic than previously envisioned, and they fluctuate between multiple conformations in an equilibrium on various timescales. In addition, NMR analyses, along with biochemical and cell biological experiments indicated that such dynamical properties are critical for the proper functions of the receptors. In this review, we will describe several NMR studies that revealed direct linkage between the structural dynamics and the functions of the cell surface membrane proteins, such as G-protein coupled receptors (GPCRs), ion channels, membrane transporters, and cell adhesion molecules.

  4. Neurons Differentiated from Transplanted Stem Cells Respond Functionally to Acoustic Stimuli in the Awake Monkey Brain.

    Science.gov (United States)

    Wei, Jing-Kuan; Wang, Wen-Chao; Zhai, Rong-Wei; Zhang, Yu-Hua; Yang, Shang-Chuan; Rizak, Joshua; Li, Ling; Xu, Li-Qi; Liu, Li; Pan, Ming-Ke; Hu, Ying-Zhou; Ghanemi, Abdelaziz; Wu, Jing; Yang, Li-Chuan; Li, Hao; Lv, Long-Bao; Li, Jia-Li; Yao, Yong-Gang; Xu, Lin; Feng, Xiao-Li; Yin, Yong; Qin, Dong-Dong; Hu, Xin-Tian; Wang, Zheng-Bo

    2016-07-26

    Here, we examine whether neurons differentiated from transplanted stem cells can integrate into the host neural network and function in awake animals, a goal of transplanted stem cell therapy in the brain. We have developed a technique in which a small "hole" is created in the inferior colliculus (IC) of rhesus monkeys, then stem cells are transplanted in situ to allow for investigation of their integration into the auditory neural network. We found that some transplanted cells differentiated into mature neurons and formed synaptic input/output connections with the host neurons. In addition, c-Fos expression increased significantly in the cells after acoustic stimulation, and multichannel recordings indicated IC specific tuning activities in response to auditory stimulation. These results suggest that the transplanted cells have the potential to functionally integrate into the host neural network.

  5. Induction of Functional Hair-Cell-Like Cells from Mouse Cochlear Multipotent Cells

    Directory of Open Access Journals (Sweden)

    Quanwen Liu

    2016-01-01

    Full Text Available In this paper, we developed a two-step-induction method of generating functional hair cells from inner ear multipotent cells. Multipotent cells from the inner ear were established and induced initially into progenitor cells committed to the inner ear cell lineage on the poly-L-lysine substratum. Subsequently, the committed progenitor cells were cultured on the mitotically inactivated chicken utricle stromal cells and induced into hair-cell-like cells containing characteristic stereocilia bundles. The hair-cell-like cells exhibited rapid permeation of FM1-43FX. The whole-cell patch-clamp technique was used to measure the membrane currents of cells differentiated for 7 days on chicken utricle stromal cells and analyze the biophysical properties of the hair-cell-like cells by recording membrane properties of cells. The results suggested that the hair-cell-like cells derived from inner ear multipotent cells were functional following differentiation in an enabling environment.

  6. Efficient VLSI Architecture for Training Radial Basis Function Networks

    Directory of Open Access Journals (Sweden)

    Wen-Jyi Hwang

    2013-03-01

    Full Text Available This paper presents a novel VLSI architecture for the training of radial basis function (RBF networks. The architecture contains the circuits for fuzzy C-means (FCM and the recursive Least Mean Square (LMS operations. The FCM circuit is designed for the training of centers in the hidden layer of the RBF network. The recursive LMS circuit is adopted for the training of connecting weights in the output layer. The architecture is implemented by the field programmable gate array (FPGA. It is used as a hardware accelerator in a system on programmable chip (SOPC for real-time training and classification. Experimental results reveal that the proposed RBF architecture is an effective alternative for applications where fast and efficient RBF training is desired.

  7. Efficient VLSI architecture for training radial basis function networks.

    Science.gov (United States)

    Fan, Zhe-Cheng; Hwang, Wen-Jyi

    2013-03-19

    This paper presents a novel VLSI architecture for the training of radial basis function (RBF) networks. The architecture contains the circuits for fuzzy C-means (FCM) and the recursive Least Mean Square (LMS) operations. The FCM circuit is designed for the training of centers in the hidden layer of the RBF network. The recursive LMS circuit is adopted for the training of connecting weights in the output layer. The architecture is implemented by the field programmable gate array (FPGA). It is used as a hardware accelerator in a system on programmable chip (SOPC) for real-time training and classification. Experimental results reveal that the proposed RBF architecture is an effective alternative for applications where fast and efficient RBF training is desired.

  8. Network Packet Length Covert Channel Based on Empirical Distribution Function

    Directory of Open Access Journals (Sweden)

    Lihua Zhang

    2014-06-01

    Full Text Available Network packet length covert channel modulates secret message bits onto the packet lengths to transmit secret messages. In this paper, a novel network packet length covert channel is proposed. The proposed scheme is based on the empirical distribution function of packet length series of legitimate traffic. Different from the existing schemes, the lengths of packets which are generated by the covert sender follow the distribution of normal traffic more closely in our scheme. To validate the security of the proposed scheme, the state-of-the-art packet length covert channel detection algorithm is adopted. The experimental results show that the packet length covert channel provides a significant performance improvement in detection resistance meanings

  9. Microtubule networks for plant cell division

    NARCIS (Netherlands)

    Keijzer, de Jeroen; Mulder, B.M.; Janson, M.E.

    2014-01-01

    During cytokinesis the cytoplasm of a cell is divided to form two daughter cells. In animal cells, the existing plasma membrane is first constricted and then abscised to generate two individual plasma membranes. Plant cells on the other hand divide by forming an interior dividing wall, the so-called

  10. Altered default mode network functional connectivity in schizotypal personality disorder.

    Science.gov (United States)

    Zhang, Qing; Shen, Jing; Wu, Jianlin; Yu, Xiao; Lou, Wutao; Fan, Hongyu; Shi, Lin; Wang, Defeng

    2014-12-01

    The default mode network (DMN) has been identified to play a critical role in many mental disorders, but such abnormalities have not yet been determined in patients with schizotypal personality disorder (SPD). The purpose of this study was to analyze the alteration of the DMN functional connectivity in subjects with (SPD) and compared it to healthy control subjects. Eighteen DSM-IV diagnosed SPD subjects (all male, average age: 19.7±0.9) from a pool of 3000 first year college students, and eighteen age and gender matched healthy control subjects were recruited (all male, average age: 20.3±0.9). Independent component analysis (ICA) was used to analyze the DMN functional connectivity alteration. Compared to the healthy control group, SPD subjects had significantly decreased functional connectivity in the frontal areas, including the superior and medial frontal gyrus, and greater functional connectivity in the bilateral superior temporal gyrus and sub-lobar regions, including the bilateral putamen and caudate. Compared to subjects with SPD, the healthy control group showed decreased functional connectivity in the bilateral posterior cingulate gyrus, but showed greater functional connectivity in the right transverse temporal gyrus and left middle temporal gyrus. The healthy control group also showed greater activation in the cerebellum compared to the SPD group. These findings suggest that DMN functional connectivity, particularly that involving cognitive or emotional regulation, is altered in SPD subjects, and thus may be helpful in studying schizophrenia.

  11. Snow cover thickness estimation using radial basis function networks

    Directory of Open Access Journals (Sweden)

    E. Binaghi

    2013-05-01

    Full Text Available This paper reports an experimental study designed for the in-depth investigation of how the radial basis function network (RBFN estimates snow cover thickness as a function of climate and topographic parameters. The estimation problem is modeled in terms of both function regression and classification, obtaining continuous and discrete thickness values, respectively. The model is based on a minimal set of climatic and topographic data collected from a limited number of stations located in the Italian Central Alps. Several experiments have been conceived and conducted adopting different evaluation indexes. A comparison analysis was also developed for a quantitative evaluation of the advantages of the RBFN method over to conventional widely used spatial interpolation techniques when dealing with critical situations originated by lack of data and limited n-homogeneously distributed instrumented sites. The RBFN model proved competitive behavior and a valuable tool in critical situations in which conventional techniques suffer from a lack of representative data.

  12. Blood cells and endothelial barrier function.

    Science.gov (United States)

    Rodrigues, Stephen F; Granger, D Neil

    2015-01-01

    The barrier properties of endothelial cells are critical for the maintenance of water and protein balance between the intravascular and extravascular compartments. An impairment of endothelial barrier function has been implicated in the genesis and/or progression of a variety of pathological conditions, including pulmonary edema, ischemic stroke, neurodegenerative disorders, angioedema, sepsis and cancer. The altered barrier function in these conditions is often linked to the release of soluble mediators from resident cells (e.g., mast cells, macrophages) and/or recruited blood cells. The interaction of the mediators with receptors expressed on the surface of endothelial cells diminishes barrier function either by altering the expression of adhesive proteins in the inter-endothelial junctions, by altering the organization of the cytoskeleton, or both. Reactive oxygen species (ROS), proteolytic enzymes (e.g., matrix metalloproteinase, elastase), oncostatin M, and VEGF are part of a long list of mediators that have been implicated in endothelial barrier failure. In this review, we address the role of blood borne cells, including, neutrophils, lymphocytes, monocytes, and platelets, in the regulation of endothelial barrier function in health and disease. Attention is also devoted to new targets for therapeutic intervention in disease states with morbidity and mortality related to endothelial barrier dysfunction. PMID:25838983

  13. Energy-Latency Tradeoff for In-Network Function Computation in Random Networks

    CERN Document Server

    Balister, Paul; Anandkumar, Animashree; Willsky, Alan

    2011-01-01

    The problem of designing policies for in-network function computation with minimum energy consumption subject to a latency constraint is considered. The scaling behavior of the energy consumption under the latency constraint is analyzed for random networks, where the nodes are uniformly placed in growing regions and the number of nodes goes to infinity. The special case of sum function computation and its delivery to a designated root node is considered first. A policy which achieves order-optimal average energy consumption in random networks subject to the given latency constraint is proposed. The scaling behavior of the optimal energy consumption depends on the path-loss exponent of wireless transmissions and the dimension of the Euclidean region where the nodes are placed. The policy is then extended to computation of a general class of functions which decompose according to maximal cliques of a proximity graph such as the $k$-nearest neighbor graph or the geometric random graph. The modified policy achiev...

  14. Surface Functionalization for Protein and Cell Patterning

    Science.gov (United States)

    Colpo, Pascal; Ruiz, Ana; Ceriotti, Laura; Rossi, François

    The interaction of biological systems with synthetic material surfaces is an important issue for many biological applications such as implanted devices, tissue engineering, cell-based sensors and assays, and more generally biologic studies performed ex vivo. To ensure reliable outcomes, the main challenge resides in the ability to design and develop surfaces or artificial micro-environment that mimic 'natural environment' in interacting with biomolecules and cells without altering their function and phenotype. At this effect, microfabrication, surface chemistry and material science play a pivotal role in the design of advanced in-vitro systems for cell culture applications. In this chapter, we discuss and describe different techniques enabling the control of cell-surface interactions, including the description of some techniques for immobilization of ligands for controlling cell-surface interactions and some methodologies for the creation of well confined cell rich areas.

  15. Functions of proteoglycans at the cell surface

    DEFF Research Database (Denmark)

    Höök, M; Woods, A; Johansson, S;

    1986-01-01

    Proteoglycans (primarily heparan sulphate proteoglycans) are found at the surface of most adherent eukaryotic cells. Earlier studies suggest that these molecules can be associated with the cell surface principally by two different mechanisms. Proteoglycans may occur as membrane-intercalated glyco......Proteoglycans (primarily heparan sulphate proteoglycans) are found at the surface of most adherent eukaryotic cells. Earlier studies suggest that these molecules can be associated with the cell surface principally by two different mechanisms. Proteoglycans may occur as membrane......-intercalated glycoproteins, where the core protein of the proteoglycan is anchored in the lipid interior of the plasma membrane, or they may be bound via the polysaccharide components of the molecule to specific anchoring proteins present at the cell surface. A number of functions have been proposed for cell surface...

  16. Dynamics of learning near singularities in radial basis function networks.

    Science.gov (United States)

    Wei, Haikun; Amari, Shun-Ichi

    2008-09-01

    The radial basis function (RBF) networks are one of the most widely used models for function approximation in the regression problem. In the learning paradigm, the best approximation is recursively or iteratively searched for based on observed data (teacher signals). One encounters difficulties in such a process when two component basis functions become identical, or when the magnitude of one component becomes null. In this case, the number of the components reduces by one, and then the reduced component recovers as the learning process proceeds further, provided such a component is necessary for the best approximation. Strange behaviors, especially the plateau phenomena, have been observed in dynamics of learning when such reduction occurs. There exist singularities in the space of parameters, and the above reduction takes place at the singular regions. This paper focuses on a detailed analysis of the dynamical behaviors of learning near the overlap and elimination singularities in RBF networks, based on the averaged learning equation that is applicable to both on-line and batch mode learning. We analyze the stability on the overlap singularity by solving the eigenvalues of the Hessian explicitly. Based on the stability analysis, we plot the analytical dynamic vector fields near the singularity, which are then compared to those real trajectories obtained by a numeric method. We also confirm the existence of the plateaus in both batch and on-line learning by simulation.

  17. Network inference from functional experimental data (Conference Presentation)

    Science.gov (United States)

    Desrosiers, Patrick; Labrecque, Simon; Tremblay, Maxime; Bélanger, Mathieu; De Dorlodot, Bertrand; Côté, Daniel C.

    2016-03-01

    Functional connectivity maps of neuronal networks are critical tools to understand how neurons form circuits, how information is encoded and processed by neurons, how memory is shaped, and how these basic processes are altered under pathological conditions. Current light microscopy allows to observe calcium or electrical activity of thousands of neurons simultaneously, yet assessing comprehensive connectivity maps directly from such data remains a non-trivial analytical task. There exist simple statistical methods, such as cross-correlation and Granger causality, but they only detect linear interactions between neurons. Other more involved inference methods inspired by information theory, such as mutual information and transfer entropy, identify more accurately connections between neurons but also require more computational resources. We carried out a comparative study of common connectivity inference methods. The relative accuracy and computational cost of each method was determined via simulated fluorescence traces generated with realistic computational models of interacting neurons in networks of different topologies (clustered or non-clustered) and sizes (10-1000 neurons). To bridge the computational and experimental works, we observed the intracellular calcium activity of live hippocampal neuronal cultures infected with the fluorescent calcium marker GCaMP6f. The spontaneous activity of the networks, consisting of 50-100 neurons per field of view, was recorded from 20 to 50 Hz on a microscope controlled by a homemade software. We implemented all connectivity inference methods in the software, which rapidly loads calcium fluorescence movies, segments the images, extracts the fluorescence traces, and assesses the functional connections (with strengths and directions) between each pair of neurons. We used this software to assess, in real time, the functional connectivity from real calcium imaging data in basal conditions, under plasticity protocols, and epileptic

  18. Snow cover thickness estimation by using radial basis function networks

    Directory of Open Access Journals (Sweden)

    A. Guidali

    2012-07-01

    Full Text Available This work investigates learning and generalisation capabilities of radial basis function networks (RBFN used to solve snow cover thickness estimation model as regression and classification. The model is based on a minimal set of climatic and topographic data collected from a limited number of stations located in the Italian Central Alps. Several experiments have been conceived and conducted adopting different evaluation indexes in both regression and classification tasks. The snow cover thickness estimation by RBFN has been proved a valuable tool able to deal with several critical aspects arising from the specific experimental context.

  19. Nonlinear Time Series Forecast Using Radial Basis Function Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHENGXin; CHENTian-Lun

    2003-01-01

    In the research of using Radial Basis Function Neural Network (RBF NN) forecasting nonlinear time series, we investigate how the different clusterings affect the process of learning and forecasting. We find that k-means clustering is very suitable. In order to increase the precision we introduce a nonlinear feedback term to escape from the local minima of energy, then we use the model to forecast the nonlinear time series which are produced by Mackey-Glass equation and stocks. By selecting the k-means clustering and the suitable feedback term, much better forecasting results are obtained.

  20. Nonlinear Time Series Forecast Using Radial Basis Function Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHENG Xin; CHEN Tian-Lun

    2003-01-01

    In the research of using Radial Basis Function Neural Network (RBF NN) forecasting nonlinear timeseries, we investigate how the different clusterings affect the process of learning and forecasting. We find that k-meansclustering is very suitable. In order to increase the precision we introduce a nonlinear feedback term to escape from thelocal minima of energy, then we use the model to forecast the nonlinear time series which are produced by Mackey-Glassequation and stocks. By selecting the k-means clustering and the suitable feedback term, much better forecasting resultsare obtained.

  1. Synchronization of chaos using radial basis functions neural networks

    Institute of Scientific and Technical Information of China (English)

    Ren Haipeng; Liu Ding

    2007-01-01

    The Radial Basis Functions Neural Network (RBFNN) is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.

  2. Cell proliferation along vascular islands during microvascular network growth

    Directory of Open Access Journals (Sweden)

    Kelly-Goss Molly R

    2012-06-01

    Full Text Available Abstract Background Observations in our laboratory provide evidence of vascular islands, defined as disconnected endothelial cell segments, in the adult microcirculation. The objective of this study was to determine if vascular islands are involved in angiogenesis during microvascular network growth. Results Mesenteric tissues, which allow visualization of entire microvascular networks at a single cell level, were harvested from unstimulated adult male Wistar rats and Wistar rats 3 and 10 days post angiogenesis stimulation by mast cell degranulation with compound 48/80. Tissues were immunolabeled for PECAM and BRDU. Identification of vessel lumens via injection of FITC-dextran confirmed that endothelial cell segments were disconnected from nearby patent networks. Stimulated networks displayed increases in vascular area, length density, and capillary sprouting. On day 3, the percentage of islands with at least one BRDU-positive cell increased compared to the unstimulated level and was equal to the percentage of capillary sprouts with at least one BRDU-positive cell. At day 10, the number of vascular islands per vascular area dramatically decreased compared to unstimulated and day 3 levels. Conclusions These results show that vascular islands have the ability to proliferate and suggest that they are able to incorporate into the microcirculation during the initial stages of microvascular network growth.

  3. Mast cell function: a new vision of an old cell.

    Science.gov (United States)

    da Silva, Elaine Zayas Marcelino; Jamur, Maria Célia; Oliver, Constance

    2014-10-01

    Since first described by Paul Ehrlich in 1878, mast cells have been mostly viewed as effectors of allergy. It has been only in the past two decades that mast cells have gained recognition for their involvement in other physiological and pathological processes. Mast cells have a widespread distribution and are found predominantly at the interface between the host and the external environment. Mast cell maturation, phenotype and function are a direct consequence of the local microenvironment and have a marked influence on their ability to specifically recognize and respond to various stimuli through the release of an array of biologically active mediators. These features enable mast cells to act as both first responders in harmful situations as well as to respond to changes in their environment by communicating with a variety of other cells implicated in physiological and immunological responses. Therefore, the critical role of mast cells in both innate and adaptive immunity, including immune tolerance, has gained increased prominence. Conversely, mast cell dysfunction has pointed to these cells as the main offenders in several chronic allergic/inflammatory disorders, cancer and autoimmune diseases. This review summarizes the current knowledge of mast cell function in both normal and pathological conditions with regards to their regulation, phenotype and role.

  4. Study of Robustness in Functionally Identical Coupled Networks against Cascading Failures.

    Science.gov (United States)

    Wang, Xingyuan; Cao, Jianye; Qin, Xiaomeng

    2016-01-01

    Based on coupled networks, taking node load, capacity and load redistribution between two networks into consideration, we propose functionally identical coupled networks, which consist of two networks connected by interlinks. Functionally identical coupled networks are derived from the power grid of the United States, which consists of many independent grids. Many power transmission lines are planned to interconnect those grids and, therefore, the study of the robustness of functionally identical coupled networks becomes important. In this paper, we find that functionally identical coupled networks are more robust than single networks under random attack. By studying the effect of the broadness and average degree of the degree distribution on the robustness of the network, we find that a broader degree distribution and a higher average degree increase the robustness of functionally identical coupled networks under random failure. And SF-SF (two coupled scale-free networks) is more robust than ER-ER (two coupled random networks) or SF-ER (coupled random network and scale-free network). This research is useful to construct robust functionally identical coupled networks such as two cooperative power grids. PMID:27494715

  5. Investigations into the relationship between feedback loops and functional importance of a signal transduction network based on Boolean network modeling

    OpenAIRE

    Cho Kwang-Hyun; Choi Sun; Kwon Yung-Keun

    2007-01-01

    Abstract Background A number of studies on biological networks have been carried out to unravel the topological characteristics that can explain the functional importance of network nodes. For instance, connectivity, clustering coefficient, and shortest path length were previously proposed for this purpose. However, there is still a pressing need to investigate another topological measure that can better describe the functional importance of network nodes. In this respect, we considered a fee...

  6. Functional diversification of taste cells in vertebrates

    OpenAIRE

    Matsumoto, Ichiro; Ohmoto, Makoto; Abe, Keiko

    2012-01-01

    Tastes are senses resulting from the activation of taste cells distributed in oral epithelia. Sweet, umami, bitter, sour, and salty tastes are called the five “basic” tastes, but why five, and why these five? In this review, we dissect the peripheral gustatory system in vertebrates from molecular and cellular perspectives. Recent behavioral and molecular genetic studies have revealed the nature of functional taste receptors and cells and show that different taste qualities are accounted for b...

  7. Computational identification of a p38SAPK regulated transcription factor network required for tumor cell quiescence

    OpenAIRE

    Adam, Alejandro P.; George, Ajish; Schewe, Denis; Bragado, Paloma; Iglesias, Bibiana V.; Ranganathan, Aparna C.; Kourtidis, Antonis; Conklin, Douglas S.; Julio A Aguirre-Ghiso

    2009-01-01

    The stress activated kinase p38 plays key roles in tumor suppression and induction of tumor cell dormancy. However, the mechanisms behind these functions remain poorly understood. Using computational tools we identified a transcription factor (TF) network regulated by p38α/β and required for human squamous carcinoma cell quiescence in vivo. We found that p38 transcriptionally regulates a core network of 46 genes that includes 16 TFs. Activation of p38 induced the expression of the TFs p53 and...

  8. Oct4 targets regulatory nodes to modulate stem cell function.

    Directory of Open Access Journals (Sweden)

    Pearl A Campbell

    Full Text Available Stem cells are characterized by two defining features, the ability to self-renew and to differentiate into highly specialized cell types. The POU homeodomain transcription factor Oct4 (Pou5f1 is an essential mediator of the embryonic stem cell state and has been implicated in lineage specific differentiation, adult stem cell identity, and cancer. Recent description of the regulatory networks which maintain 'ES' have highlighted a dual role for Oct4 in the transcriptional activation of genes required to maintain self-renewal and pluripotency while concomitantly repressing genes which facilitate lineage specific differentiation. However, the molecular mechanism by which Oct4 mediates differential activation or repression at these loci to either maintain stem cell identity or facilitate the emergence of alternate transcriptional programs required for the realization of lineage remains to be elucidated. To further investigate Oct4 function, we employed gene expression profiling together with a robust statistical analysis to identify genes highly correlated to Oct4. Gene Ontology analysis to categorize overrepresented genes has led to the identification of themes which may prove essential to stem cell identity, including chromatin structure, nuclear architecture, cell cycle control, DNA repair, and apoptosis. Our experiments have identified previously unappreciated roles for Oct4 for firstly, regulating chromatin structure in a state consistent with self-renewal and pluripotency, and secondly, facilitating the expression of genes that keeps the cell poised to respond to cues that lead to differentiation. Together, these data define the mechanism by which Oct4 orchestrates cellular regulatory pathways to enforce the stem cell state and provides important insight into stem cell function and cancer.

  9. Induction of Functional Hair-Cell-Like Cells from Mouse Cochlear Multipotent Cells

    OpenAIRE

    Quanwen Liu; Yi Shen; Jiarong Chen; Jie Ding; Zihua Tang; Cui Zhang; Jianling Chen; Liang Li; Ping Chen; Jinfu Wang

    2016-01-01

    In this paper, we developed a two-step-induction method of generating functional hair cells from inner ear multipotent cells. Multipotent cells from the inner ear were established and induced initially into progenitor cells committed to the inner ear cell lineage on the poly-L-lysine substratum. Subsequently, the committed progenitor cells were cultured on the mitotically inactivated chicken utricle stromal cells and induced into hair-cell-like cells containing characteristic stereocilia bund...

  10. Blood cells and endothelial barrier function

    OpenAIRE

    Rodrigues, Stephen F.; Granger, D Neil

    2015-01-01

    The barrier properties of endothelial cells are critical for the maintenance of water and protein balance between the intravascular and extravascular compartments. An impairment of endothelial barrier function has been implicated in the genesis and/or progression of a variety of pathological conditions, including pulmonary edema, ischemic stroke, neurodegenerative disorders, angioedema, sepsis and cancer. The altered barrier function in these conditions is often linked to the release of solub...

  11. A radial basis function network approach for the computation of inverse continuous time variant functions.

    Science.gov (United States)

    Mayorga, René V; Carrera, Jonathan

    2007-06-01

    This Paper presents an efficient approach for the fast computation of inverse continuous time variant functions with the proper use of Radial Basis Function Networks (RBFNs). The approach is based on implementing RBFNs for computing inverse continuous time variant functions via an overall damped least squares solution that includes a novel null space vector for singularities prevention. The singularities avoidance null space vector is derived from developing a sufficiency condition for singularities prevention that conduces to establish some characterizing matrices and an associated performance index.

  12. The brain's functional network architecture reveals human motives.

    Science.gov (United States)

    Hein, Grit; Morishima, Yosuke; Leiberg, Susanne; Sul, Sunhae; Fehr, Ernst

    2016-03-01

    Goal-directed human behaviors are driven by motives. Motives are, however, purely mental constructs that are not directly observable. Here, we show that the brain's functional network architecture captures information that predicts different motives behind the same altruistic act with high accuracy. In contrast, mere activity in these regions contains no information about motives. Empathy-based altruism is primarily characterized by a positive connectivity from the anterior cingulate cortex (ACC) to the anterior insula (AI), whereas reciprocity-based altruism additionally invokes strong positive connectivity from the AI to the ACC and even stronger positive connectivity from the AI to the ventral striatum. Moreover, predominantly selfish individuals show distinct functional architectures compared to altruists, and they only increase altruistic behavior in response to empathy inductions, but not reciprocity inductions. PMID:26941317

  13. Learning and generalization in radial basis function networks.

    Science.gov (United States)

    Freeman, J A; Saad, D

    1995-09-01

    The two-layer radial basis function network, with fixed centers of the basis functions, is analyzed within a stochastic training paradigm. Various definitions of generalization error are considered, and two such definitions are employed in deriving generic learning curves and generalization properties, both with and without a weight decay term. The generalization error is shown analytically to be related to the evidence and, via the evidence, to the prediction error and free energy. The generalization behavior is explored; the generic learning curve is found to be inversely proportional to the number of training pairs presented. Optimization of training is considered by minimizing the generalization error with respect to the free parameters of the training algorithms. Finally, the effect of the joint activations between hidden-layer units is examined and shown to speed training.

  14. Functional networks in emotional moral and nonmoral social judgments.

    Science.gov (United States)

    Moll, Jorge; de Oliveira-Souza, Ricardo; Bramati, Ivanei E; Grafman, Jordan

    2002-07-01

    Reading daily newspaper articles often evokes opinions and social judgments about the characters and stories. Social and moral judgments rely on the proper functioning of neural circuits concerned with complex cognitive and emotional processes. To examine whether dissociable neural systems mediate emotionally charged moral and nonmoral social judgments, we used a visual sentence verification task in conjunction with functional magnetic resonance imaging (fMRI). We found that a network comprising the medial orbitofrontal cortex, the temporal pole and the superior temporal sulcus of the left hemisphere was specifically activated by moral judgments. In contrast, judgment of emotionally evocative, but non-moral statements activated the left amygdala, lingual gyri, and the lateral orbital gyrus. These findings provide new evidence that the orbitofrontal cortex has dedicated subregions specialized in processing specific forms of social behavior. PMID:12169253

  15. Intrinsic Functional-Connectivity Networks for Diagnosis: Just Beautiful Pictures?

    Science.gov (United States)

    Moeller, James R.

    2011-01-01

    Abstract Resting-state functional connectivity has become a topic of enormous interest in the Neuroscience community in the last decade. Because resting-state data (1) harbor important information that often is diagnostically relevant and (2) are easy to acquire, there has been a rapid increase in the development of a variety of network analytic techniques for diagnostic applications, stimulating methodological research in general. While we are among those who welcome the increased interest in the resting state and multivariate analytic tools, we would like to draw attention to some entrenched practices that undermine the scientific quality of diagnostic functional-connectivity research, but whose correction is relatively easy to accomplish. With the current commentary we also hope to benefit the field at large and contribute to a healthy debate about research goals and best practices. PMID:22433005

  16. Accelerated intoxication of GABAergic synapses by botulinum neurotoxin A disinhibits stem cell-derived neuron networks prior to network silencing

    Directory of Open Access Journals (Sweden)

    Phillip H Beske

    2015-04-01

    Full Text Available Botulinum neurotoxins (BoNTs are extremely potent toxins that specifically cleave SNARE proteins in peripheral synapses, preventing neurotransmitter release. Neuronal responses to BoNT intoxication are traditionally studied by quantifying SNARE protein cleavage in vitro or monitoring physiological paralysis in vivo. Consequently, the dynamic effects of intoxication on synaptic behaviors are not well understood. We have reported that mouse embryonic stem cell-derived neurons (ESNs are highly sensitive to BoNT based on molecular readouts of intoxication. Here we study the time-dependent changes in synapse- and network-level behaviors following addition of BoNT/A to spontaneously active networks of glutamatergic and GABAergic ESNs. Whole-cell patch-clamp recordings indicated that BoNT/A rapidly blocked synaptic neurotransmission, confirming that ESNs replicate the functional pathophysiology responsible for clinical botulism. Quantitation of spontaneous neurotransmission in pharmacologically isolated synapses revealed accelerated silencing of GABAergic synapses compared to glutamatergic synapses, which was consistent with the selective accumulation of cleaved SNAP-25 at GAD1+ presynaptic terminals at early timepoints. Different latencies of intoxication resulted in complex network responses to BoNT/A addition, involving rapid disinhibition of stochastic firing followed by network silencing. Synaptic activity was found to be highly sensitive to SNAP-25 cleavage, reflecting the functional consequences of the localized cleavage of the small subpopulation of SNAP-25 that is engaged in neurotransmitter release in the nerve terminal. Collectively these findings illustrate that use of synaptic function assays in networked neurons cultures offers a novel and highly sensitive approach for mechanistic studies of toxin:neuron interactions and synaptic responses to BoNT.

  17. The Function Biomedical Informatics Research Network Data Repository.

    Science.gov (United States)

    Keator, David B; van Erp, Theo G M; Turner, Jessica A; Glover, Gary H; Mueller, Bryon A; Liu, Thomas T; Voyvodic, James T; Rasmussen, Jerod; Calhoun, Vince D; Lee, Hyo Jong; Toga, Arthur W; McEwen, Sarah; Ford, Judith M; Mathalon, Daniel H; Diaz, Michele; O'Leary, Daniel S; Jeremy Bockholt, H; Gadde, Syam; Preda, Adrian; Wible, Cynthia G; Stern, Hal S; Belger, Aysenil; McCarthy, Gregory; Ozyurt, Burak; Potkin, Steven G

    2016-01-01

    The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical data sets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 data set consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4 T scanners. The FBIRN Phase 2 and Phase 3 data sets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN's multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data. PMID:26364863

  18. Altered balance of functional brain networks in Schizophrenia.

    Science.gov (United States)

    Woodward, Todd S; Leong, KaWai; Sanford, Nicole; Tipper, Christine M; Lavigne, Katie M

    2016-02-28

    Activity in dorsal attention (DAN) and frontoparietal (FPN) functional brain networks is linked to allocation of attention to external stimuli, and activity in the default-mode network (DMN) is linked to allocation of attention to internal representations. Tasks requiring attention to external stimuli shift activity to the DAN/FPN and away from the DMN, and optimal task performance depends on balancing DAN/FPN against DMN activity. The current functional magnetic resonance imaging (fMRI) study assessed the balance of DAN/FPN and DMN activity in 13 schizophrenia patients and 13 healthy controls while they were engaged in a task switching Stroop paradigm which demanded internally directed attention to task instructions. The typical pattern of reciprocity between the DAN/FPN and DMN was observed for healthy controls but not for patients, suggesting a reduction in the internally focussed thought important for maintenance of instructions and strategies in schizophrenia. The observed alteration in the balance between DAN/FPN and DMN in patients may reflect a general mechanism underlying multiple forms of cognitive impairment in schizophrenia, including global processing deficits such as cognitive inefficiency and impaired context processing. PMID:26786152

  19. Platelet serotonin transporter function predicts default-mode network activity.

    Directory of Open Access Journals (Sweden)

    Christian Scharinger

    Full Text Available The serotonin transporter (5-HTT is abundantly expressed in humans by the serotonin transporter gene SLC6A4 and removes serotonin (5-HT from extracellular space. A blood-brain relationship between platelet and synaptosomal 5-HT reuptake has been suggested, but it is unknown today, if platelet 5-HT uptake can predict neural activation of human brain networks that are known to be under serotonergic influence.A functional magnetic resonance study was performed in 48 healthy subjects and maximal 5-HT uptake velocity (Vmax was assessed in blood platelets. We used a mixed-effects multilevel analysis technique (MEMA to test for linear relationships between whole-brain, blood-oxygen-level dependent (BOLD activity and platelet Vmax.The present study demonstrates that increases in platelet Vmax significantly predict default-mode network (DMN suppression in healthy subjects independent of genetic variation within SLC6A4. Furthermore, functional connectivity analyses indicate that platelet Vmax is related to global DMN activation and not intrinsic DMN connectivity.This study provides evidence that platelet Vmax predicts global DMN activation changes in healthy subjects. Given previous reports on platelet-synaptosomal Vmax coupling, results further suggest an important role of neuronal 5-HT reuptake in DMN regulation.

  20. Regulatory Roles of Metabolites in Cell Signaling Networks

    Institute of Scientific and Technical Information of China (English)

    Feng Li; Wei Xu; Shimin Zhao

    2013-01-01

    Mounting evidence suggests that cellular metabolites,in addition to being sources of fuel and macromolecular substrates,are actively involved in signaling and epigenetic regulation.Many metabolites,such as cyclic AMP,which regulates phosphorylation/dephosphorylation,have been identified to modulate DNA and histone methylation and protein stability.Metabolite-driven cellular regulation occurs through two distinct mechanisms:proteins allosterically bind or serve as substrates for protein signaling pathways,and metabolites covalently modify proteins to regulate their functions.Such novel protein metabolites include fumarate,succinyl-CoA,propionyl-CoA,butyryl-CoA and crontonyl-CoA.Other metabolites,including α-ketoglutarate,succinate and fumarate,regulate epigenetic processes and cell signaling via protein binding.Here,we summarize recent progress in metabolite-derived post-translational protein modification and metabolite-binding associated signaling regulation.Uncovering metabolites upstream of cell signaling and epigenetic networks permits the linkage of metabolic disorders and human diseases,and suggests that metabolite modulation may be a strategy for innovative therapeutics and disease prevention techniques.

  1. Structural and Functional Characteristics of the Social Networks of People with Mild Intellectual Disabilities

    Science.gov (United States)

    van Asselt-Goverts, A. E.; Embregts, P. J. C. M.; Hendriks, A. H. C.

    2013-01-01

    In the research on people with intellectual disabilities and their social networks, the functional characteristics of their networks have been examined less often than the structural characteristics. Research on the structural characteristics of their networks is also usually restricted to the size and composition of the networks, moreover, with…

  2. Computer generation of symbolic network functions - A new theory and implementation.

    Science.gov (United States)

    Alderson, G. E.; Lin, P.-M.

    1972-01-01

    A new method is presented for obtaining network functions in which some, none, or all of the network elements are represented by symbolic parameters (i.e., symbolic network functions). Unlike the topological tree enumeration or signal flow graph methods generally used to derive symbolic network functions, the proposed procedure employs fast, efficient, numerical-type algorithms to determine the contribution of those network branches that are not represented by symbolic parameters. A computer program called NAPPE (for Network Analysis Program using Parameter Extractions) and incorporating all of the concepts discussed has been written. Several examples illustrating the usefulness and efficiency of NAPPE are presented.

  3. Genes and Gene Networks Involved in Sodium Fluoride-Elicited Cell Death Accompanying Endoplasmic Reticulum Stress in Oral Epithelial Cells

    Directory of Open Access Journals (Sweden)

    Yoshiaki Tabuchi

    2014-05-01

    Full Text Available Here, to understand the molecular mechanisms underlying cell death induced by sodium fluoride (NaF, we analyzed gene expression patterns in rat oral epithelial ROE2 cells exposed to NaF using global-scale microarrays and bioinformatics tools. A relatively high concentration of NaF (2 mM induced cell death concomitant with decreases in mitochondrial membrane potential, chromatin condensation and caspase-3 activation. Using 980 probe sets, we identified 432 up-regulated and 548 down-regulated genes, that were differentially expressed by >2.5-fold in the cells treated with 2 mM of NaF and categorized them into 4 groups by K-means clustering. Ingenuity® pathway analysis revealed several gene networks from gene clusters. The gene networks Up-I and Up-II included many up-regulated genes that were mainly associated with the biological function of induction or prevention of cell death, respectively, such as Atf3, Ddit3 and Fos (for Up-I and Atf4 and Hspa5 (for Up-II. Interestingly, knockdown of Ddit3 and Hspa5 significantly increased and decreased the number of viable cells, respectively. Moreover, several endoplasmic reticulum (ER stress-related genes including, Ddit3, Atf4 and Hapa5, were observed in these gene networks. These findings will provide further insight into the molecular mechanisms of NaF-induced cell death accompanying ER stress in oral epithelial cells.

  4. Microbuckling in fibrin networks enables long-range cell mechanosensing

    CERN Document Server

    Notbohm, Jacob; Rosakis, Phoebus; Tirrell, David A; Ravichandran, Guruswami

    2014-01-01

    We show that cells in a fibrous matrix induce deformation fields that propagate over a longer range than predicted by linear elasticity. Synthetic, linear elastic hydrogels used in many mechanotrans- duction studies fail to capture this effect. We develop a nonlinear microstructural finite element model for a fiber network to simulate localized deformations induced by cells. The model captures measured cell-induced matrix displacements from experiments and identifies an important mech- anism for long range cell mechanosensing: loss of compression stiffness due to microbuckling of individual fibers. We show evidence that cells sense each other through the formation of localized intercellular bands of tensile deformations caused by this mechanism.

  5. CO-GENERATION AND OPERATING NETWORK CELLS

    DEFF Research Database (Denmark)

    Nielsen, John Eli

    2008-01-01

    In Denmark several thousands of generators are connected to the distribution system (10 kV and 0.4 kV). The production from these generators many times exceeds the load. The generators can be divided into two types, Wind turbines and CHP generators. These generators have one thing in common, the ...... concept in along these lines is the “Network Cell”....

  6. Equal modulation of endothelial cell function by four distinct tissue-specific mesenchymal stem cells.

    Science.gov (United States)

    Lin, Ruei-Zeng; Moreno-Luna, Rafael; Zhou, Bin; Pu, William T; Melero-Martin, Juan M

    2012-09-01

    Mesenchymal stem cells (MSCs) can generate multiple end-stage mesenchymal cell types and constitute a promising population of cells for regenerative therapies. Additionally, there is increasing evidence supporting other trophic activities of MSCs, including the ability to enable formation of vasculature in vivo. Although MSCs were originally isolated from the bone marrow, the presence of these cells in the stromal vascular fraction of multiple adult tissues has been recently recognized. However, it is unknown whether the capacity to modulate vasculogenesis is ubiquitous to all MSCs regardless of their tissue of origin. Here, we demonstrated that tissue-resident MSCs isolated from four distinct tissues have equal capacity to modulate endothelial cell function, including formation of vascular networks in vivo. MSCs were isolated from four murine tissues, including bone marrow, white adipose tissue, skeletal muscle, and myocardium. In culture, all four MSC populations secreted a plethora of pro-angiogenic factors that unequivocally induced proliferation, migration, and tube formation of endothelial colony-forming cells (ECFCs). In vivo, co-implantation of MSCs with ECFCs into mice generated an extensive network of blood vessels with ECFCs specifically lining the lumens and MSCs occupying perivascular positions. Importantly, there were no differences among all four MSCs evaluated. Our studies suggest that the capacity to modulate the formation of vasculature is a ubiquitous property of all MSCs, irrespective of their original anatomical location. These results validate multiple tissues as potential sources of MSCs for future cell-based vascular therapies.

  7. Functional integration of human neural precursor cells in mouse cortex.

    Directory of Open Access Journals (Sweden)

    Fu-Wen Zhou

    Full Text Available This study investigates the electrophysiological properties and functional integration of different phenotypes of transplanted human neural precursor cells (hNPCs in immunodeficient NSG mice. Postnatal day 2 mice received unilateral injections of 100,000 GFP+ hNPCs into the right parietal cortex. Eight weeks after transplantation, 1.21% of transplanted hNPCs survived. In these hNPCs, parvalbumin (PV-, calretinin (CR-, somatostatin (SS-positive inhibitory interneurons and excitatory pyramidal neurons were confirmed electrophysiologically and histologically. All GFP+ hNPCs were immunoreactive with anti-human specific nuclear protein. The proportions of PV-, CR-, and SS-positive cells among GFP+ cells were 35.5%, 15.7%, and 17.1%, respectively; around 15% of GFP+ cells were identified as pyramidal neurons. Those electrophysiologically and histological identified GFP+ hNPCs were shown to fire action potentials with the appropriate firing patterns for different classes of neurons and to display spontaneous excitatory and inhibitory postsynaptic currents (sEPSCs and sIPSCs. The amplitude, frequency and kinetic properties of sEPSCs and sIPSCs in different types of hNPCs were comparable to host cells of the same type. In conclusion, GFP+ hNPCs produce neurons that are competent to integrate functionally into host neocortical neuronal networks. This provides promising data on the potential for hNPCs to serve as therapeutic agents in neurological diseases with abnormal neuronal circuitry such as epilepsy.

  8. Quantitative proteomics reveals middle infrared radiation-interfered networks in breast cancer cells.

    Science.gov (United States)

    Chang, Hsin-Yi; Li, Ming-Hua; Huang, Tsui-Chin; Hsu, Chia-Lang; Tsai, Shang-Ru; Lee, Si-Chen; Huang, Hsuan-Cheng; Juan, Hsueh-Fen

    2015-02-01

    Breast cancer is one of the leading cancer-related causes of death worldwide. Treatment of triple-negative breast cancer (TNBC) is complex and challenging, especially when metastasis has developed. In this study, we applied infrared radiation as an alternative approach for the treatment of TNBC. We used middle infrared (MIR) with a wavelength range of 3-5 μm to irradiate breast cancer cells. MIR significantly inhibited cell proliferation in several breast cancer cells but did not affect the growth of normal breast epithelial cells. We performed iTRAQ-coupled LC-MS/MS analysis to investigate the MIR-triggered molecular mechanisms in breast cancer cells. A total of 1749 proteins were identified, quantified, and subjected to functional enrichment analysis. From the constructed functionally enriched network, we confirmed that MIR caused G2/M cell cycle arrest, remodeled the microtubule network to an astral pole arrangement, altered the actin filament formation and focal adhesion molecule localization, and reduced cell migration activity and invasion ability. Our results reveal the coordinative effects of MIR-regulated physiological responses in concentrated networks, demonstrating the potential implementation of infrared radiation in breast cancer therapy. PMID:25556991

  9. Stem Cells in Functional Bladder Engineering

    Science.gov (United States)

    Smolar, Jakub; Salemi, Souzan; Horst, Maya; Sulser, Tullio; Eberli, Daniel

    2016-01-01

    Conditions impairing bladder function in children and adults, such as myelomeningocele, posterior urethral valves, bladder exstrophy or spinal cord injury, often need urinary diversion or augmentation cystoplasty as when untreated they may cause severe bladder dysfunction and kidney failure. Currently, the gold standard therapy of end-stage bladder disease refractory to conservative management is enterocystoplasty, a surgical enlargement of the bladder with intestinal tissue. Despite providing functional improvement, enterocystoplasty is associated with significant long-term complications, such as recurrent urinary tract infections, metabolic abnormalities, stone formation, and malignancies. Therefore, there is a strong clinical need for alternative therapies for these reconstructive procedures, of which stem cell-based tissue engineering (TE) is considered to be the most promising future strategy. This review is focused on the recent progress in bladder stem cell research and therapy and the challenges that remain for the development of a functional bladder wall.

  10. Emergence of Complexity in Protein Functions and Metabolic Networks

    Science.gov (United States)

    Pohorille, Andzej

    2009-01-01

    In modern organisms proteins perform a majority of cellular functions, such as chemical catalysis, energy transduction and transport of material across cell walls. Although great strides have been made towards understanding protein evolution, a meaningful extrapolation from contemporary proteins to their earliest ancestors is virtually impossible. In an alternative approach, the origin of water-soluble proteins was probed through the synthesis of very large libraries of random amino acid sequences and subsequently subjecting them to in vitro evolution. In combination with computer modeling and simulations, these experiments allow us to address a number of fundamental questions about the origins of proteins. Can functionality emerge from random sequences of proteins? How did the initial repertoire of functional proteins diversify to facilitate new functions? Did this diversification proceed primarily through drawing novel functionalities from random sequences or through evolution of already existing proto-enzymes? Did protein evolution start from a pool of proteins defined by a frozen accident and other collections of proteins could start a different evolutionary pathway? Although we do not have definitive answers to these questions, important clues have been uncovered. Considerable progress has been also achieved in understanding the origins of membrane proteins. We will address this issue in the example of ion channels - proteins that mediate transport of ions across cell walls. Remarkably, despite overall complexity of these proteins in contemporary cells, their structural motifs are quite simple, with -helices being most common. By combining results of experimental and computer simulation studies on synthetic models and simple, natural channels, I will show that, even though architectures of membrane proteins are not nearly as diverse as those of water-soluble proteins, they are sufficiently flexible to adapt readily to the functional demands arising during

  11. Effect of tumor resection on the characteristics of functional brain networks

    NARCIS (Netherlands)

    Wang, H.; Douw, L.; Hernández, J.M.; Reijneveld, J.C.; Stam, C.J.; Van Mieghem, P.

    2010-01-01

    Brain functioning such as cognitive performance depends on the functional interactions between brain areas, namely, the functional brain networks. The functional brain networks of a group of patients with brain tumors are measured before and after tumor resection. In this work, we perform a weighted

  12. Boolean network model predicts cell cycle sequence of fission yeast.

    Directory of Open Access Journals (Sweden)

    Maria I Davidich

    Full Text Available A Boolean network model of the cell-cycle regulatory network of fission yeast (Schizosaccharomyces Pombe is constructed solely on the basis of the known biochemical interaction topology. Simulating the model in the computer faithfully reproduces the known activity sequence of regulatory proteins along the cell cycle of the living cell. Contrary to existing differential equation models, no parameters enter the model except the structure of the regulatory circuitry. The dynamical properties of the model indicate that the biological dynamical sequence is robustly implemented in the regulatory network, with the biological stationary state G1 corresponding to the dominant attractor in state space, and with the biological regulatory sequence being a strongly attractive trajectory. Comparing the fission yeast cell-cycle model to a similar model of the corresponding network in S. cerevisiae, a remarkable difference in circuitry, as well as dynamics is observed. While the latter operates in a strongly damped mode, driven by external excitation, the S. pombe network represents an auto-excited system with external damping.

  13. Function of laccases in cell wall biosynthesis

    DEFF Research Database (Denmark)

    Larsen, Anders; Holm, Preben Bach; Andersen, Jeppe Reitan

    2011-01-01

    substrate specificities and expression patterns. As part of the strategic research centre Bio4Bio, the present project deals with laccase functions in relation to cell wall formation in grasses based on a study of the model species Brachypodium distachyon. Thirty-one isozymes have been retrieved from......Laccases are multicopper oxidases capable of polymerizing monolignols. Histochemical assays have shown temporal and spatial correlation with secondary cell wall formation in both herbs and woody perennials. However, in plants laccases constitutes a relatively large group of isoenzymes with unique...... hybridization. Specific isozymes that show high correlation with the process of secondary cell wall formation will be further studied in a reverse genetic study in which candidates will be knocked out using RNA interference. Phenotypes of knock-out mutants are to be described in relation to cell wall...

  14. Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

    CERN Document Server

    Donges, Jonathan F; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V; Marwan, Norbert; Dijkstra, Henk A; Kurths, Jürgen

    2015-01-01

    We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence qua...

  15. MicroRNA functional network in pancreatic cancer: From biology to biomarkers of disease

    Indian Academy of Sciences (India)

    Jin Wang; Subrata Sen

    2011-08-01

    MicroRNAs (miRs), the 17- to 25-nucleotide-long non-coding RNAs, regulate expression of approximately 30% of the protein-coding genes at the post-transcriptional level and have emerged as critical components of the complex functional pathway networks controlling important cellular processes, such as proliferation, development, differentiation, stress response' and apoptosis. Abnormal expression levels of miRs, regulating critical cancerassociated pathways, have been implicated to play important roles in the oncogenic processes, functioning both as oncogenes and as tumour suppressor genes. Elucidation of the genetic networks regulated by the abnormally expressing miRs in cancer cells is proving to be extremely significant in understanding the role of these miRs in the induction of malignant-transformation-associated phenotypic changes. As a result, the miRs involved in the oncogenic transformation process are being investigated as novel biomarkers of disease detection and prognosis as well as potential therapeutic targets for human cancers. In this \\article, we review the existing literature in the field documenting the significance of aberrantly expressed miRs in human pancreatic cancer and discuss how the oncogenic miRs may be involved in the genetic networks regulating functional pathways deregulated in this malignancy.

  16. Cortical network from human embryonic stem cells

    OpenAIRE

    Nat, Roxana

    2010-01-01

    Abstract The connection of embryonic stem cell technology and developmental biology provides valuable tools to decipher the mechanisms underlying human brain development and diseases, especially among neuronal populations, that are not readily available in primary cultures. It is obviously the case of neurons forming the human cerebral cortex. In the images that are presented, the neurons were generated in vitro from human embryonic stem cells via forebrain-like progenitors. Maintained in cul...

  17. Finding missing interactions of the Arabidopsis thaliana root stem cell niche gene regulatory network

    Directory of Open Access Journals (Sweden)

    Eugenio eAzpeitia

    2013-04-01

    Full Text Available AbstractOver the last few decades, the Arabidopsis thaliana root stem cell niche has become a model system for the study of plant development and the stem cell niche. Currently, many of the molecular mechanisms involved in root stem cell niche maintenance and development have been described. A few years ago, we published a gene regulatory network model integrating this information. This model suggested that there were missing components or interactions. Upon updating the model, the observed stable gene configurations of the root stem cell niche could not be recovered, indicating that there are additional missing components or interactions in the model. In fact, due to the lack of experimental data, gene regulatory networks inferred from published data are usually incomplete. However, predicting the location and nature of the missing data is a not trivial task. Here, we propose a set of procedures for detecting and predicting missing interactions in Boolean networks. We used these procedures to predict putative missing interactions in the A. thaliana root stem cell niche network model. Using our approach, we identified three necessary interactions to recover the reported gene activation configurations that have been experimentally uncovered for the different cell types within the root stem cell niche: 1 a regulation of PHABULOSA to restrict its expression domain to the vascular cells, 2 a self-regulation of WOX5, possibly by an indirect mechanism through the auxin signalling pathway and 3 a positive regulation of JACKDAW by MAGPIE. The procedures proposed here greatly reduce the number of possible Boolean functions that are biologically meaningful and experimentally testable and that do not contradict previous data. We believe that these procedures can be used on any Boolean network. However, because the procedures were designed for the specific case of the root stem cell niche, formal demonstrations of the procedures should be shown in future

  18. Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model.

    Directory of Open Access Journals (Sweden)

    Naiqian Zhang

    Full Text Available The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN data and drug similarity network (DSN data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE and Cancer Genome Project (CGP studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.

  19. Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells

    Directory of Open Access Journals (Sweden)

    Zhou Qing

    2009-07-01

    Full Text Available Abstract Background Recent work has revealed that a core group of transcription factors (TFs regulates the key characteristics of embryonic stem (ES cells: pluripotency and self-renewal. Current efforts focus on identifying genes that play important roles in maintaining pluripotency and self-renewal in ES cells and aim to understand the interactions among these genes. To that end, we investigated the use of unsigned and signed network analysis to identify pluripotency and differentiation related genes. Results We show that signed networks provide a better systems level understanding of the regulatory mechanisms of ES cells than unsigned networks, using two independent murine ES cell expression data sets. Specifically, using signed weighted gene co-expression network analysis (WGCNA, we found a pluripotency module and a differentiation module, which are not identified in unsigned networks. We confirmed the importance of these modules by incorporating genome-wide TF binding data for key ES cell regulators. Interestingly, we find that the pluripotency module is enriched with genes related to DNA damage repair and mitochondrial function in addition to transcriptional regulation. Using a connectivity measure of module membership, we not only identify known regulators of ES cells but also show that Mrpl15, Msh6, Nrf1, Nup133, Ppif, Rbpj, Sh3gl2, and Zfp39, among other genes, have important roles in maintaining ES cell pluripotency and self-renewal. We also report highly significant relationships between module membership and epigenetic modifications (histone modifications and promoter CpG methylation status, which are known to play a role in controlling gene expression during ES cell self-renewal and differentiation. Conclusion Our systems biologic re-analysis of gene expression, transcription factor binding, epigenetic and gene ontology data provides a novel integrative view of ES cell biology.

  20. Prediction of the residual strength of clay using functional networks

    Institute of Scientific and Technical Information of China (English)

    S.Z. Khan; Shakti Suman; M. Pavani; S.K. Das

    2016-01-01

    Landslides are common natural hazards occurring in most parts of the world and have considerable adverse economic effects. Residual shear strength of clay is one of the most important factors in the determination of stability of slopes or landslides. This effect is more pronounced in sensitive clays which show large changes in shear strength from peak to residual states. This study analyses the prediction of the residual strength of clay based on a new prediction model, functional networks (FN) using data available in the literature. The performance of FN was compared with support vector machine (SVM) and artificial neural network (ANN) based on statistical parameters like correlation coefficient (R), Nash–Sutcliff coefficient of efficiency (E), absolute average error (AAE), maximum average error (MAE) and root mean square error (RMSE). Based on R and E parameters, FN is found to be a better prediction tool than ANN for the given data. However, the R and E values for FN are less than SVM. A prediction equation is presented that can be used by practicing geotechnical engineers. A sensitivity analysis is carried out to ascertain the importance of various inputs in the prediction of the output.

  1. Ultrasonic flaw detection using radial basis function networks (RBFNs).

    Science.gov (United States)

    Gil Pita, R; Vicen, R; Rosa, M; Jarabo, M P; Vera, P; Curpian, J

    2004-04-01

    Ultrasonic flaw detection has been studied many times in the literature. Schemes based on thresholding after a previous matched filter use to be the best solution, but results obtained with this method are only satisfactory when scattering and attenuation are not considered. In this paper, we propose an alternative solution to thresholding detection method. We deal with the usage of different flaw detection methods comparing them with the proposed one. The experiment tries to determinate whether a given ultrasonic signal contains a flaw echo or not. Starting with a set of 24,000 patterns with 750 samples each one, two subsets are defined for the experiments. The first one, the training set, is used to obtain the detection parameters of the different methods, and the second one is used to test the performance of them. The proposed method is based on radial basis functions networks, one of the most powerful neural network techniques. This signal processing technique tries to find the optimal decision criterion. Comparing this method with thresholding based ones, an improvement over 25-30% is obtained, depending on the probability of false alarm. So our new method is a good alternative to flaw detection problem.

  2. Radial basis function networks GPU-based implementation.

    Science.gov (United States)

    Brandstetter, Andreas; Artusi, Alessandro

    2008-12-01

    Neural networks (NNs) have been used in several areas, showing their potential but also their limitations. One of the main limitations is the long time required for the training process; this is not useful in the case of a fast training process being required to respond to changes in the application domain. A possible way to accelerate the learning process of an NN is to implement it in hardware, but due to the high cost and the reduced flexibility of the original central processing unit (CPU) implementation, this solution is often not chosen. Recently, the power of the graphic processing unit (GPU), on the market, has increased and it has started to be used in many applications. In particular, a kind of NN named radial basis function network (RBFN) has been used extensively, proving its power. However, their limiting time performances reduce their application in many areas. In this brief paper, we describe a GPU implementation of the entire learning process of an RBFN showing the ability to reduce the computational cost by about two orders of magnitude with respect to its CPU implementation.

  3. Applying fuzzy analytic network process in quality function deployment model

    Directory of Open Access Journals (Sweden)

    Mohammad Ali Afsharkazemi

    2012-08-01

    Full Text Available In this paper, we propose an empirical study of QFD implementation when fuzzy numbers are used to handle the uncertainty associated with different components of the proposed model. We implement fuzzy analytical network to find the relative importance of various criteria and using fuzzy numbers we calculate the relative importance of these factors. The proposed model of this paper uses fuzzy matrix and house of quality to study the products development in QFD and also the second phase i.e. part deployment. In most researches, the primary objective is only on CRs to implement the quality function deployment and some other criteria such as production costs, manufacturing costs etc were disregarded. The results of using fuzzy analysis network process based on the QFD model in Daroupat packaging company to develop PVDC show that the most important indexes are being waterproof, resistant pill packages, and production cost. In addition, the PVDC coating is the most important index in terms of company experts’ point of view.

  4. Construction of a computable cell proliferation network focused on non-diseased lung cells

    Directory of Open Access Journals (Sweden)

    Veljkovic Emilija

    2011-07-01

    Full Text Available Abstract Background Critical to advancing the systems-level evaluation of complex biological processes is the development of comprehensive networks and computational methods to apply to the analysis of systems biology data (transcriptomics, proteomics/phosphoproteomics, metabolomics, etc.. Ideally, these networks will be specifically designed to capture the normal, non-diseased biology of the tissue or cell types under investigation, and can be used with experimentally generated systems biology data to assess the biological impact of perturbations like xenobiotics and other cellular stresses. Lung cell proliferation is a key biological process to capture in such a network model, given the pivotal role that proliferation plays in lung diseases including cancer, chronic obstructive pulmonary disease (COPD, and fibrosis. Unfortunately, no such network has been available prior to this work. Results To further a systems-level assessment of the biological impact of perturbations on non-diseased mammalian lung cells, we constructed a lung-focused network for cell proliferation. The network encompasses diverse biological areas that lead to the regulation of normal lung cell proliferation (Cell Cycle, Growth Factors, Cell Interaction, Intra- and Extracellular Signaling, and Epigenetics, and contains a total of 848 nodes (biological entities and 1597 edges (relationships between biological entities. The network was verified using four published gene expression profiling data sets associated with measured cell proliferation endpoints in lung and lung-related cell types. Predicted changes in the activity of core machinery involved in cell cycle regulation (RB1, CDKN1A, and MYC/MYCN are statistically supported across multiple data sets, underscoring the general applicability of this approach for a network-wide biological impact assessment using systems biology data. Conclusions To the best of our knowledge, this lung-focused Cell Proliferation Network

  5. Cell-specific synaptic plasticity induced by network oscillations.

    Science.gov (United States)

    Zarnadze, Shota; Bäuerle, Peter; Santos-Torres, Julio; Böhm, Claudia; Schmitz, Dietmar; Geiger, Jörg Rp; Dugladze, Tamar; Gloveli, Tengis

    2016-01-01

    Gamma rhythms are known to contribute to the process of memory encoding. However, little is known about the underlying mechanisms at the molecular, cellular and network levels. Using local field potential recording in awake behaving mice and concomitant field potential and whole-cell recordings in slice preparations we found that gamma rhythms lead to activity-dependent modification of hippocampal networks, including alterations in sharp wave-ripple complexes. Network plasticity, expressed as long-lasting increases in sharp wave-associated synaptic currents, exhibits enhanced excitatory synaptic strength in pyramidal cells that is induced postsynaptically and depends on metabotropic glutamate receptor-5 activation. In sharp contrast, alteration of inhibitory synaptic strength is independent of postsynaptic activation and less pronounced. Further, we found a cell type-specific, directionally biased synaptic plasticity of two major types of GABAergic cells, parvalbumin- and cholecystokinin-expressing interneurons. Thus, we propose that gamma frequency oscillations represent a network state that introduces long-lasting synaptic plasticity in a cell-specific manner. PMID:27218453

  6. Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates

    Science.gov (United States)

    Dann, Benjamin; Michaels, Jonathan A; Schaffelhofer, Stefan; Scherberger, Hansjörg

    2016-01-01

    The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks. DOI: http://dx.doi.org/10.7554/eLife.15719.001 PMID:27525488

  7. Highly ordered large-scale neuronal networks of individual cells - toward single cell to 3D nanowire intracellular interfaces.

    Science.gov (United States)

    Kwiat, Moria; Elnathan, Roey; Pevzner, Alexander; Peretz, Asher; Barak, Boaz; Peretz, Hagit; Ducobni, Tamir; Stein, Daniel; Mittelman, Leonid; Ashery, Uri; Patolsky, Fernando

    2012-07-25

    The use of artificial, prepatterned neuronal networks in vitro is a promising approach for studying the development and dynamics of small neural systems in order to understand the basic functionality of neurons and later on of the brain. The present work presents a high fidelity and robust procedure for controlling neuronal growth on substrates such as silicon wafers and glass, enabling us to obtain mature and durable neural networks of individual cells at designed geometries. It offers several advantages compared to other related techniques that have been reported in recent years mainly because of its high yield and reproducibility. The procedure is based on surface chemistry that allows the formation of functional, tailormade neural architectures with a micrometer high-resolution partition, that has the ability to promote or repel cells attachment. The main achievements of this work are deemed to be the creation of a large scale neuronal network at low density down to individual cells, that develop intact typical neurites and synapses without any glia-supportive cells straight from the plating stage and with a relatively long term survival rate, up to 4 weeks. An important application of this method is its use on 3D nanopillars and 3D nanowire-device arrays, enabling not only the cell bodies, but also their neurites to be positioned directly on electrical devices and grow with registration to the recording elements underneath.

  8. Generalized profile function model based on neural networks

    Directory of Open Access Journals (Sweden)

    Radonja Pero

    2009-01-01

    Full Text Available Generalized profile function model (GPFM provides approximations of the individual models (individual stem profile models of the objects using only two basic measurements. In this paper it is shown that this GPFM can be successfully derived by using artificial computational intelligence, that is, neural networks. GPFM is obtained as a mean value of all the available normalized individual models. Generation of GPFM is performed by using the basic dataset, and verification is done by using the validation data set. Statistical properties of the original, measured data and estimated data based on the generalized model are presented and compared. Testing of the obtained GPFM is performed also by the regression analysis. The obtained correlation coefficients between the real data and the estimated data are very high, 0.9946 for the basic data set, and 0.9933 for the validation dataset. .

  9. Hash function construction using weighted complex dynamical networks

    Institute of Scientific and Technical Information of China (English)

    Song Yu-Rong; Jiang Guo-Ping

    2013-01-01

    A novel scheme to construct a hash function based on a weighted complex dynamical network (WCDN) generated from an original message is proposed in this paper.First,the original message is divided into blocks.Then,each block is divided into components,and the nodes and weighted edges are well defined from these components and their relations.Namely,the WCDN closely related to the original message is established.Furthermore,the node dynamics of the WCDN are chosen as a chaotic map.After chaotic iterations,quantization and exclusive-or operations,the fixed-length hash value is obtained.This scheme has the property that any tiny change in message can be diffused rapidly through the WCDN,leading to very different hash values.Analysis and simulation show that the scheme possesses good statistical properties,excellent confusion and diffusion,strong collision resistance and high efficiency.

  10. Impaired default network functional connectivity in autosomal dominant Alzheimer disease

    Science.gov (United States)

    Chhatwal, Jasmeer P.; Schultz, Aaron P.; Johnson, Keith; Benzinger, Tammie L.S.; Jack, Clifford; Ances, Beau M.; Sullivan, Caroline A.; Salloway, Stephen P.; Ringman, John M.; Koeppe, Robert A.; Marcus, Daniel S.; Thompson, Paul; Saykin, Andrew J.; Correia, Stephen; Schofield, Peter R.; Rowe, Christopher C.; Fox, Nick C.; Brickman, Adam M.; Mayeux, Richard; McDade, Eric; Bateman, Randall; Fagan, Anne M.; Goate, Allison M.; Xiong, Chengjie; Buckles, Virginia D.; Morris, John C.

    2013-01-01

    Objective: To investigate default mode network (DMN) functional connectivity MRI (fcMRI) in a large cross-sectional cohort of subjects from families harboring pathogenic presenilin-1 (PSEN1), presenilin-2 (PSEN2), and amyloid precursor protein (APP) mutations participating in the Dominantly Inherited Alzheimer Network. Methods: Eighty-three mutation carriers and 37 asymptomatic noncarriers from the same families underwent fMRI during resting state at 8 centers in the United States, United Kingdom, and Australia. Using group-independent component analysis, fcMRI was compared using mutation status and Clinical Dementia Rating to stratify groups, and related to each participant's estimated years from expected symptom onset (eYO). Results: We observed significantly decreased DMN fcMRI in mutation carriers with increasing Clinical Dementia Rating, most evident in the precuneus/posterior cingulate and parietal cortices (p < 0.001). Comparison of asymptomatic mutation carriers with noncarriers demonstrated decreased fcMRI in the precuneus/posterior cingulate (p = 0.014) and right parietal cortex (p = 0.0016). We observed a significant interaction between mutation carrier status and eYO, with decreases in DMN fcMRI observed as mutation carriers approached and surpassed their eYO. Conclusion: Functional disruption of the DMN occurs early in the course of autosomal dominant Alzheimer disease, beginning before clinically evident symptoms, and worsening with increased impairment. These findings suggest that DMN fcMRI may prove useful as a biomarker across a wide spectrum of disease, and support the feasibility of DMN fcMRI as a secondary endpoint in upcoming multicenter clinical trials in Alzheimer disease. PMID:23884042

  11. Construction of cell type-specific logic models of signaling networks using CellNOpt.

    Science.gov (United States)

    Morris, Melody K; Melas, Ioannis; Saez-Rodriguez, Julio

    2013-01-01

    Mathematical models are useful tools for understanding protein signaling networks because they provide an integrated view of pharmacological and toxicological processes at the molecular level. Here we describe an approach previously introduced based on logic modeling to generate cell-specific, mechanistic and predictive models of signal transduction. Models are derived from a network encoding prior knowledge that is trained to signaling data, and can be either binary (based on Boolean logic) or quantitative (using a recently developed formalism, constrained fuzzy logic). The approach is implemented in the freely available tool CellNetOptimizer (CellNOpt). We explain the process CellNOpt uses to train a prior knowledge network to data and illustrate its application with a toy example as well as a realistic case describing signaling networks in the HepG2 liver cancer cell line.

  12. Temporal modulation of collective cell behavior controls vascular network topology.

    Science.gov (United States)

    Kur, Esther; Kim, Jiha; Tata, Aleksandra; Comin, Cesar H; Harrington, Kyle I; Costa, Luciano da F; Bentley, Katie; Gu, Chenghua

    2016-01-01

    Vascular network density determines the amount of oxygen and nutrients delivered to host tissues, but how the vast diversity of densities is generated is unknown. Reiterations of endothelial-tip-cell selection, sprout extension and anastomosis are the basis for vascular network generation, a process governed by the VEGF/Notch feedback loop. Here, we find that temporal regulation of this feedback loop, a previously unexplored dimension, is the key mechanism to determine vascular density. Iterating between computational modeling and in vivo live imaging, we demonstrate that the rate of tip-cell selection determines the length of linear sprout extension at the expense of branching, dictating network density. We provide the first example of a host tissue-derived signal (Semaphorin3E-Plexin-D1) that accelerates tip cell selection rate, yielding a dense network. We propose that temporal regulation of this critical, iterative aspect of network formation could be a general mechanism, and additional temporal regulators may exist to sculpt vascular topology.

  13. Composite three-dimensional woven scaffolds with interpenetrating network hydrogels to create functional synthetic articular cartilage.

    Science.gov (United States)

    Liao, I-Chien; Moutos, Franklin T; Estes, Bradley T; Zhao, Xuanhe; Guilak, Farshid

    2013-12-17

    The development of synthetic biomaterials that possess mechanical properties that mimic those of native tissues remains an important challenge to the field of materials. In particular, articular cartilage is a complex nonlinear, viscoelastic, and anisotropic material that exhibits a very low coefficient of friction, allowing it to withstand millions of cycles of joint loading over decades of wear. Here we show that a three-dimensionally woven fiber scaffold that is infiltrated with an interpenetrating network hydrogel can provide a functional biomaterial that provides the load-bearing and tribological properties of native cartilage. An interpenetrating dual-network "tough-gel" consisting of alginate and polyacrylamide was infused into a porous three-dimensionally woven poly(ε-caprolactone) fiber scaffold, providing a versatile fiber-reinforced composite structure as a potential acellular or cell-based replacement for cartilage repair. PMID:24578679

  14. Medical Data Transmission Using Cell Phone Networks

    International Nuclear Information System (INIS)

    A big challenge in telemedicine systems is related to have the technical requirements needed for a successful implementation in remote locations where the available hardware and communication infrastructure is not adequate for a good medical data transmission. Despite of the wide standards availability, methodologies, applications and systems integration facilities in telemedicine, in many cases the implementation requirements are not achievable to allow the system execution in remote areas of our country. Therefore, this paper presents an alternative for the messages transmission related to medical studies using the cellular network and the standard HL7 V3 [1] for data modeling. The messages are transmitted to a web server and stored in a centralized database which allows data sharing with other specialists.

  15. Dynamic neural network controller model of PEM fuel cell system

    Energy Technology Data Exchange (ETDEWEB)

    Hatti, Mustapha [Nuclear Technologies Division, Nuclear Research Center of Birine, Ain Oussera, B.P 180, 17200 Djelfa (Algeria); Tioursi, Mustapha [Electrical Engineering Department, University of Sciences and Technology of Oran, B.P 1505, El M' Naouar, 31000 Oran (Algeria)

    2009-06-15

    This paper presents the artificial intelligence techniques to control a proton exchange membrane fuel cell system process, using particularly a methodology of dynamic neural network. In this work a dynamic neural network control model is obtained by introducing a delay line in the input of the neural network. A static production system including a PEMFC is subjected to variations of active and reactive power. Therefore the goal is to make the system follow these imposed variations. The simulation requires the modelling of the principal element (PEMFC) in dynamic mode. The simulation results demonstrate that the model-based dynamic neural network control scheme is appropriate for controlling, the stability of the identification and the tracking error were analyzed, and some reasons for the usefulness of this methodology are given. (author)

  16. Dynamic Enhanced Inter-Cell Interference Coordination for Realistic Networks

    DEFF Research Database (Denmark)

    Pedersen, Klaus I.; Alvarez, Beatriz Soret; Barcos, Sonia;

    2016-01-01

    area. Rather than the classical semi-static and network-wise configuration, the importance of having highly dynamic and distributed mechanisms that are able to adapt to local environment conditions is revealed. We propose two promising cell association algorithms: one aiming at pure load balancing...... user association to offload more users to the picocells. However, its application to realistic irregular deployments opens a number of research questions. In this paper, we investigate the operation of eICIC in a realistic deployment based on three-dimensional data from a dense urban European capital...... and an opportunistic approach exploiting the varying cell conditions. Moreover, an autonomous fast distributed muting algorithm is presented, which is simple, robust, and well suited for irregular network deployments. Performance results for realistic network deployments show that the traditional semi-static e...

  17. A gene regulatory network for root epidermis cell differentiation in Arabidopsis.

    Directory of Open Access Journals (Sweden)

    Angela Bruex

    2012-01-01

    Full Text Available The root epidermis of Arabidopsis provides an exceptional model for studying the molecular basis of cell fate and differentiation. To obtain a systems-level view of root epidermal cell differentiation, we used a genome-wide transcriptome approach to define and organize a large set of genes into a transcriptional regulatory network. Using cell fate mutants that produce only one of the two epidermal cell types, together with fluorescence-activated cell-sorting to preferentially analyze the root epidermis transcriptome, we identified 1,582 genes differentially expressed in the root-hair or non-hair cell types, including a set of 208 "core" root epidermal genes. The organization of the core genes into a network was accomplished by using 17 distinct root epidermis mutants and 2 hormone treatments to perturb the system and assess the effects on each gene's transcript accumulation. In addition, temporal gene expression information from a developmental time series dataset and predicted gene associations derived from a Bayesian modeling approach were used to aid the positioning of genes within the network. Further, a detailed functional analysis of likely bHLH regulatory genes within the network, including MYC1, bHLH54, bHLH66, and bHLH82, showed that three distinct subfamilies of bHLH proteins participate in root epidermis development in a stage-specific manner. The integration of genetic, genomic, and computational analyses provides a new view of the composition, architecture, and logic of the root epidermal transcriptional network, and it demonstrates the utility of a comprehensive systems approach for dissecting a complex regulatory network.

  18. Schwann cell myelination requires Dynein function

    Directory of Open Access Journals (Sweden)

    Langworthy Melissa M

    2012-11-01

    Full Text Available Abstract Background Interaction of Schwann cells with axons triggers signal transduction that drives expression of Pou3f1 and Egr2 transcription factors, which in turn promote myelination. Signal transduction appears to be mediated, at least in part, by cyclic adenosine monophosphate (cAMP because elevation of cAMP levels can stimulate myelination in the absence of axon contact. The mechanisms by which the myelinating signal is conveyed remain unclear. Results By analyzing mutations that disrupt myelination in zebrafish, we learned that Dynein cytoplasmic 1 heavy chain 1 (Dync1h1, which functions as a motor for intracellular molecular trafficking, is required for peripheral myelination. In dync1h1 mutants, Schwann cell progenitors migrated to peripheral nerves but then failed to express Pou3f1 and Egr2 or make myelin membrane. Genetic mosaic experiments revealed that robust Myelin Basic Protein expression required Dync1h1 function within both Schwann cells and axons. Finally, treatment of dync1h1 mutants with a drug to elevate cAMP levels stimulated myelin gene expression. Conclusion Dync1h1 is required for retrograde transport in axons and mutations of Dync1h1 have been implicated in axon disease. Our data now provide evidence that Dync1h1 is also required for efficient myelination of peripheral axons by Schwann cells, perhaps by facilitating signal transduction necessary for myelination.

  19. A Novel Algorithm of Network Trade Customer Classification Based on Fourier Basis Functions

    Directory of Open Access Journals (Sweden)

    Li Xinwu

    2013-11-01

    Full Text Available Learning algorithm of neural network is always an important research contents in neural network theory research and application field, learning algorithm about the feed-forward neural network has no satisfactory solution in particular for its defects in calculation speed. The paper presents a new Fourier basis functions neural network algorithm and applied it to classify network trade customer. First, 21 customer classification indicators are designed, based on characteristics and behaviors analysis of network trade customer, including customer characteristics type variables and customer behaviors type variables,; Second, Fourier basis functions is used to improve the calculation flow and algorithm structure of original BP neural network algorithm to speed up its convergence and then a new Fourier basis neural network model is constructed. Finally the experimental results show that the problem of convergence speed can been solved, and the accuracy of the customer classification are ensured when the new algorithm is used in network trade customer classification practically.

  20. Dynamic mechanisms of cell rigidity sensing: insights from a computational model of actomyosin networks.

    Directory of Open Access Journals (Sweden)

    Carlos Borau

    Full Text Available Cells modulate themselves in response to the surrounding environment like substrate elasticity, exhibiting structural reorganization driven by the contractility of cytoskeleton. The cytoskeleton is the scaffolding structure of eukaryotic cells, playing a central role in many mechanical and biological functions. It is composed of a network of actins, actin cross-linking proteins (ACPs, and molecular motors. The motors generate contractile forces by sliding couples of actin filaments in a polar fashion, and the contractile response of the cytoskeleton network is known to be modulated also by external stimuli, such as substrate stiffness. This implies an important role of actomyosin contractility in the cell mechano-sensing. However, how cells sense matrix stiffness via the contractility remains an open question. Here, we present a 3-D Brownian dynamics computational model of a cross-linked actin network including the dynamics of molecular motors and ACPs. The mechano-sensing properties of this active network are investigated by evaluating contraction and stress in response to different substrate stiffness. Results demonstrate two mechanisms that act to limit internal stress: (i In stiff substrates, motors walk until they exert their maximum force, leading to a plateau stress that is independent of substrate stiffness, whereas (ii in soft substrates, motors walk until they become blocked by other motors or ACPs, leading to submaximal stress levels. Therefore, this study provides new insights into the role of molecular motors in the contraction and rigidity sensing of cells.

  1. The Role of Functional Interdependencies in Global Operations Networks

    DEFF Research Database (Denmark)

    Slepniov, Dmitrij; Wæhrens, Brian Vejrum

    2011-01-01

    The existing studies do not adequately address the complex interplay between co-evolving production, innovation and service networks. The widening geographical and cognitive gap between these networks means that managing their interfaces in global operations context is becoming strategically...

  2. Detecting small attractors of large Boolean networks by function-reduction-based strategy.

    Science.gov (United States)

    Zheng, Qiben; Shen, Liangzhong; Shang, Xuequn; Liu, Wenbin

    2016-04-01

    Boolean networks (BNs) are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long-term behaviour of systems. A central aim of Boolean-network analysis is to find attractors that correspond to various cellular states, such as cell types or the stage of cell differentiation. This problem is NP-hard and various algorithms have been used to tackle it with considerable success. The idea is that a singleton attractor corresponds to n consistent subsequences in the truth table. To find these subsequences, the authors gradually reduce the entire truth table of Boolean functions by extending a partial gene activity profile (GAP). Not only does this process delete inconsistent subsequences in truth tables, it also directly determines values for some nodes not extended, which means it can abandon the partial GAPs that cannot lead to an attractor as early as possible. The results of simulation show that the proposed algorithm can detect small attractors with length p = 4 in BNs of up to 200 nodes with average indegree K = 2.

  3. Affected functional networks associated with sentence production in classic galactosemia.

    Science.gov (United States)

    Timmers, Inge; van den Hurk, Job; Hofman, Paul Am; Zimmermann, Luc Ji; Uludağ, Kâmil; Jansma, Bernadette M; Rubio-Gozalbo, M Estela

    2015-08-01

    Patients with the inherited metabolic disorder classic galactosemia have language production impairments in several planning stages. Here, we assessed potential deviations in recruitment and connectivity across brain areas responsible for language production that may explain these deficits. We used functional magnetic resonance imaging (fMRI) to study neural activity and connectivity while participants carried out a language production task. This study included 13 adolescent patients and 13 age- and gender-matched healthy controls. Participants passively watched or actively described an animated visual scene using two conditions, varying in syntactic complexity (single words versus a sentence). Results showed that patients recruited additional and more extensive brain regions during sentence production. Both groups showed modulations with syntactic complexity in left inferior frontal gyrus (IFG), a region associated with syntactic planning, and in right insula. In addition, patients showed a modulation with syntax in left superior temporal gyrus (STG), whereas the controls did not. Further, patients showed increased activity in right STG and right supplementary motor area (SMA). The functional connectivity data showed similar patterns, with more extensive connectivity with frontal and motor regions, and restricted and weaker connectivity with superior temporal regions. Patients also showed higher baseline cerebral blood flow (CBF) in right IFG and trends towards higher CBF in bilateral STG, SMA and the insula. Taken together, the data demonstrate that language abnormalities in classic galactosemia are associated with specific changes within the language network. These changes point towards impairments related to both syntactic planning and speech motor planning in these patients. PMID:25979518

  4. Dynamic functional brain networks involved in simple visual discrimination learning.

    Science.gov (United States)

    Fidalgo, Camino; Conejo, Nélida María; González-Pardo, Héctor; Arias, Jorge Luis

    2014-10-01

    Visual discrimination tasks have been widely used to evaluate many types of learning and memory processes. However, little is known about the brain regions involved at different stages of visual discrimination learning. We used cytochrome c oxidase histochemistry to evaluate changes in regional brain oxidative metabolism during visual discrimination learning in a water-T maze at different time points during training. As compared with control groups, the results of the present study reveal the gradual activation of cortical (prefrontal and temporal cortices) and subcortical brain regions (including the striatum and the hippocampus) associated to the mastery of a simple visual discrimination task. On the other hand, the brain regions involved and their functional interactions changed progressively over days of training. Regions associated with novelty, emotion, visuo-spatial orientation and motor aspects of the behavioral task seem to be relevant during the earlier phase of training, whereas a brain network comprising the prefrontal cortex was found along the whole learning process. This study highlights the relevance of functional interactions among brain regions to investigate learning and memory processes. PMID:24937013

  5. An arabidopsis gene regulatory network for secondary cell wall synthesis

    Science.gov (United States)

    The plant cell wall is an important factor for determining cell shape, function and response to the environment. Secondary cell walls, such as those found in xylem, are composed of cellulose, hemicelluloses and lignin and account for the bulk of plant biomass. The coordination between transcriptiona...

  6. Mapping Thalamocortical Networks in Rat Brain using Resting-State Functional Connectivity

    OpenAIRE

    Liang, Zhifeng; Li, Tao; King, Jean; Zhang, Nanyin

    2013-01-01

    Thalamocortical connectivity plays a vital role in brain function. The anatomy and function of thalamocortical networks have been extensively studied in animals by numerous invasive techniques. Non-invasively mapping thalamocortical networks in humans has also been demonstrated by utilizing resting-state functional magnetic resonance imaging (rsfMRI). However, success in simultaneously imaging multiple thalamocortical networks in animals is rather limited. This is largely due to the profound ...

  7. Analysis of Neural Networks in Terms of Domain Functions

    NARCIS (Netherlands)

    Zwaag, van der Berend Jan; Slump, Cees; Spaanenburg, Lambert

    2002-01-01

    Despite their success-story, artificial neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more as a my

  8. Clique of functional hubs orchestrates population bursts in developmentally regulated neural networks.

    Directory of Open Access Journals (Sweden)

    Stefano Luccioli

    2014-09-01

    Full Text Available It has recently been discovered that single neuron stimulation can impact network dynamics in immature and adult neuronal circuits. Here we report a novel mechanism which can explain in neuronal circuits, at an early stage of development, the peculiar role played by a few specific neurons in promoting/arresting the population activity. For this purpose, we consider a standard neuronal network model, with short-term synaptic plasticity, whose population activity is characterized by bursting behavior. The addition of developmentally inspired constraints and correlations in the distribution of the neuronal connectivities and excitabilities leads to the emergence of functional hub neurons, whose stimulation/deletion is critical for the network activity. Functional hubs form a clique, where a precise sequential activation of the neurons is essential to ignite collective events without any need for a specific topological architecture. Unsupervised time-lagged firings of supra-threshold cells, in connection with coordinated entrainments of near-threshold neurons, are the key ingredients to orchestrate population activity.

  9. Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression

    Directory of Open Access Journals (Sweden)

    Yunfeng Wu

    2014-01-01

    Full Text Available This paper presents a novel adaptive linear and normalized combination (ALNC method that can be used to combine the component radial basis function networks (RBFNs to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error and the better fidelity (characterized by normalized correlation coefficient of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.

  10. Construction of Large-Volume Tissue Mimics with 3D Functional Vascular Networks.

    Science.gov (United States)

    Kang, Tae-Yun; Hong, Jung Min; Jung, Jin Woo; Kang, Hyun-Wook; Cho, Dong-Woo

    2016-01-01

    We used indirect stereolithography (SL) to form inner-layered fluidic networks in a porous scaffold by introducing a hydrogel barrier on the luminal surface, then seeded the networks separately with human umbilical vein endothelial cells and human lung fibroblasts to form a tissue mimic containing vascular networks. The artificial vascular networks provided channels for oxygen transport, thus reducing the hypoxic volume and preventing cell death. The endothelium of the vascular networks significantly retarded the occlusion of channels during whole-blood circulation. The tissue mimics have the potential to be used as an in vitro platform to examine the physiologic and pathologic phenomena through vascular architecture. PMID:27228079

  11. Construction of Large-Volume Tissue Mimics with 3D Functional Vascular Networks

    Science.gov (United States)

    Kang, Tae-Yun; Hong, Jung Min; Jung, Jin Woo; Kang, Hyun-Wook; Cho, Dong-Woo

    2016-01-01

    We used indirect stereolithography (SL) to form inner-layered fluidic networks in a porous scaffold by introducing a hydrogel barrier on the luminal surface, then seeded the networks separately with human umbilical vein endothelial cells and human lung fibroblasts to form a tissue mimic containing vascular networks. The artificial vascular networks provided channels for oxygen transport, thus reducing the hypoxic volume and preventing cell death. The endothelium of the vascular networks significantly retarded the occlusion of channels during whole-blood circulation. The tissue mimics have the potential to be used as an in vitro platform to examine the physiologic and pathologic phenomena through vascular architecture. PMID:27228079

  12. Construction of Large-Volume Tissue Mimics with 3D Functional Vascular Networks.

    Directory of Open Access Journals (Sweden)

    Tae-Yun Kang

    Full Text Available We used indirect stereolithography (SL to form inner-layered fluidic networks in a porous scaffold by introducing a hydrogel barrier on the luminal surface, then seeded the networks separately with human umbilical vein endothelial cells and human lung fibroblasts to form a tissue mimic containing vascular networks. The artificial vascular networks provided channels for oxygen transport, thus reducing the hypoxic volume and preventing cell death. The endothelium of the vascular networks significantly retarded the occlusion of channels during whole-blood circulation. The tissue mimics have the potential to be used as an in vitro platform to examine the physiologic and pathologic phenomena through vascular architecture.

  13. Visualization of the interstitial cells of cajal (ICC) network in mice.

    Science.gov (United States)

    Chen, Yu; Shamu, Tambudzai; Chen, Hui; Besmer, Peter; Sawyers, Charles L; Chi, Ping

    2011-01-01

    The interstitial cells of Cajal (ICC) are mesenchymal derived "pacemaker cells" of the gastrointestinal (GI) tract that generate spontaneous slow waves required for peristalsis and mediate neuronal input from the enteric nervous system 1. Different subtypes of ICC form distinct networks in the muscularis of the GI tract (2,3). Loss or injury to these networks is associated with a number of motility disorders(4). ICC cells express the KIT receptor tyrosine kinase on the plasma membrane and KIT immunostaining has been used for the past 15 years to label the ICC network(5,6). Importantly, normal KIT activity is required for ICC development(5,6). Neoplastic transformation of ICC cells results in gastrointestinal stromal tumor (GIST), that frequently harbor gain-of-function KIT mutations(7,8). We recently showed that ETV1 is a lineage-specific survival factor expressed in the ICC/GIST lineage and is a master transcriptional regulator required for both normal ICC network formation and for of GIST tumorigenesis(9). We further demonstrate that it cooperates with activating KIT mutations in tumorigenesis. Here, we describe methods for visualization of ICC networks in mice, largely based on previously published protocols(10,11). More recently, the chloride channel anoctamin 1 (ANO1) has also been characterized as a specific membrane marker of ICC(11,12). Because of their plasma membrane localization, immunofluorescence of both proteins can be used to visualize the ICC networks. Here, we describe visualization of the ICC networks by fixed-frozen cyrosections and whole mount preparations.

  14. Neuronal spike sorting based on radial basis function neural networks

    Directory of Open Access Journals (Sweden)

    Taghavi Kani M

    2011-02-01

    Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.

  15. p42.3 gene expression in gastric cancer cell and its protein regulatory network analysis

    Directory of Open Access Journals (Sweden)

    Zhang Jianhua

    2012-12-01

    Full Text Available Abstract Background To analyze the p42.3 gene expression in gastric cancer (GC cell, find the relationship between protein structure and function, establish the regulatory network of p42.3 protein molecule and then to obtain the optimal regulatory pathway. Methods The expression of p42.3 gene was analyzed by RT-PCR, Western Blot and other biotechnologies. The relationship between the spatial conformation of p42.3 protein molecule and its function was analyzed using bioinformatics, MATLAB and related knowledge about protein structure and function. Furthermore, based on similarity algorithm of spatial layered spherical coordinate, we compared p42.3 molecule with several similar structured proteins which are known for the function, screened the characteristic nodes related to tumorigenesis and development, and established the multi variable relational model between p42.3 protein expression, cell cycle regulation and biological characteristics in the level of molecular regulatory networks. Finally, the optimal regulatory network was found by using Bayesian network. Results (1 The expression amount of p42.3 in G1 and M phase was higher than that in S and G2 phase; (2 The space coordinate systems of different structural domains of p42.3 protein were established in Matlab7.0 software; (3 The optimal pathway of p42.3 gene in protein regulatory network in gastric cancer is Ras protein, Raf-1 protein, MEK, MAPK kinase, MAPK, tubulin, spindle protein, centromere protein and tumor. Conclusion It is of vital significance for mechanism research to find out the action pathway of p42.3 in protein regulatory network, since p42.3 protein plays an important role in the generation and development of GC.

  16. Large-Scale Functional Brain Network Abnormalities in Alzheimer’s Disease: Insights from Functional Neuroimaging

    Directory of Open Access Journals (Sweden)

    Bradford C. Dickerson

    2009-01-01

    Full Text Available Functional MRI (fMRI studies of mild cognitive impairment (MCI and Alzheimer’s disease (AD have begun to reveal abnormalities in large-scale memory and cognitive brain networks. Since the medial temporal lobe (MTL memory system is a site of very early pathology in AD, a number of studies have focused on this region of the brain. Yet it is clear that other regions of the large-scale episodic memory network are affected early in the disease as well, and fMRI has begun to illuminate functional abnormalities in frontal, temporal, and parietal cortices as well in MCI and AD. Besides predictable hypoactivation of brain regions as they accrue pathology and undergo atrophy, there are also areas of hyperactivation in brain memory and cognitive circuits, possibly representing attempted compensatory activity. Recent fMRI data in MCI and AD are beginning to reveal relationships between abnormalities of functional activity in the MTL memory system and in functionally connected brain regions, such as the precuneus. Additional work with “resting state” fMRI data is illuminating functional-anatomic brain circuits and their disruption by disease. As this work continues to mature, it will likely contribute to our understanding of fundamental memory processes in the human brain and how these are perturbed in memory disorders. We hope these insights will translate into the incorporation of measures of task-related brain function into diagnostic assessment or therapeutic monitoring, which will hopefully one day be useful for demonstrating beneficial effects of treatments being tested in clinical trials.

  17. A Network Partition Algorithm for Mining Gene Functional Modules of Colon Cancer from DNA Microarray Data

    Institute of Scientific and Technical Information of China (English)

    Xiao-Gang Ruan; Jin-Lian Wang; Jian-Geng Li

    2006-01-01

    Computational analysis is essential for transforming the masses of microarray data into a mechanistic understanding of cancer. Here we present a method for finding gene functional modules of cancer from microarray data and have applied it to colon cancer. First, a colon cancer gene network and a normal colon tissue gene network were constructed using correlations between the genes. Then the modules that tended to have a homogeneous functional composition were identified by splitting up the network. Analysis of both networks revealed that they are scale-free.Comparison of the gene functional modules for colon cancer and normal tissues showed that the modules' functions changed with their structures.

  18. Altered functional and structural connectivity networks in psychogenic non-epileptic seizures.

    Directory of Open Access Journals (Sweden)

    Ju-Rong Ding

    Full Text Available Psychogenic non-epileptic seizures (PNES are paroxysmal behaviors that resemble epileptic seizures but lack abnormal electrical activity. Recent studies suggest aberrant functional connectivity involving specific brain regions in PNES. Little is known, however, about alterations of topological organization of whole-brain functional and structural connectivity networks in PNES. We constructed functional connectivity networks from resting-state functional MRI signal correlations and structural connectivity networks from diffusion tensor imaging tractography in 17 PNES patients and 20 healthy controls. Graph theoretical analysis was employed to compute network properties. Moreover, we investigated the relationship between functional and structural connectivity networks. We found that PNES patients exhibited altered small-worldness in both functional and structural networks and shifted towards a more regular (lattice-like organization, which could serve as a potential imaging biomarker for PNES. In addition, many regional characteristics were altered in structural connectivity network, involving attention, sensorimotor, subcortical and default-mode networks. These regions with altered nodal characteristics likely reflect disease-specific pathophysiology in PNES. Importantly, the coupling strength of functional-structural connectivity was decreased and exhibited high sensitivity and specificity to differentiate PNES patients from healthy controls, suggesting that the decoupling strength of functional-structural connectivity might be an important characteristic reflecting the mechanisms of PNES. This is the first study to explore the altered topological organization in PNES combining functional and structural connectivity networks, providing a new way to understand the pathophysiological mechanisms of PNES.

  19. Functional MRI neurofeedback training on connectivity between two regions induces long-lasting changes in intrinsic functional network

    OpenAIRE

    Megumi, Fukuda; Yamashita, Ayumu; Kawato, Mitsuo; Imamizu, Hiroshi

    2015-01-01

    Motor or perceptual learning is known to influence functional connectivity between brain regions and induce short-term changes in the intrinsic functional networks revealed as correlations in slow blood-oxygen-level dependent (BOLD) signal fluctuations. However, no cause-and-effect relationship has been elucidated between a specific change in connectivity and a long-term change in global networks. Here, we examine the hypothesis that functional connectivity (i.e., temporal correlation between...

  20. Rebound spiking in layer II medial entorhinal cortex stellate cells: Possible mechanism of grid cell function.

    Science.gov (United States)

    Shay, Christopher F; Ferrante, Michele; Chapman, G William; Hasselmo, Michael E

    2016-03-01

    Rebound spiking properties of medial entorhinal cortex (mEC) stellate cells induced by inhibition may underlie their functional properties in awake behaving rats, including the temporal phase separation of distinct grid cells and differences in grid cell firing properties. We investigated rebound spiking properties using whole cell patch recording in entorhinal slices, holding cells near spiking threshold and delivering sinusoidal inputs, superimposed with realistic inhibitory synaptic inputs to test the capacity of cells to selectively respond to specific phases of inhibitory input. Stellate cells showed a specific phase range of hyperpolarizing inputs that elicited spiking, but non-stellate cells did not show phase specificity. In both cell types, the phase range of spiking output occurred between the peak and subsequent descending zero crossing of the sinusoid. The phases of inhibitory inputs that induced spikes shifted earlier as the baseline sinusoid frequency increased, while spiking output shifted to later phases. Increases in magnitude of the inhibitory inputs shifted the spiking output to earlier phases. Pharmacological blockade of h-current abolished the phase selectivity of hyperpolarizing inputs eliciting spikes. A network computational model using cells possessing similar rebound properties as found in vitro produces spatially periodic firing properties resembling grid cell firing when a simulated animal moves along a linear track. These results suggest that the ability of mEC stellate cells to fire rebound spikes in response to a specific range of phases of inhibition could support complex attractor dynamics that provide completion and separation to maintain spiking activity of specific grid cell populations. PMID:26385258

  1. Towards a Queueing-Based Framework for In-Network Function Computation

    CERN Document Server

    Banerjee, Siddhartha; Shakkottai, Sanjay

    2011-01-01

    We seek to develop network algorithms for function computation in sensor networks. Specifically, we want dynamic joint aggregation, routing, and scheduling algorithms that have analytically provable performance benefits due to in-network computation as compared to simple data forwarding. To this end, we define a class of functions, the Fully-Multiplexible functions, which includes several functions such as parity, MAX, and k th -order statistics. For such functions we exactly characterize the maximum achievable refresh rate of the network in terms of an underlying graph primitive, the min-mincut. In wireline networks, we show that the maximum refresh rate is achievable by a simple algorithm that is dynamic, distributed, and only dependent on local information. In the case of wireless networks, we provide a MaxWeight-like algorithm with dynamic flow splitting, which is shown to be throughput-optimal.

  2. Determination of Activation Functions in A Feedforward Neural Network by using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Oğuz ÜSTÜN

    2009-03-01

    Full Text Available In this study, activation functions of all layers of the multilayered feedforward neural network have been determined by using genetic algorithm. The main criteria that show the efficiency of the neural network is to approximate to the desired output with the same number nodes and connection weights. One of the important parameter to determine this performance is to choose a proper activation function. In the classical neural network designing, a network is designed by choosing one of the generally known activation function. In the presented study, a table has been generated for the activation functions. The ideal activation function for each node has been chosen from this table by using the genetic algorithm. Two dimensional regression problem clusters has been used to compare the performance of the classical static neural network and the genetic algorithm based neural network. Test results reveal that the proposed method has a high level approximation capacity.

  3. Can we neglect the multi-layer structure of functional networks?

    CERN Document Server

    Zanin, Massimiliano

    2015-01-01

    Functional networks, i.e. networks representing dynamic relationships between the components of a complex system, have been instrumental for our understanding of, among others, the human brain. Due to limited data availability, the multi-layer nature of numerous functional networks has hitherto been neglected, and nodes are endowed with a single type of links even when multiple relationships coexist at different physical levels. A relevant problem is the assessment of the benefits yielded by studying a multi-layer functional network, against the simplicity guaranteed by the reconstruction and use of the corresponding single layer projection. Here, I tackle this issue by using as a test case, the functional network representing the dynamics of delay propagation through European airports. Neglecting the multi-layer structure of a functional network has dramatic consequences on our understanding of the underlying system, a fact to be taken into account when a projection is the only available information.

  4. Novel functional view of the crocidolite asbestos-treated A549 human lung epithelial transcriptome reveals an intricate network of pathways with opposing functions

    Directory of Open Access Journals (Sweden)

    Stevens John R

    2008-08-01

    Full Text Available Abstract Background Although exposure to asbestos is now regulated, patients continue to be diagnosed with mesothelioma, asbestosis, fibrosis and lung carcinoma because of the long latent period between exposure and clinical disease. Asbestosis is observed in approximately 200,000 patients annually and asbestos-related deaths are estimated at 4,000 annually1. Although advances have been made using single gene/gene product or pathway studies, the complexity of the response to asbestos and the many unanswered questions suggested the need for a systems biology approach. The objective of this study was to generate a comprehensive view of the transcriptional changes induced by crocidolite asbestos in A549 human lung epithelial cells. Results A statistically robust, comprehensive data set documenting the crocidolite-induced changes in the A549 transcriptome was collected. A systems biology approach involving global observations from gene ontological analyses coupled with functional network analyses was used to explore the effects of crocidolite in the context of known molecular interactions. The analyses uniquely document a transcriptome with function-based networks in cell death, cancer, cell cycle, cellular growth, proliferation, and gene expression. These functional modules show signs of a complex interplay between signaling pathways consisting of both novel and previously described asbestos-related genes/gene products. These networks allowed for the identification of novel, putative crocidolite-related genes, leading to several new hypotheses regarding genes that are important for the asbestos response. The global analysis revealed a transcriptome that bears signatures of both apoptosis/cell death and cell survival/proliferation. Conclusion Our analyses demonstrate the power of combining a statistically robust, comprehensive dataset and a functional network genomics approach to 1 identify and explore relationships between genes of known importance

  5. Creep Function of a Single Living Cell

    Science.gov (United States)

    Desprat, Nicolas; Richert, Alain; Simeon, Jacqueline; Asnacios, Atef

    2005-01-01

    We used a novel uniaxial stretching rheometer to measure the creep function J(t) of an isolated living cell. We show, for the first time at the scale of the whole cell, that J(t) behaves as a power-law J(t) = Atα. For N = 43 mice myoblasts (C2-7), we find α = 0.24 ± 0.01 and A = (2.4 ± 0.3) 10−3 Pa−1 s−α. Using Laplace Transforms, we compare A and α to the parameters G0 and β of the complex modulus G*(ω) = G0ωβ measured by other authors using magnetic twisting cytometry and atomic force microscopy. Excellent agreement between A and G0 on the one hand, and between α and β on the other hand, indicated that the power-law is an intrinsic feature of cell mechanics and not the signature of a particular technique. Moreover, the agreement between measurements at very different size scales, going from a few tens of nanometers to the scale of the whole cell, suggests that self-similarity could be a central feature of cell mechanical structure. Finally, we show that the power-law behavior could explain previous results first interpreted as instantaneous elasticity. Thus, we think that the living cell must definitely be thought of as a material with a large and continuous distribution of relaxation time constants which cannot be described by models with a finite number of springs and dash-pots. PMID:15596508

  6. Functions of Xyloglucan in Plant Cells

    Institute of Scientific and Technical Information of China (English)

    Takahisa Hayashi; Rumi Kaida

    2011-01-01

    While an increase in the number of xyloglucan tethers between the cellulose microfibrils in plant cell walls increases the walls' rigidity, the degradation of these tethers causes the walls to loosen. Degradation can occur either through the integration of xyloglucan oligosaccharides due to the action of xyloglucan endotransglucosylase or through direct hydrolysis due to the action of xyloglucanase. This is why the addition of xyloglucan and its fragment oligosac-charides causes plant tissue tension to increase and decrease so dramatically. Experiments involving the overexpression of xyloglucanase and cellulase have revealed the roles of xyloglucans in the walls. The degradation of wall xyloglucan in poplar by the transgenic expression of xyloglucanase, for example, not only accelerated stem elongation in the primary wall, but also blocked upright-stem gravitropism in the secondary wall. Overexpression of cellulase also reduced xyloglucan content in the walls as cellulose microfibrils were trimmed at their amorphous region, resulting in increased cell volume in Arabidopsis leaves and in sengon with disturbed leaf movements. The hemicellulose xyloglucan, in its function as a tether, plays a key role in the loosening and tightening of cellulose microfibrils: it enables the cell to change its shape in growth and differentiation zones and to retain its final shape after cell maturation.

  7. Gap junctions between CA3 pyramidal cells contribute to network synchronization in neonatal hippocampus.

    Science.gov (United States)

    Molchanova, Svetlana M; Huupponen, Johanna; Lauri, Sari E; Taira, Tomi

    2016-08-01

    Direct electrical coupling between neurons through gap junctions is prominent during development, when synaptic connectivity is scarce, providing the additional intercellular connectivity. However, functional studies of gap junctions are hampered by the unspecificity of pharmacological tools available. Here we have investigated gap-junctional coupling between CA3 pyramidal cells in neonatal hippocampus and its contribution to early network activity. Four different gap junction inhibitors, including the general blocker carbenoxolone, decreased the frequency of network activity bursts in CA3 area of hippocampus of P3-6 rats, suggesting the involvement of electrical connections in the generation of spontaneous network activity. In CA3 pyramidal cells, spikelets evoked by local stimulation of stratum oriens, were inhibited by carbenoxolone, but not by inhibitors of glutamatergic and GABAergic synaptic transmission, signifying the presence of electrical connectivity through axo-axonic gap junctions. Carbenoxolone also decreased the success rate of firing antidromic action potentials in response to stimulation, and changed the pattern of spontaneous action potential firing of CA3 pyramidal cells. Altogether, these data suggest that electrical coupling of CA3 pyramidal cells contribute to the generation of the early network events in neonatal hippocampus by modulating their firing pattern and synchronization. PMID:26926429

  8. Feedback through graph motifs relates structure and function in complex networks

    CERN Document Server

    Hu, Yu; Cain, Nicholas; Mihalas, Stefan; Kutz, J Nathan; Shea-Brown, Eric

    2016-01-01

    How does the connectivity of a network system combine with the behavior of its individual components to determine its collective function? We approach this question by relating the internal network feedback to the statistical prevalence of connectivity motifs, a set of surprisingly simple and local statistics on the network topology. The resulting motif description provides a reduced order model of the network input-output dynamics and it relates the overall network function to feedback control theory. For example, this new formulation dramatically simplifies the classic Erdos-Renyi graph, reducing the overall graph behavior to a simple proportional feedback wrapped around the dynamics of a single node. Higher-order motifs systematically provide further layers and types of feedback to regulate the network response. Thus, the local connectivity shapes temporal and spectral processing by the network as a whole, and we show how this enables robust, yet tunable, functionality such as extending the time constant w...

  9. An Efficient Weather Forecasting System using Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Tiruvenkadam Santhanam

    2011-01-01

    Full Text Available Problem statement: Accurate weather forecasting plays a vital role for planning day to day activities. Neural network has been use in numerous meteorological applications including weather forecasting. Approach: A neural network model has been developed for weather forecasting, based on various factors obtained from meteorological experts. This study evaluates the performance of Radial Basis Function (RBF with Back Propagation (BPN neural network. The back propagation neural network and radial basis function neural network were used to test the performance in order to investigate effective forecasting technique. Results: The prediction accuracy of RBF was 88.49%. Conclusion: The results indicate that proposed radial basis function neural network is better than back propagation neural network.

  10. Proposed spring network cell model based on a minimum energy concept.

    Science.gov (United States)

    Ujihara, Yoshihiro; Nakamura, Masanori; Miyazaki, Hiroshi; Wada, Shigeo

    2010-04-01

    We developed a mechano-cell model incorporating a cell membrane, a nuclear envelope, and actin filaments to simulate the mechanical behavior of a cell during tensile tests. The computational model depicts a cell as a combination of various spring elements in the framework of the minimum energy concept. A cell membrane and a nuclear envelope are both modeled as shells of a spring network that express elastic resistance to changes in bending, stretching, and surface area. A bundle of actin filaments is represented by a mechanical spring that generates a force as a function of its extension. The interaction between the nuclear envelope and the cell membrane is expressed by a potential energy function with respect to the distance between them. Incompressibility of a cell is assured by a volume elastic energy function. The cell shape during a tensile test is determined by a quasi-static approach, such that the total elastic energy converges to the minimum. The load-deformation curve obtained from the simulation shows a significant increase in stretching load with deformation of the cell and lies within a range of experimentally obtained load-deformation curves. The total elastic energy is dominated by the energy stored in the actin fibers. Actin fibers that are randomly oriented before loading tend to become aligned, passively, in the stretched direction. These results attribute the non-linearity in the load-deformation curve to passive reorientation of actin fibers in the stretched direction. PMID:20108165

  11. A cortical attractor network with Martinotti cells driven by facilitating synapses.

    Directory of Open Access Journals (Sweden)

    Pradeep Krishnamurthy

    Full Text Available The population of pyramidal cells significantly outnumbers the inhibitory interneurons in the neocortex, while at the same time the diversity of interneuron types is much more pronounced. One acknowledged key role of inhibition is to control the rate and patterning of pyramidal cell firing via negative feedback, but most likely the diversity of inhibitory pathways is matched by a corresponding diversity of functional roles. An important distinguishing feature of cortical interneurons is the variability of the short-term plasticity properties of synapses received from pyramidal cells. The Martinotti cell type has recently come under scrutiny due to the distinctly facilitating nature of the synapses they receive from pyramidal cells. This distinguishes these neurons from basket cells and other inhibitory interneurons typically targeted by depressing synapses. A key aspect of the work reported here has been to pinpoint the role of this variability. We first set out to reproduce quantitatively based on in vitro data the di-synaptic inhibitory microcircuit connecting two pyramidal cells via one or a few Martinotti cells. In a second step, we embedded this microcircuit in a previously developed attractor memory network model of neocortical layers 2/3. This model network demonstrated that basket cells with their characteristic depressing synapses are the first to discharge when the network enters an attractor state and that Martinotti cells respond with a delay, thereby shifting the excitation-inhibition balance and acting to terminate the attractor state. A parameter sensitivity analysis suggested that Martinotti cells might, in fact, play a dominant role in setting the attractor dwell time and thus cortical speed of processing, with cellular adaptation and synaptic depression having a less prominent role than previously thought.

  12. Network Analysis of Strategic Marketing Actions and Quality Function Deployment

    Directory of Open Access Journals (Sweden)

    fatema daneshian

    2011-12-01

    Nowadays, strategic marketing management has become an accepted practice in the strategic field. An increasing number of researchers consider marketing strategies for offering key competitive advantages associated with strategic marketing management. Every decision in the strategic field should be based on three dimensions of evaluating market, evaluating competitors and evaluating company. In this research, a model has been developed for selecting and ranking marketing strategies considering the evaluation of market (customer satisfaction elements, competitors and company based on Kano model. Quality function deployment (QFD and the analytic network process (ANP approaches have been used for market prioritization. The research has been carried out in three phases. In Phase one, the Kano model of customer satisfaction has been used to determine which requirements of a product or service brings more satisfaction to the customers, followed by the evaluation of competitors and gap analyze. In Phase two, The QFD approach has been used to incorporate the voice of customer (VOC into the marketing strategies of the company and has provided a systematic planning tool for considering the information of elements (in the last phase to make appropriate decisions effectively and efficiently. In Phase three, the ANP method has been used to analyze strategic actions considering company conditions. Finally the outputs of QFD have been corrected by ANP weights. Findings imply that the three most important strategic actions which are important for the company include offering differentiated and new generation of products to the market (leapfrog strategy, optimizing visual properties of products, and widespread and attractive advertising (frontal attack.

  13. Memory networks in tinnitus: a functional brain image study.

    Directory of Open Access Journals (Sweden)

    Maura Regina Laureano

    Full Text Available Tinnitus is characterized by the perception of sound in the absence of an external auditory stimulus. The network connectivity of auditory and non-auditory brain structures associated with emotion, memory and attention are functionally altered in debilitating tinnitus. Current studies suggest that tinnitus results from neuroplastic changes in the frontal and limbic temporal regions. The objective of this study was to use Single-Photon Emission Computed Tomography (SPECT to evaluate changes in the cerebral blood flow in tinnitus patients with normal hearing compared with healthy controls.Twenty tinnitus patients with normal hearing and 17 healthy controls, matched for sex, age and years of education, were subjected to Single Photon Emission Computed Tomography using the radiotracer ethylenedicysteine diethyl ester, labeled with Technetium 99 m (99 mTc-ECD SPECT. The severity of tinnitus was assessed using the "Tinnitus Handicap Inventory" (THI. The images were processed and analyzed using "Statistical Parametric Mapping" (SPM8.A significant increase in cerebral perfusion in the left parahippocampal gyrus (pFWE <0.05 was observed in patients with tinnitus compared with healthy controls. The average total THI score was 50.8+18.24, classified as moderate tinnitus.It was possible to identify significant changes in the limbic system of the brain perfusion in tinnitus patients with normal hearing, suggesting that central mechanisms, not specific to the auditory pathway, are involved in the pathophysiology of symptoms, even in the absence of clinically diagnosed peripheral changes.

  14. Funneled landscape leads to robustness of cell networks: yeast cell cycle.

    Directory of Open Access Journals (Sweden)

    Jin Wang

    2006-11-01

    Full Text Available We uncovered the underlying energy landscape for a cellular network. We discovered that the energy landscape of the yeast cell-cycle network is funneled towards the global minimum (G0/G1 phase from the experimentally measured or inferred inherent chemical reaction rates. The funneled landscape is quite robust against random perturbations. This naturally explains robustness from a physical point of view. The ratio of slope versus roughness of the landscape becomes a quantitative measure of robustness of the network. The funneled landscape can be seen as a possible realization of the Darwinian principle of natural selection at the cellular network level. It provides an optimal criterion for network connections and design. Our approach is general and can be applied to other cellular networks.

  15. GeneNet: a database on structure and functional organisation of gene networks

    OpenAIRE

    Ananko, E A; Podkolodny, N. L.; Stepanenko, I. L.; Ignatieva, E. V.; Podkolodnaya, O. A.; Kolchanov, N. A.

    2002-01-01

    The GeneNet database is designed for accumulation of information on gene networks. Original technology applied in GeneNet enables description of not only a gene network structure and functional relationships between components, but also metabolic and signal transduction pathways. Specialised software, GeneNet Viewer, automatically displays the graphical diagram of gene networks described in the database. Current release 3.0 of GeneNet database contains descriptions of 25 gene networks, 945 pr...

  16. Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations.

    Directory of Open Access Journals (Sweden)

    Xin Wang

    Full Text Available Combinatorial gene perturbations provide rich information for a systematic exploration of genetic interactions. Despite successful applications to bacteria and yeast, the scalability of this approach remains a major challenge for higher organisms such as humans. Here, we report a novel experimental and computational framework to efficiently address this challenge by limiting the 'search space' for important genetic interactions. We propose to integrate rich phenotypes of multiple single gene perturbations to robustly predict functional modules, which can subsequently be subjected to further experimental investigations such as combinatorial gene silencing. We present posterior association networks (PANs to predict functional interactions between genes estimated using a Bayesian mixture modelling approach. The major advantage of this approach over conventional hypothesis tests is that prior knowledge can be incorporated to enhance predictive power. We demonstrate in a simulation study and on biological data, that integrating complementary information greatly improves prediction accuracy. To search for significant modules, we perform hierarchical clustering with multiscale bootstrap resampling. We demonstrate the power of the proposed methodologies in applications to Ewing's sarcoma and human adult stem cells using publicly available and custom generated data, respectively. In the former application, we identify a gene module including many confirmed and highly promising therapeutic targets. Genes in the module are also significantly overrepresented in signalling pathways that are known to be critical for proliferation of Ewing's sarcoma cells. In the latter application, we predict a functional network of chromatin factors controlling epidermal stem cell fate. Further examinations using ChIP-seq, ChIP-qPCR and RT-qPCR reveal that the basis of their genetic interactions may arise from transcriptional cross regulation. A Bioconductor package

  17. Partition and Correlation Functions of a Freely Crossed Network Using Ising Model-Type Interactions

    CERN Document Server

    Saito, Akira

    2016-01-01

    We set out to determine the partition and correlation functions of a network under the assumption that its elements are freely connected, with an Ising model-type interaction energy associated with each connection. The partition function is obtained from all combinations of loops on the free network, while the correlation function between two elements is obtained based on all combinations of routes between these points, as well as all loops on the network. These functions allow measurement of the dynamics over the whole of any network, regardless of its form. Furthermore, even as parts are added to the network, the partition and correlation functions can still be obtained. As an example, we obtain the partition and correlation functions in a crystal system under the repeated addition of fixed parts.

  18. The function of neurocognitive networks. Comment on “Understanding brain networks and brain organization” by Pessoa

    Science.gov (United States)

    Bressler, Steven L.

    2014-09-01

    Pessoa [5] has performed a valuable service by reviewing the extant literature on brain networks and making a number of interesting proposals about their cognitive function. The term function is at the core of understanding the brain networks of cognition, or neurocognitive networks (NCNs) [1]. The great Russian neuropsychologist, Luria [4], defined brain function as the common task executed by a distributed brain network of complex dynamic structures united by the demands of cognition. Casting Luria in a modern light, we can say that function emerges from the interactions of brain regions in NCNs as they dynamically self-organize according to cognitive demands. Pessoa rightly details the mapping between brain function and structure, emphasizing both its pluripotency (one structure having multiple functions) and degeneracy (many structures having the same function). However, he fails to consider the potential importance of a one-to-one mapping between NCNs and function. If NCNs are uniquely composed of specific collections of brain areas, then each NCN has a unique function determined by that composition.

  19. Conditions for Multi-functionality in a Rhythm Generating Network Inspired by Turtle Scratching.

    Science.gov (United States)

    Snyder, Abigail C; Rubin, Jonathan E

    2015-12-01

    Rhythmic behaviors such as breathing, walking, and scratching are vital to many species. Such behaviors can emerge from groups of neurons, called central pattern generators, in the absence of rhythmic inputs. In vertebrates, the identification of the cells that constitute the central pattern generator for particular rhythmic behaviors is difficult, and often, its existence has only been inferred. For example, under experimental conditions, intact turtles generate several rhythmic scratch motor patterns corresponding to non-rhythmic stimulation of different body regions. These patterns feature alternating phases of motoneuron activation that occur repeatedly, with different patterns distinguished by the relative timing and duration of activity of hip extensor, hip flexor, and knee extensor motoneurons. While the central pattern generator network responsible for these outputs has not been located, there is hope to use motoneuron recordings to deduce its properties. To this end, this work presents a model of a previously proposed central pattern generator network and analyzes its capability to produce two distinct scratch rhythms from a single neuron pool, selected by different combinations of tonic drive parameters but with fixed strengths of connections within the network. We show through simulation that the proposed network can achieve the desired multi-functionality, even though it relies on hip unit generators to recruit appropriately timed knee extensor motoneuron activity, including a delay relative to hip activation in rostral scratch. Furthermore, we develop a phase space representation, focusing on the inputs to and the intrinsic slow variable of the knee extensor motoneuron, which we use to derive sufficient conditions for the network to realize each rhythm and which illustrates the role of a saddle-node bifurcation in achieving the knee extensor delay. This framework is harnessed to consider bistability and to make predictions about the responses of the

  20. Conditions for Multi-functionality in a Rhythm Generating Network Inspired by Turtle Scratching.

    Science.gov (United States)

    Snyder, Abigail C; Rubin, Jonathan E

    2015-12-01

    Rhythmic behaviors such as breathing, walking, and scratching are vital to many species. Such behaviors can emerge from groups of neurons, called central pattern generators, in the absence of rhythmic inputs. In vertebrates, the identification of the cells that constitute the central pattern generator for particular rhythmic behaviors is difficult, and often, its existence has only been inferred. For example, under experimental conditions, intact turtles generate several rhythmic scratch motor patterns corresponding to non-rhythmic stimulation of different body regions. These patterns feature alternating phases of motoneuron activation that occur repeatedly, with different patterns distinguished by the relative timing and duration of activity of hip extensor, hip flexor, and knee extensor motoneurons. While the central pattern generator network responsible for these outputs has not been located, there is hope to use motoneuron recordings to deduce its properties. To this end, this work presents a model of a previously proposed central pattern generator network and analyzes its capability to produce two distinct scratch rhythms from a single neuron pool, selected by different combinations of tonic drive parameters but with fixed strengths of connections within the network. We show through simulation that the proposed network can achieve the desired multi-functionality, even though it relies on hip unit generators to recruit appropriately timed knee extensor motoneuron activity, including a delay relative to hip activation in rostral scratch. Furthermore, we develop a phase space representation, focusing on the inputs to and the intrinsic slow variable of the knee extensor motoneuron, which we use to derive sufficient conditions for the network to realize each rhythm and which illustrates the role of a saddle-node bifurcation in achieving the knee extensor delay. This framework is harnessed to consider bistability and to make predictions about the responses of the

  1. Dual-Cell HSDPA for Network Energy Saving

    DEFF Research Database (Denmark)

    Micallef, Gilbert

    2010-01-01

    consumption. This paper proposes an energy saving feature that exploits variations in network traffic. Based on the individual load of each sector, the feature determines if the secondary carrier is detrimental for reaching some pre-set minimum requirements. Each sector is allowed to switch off one of their...... two carriers, during periods of low network traffic. It is concluded that an energy saving ranging between 14% and 36% is possible. This saving comes without degradation of network performance.......-Cell HSDPA (or Dual-Carrier HSDPA). This feature allows for a single user to be simultaneously scheduled over two carriers, effectively doubling its achievable data rate. The addition of a secondary carrier will require additional radio equipment at the base station site, increasing the overall energy...

  2. Gene expression correlations in human cancer cell lines define molecular interaction networks for epithelial phenotype.

    Directory of Open Access Journals (Sweden)

    Kurt W Kohn

    Full Text Available Using gene expression data to enhance our knowledge of control networks relevant to cancer biology and therapy is a challenging but urgent task. Based on the premise that genes that are expressed together in a variety of cell types are likely to functions together, we derived mutually correlated genes that function together in various processes in epithelial-like tumor cells. Expression-correlated genes were derived from data for the NCI-60 human tumor cell lines, as well as data from the Broad Institute's CCLE cell lines. NCI-60 cell lines that selectively expressed a mutually correlated subset of tight junction genes served as a signature for epithelial-like cancer cells. Those signature cell lines served as a seed to derive other correlated genes, many of which had various other epithelial-related functions. Literature survey yielded molecular interaction and function information about those genes, from which molecular interaction maps were assembled. Many of the genes had epithelial functions unrelated to tight junctions, demonstrating that new function categories were elicited. The most highly correlated genes were implicated in the following epithelial functions: interactions at tight junctions (CLDN7, CLDN4, CLDN3, MARVELD3, MARVELD2, TJP3, CGN, CRB3, LLGL2, EPCAM, LNX1; interactions at adherens junctions (CDH1, ADAP1, CAMSAP3; interactions at desmosomes (PPL, PKP3, JUP; transcription regulation of cell-cell junction complexes (GRHL1 and 2; epithelial RNA splicing regulators (ESRP1 and 2; epithelial vesicle traffic (RAB25, EPN3, GRHL2, EHF, ADAP1, MYO5B; epithelial Ca(+2 signaling (ATP2C2, S100A14, BSPRY; terminal differentiation of epithelial cells (OVOL1 and 2, ST14, PRSS8, SPINT1 and 2; maintenance of apico-basal polarity (RAB25, LLGL2, EPN3. The findings provide a foundation for future studies to elucidate the functions of regulatory networks specific to epithelial-like cancer cells and to probe for anti-cancer drug targets.

  3. The conundrum of functional brain networks: small-world efficiency or fractal modularity

    CERN Document Server

    Gallos, Lazaros K; Makse, Hernan A

    2012-01-01

    The human brain has been studied at multiple scales, from neurons, circuits, areas with well defined anatomical and functional boundaries, to large-scale functional networks which mediate coherent cognition. In a recent work, we addressed the problem of the hierarchical organization in the brain through network analysis. Our analysis identified functional brain modules of fractal structure that were inter-connected in a small-world topology. Here, we provide more details on the use of network science tools to elaborate on this behavior. We indicate the importance of using percolation theory to highlight the modular character of the functional brain network. These modules present a fractal, self-similar topology, identified through fractal network methods. When we lower the threshold of correlations to include weaker ties, the network as a whole assumes a small-world character. These weak ties are organized precisely as predicted by theory maximizing information transfer with minimal wiring costs.

  4. Investigating Upper Bounds on Network Lifetime Extension for Cell-Based Energy Conservation Techniques in Stationary Ad Hoc Networks

    OpenAIRE

    Santi, Paolo

    2002-01-01

    Cooperative cell-based strategies have been recently proposed as a technique for extending the lifetime of wireless adhoc networks, while only slightly impacting network performance. The effectiveness of this approach depends heavilyon the node density: the higher it is, the more consistent energy savings can potentially be achieved. However, nogeneral analyses of network lifetime have been done either for a base network (one without any energy conservationtechnique) or for one using cooperat...

  5. Sox2 transcription network acts as a molecular switch to regulate properties of neural stem cells

    Institute of Scientific and Technical Information of China (English)

    Koji; Shimozaki

    2014-01-01

    Neural stem cells(NSCs) contribute to ontogeny by producing neurons at the appropriate time and location. Neurogenesis from NSCs is also involved in various biological functions in adults. Thus, NSCs continue to exert their effects throughout the lifespan of the organism. The mechanism regulating the core functional properties of NSCs is governed by intra- and extracellular signals. Among the transcription factors that serve as molecular switches, Sox2 is considered a key factor in NSCs. Sox2 forms a core network with partner factors, thereby functioning as a molecular switch. This review discusses how the network of Sox2 partner and target genes illustrates the molecular characteristics of the mechanism underlying the self-renewal and multipotency of NSCs.

  6. Meditation-State Functional Connectivity (msFC): Strengthening of the Dorsal Attention Network and Beyond

    OpenAIRE

    Brett Froeliger; Garland, Eric L.; Kozink, Rachel V.; Modlin, Leslie A.; Nan-Kuei Chen; F. Joseph McClernon; Greeson, Jeffrey M.; Paul Sobin

    2012-01-01

    Meditation practice alters intrinsic resting-state functional connectivity (rsFC) in the default mode network (DMN). However, little is known regarding the effects of meditation on other resting-state networks. The aim of current study was to investigate the effects of meditation experience and meditation-state functional connectivity (msFC) on multiple resting-state networks (RSNs). Meditation practitioners (MPs) performed two 5-minute scans, one during rest, one while meditating. A meditati...

  7. Lumbar Myeloid Cell Trafficking into Locomotor Networks after Thoracic Spinal Cord Injury.

    Science.gov (United States)

    Hansen, Christopher N; Norden, Diana M; Faw, Timothy D; Deibert, Rochelle; Wohleb, Eric S; Sheridan, John F; Godbout, Jonathan P; Basso, D Michele

    2016-08-01

    Spinal cord injury (SCI) promotes inflammation along the neuroaxis that jeopardizes plasticity, intrinsic repair and recovery. While inflammation at the injury site is well-established, less is known within remote spinal networks. The presence of bone marrow-derived immune (myeloid) cells in these areas may further impede functional recovery. Previously, high levels of the gelatinase, matrix metalloproteinase-9 (MMP-9) occurred within the lumbar enlargement after thoracic SCI and impeded activity-dependent recovery. Since SCI-induced MMP-9 potentially increases vascular permeability, myeloid cell infiltration may drive inflammatory toxicity in locomotor networks. Therefore, we examined neurovascular reactivity and myeloid cell infiltration in the lumbar cord after thoracic SCI. We show evidence of region-specific recruitment of myeloid cells into the lumbar but not cervical region. Myeloid infiltration occurred with concomitant increases in chemoattractants (CCL2) and cell adhesion molecules (ICAM-1) around lumbar vasculature 24h and 7days post injury. Bone marrow GFP chimeric mice established robust infiltration of bone marrow-derived myeloid cells into the lumbar gray matter 24h after SCI. This cell infiltration occurred when the blood-spinal cord barrier was intact, suggesting active recruitment across the endothelium. Myeloid cells persisted as ramified macrophages at 7days post injury in parallel with increased inhibitory GAD67 labeling. Importantly, macrophage infiltration required MMP-9. PMID:27191729

  8. Reduced functional segregation between the default mode network and the executive control network in healthy older adults: A longitudinal study.

    Science.gov (United States)

    Ng, Kwun Kei; Lo, June C; Lim, Joseph K W; Chee, Michael W L; Zhou, Juan

    2016-06-01

    The effects of age on functional connectivity (FC) of intrinsic connectivity networks (ICNs) have largely been derived from cross-sectional studies. Far less is known about longitudinal changes in FC and how they relate to ageing-related cognitive decline. We evaluated intra- and inter-network FC in 78 healthy older adults two or three times over a period of 4years. Using linear mixed modeling we found progressive loss of functional specialization with ageing, evidenced by a decline in intra-network FC within the executive control (ECN) and default mode networks (DMN). In contrast, longitudinal inter-network FC between ECN and DMN showed a u-shaped trajectory whereby functional segregation between these two networks initially increased over time and later decreased as participants aged. The rate of loss in functional segregation between ECN and DMN was associated with ageing-related decline in processing speed. The observed longitudinal FC changes and their associations with processing speed remained after correcting for longitudinal reduction in gray matter volume. These findings help connect ageing-related changes in FC with ageing-related decline in cognitive performance and underscore the value of collecting concurrent longitudinal imaging and behavioral data.

  9. An abnormal resting-state functional brain network indicates progression towards Alzheimer’s disease*****

    Institute of Scientific and Technical Information of China (English)

    Jie Xiang; Hao Guo; Rui Cao; Hong Liang; Junjie Chen

    2013-01-01

    Brain structure and cognitive function change in the temporal lobe, hippocampus, and prefrontal cortex of patients with mild cognitive impairment and Alzheimer’s disease, and brain network-connection strength, network efficiency, and nodal attributes are abnormal. However, existing research has only analyzed the differences between these patients and normal controls. In this study, we constructed brain networks using resting-state functional MRI data that was extracted from four populations mal controls, patients with early mild cognitive impairment, patients with late mild cognitive impairment, and patients with Alzheimer’s disease) using the Alzheimer’s Disease Neuroimaging Initiative data set. The aim was to analyze the characteristics of resting-state functional neural networks, and to observe mild cognitive impairment at different stages before the transformation to Alzheimer’s disease. Results showed that as cognitive deficits increased across the four groups, the shortest path in the rest-ing-state functional network gradual y increased, while clustering coefficients gradual y decreased. This evidence indicates that dementia is associated with a decline of brain network efficiency. In tion, the changes in functional networks revealed the progressive deterioration of network function across brain regions from healthy elderly adults to those with mild cognitive impairment and Alzhei-mer’s disease. The alterations of node attributes in brain regions may reflect the cognitive functions in brain regions, and we speculate that early impairments in memory, hearing, and language function can eventual y lead to diffuse brain injury and other cognitive impairments.

  10. Origins of Protein Functions in Cells

    Science.gov (United States)

    Seelig, Burchard; Pohorille, Andrzej

    2011-01-01

    In modern organisms proteins perform a majority of cellular functions, such as chemical catalysis, energy transduction and transport of material across cell walls. Although great strides have been made towards understanding protein evolution, a meaningful extrapolation from contemporary proteins to their earliest ancestors is virtually impossible. In an alternative approach, the origin of water-soluble proteins was probed through the synthesis and in vitro evolution of very large libraries of random amino acid sequences. In combination with computer modeling and simulations, these experiments allow us to address a number of fundamental questions about the origins of proteins. Can functionality emerge from random sequences of proteins? How did the initial repertoire of functional proteins diversify to facilitate new functions? Did this diversification proceed primarily through drawing novel functionalities from random sequences or through evolution of already existing proto-enzymes? Did protein evolution start from a pool of proteins defined by a frozen accident and other collections of proteins could start a different evolutionary pathway? Although we do not have definitive answers to these questions yet, important clues have been uncovered. In one example (Keefe and Szostak, 2001), novel ATP binding proteins were identified that appear to be unrelated in both sequence and structure to any known ATP binding proteins. One of these proteins was subsequently redesigned computationally to bind GTP through introducing several mutations that introduce targeted structural changes to the protein, improve its binding to guanine and prevent water from accessing the active center. This study facilitates further investigations of individual evolutionary steps that lead to a change of function in primordial proteins. In a second study (Seelig and Szostak, 2007), novel enzymes were generated that can join two pieces of RNA in a reaction for which no natural enzymes are known

  11. Reduced synaptic activity in neuronal networks derived from embryonic stem cells of murine Rett syndrome model.

    Science.gov (United States)

    Barth, Lydia; Sütterlin, Rosmarie; Nenniger, Markus; Vogt, Kaspar E

    2014-01-01

    Neurodevelopmental diseases such as the Rett syndrome (RTT) have received renewed attention, since the mechanisms involved may underlie a broad range of neuropsychiatric disorders such as schizophrenia and autism. In vertebrates early stages in the functional development of neurons and neuronal networks are difficult to study. Embryonic stem cell-derived neurons provide an easily accessible tool to investigate neuronal differentiation and early network formation. We used in vitro cultures of neurons derived from murine embryonic stem cells missing the methyl-CpG-binding protein 2 (MECP2) gene (MeCP2-/y) and from wild type cells of the corresponding background. Cultures were assessed using whole-cell patch-clamp electrophysiology and immunofluorescence. We studied the functional maturation of developing neurons and the activity of the synaptic connections they formed. Neurons exhibited minor differences in the developmental patterns for their intrinsic parameters, such as resting membrane potential and excitability; with the MeCP2-/y cells showing a slightly accelerated development, with shorter action potential half-widths at early stages. There was no difference in the early phase of synapse development, but as the cultures matured, significant deficits became apparent, particularly for inhibitory synaptic activity. MeCP2-/y embryonic stem cell-derived neuronal cultures show clear developmental deficits that match phenotypes observed in slice preparations and thus provide a compelling tool to further investigate the mechanisms behind RTT pathophysiology.

  12. Chinese lexical networks: The structure, function and formation

    Science.gov (United States)

    Li, Jianyu; Zhou, Jie; Luo, Xiaoyue; Yang, Zhanxin

    2012-11-01

    In this paper Chinese phrases are modeled using complex networks theory. We analyze statistical properties of the networks and find that phrase networks display some important features: not only small world and the power-law distribution, but also hierarchical structure and disassortative mixing. These statistical traits display the global organization of Chinese phrases. The origin and formation of such traits are analyzed from a macroscopic Chinese culture and philosophy perspective. It is interesting to find that Chinese culture and philosophy may shape the formation and structure of Chinese phrases. To uncover the structural design principles of networks, network motif patterns are studied. It is shown that they serve as basic building blocks to form the whole phrase networks, especially triad 38 (feed forward loop) plays a more important role in forming most of the phrases and other motifs. The distinct structure may not only keep the networks stable and robust, but also be helpful for information processing. The results of the paper can give some insight into Chinese language learning and language acquisition. It strengthens the idea that learning the phrases helps to understand Chinese culture. On the other side, understanding Chinese culture and philosophy does help to learn Chinese phrases. The hub nodes in the networks show the close relationship with Chinese culture and philosophy. Learning or teaching the hub characters, hub-linking phrases and phrases which are meaning related based on motif feature should be very useful and important for Chinese learning and acquisition.

  13. Impaired small-world network efficiency and dynamic functional distribution in patients with cirrhosis.

    Directory of Open Access Journals (Sweden)

    Tun-Wei Hsu

    Full Text Available Hepatic encephalopathy (HE is a complex neuropsychiatric syndrome and a major complication of liver cirrhosis. Dysmetabolism of the brain, related to elevated ammonia levels, interferes with intercortical connectivity and cognitive function. For evaluation of network efficiency, a 'small-world' network model can quantify the effectiveness of information transfer within brain networks. This study aimed to use small-world topology to investigate abnormalities of neuronal connectivity among widely distributed brain regions in patients with liver cirrhosis using resting-state functional magnetic resonance imaging (rs-fMRI. Seventeen cirrhotic patients without HE, 9 with minimal HE, 9 with overt HE, and 35 healthy controls were compared. The interregional correlation matrix was obtained by averaging the rs-fMRI time series over all voxels in each of the 90 regions using the automated anatomical labeling model. Cost and correlation threshold values were then applied to construct the functional brain network. The absolute and relative network efficiencies were calculated; quantifying distinct aspects of the local and global topological network organization. Correlations between network topology parameters, ammonia levels, and the severity of HE were determined using linear regression and ANOVA. The local and global topological efficiencies of the functional connectivity network were significantly disrupted in HE patients; showing abnormal small-world properties. Alterations in regional characteristics, including nodal efficiency and nodal strength, occurred predominantly in the association, primary, and limbic/paralimbic regions. The degree of network organization disruption depended on the severity of HE. Ammonia levels were also significantly associated with the alterations in local network properties. Results indicated that alterations in the rs-fMRI network topology of the brain were associated with HE grade; and that focal or diffuse lesions

  14. Towards stable kinetics of large metabolic networks: Nonequilibrium potential function approach

    Science.gov (United States)

    Chen, Yong-Cong; Yuan, Ruo-Shi; Ao, Ping; Xu, Min-Juan; Zhu, Xiao-Mei

    2016-06-01

    While the biochemistry of metabolism in many organisms is well studied, details of the metabolic dynamics are not fully explored yet. Acquiring adequate in vivo kinetic parameters experimentally has always been an obstacle. Unless the parameters of a vast number of enzyme-catalyzed reactions happened to fall into very special ranges, a kinetic model for a large metabolic network would fail to reach a steady state. In this work we show that a stable metabolic network can be systematically established via a biologically motivated regulatory process. The regulation is constructed in terms of a potential landscape description of stochastic and nongradient systems. The constructed process draws enzymatic parameters towards stable metabolism by reducing the change in the Lyapunov function tied to the stochastic fluctuations. Biologically it can be viewed as interplay between the flux balance and the spread of workloads on the network. Our approach allows further constraints such as thermodynamics and optimal efficiency. We choose the central metabolism of Methylobacterium extorquens AM1 as a case study to demonstrate the effectiveness of the approach. Growth efficiency on carbon conversion rate versus cell viability and futile cycles is investigated in depth.

  15. Vestibular and Attractor Network Basis of the Head Direction Cell Signal in Subcortical Circuits

    Directory of Open Access Journals (Sweden)

    Benjamin J Clark

    2012-03-01

    Full Text Available Accurate navigation depends on a network of neural systems that encode the moment-to-moment changes in an animal’s directional orientation and location in space. Within this navigation system are head direction (HD cells, which fire persistently when an animal’s head is pointed in a particular direction (Sharp et al., 2001a; Taube, 2007. HD cells are widely thought to underlie an animal’s sense of spatial orientation, and research over the last 25+ years has revealed that this robust spatial signal is widely distributed across subcortical and cortical limbic areas. Much of this work has been directed at understanding the functional organization of the HD cell circuitry, and precisely how this signal is generated from sensory and motor systems. The purpose of the present review is to summarize some of the recent studies arguing that the HD cell circuit is largely processed in a hierarchical fashion, following a pathway involving the dorsal tegmental nuclei → lateral mammillary nuclei → anterior thalamus → parahippocampal and retrosplenial cortical regions. We also review recent work identifying bursting cellular activity in the HD cell circuit after lesions of the vestibular system, and relate these observations to the long held view that attractor network mechanisms underlie HD signal generation. Finally, we summarize the work to date suggesting that this network architecture may reside within the tegmento-mammillary circuit.

  16. Properties of Healthcare Teaming Networks as a Function of Network Construction Algorithms

    CERN Document Server

    Zand, Martin S; Farooq, Samir A; Fucile, Christopher; Ghoshal, Grourab; White, Robert J; Quill, Caroline M; Rosenberg, Alexander; Serrano, Hugo; Chafi, Hassan; Boudreau, Timothy

    2016-01-01

    Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other. Most healthcare service network models have been constructed from patient claims data, using billing claims to link patients with providers. The data sets can be quite large, making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks. To address this issue, we compared the properties of healthcare networks constructed using different algorithms and the 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We...

  17. Network Analysis of Strategic Marketing Actions and Quality Function Deployment

    Directory of Open Access Journals (Sweden)

    Fatemeh Daneshian

    2011-01-01

    Full Text Available Nowadays, strategic marketing management has become an accepted practice in the strategic field. An increasing number of researchers consider marketing strategies for offering key competitive advantages associated with strategic marketing management. Every decision in the strategic field should be based on three dimensions of evaluating market, evaluating competitors and evaluating company. In this research, a model has been developed for selecting and ranking marketing strategies considering the evaluation of market (customer satisfaction elements, competitors and company based on Kano model. Quality function deployment (QFD and the analytic network process (ANP approaches have been used for market prioritization. The research has been carried out in three phases. In Phase one, the Kano model of customer satisfaction has been used to determine which requirements of a product or service brings more satisfaction to the customers, followed by the evaluation of competitors and gap analyze. In Phase two, The QFD approach has been used to incorporate the voice of customer (VOC into the marketing strategies of the company and has provided a systematic planning tool for considering the information of elements (in the last phase to make appropriate decisions effectively and efficiently. In Phase three, the ANP method has been used to analyze strategic actions considering company conditions. Finally the outputs of QFD have been corrected by ANP weights. Findings imply that the three most important strategic actions which are important for the company include offering differentiated and new generation of products to the market (leapfrog strategy, optimizing visual properties of products, and widespread and attractive advertising (frontal attack.

  18. Estimation of equivalent internal-resistance of PEM fuel cell using artificial neural networks

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A practical method of estimation for the internal-resistance of polymer electrolyte membrane fuel cell (PEMFC) stack was adopted based on radial basis function (RBF) neural networks. In the training process, k-means clustering algorithm was applied to select the network centers of the input training data. Furthermore, an equivalent electrical-circuit model with this internal-resistance was developed for investigation on the stack. Finally using the neural networks model of the equivalent resistance in the PEMFC stack, the simulation results of the estimation of equivalent internal-resistance of PEMFC were presented. The results show that this electrical PEMFC model is effective and is suitable for the study of control scheme, fault detection and the engineering analysis of electrical circuits.

  19. Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis

    Directory of Open Access Journals (Sweden)

    Cristina Pina

    2015-06-01

    Full Text Available We explore cell heterogeneity during spontaneous and transcription-factor-driven commitment for network inference in hematopoiesis. Since individual genes display discrete OFF states or a distribution of ON levels, we compute and combine pairwise gene associations from binary and continuous components of gene expression in single cells. Ddit3 emerges as a regulatory node with positive linkage to erythroid regulators and negative association with myeloid determinants. Ddit3 loss impairs erythroid colony output from multipotent cells, while forcing Ddit3 in granulo-monocytic progenitors (GMPs enhances self-renewal and impedes differentiation. Network analysis of Ddit3-transduced GMPs reveals uncoupling of myeloid networks and strengthening of erythroid linkages. RNA sequencing suggests that Ddit3 acts through development or stabilization of a precursor upstream of GMPs with inherent Meg-E potential. The enrichment of Gata2 target genes in Ddit3-dependent transcriptional responses suggests that Ddit3 functions in an erythroid transcriptional network nucleated by Gata2.

  20. A novel joint sparse partial correlation method for estimating group functional networks.

    Science.gov (United States)

    Liang, Xiaoyun; Connelly, Alan; Calamante, Fernando

    2016-03-01

    Advances in graph theory have provided a powerful tool to characterize brain networks. In particular, functional networks at group-level have great appeal to gain further insight into complex brain function, and to assess changes across disease conditions. These group networks, however, often have two main limitations. First, they are popularly estimated by directly averaging individual networks that are compromised by confounding variations. Secondly, functional networks have been estimated mainly through Pearson cross-correlation, without taking into account the influence of other regions. In this study, we propose a sparse group partial correlation method for robust estimation of functional networks based on a joint graphical models approach. To circumvent the issue of choosing the optimal regularization parameters, a stability selection method is employed to extract networks. The proposed method is, therefore, denoted as JGMSS. By applying JGMSS across simulated datasets, the resulting networks show consistently higher accuracy and sensitivity than those estimated using an alternative approach (the elastic-net regularization with stability selection, ENSS). The robustness of the JGMSS is evidenced by the independence of the estimated networks to choices of the initial set of regularization parameters. The performance of JGMSS in estimating group networks is further demonstrated with in vivo fMRI data (ASL and BOLD), which show that JGMSS can more robustly estimate brain hub regions at group-level and can better control intersubject variability than it is achieved using ENSS. PMID:26859311

  1. Altered resting-state functional connectivity in cortical networks in psychopathy.

    Science.gov (United States)

    Philippi, Carissa L; Pujara, Maia S; Motzkin, Julian C; Newman, Joseph; Kiehl, Kent A; Koenigs, Michael

    2015-04-15

    Psychopathy is a personality disorder characterized by callous antisocial behavior and criminal recidivism. Here we examine whether psychopathy is associated with alterations in functional connectivity in three large-scale cortical networks. Using fMRI in 142 adult male prison inmates, we computed resting-state functional connectivity using seeds from the default mode network, frontoparietal network, and cingulo-opercular network. To determine the specificity of our findings to these cortical networks, we also calculated functional connectivity using seeds from two comparison primary sensory networks: visual and auditory networks. Regression analyses related network connectivity to overall psychopathy scores and to subscores for the "factors" and "facets" of psychopathy: Factor 1, interpersonal/affective traits; Factor 2, lifestyle/antisocial traits; Facet 1, interpersonal; Facet 2, affective; Facet 3, lifestyle; Facet 4, antisocial. Overall psychopathy severity was associated with reduced functional connectivity between lateral parietal cortex and dorsal anterior cingulate cortex. The two factor scores exhibited contrasting relationships with functional connectivity: Factor 1 scores were associated with reduced functional connectivity in the three cortical networks, whereas Factor 2 scores were associated with heightened connectivity in the same networks. This dissociation was evident particularly in the functional connectivity between anterior insula and dorsal anterior cingulate cortex. The facet scores also demonstrated distinct patterns of connectivity. We found no associations between psychopathy scores and functional connectivity within visual or auditory networks. These findings provide novel evidence on the neural correlates of psychopathy and suggest that connectivity between cortical association hubs, such as the dorsal anterior cingulate cortex, may be a neurobiological marker of the disorder.

  2. Functional Organization of the Action Observation Network in Autism: A Graph Theory Approach.

    Directory of Open Access Journals (Sweden)

    Kaat Alaerts

    Full Text Available The ability to recognize, understand and interpret other's actions and emotions has been linked to the mirror system or action-observation-network (AON. Although variations in these abilities are prevalent in the neuro-typical population, persons diagnosed with autism spectrum disorders (ASD have deficits in the social domain and exhibit alterations in this neural network.Here, we examined functional network properties of the AON using graph theory measures and region-to-region functional connectivity analyses of resting-state fMRI-data from adolescents and young adults with ASD and typical controls (TC.Overall, our graph theory analyses provided convergent evidence that the network integrity of the AON is altered in ASD, and that reductions in network efficiency relate to reductions in overall network density (i.e., decreased overall connection strength. Compared to TC, individuals with ASD showed significant reductions in network efficiency and increased shortest path lengths and centrality. Importantly, when adjusting for overall differences in network density between ASD and TC groups, participants with ASD continued to display reductions in network integrity, suggesting that also network-level organizational properties of the AON are altered in ASD.While differences in empirical connectivity contributed to reductions in network integrity, graph theoretical analyses provided indications that also changes in the high-level network organization reduced integrity of the AON.

  3. Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?

    Science.gov (United States)

    Alavash, Mohsen; Doebler, Philipp; Holling, Heinz; Thiel, Christiane M; Gießing, Carsten

    2015-03-01

    Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing

  4. Invertebrate diversity classification using self-organizing map neural network: with some special topological functions

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2014-06-01

    Full Text Available In present study we used self-organizing map (SOM neural network to conduct the non-supervisory clustering of invertebrate orders in rice field. Four topological functions, i.e., cossintopf, sincostopf, acossintopf, and expsintopf, established on the template in toolbox of Matlab, were used in SOM neural network learning. Results showed that clusters were different when using different topological functions because different topological functions will generate different spatial structure of neurons in neural network. We may chose these functions and results based on comparison with the practical situation.

  5. Modeling dynamic functional information flows on large-scale brain networks.

    Science.gov (United States)

    Lv, Peili; Guo, Lei; Hu, Xintao; Li, Xiang; Jin, Changfeng; Han, Junwei; Li, Lingjiang; Liu, Tianming

    2013-01-01

    Growing evidence from the functional neuroimaging field suggests that human brain functions are realized via dynamic functional interactions on large-scale structural networks. Even in resting state, functional brain networks exhibit remarkable temporal dynamics. However, it has been rarely explored to computationally model such dynamic functional information flows on large-scale brain networks. In this paper, we present a novel computational framework to explore this problem using multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. Basically, recent literature reports including our own studies have demonstrated that the resting state brain networks dynamically undergo a set of distinct brain states. Within each quasi-stable state, functional information flows from one set of structural brain nodes to other sets of nodes, which is analogous to the message package routing on the Internet from the source node to the destination. Therefore, based on the large-scale structural brain networks constructed from DTI data, we employ a dynamic programming strategy to infer functional information transition routines on structural networks, based on which hub routers that most frequently participate in these routines are identified. It is interesting that a majority of those hub routers are located within the default mode network (DMN), revealing a possible mechanism of the critical functional hub roles played by the DMN in resting state. Also, application of this framework on a post trauma stress disorder (PTSD) dataset demonstrated interesting difference in hub router distributions between PTSD patients and healthy controls. PMID:24579202

  6. Novel Online Dimensionality Reduction Method with Improved Topology Representing and Radial Basis Function Networks.

    Science.gov (United States)

    Ni, Shengqiao; Lv, Jiancheng; Cheng, Zhehao; Li, Mao

    2015-01-01

    This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.

  7. Novel Online Dimensionality Reduction Method with Improved Topology Representing and Radial Basis Function Networks.

    Directory of Open Access Journals (Sweden)

    Shengqiao Ni

    Full Text Available This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.

  8. Organisms modeling: The question of radial basis function networks

    Directory of Open Access Journals (Sweden)

    Muzy Alexandre

    2014-01-01

    Full Text Available There exists usually a gap between bio-inspired computational techniques and what biologists can do with these techniques in their current researches. Although biology is the root of system-theory and artifical neural networks, computer scientists are tempted to build their own systems independently of biological issues. This publication is a first-step re-evalution of an usual machine learning technique (radial basis funtion(RBF networks in the context of systems and biological reactive organisms.

  9. Cost functions in optical burst-switched networks

    OpenAIRE

    Klusek, Bartlomiej

    2006-01-01

    Optical Burst Switching (OBS) is a new paradigm for an all-optical Internet. It combines the best features of Optical Circuit Switching (OCS) and Optical Packet Switching (OPS) while avoidmg the mam problems associated with those networks .Namely, it offers good granularity, but its hardware requirements are lower than those of OPS. In a backbone network, low loss ratio is of particular importance. Also, to meet varying user requirements, it should support multiple classes of service. In ...

  10. Semiautomatic transfer function initialization for abdominal visualization using self-generating hierarchical radial basis function networks.

    Science.gov (United States)

    Selver, M Alper; Güzeliş, Cüneyt

    2009-01-01

    As being a tool that assigns optical parameters used in interactive visualization, Transfer Functions (TF) have important effects on the quality of volume rendered medical images. Unfortunately, finding accurate TFs is a tedious and time consuming task because of the trade off between using extensive search spaces and fulfilling the physician's expectations with interactive data exploration tools and interfaces. By addressing this problem, we introduce a semi-automatic method for initial generation of TFs. The proposed method uses a Self Generating Hierarchical Radial Basis Function Network to determine the lobes of a Volume Histogram Stack (VHS) which is introduced as a new domain by aligning the histograms of slices of a image series. The new self generating hierarchical design strategy allows the recognition of suppressed lobes corresponding to suppressed tissues and representation of the overlapping regions which are parts of the lobes but can not be represented by the Gaussian bases in VHS. Moreover, approximation with a minimum set of basis functions provides the possibility of selecting and adjusting suitable units to optimize the TF. Applications on different CT and MR data sets show enhanced rendering quality and reduced optimization time in abdominal studies.

  11. The Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks

    CERN Document Server

    Yao, Kun

    2015-01-01

    We demonstrate a convolutional neural network trained to reproduce the Kohn-Sham kinetic energy of hydrocarbons from electron density. The output of the network is used as a non-local correction to the conventional local and semi-local kinetic functionals. We show that this approximation qualitatively reproduces Kohn-Sham potential energy surfaces when used with conventional exchange correlation functionals. Numerical noise inherited from the non-linearity of the neural network is identified as the major challenge for the model. Finally we examine the features in the density learned by the neural network to anticipate the prospects of generalizing these models.

  12. Extrasynaptic neurotransmission in the modulation of brain function. Focus on the striatal neuronal-glial networks

    Directory of Open Access Journals (Sweden)

    Kjell eFuxe

    2012-06-01

    Full Text Available Extrasynaptic neurotransmission is an important short distance form of volume transmission (VT and describes the extracellular diffusion of transmitters and modulators after synaptic spillover or extrasynaptic release in the local circuit regions binding to and activating mainly extrasynaptic neuronal and glial receptors in the neuroglial networks of the brain. Receptor-receptor interactions in G protein-coupled receptor (GPCR heteromers play a major role, on dendritic spines and nerve terminals including glutamate synapses, in the integrative processes of the extrasynaptic signaling. Heteromeric complexes between GPCR and ion-channel receptors play a special role in the integration of the synaptic and extrasynaptic signals. Changes in extracellular concentrations of the classical synaptic neurotransmitters glutamate and GABA found with microdialysis is likely an expression of the activity of the neuron-astrocyte unit of the brain and can be used as an index of VT-mediated actions of these two neurotransmitters in the brain. Thus, the activity of neurons may be functionally linked to the activity of astrocytes, which may release glutamate and GABA to the extracellular space where extrasynaptic glutamate and GABA receptors do exist. Wiring transmission (WT and VT are fundamental properties of all neurons of the CNS but the balance between WT and VT varies from one nerve cell population to the other. The focus is on the striatal cellular networks, and the WT and VT and their integration via receptor heteromers are described in the GABA projection neurons, the glutamate, dopamine, 5-hydroxytryptamine (5-HT and histamine striatal afferents, the cholinergic interneurons and different types of GABA interneurons. In addition, the role in these networks of VT signaling of the energy-dependent modulator adenosine and of endocannabinoids mainly formed in the striatal projection neurons will be underlined to understand the communication in the striatal

  13. Evolutionary trends and functional anatomy of the human expanded autophagy network.

    Science.gov (United States)

    Till, Andreas; Saito, Rintaro; Merkurjev, Daria; Liu, Jing-Jing; Syed, Gulam Hussain; Kolnik, Martin; Siddiqui, Aleem; Glas, Martin; Scheffler, Björn; Ideker, Trey; Subramani, Suresh

    2015-01-01

    All eukaryotic cells utilize autophagy for protein and organelle turnover, thus assuring subcellular quality control, homeostasis, and survival. In order to address recent advances in identification of human autophagy associated genes, and to describe autophagy on a system-wide level, we established an autophagy-centered gene interaction network by merging various primary data sets and by retrieving respective interaction data. The resulting network ('AXAN') was analyzed with respect to subnetworks, e.g. the prime gene subnetwork (including the core machinery, signaling pathways and autophagy receptors) and the transcription subnetwork. To describe aspects of evolution within this network, we assessed the presence of protein orthologs across 99 eukaryotic model organisms. We visualized evolutionary trends for prime gene categories and evolutionary tracks for selected AXAN genes. This analysis confirms the eukaryotic origin of autophagy core genes while it points to a diverse evolutionary history of autophagy receptors. Next, we used module identification to describe the functional anatomy of the network at the level of pathway modules. In addition to obvious pathways (e.g., lysosomal degradation, insulin signaling) our data unveil the existence of context-related modules such as Rho GTPase signaling. Last, we used a tripartite, image-based RNAi - screen to test candidate genes predicted to play a role in regulation of autophagy. We verified the Rho GTPase, CDC42, as a novel regulator of autophagy-related signaling. This study emphasizes the applicability of system-wide approaches to gain novel insights into a complex biological process and to describe the human autophagy pathway at a hitherto unprecedented level of detail.

  14. Neural network model for the efficient calculation of Green's functions in layered media

    CERN Document Server

    Soliman, E A; El-Gamal, M A; 10.1002/mmce.10066

    2003-01-01

    In this paper, neural networks are employed for fast and efficient calculation of Green's functions in a layered medium. Radial basis function networks (RBFNs) are effectively trained to estimate the coefficients and the exponents that represent a Green's function in the discrete complex image method (DCIM). Results show very good agreement with the DCIM, and the trained RBFNs are very fast compared with the corresponding DCIM. (23 refs).

  15. Modeling the Flux-Charge Relation of Memristor with Neural Network of Smooth Hinge Functions

    OpenAIRE

    X. Mu; Yu, J.; Wang, S.

    2014-01-01

    The memristor was proposed to characterize the flux-charge relation. We propose the generalized flux-charge relation model of memristor with neural network of smooth hinge functions. There is effective identification algorithm for the neural network of smooth hinge functions. The representation capability of this model is theoretically guaranteed. Any functional flux-charge relation of a memristor can be approximated by the model. We also give application examples to show that the given model...

  16. Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks

    OpenAIRE

    Ru Shen; Xiaosheng Wang; Chittibabu Guda

    2015-01-01

    Background. The molecular profiles exhibited in different cancer types are very different; hence, discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them. Protein-protein interaction networks carry vital information about molecular interactions in cellular systems, and identification of functional modules (subgraphs) in these networks is one of the most important applications of biological networ...

  17. Invertebrate diversity classification using self-organizing map neural network: with some special topological functions

    OpenAIRE

    WenJun Zhang; QuHuan Li

    2014-01-01

    In present study we used self-organizing map (SOM) neural network to conduct the non-supervisory clustering of invertebrate orders in rice field. Four topological functions, i.e., cossintopf, sincostopf, acossintopf, and expsintopf, established on the template in toolbox of Matlab, were used in SOM neural network learning. Results showed that clusters were different when using different topological functions because different topological functions will generate different spatial structure of ...

  18. Metabolic regulation of regulatory T cell development and function

    Directory of Open Access Journals (Sweden)

    David John Coe

    2014-11-01

    Full Text Available It is now well established that the effector T cell (Teff response is regulated by a series of metabolic switches. Quiescent T cells predominantly require ATP-generating processes, whereas proliferating Teff require high metabolic flux through growth-promoting pathways, such as glycolysis. Pathways that control metabolism and immune cell function are intimately linked, and changes in cell metabolism at both the cell and system levels have been shown to enhance or suppress specific T cell effector functions. Furthermore, functionally distinct T cell subsets have been shown to require distinct energetic and biosynthetic pathways to support their specific functional needs. In particular, naturally occurring regulatory T cells (Treg are characterized by a unique metabolic signature distinct to that of conventional Teff cells. We here briefly review the signaling pathways that control Treg metabolism and how this metabolic phenotype integrates their differentiation and function. Ultimately, these metabolic features may provide new opportunities for the therapeutic modulation of unwanted immune responses.

  19. Automatic selection of resting-state networks with functional magnetic resonance imaging

    Directory of Open Access Journals (Sweden)

    Silvia Francesca eStorti

    2013-05-01

    Full Text Available Functional magnetic resonance imaging (fMRI during a resting-state condition can reveal the co-activation of specific brain regions in distributed networks, called resting-state networks, which are selected by independent component analysis (ICA of the fMRI data. One of the major difficulties with component analysis is the automatic selection of the ICA features related to brain activity. In this study we describe a method designed to automatically select networks of potential functional relevance, specifically, those regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. To do this, image analysis was based on probabilistic ICA as implemented in FSL software. After decomposition, the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, Pearson's median coefficient of skewness of the spatial maps generated by FSL, followed by clustering, segmentation, and spectral analysis. To evaluate the performance of the approach, we investigated the resting-state networks in 25 subjects. For each subject, three resting-state scans were obtained with a Siemens Allegra 3 T scanner (NYU data set. Comparison of the visually and the automatically identified neuronal networks showed that the algorithm had high accuracy (first scan: 95%, second scan: 95%, third scan: 93% and precision (90%, 90%, 84%. The reproducibility of the networks for visual and automatic selection was very close: it was highly consistent in each subject for the default-mode network (≥ 92% and the occipital network, which includes the medial visual cortical areas (≥ 94%, and consistent for the attention network (≥ 80%, the right and/or left lateralized frontoparietal attention networks, and the temporal-motor network (≥ 80%. The automatic selection method may be used to detect neural networks and reduce subjectivity in ICA

  20. Application of functional-link neural network in evaluation of sublayer suspension based on FWD test

    Institute of Scientific and Technical Information of China (English)

    陈瑜; 张起森

    2004-01-01

    Several methods for evaluating the sublayer suspension beneath old pavement with falling weight deflectormeter(FWD), were summarized and the respective advantages and disadvantages were analyzed. Based on these methods, the evaluation principles were improved and a new type of the neural network, functional-link neural network was proposed to evaluate the sublayer suspension with FWD test results. The concept of function link, learning method of functional-link neural network and the establishment process of neural network model were studied in detail. Based on the old pavement over-repairing engineering of Kaiping section, Guangdong Province in G325 National Highway, the application of functional-link neural network in evaluation of sublayer suspension beneath old pavement based on FWD test data on the spot was investigated. When learning rate is 0.1 and training cycles are 405, the functional-link network error is less than 0.0001, while the optimum chosen 4-8-1 BP needs over 10000 training cycles to reach the same accuracy with less precise evaluation results. Therefore, in contrast to common BP neural network,the functional-link neural network adopts single layer structure to learn and calculate, which simplifies the network, accelerates the convergence speed and improves the accuracy. Moreover the trained functional-link neural network can be adopted to directly evaluate the sublayer suspension based on FWD test data on the site. Engineering practice indicates that the functional-link neural model gains very excellent results and effectively guides the pavement over-repairing construction.

  1. Functional Brain Networks Associated with Cognitive Control, Cocaine Dependence and Treatment Outcome

    Science.gov (United States)

    Worhunsky, Patrick D.; Stevens, Michael C.; Carroll, Kathleen M.; Rounsaville, Bruce J.; Calhoun, Vince D.; Pearlson, Godfrey D.; Potenza, Marc N.

    2013-01-01

    Individuals with cocaine dependence often evidence poor cognitive control. The purpose of this exploratory study was to investigate networks of functional connectivity underlying cognitive control in cocaine dependence and examine the relationship of the networks to the disorder and its treatment. Independent component analysis (ICA) was applied to fMRI data to investigate if regional activations underlying cognitive control processes operate in functional networks, and whether these networks relate to performance and treatment outcome measures in cocaine dependence. Twenty patients completed a Stroop task during fMRI prior to entering outpatient treatment and were compared to 20 control participants. ICA identified five distinct functional networks related to cognitive control interference events. Cocaine-dependent patients displayed differences in performance-related recruitment of three networks. Reduced involvement of a “top-down” fronto-cingular network contributing to conflict monitoring correlated with better treatment retention. Greater engagement of two “bottom-up” subcortical and ventral prefrontal networks related to cue-elicited motivational processing correlated with abstinence during treatment. The identification of subcortical networks linked to cocaine abstinence and cortical networks to treatment retention suggests that specific circuits may represent important, complementary targets in treatment development for cocaine dependence. PMID:22775772

  2. Interrogating a cell signalling network sensitively monitors cell fate transition during early differentiation of mouse embryonic stem cells

    Institute of Scientific and Technical Information of China (English)

    LIU; Yi-Hsin; HO; Chih-ming

    2010-01-01

    The different cell types in an animal are often considered to be specified by combinations of transcription factors,and defined by marker gene expression.This paradigm is challenged,however,in stem cell research and application.Using a mouse embryonic stem cell(mESC) culture system,here we show that the expression level of many key stem cell marker genes/transcription factors such as Oct4,Sox2 and Nanog failed to monitor cell status transition during mESC differentiation.On the other hand,the response patterns of cell signalling network to external stimuli,as monitored by the dynamics of protein phosphorylation,changed dramatically.Our results also suggest that an irreversible alternation in the cell signalling network precedes the adjustment of transcription factor levels.This is consistent with the notion that signal transduction events regulate cell fate specification.We propose that interrogating a cell signalling network can assess the cell property more precisely,and provide a sensitive measurement for the early events in cell fate transition.We wish to bring attention to the potential problem of cell identification using a few marker genes,and suggest a novel methodology to address this issue.

  3. Characterizing short-term stability for Boolean networks over any distribution of transfer functions

    Science.gov (United States)

    Seshadhri, C.; Smith, Andrew M.; Vorobeychik, Yevgeniy; Mayo, Jackson R.; Armstrong, Robert C.

    2016-07-01

    We present a characterization of short-term stability of Kauffman's N K (random) Boolean networks under arbitrary distributions of transfer functions. Given such a Boolean network where each transfer function is drawn from the same distribution, we present a formula that determines whether short-term chaos (damage spreading) will happen. Our main technical tool which enables the formal proof of this formula is the Fourier analysis of Boolean functions, which describes such functions as multilinear polynomials over the inputs. Numerical simulations on mixtures of threshold functions and nested canalyzing functions demonstrate the formula's correctness.

  4. Yamanaka factors critically regulate the developmental signaling network in mouse embryonic stem cells

    Institute of Scientific and Technical Information of China (English)

    Xiaosong Liu; Jinyan Huang; Taotao Chen; Ying Wang; Shunmei Xin; Jian Li; Gang Pei; Jiuhong Kang

    2008-01-01

    Yamanaka factors (Oct3/4,Sox2,KIf4,c-Myc) are highly expressed in embryonic stem (ES) cells,and their overexpression can induce pluripotency in both mouse and human somatic cells,indicating that these factors regulate the developmental signaling network necessary for ES cell pluripotency.However,systemic analysis of the signaling pathways regulated by Yamanaka factors has not yet been fully described.In this study,we identified the target promoters of endogenous Yamanaka factors on a whole genome scale using ChIP (chromatin immunoprecipitation)-on-chip in E14.1 mouse ES cells,and we found that these four factors co-occupied 58 promoters.Interestingly,when Oct4 and Sox2 were analyzed as core factors,Kif4 functioned to enhance the core factors for development regulation,whereas c-Myc seemed to play a distinct role in regulating metabolism.The pathway analysis revealed that Yamanaka factors collectively regulate a developmental signaling network composed of 16 developmental signaling pathways,nine of which represent earlier unknown pathways in ES cells,including apoptosis and cellcycle pathways.We further analyzed data from a recent study examining Yamanaka factors in mouse ES cells.Interestingly,this analysis also revealed 16 developmental signaling pathways,of which 14 pathways overlap with the ones revealed by this study,despite that the target genes and the signaling pathways regulated by each individual Yamanaka factor differ significantly between these two datasets.We suggest that Yamanaka factors critically regulate a developmental signaling network composed of approximately a dozen crucial developmental signaling pathways to maintain the pluripotency of ES cells and probably also to induce pluripotent stem cells.

  5. Networks of neuroblastoma cells on porous silicon substrates reveal a small world topology

    KAUST Repository

    Marinaro, Giovanni

    2015-01-01

    The human brain is a tightly interweaving network of neural cells where the complexity of the network is given by the large number of its constituents and its architecture. The topological structure of neurons in the brain translates into its increased computational capabilities, low energy consumption, and nondeterministic functions, which differentiate human behavior from artificial computational schemes. In this manuscript, we fabricated porous silicon chips with a small pore size ranging from 8 to 75 nm and large fractal dimensions up to Df ∼ 2.8. In culturing neuroblastoma N2A cells on the described substrates, we found that those cells adhere more firmly to and proliferate on the porous surfaces compared to the conventional nominally flat silicon substrates, which were used as controls. More importantly, we observed that N2A cells on the porous substrates create highly clustered, small world topology patterns. We conjecture that neurons with a similar architecture may elaborate information more efficiently than in random or regular grids. Moreover, we hypothesize that systems of neurons on nano-scale geometry evolve in time to form networks in which the propagation of information is maximized. This journal is

  6. Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

    Science.gov (United States)

    Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen

    2015-11-01

    We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.

  7. Cell Phone’s Functions on Language Learning

    Institute of Scientific and Technical Information of China (English)

    伍文忠; 王娜

    2012-01-01

      With the time passing by and development of this society, almost everyone has a cell phone, we may see that cell phone as a new fifth medium after computer, having its own characters and advantages, many learners research the communica⁃tive function of cell phone and some from the technological level. This thesis aims at trying to reveal cultural functions of culture, aiming at improve cell phone’ s learning and culture functions in a more proper way.

  8. Dynamic competition between large-scale functional networks differentiates fear conditioning and extinction in humans.

    Science.gov (United States)

    Marstaller, Lars; Burianová, Hana; Reutens, David C

    2016-07-01

    The high evolutionary value of learning when to respond to threats or when to inhibit previously learned associations after changing threat contingencies is reflected in dedicated networks in the animal and human brain. Recent evidence further suggests that adaptive learning may be dependent on the dynamic interaction of meta-stable functional brain networks. However, it is still unclear which functional brain networks compete with each other to facilitate associative learning and how changes in threat contingencies affect this competition. The aim of this study was to assess the dynamic competition between large-scale networks related to associative learning in the human brain by combining a repeated differential conditioning and extinction paradigm with independent component analysis of functional magnetic resonance imaging data. The results (i) identify three task-related networks involved in initial and sustained conditioning as well as extinction, and demonstrate that (ii) the two main networks that underlie sustained conditioning and extinction are anti-correlated with each other and (iii) the dynamic competition between these two networks is modulated in response to changes in associative contingencies. These findings provide novel evidence for the view that dynamic competition between large-scale functional networks differentiates fear conditioning from extinction learning in the healthy brain and suggest that dysfunctional network dynamics might contribute to learning-related neuropsychiatric disorders. PMID:27079532

  9. Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging.

    Science.gov (United States)

    Voss, Michelle W; Weng, Timothy B; Burzynska, Agnieszka Z; Wong, Chelsea N; Cooke, Gillian E; Clark, Rachel; Fanning, Jason; Awick, Elizabeth; Gothe, Neha P; Olson, Erin A; McAuley, Edward; Kramer, Arthur F

    2016-05-01

    Greater physical activity and cardiorespiratory fitness are associated with reduced age-related cognitive decline and lower risk for dementia. However, significant gaps remain in the understanding of how physical activity and fitness protect the brain from adverse effects of brain aging. The primary goal of the current study was to empirically evaluate the independent relationships between physical activity and fitness with functional brain health among healthy older adults, as measured by the functional connectivity of cognitively and clinically relevant resting state networks. To build context for fitness and physical activity associations in older adults, we first demonstrate that young adults have greater within-network functional connectivity across a broad range of cortical association networks. Based on these results and previous research, we predicted that individual differences in fitness and physical activity would be most strongly associated with functional integrity of the networks most sensitive to aging. Consistent with this prediction, and extending on previous research, we showed that cardiorespiratory fitness has a positive relationship with functional connectivity of several cortical networks associated with age-related decline, and effects were strongest in the default mode network (DMN). Furthermore, our results suggest that the positive association of fitness with brain function can occur independent of habitual physical activity. Overall, our findings provide further support that cardiorespiratory fitness is an important factor in moderating the adverse effects of aging on cognitively and clinically relevant functional brain networks.

  10. Expression and function of nicotinic acetylcholine receptors in stem cells

    Directory of Open Access Journals (Sweden)

    Carlos M. Carballosa

    2016-07-01

    Full Text Available Nicotinic acetylcholine receptors are prototypical ligand gated ion channels typically found in muscular and neuronal tissues. Functional nicotinic acetylcholine receptors, however, have also recently been identified on other cell types, including stem cells. Activation of these receptors by the binding of agonists like choline, acetylcholine, or nicotine has been implicated in many cellular changes. In regards to stem cell function, nicotinic acetylcholine receptor activation leads to changes in stem cell proliferation, migration and differentiation potential. In this review we summarize the expression and function of known nicotinic acetylcholine receptors in different classes of stem cells including: pluripotent stem cells, mesenchymal stem cells, periodontal ligament derived stem cells, and neural progenitor cells and discuss the potential downstream effects of receptor activation on stem cell function.

  11. Beijing Lightning Network (BLNET): Configuration, Function and Preliminary Results

    Science.gov (United States)

    Qie, X.; Wang, D.; Wang, Y.; Liu, M.; Tian, Y.; Lu, G.

    2015-12-01

    A regional multi-frequency-band lightning detection network in Beijing (BLNET) has been developed for both research and operational purposes. The network is employed in the experiment of Dynamic-microphysical-electrical Processes in Severe Thunderstorms and Lightning Hazards (Storm973), supported by Ministry of Science and Technology as National Key Basic Research Program of China or 973 Program. The network consisted of 16 stations in 2015 covering most part of The "Jing-Jin-Ji" (Beijing-Tianjin-Hebei) metropolis zone, one of the most developed areas in China. Four different sensors, including slow antenna, fast antenna, magnetic antenna, and VHF antenna, are integrated in each station to detect lightning radiation signals in different frequency band. The Chan algorithm and Levenberg-Marquardt method are adopted jointly in the lightning location algorithm. In addition to locate the lightning radiation pulses in two-dimension or three-dimension in different band, the charge source neutralized by the lightning discharge can be retrieved either. The theoretical horizontal error over the network is less than 200 m and the vertical error is less than 500 m over the network. The comparison of total lightning location results with corresponding radar echoes for thunderstorm cases indicates the good performance of BLNET in the severe convection surveillance. The actual two-dimensional location error in VLF/LF band, compared with a ground truth that acquired with a GPS-synchronized high-speed video camera, is about 250 m.

  12. Metal-Insulator-Semiconductor Nanowire Network Solar Cells.

    Science.gov (United States)

    Oener, Sebastian Z; van de Groep, Jorik; Macco, Bart; Bronsveld, Paula C P; Kessels, W M M; Polman, Albert; Garnett, Erik C

    2016-06-01

    Metal-insulator-semiconductor (MIS) junctions provide the charge separating properties of Schottky junctions while circumventing the direct and detrimental contact of the metal with the semiconductor. A passivating and tunnel dielectric is used as a separation layer to reduce carrier recombination and remove Fermi level pinning. When applied to solar cells, these junctions result in two main advantages over traditional p-n-junction solar cells: a highly simplified fabrication process and excellent passivation properties and hence high open-circuit voltages. However, one major drawback of metal-insulator-semiconductor solar cells is that a continuous metal layer is needed to form a junction at the surface of the silicon, which decreases the optical transmittance and hence short-circuit current density. The decrease of transmittance with increasing metal coverage, however, can be overcome by nanoscale structures. Nanowire networks exhibit precisely the properties that are required for MIS solar cells: closely spaced and conductive metal wires to induce an inversion layer for homogeneous charge carrier extraction and simultaneously a high optical transparency. We experimentally demonstrate the nanowire MIS concept by using it to make silicon solar cells with a measured energy conversion efficiency of 7% (∼11% after correction), an effective open-circuit voltage (Voc) of 560 mV and estimated short-circuit current density (Jsc) of 33 mA/cm(2). Furthermore, we show that the metal nanowire network can serve additionally as an etch mask to pattern inverted nanopyramids, decreasing the reflectivity substantially from 36% to ∼4%. Our extensive analysis points out a path toward nanowire based MIS solar cells that exhibit both high Voc and Jsc values.

  13. Metal-Insulator-Semiconductor Nanowire Network Solar Cells.

    Science.gov (United States)

    Oener, Sebastian Z; van de Groep, Jorik; Macco, Bart; Bronsveld, Paula C P; Kessels, W M M; Polman, Albert; Garnett, Erik C

    2016-06-01

    Metal-insulator-semiconductor (MIS) junctions provide the charge separating properties of Schottky junctions while circumventing the direct and detrimental contact of the metal with the semiconductor. A passivating and tunnel dielectric is used as a separation layer to reduce carrier recombination and remove Fermi level pinning. When applied to solar cells, these junctions result in two main advantages over traditional p-n-junction solar cells: a highly simplified fabrication process and excellent passivation properties and hence high open-circuit voltages. However, one major drawback of metal-insulator-semiconductor solar cells is that a continuous metal layer is needed to form a junction at the surface of the silicon, which decreases the optical transmittance and hence short-circuit current density. The decrease of transmittance with increasing metal coverage, however, can be overcome by nanoscale structures. Nanowire networks exhibit precisely the properties that are required for MIS solar cells: closely spaced and conductive metal wires to induce an inversion layer for homogeneous charge carrier extraction and simultaneously a high optical transparency. We experimentally demonstrate the nanowire MIS concept by using it to make silicon solar cells with a measured energy conversion efficiency of 7% (∼11% after correction), an effective open-circuit voltage (Voc) of 560 mV and estimated short-circuit current density (Jsc) of 33 mA/cm(2). Furthermore, we show that the metal nanowire network can serve additionally as an etch mask to pattern inverted nanopyramids, decreasing the reflectivity substantially from 36% to ∼4%. Our extensive analysis points out a path toward nanowire based MIS solar cells that exhibit both high Voc and Jsc values. PMID:27172429

  14. Altered topological properties of functional network connectivity in schizophrenia during resting state: a small-world brain network study.

    Directory of Open Access Journals (Sweden)

    Qingbao Yu

    Full Text Available Aberrant topological properties of small-world human brain networks in patients with schizophrenia (SZ have been documented in previous neuroimaging studies. Aberrant functional network connectivity (FNC, temporal relationships among independent component time courses has also been found in SZ by a previous resting state functional magnetic resonance imaging (fMRI study. However, no study has yet determined if topological properties of FNC are also altered in SZ. In this study, small-world network metrics of FNC during the resting state were examined in both healthy controls (HCs and SZ subjects. FMRI data were obtained from 19 HCs and 19 SZ. Brain images were decomposed into independent components (ICs by group independent component analysis (ICA. FNC maps were constructed via a partial correlation analysis of ICA time courses. A set of undirected graphs were built by thresholding the FNC maps and the small-world network metrics of these maps were evaluated. Our results demonstrated significantly altered topological properties of FNC in SZ relative to controls. In addition, topological measures of many ICs involving frontal, parietal, occipital and cerebellar areas were altered in SZ relative to controls. Specifically, topological measures of whole network and specific components in SZ were correlated with scores on the negative symptom scale of the Positive and Negative Symptom Scale (PANSS. These findings suggest that aberrant architecture of small-world brain topology in SZ consists of ICA temporally coherent brain networks.

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

    Science.gov (United States)

    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. PMID:26097169

  16. A Comprehensive Nuclear Receptor Network for Breast Cancer Cells

    Directory of Open Access Journals (Sweden)

    Ralf Kittler

    2013-02-01

    Full Text Available In breast cancer, nuclear receptors (NRs play a prominent role in governing gene expression, have prognostic utility, and are therapeutic targets. We built a regulatory map for 24 NRs, six chromatin state markers, and 14 breast-cancer-associated transcription factors (TFs that are expressed in the breast cancer cell line MCF-7. The resulting network reveals a highly interconnected regulatory matrix where extensive crosstalk occurs among NRs and other breast -cancer-associated TFs. We show that large numbers of factors are coordinately bound to highly occupied target regions throughout the genome, and these regions are associated with active chromatin state and hormone-responsive gene expression. This network also provides a framework for stratifying and predicting patient outcomes, and we use it to show that the peroxisome proliferator-activated receptor delta binds to a set of genes also regulated by the retinoic acid receptors and whose expression is associated with poor prognosis in breast cancer.

  17. BAX supports the mitochondrial network, promoting bioenergetics in nonapoptotic cells

    Science.gov (United States)

    Boohaker, Rebecca J.; Zhang, Ge; Carlson, Adina Loosley; Nemec, Kathleen N.

    2011-01-01

    The dual functionality of the tumor suppressor BAX is implied by the nonapoptotic functions of other members of the BCL-2 family. To explore this, mitochondrial metabolism was examined in BAX-deficient HCT-116 cells as well as primary hepatocytes from BAX-deficient mice. Although mitochondrial density and mitochondrial DNA content were the same in BAX-containing and BAX-deficient cells, MitoTracker staining patterns differed, suggesting the existence of BAX-dependent functional differences in mitochondrial physiology. Oxygen consumption and cellular ATP levels were reduced in BAX-deficient cells, while glycolysis was increased. These results suggested that cells lacking BAX have a deficiency in the ability to generate ATP through cellular respiration. This conclusion was supported by detection of reduced citrate synthase activity in BAX-deficient cells. In nonapoptotic cells, a portion of BAX associated with mitochondria and a sequestered, protease-resistant form was detected. Inhibition of BAX with small interfering RNAs reduced intracellular ATP content in BAX-containing cells. Expression of either full-length or COOH-terminal-truncated BAX in BAX-deficient cells rescued ATP synthesis and oxygen consumption and reduced glycolytic activity, suggesting that this metabolic function of BAX was not dependent upon its COOH-terminal helix. Expression of BCL-2 in BAX-containing cells resulted in a subsequent loss of ATP measured, implying that, even under nonapoptotic conditions, an antagonistic interaction exists between the two proteins. These findings infer that a basal amount of BAX is necessary to maintain energy production via aerobic respiration. PMID:21289292

  18. A Theory of Decomposition of Complex Chemical Networks using the Hill Functions

    CERN Document Server

    Chikayama, Eisuke

    2014-01-01

    The design and synthesis of complex and large mimicked biochemical networks de novo is an unsolved problem in synthetic biology. To address this limitation without resorting to ad hoc computations and experiments, a predictive mathematical theory is required to reduce these complex chemical networks into natural physico-chemical expressions. Here we provide a mathematical theory that offers a physico-chemical expression for a large chemical network that is almost arbitrarily both nonlinear and complex. Unexpectedly, the theory demonstrates that such networks can be decomposed into reactions based solely on the Hill equation, a simple chemical logic gate. This theory, analogous to implemented electrical logic gates or functional algorithms in a computer, is proposed for implementing regulated sequences of functional chemical reactions, such as mimicked genes, transcriptional regulation, signal transduction, protein interaction, and metabolic networks, into an artificial designed chemical network.

  19. Functional MRI neurofeedback training on connectivity between two regions induces long-lasting changes in intrinsic functional network

    Directory of Open Access Journals (Sweden)

    Fukuda eMegumi

    2015-03-01

    Full Text Available Motor or perceptual learning is known to influence functional connectivity between brain regions and induce short-term changes in the intrinsic functional networks revealed as correlations in slow blood-oxygen-level dependent (BOLD signal fluctuations. However, no cause-and-effect relationship has been elucidated between a specific change in connectivity and a long-term change in global networks. Here, we examine the hypothesis that functional connectivity (i.e. temporal correlation between two regions is increased and preserved for a long time when two regions are simultaneously activated or deactivated. Using the connectivity-neurofeedback training paradigm, subjects successfully learned to increase the correlation of activity between the lateral parietal and primary motor areas, regions that belong to different intrinsic networks and negatively correlated before training under the resting conditions. Furthermore, whole-brain hypothesis-free analysis as well as functional network analyses demonstrated that the correlation in the resting state between these areas as well as the correlation between the intrinsic networks that include the areas increased for at least two months. These findings indicate that the connectivity-neurofeedback training can cause long-term changes in intrinsic connectivity and that intrinsic networks can be shaped by experience-driven modulation of regional correlation.

  20. Calibration of the head direction network: a role for symmetric angular head velocity cells.

    Science.gov (United States)

    Stratton, Peter; Wyeth, Gordon; Wiles, Janet

    2010-06-01

    Continuous attractor networks require calibration. Computational models of the head direction (HD) system of the rat usually assume that the connections that maintain HD neuron activity are pre-wired and static. Ongoing activity in these models relies on precise continuous attractor dynamics. It is currently unknown how such connections could be so precisely wired, and how accurate calibration is maintained in the face of ongoing noise and perturbation. Our adaptive attractor model of the HD system that uses symmetric angular head velocity (AHV) cells as a training signal shows that the HD system can learn to support stable firing patterns from poorly-performing, unstable starting conditions. The proposed calibration mechanism suggests a requirement for symmetric AHV cells, the existence of which has previously been unexplained, and predicts that symmetric and asymmetric AHV cells should be distinctly different (in morphology, synaptic targets and/or methods of action on postsynaptic HD cells) due to their distinctly different functions. PMID:20354898

  1. Application of neural networks with orthogonal activation functions in control of dynamical systems

    Science.gov (United States)

    Nikolić, Saša S.; Antić, Dragan S.; Milojković, Marko T.; Milovanović, Miroslav B.; Perić, Staniša Lj.; Mitić, Darko B.

    2016-04-01

    In this article, we present a new method for the synthesis of almost and quasi-orthogonal polynomials of arbitrary order. Filters designed on the bases of these functions are generators of generalised quasi-orthogonal signals for which we derived and presented necessary mathematical background. Based on theoretical results, we designed and practically implemented generalised first-order (k = 1) quasi-orthogonal filter and proved its quasi-orthogonality via performed experiments. Designed filters can be applied in many scientific areas. In this article, generated functions were successfully implemented in Nonlinear Auto Regressive eXogenous (NARX) neural network as activation functions. One practical application of the designed orthogonal neural network is demonstrated through the example of control of the complex technical non-linear system - laboratory magnetic levitation system. Obtained results were compared with neural networks with standard activation functions and orthogonal functions of trigonometric shape. The proposed network demonstrated superiority over existing solutions in the sense of system performances.

  2. The early history and emergence of molecular functions and modular scale-free network behavior.

    Science.gov (United States)

    Aziz, M Fayez; Caetano-Anollés, Kelsey; Caetano-Anollés, Gustavo

    2016-01-01

    The formation of protein structural domains requires that biochemical functions, defined by conserved amino acid sequence motifs, be embedded into a structural scaffold. Here we trace domain history onto a bipartite network of elementary functional loop sequences and domain structures defined at the fold superfamily level of SCOP classification. The resulting 'elementary functionome' network and its loop motif and structural domain graph projections create evolutionary 'waterfalls' describing the emergence of primordial functions. Waterfalls reveal how ancient loops are shared by domain structures in two initial waves of functional innovation that involve founder 'p-loop' and 'winged helix' domain structures. They also uncover a dynamics of modular motif embedding in domain structures that is ongoing, which transfers 'preferential' cooption properties of ancient loops to emerging domains. Remarkably, we find that the emergence of molecular functions induces hierarchical modularity and power law behavior in network evolution as the network of motifs and structures expand metabolic pathways and translation. PMID:27121452

  3. Acute functional reactivation of the language network during awake intraoperative brain mapping.

    Science.gov (United States)

    Spena, Giannantonio; Costi, Emanuele; Panciani, Pier Paolo; Roca, Elena; Migliorati, Karol; Fontanella, Marco Maria

    2015-01-01

    Acute brain plasticity during resection of central lesions has been recently described. In the cases reported, perilesional latent networks, useful to preserve the neurological functions, were detected in asymptomatic patients. In this paper, we presented a case of acute functional reactivation (AFR) of the language network in a symptomatic patient. Tumor resection allowed to acutely restore the neurological deficit. Intraoperative direct cortical stimulation (DCS) and functional neuroimaging showed new epicentres of activation of the language network after tumor excision. DCS in awake surgery is mandatory to reveal AFR needful to improve the extent of resection preserving the quality of life.

  4. Highly Enhanced Vapor Sensing of Multiwalled Carbon Nanotube Network Sensors by n-Butylamine Functionalization

    Directory of Open Access Journals (Sweden)

    P. Slobodian

    2014-01-01

    Full Text Available The sensing of volatile organic compounds by multiwall carbon nanotube networks of randomly entangled pristine nanotubes or the nanotubes functionalized by n-butylamine, which were deposited on polyurethane supporting electrospinned nonwoven membrane, has been investigated. The results show that the sensing of volatile organic compounds by functionalized nanotubes considerably increases with respect to pristine nanotubes. The increase is highly dependent on used vapor polarity. For the case of highly polar methanol, the functionalized MWCNT network exhibits even more than eightfold higher sensitivity in comparison to the network prepared from pristine nanotubes.

  5. Bow-tie topological features of metabolic networks and the functional significance

    Institute of Scientific and Technical Information of China (English)

    ZHAO Jing; TAO Lin; YU Hong; LUO JianHua; GAO ZhiWei; LI YiXue

    2007-01-01

    Exploring the structural topology of genome-based large-scale metabolic network is essential for in vestigating possible relations between structure and functionality. Visualization would be helpful for obtaining immediate information about structural organization. In this work, metabolic networks of 75 organisms were investigated from a topological point of view. A spread bow-tie model was proposed to give a clear visualization of the bow-tie structure for metabolic networks. The revealed topological pattern helps to design more efficient algorithm specifically for metabolic networks. This coarsegrained graph also visualizes the vulnerable connections in the network, and thus could have important implication for disease studies and drug target identifications. In addition, analysis on the reciprocal links and main cores in the GSC part of bow-tie also reveals that the bow-tie structure of metabolic networks has its own intrinsic and significant features which are significantly different from those of random networks.

  6. On the efficiency of the orthogonal least squares training method for radial basis function networks.

    Science.gov (United States)

    Sherstinsky, A; Picard, R W

    1996-01-01

    The efficiency of the orthogonal least squares (OLS) method for training approximation networks is examined using the criterion of energy compaction. We show that the selection of basis vectors produced by the procedure is not the most compact when the approximation is performed using a nonorthogonal basis. Hence, the algorithm does not produce the smallest possible networks for a given approximation error. Specific examples are given using the Gaussian radial basis functions type of approximation networks.

  7. Properties of functional brain networks correlate frequency of psychogenic non-epileptic seizures

    OpenAIRE

    Mahdi Jalili; Rossetti, Andrea O

    2012-01-01

    Abnormalities in the topology of brain networks may be an important feature and etiological factor for psychogenic non-epileptic seizures (PNES). To explore this possibility, we applied a graph theoretical approach to functional networks based on resting state EEGs from 13 PNES patients and 13 age- and gender-matched controls. The networks were extracted from Laplacian-transformed time-series by a cross-correlation method. PNES patients showed close to normal local and global connectivity and...

  8. Properties of functional brain networks correlate with frequency of psychogenic non-epileptic seizures.

    OpenAIRE

    Barzegaran, Elham; Joudaki, Amir; Jalili, Mahdi; Rossetti, Andrea O; Frackowiak, Richard S.; Knyazeva, Maria G.

    2012-01-01

    Abnormalities in the topology of brain networks may be an important feature and etiological factor for psychogenic non-epileptic seizures (PNES). To explore this possibility, we applied a graph theoretical approach to functional networks based on resting state EEGs from 13 PNES patients and 13 age- and gender-matched controls. The networks were extracted from Laplacian-transformed time-series by a cross-correlation method. PNES patients showed close to normal local and global connectivity and...

  9. Bow-tie topological features of metabolic networks and the functional significance

    OpenAIRE

    Jing, Zhao; Lin, Tao; Hong, Yu; Jian-Hua, Luo; Cao, Z W; Yixue, Li

    2006-01-01

    Exploring the structural topology of genome-based large-scale metabolic network is essential for investigating possible relations between structure and functionality. Visualization would be helpful for obtaining immediate information about structural organization. In this work, metabolic networks of 75 organisms were investigated from a topological point of view. A spread bow-tie model was proposed to give a clear visualization of the bow-tie structure for metabolic networks. The revealed top...

  10. Functional Organization of the Action Observation Network in Autism: A Graph Theory Approach

    OpenAIRE

    Kaat Alaerts; Franca Geerlings; Lynn Herremans; Stephan P Swinnen; Judith Verhoeven; Stefan Sunaert; Nicole Wenderoth

    2015-01-01

    Background The ability to recognize, understand and interpret other’s actions and emotions has been linked to the mirror system or action-observation-network (AON). Although variations in these abilities are prevalent in the neuro-typical population, persons diagnosed with autism spectrum disorders (ASD) have deficits in the social domain and exhibit alterations in this neural network. Method Here, we examined functional network properties of the AON using graph theory measures and region-to-...

  11. Network analysis identifies protein clusters of functional importance in juvenile idiopathic arthritis

    OpenAIRE

    Stevens, Adam; Meyer, Stefan; Hanson, Daniel; Clayton, Peter; Donn, Rachelle

    2014-01-01

    Introduction Our objective was to utilise network analysis to identify protein clusters of greatest potential functional relevance in the pathogenesis of oligoarticular and rheumatoid factor negative (RF-ve) polyarticular juvenile idiopathic arthritis (JIA). Methods JIA genetic association data were used to build an interactome network model in BioGRID 3.2.99. The top 10% of this protein:protein JIA Interactome was used to generate a minimal essential network (MEN). Reactome FI Cytoscape 2.83...

  12. Functional brain networks formed using cross-sample entropy are scale free.

    Science.gov (United States)

    Pritchard, Walter S; Laurienti, Paul J; Burdette, Jonathan H; Hayasaka, Satoru

    2014-08-01

    Over the previous decade, there has been an explosion of interest in network science, in general, and its application to the human brain, in particular. Most brain network investigations to date have used linear correlations (LinCorr) between brain areas to construct and then interpret brain networks. In this study, we applied an entropy-based method to establish functional connectivity between brain areas. This method is sensitive to both nonlinear and linear associations. The LinCorr-based and entropy-based techniques were applied to resting-state functional magnetic resonance imaging data from 10 subjects, and the resulting networks were compared. The networks derived from the entropy-based method exhibited power-law degree distributions. Moreover, the entropy-based networks had a higher clustering coefficient and a shorter path length compared with that of the LinCorr-based networks. While the LinCorr-based networks were assortative, with nodes with similar degrees preferentially connected, the entropy-based networks were disassortative, with high-degree hubs directly connected to low-degree nodes. It is likely that the differences in clustering and assortativity are due to "mega-hubs" in the entropy-based networks. These mega-hubs connect to a large majority of the nodes in the network. This is the first work clearly demonstrating differences between functional brain networks using linear and nonlinear techniques. The key finding is that the nonlinear technique produced networks with scale-free degree distributions. There remains debate among the neuroscience community as to whether human brains are scale free. These data support the argument that at least some aspects of the human brain are perhaps scale free.

  13. Immunosurveillance function of human mast cell?

    Institute of Scientific and Technical Information of China (English)

    (O)ner (O)zdemir

    2005-01-01

    Mast cell (MC) is so widely recognized as a critical effector in allergic disorders that it can be difficult to think of MC in any other context. Indeed, MCs are multifunctional and recently shown that MCs can also act as antigen presenters as well as effector elements of human immune system. First observations of their possible role as anti-tumor cells in peri- or intra-tumoral tissue were mentioned five decades ago and a high content of MCs is considered as a favorable prognosis,consistent with this study. Believers of this hypothesis assumed them to be inhibitors of tumor development through their pro-apoptotic and -necrolytic granules e.g.,granzymes and TNF-α. However, some still postulate them to be enhancers of tumor development through their effects on angiogenesis due to mostly tryptase.There are also some data suggesting increased MC density causes tumor development and indicates bad prognosis. Furthermore, since MC-associated mediators have shown to influence various aspects of tumor biology, the net effect of MCs on the development/progression of tumors has been difficult to evaluate. For instance, chymase induces apoptosis in targets; yet,tryptase, another MC protease, is a well-known mitogen.MCs with these various enzyme expression patterns may mediate different functions and the predominant MC type in tissues may be determined by the environmental needs. The coexistence of tryptase-expressing MCs(MCT) and chymase and tryptase-expressing MCs (MCTC)in physiological conditions reflects a naturally occurring balance that contributes to tissue homeostasis. We have recently discussed the role and relevance of MC serine proteases in different bone marrow diseases.

  14. Radial basis function neural network for power system load-flow

    Energy Technology Data Exchange (ETDEWEB)

    Karami, A.; Mohammadi, M.S. [Faculty of Engineering, The University of Guilan, P.O. Box 41635-3756, Rasht (Iran)

    2008-01-15

    This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)

  15. Incidental and intentional learning of verbal episodic material differentially modifies functional brain networks.

    Directory of Open Access Journals (Sweden)

    Marie-Therese Kuhnert

    Full Text Available Learning- and memory-related processes are thought to result from dynamic interactions in large-scale brain networks that include lateral and mesial structures of the temporal lobes. We investigate the impact of incidental and intentional learning of verbal episodic material on functional brain networks that we derive from scalp-EEG recorded continuously from 33 subjects during a neuropsychological test schedule. Analyzing the networks' global statistical properties we observe that intentional but not incidental learning leads to a significantly increased clustering coefficient, and the average shortest path length remains unaffected. Moreover, network modifications correlate with subsequent recall performance: the more pronounced the modifications of the clustering coefficient, the higher the recall performance. Our findings provide novel insights into the relationship between topological aspects of functional brain networks and higher cognitive functions.

  16. A practical algorithm for optimal operation management of distribution network including fuel cell power plants

    Energy Technology Data Exchange (ETDEWEB)

    Niknam, Taher; Meymand, Hamed Zeinoddini; Nayeripour, Majid [Electrical and Electronic Engineering Department, Shiraz University of Technology, Shiraz (Iran)

    2010-08-15

    Fuel cell power plants (FCPPs) have been taken into a great deal of consideration in recent years. The continuing growth of the power demand together with environmental constraints is increasing interest to use FCPPs in power system. Since FCPPs are usually connected to distribution network, the effect of FCPPs on distribution network is more than other sections of power system. One of the most important issues in distribution networks is optimal operation management (OOM) which can be affected by FCPPs. This paper proposes a new approach for optimal operation management of distribution networks including FCCPs. In the article, we consider the total electrical energy losses, the total electrical energy cost and the total emission as the objective functions which should be minimized. Whereas the optimal operation in distribution networks has a nonlinear mixed integer optimization problem, the optimal solution could be obtained through an evolutionary method. We use a new evolutionary algorithm based on Fuzzy Adaptive Particle Swarm Optimization (FAPSO) to solve the optimal operation problem and compare this method with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO) and Tabu Search (TS) over two distribution test feeders. (author)

  17. Aberrant topologies and reconfiguration pattern of functional brain network in children with second language reading impairment.

    Science.gov (United States)

    Liu, Lanfang; Li, Hehui; Zhang, Manli; Wang, Zhengke; Wei, Na; Liu, Li; Meng, Xiangzhi; Ding, Guosheng

    2016-07-01

    Prior work has extensively studied neural deficits in children with reading impairment (RI) in their native language but has rarely examined those of RI children in their second language (L2). A recent study revealed that the function of the local brain regions was disrupted in children with RI in L2, but it is not clear whether the disruption also occurs at a large-scale brain network level. Using fMRI and graph theoretical analysis, we explored the topology of the whole-brain functional network during a phonological rhyming task and network reconfigurations across task and short resting phases in Chinese children with English reading impairment versus age-matched typically developing (TD) children. We found that, when completing the phonological task, the RI group exhibited higher local network efficiency and network modularity compared with the TD group. When switching between the phonological task and the short resting phase, the RI group showed difficulty with network reconfiguration, as reflected in fewer changes in the local efficiency and modularity properties and less rearrangement of the modular communities. These findings were reproducible after controlling for the effects of in-scanner accuracy, participant gender, and L1 reading performance. The results from the whole-brain network analyses were largely replicated in the task-activated network. These findings provide preliminary evidence supporting that RI in L2 is associated with not only abnormal functional network organization but also poor flexibility of the neural system in responding to changing cognitive demands. PMID:27321248

  18. Optimization of Cellular Resources Evading Intra and Inter Tier Interference in Femto cells Equipped Macro cell Networks

    CERN Document Server

    Shakhakarmi, Niraj

    2012-01-01

    Cellular network resources are essential to be optimized in Femto cells equipped macro cell networks. This is achieved by increasing the cellular coverage and channel capacity, and reducing power usage and interference between femto cells and macro cells. In this paper, the optimization approach for cellular resources with installed femto cells in macro cell networks has been addressed by deploying smart antennas applications and effect power adaptation method which significantly optimize the cellular coverage, channel capacity, power usage, and intra and inter tier interference. The simulation results also illustrate the outstanding performance of this optimization methodology.

  19. Precuneus is a functional core of the default-mode network.

    Science.gov (United States)

    Utevsky, Amanda V; Smith, David V; Huettel, Scott A

    2014-01-15

    Efforts to understand the functional architecture of the brain have consistently identified multiple overlapping large-scale neural networks that are observable across multiple states. Despite the ubiquity of these networks, it remains unclear how regions within these large-scale neural networks interact to orchestrate behavior. Here, we collected functional magnetic resonance imaging data from 188 human subjects who engaged in three cognitive tasks and a resting-state scan. Using multiple tasks and a large sample allowed us to use split-sample validations to test for replication of results. We parceled the task-rest pairs into functional networks using a probabilistic spatial independent components analysis. We examined changes in connectivity between task and rest states using dual-regression analysis, which quantifies voxelwise connectivity estimates for each network of interest while controlling for the influence of signals arising from other networks and artifacts. Our analyses revealed systematic state-dependent functional connectivity in one brain region: the precuneus. Specifically, task performance led to increased connectivity (compared with rest) between the precuneus and the right frontoparietal network, whereas rest increased connectivity between the precuneus and the default-mode network (DMN). The absolute magnitude of this effect was greater for DMN, suggesting a heightened specialization for resting-state cognition. All results replicated within the two independent samples. Our results indicate that the precuneus plays a core role not only in DMN, but also more broadly through its engagement under a variety of processing states. PMID:24431451

  20. Embedding multiple self-organisation functionalities in future radio access networks

    NARCIS (Netherlands)

    Jansen, T.; Amirijoo, M.; Türke, U.; Jorguseski, L.; Zetterberg, K.; Nascimento, J.R.V. do; Schmelz, L.C.; Turk, J.; Balan, I.

    2009-01-01

    Wireless network operators today allocate considerable manual effort in managing their networks. A viable solution for lowering the manual effort is to introduce self-organisation functionalities. In this paper we discuss the challenges that are encountered when embedding multiple self-organisation

  1. Resting state functional MRI reveals abnormal network connectivity in orthostatic tremor.

    Science.gov (United States)

    Benito-León, Julián; Louis, Elan D; Manzanedo, Eva; Hernández-Tamames, Juan Antonio; Álvarez-Linera, Juan; Molina-Arjona, José Antonio; Matarazzo, Michele; Romero, Juan Pablo; Domínguez-González, Cristina; Domingo-Santos, Ángela; Sánchez-Ferro, Álvaro

    2016-07-01

    Very little is known about the pathogenesis of orthostatic tremor (OT). We have observed that OT patients might have deficits in specific aspects of neuropsychological function, particularly those thought to rely on the integrity of the prefrontal cortex, which suggests a possible involvement of frontocerebellar circuits. We examined whether resting-state functional magnetic resonance imaging (fMRI) might provide further insights into the pathogenesis on OT. Resting-state fMRI data in 13 OT patients (11 women and 2 men) and 13 matched healthy controls were analyzed using independent component analysis, in combination with a "dual-regression" technique, to identify group differences in several resting-state networks (RSNs). All participants also underwent neuropsychological testing during the same session. Relative to healthy controls, OT patients showed increased connectivity in RSNs involved in cognitive processes (default mode network [DMN] and frontoparietal networks), and decreased connectivity in the cerebellum and sensorimotor networks. Changes in network integrity were associated not only with duration (DMN and medial visual network), but also with cognitive function. Moreover, in at least 2 networks (DMN and medial visual network), increased connectivity was associated with worse performance on different cognitive domains (attention, executive function, visuospatial ability, visual memory, and language). In this exploratory study, we observed selective impairments of RSNs in OT patients. This and other future resting-state fMRI studies might provide a novel method to understand the pathophysiological mechanisms of motor and nonmotor features of OT. PMID:27442678

  2. Dynamical patterns of calcium signaling in a functional model of neuron–astrocyte networks

    OpenAIRE

    Postnov, D. E.; Koreshkov, R. N.; Brazhe, N. A.; Brazhe, A. R.; Sosnovtseva, O. V.

    2009-01-01

    We propose a functional mathematical model for neuron-astrocyte networks. The model incorporates elements of the tripartite synapse and the spatial branching structure of coupled astrocytes. We consider glutamate-induced calcium signaling as a specific mode of excitability and transmission in astrocytic–neuronal networks. We reproduce local and global dynamical patterns observed experimentally.

  3. Inferring the physical connectivity of complex networks from their functional dynamics

    Directory of Open Access Journals (Sweden)

    Holm Liisa

    2010-05-01

    Full Text Available Abstract Background Biological networks, such as protein-protein interactions, metabolic, signalling, transcription-regulatory networks and neural synapses, are representations of large-scale dynamic systems. The relationship between the network structure and functions remains one of the central problems in current multidisciplinary research. Significant progress has been made toward understanding the implication of topological features for the network dynamics and functions, especially in biological networks. Given observations of a network system's behaviours or measurements of its functional dynamics, what can we conclude of the details of physical connectivity of the underlying structure? Results We modelled the network system by employing a scale-free network of coupled phase oscillators. Pairwise phase coherence (PPC was calculated for all the pairs of oscillators to present functional dynamics induced by the system. At the regime of global incoherence, we observed a Significant pairwise synchronization only between two nodes that are physically connected. Right after the onset of global synchronization, disconnected nodes begin to oscillate in a correlated fashion and the PPC of two nodes, either connected or disconnected, depends on their degrees. Based on the observation of PPCs, we built a weighted network of synchronization (WNS, an all-to-all functionally connected network where each link is weighted by the PPC of two oscillators at the ends of the link. In the regime of strong coupling, we observed a Significant similarity in the organization of WNSs induced by systems sharing the same substrate network but different configurations of initial phases and intrinsic frequencies of oscillators. We reconstruct physical network from the WNS by choosing the links whose weights are higher than a given threshold. We observed an optimal reconstruction just before the onset of global synchronization. Finally, we correlated the topology of the

  4. A spiking network model of cerebellar Purkinje cells and molecular layer interneurons exhibiting irregular firing

    Directory of Open Access Journals (Sweden)

    William eLennon

    2014-12-01

    Full Text Available While the anatomy of the cerebellar microcircuit is well studied, how it implements cerebellar function is not understood. A number of models have been proposed to describe this mechanism but few emphasize the role of the vast network Purkinje cells (PKJs form with the molecular layer interneurons (MLIs – the stellate and basket cells. We propose a model of the MLI-PKJ network composed of simple spiking neurons incorporating the major anatomical and physiological features. In computer simulations, the model reproduces the irregular firing patterns observed in PKJs and MLIs in vitro and a shift toward faster, more regular firing patterns when inhibitory synaptic currents are blocked. In the model, the time between PKJ spikes is shown to be proportional to the amount of feedforward inhibition from an MLI on average. The two key elements of the model are: (1 spontaneously active PKJs and MLIs due to an endogenous depolarizing current, and (2 adherence to known anatomical connectivity along a parasagittal strip of cerebellar cortex. We propose this model to extend previous spiking network models of the cerebellum and for further computational investigation into the role of irregular firing and MLIs in cerebellar learning and function.

  5. Whole-brain circuit dissection in free-moving animals reveals cell-specific mesocorticolimbic networks

    Science.gov (United States)

    Michaelides, Michael; Anderson, Sarah Ann R.; Ananth, Mala; Smirnov, Denis; Thanos, Panayotis K.; Neumaier, John F.; Wang, Gene-Jack; Volkow, Nora D.; Hurd, Yasmin L.

    2013-01-01

    The ability to map the functional connectivity of discrete cell types in the intact mammalian brain during behavior is crucial for advancing our understanding of brain function in normal and disease states. We combined designer receptor exclusively activated by designer drug (DREADD) technology and behavioral imaging with μPET and [18F]fluorodeoxyglucose (FDG) to generate whole-brain metabolic maps of cell-specific functional circuits during the awake, freely moving state. We have termed this approach DREADD-assisted metabolic mapping (DREAMM) and documented its ability in rats to map whole-brain functional anatomy. We applied this strategy to evaluating changes in the brain associated with inhibition of prodynorphin-expressing (Pdyn-expressing) and of proenkephalin-expressing (Penk-expressing) medium spiny neurons (MSNs) of the nucleus accumbens shell (NAcSh), which have been implicated in neuropsychiatric disorders. DREAMM revealed discrete behavioral manifestations and concurrent engagement of distinct corticolimbic networks associated with dysregulation of Pdyn and Penk in MSNs of the NAcSh. Furthermore, distinct neuronal networks were recruited in awake versus anesthetized conditions. These data demonstrate that DREAMM is a highly sensitive, molecular, high-resolution quantitative imaging approach. PMID:24231358

  6. Disrupted Brain Functional Network in Internet Addiction Disorder: A Resting-State Functional Magnetic Resonance Imaging Study

    OpenAIRE

    Wee, Chong-Yaw; Zhao, Zhimin; Yap, Pew-Thian; Wu, Guorong; Shi, Feng; Price, True; Du, Yasong; Xu, Jianrong; Zhou, Yan; Shen, Dinggang

    2014-01-01

    Internet addiction disorder (IAD) is increasingly recognized as a mental health disorder, particularly among adolescents. The pathogenesis associated with IAD, however, remains unclear. In this study, we aim to explore the encephalic functional characteristics of IAD adolescents at rest using functional magnetic resonance imaging data. We adopted a graph-theoretic approach to investigate possible disruptions of functional connectivity in terms of network properties including small-worldness, ...

  7. Assessment of pancreatic islet cell function and survival

    OpenAIRE

    Köhler, Martin

    2015-01-01

    Function and survival of pancreatic islet insulin-producing beta-cells (β-cells) and glucagonproducing alpha-cells (α-cells) were studied, and methods for this purpose were developed or refined. Dynamic control of glucose metabolism is essential for β-cell stimulus-secretion coupling. ATP is an important metabolic parameter and therefore we set up a technique to monitor dynamic changes of ATP in insulin-producing cells using luciferase bioluminescence at the level of single...

  8. The Neural Network analysis for the single cell of Molten Carbonate Fuel cell (MCFC

    Directory of Open Access Journals (Sweden)

    S. K. Dhakad, S.C.soni, Pankaj Agrawal, Prashant Baredaer

    2012-11-01

    Full Text Available In the present work try to trained the performance and evolution for the single cell of the MCFC by using the Neural Network tool in the MAT-Lab software. The data used for the Neural Network training are, simulated results, these are obtained for the single cell of the MCFC [1].The analysis carried out for n input vectors (known input variables i.e. temperature and load current and power as output vector. Figure 2 shown simulated powers at the different values of input variables, as load current & temperature. Figures 3 shown the trained results are obtained using model in the form of approximate feed forward neural network for the 4 layers & 2:3:2 neurons. Power as the output vector of the MCFC is well compare to the simulated results shown in figure 5.

  9. Beyond growth: novel functions for bacterial cell wall hydrolases.

    Science.gov (United States)

    Wyckoff, Timna J; Taylor, Jennifer A; Salama, Nina R

    2012-11-01

    The peptidoglycan cell wall maintains turgor pressure and cell shape of most bacteria. Cell wall hydrolases are essential, together with synthases, for growth and daughter cell separation. Recent work in diverse organisms has uncovered new cell wall hydrolases that act autonomously or on neighboring cells to modulate invasion of prey cells, cell shape, innate immune detection, intercellular communication, and competitor lysis. The hydrolases involved in these processes catalyze the cleavage of bonds throughout the sugar and peptide moities of peptidoglycan. Phenotypes associated with these diverse hydrolases reveal new functions of the bacterial cell wall beyond growth and division.

  10. Functional connectivity dynamics during film viewing reveal common networks for different emotional experiences.

    Science.gov (United States)

    Raz, Gal; Touroutoglou, Alexandra; Wilson-Mendenhall, Christine; Gilam, Gadi; Lin, Tamar; Gonen, Tal; Jacob, Yael; Atzil, Shir; Admon, Roee; Bleich-Cohen, Maya; Maron-Katz, Adi; Hendler, Talma; Barrett, Lisa Feldman

    2016-08-01

    Recent theoretical and empirical work has highlighted the role of domain-general, large-scale brain networks in generating emotional experiences. These networks are hypothesized to process aspects of emotional experiences that are not unique to a specific emotional category (e.g., "sadness," "happiness"), but rather that generalize across categories. In this article, we examined the dynamic interactions (i.e., changing cohesiveness) between specific domain-general networks across time while participants experienced various instances of sadness, fear, and anger. We used a novel method for probing the network connectivity dynamics between two salience networks and three amygdala-based networks. We hypothesized, and found, that the functional connectivity between these networks covaried with the intensity of different emotional experiences. Stronger connectivity between the dorsal salience network and the medial amygdala network was associated with more intense ratings of emotional experience across six different instances of the three emotion categories examined. Also, stronger connectivity between the dorsal salience network and the ventrolateral amygdala network was associated with more intense ratings of emotional experience across five out of the six different instances. Our findings demonstrate that a variety of emotional experiences are associated with dynamic interactions of domain-general neural systems. PMID:27142636

  11. Impaired functional default mode network in patients with mild neurological Wilson's disease.

    Science.gov (United States)

    Han, Yongsheng; Cheng, Hewei; Toledo, Jon B; Wang, Xun; Li, Bo; Han, Yongzhu; Wang, Kai; Fan, Yong

    2016-09-01

    Wilson's disease (WD) is an autosomal recessive metabolic disorder characterized by cognitive, psychiatric and motor signs and symptoms that are associated with structural and pathological brain abnormalities, in addition to liver changes. However, functional brain connectivity pattern of WD patients remains largely unknown. In the present study, we investigated functional brain connectivity pattern of WD patients using resting state functional magnetic resonance imaging. Particularly, we studied default mode network (DMN) using posterior cingulate cortex (PCC) based seed functional connectivity analysis and graph theoretic functional brain network analysis tools, and investigated the relationship between the DMN's functional connectivity pattern of WD patients and their attention functions examined using the attention network test (ANT). Our results demonstrated that WD patients had altered DMN's functional connectivity and lower local and global network efficiency compared with normal controls (NCs). In addition, the functional connectivity between left inferior temporal cortex and right lateral parietal cortex was correlated with altering function, one of the attention functions, across WD and NC subjects. These findings indicated that the DMN's functional connectivity was altered in WD patients, which might be correlated with their attention dysfunction.

  12. Collective Calcium Dynamics in Networks of Communicating Cells

    Science.gov (United States)

    Byrd, Tommy; Potter, Garrett; Sun, Bo; Mugler, Andrew

    Cells can sense and encode information about their environment with remarkable precision. These properties have been studied extensively for single cells, but intercellular communication is known to be important for both single- and multicellular organisms. Here, we examine calcium dynamics of fibroblast cells exposed to external ATP stimuli, and the effects of communication and stimulus strength on cells' response. Experimental results show that increasing communication strength induces a greater fraction of cells to exhibit oscillatory calcium dynamics, but the frequencies of oscillation do not systematically shift with ATP strength. We developed a model of calcium signaling by adding noise, communication, and cell-to-cell variability to the model of Tang and Othmer. This model reproduces cells' increased tendency to oscillate as a function of communication strength, and frequency encoding is nearly removed at the global level. Our model therefore suggests that the propensity of cells to oscillate, rather than frequency encoding, determines the response to external ATP. These results suggest that the system lies near a critical boundary separating non-oscillatory and oscillatory calcium dynamics.

  13. Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

    Science.gov (United States)

    Donges, Jonathan; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik; Marwan, Norbert; Dijkstra, Henk; Kurths, Jürgen

    2016-04-01

    We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology. pyunicorn is available online at https://github.com/pik-copan/pyunicorn. Reference: J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng, L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra, and J. Kurths, Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, Chaos 25, 113101 (2015), DOI: 10.1063/1.4934554, Preprint: arxiv.org:1507.01571 [physics.data-an].

  14. Alterations of Functional Connectivity Among Resting-State Networks in Hypothyroidism.

    Science.gov (United States)

    Singh, S; Kumar, M; Modi, S; Kaur, P; Shankar, L R; Khushu, S

    2015-07-01

    Hypothyroidism affects brain functioning as suggested by various neuroimaging studies. The primary focus of the present study was to examine whether hypothyroidism would impact connectivity among resting-state networks (RSNs) using resting-state functional magnetic resonance imaging (rsfMRI). Twenty-two patients with hypothyroidism and 22 healthy controls were recruited and scanned using rsfMRI. The data were analysed using independent component analysis and a dual regression approach that was applied on five RSNs that were identified using fsl software (http://fsl.fmrib.ox.ac.uk). Hypothyroid patients showed significantly decreased functional connectivity in the regions of the right frontoparietal network (frontal pole), the medial visual network (lateral occipital gyrus, precuneus cortex and cuneus) and the motor network (precentral gyrus, postcentral gyrus, precuneus cortex, paracingulate gyrus, cingulate gyrus and supramarginal gyrus) compared to healthy controls. The reduced functional connectivity in the right frontoparietal network, the medial visual network and the motor network suggests neurocognitive alterations in hypothyroid patients in the corresponding functions. However, the study would be further continued to investigate the effects of thyroxine treatment and correlation with neurocognitive scores. The findings of the present study provide further interesting insights into our understanding of the action of thyroid hormone on the adult human brain.

  15. Satisfiability of logic programming based on radial basis function neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)

    2014-07-10

    In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.

  16. A clustering technique for digital communications channel equalization using radial basis function networks.

    Science.gov (United States)

    Chen, S; Mulgrew, B; Grant, P M

    1993-01-01

    The application of a radial basis function network to digital communications channel equalization is examined. It is shown that the radial basis function network has an identical structure to the optimal Bayesian symbol-decision equalizer solution and, therefore, can be employed to implement the Bayesian equalizer. The training of a radial basis function network to realize the Bayesian equalization solution can be achieved efficiently using a simple and robust supervised clustering algorithm. During data transmission a decision-directed version of the clustering algorithm enables the radial basis function network to track a slowly time-varying environment. Moreover, the clustering scheme provides an automatic compensation for nonlinear channel and equipment distortion. Computer simulations are included to illustrate the analytical results.

  17. Functional Integration between Salience and Central Executive Networks: A Role for Action Video Game Experience

    Directory of Open Access Journals (Sweden)

    Diankun Gong

    2016-01-01

    Full Text Available Action video games (AVGs have attracted increasing research attention as they offer a unique perspective into the relation between active learning and neural plasticity. However, little research has examined the relation between AVG experience and the plasticity of neural network mechanisms. It has been proposed that AVG experience is related to the integration between Salience Network (SN and Central Executive Network (CEN, which are responsible for attention and working memory, respectively, two cognitive functions essential for AVG playing. This study initiated a systematic investigation of this proposition by analyzing AVG experts’ and amateurs’ resting-state brain functions through graph theoretical analyses and functional connectivity. Results reveal enhanced intra- and internetwork functional integrations in AVG experts compared to amateurs. The findings support the possible relation between AVG experience and the neural network plasticity.

  18. Functional Integration between Salience and Central Executive Networks: A Role for Action Video Game Experience.

    Science.gov (United States)

    Gong, Diankun; He, Hui; Ma, Weiyi; Liu, Dongbo; Huang, Mengting; Dong, Li; Gong, Jinnan; Li, Jianfu; Luo, Cheng; Yao, Dezhong

    2016-01-01

    Action video games (AVGs) have attracted increasing research attention as they offer a unique perspective into the relation between active learning and neural plasticity. However, little research has examined the relation between AVG experience and the plasticity of neural network mechanisms. It has been proposed that AVG experience is related to the integration between Salience Network (SN) and Central Executive Network (CEN), which are responsible for attention and working memory, respectively, two cognitive functions essential for AVG playing. This study initiated a systematic investigation of this proposition by analyzing AVG experts' and amateurs' resting-state brain functions through graph theoretical analyses and functional connectivity. Results reveal enhanced intra- and internetwork functional integrations in AVG experts compared to amateurs. The findings support the possible relation between AVG experience and the neural network plasticity.

  19. Satisfiability of logic programming based on radial basis function neural networks

    Science.gov (United States)

    Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong

    2014-07-01

    In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.

  20. Functional Integration between Salience and Central Executive Networks: A Role for Action Video Game Experience.

    Science.gov (United States)

    Gong, Diankun; He, Hui; Ma, Weiyi; Liu, Dongbo; Huang, Mengting; Dong, Li; Gong, Jinnan; Li, Jianfu; Luo, Cheng; Yao, Dezhong

    2016-01-01

    Action video games (AVGs) have attracted increasing research attention as they offer a unique perspective into the relation between active learning and neural plasticity. However, little research has examined the relation between AVG experience and the plasticity of neural network mechanisms. It has been proposed that AVG experience is related to the integration between Salience Network (SN) and Central Executive Network (CEN), which are responsible for attention and working memory, respectively, two cognitive functions essential for AVG playing. This study initiated a systematic investigation of this proposition by analyzing AVG experts' and amateurs' resting-state brain functions through graph theoretical analyses and functional connectivity. Results reveal enhanced intra- and internetwork functional integrations in AVG experts compared to amateurs. The findings support the possible relation between AVG experience and the neural network plasticity. PMID:26885408