WorldWideScience

Sample records for cell network function

  1. Transcriptional networks that regulate muscle stem cell function.

    Science.gov (United States)

    Punch, Vincent G; Jones, Andrew E; Rudnicki, Michael A

    2009-01-01

    Muscle stem cells comprise different populations of stem and progenitor cells found in embryonic and adult tissues. A number of signaling and transcriptional networks are responsible for specification and survival of these cell populations and regulation of their behavior during growth and regeneration. Muscle progenitor cells are mostly derived from the somites of developing embryos, while satellite cells are the progenitor cells responsible for the majority of postnatal growth and adult muscle regeneration. In resting muscle, these stem cells are quiescent, but reenter the cell cycle during their activation, whereby they undergo decisions to self-renew, proliferate, or differentiate and fuse into multinucleated myofibers to repair damaged muscle. Regulation of muscle stem cell activity is under the precise control of a number of extrinsic signaling pathways and active transcriptional networks that dictate their behavior, fate, and regenerative potential. Here, we review the networks responsible for these different aspects of muscle stem cell biology and discuss prevalent parallels between mechanisms regulating the activity of embryonic muscle progenitor cells and adult satellite cells.

  2. Developmental changes in postnatal murine intestinal interstitial cell of Cajal network structure and function.

    Science.gov (United States)

    Gao, Jerry; Sathar, Shameer; O'Grady, Gregory; Han, Juan; Cheng, Leo K

    2014-08-01

    The mammalian gastrointestinal (GI) tract undergoes rapid development during early postnatal life in order to transition from a milk to solid diet. Interstitial cells of Cajal (ICC) are the pacemaker cells that coordinate smooth muscle contractility within the GI tract, and hence we hypothesized that ICC networks undergo significant developmental changes during this early postnatal period. Numerical metrics for quantifying ICC network structural properties were applied on confocal ICC network imaging data obtained from the murine small intestine at various postnatal ages spanning birth to weaning. These imaging data were also coupled to a biophysically-based computational model to simulate pacemaker activity in the networks, to quantify how changes in structure may alter function. The results showed a pruning-like mechanism which occurs during postnatal development, and the temporal course of this phenomenon was defined. There was an initial ICC process overgrowth to optimize network efficiency and increase functional output volume. This was followed by a selective retaining and strengthening of processes, while others were discarded to further elevate functional output volume. Subsequently, new ICC processes were formed and the network was adjusted to its adult morphology. These postnatal ICC network developmental events may be critical in facilitating mature digestive function.

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

    Directory of Open Access Journals (Sweden)

    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

  4. Development and function of human cerebral cortex neural networks from pluripotent stem cells in vitro.

    Science.gov (United States)

    Kirwan, Peter; Turner-Bridger, Benita; Peter, Manuel; Momoh, Ayiba; Arambepola, Devika; Robinson, Hugh P C; Livesey, Frederick J

    2015-09-15

    A key aspect of nervous system development, including that of the cerebral cortex, is the formation of higher-order neural networks. Developing neural networks undergo several phases with distinct activity patterns in vivo, which are thought to prune and fine-tune network connectivity. We report here that human pluripotent stem cell (hPSC)-derived cerebral cortex neurons form large-scale networks that reflect those found in the developing cerebral cortex in vivo. Synchronised oscillatory networks develop in a highly stereotyped pattern over several weeks in culture. An initial phase of increasing frequency of oscillations is followed by a phase of decreasing frequency, before giving rise to non-synchronous, ordered activity patterns. hPSC-derived cortical neural networks are excitatory, driven by activation of AMPA- and NMDA-type glutamate receptors, and can undergo NMDA-receptor-mediated plasticity. Investigating single neuron connectivity within PSC-derived cultures, using rabies-based trans-synaptic tracing, we found two broad classes of neuronal connectivity: most neurons have small numbers (40). These data demonstrate that the formation of hPSC-derived cortical networks mimics in vivo cortical network development and function, demonstrating the utility of in vitro systems for mechanistic studies of human forebrain neural network biology.

  5. Haspin has Multiple Functions in the Plant Cell Division Regulatory Network.

    Science.gov (United States)

    Kozgunova, Elena; Suzuki, Takamasa; Ito, Masaki; Higashiyama, Tetsuya; Kurihara, Daisuke

    2016-04-01

    Progression of cell division is controlled by various mitotic kinases. In animal cells, phosphorylation of histone H3 at Thr3 by the kinase Haspin (haploid germ cell-specific nuclear protein kinase) promotes centromeric Aurora B localization to regulate chromosome segregation. However, less is known about the function of Haspin in regulatory networks in plant cells. Here, we show that inhibition of Haspin with 5-iodotubercidin (5-ITu) in Bright Yellow-2 (BY-2) cells delayed chromosome alignment. Haspin inhibition also prevented the centromeric localization of Aurora3 kinase (AUR3) and disrupted its function. This suggested that Haspin plays a role in the specific positioning of AUR3 on chromosomes in plant cells, a function conserved in animals. The results also indicated that Haspin and AUR3 are involved in the same pathway, which regulates chromosome alignment during prometaphase/metaphase. Remarkably, Haspin inhibition by 5-ITu also led to a severe cytokinesis defect, resulting in binuclear cells with a partially formed cell plate. The 5-ITu treatment did not affect microtubules, AUR1/2 or the NACK-PQR pathway; however, it did alter the distribution of actin filaments on the cell plate. Together, these results suggested that Haspin has several functions in regulating cell division in plant cells: in the localization of AUR3 on centromeres and in regulating late cell plate expansion during cytokinesis.

  6. A systems view of epigenetic networks regulating pancreas development and β-cell function.

    Science.gov (United States)

    Xie, Ruiyu; Carrano, Andrea C; Sander, Maike

    2015-01-01

    The development of the pancreas and determination of endocrine cell fate are controlled by a highly complex interplay of signaling events and transcriptional networks. It is now known that an interconnected epigenetic program is also required to drive these processes. Recent studies using genome-wide approaches have implicated epigenetic regulators, such as DNA and histone-modifying enzymes and noncoding RNAs, to play critical roles in pancreas development and the maintenance of cell identity and function. Furthermore, genome-wide analyses have implicated epigenetic changes as a casual factor in the pathogenesis of diabetes. In the future, genomic approaches to further our understanding of the role of epigenetics in endocrine cell development and function will be useful for devising strategies to produce or manipulate β-cells for therapies of diabetes.

  7. Programmable chemical reaction networks: emulating regulatory functions in living cells using a bottom-up approach.

    Science.gov (United States)

    van Roekel, Hendrik W H; Rosier, Bas J H M; Meijer, Lenny H H; Hilbers, Peter A J; Markvoort, Albert J; Huck, Wilhelm T S; de Greef, Tom F A

    2015-11-07

    Living cells are able to produce a wide variety of biological responses when subjected to biochemical stimuli. It has become apparent that these biological responses are regulated by complex chemical reaction networks (CRNs). Unravelling the function of these circuits is a key topic of both systems biology and synthetic biology. Recent progress at the interface of chemistry and biology together with the realisation that current experimental tools are insufficient to quantitatively understand the molecular logic of pathways inside living cells has triggered renewed interest in the bottom-up development of CRNs. This builds upon earlier work of physical chemists who extensively studied inorganic CRNs and showed how a system of chemical reactions can give rise to complex spatiotemporal responses such as oscillations and pattern formation. Using purified biochemical components, in vitro synthetic biologists have started to engineer simplified model systems with the goal of mimicking biological responses of intracellular circuits. Emulation and reconstruction of system-level properties of intracellular networks using simplified circuits are able to reveal key design principles and molecular programs that underlie the biological function of interest. In this Tutorial Review, we present an accessible overview of this emerging field starting with key studies on inorganic CRNs followed by a discussion of recent work involving purified biochemical components. Finally, we review recent work showing the versatility of programmable biochemical reaction networks (BRNs) in analytical and diagnostic applications.

  8. Adhesion protein networks reveal functions proximal and distal to cell-matrix contacts.

    Science.gov (United States)

    Byron, Adam; Frame, Margaret C

    2016-04-01

    Cell adhesion to the extracellular matrix is generally mediated by integrin receptors, which bind to intracellular adhesion proteins that form multi-molecular scaffolding and signalling complexes. The networks of proteins, and their interactions, are dynamic, mechanosensitive and extremely complex. Recent efforts to characterise adhesions using a variety of technologies, including imaging, proteomics and bioinformatics, have provided new insights into their composition, organisation and how they are regulated, and have also begun to reveal unexpected roles for so-called adhesion proteins in other cellular compartments (for example, the nucleus or centrosomes) in diseases such as cancer. We believe this is opening a new chapter on understanding the wider functions of adhesion proteins, both proximal and distal to cell-matrix contacts.

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

    Directory of Open Access Journals (Sweden)

    Mario Novkovic

    2016-07-01

    Full Text Available 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.

  10. 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

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

    Science.gov (United States)

    Novkovic, Mario; Onder, Lucas; Cupovic, Jovana; Abe, Jun; Bomze, David; Cremasco, Viviana; Scandella, Elke; Stein, Jens V; Bocharov, Gennady; Turley, Shannon J; Ludewig, Burkhard

    2016-07-01

    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.

  12. On Network Functional Compression

    CERN Document Server

    Feizi, Soheil

    2010-01-01

    In this paper, we consider different aspects of the network functional compression problem where computation of a function (or, some functions) of sources located at certain nodes in a network is desired at receiver(s). The rate region of this problem has been considered in the literature under certain restrictive assumptions, particularly in terms of the network topology, the functions and the characteristics of the sources. In this paper, we present results that significantly relax these assumptions. Firstly, we consider this problem for an arbitrary tree network and asymptotically lossless computation. We show that, for depth one trees with correlated sources, or for general trees with independent sources, a modularized coding scheme based on graph colorings and Slepian-Wolf compression performs arbitrarily closely to rate lower bounds. For a general tree network with independent sources, optimal computation to be performed at intermediate nodes is derived. We introduce a necessary and sufficient condition...

  13. Bioprinting: Functional droplet networks

    Science.gov (United States)

    Durmus, Naside Gozde; Tasoglu, Savas; Demirci, Utkan

    2013-06-01

    Tissue-mimicking printed networks of droplets separated by lipid bilayers that can be functionalized with membrane proteins are able to spontaneously fold and transmit electrical currents along predefined paths.

  14. Sertoli Cells Modulate Testicular Vascular Network Development, Structure, and Function to Influence Circulating Testosterone Concentrations in Adult Male Mice.

    Science.gov (United States)

    Rebourcet, Diane; Wu, Junxi; Cruickshanks, Lyndsey; Smith, Sarah E; Milne, Laura; Fernando, Anuruddika; Wallace, Robert J; Gray, Calum D; Hadoke, Patrick W F; Mitchell, Rod T; O'Shaughnessy, Peter J; Smith, Lee B

    2016-06-01

    The testicular vasculature forms a complex network, providing oxygenation, micronutrients, and waste clearance from the testis. The vasculature is also instrumental to testis function because it is both the route by which gonadotropins are delivered to the testis and by which T is transported away to target organs. Whether Sertoli cells play a role in regulating the testicular vasculature in postnatal life has never been unequivocally demonstrated. In this study we used models of acute Sertoli cell ablation and acute germ cell ablation to address whether Sertoli cells actively influence vascular structure and function in the adult testis. Our findings suggest that Sertoli cells play a key role in supporting the structure of the testicular vasculature. Ablating Sertoli cells (and germ cells) or germ cells alone results in a similar reduction in testis size, yet only the specific loss of Sertoli cells leads to a reduction in total intratesticular vascular volume, the number of vascular branches, and the numbers of small microvessels; loss of germ cells alone has no effect on the testicular vasculature. These perturbations to the testicular vasculature leads to a reduction in fluid exchange between the vasculature and testicular interstitium, which reduces gonadotropin-stimulated circulating T concentrations, indicative of reduced Leydig cell stimulation and/or reduced secretion of T into the vasculature. These findings describe a new paradigm by which the transport of hormones and other factors into and out of the testis may be influenced by Sertoli cells and highlights these cells as potential targets for enhancing this endocrine relationship.

  15. 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.

  16. Functional roles of distributed synaptic clusters in the mitral-granule cell network of the olfactory bulb

    Directory of Open Access Journals (Sweden)

    Michele Migliore

    2010-09-01

    Full Text Available Odors are encoded in spatio-temporal patterns within the olfactory bulb, but the mechanisms of odor recognition and discrimination are poorly understood. It is reasonable to postulate that the olfactory code is sculpted by lateral and feedforward inhibition mediated by granule cells onto the mitral cells. Recent viral tracing and physiological studies revealed patterns of distributed granule cell synaptic clusters that provided additional clues to the possible mechanisms at the network level. The emerging properties and functional roles of these patterns, however, are unknown. Here, using a realistic model of 5 mitral and 100 granule cells we show how their synaptic network can dynamically self-organize and interact through an activity-dependent dendrodendritic mechanism. The results suggest that the patterns of distributed mitral-granule cell connectivity may represent the most recent history of odor inputs, and may contribute to the basic processes underlying mixture perception and odor qualities. The model predicts how and why the dynamical interactions between the active mitral cells through the granule cell synaptic clusters can account for a variety of puzzling behavioral results on odor mixtures and on the emergence of synthetic or analytic perception.

  17. 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.

  18. Localizing and placement of network node functions in a network

    NARCIS (Netherlands)

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

    2014-01-01

    The invention enables placement and use of a network node function in a second network node instead of using the network node function in a first network node. The network node function is e.g. a server function or a router function. The second network node is typically located in or close to the cl

  19. 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...

  20. Network-based functional enrichment

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    Poirel Christopher L

    2011-11-01

    Full Text Available Abstract Background Many methods have been developed to infer and reason about molecular interaction networks. These approaches often yield networks with hundreds or thousands of nodes and up to an order of magnitude more edges. It is often desirable to summarize the biological information in such networks. A very common approach is to use gene function enrichment analysis for this task. A major drawback of this method is that it ignores information about the edges in the network being analyzed, i.e., it treats the network simply as a set of genes. In this paper, we introduce a novel method for functional enrichment that explicitly takes network interactions into account. Results Our approach naturally generalizes Fisher’s exact test, a gene set-based technique. Given a function of interest, we compute the subgraph of the network induced by genes annotated to this function. We use the sequence of sizes of the connected components of this sub-network to estimate its connectivity. We estimate the statistical significance of the connectivity empirically by a permutation test. We present three applications of our method: i determine which functions are enriched in a given network, ii given a network and an interesting sub-network of genes within that network, determine which functions are enriched in the sub-network, and iii given two networks, determine the functions for which the connectivity improves when we merge the second network into the first. Through these applications, we show that our approach is a natural alternative to network clustering algorithms. Conclusions We presented a novel approach to functional enrichment that takes into account the pairwise relationships among genes annotated by a particular function. Each of the three applications discovers highly relevant functions. We used our methods to study biological data from three different organisms. Our results demonstrate the wide applicability of our methods. Our algorithms are

  1. Aging and functional brain networks

    Energy Technology Data Exchange (ETDEWEB)

    Tomasi D.; Tomasi, D.; Volkow, N.D.

    2011-07-11

    Aging is associated with changes in human brain anatomy and function and cognitive decline. Recent studies suggest the aging decline of major functional connectivity hubs in the 'default-mode' network (DMN). Aging effects on other networks, however, are largely unknown. We hypothesized that aging would be associated with a decline of short- and long-range functional connectivity density (FCD) hubs in the DMN. To test this hypothesis, we evaluated resting-state data sets corresponding to 913 healthy subjects from a public magnetic resonance imaging database using functional connectivity density mapping (FCDM), a voxelwise and data-driven approach, together with parallel computing. Aging was associated with pronounced long-range FCD decreases in DMN and dorsal attention network (DAN) and with increases in somatosensory and subcortical networks. Aging effects in these networks were stronger for long-range than for short-range FCD and were also detected at the level of the main functional hubs. Females had higher short- and long-range FCD in DMN and lower FCD in the somatosensory network than males, but the gender by age interaction effects were not significant for any of the networks or hubs. These findings suggest that long-range connections may be more vulnerable to aging effects than short-range connections and that, in addition to the DMN, the DAN is also sensitive to aging effects, which could underlie the deterioration of attention processes that occurs with aging.

  2. Integrating phosphorylation network with transcriptional network reveals novel functional relationships.

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    Lin Wang

    Full Text Available Phosphorylation and transcriptional regulation events are critical for cells to transmit and respond to signals. In spite of its importance, systems-level strategies that couple these two networks have yet to be presented. Here we introduce a novel approach that integrates the physical and functional aspects of phosphorylation network together with the transcription network in S.cerevisiae, and demonstrate that different network motifs are involved in these networks, which should be considered in interpreting and integrating large scale datasets. Based on this understanding, we introduce a HeRS score (hetero-regulatory similarity score to systematically characterize the functional relevance of kinase/phosphatase involvement with transcription factor, and present an algorithm that predicts hetero-regulatory modules. When extended to signaling network, this approach confirmed the structure and cross talk of MAPK pathways, inferred a novel functional transcription factor Sok2 in high osmolarity glycerol pathway, and explained the mechanism of reduced mating efficiency upon Fus3 deletion. This strategy is applicable to other organisms as large-scale datasets become available, providing a means to identify the functional relationships between kinases/phosphatases and transcription factors.

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

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    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.

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

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    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.

  5. 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.

  6. 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.

  7. Indirect photobiomodulation in functional networks

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    Liu, Timon Cheng-Yi; Zhu, Wei-Wei; Yang, Xiang-Bo

    2012-12-01

    Photobiomodulation (PBM) is a non-damaged modulation of laser irradiation or monochromatic light (LI) on a biosystem function. It depends on whether the function is in its function-specific homeostasis (FSH), a negative feedback response for the function to be performed perfectly. Many redundant pathways (RPs) maintain the same cellular function. The full activation of any of RPs can maintain a normal function in its FSH, but partial activation of all the RPs can only maintain a dysfunctional function far from its FSH. A PBM may self-adaptively modulate the activation of a partially activated RP of a normal function until it is fully activated and the normal function is then upgraded. This PBM is called indirect PBM (iPBM). The iPBM on cells such as tumor cells, myoblast cells and fibroblasts and other biosystems and their applications would be reviewed in this paper.

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

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    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

  9. 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…

  10. The core regulatory network in human cells.

    Science.gov (United States)

    Kim, Man-Sun; Kim, Dongsan; Kang, Nam Sook; Kim, Jeong-Rae

    2017-03-04

    In order to discover the common characteristics of various cell types in the human body, many researches have been conducted to find the set of genes commonly expressed in various cell types and tissues. However, the functional characteristics of a cell is determined by the complex regulatory relationships among the genes rather than by expressed genes themselves. Therefore, it is more important to identify and analyze a core regulatory network where all regulatory relationship between genes are active across all cell types to uncover the common features of various cell types. Here, based on hundreds of tissue-specific gene regulatory networks constructed by recent genome-wide experimental data, we constructed the core regulatory network. Interestingly, we found that the core regulatory network is organized by simple cascade and has few complex regulations such as feedback or feed-forward loops. Moreover, we discovered that the regulatory links from genes in the core regulatory network to genes in the peripheral regulatory network are much more abundant than the reverse direction links. These results suggest that the core regulatory network locates at the top of regulatory network and plays a role as a 'hub' in terms of information flow, and the information that is common to all cells can be modified to achieve the tissue-specific characteristics through various types of feedback and feed-forward loops in the peripheral regulatory networks. We also found that the genes in the core regulatory network are evolutionary conserved, essential and non-disease, non-druggable genes compared to the peripheral genes. Overall, our study provides an insight into how all human cells share a common function and generate tissue-specific functional traits by transmitting and processing information through regulatory network.

  11. 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.

  12. 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...

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Functional brain network efficiency predicts intelligence.

    Science.gov (United States)

    Langer, Nicolas; Pedroni, Andreas; Gianotti, Lorena R R; Hänggi, Jürgen; Knoch, Daria; Jäncke, Lutz

    2012-06-01

    The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small-world network attributes (high clustering and short path length) and whether these small-world network parameters are associated with intellectual performance. High-density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph-theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small-world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto-Frontal Integration Theory (P-FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for highly productive cognitions.

  19. 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

  20. 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...

  1. Structure and function in flow networks

    CERN Document Server

    Rubido, Nicolás; Baptista, Murilo S

    2013-01-01

    This Letter presents a unified approach for the fundamental relationship between structure and function in flow networks by solving analytically the voltages in a resistor network, transforming the network structure to an effective all-to-all topology, and then measuring the resultant flows. Moreover, it defines a way to study the structural resilience of the graph and to detect possible communities.

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. RBF networks with mixed radial basis functions

    NARCIS (Netherlands)

    Ciftcioglu, O.; Sariyildiz, I.S.

    2000-01-01

    After the introduction to neural network technology as multivariable function approximation, radial basis function (RBF) networks have been studied in many different aspects in recent years. From the theoretical viewpoint, approximation and uniqueness of the interpolation is studied and it has been

  7. 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 ...

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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......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 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....

  14. Industrial entrepreneurial network: Structural and functional analysis

    Science.gov (United States)

    Medvedeva, M. A.; Davletbaev, R. H.; Berg, D. B.; Nazarova, J. J.; Parusheva, S. S.

    2016-12-01

    Structure and functioning of two model industrial entrepreneurial networks are investigated in the present paper. One of these networks is forming when implementing an integrated project and consists of eight agents, which interact with each other and external environment. The other one is obtained from the municipal economy and is based on the set of the 12 real business entities. Analysis of the networks is carried out on the basis of the matrix of mutual payments aggregated over the certain time period. The matrix is created by the methods of experimental economics. Social Network Analysis (SNA) methods and instruments were used in the present research. The set of basic structural characteristics was investigated: set of quantitative parameters such as density, diameter, clustering coefficient, different kinds of centrality, and etc. They were compared with the random Bernoulli graphs of the corresponding size and density. Discovered variations of random and entrepreneurial networks structure are explained by the peculiarities of agents functioning in production network. Separately, were identified the closed exchange circuits (cyclically closed contours of graph) forming an autopoietic (self-replicating) network pattern. The purpose of the functional analysis was to identify the contribution of the autopoietic network pattern in its gross product. It was found that the magnitude of this contribution is more than 20%. Such value allows using of the complementary currency in order to stimulate economic activity of network agents.

  15. 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...

  16. Effectiveness of cell outage compensation in LTE networks

    NARCIS (Netherlands)

    Amirijoo, M.; Jorguseski, L.; Litjens, R.; Nascimento, R.

    2011-01-01

    Cell outage management is a self-healing functionality in future mobile cellular networks, aiming to automatically detect cell or site level outages (cell outage detection) as well as to mitigate as much as possible the caused degradation of coverage, capacity and/or service quality (cell outage com

  17. 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.

  18. Functional network organization of the human brain.

    Science.gov (United States)

    Power, Jonathan D; Cohen, Alexander L; Nelson, Steven M; Wig, Gagan S; Barnes, Kelly Anne; Church, Jessica A; Vogel, Alecia C; Laumann, Timothy O; Miezin, Fran M; Schlaggar, Bradley L; Petersen, Steven E

    2011-11-17

    Real-world complex systems may be mathematically modeled as graphs, revealing properties of the system. Here we study graphs of functional brain organization in healthy adults using resting state functional connectivity MRI. We propose two novel brain-wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships. These graphs contain many subgraphs in good agreement with known functional brain systems. Other subgraphs lack established functional identities; we suggest possible functional characteristics for these subgraphs. Further, graph measures of the areal network indicate that the default mode subgraph shares network properties with sensory and motor subgraphs: it is internally integrated but isolated from other subgraphs, much like a "processing" system. The modified voxelwise graph also reveals spatial motifs in the patterning of systems across the cortex.

  19. Identifying Functional Modules in Complex Networks

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In this paper, we propose a new method that enables us to detect and describe the functional modules in complex networks. Using the proposed method, we can classify the nodes of networks into different modules according to their pattern of intra- and extra-module links. We use our method to analyze the modular structures of the ER random networks. We find that different modules of networks have different structure properties, such as the clustering coefficient. Moreover, at the same time, many nodes of networks participate different modules. Remarkably, we find that in the ER random networks, when the probability p is small, different modules or different roles of nodes can be identified by different regionsin the c-p parameter space.

  20. 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.

  1. Genetic Networks in Mouse Retinal Ganglion Cells

    Directory of Open Access Journals (Sweden)

    Felix L Struebing

    2016-09-01

    Full Text Available Retinal ganglion cells (RGCs are the output neuron of the eye, transmitting visual information from the retina through the optic nerve to the brain. The importance of RGCs for vision is demonstrated in blinding diseases where RGCs are lost, such as in glaucoma or after optic nerve injury. In the present study, we hypothesize that normal RGC function is transcriptionally regulated. To test our hypothesis, we examine large retinal expression microarray datasets from recombinant inbred mouse strains in GeneNetwork and define transcriptional networks of RGCs and their subtypes. Two major and functionally distinct transcriptional networks centering around Thy1 and Tubb3 (Class III beta-tubulin were identified. Each network is independently regulated and modulated by unique genomic loci. Meta-analysis of publically available data confirms that RGC subtypes are differentially susceptible to death, with alpha-RGCs and intrinsically photosensitive RGCs (ipRGCs being less sensitive to cell death than other RGC subtypes in a mouse model of glaucoma.

  2. From molecular networks to qualitative cell behavior.

    Science.gov (United States)

    Gagneur, Julien; Casari, Georg

    2005-03-21

    Adaptation and behavior are characteristics of life which are fundamentally dynamic. If we want to model the living cell we have to describe it as a dynamic system. Typical dynamic models are based on quantitative differential equations requiring very detailed kinetic knowledge. Alternative modeling techniques for less fine-grained information are better suited to available functional genomics data. As such, constraint-based techniques and qualitative modeling have proven themselves to be valid approaches in cell biology. These approaches offer formal support to check the consistency of molecular networks against phenotypic observations in the light of dynamic systems.

  3. 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.

  4. 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

  5. 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...

  6. 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.

  7. Boolean logic functions of a synthetic peptide network.

    Science.gov (United States)

    Ashkenasy, Gonen; Ghadiri, M Reza

    2004-09-15

    Living cells can process rapidly and simultaneously multiple extracellular input signals through the complex networks of evolutionary selected biomolecular interactions and chemical transformations. Recent approaches to molecular computation have increasingly sought to mimic or exploit various aspects of biology. A number of studies have adapted nucleic acids and proteins to the design of molecular logic gates and computational systems, while other works have affected computation in living cells via biochemical pathway engineering. Here we report that de novo designed synthetic peptide networks can also mimic some of the basic logic functions of the more complex biological networks. We show that segments of a small network whose graph structure is composed of five nodes and 15 directed edges can express OR, NOR, and NOTIF logic.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Developing Functional Networks of Frontier Capital Markets

    Science.gov (United States)

    2011-10-01

    capital. As Stiglitz and Gallegati (2011) note, “Some network designs may be good at absorbing small shocks, when there can be systemic failure when...functions in innovative ways. 3 Stiglitz , Joseph E. and Mauro Gallegati, “Heterogeneous Interacting Agent Models for Understanding Monetary

  15. Functional model of biological neural networks.

    Science.gov (United States)

    Lo, James Ting-Ho

    2010-12-01

    A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

  16. 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.

  17. 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...

  18. 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.

  19. 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...

  20. 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.

  1. Coded Network Function Virtualization: Fault Tolerance via In-Network Coding

    DEFF Research Database (Denmark)

    Al-Shuwaili, A.; Simone, O.; Kliewer, J.

    2016-01-01

    Network function virtualization (NFV) prescribes the instantiation of network functions on general-purpose network devices, such as servers and switches. While yielding a more flexible and cost-effective network architecture, NFV is potentially limited by the fact that commercial off-the-shelf ha......Network function virtualization (NFV) prescribes the instantiation of network functions on general-purpose network devices, such as servers and switches. While yielding a more flexible and cost-effective network architecture, NFV is potentially limited by the fact that commercial off...

  2. BioFNet: biological functional network database for analysis and synthesis of biological systems.

    Science.gov (United States)

    Kurata, Hiroyuki; Maeda, Kazuhiro; Onaka, Toshikazu; Takata, Takenori

    2014-09-01

    In synthetic biology and systems biology, a bottom-up approach can be used to construct a complex, modular, hierarchical structure of biological networks. To analyze or design such networks, it is critical to understand the relationship between network structure and function, the mechanism through which biological parts or biomolecules are assembled into building blocks or functional networks. A functional network is defined as a subnetwork of biomolecules that performs a particular function. Understanding the mechanism of building functional networks would help develop a methodology for analyzing the structure of large-scale networks and design a robust biological circuit to perform a target function. We propose a biological functional network database, named BioFNet, which can cover the whole cell at the level of molecular interactions. The BioFNet takes an advantage in implementing the simulation program for the mathematical models of the functional networks, visualizing the simulated results. It presents a sound basis for rational design of biochemical networks and for understanding how functional networks are assembled to create complex high-level functions, which would reveal design principles underlying molecular architectures.

  3. A functional network module for Smith-Magenis syndrome.

    Science.gov (United States)

    Girirajan, S; Truong, H T; Blanchard, C L; Elsea, S H

    2009-04-01

    Disorders with overlapping diagnostic features are grouped into a network module. Based on phenotypic similarities or differential diagnoses, it is possible to identify functional pathways leading to individual features. We generated a Smith-Magenis syndrome (SMS)-specific network module utilizing patient clinical data, text mining from the Online Mendelian Inheritance in Man database, and in vitro functional analysis. We tested our module by functional studies based on a hypothesis that RAI1 acts through phenotype-specific pathways involving several downstream genes, which are altered due to RAI1 haploinsufficiency. A preliminary genome-wide gene expression study was performed using microarrays on RAI1 haploinsufficient cells created by RNAi-based approximately 50% knockdown of RAI1 in HEK293T cells. The top dysregulated genes were involved in growth signaling and insulin sensitivity, neuronal differentiation, lipid biosynthesis and fat mobilization, circadian activity, behavior, renal, cardiovascular and skeletal development, gene expression, and cell-cycle regulation and recombination, reflecting the spectrum of clinical features observed in SMS. Validation using real-time quantitative reverse transcriptase polymerase chain reaction confirmed the gene expression profile of 75% of the selected genes analyzed in both HEK293T RAI1 knockdown cells and SMS lymphoblastoid cell lines. Overall, these data support a method for identifying genes and pathways responsible for individual clinical features in a complex disorder such as SMS.

  4. 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.

  5. A Squeezed Artificial Neural Network for the Symbolic Network Reliability Functions of Binary-State Networks.

    Science.gov (United States)

    Yeh, Wei-Chang

    2016-08-18

    Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.

  6. Advanced Functionalities for Highly Reliable Optical Networks

    DEFF Research Database (Denmark)

    An, Yi

    This thesis covers two research topics concerning optical solutions for networks e.g. avionic systems. One is to identify the applications for silicon photonic devices for cost-effective solutions in short-range optical networks. The other one is to realise advanced functionalities in order to in......) using two exclusive OR (XOR) gates realised by four-wave mixing (FWM) in semiconductor optical amplifiers (SOAs) is experimentally demonstrated and very low (~ 1 dB) total operation penalty is achieved....... 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...... interconnects and network-on-chips. A novel concept of all-optical protection switching scheme is proposed, where fault detection and protection trigger are all implemented in the optical domain. This scheme can provide ultra-fast establishment of the protection path resulting in a minimum loss of data...

  7. 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.

  8. 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.

  9. 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/.

  10. Controlling Cell Function with Geometry

    Science.gov (United States)

    Mrksich, Milan

    2012-02-01

    This presentation will describe the use of patterned substrates to control cell shape with examples that illustrate the ways in which cell shape can regulate cell function. Most cells are adherent and must attach to and spread on a surface in order to survive, proliferate and function. In tissue, this surface is the extracellular matrix (ECM), an insoluble scaffold formed by the assembly of several large proteins---including fibronectin, the laminins and collagens and others---but in the laboratory, the surface is prepared by adsorbing protein to glass slides. To pattern cells, gold-coated slides are patterned with microcontact printing to create geometric features that promote cell attachment and that are surrounded by inert regions. Cells attach to these substrates and spread to adopt the shape defined by the underlying pattern and remain stable in culture for several days. Examples will be described that used a series of shapes to reveal the relationship between the shape of the cell and the structure of its cytoskeleton. These geometric cues were used to control cell polarity and the tension, or contractility, present in the cytoskeleton. These rules were further used to control the shapes of mesenchymal stem cells and in turn to control the differentiation of these cells into specialized cell types. For example, stem cells that were patterned into a ``star'' shape preferentially differentiated into bone cells whereas those that were patterned into a ``flower'' shape preferred a fat cell fate. These influences of shape on differentiation depend on the mechanical properties of the cytoskeleton. These examples, and others, reveal that shape is an important cue that informs cell function and that can be combined with the more common soluble cues to direct and study cell function.

  11. Mechanical Cell-Cell Communication in Fibrous Networks: The Importance of Network Geometry.

    Science.gov (United States)

    Humphries, D L; Grogan, J A; Gaffney, E A

    2017-03-01

    Cells contracting in extracellular matrix (ECM) can transmit stress over long distances, communicating their position and orientation to cells many tens of micrometres away. Such phenomena are not observed when cells are seeded on substrates with linear elastic properties, such as polyacrylamide (PA) gel. The ability for fibrous substrates to support far reaching stress and strain fields has implications for many physiological processes, while the mechanical properties of ECM are central to several pathological processes, including tumour invasion and fibrosis. Theoretical models have investigated the properties of ECM in a variety of network geometries. However, the effects of network architecture on mechanical cell-cell communication have received little attention. This work investigates the effects of geometry on network mechanics, and thus the ability for cells to communicate mechanically through different networks. Cell-derived displacement fields are quantified for various network geometries while controlling for network topology, cross-link density and micromechanical properties. We find that the heterogeneity of response, fibre alignment, and substrate displacement fields are sensitive to network choice. Further, we show that certain geometries support mechanical communication over longer distances than others. As such, we predict that the choice of network geometry is important in fundamental modelling of cell-cell interactions in fibrous substrates, as well as in experimental settings, where mechanical signalling at the cellular scale plays an important role. This work thus informs the construction of theoretical models for substrate mechanics and experimental explorations of mechanical cell-cell communication.

  12. CellNet: network biology applied to stem cell engineering.

    Science.gov (United States)

    Cahan, Patrick; Li, Hu; Morris, Samantha A; Lummertz da Rocha, Edroaldo; Daley, George Q; Collins, James J

    2014-08-14

    Somatic cell reprogramming, directed differentiation of pluripotent stem cells, and direct conversions between differentiated cell lineages represent powerful approaches to engineer cells for research and regenerative medicine. We have developed CellNet, a network biology platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. Analyzing expression data from 56 published reports, we found that cells derived via directed differentiation more closely resemble their in vivo counterparts than products of direct conversion, as reflected by the establishment of target cell-type gene regulatory networks (GRNs). Furthermore, we discovered that directly converted cells fail to adequately silence expression programs of the starting population and that the establishment of unintended GRNs is common to virtually every cellular engineering paradigm. CellNet provides a platform for quantifying how closely engineered cell populations resemble their target cell type and a rational strategy to guide enhanced cellular engineering.

  13. 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.

  14. Network physiology reveals relations between network topology and physiological function

    OpenAIRE

    Bashan, Amir; Bartsch, Ronny P.; Kantelhardt, Jan W.; Havlin, Shlomo; Ivanov, Plamen Ch.

    2012-01-01

    The human organism is an integrated network where complex physiological 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 physiological network. We find that each physiological state is...

  15. 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.

  16. Manifold learning on brain functional networks in aging.

    Science.gov (United States)

    Qiu, Anqi; Lee, Annie; Tan, Mingzhen; Chung, Moo K

    2015-02-01

    We propose a new analysis framework to utilize the full information of brain functional networks for computing the mean of a set of brain functional networks and embedding brain functional networks into a low-dimensional space in which traditional regression and classification analyses can be easily employed. For this, we first represent the brain functional network by a symmetric positive matrix computed using sparse inverse covariance estimation. We then impose a Log-Euclidean Riemannian manifold structure on brain functional networks whose norm gives a convenient and practical way to define a mean. Finally, based on the fact that the computation of linear operations can be done in the tangent space of this Riemannian manifold, we adopt Locally Linear Embedding (LLE) to the Log-Euclidean Riemannian manifold space in order to embed the brain functional networks into a low-dimensional space. We show that the integration of the Log-Euclidean manifold with LLE provides more efficient and succinct representation of the functional network and facilitates regression analysis, such as ridge regression, on the brain functional network to more accurately predict age when compared to that of the Euclidean space of functional networks with LLE. Interestingly, using the Log-Euclidean analysis framework, we demonstrate the integration and segregation of cortical-subcortical networks as well as among the salience, executive, and emotional networks across lifespan.

  17. DevOps for network function virtualisation: an architectural approach

    OpenAIRE

    Karl, H.; Draexler, S.; Peuster, M; Galis, A.; Bredel, M.; RAMOS, A.; Martrat, J.; Siddiqui, M S; Van Rossem, S.; Tavernier, W; Xilouris, G.

    2016-01-01

    The Service Programming and Orchestration for Virtualised Software Networks (SONATA) project targets both the flexible programmability of software networks and the optimisation of their deployments by means of integrating Development and Operations in order to accelerate industry adoption of software networks and reduce time-to-market for networked services. SONATA supports network function chaining and orchestration, making service platforms modular and easier to customise to the needs of di...

  18. 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.

  19. 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...

  20. 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.

  1. 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

  2. ``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.

  3. 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.

  4. 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.

  5. Using neural networks to predict the functionality of reconfigurable nano-material networks

    NARCIS (Netherlands)

    Greff, Klaus; Damme, van Ruud; Koutnik, Jan; Broersma, Hajo; Mikhal, Julia; Lawrence, Celestine; Wiel, van der Wilfred; Schmidhuber, Jürgen

    2017-01-01

    This paper demonstrates how neural networks can be applied to model and predict the functional behaviour of disordered nano-particle and nano-tube networks. In recently published experimental work, nano-particle and nano-tube networks show promising functionality for future reconfigurable devices, w

  6. Models of neural networks with fuzzy activation functions

    Science.gov (United States)

    Nguyen, A. T.; Korikov, A. M.

    2017-02-01

    This paper investigates the application of a new form of neuron activation functions that are based on the fuzzy membership functions derived from the theory of fuzzy systems. On the basis of the results regarding neuron models with fuzzy activation functions, we created the models of fuzzy-neural networks. These fuzzy-neural network models differ from conventional networks that employ the fuzzy inference systems using the methods of neural networks. While conventional fuzzy-neural networks belong to the first type, fuzzy-neural networks proposed here are defined as the second-type models. The simulation results show that the proposed second-type model can successfully solve the problem of the property prediction for time – dependent signals. Neural networks with fuzzy impulse activation functions can be widely applied in many fields of science, technology and mechanical engineering to solve the problems of classification, prediction, approximation, etc.

  7. 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.

  8. 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

  9. 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.

  10. 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...

  11. Elucidating gene function and function evolution through comparison of co-expression networks in plants

    Directory of Open Access Journals (Sweden)

    Marek eMutwil

    2014-08-01

    Full Text Available The analysis of gene expression data has shown that transcriptionally coordinated (co-expressed genes are often functionally related, enabling scientists to use expression data in gene function prediction. This Focused Review discusses our original paper (Large-scale co-expression approach to dissect secondary cell wall formation across plant species, Frontiers in Plant Science 2:23. In this paper we applied cross-species analysis to co-expression networks of genes involved in cellulose biosynthesis. We show that the co-expression networks from different species are highly similar, indicating that whole biological pathways are conserved across species. This finding has two important implications. First, the analysis can transfer gene function annotation from well-studied plants, such as Arabidopsis, to other, uncharacterized plant species. As the analysis finds genes that have similar sequence and similar expression pattern across different organisms, functionally equivalent genes can be identified. Second, since co-expression analyses are often noisy, a comparative analysis should have higher performance, as parts of co-expression networks that are conserved are more likely to be functionally relevant. In this Focused Review, we outline the comparative analysis done in the original paper and comment on the recent advances and approaches that allow comparative analyses of co-function networks. We hypothesize that, in comparison to simple co-expression analysis, comparative analysis would yield more accurate gene function predictions. Finally, by combining comparative analysis with genomic information of green plants, we propose a possible composition of cellulose biosynthesis machinery during earlier stages of plant evolution.

  12. 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.

  13. Discovering functional interaction patterns in protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Can Tolga

    2008-06-01

    Full Text Available Abstract Background In recent years, a considerable amount of research effort has been directed to the analysis of biological networks with the availability of genome-scale networks of genes and/or proteins of an increasing number of organisms. A protein-protein interaction (PPI network is a particular biological network which represents physical interactions between pairs of proteins of an organism. Major research on PPI networks has focused on understanding the topological organization of PPI networks, evolution of PPI networks and identification of conserved subnetworks across different species, discovery of modules of interaction, use of PPI networks for functional annotation of uncharacterized proteins, and improvement of the accuracy of currently available networks. Results In this article, we map known functional annotations of proteins onto a PPI network in order to identify frequently occurring interaction patterns in the functional space. We propose a new frequent pattern identification technique, PPISpan, adapted specifically for PPI networks from a well-known frequent subgraph identification method, gSpan. Existing module discovery techniques either look for specific clique-like highly interacting protein clusters or linear paths of interaction. However, our goal is different; instead of single clusters or pathways, we look for recurring functional interaction patterns in arbitrary topologies. We have applied PPISpan on PPI networks of Saccharomyces cerevisiae and identified a number of frequently occurring functional interaction patterns. Conclusion With the help of PPISpan, recurring functional interaction patterns in an organism's PPI network can be identified. Such an analysis offers a new perspective on the modular organization of PPI networks. The complete list of identified functional interaction patterns is available at http://bioserver.ceng.metu.edu.tr/PPISpan/.

  14. Structure-function clustering in multiplex brain networks

    Science.gov (United States)

    Crofts, J. J.; Forrester, M.; O'Dea, R. D.

    2016-10-01

    A key question in neuroscience is to understand how a rich functional repertoire of brain activity arises within relatively static networks of structurally connected neural populations: elucidating the subtle interactions between evoked “functional connectivity” and the underlying “structural connectivity” has the potential to address this. These structural-functional networks (and neural networks more generally) are more naturally described using a multilayer or multiplex network approach, in favour of standard single-layer network analyses that are more typically applied to such systems. In this letter, we address such issues by exploring important structure-function relations in the Macaque cortical network by modelling it as a duplex network that comprises an anatomical layer, describing the known (macro-scale) network topology of the Macaque monkey, and a functional layer derived from simulated neural activity. We investigate and characterize correlations between structural and functional layers, as system parameters controlling simulated neural activity are varied, by employing recently described multiplex network measures. Moreover, we propose a novel measure of multiplex structure-function clustering which allows us to investigate the emergence of functional connections that are distinct from the underlying cortical structure, and to highlight the dependence of multiplex structure on the neural dynamical regime.

  15. 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.

  16. 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.

  17. Resting-state brain organization revealed by functional covariance networks.

    Directory of Open Access Journals (Sweden)

    Zhiqiang Zhang

    Full Text Available BACKGROUND: Brain network studies using techniques of intrinsic connectivity network based on fMRI time series (TS-ICN and structural covariance network (SCN have mapped out functional and structural organization of human brain at respective time scales. However, there lacks a meso-time-scale network to bridge the ICN and SCN and get insights of brain functional organization. METHODOLOGY AND PRINCIPAL FINDINGS: We proposed a functional covariance network (FCN method by measuring the covariance of amplitude of low-frequency fluctuations (ALFF in BOLD signals across subjects, and compared the patterns of ALFF-FCNs with the TS-ICNs and SCNs by mapping the brain networks of default network, task-positive network and sensory networks. We demonstrated large overlap among FCNs, ICNs and SCNs and modular nature in FCNs and ICNs by using conjunctional analysis. Most interestingly, FCN analysis showed a network dichotomy consisting of anti-correlated high-level cognitive system and low-level perceptive system, which is a novel finding different from the ICN dichotomy consisting of the default-mode network and the task-positive network. CONCLUSION: The current study proposed an ALFF-FCN approach to measure the interregional correlation of brain activity responding to short periods of state, and revealed novel organization patterns of resting-state brain activity from an intermediate time scale.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. Hierarchical organization of brain functional network during visual task

    CERN Document Server

    Zhuo, Zhao; Fu, Zhong-Qian; Zhang, Jie

    2011-01-01

    In this paper, the brain functional networks derived from high-resolution synchronous EEG time series during visual task are generated by calculating the phase synchronization among the time series. The hierarchical modular organizations of these networks are systematically investigated by the fast Girvan-Newman algorithm. At the same time, the spatially adjacent electrodes (corresponding to EEG channels) are clustered into functional groups based on anatomical parcellation of brain cortex, and this clustering information are compared to that of the functional network. The results show that the modular architectures of brain functional network are in coincidence with that from the anatomical structures over different levels of hierarchy, which suggests that population of neurons performing the same function excite and inhibit in identical rhythms. The structure-function relationship further reveals that the correlations among EEG time series in the same functional group are much stronger than those in differe...

  3. Adiabatic superconducting cells for ultra-low-power artificial neural networks

    Directory of Open Access Journals (Sweden)

    Andrey E. Schegolev

    2016-10-01

    Full Text Available We propose the concept of using superconducting quantum interferometers for the implementation of neural network algorithms with extremely low power dissipation. These adiabatic elements are Josephson cells with sigmoid- and Gaussian-like activation functions. We optimize their parameters for application in three-layer perceptron and radial basis function networks.

  4. Adiabatic superconducting cells for ultra-low-power artificial neural networks.

    Science.gov (United States)

    Schegolev, Andrey E; Klenov, Nikolay V; Soloviev, Igor I; Tereshonok, Maxim V

    2016-01-01

    We propose the concept of using superconducting quantum interferometers for the implementation of neural network algorithms with extremely low power dissipation. These adiabatic elements are Josephson cells with sigmoid- and Gaussian-like activation functions. We optimize their parameters for application in three-layer perceptron and radial basis function networks.

  5. 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.

  6. 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.

  7. Optimizing cell arrays for accurate functional genomics

    Directory of Open Access Journals (Sweden)

    Fengler Sven

    2012-07-01

    Full Text Available Abstract Background Cellular responses emerge from a complex network of dynamic biochemical reactions. In order to investigate them is necessary to develop methods that allow perturbing a high number of gene products in a flexible and fast way. Cell arrays (CA enable such experiments on microscope slides via reverse transfection of cellular colonies growing on spotted genetic material. In contrast to multi-well plates, CA are susceptible to contamination among neighboring spots hindering accurate quantification in cell-based screening projects. Here we have developed a quality control protocol for quantifying and minimizing contamination in CA. Results We imaged checkered CA that express two distinct fluorescent proteins and segmented images into single cells to quantify the transfection efficiency and interspot contamination. Compared with standard procedures, we measured a 3-fold reduction of contaminants when arrays containing HeLa cells were washed shortly after cell seeding. We proved that nucleic acid uptake during cell seeding rather than migration among neighboring spots was the major source of contamination. Arrays of MCF7 cells developed without the washing step showed 7-fold lower percentage of contaminant cells, demonstrating that contamination is dependent on specific cell properties. Conclusions Previously published methodological works have focused on achieving high transfection rate in densely packed CA. Here, we focused in an equally important parameter: The interspot contamination. The presented quality control is essential for estimating the rate of contamination, a major source of false positives and negatives in current microscopy based functional genomics screenings. We have demonstrated that a washing step after seeding enhances CA quality for HeLA but is not necessary for MCF7. The described method provides a way to find optimal seeding protocols for cell lines intended to be used for the first time in CA.

  8. Mapping multiplex hubs in human functional brain networks

    Directory of Open Access Journals (Sweden)

    Alex Arenas

    2016-07-01

    Full Text Available Typical brain networks consist of many peripheral regions and a few highly centralones, i.e. hubs, playing key functional roles in cerebral inter-regional interactions. Studieshave shown that networks, obtained from the analysis of specific frequency components ofbrain activity, present peculiar architectures with unique profiles of region centrality. However,the identification of hubs in networks built from different frequency bands simultaneouslyis still a challenging problem, remaining largely unexplored. Here we identify eachfrequency component with one layer of a multiplex network and face this challenge by exploitingthe recent advances in the analysis of multiplex topologies. First, we show that eachfrequency band carries unique topological information, fundamental to accurately modelbrain functional networks. We then demonstrate that hubs in the multiplex network, in generaldifferent from those ones obtained after discarding or aggregating the measured signalsas usual, provide a more accurate map of brain’s most important functional regions, allowingto distinguish between healthy and schizophrenic populations better than conventionalnetwork approaches.

  9. 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.

  10. 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.

  11. 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

  12. Cell elasticity determines macrophage function.

    Directory of Open Access Journals (Sweden)

    Naimish R Patel

    Full Text Available Macrophages serve to maintain organ homeostasis in response to challenges from injury, inflammation, malignancy, particulate exposure, or infection. Until now, receptor ligation has been understood as being the central mechanism that regulates macrophage function. Using macrophages of different origins and species, we report that macrophage elasticity is a major determinant of innate macrophage function. Macrophage elasticity is modulated not only by classical biologic activators such as LPS and IFN-γ, but to an equal extent by substrate rigidity and substrate stretch. Macrophage elasticity is dependent upon actin polymerization and small rhoGTPase activation, but functional effects of elasticity are not predicted by examination of gene expression profiles alone. Taken together, these data demonstrate an unanticipated role for cell elasticity as a common pathway by which mechanical and biologic factors determine macrophage function.

  13. 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.

  14. Neural networks for function approximation in nonlinear control

    Science.gov (United States)

    Linse, Dennis J.; Stengel, Robert F.

    1990-01-01

    Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.

  15. 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.

  16. Density functional and neural network analysis

    DEFF Research Database (Denmark)

    Jalkanen, K. J.; 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....

  17. Discovering and Analyzing Network Function and Structure

    Science.gov (United States)

    2015-07-08

    that the numerical linear algebra community has been seeking for a long time: sparse approximate inverses. To explain these, I recall that the classical...accelerate the computation. Our presently best algorithm computes the matrices L and U and applies them to solve a linear system in parallel time O(log6 n...imization, comes from Zhu, Ghahramani and Lafferty [ZGL+03], and only applies to undirected networks. Formally, one is given a network with vertex set

  18. 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

  19. 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.

  20. Layered neural networks with non-monotonic transfer functions

    Science.gov (United States)

    Katayama, Katsuki; Sakata, Yasuo; Horiguchi, Tsuyoshi

    2003-01-01

    We investigate storage capacity and generalization ability for two types of fully connected layered neural networks with non-monotonic transfer functions; random patterns are embedded into the networks by a Hebbian learning rule. One of them is a layered network in which a non-monotonic transfer function of even layers is different from that of odd layers. The other is a layered network with intra-layer connections, in which the non-monotonic transfer function of inter-layer is different from that of intra-layer, and inter-layered neurons and intra-layered neurons are updated alternately. We derive recursion relations for order parameters for those layered networks by the signal-to-noise ratio method. We clarify that the storage capacity and the generalization ability for those layered networks are enhanced in comparison with those with a conventional monotonic transfer function when non-monotonicity of the transfer functions is selected optimally. We also point out that some chaotic behavior appears in the order parameters for the layered networks when non-monotonicity of the transfer functions increases.

  1. 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.

  2. 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

  3. Functional expansion representations of artificial neural networks

    Science.gov (United States)

    Gray, W. Steven

    1992-01-01

    In the past few years, significant interest has developed in using artificial neural networks to model and control nonlinear dynamical systems. While there exists many proposed schemes for accomplishing this and a wealth of supporting empirical results, most approaches to date tend to be ad hoc in nature and rely mainly on heuristic justifications. The purpose of this project was to further develop some analytical tools for representing nonlinear discrete-time input-output systems, which when applied to neural networks would give insight on architecture selection, pruning strategies, and learning algorithms. A long term goal is to determine in what sense, if any, a neural network can be used as a universal approximator for nonliner input-output maps with memory (i.e., realized by a dynamical system). This property is well known for the case of static or memoryless input-output maps. The general architecture under consideration in this project was a single-input, single-output recurrent feedforward network.

  4. 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.

  5. Functional characterization and topological modularity of molecular interaction networks

    Directory of Open Access Journals (Sweden)

    Koyutürk Mehmet

    2010-01-01

    Full Text Available Abstract Background Analyzing interaction networks for functional characterization poses significant challenges arising from the noisy, incomplete, and generic nature of both the interaction data as well as functional annotation of molecules. Network-based methods focus on interacting molecules (pairs or sets occurring in close proximity to infer functional associations. Results In this paper we perform a formal comparative investigation of the relationship between functional coherence and topological proximity in networks. We investigate the problem of assessing the coherence of sets of biomolecules (or segments thereof taking into account functional specificity as well as the distribution of functional attributes across entity groups. We also propose novel measures of topological proximity that are more robust to noisy and incomplete interaction data. Conclusion We derive the following results in this paper: (i there exists strong correlation between functional similarity and topological proximity in various network abstractions, with domain interaction networks (DDIs demonstrating higher correlation than protein interaction networks (PPIs; (ii measures that quantify coherence among entire sets of proteins are superior to aggregates of known pair-wise measures; and (iii random-walk based measures of topological proximity are better suited to existing interaction data. We validate our methods on diverse data, including experimentally and computationally derived PPIs and DDIs, as well as on sets of known biologically related groups of molecules.

  6. 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.

  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...... 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.......-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...

  8. The Reticular Cell Network : A Robust Backbone for Immune Responses

    NARCIS (Netherlands)

    Textor, Johannes; Mandl, Judith N; de Boer, Rob J

    2016-01-01

    Lymph nodes are meeting points for circulating immune cells. A network of reticular cells that ensheathe a mesh of collagen fibers crisscrosses the tissue in each lymph node. This reticular cell network distributes key molecules and provides a structure for immune cells to move around on. During inf

  9. Functional Reorganizations of Brain Network in Prelingually Deaf Adolescents

    OpenAIRE

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

    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 ne...

  10. 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.

  11. Language networks in children: Evidence from functional MRI studies

    OpenAIRE

    2009-01-01

    We review functional MRI and other neuroimaging studies of language skills in children from infancy to adulthood. These studies show developmental changes in the networks of brain regions supporting language, which can be affected by brain injuries or neurological disorders. Particular aspects of language rely on networks that lateralize to the dominant hemisphere; others rely on bilateral or non-dominant mechanisms. Multiple fMRI tasks for pediatric patients characterize functional brain reo...

  12. Statistical Network Analysis for Functional MRI: Mean Networks and Group Comparisons.

    Directory of Open Access Journals (Sweden)

    Cedric E Ginestet

    2014-05-01

    Full Text Available Comparing networks in neuroscience is hard, because the topological properties of a given network are necessarily dependent on the number of edges of that network. This problem arises in the analysis of both weighted and unweighted networks. The term density is often used in this context, in order to refer to the mean edge weight of a weighted network, or to the number of edges in an unweighted one. Comparing families of networks is therefore statistically difficult because differences in topology are necessarily associated with differences in density. In this review paper, we consider this problem from two different perspectives, which include (i the construction of summary networks, such as how to compute and visualize the mean network from a sample of network-valued data points; and (ii how to test for topological differences, when two families of networks also exhibit significant differences in density. In the first instance, we show that the issue of summarizing a family of networks can be conducted by either adopting a mass-univariate approach, which produces a statistical parametric network (SPN, or by directly computing the mean network, provided that a metric has been specified on the space of all networks with a given number of nodes. In the second part of this review, we then highlight the inherent problems associated with the comparison of topological functions of families of networks that differ in density. In particular, we show that a wide range of topological summaries, such as global efficiency and network modularity are highly sensitive to differences in density. Moreover, these problems are not restricted to unweighted metrics, as we demonstrate that the same issues remain present when considering the weighted versions of these metrics. We conclude by encouraging caution, when reporting such statistical comparisons, and by emphasizing the importance of constructing summary networks.

  13. Cell cycle control by a minimal Cdk network.

    Directory of Open Access Journals (Sweden)

    Claude Gérard

    2015-02-01

    Full Text Available In present-day eukaryotes, the cell division cycle is controlled by a complex network of interacting proteins, including members of the cyclin and cyclin-dependent protein kinase (Cdk families, and the Anaphase Promoting Complex (APC. Successful progression through the cell cycle depends on precise, temporally ordered regulation of the functions of these proteins. In light of this complexity, it is surprising that in fission yeast, a minimal Cdk network consisting of a single cyclin-Cdk fusion protein can control DNA synthesis and mitosis in a manner that is indistinguishable from wild type. To improve our understanding of the cell cycle regulatory network, we built and analysed a mathematical model of the molecular interactions controlling the G1/S and G2/M transitions in these minimal cells. The model accounts for all observed properties of yeast strains operating with the fusion protein. Importantly, coupling the model's predictions with experimental analysis of alternative minimal cells, we uncover an explanation for the unexpected fact that elimination of inhibitory phosphorylation of Cdk is benign in these strains while it strongly affects normal cells. Furthermore, in the strain without inhibitory phosphorylation of the fusion protein, the distribution of cell size at division is unusually broad, an observation that is accounted for by stochastic simulations of the model. Our approach provides novel insights into the organization and quantitative regulation of wild type cell cycle progression. In particular, it leads us to propose a new mechanistic model for the phenomenon of mitotic catastrophe, relying on a combination of unregulated, multi-cyclin-dependent Cdk activities.

  14. A Statistical Method to Distinguish Functional Brain Networks

    Science.gov (United States)

    Fujita, André; Vidal, Maciel C.; Takahashi, Daniel Y.

    2017-01-01

    One major problem in neuroscience is the comparison of functional brain networks of different populations, e.g., distinguishing the networks of controls and patients. Traditional algorithms are based on search for isomorphism between networks, assuming that they are deterministic. However, biological networks present randomness that cannot be well modeled by those algorithms. For instance, functional brain networks of distinct subjects of the same population can be different due to individual characteristics. Moreover, networks of subjects from different populations can be generated through the same stochastic process. Thus, a better hypothesis is that networks are generated by random processes. In this case, subjects from the same group are samples from the same random process, whereas subjects from different groups are generated by distinct processes. Using this idea, we developed a statistical test called ANOGVA to test whether two or more populations of graphs are generated by the same random graph model. Our simulations' results demonstrate that we can precisely control the rate of false positives and that the test is powerful to discriminate random graphs generated by different models and parameters. The method also showed to be robust for unbalanced data. As an example, we applied ANOGVA to an fMRI dataset composed of controls and patients diagnosed with autism or Asperger. ANOGVA identified the cerebellar functional sub-network as statistically different between controls and autism (p < 0.001). PMID:28261045

  15. The structure and function of fungal cells

    Science.gov (United States)

    Nozawa, Y.

    1984-01-01

    The structure and function of fungal cell walls were studied with particular emphasis on dermatophytes. Extraction, isolation, analysis, and observation of the cell wall structure and function were performed. The structure is described microscopically and chemically.

  16. 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

  17. A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation.

    Science.gov (United States)

    Vuković, Najdan; Miljković, Zoran

    2013-10-01

    Radial basis function (RBF) neural network is constructed of certain number of RBF neurons, and these networks are among the most used neural networks for modeling of various nonlinear problems in engineering. Conventional RBF neuron is usually based on Gaussian type of activation function with single width for each activation function. This feature restricts neuron performance for modeling the complex nonlinear problems. To accommodate limitation of a single scale, this paper presents neural network with similar but yet different activation function-hyper basis function (HBF). The HBF allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The HBF is based on generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. Compared to the RBF, the HBF neuron has more parameters to optimize, but HBF neural network needs less number of HBF neurons to memorize relationship between input and output sets in order to achieve good generalization property. However, recent research results of HBF neural network performance have shown that optimal way of constructing this type of neural network is needed; this paper addresses this issue and modifies sequential learning algorithm for HBF neural network that exploits the concept of neuron's significance and allows growing and pruning of HBF neuron during learning process. Extensive experimental study shows that HBF neural network, trained with developed learning algorithm, achieves lower prediction error and more compact neural network.

  18. 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.

  19. Mapping distributed brain function and networks with diffuse optical tomography

    Science.gov (United States)

    Eggebrecht, Adam T.; Ferradal, Silvina L.; Robichaux-Viehoever, Amy; Hassanpour, Mahlega S.; Dehghani, Hamid; Snyder, Abraham Z.; Hershey, Tamara; Culver, Joseph P.

    2014-06-01

    Mapping of human brain function has revolutionized systems neuroscience. However, traditional functional neuroimaging by positron emission tomography or functional magnetic resonance imaging cannot be used when applications require portability, or are contraindicated because of ionizing radiation (positron emission tomography) or implanted metal (functional magnetic resonance imaging). Optical neuroimaging offers a non-invasive alternative that is radiation free and compatible with implanted metal and electronic devices (for example, pacemakers). However, optical imaging technology has heretofore lacked the combination of spatial resolution and wide field of view sufficient to map distributed brain functions. Here, we present a high-density diffuse optical tomography imaging array that can map higher-order, distributed brain function. The system was tested by imaging four hierarchical language tasks and multiple resting-state networks including the dorsal attention and default mode networks. Finally, we imaged brain function in patients with Parkinson's disease and implanted deep brain stimulators that preclude functional magnetic resonance imaging.

  20. Development of large-scale functional networks over the lifespan.

    Science.gov (United States)

    Schlee, Winfried; Leirer, Vera; Kolassa, Stephan; Thurm, Franka; Elbert, Thomas; Kolassa, Iris-Tatjana

    2012-10-01

    The development of large-scale functional organization of the human brain across the lifespan is not well understood. Here we used magnetoencephalographic recordings of 53 adults (ages 18-89) to characterize functional brain networks in the resting state. Slow frequencies engage larger networks than higher frequencies and show different development over the lifespan. Networks in the delta (2-4 Hz) frequency range decrease, while networks in the beta/gamma frequency range (> 16 Hz) increase in size with advancing age. Results show that the right frontal lobe and the temporal areas in both hemispheres are important relay stations in the expanding high-frequency networks. Neuropsychological tests confirmed the tendency of cognitive decline with older age. The decrease in visual memory and visuoconstructive functions was strongly associated with the age-dependent enhancement of functional connectivity in both temporal lobes. Using functional network analysis this study elucidates important neuronal principles underlying age-related cognitive decline paving mental deterioration in senescence.

  1. 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.

  2. 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.

  3. 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.

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

    Science.gov (United States)

    Zhang, Dongliang; Wu, Jiayi; Ouyang, Qi

    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, continuous simulation is applied to evaluate the performance of these topologies. We demonstrate the usefulness of this method by designing an example biological function: the SOS response of E. coli. Our numerical results show that the desired function can be faithfully reproduced by candidate networks with different parameters and initial conditions. Possible circuits are ranked according to their robustness against perturbations in parameter and gene expressions. The biological network is among the candidate networks, yet novel designs can be generated. Our method provides a scalable way to design robust circuits that can achieve complex functions, and makes it possible to uncover design principles of biological networks. PMID:26061094

  5. Connectomics and neuroticism: an altered functional network organization.

    Science.gov (United States)

    Servaas, Michelle N; Geerligs, Linda; Renken, Remco J; Marsman, Jan-Bernard C; Ormel, Johan; Riese, Harriëtte; Aleman, André

    2015-01-01

    The personality trait neuroticism is a potent risk marker for psychopathology. Although the neurobiological basis remains unclear, studies have suggested that alterations in connectivity may underlie it. Therefore, the aim of the current study was to shed more light on the functional network organization in neuroticism. To this end, we applied graph theory on resting-state functional magnetic resonance imaging (fMRI) data in 120 women selected based on their neuroticism score. Binary and weighted brain-wide graphs were constructed to examine changes in the functional network structure and functional connectivity strength. Furthermore, graphs were partitioned into modules to specifically investigate connectivity within and between functional subnetworks related to emotion processing and cognitive control. Subsequently, complex network measures (ie, efficiency and modularity) were calculated on the brain-wide graphs and modules, and correlated with neuroticism scores. Compared with low neurotic individuals, high neurotic individuals exhibited a whole-brain network structure resembling more that of a random network and had overall weaker functional connections. Furthermore, in these high neurotic individuals, functional subnetworks could be delineated less clearly and the majority of these subnetworks showed lower efficiency, while the affective subnetwork showed higher efficiency. In addition, the cingulo-operculum subnetwork demonstrated more ties with other functional subnetworks in association with neuroticism. In conclusion, the 'neurotic brain' has a less than optimal functional network organization and shows signs of functional disconnectivity. Moreover, in high compared with low neurotic individuals, emotion and salience subnetworks have a more prominent role in the information exchange, while sensory(-motor) and cognitive control subnetworks have a less prominent role.

  6. Performance verification of network function virtualization in software defined optical transport networks

    Science.gov (United States)

    Zhao, Yongli; Hu, Liyazhou; Wang, Wei; Li, Yajie; Zhang, Jie

    2017-01-01

    With the continuous opening of resource acquisition and application, there are a large variety of network hardware appliances deployed as the communication infrastructure. To lunch a new network application always implies to replace the obsolete devices and needs the related space and power to accommodate it, which will increase the energy and capital investment. Network function virtualization1 (NFV) aims to address these problems by consolidating many network equipment onto industry standard elements such as servers, switches and storage. Many types of IT resources have been deployed to run Virtual Network Functions (vNFs), such as virtual switches and routers. Then how to deploy NFV in optical transport networks is a of great importance problem. This paper focuses on this problem, and gives an implementation architecture of NFV-enabled optical transport networks based on Software Defined Optical Networking (SDON) with the procedure of vNFs call and return. Especially, an implementation solution of NFV-enabled optical transport node is designed, and a parallel processing method for NFV-enabled OTN nodes is proposed. To verify the performance of NFV-enabled SDON, the protocol interaction procedures of control function virtualization and node function virtualization are demonstrated on SDON testbed. Finally, the benefits and challenges of the parallel processing method for NFV-enabled OTN nodes are simulated and analyzed.

  7. Hemispheric asymmetry of electroencephalography-based functional brain networks.

    Science.gov (United States)

    Jalili, Mahdi

    2014-11-12

    Electroencephalography (EEG)-based functional brain networks have been investigated frequently in health and disease. It has been shown that a number of graph theory metrics are disrupted in brain disorders. EEG-based brain networks are often studied in the whole-brain framework, where all the nodes are grouped into a single network. In this study, we studied the brain networks in two hemispheres and assessed whether there are any hemispheric-specific patterns in the properties of the networks. To this end, resting state closed-eyes EEGs from 44 healthy individuals were processed and the network structures were extracted separately for each hemisphere. We examined neurophysiologically meaningful graph theory metrics: global and local efficiency measures. The global efficiency did not show any hemispheric asymmetry, whereas the local connectivity showed rightward asymmetry for a range of intermediate density values for the constructed networks. Furthermore, the age of the participants showed significant direct correlations with the global efficiency of the left hemisphere, but only in the right hemisphere, with local connectivity. These results suggest that only local connectivity of EEG-based functional networks is associated with brain hemispheres.

  8. 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.

  9. 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

  10. Modeling Marine Electromagnetic Survey with Radial Basis Function Networks

    Directory of Open Access Journals (Sweden)

    Agus Arif

    2014-11-01

    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[1].[1] This manuscript is an extended version of our previous paper, entitled Radial Basis Function Networks for Modeling Marine Electromagnetic Survey, which had been presented on 2011 International Conference on Electrical Engineering and Informatics, 17-19 July 2011, Bandung, Indonesia.

  11. 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.

  12. 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.

  13. A methodology for the structural and functional analysis of signaling and regulatory networks

    Directory of Open Access Journals (Sweden)

    Simeoni Luca

    2006-02-01

    states are crucial for the dynamic behavior. We have implemented these methods in our software tool CellNetAnalyzer (successor of FluxAnalyzer and illustrate their applicability using a logical model of T-Cell receptor signaling providing non-intuitive results regarding feedback loops, essential elements, and (logical signal processing upon different stimuli. Conclusion The methods and formalisms we propose herein are another step towards the comprehensive functional analysis of cellular interaction networks. Their potential, shown on a realistic T-cell signaling model, makes them a promising tool.

  14. 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.

  15. A candidate multimodal functional genetic network for thermal adaptation

    Directory of Open Access Journals (Sweden)

    Katharina C. Wollenberg Valero

    2014-09-01

    Full Text Available Vertebrate ectotherms such as reptiles provide ideal organisms for the study of adaptation to environmental thermal change. Comparative genomic and exomic studies can recover markers that diverge between warm and cold adapted lineages, but the genes that are functionally related to thermal adaptation may be difficult to identify. We here used a bioinformatics genome-mining approach to predict and identify functions for suitable candidate markers for thermal adaptation in the chicken. We first established a framework of candidate functions for such markers, and then compiled the literature on genes known to adapt to the thermal environment in different lineages of vertebrates. We then identified them in the genomes of human, chicken, and the lizard Anolis carolinensis, and established a functional genetic interaction network in the chicken. Surprisingly, markers initially identified from diverse lineages of vertebrates such as human and fish were all in close functional relationship with each other and more associated than expected by chance. This indicates that the general genetic functional network for thermoregulation and/or thermal adaptation to the environment might be regulated via similar evolutionarily conserved pathways in different vertebrate lineages. We were able to identify seven functions that were statistically overrepresented in this network, corresponding to four of our originally predicted functions plus three unpredicted functions. We describe this network as multimodal: central regulator genes with the function of relaying thermal signal (1, affect genes with different cellular functions, namely (2 lipoprotein metabolism, (3 membrane channels, (4 stress response, (5 response to oxidative stress, (6 muscle contraction and relaxation, and (7 vasodilation, vasoconstriction and regulation of blood pressure. This network constitutes a novel resource for the study of thermal adaptation in the closely related nonavian reptiles and

  16. 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...

  17. 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...

  18. Neural network design for J function approximation in dynamic programming

    CERN Document Server

    Pang, X

    1998-01-01

    This paper shows that a new type of artificial neural network (ANN) -- the Simultaneous Recurrent Network (SRN) -- can, if properly trained, solve a difficult function approximation problem which conventional ANNs -- either feedforward or Hebbian -- cannot. This problem, the problem of generalized maze navigation, is typical of problems which arise in building true intelligent control systems using neural networks. (Such systems are discussed in the chapter by Werbos in K.Pribram, Brain and Values, Erlbaum 1998.) The paper provides a general review of other types of recurrent networks and alternative training techniques, including a flowchart of the Error Critic training design, arguable the only plausible approach to explain how the brain adapts time-lagged recurrent systems in real-time. The C code of the test is appended. As in the first tests of backprop, the training here was slow, but there are ways to do better after more experience using this type of network.

  19. 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.

  20. Empirical evaluation of scoring functions for Bayesian network model selection.

    Science.gov (United States)

    Liu, Zhifa; Malone, Brandon; Yuan, Changhe

    2012-01-01

    In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures. Similar investigations have been carried out before, but they typically relied on approximate learning algorithms to learn the network structures. The suboptimal structures found by the approximation methods have unknown quality and may affect the reliability of their conclusions. Our study uses an optimal algorithm to learn Bayesian network structures from datasets generated from a set of gold standard Bayesian networks. Because all optimal algorithms always learn equivalent networks, this ensures that only the choice of scoring function affects the learned networks. Another shortcoming of the previous studies stems from their use of random synthetic networks as test cases. There is no guarantee that these networks reflect real-world data. We use real-world data to generate our gold-standard structures, so our experimental design more closely approximates real-world situations. A major finding of our study suggests that, in contrast to results reported by several prior works, the Minimum Description Length (MDL) (or equivalently, Bayesian information criterion (BIC)) consistently outperforms other scoring functions such as Akaike's information criterion (AIC), Bayesian Dirichlet equivalence score (BDeu), and factorized normalized maximum likelihood (fNML) in recovering the underlying Bayesian network structures. We believe this finding is a result of using both datasets generated from real-world applications rather than from random processes used in previous studies and learning algorithms to select high-scoring structures rather than selecting random models. Other findings of our study support existing work, e.g., large sample sizes result in learning structures closer to the true underlying structure; the BDeu score is sensitive to the parameter settings; and the fNML performs pretty well on small datasets. We also

  1. 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.

  2. Efficiency and cost of economical brain functional networks.

    Directory of Open Access Journals (Sweden)

    Sophie Achard

    2007-02-01

    Full Text Available Brain anatomical networks are sparse, complex, and have economical small-world properties. We investigated the efficiency and cost of human brain functional networks measured using functional magnetic resonance imaging (fMRI in a factorial design: two groups of healthy old (N = 11; mean age = 66.5 years and healthy young (N = 15; mean age = 24.7 years volunteers were each scanned twice in a no-task or "resting" state following placebo or a single dose of a dopamine receptor antagonist (sulpiride 400 mg. Functional connectivity between 90 cortical and subcortical regions was estimated by wavelet correlation analysis, in the frequency interval 0.06-0.11 Hz, and thresholded to construct undirected graphs. These brain functional networks were small-world and economical in the sense of providing high global and local efficiency of parallel information processing for low connection cost. Efficiency was reduced disproportionately to cost in older people, and the detrimental effects of age on efficiency were localised to frontal and temporal cortical and subcortical regions. Dopamine antagonism also impaired global and local efficiency of the network, but this effect was differentially localised and did not interact with the effect of age. Brain functional networks have economical small-world properties-supporting efficient parallel information transfer at relatively low cost-which are differently impaired by normal aging and pharmacological blockade of dopamine transmission.

  3. 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.

  4. Rational function systems and electrical networks with multiparameters

    CERN Document Server

    Lu, KaiSheng

    2012-01-01

    To overcome the problems of system theory and network theory over real field, this book uses matrices over the field F(z) of rational functions in multiparameters describing coefficient matrices of systems and networks and makes systems and network description over F(z) and researches their structural properties: reducible condition of a class of matrices over F(z) and their characteristic polynomial; type1 matrix and two basic properties; variable replacement conditions for independent parameters; structural controllability and observability of linear systems over F(z); separability, reducibi

  5. Remote synchronization reveals network symmetries and functional modules

    CERN Document Server

    Nicosia, Vincenzo; Chavez, Mario; Díaz-Guilera, Albert; Latora, Vito

    2012-01-01

    We study a Kuramoto model in which the oscillators are associated to the nodes of a complex network and the interactions include a phase frustration, thus preventing full synchronization. The system organizes into a regime of remote synchronization where pairs of nodes with the same network symmetry are fully synchronized, despite their distance on the graph. We provide analytical arguments to explain this result and we show how the frustration parameter affects the distribution of phases. An application to brain networks suggests that anatomical symmetry plays a role in neural synchronization by determining correlated functional modules across distant locations.

  6. Learning ambiguous functions by neural networks

    CERN Document Server

    Ligeiro, Rui

    2013-01-01

    It is not, in general, possible to have access to all variables that determine the behavior of a system. Having identified a number of variables whose values can be accessed, there may still be hidden variables which influence the dynamics of the system. The result is model ambiguity in the sense that, for the same (or very similar) input values, different objective outputs should have been obtained. In addition, the degree of ambiguity may vary widely across the whole range of input values. Thus, to evaluate the accuracy of a model it is of utmost importance to create a method to obtain the degree of reliability of each output result. In this paper we present such a scheme composed of two coupled artificial neural networks: the first one being responsible for outputting the predicted value, whereas the other evaluates the reliability of the output, which is learned from the error values of the first one. As an illustration, the scheme is applied to a model for tracking slopes in a straw chamber and to a cred...

  7. 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...

  8. 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.

  9. Least dissipation cost as a design principle for robustness and function of cellular networks

    Science.gov (United States)

    Han, Bo; Wang, Jin

    2008-03-01

    From a study of the budding yeast cell cycle, we found that the cellular network evolves to have the least cost for realizing its biological function. We quantify the cost in terms of the dissipation or heat loss characterized through the steady-state properties: the underlying landscape and the associated flux. We found that the dissipation cost is intimately related to the stability and robustness of the network. With the least dissipation cost, the network becomes most stable and robust under mutations and perturbations on the sharpness of the response from input to output as well as self-degradations. The least dissipation cost may provide a general design principle for the cellular network to survive from the evolution and realize the biological function.

  10. Identification of functional modules in a PPI network by clique percolation clustering.

    Science.gov (United States)

    Zhang, Shihua; Ning, Xuemei; Zhang, Xiang-Sun

    2006-12-01

    Large-scale experiments and data integration have provided the opportunity to systematically analyze and comprehensively understand the topology of biological networks and biochemical processes in cells. Modular architecture which encompasses groups of genes/proteins involved in elementary biological functional units is a basic form of the organization of interacting proteins. Here we apply a graph clustering algorithm based on clique percolation clustering to detect overlapping network modules of a protein-protein interaction (PPI) network. Our analysis of the yeast Sacchromyces cerevisiae suggests that most of the detected modules correspond to one or more experimentally functional modules and half of these annotated modules match well with experimentally determined protein complexes. Our method of analysis can of course be applied to protein-protein interaction data for any species and even other biological networks.

  11. 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

  12. Functional evolution of new and expanded attention networks in humans.

    Science.gov (United States)

    Patel, Gaurav H; Yang, Danica; Jamerson, Emery C; Snyder, Lawrence H; Corbetta, Maurizio; Ferrera, Vincent P

    2015-07-28

    Macaques are often used as a model system for invasive investigations of the neural substrates of cognition. However, 25 million years of evolution separate humans and macaques from their last common ancestor, and this has likely substantially impacted the function of the cortical networks underlying cognitive processes, such as attention. We examined the homology of frontoparietal networks underlying attention by comparing functional MRI data from macaques and humans performing the same visual search task. Although there are broad similarities, we found fundamental differences between the species. First, humans have more dorsal attention network areas than macaques, indicating that in the course of evolution the human attention system has expanded compared with macaques. Second, potentially homologous areas in the dorsal attention network have markedly different biases toward representing the contralateral hemifield, indicating that the underlying neural architecture of these areas may differ in the most basic of properties, such as receptive field distribution. Third, despite clear evidence of the temporoparietal junction node of the ventral attention network in humans as elicited by this visual search task, we did not find functional evidence of a temporoparietal junction in macaques. None of these differences were the result of differences in training, experimental power, or anatomical variability between the two species. The results of this study indicate that macaque data should be applied to human models of cognition cautiously, and demonstrate how evolution may shape cortical networks.

  13. Alteration of Motor Network Function Following Injury

    Science.gov (United States)

    2013-10-01

    843–852, 1996. Sutherland H, Bickmore WA. Transcription factories: gene expression in unions ? Nat Rev Genet 10: 457–466, 2009. Swensen AM, Marder E...electrodes filled with 3 M KCl (8- to 17-M resistance) with an Axoclamp 2A amplifier (Axon Instru- ments, Union City, CA). In TEVC experiments when all... neuromuscular junction. J Physiol 462: 243–260, 1993. Olson RO, Liu Z, Nomura Y, Song W, Dong K. Molecular and functional characterization of voltage-gated

  14. 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...

  15. 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.

  16. 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.

  17. Variability in functional brain networks predicts expertise during action observation.

    Science.gov (United States)

    Amoruso, Lucía; Ibáñez, Agustín; Fonseca, Bruno; Gadea, Sebastián; Sedeño, Lucas; Sigman, Mariano; García, Adolfo M; Fraiman, Ricardo; Fraiman, Daniel

    2017-02-01

    Observing an action performed by another individual activates, in the observer, similar circuits as those involved in the actual execution of that action. This activation is modulated by prior experience; indeed, sustained training in a particular motor domain leads to structural and functional changes in critical brain areas. Here, we capitalized on a novel graph-theory approach to electroencephalographic data (Fraiman et al., 2016) to test whether variability in functional brain networks implicated in Tango observation can discriminate between groups differing in their level of expertise. We found that experts and beginners significantly differed in the functional organization of task-relevant networks. Specifically, networks in expert Tango dancers exhibited less variability and a more robust functional architecture. Notably, these expertise-dependent effects were captured within networks derived from electrophysiological brain activity recorded in a very short time window (2s). In brief, variability in the organization of task-related networks seems to be a highly sensitive indicator of long-lasting training effects. This finding opens new methodological and theoretical windows to explore the impact of domain-specific expertise on brain plasticity, while highlighting variability as a fruitful measure in neuroimaging research.

  18. 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.

  19. Wearable sensor network to study laterality of brain functions.

    Science.gov (United States)

    Postolache, Gabriela B; Girao, Pedro S; Postolache, Octavian A

    2015-08-01

    In the last decade researches on laterality of brain functions have been reinvigorated. New models of lateralization of brain functions were proposed and new methods for understanding mechanisms of asymmetry between right and left brain functions were described. We design a system to study laterality of motor and autonomic nervous system based on wearable sensors network. A mobile application was developed for analysis of upper and lower limbs movements, cardiac and respiratory function. The functionalities and experience gained with deployment of the system are described.

  20. Structure and function of endosomes in plant cells.

    Science.gov (United States)

    Contento, Anthony L; Bassham, Diane C

    2012-08-01

    Endosomes are a heterogeneous collection of organelles that function in the sorting and delivery of internalized material from the cell surface and the transport of materials from the Golgi to the lysosome or vacuole. Plant endosomes have some unique features, with an organization distinct from that of yeast or animal cells. Two clearly defined endosomal compartments have been studied in plant cells, the trans-Golgi network (equivalent to the early endosome) and the multivesicular body (equivalent to the late endosome), with additional endosome types (recycling endosome, late prevacuolar compartment) also a possibility. A model has been proposed in which the trans-Golgi network matures into a multivesicular body, which then fuses with the vacuole to release its cargo. In addition to basic trafficking functions, endosomes in plant cells are known to function in maintenance of cell polarity by polar localization of hormone transporters and in signaling pathways after internalization of ligand-bound receptors. These signaling functions are exemplified by the BRI1 brassinosteroid hormone receptor and by receptors for pathogen elicitors that activate defense responses. After endocytosis of these receptors from the plasma membrane, endosomes act as a signaling platform, thus playing an essential role in plant growth, development and defense responses. Here we describe the key features of plant endosomes and their differences from those of other organisms and discuss the role of these organelles in cell polarity and signaling pathways.

  1. 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.

  2. Structure and functions of fungal cell surfaces

    Science.gov (United States)

    Nozawa, Y.

    1984-01-01

    A review with 24 references on the biochemistry, molecular structure, and function of cell surfaces of fungi, especially dermatophytes: the chemistry and structure of the cell wall, the effect of polyene antibiotics on the morphology and function of cytoplasmic membranes, and the chemical structure and function of pigments produced by various fungi are discussed.

  3. 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

  4. Harmonic Functions and Potentials on Finite or Infinite Networks

    CERN Document Server

    Anandam, Victor

    2011-01-01

    Random walks, Markov chains and electrical networks serve as an introduction to the study of real-valued functions on finite or infinite graphs, with appropriate interpretations using probability theory and current-voltage laws. The relation between this type of function theory and the (Newton) potential theory on the Euclidean spaces is well-established. The latter theory has been variously generalized, one example being the axiomatic potential theory on locally compact spaces developed by Brelot, with later ramifications from Bauer, Constantinescu and Cornea. A network is a graph with edge-w

  5. Three functions of cadherins in cell adhesion.

    Science.gov (United States)

    Maître, Jean-Léon; Heisenberg, Carl-Philipp

    2013-07-22

    Cadherins are transmembrane proteins that mediate cell-cell adhesion in animals. By regulating contact formation and stability, cadherins play a crucial role in tissue morphogenesis and homeostasis. Here, we review the three major functions of cadherins in cell-cell contact formation and stability. Two of those functions lead to a decrease in interfacial tension at the forming cell-cell contact, thereby promoting contact expansion--first, by providing adhesion tension that lowers interfacial tension at the cell-cell contact, and second, by signaling to the actomyosin cytoskeleton in order to reduce cortex tension and thus interfacial tension at the contact. The third function of cadherins in cell-cell contact formation is to stabilize the contact by resisting mechanical forces that pull on the contact.

  6. Loneliness, Social Networks, and Social Functioning in Borderline Personality Disorder.

    Science.gov (United States)

    Liebke, Lisa; Bungert, Melanie; Thome, Janine; Hauschild, Sophie; Gescher, Dorothee Maria; Schmahl, Christian; Bohus, Martin; Lis, Stefanie

    2016-08-08

    Persistent loneliness is often reported by patients with borderline personality disorder (BPD). However, empirical studies investigating this aspect of BPD psychopathology are sparse. Studies from social psychology revealed that social isolation and low social functioning contribute to loneliness, that is, the subjective feeling of being alone. The aim of the present study was to contribute to the understanding of loneliness in BPD by investigating its relation to social isolation and functioning in different domains of life. Subjective experience of loneliness was measured in 80 women (40 BPD patients, 40 healthy controls) with the UCLA Loneliness Scale. Social isolation and social functioning were assessed with the Social Network Inventory and the Social Functioning Scale. In addition, we assessed global functioning with the Global Assessment of Functioning. BPD patients reported stronger feelings of loneliness compared to healthy participants. In general, the level of loneliness was linked to network size, social engagement, and prosocial behavior. Diversity of social networks and functioning in the domain of interpersonal communication were associated with the level of loneliness only in BPD. A reduced variety of roles in social life together with impairments in interpersonal communication were particularly relevant for the experience of loneliness in BPD, suggesting an indirect path to target this psychopathological feature in therapeutic interventions. However, both social isolation and social functioning were not sufficient to explain the severely increased loneliness experienced by these patients, stressing the need for further investigation of determinants of loneliness in this clinical population. (PsycINFO Database Record

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. Neural progenitors organize in small-world networks to promote cell proliferation.

    Science.gov (United States)

    Malmersjö, Seth; Rebellato, Paola; Smedler, Erik; Planert, Henrike; Kanatani, Shigeaki; Liste, Isabel; Nanou, Evanthia; Sunner, Hampus; Abdelhady, Shaimaa; Zhang, Songbai; Andäng, Michael; El Manira, Abdeljabbar; Silberberg, Gilad; Arenas, Ernest; Uhlén, Per

    2013-04-16

    Coherent network activity among assemblies of interconnected cells is essential for diverse functions in the adult brain. However, cellular networks before formations of chemical synapses are poorly understood. Here, embryonic stem cell-derived neural progenitors were found to form networks exhibiting synchronous calcium ion (Ca(2+)) activity that stimulated cell proliferation. Immature neural cells established circuits that propagated electrical signals between neighboring cells, thereby activating voltage-gated Ca(2+) channels that triggered Ca(2+) oscillations. These network circuits were dependent on gap junctions, because blocking prevented electrotonic transmission both in vitro and in vivo. Inhibiting connexin 43 gap junctions abolished network activity, suppressed proliferation, and affected embryonic cortical layer formation. Cross-correlation analysis revealed highly correlated Ca(2+) activities in small-world networks that followed a scale-free topology. Graph theory predicts that such network designs are effective for biological systems. Taken together, these results demonstrate that immature cells in the developing brain organize in small-world networks that critically regulate neural progenitor proliferation.

  12. Heterogeneity assessment of functional T cell avidity

    Science.gov (United States)

    Ioannidou, Kalliopi; Baumgaertner, Petra; Gannon, Philippe O.; Speiser, Michel F.; Allard, Mathilde; Hebeisen, Michael; Rufer, Nathalie; Speiser, Daniel E.

    2017-01-01

    The potency of cellular immune responses strongly depends on T cell avidity to antigen. Yet, functional avidity measurements are rarely performed in patients, mainly due to the technical challenges of characterizing heterogeneous T cells. The mean functional T cell avidity can be determined by the IFN-γ Elispot assay, with titrated amounts of peptide. Using this assay, we developed a method revealing the heterogeneity of functional avidity, represented by the steepness/hillslope of the peptide titration curve, documented by proof of principle experiments and mathematical modeling. Our data show that not only natural polyclonal CD8 T cell populations from cancer patients, but also monoclonal T cells differ strongly in their heterogeneity of functional avidity. Interestingly, clones and polyclonal cells displayed comparable ranges of heterogeneity. We conclude that besides the mean functional avidity, it is feasible and useful to determine its heterogeneity (hillslope) for characterizing T cell responses in basic research and patient investigation. PMID:28287160

  13. Reconfiguration of Intrinsic Functional Coupling Patterns Following Circumscribed Network Lesions.

    Science.gov (United States)

    Eldaief, Mark C; McMains, Stephanie; Hutchison, R Matthew; Halko, Mark A; Pascual-Leone, Alvaro

    2016-05-25

    Communication between cortical regions is necessary for optimal cognitive processing. Functional relationships between cortical regions can be inferred through measurements of temporal synchrony in spontaneous activity patterns. These relationships can be further elaborated by surveying effects of cortical lesions upon inter-regional connectivity. Lesions to cortical hubs and heteromodal association regions are expected to induce distributed connectivity changes and higher-order cognitive deficits, yet their functional consequences remain relatively unexplored. Here, we used resting-state fMRI to investigate intrinsic functional connectivity (FC) and graph theoretical metrics in 12 patients with circumscribed lesions of the medial prefrontal cortex (mPFC) portion of the Default Network (DN), and compared these metrics with those observed in healthy matched comparison participants and a sample of 1139 healthy individuals. Despite significant mPFC destruction, patients did not demonstrate weakened intrinsic FC among undamaged DN nodes. Instead, network-specific changes were manifested as weaker negative correlations between the DN and attentional and somatomotor networks. These findings conflict with the DN being a homogenous system functionally anchored at mPFC. Rather, they implicate a role for mPFC in mediating cross-network functional interactions. More broadly, our data suggest that lesions to association cortical hubs might induce clinical deficits by disrupting communication between interacting large-scale systems.

  14. 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.

  15. Human brain networks function in connectome-specific harmonic waves.

    Science.gov (United States)

    Atasoy, Selen; Donnelly, Isaac; Pearson, Joel

    2016-01-21

    A key characteristic of human brain activity is coherent, spatially distributed oscillations forming behaviour-dependent brain networks. However, a fundamental principle underlying these networks remains unknown. Here we report that functional networks of the human brain are predicted by harmonic patterns, ubiquitous throughout nature, steered by the anatomy of the human cerebral cortex, the human connectome. We introduce a new technique extending the Fourier basis to the human connectome. In this new frequency-specific representation of cortical activity, that we call 'connectome harmonics', oscillatory networks of the human brain at rest match harmonic wave patterns of certain frequencies. We demonstrate a neural mechanism behind the self-organization of connectome harmonics with a continuous neural field model of excitatory-inhibitory interactions on the connectome. Remarkably, the critical relation between the neural field patterns and the delicate excitation-inhibition balance fits the neurophysiological changes observed during the loss and recovery of consciousness.

  16. 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.

  17. 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)

  18. Performance and Complexity of Tunable Sparse Network Coding with Gradual Growing Tuning Functions over Wireless Networks

    DEFF Research Database (Denmark)

    Garrido, Pablo; Sørensen, Chres Wiant; Roetter, Daniel Enrique Lucani;

    2016-01-01

    Random Linear Network Coding (RLNC) has been shown to be a technique with several benefits, in particular when applied over wireless mesh networks, since it provides robustness against packet losses. On the other hand, Tunable Sparse Network Coding (TSNC) is a promising concept, which leverages...... we propose two novel tuning functions with a lower computational cost, which do not increase the overhead in terms of the transmission of linear dependent packets compared with Random Linear Network Coding (RLNC) and previous proposals. Furthermore, we also broaden previous studies of TSNC techniques....... The results show a reduction of 3.5× in the number of operations without jeopardizing the network performance, in terms of goodput, even when we consider the delay effect on the feedback sent by the decoder...

  19. 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...

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Connectomics and neuroticism : an altered functional network organization

    NARCIS (Netherlands)

    Servaas, Michelle N; Geerligs, Linda; Renken, Remco J; Marsman, Jan-Bernard; Ormel, Johan; Riese, Harriëtte; Aleman, André

    2015-01-01

    The personality trait neuroticism is a potent risk marker for psychopathology. Although the neurobiological basis remains unclear, studies have suggested that alterations in connectivity may underlie it. Therefore, the aim of the current study was to shed more light on the functional network organiz

  5. Real-time estimation of dynamic functional connectivity networks.

    Science.gov (United States)

    Monti, Ricardo Pio; Lorenz, Romy; Braga, Rodrigo M; Anagnostopoulos, Christoforos; Leech, Robert; Montana, Giovanni

    2017-01-01

    Two novel and exciting avenues of neuroscientific research involve the study of task-driven dynamic reconfigurations of functional connectivity networks and the study of functional connectivity in real-time. While the former is a well-established field within neuroscience and has received considerable attention in recent years, the latter remains in its infancy. To date, the vast majority of real-time fMRI studies have focused on a single brain region at a time. This is due in part to the many challenges faced when estimating dynamic functional connectivity networks in real-time. In this work, we propose a novel methodology with which to accurately track changes in time-varying functional connectivity networks in real-time. The proposed method is shown to perform competitively when compared to state-of-the-art offline algorithms using both synthetic as well as real-time fMRI data. The proposed method is applied to motor task data from the Human Connectome Project as well as to data obtained from a visuospatial attention task. We demonstrate that the algorithm is able to accurately estimate task-related changes in network structure in real-time. Hum Brain Mapp 38:202-220, 2017. © 2016 Wiley Periodicals, Inc.

  6. Neural network-based control using Lyapunov functions

    Science.gov (United States)

    Luxemburg, Leon A.

    1993-01-01

    We have successfully demonstrated how the problem of stabilization of plants can be reduced to a problem of approximation of functions. Neural networks have been shown to have approximating and interpolating properties. This approach is good for linear and nonlinear plants. Software has been generated to demonstrate this approach.

  7. The Fractional Differential Polynomial Neural Network for Approximation of Functions

    Directory of Open Access Journals (Sweden)

    Rabha W. Ibrahim

    2013-09-01

    Full Text Available In this work, we introduce a generalization of the differential polynomial neural network utilizing fractional calculus. Fractional calculus is taken in the sense of the Caputo differential operator. It approximates a multi-parametric function with particular polynomials characterizing its functional output as a generalization of input patterns. This method can be employed on data to describe modelling of complex systems. Furthermore, the total information is calculated by using the fractional Poisson process.

  8. 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.

  9. Transcriptional networks in developing and mature B cells.

    Science.gov (United States)

    Matthias, Patrick; Rolink, Antonius G

    2005-06-01

    The development of B cells from haematopoietic stem cells proceeds along a highly ordered, yet flexible, pathway. At multiple steps along this pathway, cells are instructed by transcription factors on how to further differentiate, and several check-points have been identified. These check-points are initial commitment to lymphocytic progenitors, specification of pre-B cells, entry to the peripheral B-cell pool, maturation of B cells and differentiation into plasma cells. At each of these regulatory nodes, there are transcriptional networks that control the outcome, and much progress has recently been made in dissecting these networks. This article reviews our current understanding of this exciting field.

  10. Pro-cognitive drug effects modulate functional brain network organization

    Directory of Open Access Journals (Sweden)

    Carsten eGiessing

    2012-08-01

    Full Text Available Previous studies document that cholinergic and noradrenergic drugs improve attention, memory and cognitive control in healthy subjects and patients with neuropsychiatric disorders. In humans neural mechanisms of cholinergic and noradrenergic modulation have mainly been analyzed by investigating drug-induced changes of task-related neural activity measured with fMRI. Endogenous neural activity has often been neglected. Further, although drugs affect the coupling between neurons, only a few human studies have explicitly addressed how drugs modulate the functional connectome, i.e. the functional neural interactions within the brain. These studies have mainly focused on synchronization or correlation of brain activations. Recently, there are some drug studies using graph theory and other new mathematical approaches to model the brain as a complex network of interconnected processing nodes. Using such measures it is possible to detect not only focal, but also subtle, widely distributed drug effects on functional network topology. Most important, graph theoretical measures also quantify whether drug-induced changes in topology or network organization facilitate or hinder information processing. Several studies could show that functional brain integration is highly correlated with behavioral performance suggesting that cholinergic and noradrenergic drugs which improve measures of cognitive performance should increase functional network integration. The purpose of this paper is to show that graph theory provides a mathematical tool to develop theory-driven biomarkers of pro-cognitive drug effects, and also to discuss how these approaches can contribute to the understanding of the role of cholinergic and noradrenergic modulation in the human brain. Finally we discuss the global workspace theory as a theoretical framework of pro-cognitive drug effects and argue that pro-cognitive effects of cholinergic and noradrenergic drugs might be related to higher

  11. MIRAGE: a functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks.

    Science.gov (United States)

    Vitkin, Edward; Shlomi, Tomer

    2012-11-29

    Genome-scale metabolic network reconstructions are considered a key step in quantifying the genotype-phenotype relationship. We present a novel gap-filling approach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifies missing network reactions by integrating metabolic flux analysis and functional genomics data. MIRAGE's performance is demonstrated on the reconstruction of metabolic network models of E. coli and Synechocystis sp. and validated via existing networks for these species. Then, it is applied to reconstruct genome-scale metabolic network models for 36 sequenced cyanobacteria amenable for constraint-based modeling analysis and specifically for metabolic engineering. The reconstructed network models are supplied via standard SBML files.

  12. Reproducibility of graph metrics of human brain functional networks.

    Science.gov (United States)

    Deuker, Lorena; Bullmore, Edward T; Smith, Marie; Christensen, Soren; Nathan, Pradeep J; Rockstroh, Brigitte; Bassett, Danielle S

    2009-10-01

    Graph theory provides many metrics of complex network organization that can be applied to analysis of brain networks derived from neuroimaging data. Here we investigated the test-retest reliability of graph metrics of functional networks derived from magnetoencephalography (MEG) data recorded in two sessions from 16 healthy volunteers who were studied at rest and during performance of the n-back working memory task in each session. For each subject's data at each session, we used a wavelet filter to estimate the mutual information (MI) between each pair of MEG sensors in each of the classical frequency intervals from gamma to low delta in the overall range 1-60 Hz. Undirected binary graphs were generated by thresholding the MI matrix and 8 global network metrics were estimated: the clustering coefficient, path length, small-worldness, efficiency, cost-efficiency, assortativity, hierarchy, and synchronizability. Reliability of each graph metric was assessed using the intraclass correlation (ICC). Good reliability was demonstrated for most metrics applied to the n-back data (mean ICC=0.62). Reliability was greater for metrics in lower frequency networks. Higher frequency gamma- and beta-band networks were less reliable at a global level but demonstrated high reliability of nodal metrics in frontal and parietal regions. Performance of the n-back task was associated with greater reliability than measurements on resting state data. Task practice was also associated with greater reliability. Collectively these results suggest that graph metrics are sufficiently reliable to be considered for future longitudinal studies of functional brain network changes.

  13. 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.

  14. 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.

  15. Large-Scale Functional Brain Network Reorganization During Taoist Meditation.

    Science.gov (United States)

    Jao, Tun; Li, Chia-Wei; Vértes, Petra E; Wu, Changwei Wesley; Achard, Sophie; Hsieh, Chao-Hsien; Liou, Chien-Hui; Chen, Jyh-Horng; Bullmore, Edward T

    2016-02-01

    Meditation induces a distinct and reversible mental state that provides insights into brain correlates of consciousness. We explored brain network changes related to meditation by graph theoretical analysis of resting-state functional magnetic resonance imaging data. Eighteen Taoist meditators with varying levels of expertise were scanned using a within-subjects counterbalanced design during resting and meditation states. State-related differences in network topology were measured globally and at the level of individual nodes and edges. Although measures of global network topology, such as small-worldness, were unchanged, meditation was characterized by an extensive and expertise-dependent reorganization of the hubs (highly connected nodes) and edges (functional connections). Areas of sensory cortex, especially the bilateral primary visual and auditory cortices, and the bilateral temporopolar areas, which had the highest degree (or connectivity) during the resting state, showed the biggest decrease during meditation. Conversely, bilateral thalamus and components of the default mode network, mainly the bilateral precuneus and posterior cingulate cortex, had low degree in the resting state but increased degree during meditation. Additionally, these changes in nodal degree were accompanied by reorganization of anatomical orientation of the edges. During meditation, long-distance longitudinal (antero-posterior) edges increased proportionally, whereas orthogonal long-distance transverse (right-left) edges connecting bilaterally homologous cortices decreased. Our findings suggest that transient changes in consciousness associated with meditation introduce convergent changes in the topological and spatial properties of brain functional networks, and the anatomical pattern of integration might be as important as the global level of integration when considering the network basis for human consciousness.

  16. Altered functional brain networks in Prader-Willi syndrome.

    Science.gov (United States)

    Zhang, Yi; Zhao, Heng; Qiu, Siyou; Tian, Jie; Wen, Xiaotong; Miller, Jennifer L; von Deneen, Karen M; Zhou, Zhenyu; Gold, Mark S; Liu, Yijun

    2013-06-01

    Prader-Willi syndrome (PWS) is a genetic imprinting disorder characterized mainly by hyperphagia and early childhood obesity. Previous functional neuroimaging studies used visual stimuli to examine abnormal activities in the eating-related neural circuitry of patients with PWS. It was found that patients with PWS exhibited both excessive hunger and hyperphagia consistently, even in situations without any food stimulation. In the present study, we employed resting-state functional MRI techniques to investigate abnormal brain networks related to eating disorders in children with PWS. First, we applied amplitude of low-frequency fluctuation analysis to define the regions of interest that showed significant alterations in resting-state brain activity levels in patients compared with their sibling control group. We then applied a functional connectivity (FC) analysis to these regions of interest in order to characterize interactions among the brain regions. Our results demonstrated that patients with PWS showed decreased FC strength in the medial prefrontal cortex (MPFC)/inferior parietal lobe (IPL), MPFC/precuneus, IPL/precuneus and IPL/hippocampus in the default mode network; decreased FC strength in the pre-/postcentral gyri and dorsolateral prefrontal cortex (DLPFC)/orbitofrontal cortex (OFC) in the motor sensory network and prefrontal cortex network, respectively; and increased FC strength in the anterior cingulate cortex/insula, ventrolateral prefrontal cortex (VLPFC)/OFC and DLPFC/VLPFC in the core network and prefrontal cortex network, respectively. These findings indicate that there are FC alterations among the brain regions implicated in eating as well as rewarding, even during the resting state, which may provide further evidence supporting the use of PWS as a model to study obesity and to provide information on potential neural targets for the medical treatment of overeating.

  17. Bach2 represses plasma cell gene regulatory network in B cells to promote antibody class switch.

    Science.gov (United States)

    Muto, Akihiko; Ochiai, Kyoko; Kimura, Yoshitaka; Itoh-Nakadai, Ari; Calame, Kathryn L; Ikebe, Dai; Tashiro, Satoshi; Igarashi, Kazuhiko

    2010-12-01

    Two transcription factors, Pax5 and Blimp-1, form a gene regulatory network (GRN) with a double-negative loop, which defines either B-cell (Pax5 high) or plasma cell (Blimp-1 high) status as a binary switch. However, it is unclear how this B-cell GRN registers class switch DNA recombination (CSR), an event that takes place before the terminal differentiation to plasma cells. In the absence of Bach2 encoding a transcription factor required for CSR, mouse splenic B cells more frequently and rapidly expressed Blimp-1 and differentiated to IgM plasma cells as compared with wild-type cells. Genetic loss of Blimp-1 in Bach2(-/-) B cells was sufficient to restore CSR. These data with mathematical modelling of the GRN indicate that Bach2 achieves a time delay in Blimp-1 induction, which inhibits plasma cell differentiation and promotes CSR (Delay-Driven Diversity model for CSR). Reduction in mature B-cell numbers in Bach2(-/-) mice was not rescued by Blimp-1 ablation, indicating that Bach2 regulates B-cell differentiation and function through Blimp-1-dependent and -independent GRNs.

  18. 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.

  19. 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.

  20. 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...

  1. A Novel Learning Scheme for Chebyshev Functional Link Neural Networks

    Directory of Open Access Journals (Sweden)

    Satchidananda Dehuri

    2011-01-01

    dimensional-space where linear separability is possible. Moreover, the proposed HCFLNN combines the best attribute of particle swarm optimization (PSO, back propagation learning (BP learning, and functional link neural networks (FLNNs. The proposed method eliminates the need of hidden layer by expanding the input patterns using Chebyshev orthogonal polynomials. We have shown its effectiveness of classifying the unknown pattern using the publicly available datasets obtained from UCI repository. The computational results are then compared with functional link neural network (FLNN with a generic basis functions, PSO-based FLNN, and EFLN. From the comparative study, we observed that the performance of the HCFLNN outperforms FLNN, PSO-based FLNN, and EFLN in terms of classification accuracy.

  2. 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.

  3. Spaceflight alters immune cell function and distribution

    Science.gov (United States)

    Sonnenfeld, Gerald; Mandel, Adrian D.; Konstantinova, Irina V.; Berry, Wallace D.; Taylor, Gerald R.; Lesniak, A. T.; Fuchs, Boris B.; Rakhmilevich, Alexander L.

    1992-01-01

    Experiments are described which were performed onboard Cosmos 2044 to determine spaceflight effects on immunologically important cell function and distribution. Results indicate that bone marrow cells from flown and suspended rats exhibited a decreased response to a granulocyte/monocyte colony-stimulating factor compared with the bone marrow cells from control rats. Bone marrow cells showed an increase in the percentage of cells expressing markers for helper T-cells in the myelogenous population and increased percentages of anti-asialo granulocyte/monocyte-1-bearing interleulin-2 receptor bearing pan T- and helper T-cells in the lymphocytic population.

  4. Functional splicing network reveals extensive regulatory potential of the core spliceosomal machinery.

    Science.gov (United States)

    Papasaikas, Panagiotis; Tejedor, J Ramón; Vigevani, Luisa; Valcárcel, Juan

    2015-01-08

    Pre-mRNA splicing relies on the poorly understood dynamic interplay between >150 protein components of the spliceosome. The steps at which splicing can be regulated remain largely unknown. We systematically analyzed the effect of knocking down the components of the splicing machinery on alternative splicing events relevant for cell proliferation and apoptosis and used this information to reconstruct a network of functional interactions. The network accurately captures known physical and functional associations and identifies new ones, revealing remarkable regulatory potential of core spliceosomal components, related to the order and duration of their recruitment during spliceosome assembly. In contrast with standard models of regulation at early steps of splice site recognition, factors involved in catalytic activation of the spliceosome display regulatory properties. The network also sheds light on the antagonism between hnRNP C and U2AF, and on targets of antitumor drugs, and can be widely used to identify mechanisms of splicing regulation.

  5. 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.

  6. The effects of music on brain functional networks: a network analysis.

    Science.gov (United States)

    Wu, J; Zhang, J; Ding, X; Li, R; Zhou, C

    2013-10-10

    The human brain can dynamically adapt to the changing surroundings. To explore this issue, we adopted graph theoretical tools to examine changes in electroencephalography (EEG) functional networks while listening to music. Three different excerpts of Chinese Guqin music were played to 16 non-musician subjects. For the main frequency intervals, synchronizations between all pair-wise combinations of EEG electrodes were evaluated with phase lag index (PLI). Then, weighted connectivity networks were created and their organizations were characterized in terms of an average clustering coefficient and characteristic path length. We found an enhanced synchronization level in the alpha2 band during music listening. Music perception showed a decrease of both normalized clustering coefficient and path length in the alpha2 band. Moreover, differences in network measures were not observed between musical excerpts. These experimental results demonstrate an increase of functional connectivity as well as a more random network structure in the alpha2 band during music perception. The present study offers support for the effects of music on human brain functional networks with a trend toward a more efficient but less economical architecture.

  7. 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.

  8. Nucleolar function and size in cancer cells.

    OpenAIRE

    Derenzini, M; Trerè, D; Pession, A; Montanaro, L; Sirri, V.; Ochs, R. L.

    1998-01-01

    We have have studied the relationship between nucleolar function and size and cell doubling time in cancer cells. Seven human cancer cell lines characterized by different proliferation rates were used. Nucleolar functional activity was evaluated by measuring RNA polymerase I activity and expression of RNA polymerase I upstream binding factor (UBF), DNA topoisomerase I, and fibrillarin, three proteins involved in synthesis and processing of rRNA. Transcriptional activity of RNA polymerase I wa...

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. Recruitment of an inhibitory hippocampal network after bursting in a single granule cell

    OpenAIRE

    Mori, M; Gähwiler, B; Gerber, U.

    2007-01-01

    The hippocampal CA3 area, an associational network implicated in memory function, receives monosynaptic excitatory as well as disynaptic inhibitory input through the mossy-fiber axons of the dentate granule cells. Synapses made by mossy fibers exhibit low release probability, resulting in high failure rates at resting discharge frequencies of 0.1 Hz. In recordings from functionally connected pairs of neurons, burst firing of a granule cell increased the probability of glutamate release onto b...

  16. Ikaros in B cell development and function

    Institute of Scientific and Technical Information of China (English)

    MacLean; Sellars; Philippe; Kastner; Susan; Chan

    2011-01-01

    The zinc finger transcription factor,Ikaros,is a central regulator of hematopoiesis.It is required for the development of the earliest B cell progenitors and at later stages for VDJ recombination and B cell receptor expression.Mature B cells rely on Ikaros to set the activation threshold for various stimuli,and to choose the correct antibody isotype during class switch recombination.Thus,Ikaros contributes to nearly every level of B cell differentiation and function.

  17. 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.

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

    DEFF Research Database (Denmark)

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

    2016-01-01

    Enhanced Inter-Cell Interference Coordination (eICIC) is a key ingredient to boost the performance of co-channel Heterogeneous Networks (HetNets). eICIC encompasses two main techniques: Almost Blank Subframes (ABS), during which the macro cell remains silent to reduce the interference, and biased...... 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...

  19. Physiological functions of plant cell coverings.

    Science.gov (United States)

    Hoson, Takayuki

    2002-08-01

    The cell coverings of plants have two important functions in plant life. Plant cell coverings are deeply involved in the regulation of the life cycle of plants: each stage of the life cycle, such as germination, vegetative growth, reproductive growth, and senescence, is strongly influenced by the nature of the cell coverings. Also, the apoplast, which consists of the cell coverings, is the field where plant cells first encounter the outer environment, and so becomes the major site of plant responses to the environment. In the regulation of each stage of the life cycle and the response to each environmental signal, some specific constituents of the cell coverings, such as xyloglucans in dicotyledons and 1,3,1,4-beta-glucans in Gramineae, act as the key component. The physiological functions of plant cell coverings are sustained by the metabolic turnover of these components. The components of the cell coverings are supplied from the symplast, but then they are modified or degraded in the apoplast. Thus, the metabolism of the cell coverings is regulated through the cross-talk between the symplast and the apoplast. The understanding of physiological functions of plant cell coverings will be greatly advanced by the use of genomic approaches. At the same time, we need to introduce nanobiological techniques for clarifying the minute changes in the cell coverings that occur in a small part within each cell.

  20. Generation of functional eyes from pluripotent cells.

    Directory of Open Access Journals (Sweden)

    Andrea S Viczian

    2009-08-01

    Full Text Available Pluripotent cells such as embryonic stem (ES and induced pluripotent stem (iPS cells are the starting point from which to generate organ specific cell types. For example, converting pluripotent cells to retinal cells could provide an opportunity to treat retinal injuries and degenerations. In this study, we used an in vivo strategy to determine if functional retinas could be generated from a defined population of pluripotent Xenopus laevis cells. Animal pole cells isolated from blastula stage embryos are pluripotent. Untreated, these cells formed only epidermis, when transplanted to either the flank or eye field. In contrast, misexpression of seven transcription factors induced the formation of retinal cell types. Induced retinal cells were committed to a retinal lineage as they formed eyes when transplanted to the flanks of developing embryos. When the endogenous eye field was replaced with induced retinal cells, they formed eyes that were molecularly, anatomically, and electrophysiologically similar to normal eyes. Importantly, induced eyes could guide a vision-based behavior. These results suggest the fate of pluripotent cells may be purposely altered to generate multipotent retinal progenitor cells, which differentiate into functional retinal cell classes and form a neural circuitry sufficient for vision.

  1. 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.

  2. 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.

  3. 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...

  4. Resting-state functional brain networks in Parkinson's disease.

    Science.gov (United States)

    Baggio, Hugo C; Segura, Bàrbara; Junque, Carme

    2015-10-01

    The network approach is increasingly being applied to the investigation of normal brain function and its impairment. In the present review, we introduce the main methodological approaches employed for the analysis of resting-state neuroimaging data in Parkinson's disease studies. We then summarize the results of recent studies that used a functional network perspective to evaluate the changes underlying different manifestations of Parkinson's disease, with an emphasis on its cognitive symptoms. Despite the variability reported by many studies, these methods show promise as tools for shedding light on the pathophysiological substrates of different aspects of Parkinson's disease, as well as for differential diagnosis, treatment monitoring and establishment of imaging biomarkers for more severe clinical outcomes.

  5. 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.

  6. 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.

  7. Shape-dependent control of cell growth, differentiation, and apoptosis: switching between attractors in cell regulatory networks

    Science.gov (United States)

    Huang, S.; Ingber, D. E.

    2000-01-01

    Development of characteristic tissue patterns requires that individual cells be switched locally between different phenotypes or "fates;" while one cell may proliferate, its neighbors may differentiate or die. Recent studies have revealed that local switching between these different gene programs is controlled through interplay between soluble growth factors, insoluble extracellular matrix molecules, and mechanical forces which produce cell shape distortion. Although the precise molecular basis remains unknown, shape-dependent control of cell growth and function appears to be mediated by tension-dependent changes in the actin cytoskeleton. However, the question remains: how can a generalized physical stimulus, such as cell distortion, activate the same set of genes and signaling proteins that are triggered by molecules which bind to specific cell surface receptors. In this article, we use computer simulations based on dynamic Boolean networks to show that the different cell fates that a particular cell can exhibit may represent a preprogrammed set of common end programs or "attractors" which self-organize within the cell's regulatory networks. In this type of dynamic network model of information processing, generalized stimuli (e.g., mechanical forces) and specific molecular cues elicit signals which follow different trajectories, but eventually converge onto one of a small set of common end programs (growth, quiescence, differentiation, apoptosis, etc.). In other words, if cells use this type of information processing system, then control of cell function would involve selection of preexisting (latent) behavioral modes of the cell, rather than instruction by specific binding molecules. Importantly, the results of the computer simulation closely mimic experimental data obtained with living endothelial cells. The major implication of this finding is that current methods used for analysis of cell function that rely on characterization of linear signaling pathways or

  8. From the Cover: Design of artificial cell-cell communication using gene and metabolic networks

    Science.gov (United States)

    Bulter, Thomas; Lee, Sun-Gu; Waichun Wong, Wilson; Fung, Eileen; Connor, Michael R.; Liao, James C.

    2004-02-01

    Artificial transcriptional networks have been used to achieve novel, nonnative behavior in bacteria. Typically, these artificial circuits are isolated from cellular metabolism and are designed to function without intercellular communication. To attain concerted biological behavior in a population, synchronization through intercellular communication is highly desirable. Here we demonstrate the design and construction of a gene-metabolic circuit that uses a common metabolite to achieve tunable artificial cell-cell communication. This circuit uses a threshold concentration of acetate to induce gene expression by acetate kinase and part of the nitrogen-regulation two-component system. As one application of the cell-cell communication circuit we created an artificial quorum sensor. Engineering of carbon metabolism in Escherichia coli made acetate secretion proportional to cell density and independent of oxygen availability. In these cells the circuit induced gene expression in response to a threshold cell density. This threshold can be tuned effectively by controlling pH over the cell membrane, which determines the partition of acetate between medium and cells. Mutagenesis of the enhancer sequence of the glnAp2 promoter produced variants of the circuit with changed sensitivity demonstrating tunability of the circuit by engineering of its components. The behavior of the circuit shows remarkable predictability based on a mathematical design model.

  9. 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.

  10. 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.

  11. 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...

  12. MyosinV controls PTEN function and neuronal cell size.

    Science.gov (United States)

    van Diepen, Michiel T; Parsons, Maddy; Downes, C Peter; Leslie, Nicholas R; Hindges, Robert; Eickholt, Britta J

    2009-10-01

    The tumour suppressor PTEN can inhibit cell proliferation and migration as well as control cell growth, in different cell types. PTEN functions predominately as a lipid phosphatase, converting PtdIns(3,4,5)P(3) to PtdIns(4,5)P(2), thereby antagonizing PI(3)K (phosphoinositide 3-kinase) and its established downstream effector pathways. However, much is unclear concerning the mechanisms that regulate PTEN movement to the cell membrane, which is necessary for its activity towards PtdIns(3,4,5)P(3) (Refs 3, 4, 5). Here we show a requirement for functional motor proteins in the control of PI3K signalling, involving a previously unknown association between PTEN and myosinV. FRET (Förster resonance energy transfer) measurements revealed that PTEN interacts directly with myosinV, which is dependent on PTEN phosphorylation mediated by CK2 and/or GSK3. Inactivation of myosinV-transport function in neurons increased cell size, which, in line with known attributes of PTEN-loss, required PI(3)K and mTor. Our data demonstrate a myosin-based transport mechanism that regulates PTEN function, providing new insights into the signalling networks regulating cell growth.

  13. Data-driven quantification of the robustness and sensitivity of cell signaling networks

    Science.gov (United States)

    Mukherjee, Sayak; Seok, Sang-Cheol; Vieland, Veronica J.; Das, Jayajit

    2013-12-01

    Robustness and sensitivity of responses generated by cell signaling networks has been associated with survival and evolvability of organisms. However, existing methods analyzing robustness and sensitivity of signaling networks ignore the experimentally observed cell-to-cell variations of protein abundances and cell functions or contain ad hoc assumptions. We propose and apply a data-driven maximum entropy based method to quantify robustness and sensitivity of Escherichia coli (E. coli) chemotaxis signaling network. Our analysis correctly rank orders different models of E. coli chemotaxis based on their robustness and suggests that parameters regulating cell signaling are evolutionary selected to vary in individual cells according to their abilities to perturb cell functions. Furthermore, predictions from our approach regarding distribution of protein abundances and properties of chemotactic responses in individual cells based on cell population averaged data are in excellent agreement with their experimental counterparts. Our approach is general and can be used to evaluate robustness as well as generate predictions of single cell properties based on population averaged experimental data in a wide range of cell signaling systems.

  14. Reconstruction and signal propagation analysis of the Syk signaling network in breast cancer cells

    Science.gov (United States)

    Urbach, Serge; Montcourrier, Philippe; Roy, Christian; Solassol, Jérôme; Freiss, Gilles; Radulescu, Ovidiu

    2017-01-01

    The ability to build in-depth cell signaling networks from vast experimental data is a key objective of computational biology. The spleen tyrosine kinase (Syk) protein, a well-characterized key player in immune cell signaling, was surprisingly first shown by our group to exhibit an onco-suppressive function in mammary epithelial cells and corroborated by many other studies, but the molecular mechanisms of this function remain largely unsolved. Based on existing proteomic data, we report here the generation of an interaction-based network of signaling pathways controlled by Syk in breast cancer cells. Pathway enrichment of the Syk targets previously identified by quantitative phospho-proteomics indicated that Syk is engaged in cell adhesion, motility, growth and death. Using the components and interactions of these pathways, we bootstrapped the reconstruction of a comprehensive network covering Syk signaling in breast cancer cells. To generate in silico hypotheses on Syk signaling propagation, we developed a method allowing to rank paths between Syk and its targets. We first annotated the network according to experimental datasets. We then combined shortest path computation with random walk processes to estimate the importance of individual interactions and selected biologically relevant pathways in the network. Molecular and cell biology experiments allowed to distinguish candidate mechanisms that underlie the impact of Syk on the regulation of cortactin and ezrin, both involved in actin-mediated cell adhesion and motility. The Syk network was further completed with the results of our biological validation experiments. The resulting Syk signaling sub-networks can be explored via an online visualization platform. PMID:28306714

  15. REGULATORY T–CELLS: ORIGIN AND FUNCTION

    Directory of Open Access Journals (Sweden)

    I. S. Freidlin

    2005-01-01

    Full Text Available Abstract. Over the past decade a population of so–called “regulatory T cells” (Treg cells has been linked to the prevention of autoimmunity. In this review we discuss the molecular mechanisms of Treg cells development and function including the identification of the unique molecular marker of Treg cells – the transcription factor Foxp3. We discuss also the mechanisms of suppression, which include the direct cell contact through binding of cell surface molecules CTLA–4 on Treg cells to CD80/CD86 molecules of effector T cells and the local secretion of cytokines (IL–10, TGFβ. Deficiency in or dysfunction of these cells can be a cause of autoimmune disease. These cells are a good target for designing ways to induce or abrogate immunological tolerance to self and non–self antigens. (Med. Immunol., 2005, vol.7, № 4, pp. 347–354

  16. 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.

  17. 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.

  18. 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.

  19. Dynamic reorganization of intrinsic functional networks in the mouse brain.

    Science.gov (United States)

    Grandjean, Joanes; Preti, Maria Giulia; Bolton, Thomas A W; Buerge, Michaela; Seifritz, Erich; Pryce, Christopher R; Van De Ville, Dimitri; Rudin, Markus

    2017-03-14

    Functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) allows for the integrative study of neuronal processes at a macroscopic level. The majority of studies to date have assumed stationary interactions between brain regions, without considering the dynamic aspects of network organization. Only recently has the latter received increased attention, predominantly in human studies. Applying dynamic FC (dFC) analysis to mice is attractive given the relative simplicity of the mouse brain and the possibility to explore mechanisms underlying network dynamics using pharmacological, environmental or genetic interventions. Therefore, we have evaluated the feasibility and research potential of mouse dFC using the interventions of social stress or anesthesia duration as two case-study examples. By combining a sliding-window correlation approach with dictionary learning, several dynamic functional states (dFS) with a complex organization were identified, exhibiting highly dynamic inter- and intra-modular interactions. Each dFS displayed a high degree of reproducibility upon changes in analytical parameters and across datasets. They fluctuated at different degrees as a function of anesthetic depth, and were sensitive indicators of pathology as shown for the chronic psychosocial stress mouse model of depression. Dynamic functional states are proposed to make a major contribution to information integration and processing in the healthy and diseased brain.

  20. 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.

  1. 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.

  2. Detection of Malignancy Associated Changes in Cervical Cell Nuclei Using Feed-Forward Neural Networks

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    Roger A. Kemp

    1997-01-01

    Full Text Available Normal cells in the presence of a precancerous lesion undergo subtle changes of their DNA distribution when observed by visible microscopy. These changes have been termed Malignancy Associated Changes (MACs. Using statistical models such as neural networks and discriminant functions it is possible to design classifiers that can separate these objects from truly normal cells. The correct classification rate using feed‐forward neural networks is compared to linear discriminant analysis when applied to detecting MACs. Classifiers were designed using 53 nuclear features calculated from images for each of 25,360 normal appearing cells taken from 344 slides diagnosed as normal or containing severe dysplasia. A linear discriminant function achieved a correct classification rate of 61.6% on the test data while neural networks scored as high as 72.5% on a cell‐by‐cell basis. The cell classifiers were applied to a library of 93,494 cells from 395 slides, and the results were jackknifed using a single slide feature. The discriminant function achieved a correct classification rate of 67.6% while the neural networks managed as high as 76.2%.

  3. Functional Cortical Network in Alpha Band Correlates with Social Bargaining

    Science.gov (United States)

    Billeke, Pablo; Zamorano, Francisco; Chavez, Mario; Cosmelli, Diego; Aboitiz, Francisco

    2014-01-01

    Solving demanding tasks requires fast and flexible coordination among different brain areas. Everyday examples of this are the social dilemmas in which goals tend to clash, requiring one to weigh alternative courses of action in limited time. In spite of this fact, there are few studies that directly address the dynamics of flexible brain network integration during social interaction. To study the preceding, we carried out EEG recordings while subjects played a repeated version of the Ultimatum Game in both human (social) and computer (non-social) conditions. We found phase synchrony (inter-site-phase-clustering) modulation in alpha band that was specific to the human condition and independent of power modulation. The strength and patterns of the inter-site-phase-clustering of the cortical networks were also modulated, and these modulations were mainly in frontal and parietal regions. Moreover, changes in the individuals’ alpha network structure correlated with the risk of the offers made only in social conditions. This correlation was independent of changes in power and inter-site-phase-clustering strength. Our results indicate that, when subjects believe they are participating in a social interaction, a specific modulation of functional cortical networks in alpha band takes place, suggesting that phase synchrony of alpha oscillations could serve as a mechanism by which different brain areas flexibly interact in order to adapt ongoing behavior in socially demanding contexts. PMID:25286240

  4. Robustness under functional constraint: the genetic network for temporal expression in Drosophila neurogenesis.

    Directory of Open Access Journals (Sweden)

    Akihiko Nakajima

    2010-04-01

    Full Text Available Precise temporal coordination of gene expression is crucial for many developmental processes. One central question in developmental biology is how such coordinated expression patterns are robustly controlled. During embryonic development of the Drosophila central nervous system, neural stem cells called neuroblasts express a group of genes in a definite order, which leads to the diversity of cell types. We produced all possible regulatory networks of these genes and examined their expression dynamics numerically. From the analysis, we identified requisite regulations and predicted an unknown factor to reproduce known expression profiles caused by loss-of-function or overexpression of the genes in vivo, as well as in the wild type. Following this, we evaluated the stability of the actual Drosophila network for sequential expression. This network shows the highest robustness against parameter variations and gene expression fluctuations among the possible networks that reproduce the expression profiles. We propose a regulatory module composed of three types of regulations that is responsible for precise sequential expression. This study suggests that the Drosophila network for sequential expression has evolved to generate the robust temporal expression for neuronal specification.

  5. Mast Cell: A Multi-Functional Master Cell.

    Science.gov (United States)

    Krystel-Whittemore, Melissa; Dileepan, Kottarappat N; Wood, John G

    2015-01-01

    Mast cells are immune cells of the myeloid lineage and are present in connective tissues throughout the body. The activation and degranulation of mast cells significantly modulates many aspects of physiological and pathological conditions in various settings. With respect to normal physiological functions, mast cells are known to regulate vasodilation, vascular homeostasis, innate and adaptive immune responses, angiogenesis, and venom detoxification. On the other hand, mast cells have also been implicated in the pathophysiology of many diseases, including allergy, asthma, anaphylaxis, gastrointestinal disorders, many types of malignancies, and cardiovascular diseases. This review summarizes the current understanding of the role of mast cells in many pathophysiological conditions.

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

    Science.gov (United States)

    Torcini, Alessandro; Luccioli, Stefano; Bonifazi, Paolo; Ben-Jacob, Eshel; Barzilai, Ari

    2015-03-01

    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 developing neuronal circuits, typically composed of only excitatory cells, 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 regulated constraints on single neuron excitability and connectivity 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. This work is part of the activity of the Joint Italian-Israeli Laboratory on Integrative Network Neuroscience supported by the Italian Ministry of Foreign Affairs.

  7. Deciphering primordial cyanobacterial genome functions from protein network analysis.

    Science.gov (United States)

    Harel, Arye; Karkar, Slim; Cheng, Shu; Falkowski, Paul G; Bhattacharya, Debashish

    2015-03-02

    The Great Oxidation Event (GOE) ∼2.4 billion years ago resulted from the accumulation of oxygen by the ancestors of cyanobacteria [1-3]. Cyanobacteria continue to play a significant role in primary production [4] and in regulating the global marine and limnic nitrogen cycles [5, 6]. Relatively little is known, however, about the evolutionary history and gene content of primordial cyanobacteria [7, 8]. To address these issues, we used protein similarity networks [9], containing proteomes from 48 cyanobacteria as the test group, and reference proteomes from 84 microbes representing four distinct metabolic groups from most reducing to most oxidizing: methanogens, obligate anaerobes (nonmethanogenic), facultative aerobes, and obligate aerobes. These four metabolic groups represent extant bioinformatic proxies for ancient redox chemistries, extending from an anoxic origin through the GOE and ultimately to obligate aerobes [10-13]. Analysis of the network metric degree showed a strong relationship between cyanobacteria and obligate anaerobes, from which cyanobacteria presumably arose, for core functions that include translation, photosynthesis, energy conservation, and environmental interactions. These data were used to reconstruct primordial functions in cyanobacteria that included nine gene families involved in photosynthesis, hydrogenases, and proteins involved in defense from environmental stress. The presence of 60% of these genes in both reaction center I (RC-I) and RC-II-type bacteria may be explained by selective loss of either RC in the evolutionary history of some photosynthetic lineages. Finally, the network reveals that cyanobacteria occupy a unique position among prokaryotes as a hub between anaerobes and obligate aerobes.

  8. Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies

    Science.gov (United States)

    Ciucci, Sara; Ge, Yan; Durán, Claudio; Palladini, Alessandra; Jiménez-Jiménez, Víctor; Martínez-Sánchez, Luisa María; Wang, Yuting; Sales, Susanne; Shevchenko, Andrej; Poser, Steven W.; Herbig, Maik; Otto, Oliver; Androutsellis-Theotokis, Andreas; Guck, Jochen; Gerl, Mathias J.; Cannistraci, Carlo Vittorio

    2017-01-01

    Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics. PMID:28287094

  9. 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.

  10. Anatomy and function of an excitatory network in the visual cortex.

    Science.gov (United States)

    Lee, Wei-Chung Allen; Bonin, Vincent; Reed, Michael; Graham, Brett J; Hood, Greg; Glattfelder, Katie; Reid, R Clay

    2016-04-21

    Circuits in the cerebral cortex consist of thousands of neurons connected by millions of synapses. A precise understanding of these local networks requires relating circuit activity with the underlying network structure. For pyramidal cells in superficial mouse visual cortex (V1), a consensus is emerging that neurons with similar visual response properties excite each other, but the anatomical basis of this recurrent synaptic network is unknown. Here we combined physiological imaging and large-scale electron microscopy to study an excitatory network in V1. We found that layer 2/3 neurons organized into subnetworks defined by anatomical connectivity, with more connections within than between groups. More specifically, we found that pyramidal neurons with similar orientation selectivity preferentially formed synapses with each other, despite the fact that axons and dendrites of all orientation selectivities pass near (<5 μm) each other with roughly equal probability. Therefore, we predict that mechanisms of functionally specific connectivity take place at the length scale of spines. Neurons with similar orientation tuning formed larger synapses, potentially enhancing the net effect of synaptic specificity. With the ability to study thousands of connections in a single circuit, functional connectomics is proving a powerful method to uncover the organizational logic of cortical networks.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. Protein signaling networks from single cell fluctuations and information theory profiling.

    Science.gov (United States)

    Shin, Young Shik; Remacle, F; Fan, Rong; Hwang, Kiwook; Wei, Wei; Ahmad, Habib; Levine, R D; Heath, James R

    2011-05-18

    Protein signaling networks among cells play critical roles in a host of pathophysiological processes, from inflammation to tumorigenesis. We report on an approach that integrates microfluidic cell handling, in situ protein secretion profiling, and information theory to determine an extracellular protein-signaling network and the role of perturbations. We assayed 12 proteins secreted from human macrophages that were subjected to lipopolysaccharide challenge, which emulates the macrophage-based innate immune responses against Gram-negative bacteria. We characterize the fluctuations in protein secretion of single cells, and of small cell colonies (n = 2, 3,···), as a function of colony size. Measuring the fluctuations permits a validation of the conditions required for the application of a quantitative version of the Le Chatelier's principle, as derived using information theory. This principle provides a quantitative prediction of the role of perturbations and allows a characterization of a protein-protein interaction network.

  16. Protein Signaling Networks from Single Cell Fluctuations and Information Theory Profiling

    Science.gov (United States)

    Shin, Young Shik; Remacle, F.; Fan, Rong; Hwang, Kiwook; Wei, Wei; Ahmad, Habib; Levine, R.D.; Heath, James R.

    2011-01-01

    Protein signaling networks among cells play critical roles in a host of pathophysiological processes, from inflammation to tumorigenesis. We report on an approach that integrates microfluidic cell handling, in situ protein secretion profiling, and information theory to determine an extracellular protein-signaling network and the role of perturbations. We assayed 12 proteins secreted from human macrophages that were subjected to lipopolysaccharide challenge, which emulates the macrophage-based innate immune responses against Gram-negative bacteria. We characterize the fluctuations in protein secretion of single cells, and of small cell colonies (n = 2, 3,···), as a function of colony size. Measuring the fluctuations permits a validation of the conditions required for the application of a quantitative version of the Le Chatelier's principle, as derived using information theory. This principle provides a quantitative prediction of the role of perturbations and allows a characterization of a protein-protein interaction network. PMID:21575571

  17. A Network Model to Describe the Terminal Differentiation of B Cells

    Science.gov (United States)

    Méndez, Akram; Mendoza, Luis

    2016-01-01

    Terminal differentiation of B cells is an essential process for the humoral immune response in vertebrates and is achieved by the concerted action of several transcription factors in response to antigen recognition and extracellular signals provided by T-helper cells. While there is a wealth of experimental data regarding the molecular and cellular signals involved in this process, there is no general consensus regarding the structure and dynamical properties of the underlying regulatory network controlling this process. We developed a dynamical model of the regulatory network controlling terminal differentiation of B cells. The structure of the network was inferred from experimental data available in the literature, and its dynamical behavior was analyzed by modeling the network both as a discrete and a continuous dynamical systems. The steady states of these models are consistent with the patterns of activation reported for the Naive, GC, Mem, and PC cell types. Moreover, the models are able to describe the patterns of differentiation from the precursor Naive to any of the GC, Mem, or PC cell types in response to a specific set of extracellular signals. We simulated all possible single loss- and gain-of-function mutants, corroborating the importance of Pax5, Bcl6, Bach2, Irf4, and Blimp1 as key regulators of B cell differentiation process. The model is able to represent the directional nature of terminal B cell differentiation and qualitatively describes key differentiation events from a precursor cell to terminally differentiated B cells. PMID:26751566

  18. 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

  19. 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.

  20. 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.

  1. Generating function formula of heat transfer in harmonic networks

    Science.gov (United States)

    Saito, Keiji; Dhar, Abhishek

    2011-04-01

    We consider heat transfer across an arbitrary classical harmonic network connected to two heat baths at different temperatures. The network has N positional degrees of freedom, of which NL are connected to a bath at temperature TL and NR are connected to a bath at temperature TR. We derive an exact formula for the cumulant generating function for heat transfer between the two baths. The formula is valid even for NL≠NR and satisfies the Gallavotti-Cohen fluctuation symmetry. Since harmonic crystals in three dimensions are known to exhibit different regimes of transport such as ballistic, anomalous, and diffusive, our result implies validity of the fluctuation theorem in all regimes. Our exact formula provides a powerful tool to study other properties of nonequilibrium current fluctuations.

  2. Single-cell Transcriptional Analysis Reveals Novel Neuronal Phenotypes and Interaction Networks involved In the Central Circadian Clock

    Directory of Open Access Journals (Sweden)

    James Park

    2016-10-01

    Full Text Available Single-cell heterogeneity confounds efforts to understand how a population of cells organizes into cellular networks that underlie tissue-level function. This complexity is prominent in the mammalian suprachiasmatic nucleus (SCN. Here, individual neurons exhibit a remarkable amount of asynchronous behavior and transcriptional heterogeneity. However, SCN neurons are able to generate precisely coordinated synaptic and molecular outputs that synchronize the body to a common circadian cycle by organizing into cellular networks. To understand this emergent cellular network property, it is important to reconcile single-neuron heterogeneity with network organization. In light of recent studies suggesting that transcriptionally heterogeneous cells organize into distinct cellular phenotypes, we characterized the transcriptional, spatial, and functional organization of 352 SCN neurons from mice experiencing phase-shifts in their circadian cycle. Using the community structure detection method and multivariate analytical techniques, we identified previously undescribed neuronal phenotypes that are likely to participate in regulatory networks with known SCN cell types. Based on the newly discovered neuronal phenotypes, we developed a data-driven neuronal network structure in which multiple cell types interact through known synaptic and paracrine signaling mechanisms. These results provide a basis from which to interpret the functional variability of SCN neurons and describe methodologies towards understanding how a population of heterogeneous single cells organizes into cellular networks that underlie tissue-level function.

  3. Radial Basis Function Neural Network-based PID model for functional electrical stimulation system control.

    Science.gov (United States)

    Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong

    2009-01-01

    Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.

  4. 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.

  5. Parametrization of analytic interatomic potential functions using neural networks.

    Science.gov (United States)

    Malshe, M; Narulkar, R; Raff, L M; Hagan, M; Bukkapatnam, S; Komanduri, R

    2008-07-28

    A generalized method that permits the parameters of an arbitrary empirical potential to be efficiently and accurately fitted to a database is presented. The method permits the values of a subset of the potential parameters to be considered as general functions of the internal coordinates that define the instantaneous configuration of the system. The parameters in this subset are computed by a generalized neural network (NN) with one or more hidden layers and an input vector with at least 3n-6 elements, where n is the number of atoms in the system. The Levenberg-Marquardt algorithm is employed to efficiently affect the optimization of the weights and biases of the NN as well as all other potential parameters being treated as constants rather than as functions of the input coordinates. In order to effect this minimization, the usual Jacobian employed in NN operations is modified to include the Jacobian of the computed errors with respect to the parameters of the potential function. The total Jacobian employed in each epoch of minimization is the concatenation of two Jacobians, one containing derivatives of the errors with respect to the weights and biases of the network, and the other with respect to the constant parameters of the potential function. The method provides three principal advantages. First, it obviates the problem of selecting the form of the functional dependence of the parameters upon the system's coordinates by employing a NN. If this network contains a sufficient number of neurons, it will automatically find something close to the best functional form. This is the case since Hornik et al., [Neural Networks 2, 359 (1989)] have shown that two-layer NNs with sigmoid transfer functions in the first hidden layer and linear functions in the output layer are universal approximators for analytic functions. Second, the entire fitting procedure is automated so that excellent fits are obtained rapidly with little human effort. Third, the method provides a

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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 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......-associated proteoglycans, including: regulation of cell-substrate adhesion; regulation of cell proliferation; participation in the binding and uptake of extracellular components; and participation in the regulation of extracellular matrix formation. Evidence is discussed suggesting that the cell-associated heparan...

  11. 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.

  12. 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...

  13. DNA Damage Activates a Spatially Distinct Late Cytoplasmic Cell-Cycle Checkpoint Network Controlled by MK2-Mediated RNA Stabilization

    NARCIS (Netherlands)

    Reinhardt, H. Christian; Hasskamp, Pia; Schmedding, Ingolf; Morandell, Sandra; van Vugt, Marcel A. T. M.; Wang, XiaoZhe; Linding, Rune; Ong, Shao-En; Weaver, David; Carr, Steven A.; Yaffe, Michael B.

    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 pa

  14. 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.

  15. 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

  16. Cell functional enviromics: Unravelling the function of environmental factors

    Directory of Open Access Journals (Sweden)

    Alves Paula M

    2011-06-01

    Full Text Available Abstract Background While functional genomics, focused on gene functions and gene-gene interactions, has become a very active field of research in molecular biology, equivalent methodologies embracing the environment and gene-environment interactions are relatively less developed. Understanding the function of environmental factors is, however, of paramount importance given the complex, interactive nature of environmental and genetic factors across multiple time scales. Results Here, we propose a systems biology framework, where the function of environmental factors is set at its core. We set forth a "reverse" functional analysis approach, whereby cellular functions are reconstructed from the analysis of dynamic envirome data. Our results show these data sets can be mapped to less than 20 core cellular functions in a typical mammalian cell culture, while explaining over 90% of flux data variance. A functional enviromics map can be created, which provides a template for manipulating the environmental factors to induce a desired phenotypic trait. Conclusion Our results support the feasibility of cellular function reconstruction guided by the analysis and manipulation of dynamic envirome data.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  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. Application-Network Cross Layer Multi-variable Cost Function for Application Layer Multicast of Multimedia Delivery over Convergent Networks

    OpenAIRE

    Le, Tien Anh; Nguyen, Hang; Nguyen, Manh Cuong

    2015-01-01

    International audience; Application layer multicast (ALM) algorithms are either similar or conceptually based on network layer multicast's cost functions. In this research work, a new application-network cross layer multi-variable cost function is proposed. It optimizes the variable requirements and available resources from both the application and the network layers. It can dynamically update the available resources required for reaching a particular node on the ALM's media distribution tree...

  3. 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.

  4. Dynamics of regulatory networks in gastrin-treated adenocarcinoma cells.

    Directory of Open Access Journals (Sweden)

    Naresh Doni Jayavelu

    Full Text Available Understanding gene transcription regulatory networks is critical to deciphering the molecular mechanisms of different cellular states. Most studies focus on static transcriptional networks. In the current study, we used the gastrin-regulated system as a model to understand the dynamics of transcriptional networks composed of transcription factors (TFs and target genes (TGs. The hormone gastrin activates and stimulates signaling pathways leading to various cellular states through transcriptional programs. Dysregulation of gastrin can result in cancerous tumors, for example. However, the regulatory networks involving gastrin are highly complex, and the roles of most of the components of these networks are unknown. We used time series microarray data of AR42J adenocarcinoma cells treated with gastrin combined with static TF-TG relationships integrated from different sources, and we reconstructed the dynamic activities of TFs using network component analysis (NCA. Based on the peak expression of TGs and activity of TFs, we created active sub-networks at four time ranges after gastrin treatment, namely immediate-early (IE, mid-early (ME, mid-late (ML and very late (VL. Network analysis revealed that the active sub-networks were topologically different at the early and late time ranges. Gene ontology analysis unveiled that each active sub-network was highly enriched in a particular biological process. Interestingly, network motif patterns were also distinct between the sub-networks. This analysis can be applied to other time series microarray datasets, focusing on smaller sub-networks that are activated in a cascade, allowing better overview of the mechanisms involved at each time range.

  5. Workshop: Theory an Applications of Coupled Cell Networks

    Science.gov (United States)

    2006-03-22

    Economia and Centro de Matematica, Universidade do Porto) Application of coupled cell systems have been made to a wide range of problems in the physical and...the propagation of perturbations across the optical spectrum. Minimal coupled cell networks M. Aguiar (Faculdade de Economia do Porto), A.P.S. Dias

  6. Proteome adaptation in cell reprogramming proceeds via distinct transcriptional networks

    NARCIS (Netherlands)

    Benevento, Marco; Tonge, Peter D; Puri, Mira C; Hussein, Samer M I; Cloonan, Nicole; Wood, David L; Grimmond, Sean M; Nagy, Andras; Munoz, Javier; Heck, Albert J R

    2014-01-01

    The ectopic expression of Oct4, Klf4, c-Myc and Sox2 (OKMS) transcription factors allows reprogramming of somatic cells into induced pluripotent stem cells (iPSCs). The reprogramming process, which involves a complex network of molecular events, is not yet fully characterized. Here we perform a quan

  7. Schwann cells and their transcriptional network: Evolution of key regulators of peripheral myelination.

    Science.gov (United States)

    Stolt, C Claus; Wegner, Michael

    2016-06-15

    As derivatives of the neural crest, Schwann cells represent a vertebrate invention. Their development and differentiation is under control of a newly constructed, vertebrate-specific regulatory network that contains Sox10, Oct6 and Krox20 as cornerstones and central regulators of peripheral myelination. In this review, we discuss the function and relationship of these transcription factors among each other and in the context of their regulatory network, and present ideas of how neofunctionalization may have helped to recruit them to their novel task in Schwann cells. This article is part of a Special Issue entitled SI: Myelin Evolution.

  8. Human Embryonic Stem Cells Form Functional Thyroid Follicles

    Science.gov (United States)

    Latif, Rauf; Davies, Terry F.

    2015-01-01

    Objective: The molecular events that lead to human thyroid cell speciation remain incompletely characterized. It has been shown that overexpression of the regulatory transcription factors Pax8 and Nkx2-1 (ttf-1) directs murine embryonic stem (mES) cells to differentiate into thyroid follicular cells by initiating a transcriptional regulatory network. Such cells subsequently organized into three-dimensional follicular structures in the presence of extracellular matrix. In the current study, human embryonic stem (hES) cells were studied with the aim of recapitulating this scenario and producing functional human thyroid cell lines. Methods: Reporter gene tagged pEZ-lentiviral vectors were used to express human PAX8-eGFP and NKX2-1-mCherry in the H9 hES cell line followed by differentiation into thyroid cells directed by Activin A and thyrotropin (TSH). Results: Both transcription factors were expressed efficiently in hES cells expressing either PAX8, NKX2-1, or in combination in the hES cells, which had low endogenous expression of these transcription factors. Further differentiation of the double transfected cells showed the expression of thyroid-specific genes, including thyroglobulin (TG), thyroid peroxidase (TPO), the sodium/iodide symporter (NIS), and the TSH receptor (TSHR) as assessed by reverse transcription polymerase chain reaction and immunostaining. Most notably, the Activin/TSH-induced differentiation approach resulted in thyroid follicle formation and abundant TG protein expression within the follicular lumens. On stimulation with TSH, these hES-derived follicles were also capable of dose-dependent cAMP generation and radioiodine uptake, indicating functional thyroid epithelial cells. Conclusion: The induced expression of PAX8 and NKX2-1 in hES cells was followed by differentiation into thyroid epithelial cells and their commitment to form functional three-dimensional neo-follicular structures. The data provide proof of principal that hES cells can be

  9. 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.

  10. Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory

    Directory of Open Access Journals (Sweden)

    Gao Haichun

    2007-08-01

    Full Text Available Abstract Background Large-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT, which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory. Results Application of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under

  11. 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.

  12. Impaired Leydig cell function in infertile men

    DEFF Research Database (Denmark)

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

    2004-01-01

    To investigate whether an impaired Leydig cell function is present in severely oligospermic men, serum testosterone (T), LH, estradiol (E(2)), and SHBG levels in 357 idiopathic infertile men were compared with levels in 318 proven fertile men. In addition, the T/LH ratio, E(2)/T ratio...... 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......, and calculated free T index (cFT) were compared between the two groups.A shift toward lower serum T levels, cFT, and T/LH ratio and higher serum LH, E(2), and E(2)/T levels was observed in the group of infertile men. On average, the infertile men had 18, 26, and 34% lower serum T, cFT, and T/LH levels...

  13. 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.

  14. 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…

  15. 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.

  16. Related pituitary cell lineages develop into interdigitated 3D cell networks.

    Science.gov (United States)

    Budry, Lionel; Lafont, Chrystel; El Yandouzi, Taoufik; Chauvet, Norbert; Conéjero, Geneviève; Drouin, Jacques; Mollard, Patrice

    2011-07-26

    The pituitary gland has long been considered to be a random patchwork of hormone-producing cells. By using pituitary-scale tridimensional imaging for two of the least abundant cell lineages, the corticotropes and gonadotropes, we have now uncovered highly organized and interdigitated cell networks that reflect homotypic and heterotypic interactions between cells. Although newly differentiated corticotrope cells appear on the ventral surface of the gland, they rapidly form homotypic strands of cells that extend from the lateral tips of the anterior pituitary along its ventral surface and into the medial gland. As the corticotrope network is established away from the microvasculature, cell morphology changes from rounded, to polygonal, and finally to cells with long cytoplasmic processes or cytonemes that connect corticotropes to the perivascular space. Gonadotropes differentiate later and are positioned in close proximity to corticotropes and capillaries. Blockade of corticotrope terminal differentiation produced by knockout of the gene encoding the transcription factor Tpit results in smaller gonadotropes within an expanded cell network, particularly in the lateral gland. Thus, pituitary-scale tridimensional imaging reveals highly structured cell networks of unique topology for each pituitary lineage. The sequential development of interdigitated cell networks during organogenesis indicate that extensive cell:cell interactions lead to a highly ordered cell positioning rather than random patchwork.

  17. 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.

  18. Nuclear charge radii: density functional theory meets Bayesian neural networks

    Science.gov (United States)

    Utama, R.; Chen, Wei-Chia; Piekarewicz, J.

    2016-11-01

    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. The aim of this study is to 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 demonstrated the ability of the BNN approach to significantly increase the accuracy of nuclear models in the predictions of nuclear charge radii. However, as many before us, we failed to uncover the underlying physics behind the intriguing behavior of charge radii along the calcium isotopic chain.

  19. Noise Reduction Technique for Images using Radial Basis Function Neural Networks

    Directory of Open Access Journals (Sweden)

    Sander Ali Khowaja

    2014-07-01

    Full Text Available This paper presents a NN (Neural Network based model for reducing the noise from images. This is a RBF (Radial Basis Function network which is used to reduce the effect of noise and blurring from the captured images. The proposed network calculates the mean MSE (Mean Square Error and PSNR (Peak Signal to Noise Ratio of the noisy images. The proposed network has also been successfully applied to medical images. The performance of the trained RBF network has been compared with the MLP (Multilayer Perceptron Network and it has been demonstrated that the performance of the RBF network is better than the MLP network.

  20. Fuel cell and hydrogen network North Rhine-Westphalia

    Energy Technology Data Exchange (ETDEWEB)

    Ziolek, A.; Koch, F. [Energy Agency NRW, Dusseldorf (Germany). Fuel Cell and Hydrogen Network

    2007-07-01

    The Fuel Cell and Hydrogen Network North-Rhine-Westphalia (FCHN NRW) is a non-profit regional technology platform whose mandate is to commercialize fuel cell technologies and establish a sustainable hydrogen economy. The FCHN NRW aims to position the North Rhine-Westphalia region as international centre for fuel cell and hydrogen technology. The network consists of more 300 members from research institutes, government agencies, and private businesses who are encouraged to adapt their products to the special needs of fuel cell systems. The FCHN NRW also aids in the procurement of project partners and provides advice on funding. The region currently has a 240 km hydrogen pipeline connecting several chemical plants and producers and consumers of hydrogen. Approximately 1250 GWh of hydrogen are produced in the region, the majority of which is consumed. The network is also involved in a European-wide project to deploy fuel cell vehicles and create a hydrogen infrastructure. Other projects in the past have included the development of 10 kW fuel cell midi buses; fuel cell cargo-bikes; mobile filling stations; and outdoor terminals. The network is now involved in a national 10 year program in Germany which aims to prepare the country for a hydrogen economy. 7 figs.

  1. 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.

  2. 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.

  3. Energy Harvesting Small Cell Networks: Feasibility, Deployment and Operation

    OpenAIRE

    Mao, Yuyi; Luo, Yaming; Zhang, Jun; Letaief, Khaled B.

    2015-01-01

    Small cell networks (SCNs) have attracted great attention in recent years due to their potential to meet the exponential growth of mobile data traffic and the increasing demand for better quality of service and user experience in mobile applications. Nevertheless, a wide deployment of SCNs has not happened yet because of the complexity in the network planning and optimization, as well as the high expenditure involved in deployment and operation. In particular, it is difficult to provide grid ...

  4. 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.

  5. 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.

  6. [Structure and function of fungal cell wall].

    Science.gov (United States)

    Ohno, Naohito

    2008-12-01

    Cell wall glycans of fungi/yeasts are reviewed. Fungi/yeasts produce various kinds of polysaccharides. As part of the cell wall they are interlinked with other components forming a huge network. The insolubility and complex with multiple components makes the research very tough. Studies on beta-glucan have been performed from various views, such as chemistry, conformation, solubility, tissue distribution and metabolism, biological activity, clinical application, receptor, biosynthesis, and antibody. Studies on mannan focus on immunotoxicity, such as anaphylactoid reaction and coronary arteritis induction. alpha-glucan, chitin, and capsular polysaccharide were also mentioned in relation to structure and genes. Compared with human and animal polysaccharides, fungi/yeasts polysaccharides have very characteristic properties.

  7. A novel meta-analysis approach of cancer transcriptomes reveals prevailing transcriptional networks in cancer cells.

    Science.gov (United States)

    Niida, Atsushi; Imoto, Seiya; Nagasaki, Masao; Yamaguchi, Rui; Miyano, Satoru

    2010-01-01

    Although microarray technology has revealed transcriptomic diversities underlining various cancer phenotypes, transcriptional programs controlling them have not been well elucidated. To decode transcriptional programs governing cancer transcriptomes, we have recently developed a computational method termed EEM, which searches for expression modules from prescribed gene sets defined by prior biological knowledge like TF binding motifs. In this paper, we extend our EEM approach to predict cancer transcriptional networks. Starting from functional TF binding motifs and expression modules identified by EEM, we predict cancer transcriptional networks containing regulatory TFs, associated GO terms, and interactions between TF binding motifs. To systematically analyze transcriptional programs in broad types of cancer, we applied our EEM-based network prediction method to 122 microarray datasets collected from public databases. The data sets contain about 15000 experiments for tumor samples of various tissue origins including breast, colon, lung etc. This EEM based meta-analysis successfully revealed a prevailing cancer transcriptional network which functions in a large fraction of cancer transcriptomes; they include cell-cycle and immune related sub-networks. This study demonstrates broad applicability of EEM, and opens a way to comprehensive understanding of transcriptional networks in cancer cells.

  8. 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

  9. 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.

  10. 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.

  11. 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...

  12. Information technology - Telecommunications and information exchange between systems - Private integrated services network - Specification, functional model and information flows - Call interception additional network feature

    CERN Document Server

    International Organization for Standardization. Geneva

    2003-01-01

    Information technology - Telecommunications and information exchange between systems - Private integrated services network - Specification, functional model and information flows - Call interception additional network feature

  13. Prediction and testing of novel transcriptional networks regulating embryonic stem cell self-renewal and commitment.

    Science.gov (United States)

    Walker, Emily; Ohishi, Minako; Davey, Ryan E; Zhang, Wen; Cassar, Paul A; Tanaka, Tetsuya S; Der, Sandy D; Morris, Quaid; Hughes, Timothy R; Zandstra, Peter W; Stanford, William L

    2007-06-07

    Stem cell fate is governed by the integration of intrinsic and extrinsic positive and negative signals upon inherent transcriptional networks. To identify novel embryonic stem cell (ESC) regulators and assemble transcriptional networks controlling ESC fate, we performed temporal expression microarray analyses of ESCs after the initiation of commitment and integrated these data with known genome-wide transcription factor binding. Effects of forced under- or overexpression of predicted novel regulators, defined as differentially expressed genes with potential binding sites for known regulators of pluripotency, demonstrated greater than 90% correspondence with predicted function, as assessed by functional and high-content assays of self-renewal. We next assembled 43 theoretical transcriptional networks in ESCs, 82% (23 out of 28 tested) of which were supported by analysis of genome-wide expression in Oct4 knockdown cells. By using this integrative approach, we have formulated novel networks describing gene repression of key developmental regulators in undifferentiated ESCs and successfully predicted the outcomes of genetic manipulation of these networks.

  14. Human Dopamine Receptors Interaction Network (DRIN): a systems biology perspective on topology, stability and functionality of the network.

    Science.gov (United States)

    Podder, Avijit; Jatana, Nidhi; Latha, N

    2014-09-21

    Dopamine receptors (DR) are one of the major neurotransmitter receptors present in human brain. Malfunctioning of these receptors is well established to trigger many neurological and psychiatric disorders. Taking into consideration that proteins function collectively in a network for most of the biological processes, the present study is aimed to depict the interactions between all dopamine receptors following a systems biology approach. To capture comprehensive interactions of candidate proteins associated with human dopamine receptors, we performed a protein-protein interaction network (PPIN) analysis of all five receptors and their protein partners by mapping them into human interactome and constructed a human Dopamine Receptors Interaction Network (DRIN). We explored the topology of dopamine receptors as molecular network, revealing their characteristics and the role of central network elements. More to the point, a sub-network analysis was done to determine major functional clusters in human DRIN that govern key neurological pathways. Besides, interacting proteins in a pathway were characterized and prioritized based on their affinity for utmost drug molecules. The vulnerability of different networks to the dysfunction of diverse combination of components was estimated under random and direct attack scenarios. To the best of our knowledge, the current study is unique to put all five dopamine receptors together in a common interaction network and to understand the functionality of interacting proteins collectively. Our study pinpointed distinctive topological and functional properties of human dopamine receptors that have helped in identifying potential therapeutic drug targets in the dopamine interaction network.

  15. 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.

  16. Eicosanoids, β-cell function, and diabetes.

    Science.gov (United States)

    Luo, Pengcheng; Wang, Mong-Heng

    2011-08-01

    Arachidonic acid (AA) is metabolized by cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 (CYP) enzymes into eicosanoids, which are involved in diverse diseases, including type 1 and type 2 diabetes. During the last 30 years, evidence has been accumulated that suggests important functions for eicosanoids in the control of pancreatic β-cell function and destruction. AA metabolites of the COX pathway, especially prostaglandin E(2) (PGE(2)), appear to be significant factors to β-cell dysfunction and destruction, participating in the pathogenesis of diabetes and its complications. Several elegant studies have contributed to the sorting out of the importance of 12-LOX eicosanoids in cytokine-mediated inflammation in pancreatic β cells. The role of CYP eicosanoids in diabetes is yet to be explored. A recent publication has demonstrated that stabilizing the levels of epoxyeicosatrienoic acids (EETs), CYP eicosanoids, by inhibiting or deleting soluble epoxide hydrolase (sEH) improves β-cell function and reduces β-cell apoptosis in diabetes. In this review we summarize recent findings implicating these eicosanoid pathways in diabetes and its complications. We also discuss the development of animal models with targeted gene deletion and specific enzymatic inhibitors in each pathway to identify potential targets for the treatment of diabetes and its complications.

  17. Effect of memory in non-Markovian Boolean networks illustrated with a case study: A cell cycling process

    Science.gov (United States)

    Ebadi, H.; Saeedian, M.; Ausloos, M.; Jafari, G. R.

    2016-11-01

    The Boolean network is one successful model to investigate discrete complex systems such as the gene interacting phenomenon. The dynamics of a Boolean network, controlled with Boolean functions, is usually considered to be a Markovian (memory-less) process. However, both self-organizing features of biological phenomena and their intelligent nature should raise some doubt about ignoring the history of their time evolution. Here, we extend the Boolean network Markovian approach: we involve the effect of memory on the dynamics. This can be explored by modifying Boolean functions into non-Markovian functions, for example, by investigating the usual non-Markovian threshold function —one of the most applied Boolean functions. By applying the non-Markovian threshold function on the dynamical process of the yeast cell cycle network, we discover a power-law-like memory with a more robust dynamics than the Markovian dynamics.

  18. 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.

  19. Spatio-temporal remodeling of functional membrane microdomains organizes the signaling networks of a bacterium.

    Directory of Open Access Journals (Sweden)

    Johannes Schneider

    2015-04-01

    Full Text Available Lipid rafts are membrane microdomains specialized in the regulation of numerous cellular processes related to membrane organization, as diverse as signal transduction, protein sorting, membrane trafficking or pathogen invasion. It has been proposed that this functional diversity would require a heterogeneous population of raft domains with varying compositions. However, a mechanism for such diversification is not known. We recently discovered that bacterial membranes organize their signal transduction pathways in functional membrane microdomains (FMMs that are structurally and functionally similar to the eukaryotic lipid rafts. In this report, we took advantage of the tractability of the prokaryotic model Bacillus subtilis to provide evidence for the coexistence of two distinct families of FMMs in bacterial membranes, displaying a distinctive distribution of proteins specialized in different biological processes. One family of microdomains harbors the scaffolding flotillin protein FloA that selectively tethers proteins specialized in regulating cell envelope turnover and primary metabolism. A second population of microdomains containing the two scaffolding flotillins, FloA and FloT, arises exclusively at later stages of cell growth and specializes in adaptation of cells to stationary phase. Importantly, the diversification of membrane microdomains does not occur arbitrarily. We discovered that bacterial cells control the spatio-temporal remodeling of microdomains by restricting the activation of FloT expression to stationary phase. This regulation ensures a sequential assembly of functionally specialized membrane microdomains to strategically organize signaling networks at the right time during the lifespan of a bacterium.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Functional communication within a perceptual network processing letters and pseudoletters.

    Science.gov (United States)

    Herdman, Anthony T

    2011-10-01

    Many studies have identified regions within human ventral visual stream to be important for object identification and categorization; however, knowledge of how perceptual information is communicated within the visual network is still limited. Current theories posit that if a high correspondence between incoming sensory information and internal representations exists, then the object is rapidly identified, and if there is not, then the object requires extra detailed processing. Event-related responses from the present magnetoencephalography study showed two main effects. The N1m peak latencies were approximately 15 milliseconds earlier to familiar letters than to unfamiliar pseudoletters, and the N2m was more negative to pseudoletters than to letters. Event-related beamforming analyses identified these effects to be within bilateral visual cortices with a right lateralization for the N2m effect. Furthermore, functional connectivity analyses revealed that gamma-band (50-80 Hz) oscillatory phase synchronizations among occipital regions were greater to letters than to pseudoletters (around 85 milliseconds). However, during a later time interval between 245 and 375 milliseconds, pseudoletters elicited greater gamma-band phase synchronizations among a more distributed occipital network than did letters. These findings indicate that familiar object processing begins by at least 85 milliseconds, which could represent an initial match to an internal template. In addition, unfamiliar object processing persisted longer than that for familiar objects, which could reflect greater attention to inexperienced objects to determine their identity and/or to consolidate a new template to aid in future identification.

  5. FOOD SAFETY SYSTEMS’ FUNCTIONING IN POLISH NETWORKS OF GROCERY STORES

    Directory of Open Access Journals (Sweden)

    Paweł NOWICKI

    2013-04-01

    Full Text Available This article shows the way how the food safety systems are functioning in Polish networks of grocery stores. The study was conducted in the fourth quarter of 2012 in the south‐eastern Poland. There were chosen three organizations that meet certain conditions: medium size Polish grocery network without participation of foreign capital and up to 30 retail locations within the group. Studies based on a case study model. The research found that regular and unannounced inspections carried out to each store's, impact on increasing safety of food offered and the verification of GHP requirements on the headquarters level has a significant impact on the safety of food offered as well as on the knowledge and behavior of employees. In addition it was found that the verification and analysis of food safety management system is an effective tool for improving food safety. It was also shown that in most cases there is no formal crisis management system for the food protection in the surveyed companies and employees are only informed of what to do in case of an emergency.

  6. 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.

  7. 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.

  8. Cortical network from human embryonic stem cells

    OpenAIRE

    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...

  9. Influence of Acupuncture Stimulation on Cerebral Network in Functional Diarrhea

    Directory of Open Access Journals (Sweden)

    Siyuan Zhou

    2013-01-01

    Full Text Available Acupuncture is a commonly used therapy for treating functional diarrhea (FD, although there is limited knowledge on the mechanism. The objectives of this study were to investigate the differences in brain activities elicited by acupuncture between FD patients and healthy controls (HC so as to explore the possible mechanism. Eighteen FD patients and eighteen HC received 10 sessions of acupuncture treatment at ST25 acupoints. Functional magnetic resonance imaging (fMRI scans were, respectively, performed before and after acupuncture. The defecation frequency, Bristol stool form scale (SBFS, and MOS 36-item Short Healthy Survey (SF-36 were employed to evaluate the clinical efficacy. After acupuncture, the FD patients showed a significant decrease in defecation frequency and BSFS score. The regional homogeneity (ReHo map showed a decrease in the paracentral lobule and postcentral gyrus, and an increase in the angular gyrus, insula, anterior cingulate cortex (ACC, and precuneus in the FD group. Moreover, the changes in ReHo values in the ACC were correlated with the reduction in defecation frequency. Decreasing functional connectivity among the ACC, insula, thalamus, and orbital frontal cortex only existed in the FD group. Conclusively, acupuncture alleviated defecation frequency and improved stool formation in FD patients. The efficacy might result from the regulation of the homeostasis afferent processing network.

  10. Functional neural networks underlying semantic encoding of associative memories.

    Science.gov (United States)

    Crespo-Garcia, M; Cantero, J L; Pomyalov, A; Boccaletti, S; Atienza, M

    2010-04-15

    Evidence suggests that theta oscillations recruit distributed cortical representations to improve associative encoding under semantically congruent conditions. Here we show that positive effects of semantic context on encoding and retrieval of associations are mediated by changes in the coupling pattern between EEG theta sources. During successful encoding of semantically congruent face-location associations, the right superior parietal lobe showed enhanced theta phase synchronization with other regions within the lateral posterior parietal lobe (PPL) and left medial temporal lobe (MTL). However, functional coordination involving the inferior parietal lobe was higher in the incongruent condition. These results suggest a differential engagement of top-down and bottom-up mechanisms during encoding of semantically congruent and incongruent episodic associations, respectively. Although retrieval processes operated on a similar neural network, the main difference with the study phase was the larger amount of functional links shown by the lateral prefrontal cortex with regions of the MTL and PPL. All together, these results suggest that theta oscillations mediate, at least partially, the positive effect of semantic congruence on associative memory by (i) optimizing top-down attentional mechanisms through enhanced theta phase synchronization between dorsal regions of the PPL and MTL and (ii) by adjusting the control of automatic attention to sensory and contextual information reactivated in the MTL through functional connections with the inferior parietal lobe during both encoding and retrieval processes.

  11. Link prediction boosted psychiatry disorder classification for functional connectivity network

    Science.gov (United States)

    Li, Weiwei; Mei, Xue; Wang, Hao; Zhou, Yu; Huang, Jiashuang

    2017-02-01

    Functional connectivity network (FCN) is an effective tool in psychiatry disorders classification, and represents cross-correlation of the regional blood oxygenation level dependent signal. However, FCN is often incomplete for suffering from missing and spurious edges. To accurate classify psychiatry disorders and health control with the incomplete FCN, we first `repair' the FCN with link prediction, and then exact the clustering coefficients as features to build a weak classifier for every FCN. Finally, we apply a boosting algorithm to combine these weak classifiers for improving classification accuracy. Our method tested by three datasets of psychiatry disorder, including Alzheimer's Disease, Schizophrenia and Attention Deficit Hyperactivity Disorder. The experimental results show our method not only significantly improves the classification accuracy, but also efficiently reconstructs the incomplete FCN.

  12. 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.

  13. 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.

  14. 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...

  15. 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

  16. Proteomic and protein interaction network analysis of human T lymphocytes during cell-cycle entry

    Science.gov (United States)

    Orr, Stephen J; Boutz, Daniel R; Wang, Rong; Chronis, Constantinos; Lea, Nicholas C; Thayaparan, Thivyan; Hamilton, Emma; Milewicz, Hanna; Blanc, Eric; Mufti, Ghulam J; Marcotte, Edward M; Thomas, N Shaun B

    2012-01-01

    Regulating the transition of cells such as T lymphocytes from quiescence (G0) into an activated, proliferating state involves initiation of cellular programs resulting in entry into the cell cycle (proliferation), the growth cycle (blastogenesis, cell size) and effector (functional) activation. We show the first proteomic analysis of protein interaction networks activated during entry into the first cell cycle from G0. We also provide proof of principle that blastogenesis and proliferation programs are separable in primary human T cells. We employed a proteomic profiling method to identify large-scale changes in chromatin/nuclear matrix-bound and unbound proteins in human T lymphocytes during the transition from G0 into the first cell cycle and mapped them to form functionally annotated, dynamic protein interaction networks. Inhibiting the induction of two proteins involved in two of the most significantly upregulated cellular processes, ribosome biogenesis (eIF6) and hnRNA splicing (SF3B2/SF3B4), showed, respectively, that human T cells can enter the cell cycle without growing in size, or increase in size without entering the cell cycle. PMID:22415777

  17. Biofunctionalized 3-D Carbon Nano-Network Platform for Enhanced Fibroblast Cell Adhesion

    Science.gov (United States)

    Chowdhury, A. K. M. Rezaul Haque; Tavangar, Amirhossein; Tan, Bo; Venkatakrishnan, Krishnan

    2017-01-01

    Carbon nanomaterials have been investigated for various biomedical applications. In most cases, however, these nanomaterials must be functionalized biologically or chemically due to their biological inertness or possible cytotoxicity. Here, we report the development of a new carbon nanomaterial with a bioactive phase that significantly promotes cell adhesion. We synthesize the bioactive phase by introducing self-assembled nanotopography and altered nano-chemistry to graphite substrates using ultrafast laser. To the best of our knowledge, this is the first time that such a cytophilic bio-carbon is developed in a single step without requiring subsequent biological/chemical treatments. By controlling the nano-network concentration and chemistry, we develop platforms with different degrees of cell cytophilicity. We study quantitatively and qualitatively the cell response to nano-network platforms with NIH-3T3 fibroblasts. The findings from the in vitro study indicate that the platforms possess excellent biocompatibility and promote cell adhesion considerably. The study of the cell morphology shows a healthy attachment of cells with a well-spread shape, overextended actin filaments, and morphological symmetry, which is indicative of a high cellular interaction with the nano-network. The developed nanomaterial possesses great biocompatibility and considerably stimulates cell adhesion and subsequent cell proliferation, thus offering a promising path toward engineering various biomedical devices. PMID:28287138

  18. Real-time dynamics of emerging actin networks in cell-mimicking compartments.

    Directory of Open Access Journals (Sweden)

    Siddharth Deshpande

    Full Text Available Understanding the cytoskeletal functionality and its relation to other cellular components and properties is a prominent question in biophysics. The dynamics of actin cytoskeleton and its polymorphic nature are indispensable for the proper functioning of living cells. Actin bundles are involved in cell motility, environmental exploration, intracellular transport and mechanical stability. Though the viscoelastic properties of actin-based structures have been extensively probed, the underlying microstructure dynamics, especially their disassembly, is not fully understood. In this article, we explore the rich dynamics and emergent properties exhibited by actin bundles within flow-free confinements using a microfluidic set-up and epifluorescence microscopy. After forming entangled actin filaments within cell-sized quasi two-dimensional confinements, we induce their bundling using three different fundamental mechanisms: counterion condensation, depletion interactions and specific protein-protein interactions. Intriguingly, long actin filaments form emerging networks of actin bundles via percolation leading to remarkable properties such as stress generation and spindle-like intermediate structures. Simultaneous sharing of filaments in different links of the network is an important parameter, as short filaments do not form networks but segregated clusters of bundles instead. We encounter a hierarchical process of bundling and its subsequent disassembly. Additionally, our study suggests that such percolated networks are likely to exist within living cells in a dynamic fashion. These observations render a perspective about differential cytoskeletal responses towards numerous stimuli.

  19. Human Amniotic Fluid Cells Form Functional Gap Junctions with Cortical Cells

    Directory of Open Access Journals (Sweden)

    Anna Jezierski

    2012-01-01

    Full Text Available The usage of stem cells is a promising strategy for the repair of damaged tissue in the injured brain. Recently, amniotic fluid (AF cells have received a lot of attention as an alternative source of stem cells for cell-based therapies. However, the success of this approach relies significantly on proper interactions between graft and host tissue. In particular, the reestablishment of functional brain networks requires formation of gap junctions, as a key step to provide sufficient intercellular communication. In this study, we show that AF cells express high levels of CX43 (GJA1 and are able to establish functional gap junctions with cortical cultures. Furthermore, we report an induction of Cx43 expression in astrocytes following injury to the mouse motor cortex and demonstrate for the first time CX43 expression at the interface between implanted AF cells and host brain cells. These findings suggest that CX43-mediated intercellular communication between AF cells and cortical astrocytes may contribute to the reconstruction of damaged tissue by mediating modulatory, homeostatic, and protective factors in the injured brain and hence warrants further investigation.

  20. 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.

  1. Intermediate filaments: a dynamic network that controls cell mechanics.

    Science.gov (United States)

    Gruenbaum, Yosef; Aebi, Ueli

    2014-01-01

    In humans the superfamily of intermediate filament (IF) proteins is encoded by more than 70 different genes, which are expressed in a cell- and tissue-specific manner. IFs assemble into approximately 10 nm-wide filaments that account for the principal structural elements at the nuclear periphery, nucleoplasm, and cytoplasm. They are also required for organizing the microtubule and microfilament networks. In this review, we focus on the dynamics of IFs and how modifications regulate it. We also discuss the role of nuclear IF organization in determining nuclear mechanics as well as that of cytoplasmic IFs organization in maintaining cell stiffness, formation of lamellipodia, regulation of cell migration, and permitting cell adhesion.

  2. 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

    2016-02-24

    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.

  3. Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

    Science.gov (United States)

    Jorgensen, Charles C.

    1997-01-01

    A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

  4. Functional interactions among members of the MAX and MLX transcriptional network during oncogenesis.

    Science.gov (United States)

    Diolaiti, Daniel; McFerrin, Lisa; Carroll, Patrick A; Eisenman, Robert N

    2015-05-01

    The transcription factor MYC and its related family members MYCN and MYCL have been implicated in the etiology of a wide spectrum of human cancers. Compared to other oncoproteins, such as RAS or SRC, MYC is unique because its protein coding region is rarely mutated. Instead, MYC's oncogenic properties are unleashed by regulatory mutations leading to unconstrained high levels of expression. Under both normal and pathological conditions MYC regulates multiple aspects of cellular physiology including proliferation, differentiation, apoptosis, growth and metabolism by controlling the expression of thousands of genes. How a single transcription factor exerts such broad effects remains a fascinating puzzle. Notably, MYC is part of a network of bHLHLZ proteins centered on the MYC heterodimeric partner MAX and its counterpart, the MAX-like protein MLX. This network includes MXD1-4, MNT, MGA, MONDOA and MONDOB proteins. With some exceptions, MXD proteins have been functionally linked to cell cycle arrest and differentiation, while MONDO proteins control cellular metabolism. Although the temporal expression patterns of many of these proteins can differ markedly they are frequently expressed simultaneously in the same cellular context, and potentially bind to the same, or similar DNA consensus sequence. Here we review the activities and interactions among these proteins and propose that the broad spectrum of phenotypes elicited by MYC deregulation is intimately connected to the functions and regulation of the other network members. Furthermore, we provide a meta-analysis of TCGA data suggesting that the coordinate regulation of the network is important in MYC driven tumorigenesis. This article is part of a Special Issue entitled: Myc proteins in cell biology and pathology.

  5. 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.

  6. Three-dimensional vascular network assembly from diabetic patient-derived induced pluripotent stem cells

    Science.gov (United States)

    Chan, Xin Yi; Black, Rebecca; Dickerman, Kayla; Federico, Joseph; Levesque, Mathieu; Mumm, Jeff; Gerecht, Sharon

    2015-01-01

    Objective In diabetics, hyperglycemia results in deficient endothelial progenitors and cells, leading to cardiovascular complications. We aim to engineer three-dimensional (3D) vascular networks in synthetic hydrogels from type-1 diabetes (T1D) patient-derived human induced pluripotent stem cells (hiPSCs), to serve as a transformative autologous vascular therapy for diabetic patients. Approach and Results We validated and optimized an adherent, feeder free differentiation procedure to derive early vascular cells (EVCs) with high portions of VEcad+ cells from hiPSCs. We demonstrate similar differentiation efficiency from hiPSCs derived from healthy donor and T1D patients. T1D-hiPSC-derived VEcad+ cells can mature to functional endothelial cells (ECs) expressing mature markers: von Willebrand factor and eNOS, are capable of lectin binding and acetylated low density lipoprotein uptake, form cords in Matrigel and respond to tumor necrosis factor alpha. When embedded in engineered hyaluronic acid (HA) hydrogels, T1D-EVCs undergo morphogenesis and assemble into 3D networks. When encapsulated in a novel hypoxia-inducible (HI) hydrogel, T1D-EVCs respond to low oxygen and form 3D networks. As xenografts, T1D-EVCs incorporate into developing zebrafish vasculature. Conclusion Using our robust protocol, we can direct efficient differentiation of T1D-hiPSC to EVCs. Early ECs derived from T1D-hiPSC are functional when mature. T1D-EVCs self-assembled into 3D networks when embedded in HA and HI hydrogels. The capability of T1D-EVCs to assemble into 3D networks in engineered matrices and to respond to a hypoxic microenvironment is a significant advancement for autologous vascular therapy in diabetic patients and has broad importance for tissue engineering. PMID:26449749

  7. 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.

  8. Cognitive Memory Network

    CERN Document Server

    James, Alex Pappachen; 10.1049/el.2010.0279

    2012-01-01

    A resistive memory network that has no crossover wiring is proposed to overcome the hardware limitations to size and functional complexity that is associated with conventional analogue neural networks. The proposed memory network is based on simple network cells that are arranged in a hierarchical modular architecture. Cognitive functionality of this network is demonstrated by an example of character recognition. The network is trained by an evolutionary process to completely recognise characters deformed by random noise, rotation, scaling and shifting

  9. Cell cycle phase regulates glucocorticoid receptor function.

    Directory of Open Access Journals (Sweden)

    Laura Matthews

    Full Text Available The glucocorticoid receptor (GR is a member of the nuclear hormone receptor superfamily of ligand-activated transcription factors. In contrast to many other nuclear receptors, GR is thought to be exclusively cytoplasmic in quiescent cells, and only translocate to the nucleus on ligand binding. We now demonstrate significant nuclear GR in the absence of ligand, which requires nuclear localisation signal 1 (NLS1. Live cell imaging reveals dramatic GR import into the nucleus through interphase and rapid exclusion of the GR from the nucleus at the onset of mitosis, which persists into early G(1. This suggests that the heterogeneity in GR distribution is reflective of cell cycle phase. The impact of cell cycle-driven GR trafficking on a panel of glucocorticoid actions was profiled. In G2/M-enriched cells there was marked prolongation of glucocorticoid-induced ERK activation. This was accompanied by DNA template-specific, ligand-independent GR transactivation. Using chimeric and domain-deleted receptors we demonstrate that this transactivation effect is mediated by the AF1 transactivation domain. AF-1 harbours multiple phosphorylation sites, which are consensus sequences for kinases including CDKs, whose activity changes during the cell cycle. In G2/M there was clear ligand independent induction of GR phosphorylation on residues 203 and 211, both of which are phosphorylated after ligand activation. Ligand-independent transactivation required induction of phospho-S211GR but not S203GR, thereby directly linking cell cycle driven GR modification with altered GR function. Cell cycle phase therefore regulates GR localisation and post-translational modification which selectively impacts GR activity. This suggests that cell cycle phase is an important determinant in the cellular response to Gc, and that mitotic index contributes to tissue Gc sensitivity.

  10. Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis.

    Science.gov (United States)

    Moignard, Victoria; Macaulay, Iain C; Swiers, Gemma; Buettner, Florian; Schütte, Judith; Calero-Nieto, Fernando J; Kinston, Sarah; Joshi, Anagha; Hannah, Rebecca; Theis, Fabian J; Jacobsen, Sten Eirik; de Bruijn, Marella F; Göttgens, Berthold

    2013-04-01

    Cellular decision-making is mediated by a complex interplay of external stimuli with the intracellular environment, in particular transcription factor regulatory networks. Here we have determined the expression of a network of 18 key haematopoietic transcription factors in 597 single primary blood stem and progenitor cells isolated from mouse bone marrow. We demonstrate that different stem/progenitor populations are characterized by distinctive transcription factor expression states, and through comprehensive bioinformatic analysis reveal positively and negatively correlated transcription factor pairings, including previously unrecognized relationships between Gata2, Gfi1 and Gfi1b. Validation using transcriptional and transgenic assays confirmed direct regulatory interactions consistent with a regulatory triad in immature blood stem cells, where Gata2 may function to modulate cross-inhibition between Gfi1 and Gfi1b. Single-cell expression profiling therefore identifies network states and allows reconstruction of network hierarchies involved in controlling stem cell fate choices, and provides a blueprint for studying both normal development and human disease.

  11. 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.

  12. 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.

  13. 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

    2015-08-07

    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.

  14. 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

  15. 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

  16. 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.

  17. 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.

  18. Controlling Functional Group Architecture in Artificial Cells

    Science.gov (United States)

    2015-07-02

    further enable enzyme encapsulation to improve the efficiency of light-driven hydrogen fuel production. 5. Changes in key personnel, if applicable : -None ...Controlling Functional Group Architecture in Artificial Cells 5a. CONTRACT NUMBER W9132T-14-2-0002 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6...cycloadditions to modify reactive groups within the phospholipid membrane structure and how the nature of the reactive elements, the copper catalyst

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Proteome adaptation in cell reprogramming proceeds via distinct transcriptional networks.

    Science.gov (United States)

    Benevento, Marco; Tonge, Peter D; Puri, Mira C; Hussein, Samer M I; Cloonan, Nicole; Wood, David L; Grimmond, Sean M; Nagy, Andras; Munoz, Javier; Heck, Albert J R

    2014-12-10

    The ectopic expression of Oct4, Klf4, c-Myc and Sox2 (OKMS) transcription factors allows reprogramming of somatic cells into induced pluripotent stem cells (iPSCs). The reprogramming process, which involves a complex network of molecular events, is not yet fully characterized. Here we perform a quantitative mass spectrometry-based analysis to probe in-depth dynamic proteome changes during somatic cell reprogramming. Our data reveal defined waves of proteome resetting, with the first wave occurring 48 h after the activation of the reprogramming transgenes and involving specific biological processes linked to the c-Myc transcriptional network. A second wave of proteome reorganization occurs in a later stage of reprogramming, where we characterize the proteome of two distinct pluripotent cellular populations. In addition, the overlay of our proteome resource with parallel generated -omics data is explored to identify post-transcriptionally regulated proteins involved in key steps during reprogramming.

  9. The human functional brain network demonstrates structural and dynamical resilience to targeted attack.

    Directory of Open Access Journals (Sweden)

    Karen E Joyce

    Full Text Available In recent years, the field of network science has enabled researchers to represent the highly complex interactions in the brain in an approachable yet quantitative manner. One exciting finding since the advent of brain network research was that the brain network can withstand extensive damage, even to highly connected regions. However, these highly connected nodes may not be the most critical regions of the brain network, and it is unclear how the network dynamics are impacted by removal of these key nodes. This work seeks to further investigate the resilience of the human functional brain network. Network attack experiments were conducted on voxel-wise functional brain networks and region-of-interest (ROI networks of 5 healthy volunteers. Networks were attacked at key nodes using several criteria for assessing node importance, and the impact on network structure and dynamics was evaluated. The findings presented here echo previous findings that the functional human brain network is highly resilient to targeted attacks, both in terms of network structure and dynamics.

  10. The human functional brain network demonstrates structural and dynamical resilience to targeted attack.

    Science.gov (United States)

    Joyce, Karen E; Hayasaka, Satoru; Laurienti, Paul J

    2013-01-01

    In recent years, the field of network science has enabled researchers to represent the highly complex interactions in the brain in an approachable yet quantitative manner. One exciting finding since the advent of brain network research was that the brain network can withstand extensive damage, even to highly connected regions. However, these highly connected nodes may not be the most critical regions of the brain network, and it is unclear how the network dynamics are impacted by removal of these key nodes. This work seeks to further investigate the resilience of the human functional brain network. Network attack experiments were conducted on voxel-wise functional brain networks and region-of-interest (ROI) networks of 5 healthy volunteers. Networks were attacked at key nodes using several criteria for assessing node importance, and the impact on network structure and dynamics was evaluated. The findings presented here echo previous findings that the functional human brain network is highly resilient to targeted attacks, both in terms of network structure and dynamics.

  11. On the Virtual Cell Transmission in Ultra Dense Networks

    Directory of Open Access Journals (Sweden)

    Xiaopeng Zhu

    2016-10-01

    Full Text Available Ultra dense networks (UDN are identified as one of the key enablers for 5G, since they can provide an ultra high spectral reuse factor exploiting proximal transmissions. By densifying the network infrastructure equipment, it is highly possible that each user will have one or more dedicated serving base station antennas, introducing the user-centric virtual cell paradigm. However, due to irregular deployment of a large amount of base station antennas, the interference environment becomes rather complex, thus introducing severe interferences among different virtual cells. This paper focuses on the downlink transmission scheme in UDN where a large number of users and base station antennas is uniformly spread over a certain area. An interference graph is first created based on the large-scale fadings to give a potential description of the interference relationship among the virtual cells. Then, base station antennas and users in the virtual cells within the same maximally-connected component are grouped together and merged into one new virtual cell cluster, where users are jointly served via zero-forcing (ZF beamforming. A multi-virtual-cell minimum mean square error precoding scheme is further proposed to mitigate the inter-cluster interference. Additionally, the interference alignment framework is proposed based on the low complexity virtual cell merging to eliminate the strong interference between different virtual cells. Simulation results show that the proposed interference graph-based virtual cell merging approach can attain the average user spectral efficiency performance of the grouping scheme based on virtual cell overlapping with a smaller virtual cell size and reduced signal processing complexity. Besides, the proposed user-centric transmission scheme greatly outperforms the BS-centric transmission scheme (maximum ratio transmission (MRT in terms of both the average user spectral efficiency and edge user spectral efficiency. What is more

  12. Carbon nanotube dispersed conductive network for microbial fuel cells

    Science.gov (United States)

    Matsumoto, S.; Yamanaka, K.; Ogikubo, H.; Akasaka, H.; Ohtake, N.

    2014-08-01

    Microbial fuel cells (MFCs) are promising devices for capturing biomass energy. Although they have recently attracted considerable attention, their power densities are too low for practical use. Increasing their electrode surface area is a key factor for improving the performance of MFC. Carbon nanotubes (CNTs), which have excellent electrical conductivity and extremely high specific surface area, are promising materials for electrodes. However, CNTs are insoluble in aqueous solution because of their strong intertube van der Waals interactions, which make practical use of CNTs difficult. In this study, we revealed that CNTs have a strong interaction with Saccharomyces cerevisiae cells. CNTs attach to the cells and are dispersed in a mixture of water and S. cerevisiae, forming a three-dimensional CNT conductive network. Compared with a conventional two-dimensional electrode, such as carbon paper, the three-dimensional conductive network has a much larger surface area. By applying this conductive network to MFCs as an anode electrode, power density is increased to 176 μW/cm2, which is approximately 25-fold higher than that in the case without CNTs addition. Maximum current density is also increased to approximately 8-fold higher. These results suggest that three-dimensional CNT conductive network contributes to improve the performance of MFC by increasing surface area.

  13. Transcriptional networks in single perivascular cells sorted from human adipose tissue reveal a hierarchy of mesenchymal stem cells.

    Science.gov (United States)

    Hardy, W Reef; Moldovan, Nicanor I; Moldovan, Leni; Livak, Kenneth J; Datta; Goswami, Chirayu; Corselli, Mirko; Traktuev, Dmitry O; Murray, Iain R; Péault, Bruno; March, Keith

    2017-02-24

    Adipose tissue is a rich source of multipotent mesenchymal stem-like cells, located in the perivascular niche. Based on their surface markers, these have been assigned to two main categories: CD34+CD31-CD45-CD146- cells (adventitial stromal/stem cells, ASCs), and CD146+CD31-CD34-CD45- cells (pericytes, PCs). These populations display heterogeneity of unknown significance. We hypothesized that aldehyde dehydrogenase (ALDH) activity, a functional marker of primitivity, could help to better define ASC and PC subclasses. To this end, the stromal vascular fraction from a human lipoaspirate was simultaneously stained with fluorescent antibodies to CD31, CD45, CD34, and CD146 antigens and the ALDH substrate Aldefluor®, then sorted by FACS. Individual ASCs (n=67) and PCs (n=73) selected from the extremities of the ALDH-staining spectrum were transcriptionally profiled by Fluidigm single-cell quantitative PCR for a predefined set (n=429) of marker genes. To these single-cell data, we applied differential expression and principal component and clustering analysis, as well as an original gene co-expression network reconstruction algorithm. Despite the stochasticity at the single-cell level, covariation gene expression analysis yielded multiple network connectivity parameters suggesting that these perivascular progenitor cell subclasses possess the following order of maturity: i) ALDH(br) ASC (most primitive); ii) ALDH(dim) ASC; iii) ALDH(br) PC; iv) ALDH(dim) PC (least primitive). This order was independently supported by specific combinations of class-specific expressed genes and further confirmed by the analysis of associated signaling pathways. In conclusion, single-cell transcriptional analysis of four populations isolated from fat by surface markers and enzyme activity suggests a developmental hierarchy among perivascular mesenchymal stem cells supported by markers and co-expression networks. This article is protected by copyright. All rights reserved.

  14. Diverse functions of IGF/insulin signaling in malignant and noncancerous prostate cells: proliferation in cancer cells and differentiation in noncancerous cells.

    Science.gov (United States)

    Heidegger, Isabel; Ofer, Philipp; Doppler, Wolfgang; Rotter, Varda; Klocker, Helmut; Massoner, Petra

    2012-10-01

    The insulin-like growth factor (IGF) pathway represents one of the most studied molecular regulatory networks in oncology. Clinical trials investigating the therapeutic value of anti-IGF1 receptor (IGF1R) therapies in cancer, including prostate cancer, are ongoing. However, the multiple functions of the IGF network in the prostate are not entirely known. To elucidate the effects of IGF and insulin (INS) on prostate cells, we stimulated prostate cancer (PC3, DU145, LNCaP, DUCaP) and noncancerous prostate cells (EP156T, RWPE-1) and observed differing responses: whereas cancer cells responded to IGF and INS exposure by way of enhanced cell proliferation and glucose consumption, basal to luminal differentiation was induced in noncancerous cells. The same diverse responses were observed when the growth factor receptors IGF1R or INSR were overexpressed. Down-regulation of IGF1R or INSR isoform A (INSRA) also inhibited only proliferation of cancer cells. The proliferative response induced by the INSR in cancer cells was mediated solely by the INSRA. Moreover we observed that the receptors of the IGF network mutually influence their expression and exert redundant functions, thus underscoring the functional molecular network formed by IGF, INS, IGF1R, and INSR. Collectively we found that both IGF1R and INSRA have oncogenic effects in prostate cancer, but the IGF network also has important physiological functions in the noncancerous prostate. These data provide new insights into the biology of the IGF network in the prostate, thereby facilitating the design and interpretation of clinical studies investigating IGF1R targeting agents.

  15. 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

  16. 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...

  17. Set7 mediated interactions regulate transcriptional networks in embryonic stem cells.

    Science.gov (United States)

    Tuano, Natasha K; Okabe, Jun; Ziemann, Mark; Cooper, Mark E; El-Osta, Assam

    2016-11-02

    Histone methylation by lysine methyltransferase enzymes regulate the expression of genes implicated in lineage specificity and cellular differentiation. While it is known that Set7 catalyzes mono-methylation of histone and non-histone proteins, the functional importance of this enzyme in stem cell differentiation remains poorly understood. We show Set7 expression is increased during mouse embryonic stem cell (mESC) differentiation and is regulated by the pluripotency factors, Oct4 and Sox2. Transcriptional network analyses reveal smooth muscle (SM) associated genes are subject to Set7-mediated regulation. Furthermore, pharmacological inhibition of Set7 activity confirms this regulation. We observe Set7-mediated modification of serum response factor (SRF) and mono-methylation of histone H4 lysine 4 (H3K4me1) regulate gene expression. We conclude the broad substrate specificity of Set7 serves to control key transcriptional networks in embryonic stem cells.

  18. 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.

  19. Memory Networks in Tinnitus: A Functional Brain Image Study

    Science.gov (United States)

    Laureano, Maura Regina; Onishi, Ektor Tsuneo; Bressan, Rodrigo Affonseca; Castiglioni, Mario Luiz Vieira; Batista, Ilza Rosa; Reis, Marilia Alves; Garcia, Michele Vargas; de Andrade, Adriana Neves; de Almeida, Roberta Ribeiro; Garrido, Griselda J.; Jackowski, Andrea Parolin

    2014-01-01

    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. Methods: 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). Results: 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. Conclusion: 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. PMID:24516567

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. Topological peculiarities of mammalian networks with different functionalities: transcription, signal transduction and metabolic networks

    Directory of Open Access Journals (Sweden)

    Bjorn Goemann

    2011-12-01

    Full Text Available We have comparatively investigated three different mammalian networks - on transcription, signal transduction and metabolic processes - with respect to their common and individual topological traits. The networks have been constructed based on genome- wide data collected from human, mouse and rat. None of these three networks exhibits a pure power-law degree distribution and, therefore, could be considered scalefree. Rather, the degree distributions of all three networks were best fitted by mixed models of a power law with an exponential tail. The networks differ from one another in the quantitative parameters of the models. Moreover, the transcription network can also be very well approximated by an exponential law. The connectivity within each network is rather robust, as is seen when removing individual nodes and computing the values of their pairwise disconnectivity index (PDI, which characterizes the topological significance of each node v by the number of direct or indirect connections in the network that critically depend on the presence of v. The results evidence that the networks are not centralized: none of nodes globally controls the integrity of each network. Just a few vertices appeared to strongly affect the coherence of the networks. These nodes are characterized by a broad range of degrees, thereby indicating that the degree alone is not the decisive criteria of a node's importance. The networks reveal distinct architectures: The transcriptional network exhibits a hierarchical modularity, whereas the signaling network is mainly comprised of semi-autonomous modules. The metabolic network seems to be made by a more complex mixture of substructures. Thus, despite being encoded by the same genomes, the networks significantly differ from one another in their general architectural design. Altogether, our results indicate that the subsets of genes and relationships that constitute these networks have co-evolved very differently and

  7. 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.

  8. 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......The increasing demand for mobile broadband is pushing existing 3G networks closer to their capacity limit. Additional carriers together with new HSPA features (HSPA+) are expected to provide the next necessary boost in network capacity. One specific feature in HSPA+ is referred to as Dual......-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...

  9. 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

    2013-04-01

    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 little attention to such characteristics as the variety, accessibility, length and origin of the relationships or the frequency and initiation of the contacts. A comprehensive examination of both the structural and functional characteristics of the social networks of 33 people with intellectual disabilities was therefore undertaken. The social networks of the individuals who all lived in the community varied from 4 to 28 members (mean 14.21); 42.65% of the network members were family members, 32.84% acquaintances and 24.51% professionals. Remarkable is the high frequency of contact with network members; the finding that the participants considered themselves to be the main initiator of contact more often than the other members of their networks as the main initiators; the high scores assigned to neighbours and professionals for functional characteristics; and the relatively low scores assigned to network members for the connection characteristic of the social networks. The important role of professionals in the social networks of people with mild intellectual disabilities and practical implications to facilitate their social inclusion are discussed.

  10. Sphingomyelin homeostasis is required to form functional enzymatic domains at the trans-Golgi network.

    Science.gov (United States)

    van Galen, Josse; Campelo, Felix; Martínez-Alonso, Emma; Scarpa, Margherita; Martínez-Menárguez, José Ángel; Malhotra, Vivek

    2014-09-01

    Do lipids such as sphingomyelin (SM) that are known to assemble into specific membrane domains play a role in the organization and function of transmembrane proteins? In this paper, we show that disruption of SM homeostasis at the trans-Golgi network (TGN) by treatment of HeLa cells with d-ceramide-C6, which was converted together with phosphatidylcholine to short-chain SM and diacylglycerol by SM synthase, led to the segregation of Golgi-resident proteins from each other. We found that TGN46, which cycles between the TGN and the plasma membrane, was not sialylated by a sialyltransferase at the TGN and that this enzyme and its substrate TGN46 could not physically interact with each other. Our results suggest that SM organizes transmembrane proteins into functional enzymatic domains at the TGN.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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...... industrial companies. The paper closes with suggestions for how the tentative results of this work can be unraveled further....

  17. 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.

  18. Distribution of mitochondrial nucleoids upon mitochondrial network fragmentation and network reintegration in HEPG2 cells.

    Science.gov (United States)

    Tauber, Jan; Dlasková, Andrea; Šantorová, Jitka; Smolková, Katarína; Alán, Lukáš; Špaček, Tomáš; Plecitá-Hlavatá, Lydie; Jabůrek, Martin; Ježek, Petr

    2013-03-01

    Mitochondrial DNA (mtDNA) is organized in nucleoids in complex with accessory proteins, proteins of mtDNA replication and gene expression machinery. A robust mtDNA genome is represented by hundreds to thousands of nucleoids in cell mitochondrion. Detailed information is lacking about the dynamics of nucleoid distribution within the mitochondrial network upon physiological and pathological events. Therefore, we used confocal microscopy to study mitochondrial nucleoid redistribution upon mitochondrial fission and following reintegration of the mitochondrial network. Fission was induced by oxidative stress at respiration inhibition by rotenone or upon elimination of the protonmotive force by uncoupling or upon canceling its electrical component, ΔΨ(m), by valinomycin; and by silencing of mitofusin MFN2. Agent withdrawal resulted in concomitant mitochondrial network reintegration. We found two major principal morphological states: (i) a tubular state of the mitochondrial network with equidistant nucleoid spacing, 1.10±0.2 nucleoids per μm, and (ii) a fragmented state of solitary spheroid objects in which several nucleoids were clustered. We rarely observed singular mitochondrial fragments with a single nucleoid inside and very seldom we observed empty fragments. Reintegration of fragments into the mitochondrial network re-established the tubular state with equidistant nucleoid spacing. The two major morphological states coexisted at intermediate stages. These observations suggest that both mitochondrial network fission and reconnection of the disintegrated network are nucleoid-centric, i.e., fission and new mitochondrial tubule formation are initiated around nucleoids. Analyses of combinations of these morphological icons thus provide a basis for a future mitochondrial morphology diagnostics.

  19. 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

  20. Neural network analyses of infrared spectra for classifying cell wall architectures.

    Science.gov (United States)

    McCann, Maureen C; Defernez, Marianne; Urbanowicz, Breeanna R; Tewari, Jagdish C; Langewisch, Tiffany; Olek, Anna; Wells, Brian; Wilson, Reginald H; Carpita, Nicholas C

    2007-03-01

    About 10% of plant genomes are devoted to cell wall biogenesis. Our goal is to establish methodologies that identify and classify cell wall phenotypes of mutants on a genome-wide scale. Toward this goal, we have used a model system, the elongating maize (Zea mays) coleoptile system, in which cell wall changes are well characterized, to develop a paradigm for classification of a comprehensive range of cell wall architectures altered during development, by environmental perturbation, or by mutation. Dynamic changes in cell walls of etiolated maize coleoptiles, sampled at one-half-d intervals of growth, were analyzed by chemical and enzymatic assays and Fourier transform infrared spectroscopy. The primary walls of grasses are composed of cellulose microfibrils, glucuronoarabinoxylans, and mixed-linkage (1 --> 3),(1 --> 4)-beta-D-glucans, together with smaller amounts of glucomannans, xyloglucans, pectins, and a network of polyphenolic substances. During coleoptile development, changes in cell wall composition included a transient appearance of the (1 --> 3),(1 --> 4)-beta-D-glucans, a gradual loss of arabinose from glucuronoarabinoxylans, and an increase in the relative proportion of cellulose. Infrared spectra reflected these dynamic changes in composition. Although infrared spectra of walls from embryonic, elongating, and senescent coleoptiles were broadly discriminated from each other by exploratory principal components analysis, neural network algorithms (both genetic and Kohonen) could correctly classify infrared spectra from cell walls harvested from individuals differing at one-half-d interval of growth. We tested the predictive capabilities of the model with a maize inbred line, Wisconsin 22, and found it to be accurate in classifying cell walls representing developmental stage. The ability of artificial neural networks to classify infrared spectra from cell walls provides a means to identify many possible classes of cell wall phenotypes. This classification

  1. 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.

  2. Random matrix theory for analyzing the brain functional network in attention deficit hyperactivity disorder

    Science.gov (United States)

    Wang, Rong; Wang, Li; Yang, Yong; Li, Jiajia; Wu, Ying; Lin, Pan

    2016-11-01

    Attention deficit hyperactivity disorder (ADHD) is the most common childhood neuropsychiatric disorder and affects approximately 6 -7 % of children worldwide. Here, we investigate the statistical properties of undirected and directed brain functional networks in ADHD patients based on random matrix theory (RMT), in which the undirected functional connectivity is constructed based on correlation coefficient and the directed functional connectivity is measured based on cross-correlation coefficient and mutual information. We first analyze the functional connectivity and the eigenvalues of the brain functional network. We find that ADHD patients have increased undirected functional connectivity, reflecting a higher degree of linear dependence between regions, and increased directed functional connectivity, indicating stronger causality and more transmission of information among brain regions. More importantly, we explore the randomness of the undirected and directed functional networks using RMT. We find that for ADHD patients, the undirected functional network is more orderly than that for normal subjects, which indicates an abnormal increase in undirected functional connectivity. In addition, we find that the directed functional networks are more random, which reveals greater disorder in causality and more chaotic information flow among brain regions in ADHD patients. Our results not only further confirm the efficacy of RMT in characterizing the intrinsic properties of brain functional networks but also provide insights into the possibilities RMT offers for improving clinical diagnoses and treatment evaluations for ADHD patients.

  3. Random matrix theory for analyzing the brain functional network in attention deficit hyperactivity disorder.

    Science.gov (United States)

    Wang, Rong; Wang, Li; Yang, Yong; Li, Jiajia; Wu, Ying; Lin, Pan

    2016-11-01

    Attention deficit hyperactivity disorder (ADHD) is the most common childhood neuropsychiatric disorder and affects approximately 6-7% of children worldwide. Here, we investigate the statistical properties of undirected and directed brain functional networks in ADHD patients based on random matrix theory (RMT), in which the undirected functional connectivity is constructed based on correlation coefficient and the directed functional connectivity is measured based on cross-correlation coefficient and mutual information. We first analyze the functional connectivity and the eigenvalues of the brain functional network. We find that ADHD patients have increased undirected functional connectivity, reflecting a higher degree of linear dependence between regions, and increased directed functional connectivity, indicating stronger causality and more transmission of information among brain regions. More importantly, we explore the randomness of the undirected and directed functional networks using RMT. We find that for ADHD patients, the undirected functional network is more orderly than that for normal subjects, which indicates an abnormal increase in undirected functional connectivity. In addition, we find that the directed functional networks are more random, which reveals greater disorder in causality and more chaotic information flow among brain regions in ADHD patients. Our results not only further confirm the efficacy of RMT in characterizing the intrinsic properties of brain functional networks but also provide insights into the possibilities RMT offers for improving clinical diagnoses and treatment evaluations for ADHD patients.

  4. 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.

  5. 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...

  6. The role of the vascular dendritic cell network in atherosclerosis

    OpenAIRE

    Alberts-Grill, Noah; Denning, Timothy L.; Rezvan, Amir; Jo, Hanjoong

    2013-01-01

    A complex role has been described for dendritic cells (DCs) in the potentiation and control of vascular inflammation and atherosclerosis. Resident vascular DCs are found in the intima of atherosclerosis-prone vascular regions exposed to disturbed blood flow patterns. Several phenotypically and functionally distinct vascular DC subsets have been described. The functional heterogeneity of these cells and their contributions to vascular homeostasis, inflammation, and atherosclerosis are only rec...

  7. 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.

  8. Heterogeneous Cellular Networks with Flexible Cell Association: A Comprehensive Downlink SINR Analysis

    CERN Document Server

    Jo, Han-Shin; Xia, Ping; Andrews, Jeffrey G

    2011-01-01

    In this paper we develop a tractable framework for SINR analysis in downlink heterogeneous cellular networks (HCNs) with flexible cell association policies. The HCN is modeled as a multi-tier cellular network where each tier's base stations (BSs) are randomly located and have a particular transmit power, path loss exponent, spatial density, and bias towards admitting mobile users. For example, as compared to macrocells, picocells would usually have lower transmit power, higher path loss exponent (lower antennas), higher spatial density (many picocells per macrocell), and a positive bias so that macrocell users are actively encouraged to use the more lightly loaded picocells. In the present paper we implicitly assume all base stations have full queues; future work should relax this. For this model, we derive the outage probability of a typical user in the whole network or a certain tier, which is equivalently the downlink SINR cumulative distribution function. The results are accurate for all SINRs, and their ...

  9. Whole-brain functional connectivity during acquisition of novel grammar: Distinct functional networks depend on language learning abilities.

    Science.gov (United States)

    Kepinska, Olga; de Rover, Mischa; Caspers, Johanneke; Schiller, Niels O

    2017-03-01

    In an effort to advance the understanding of brain function and organisation accompanying second language learning, we investigate the neural substrates of novel grammar learning in a group of healthy adults, consisting of participants with high and average language analytical abilities (LAA). By means of an Independent Components Analysis, a data-driven approach to functional connectivity of the brain, the fMRI data collected during a grammar-learning task were decomposed into maps representing separate cognitive processes. These included the default mode, task-positive, working memory, visual, cerebellar and emotional networks. We further tested for differences within the components, representing individual differences between the High and Average LAA learners. We found high analytical abilities to be coupled with stronger contributions to the task-positive network from areas adjacent to bilateral Broca's region, stronger connectivity within the working memory network and within the emotional network. Average LAA participants displayed stronger engagement within the task-positive network from areas adjacent to the right-hemisphere homologue of Broca's region and typical to lower level processing (visual word recognition), and increased connectivity within the default mode network. The significance of each of the identified networks for the grammar learning process is presented next to a discussion on the established markers of inter-individual learners' differences. We conclude that in terms of functional connectivity, the engagement of brain's networks during grammar acquisition is coupled with one's language learning abilities.

  10. 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.

  11. 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.

  12. Aberrant functional connectivity of resting state networks in transient ischemic attack.

    Directory of Open Access Journals (Sweden)

    Rong Li

    Full Text Available BACKGROUND: Transient ischemic attack (TIA is usually defined as a neurologic ischemic disorder without permanent cerebral infarction. Studies have showed that patients with TIA can have lasting cognitive functional impairment. Inherent brain activity in the resting state is spatially organized in a set of specific coherent patterns named resting state networks (RSNs, which epitomize the functional architecture of memory, language, attention, visual, auditory and somato-motor networks. Here, we aimed to detect differences in RSNs between TIA patients and healthy controls (HCs. METHODS: Twenty one TIA patients suffered an ischemic event and 21 matched HCs were enrolled in the study. All subjects were investigated using cognitive tests, psychiatric tests and functional magnetic resonance imaging (fMRI. Independent component analysis (ICA was adopted to acquire the eight brain RSNs. Then one-sample t-tests were calculated in each group to gather the spatial maps of each RSNs, followed by second level analysis to investigate statistical differences on RSNs between twenty one TIA patients and 21 controls. Furthermore, a correlation analysis was performed to explore the relationship between functional connectivity (FC and cognitive and psychiatric scales in TIA group. RESULTS: Compared with the controls, TIA patients exhibited both decreased and increased functional connectivity in default mode network (DMN and self-referential network (SRN, and decreased functional connectivity in dorsal attention network (DAN, central-executive network (CEN, core network (CN, somato-motor network (SMN, visual network (VN and auditory network (AN. There was no correlation between neuropsychological scores and functional connectivity in regions of RSNs. CONCLUSIONS: We observed selective impairments of RSN intrinsic FC in TIA patients, whose all eight RSNs had aberrant functional connectivity. These changes indicate that TIA is a disease with widely abnormal brain

  13. 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

  14. 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.

  15. Boolean genetic network model for the control of C. elegans early embryonic cell cycles

    Science.gov (United States)

    2013-01-01

    Background In Caenorhabditis elegans early embryo, cell cycles only have two phases: DNA synthesis and mitosis, which are different from the typical 4-phase cell cycle. Modeling this cell-cycle process into network can fill up the gap in C. elegans cell-cycle study and provide a thorough understanding on the cell-cycle regulations and progressions at the network level. Methods In this paper, C. elegans early embryonic cell-cycle network has been constructed based on the knowledge of key regulators and their interactions from literature studies. A discrete dynamical Boolean model has been applied in computer simulations to study dynamical properties of this network. The cell-cycle network is compared with random networks and tested under several perturbations to analyze its robustness. To investigate whether our proposed network could explain biological experiment results, we have also compared the network simulation results with gene knock down experiment data. Results With the Boolean model, this study showed that the cell-cycle network was stable with a set of attractors (fixed points). A biological pathway was observed in the simulation, which corresponded to a whole cell-cycle progression. The C. elegans network was significantly robust when compared with random networks of the same size because there were less attractors and larger basins than random networks. Moreover, the network was also robust under perturbations with no significant change of the basin size. In addition, the smaller number of attractors and the shorter biological pathway from gene knock down network simulation interpreted the shorter cell-cycle lengths in mutant from the RNAi gene knock down experiment data. Hence, we demonstrated that the results in network simulation could be verified by the RNAi gene knock down experiment data. Conclusions A C. elegans early embryonic cell cycles network was constructed and its properties were analyzed and compared with those of random networks

  16. 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.

  17. Functional scale-free networks in the two-dimensional Abelian sandpile model

    Science.gov (United States)

    Zarepour, M.; Niry, M. D.; Valizadeh, A.

    2015-07-01

    Recently, the similarity of the functional network of the brain and the Ising model was investigated by Chialvo [Nat. Phys. 6, 744 (2010), 10.1038/nphys1803]. This similarity supports the idea that the brain is a self-organized critical system. In this study we derive a functional network of the two-dimensional Bak-Tang-Wiesenfeld sandpile model as a self-organized critical model, and compare its characteristics with those of the functional network of the brain, obtained from functional magnetic resonance imaging.

  18. Functional brain network changes associated with clinical and biochemical measures of the severity of hepatic encephalopathy.

    Science.gov (United States)

    Jao, Tun; Schröter, Manuel; Chen, Chao-Long; Cheng, Yu-Fan; Lo, Chun-Yi Zac; Chou, Kun-Hsien; Patel, Ameera X; Lin, Wei-Che; Lin, Ching-Po; Bullmore, Edward T

    2015-11-15

    Functional properties of the brain may be associated with changes in complex brain networks. However, little is known about how properties of large-scale functional brain networks may be altered stepwise in patients with disturbance of consciousness, e.g., an encephalopathy. We used resting-state fMRI data on patients suffering from various degrees of hepatic encephalopathy (HE) to explore how topological and spatial network properties of functional brain networks changed at different cognitive and consciousness states. Severity of HE was measured clinically and by neuropsychological tests. Fifty-eight non-alcoholic liver cirrhosis patients and 62 normal controls were studied. Patients were subdivided into liver cirrhosis with no outstanding HE (NoHE, n=23), minimal HE with cognitive impairment only detectable by neuropsychological tests (MHE, n=28), and clinically overt HE (OHE, n=7). From the earliest stage, the NoHE, functional brain networks were progressively more random, less clustered, and less modular. Since the intermediate stage (MHE), increased ammonia level was accompanied by concomitant exponential decay of mean connectivity strength, especially in the primary cortical areas and midline brain structures. Finally, at the OHE stage, there were radical reorganization of the topological centrality-i.e., the relative importance-of the hubs and reorientation of functional connections between nodes. In summary, this study illustrated progressively greater abnormalities in functional brain network organization in patients with clinical and biochemical evidence of more severe hepatic encephalopathy. The early-than-expected brain network dysfunction in cirrhotic patients suggests that brain functional connectivity and network analysis may provide useful and complementary biomarkers for more aggressive and earlier intervention of hepatic encephalopathy. Moreover, the stepwise deterioration of functional brain networks in HE patients may suggest that hierarchical

  19. 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.

  20. 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.

  1. A Complex-Valued Projection Neural Network for Constrained Optimization of Real Functions in Complex Variables.

    Science.gov (United States)

    Zhang, Songchuan; Xia, Youshen; Wang, Jun

    2015-12-01

    In this paper, we present a complex-valued projection neural network for solving constrained convex optimization problems of real functions with complex variables, as an extension of real-valued projection neural networks. Theoretically, by developing results on complex-valued optimization techniques, we prove that the complex-valued projection neural network is globally stable and convergent to the optimal solution. Obtained results are completely established in the complex domain and thus significantly generalize existing results of the real-valued projection neural networks. Numerical simulations are presented to confirm the obtained results and effectiveness of the proposed complex-valued projection neural network.

  2. Biomechanical cell regulatory networks as complex adaptive systems in relation to cancer.

    Science.gov (United States)

    Feller, Liviu; Khammissa, Razia Abdool Gafaar; Lemmer, Johan

    2017-01-01

    Physiological structure and function of cells are maintained by ongoing complex dynamic adaptive processes in the intracellular molecular pathways controlling the overall profile of gene expression, and by genes in cellular gene regulatory circuits. Cytogenetic mutations and non-genetic factors such as chronic inflammation or repetitive trauma, intrinsic mechanical stresses within extracellular matrix may induce redirection of gene regulatory circuits with abnormal reactivation of embryonic developmental programmes which can now drive cell transformation and cancer initiation, and later cancer progression and metastasis. Some of the non-genetic factors that may also favour cancerization are dysregulation in epithelial-mesenchymal interactions, in cell-to-cell communication, in extracellular matrix turnover, in extracellular matrix-to-cell interactions and in mechanotransduction pathways. Persistent increase in extracellular matrix stiffness, for whatever reason, has been shown to play an important role in cell transformation, and later in cancer cell invasion. In this article we review certain cell regulatory networks driving carcinogenesis, focussing on the role of mechanical stresses modulating structure and function of cells and their extracellular matrices.

  3. 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.

  4. A Geographic and Functional Network Flow Analysis Tool

    Science.gov (United States)

    2014-06-01

    INFORMATION SYSTEMS TOOLS AND DYSTOPIA .......................................................................................................5 B. MODELS OF...17 IV. CASE STUDY: FIBER OPTIC COMMUNICATIONS BACKBONE IN DYSTOPIA ...15 Figure 8. A simple fiber-optic backbone network for Dystopia . .....................................19 Figure 9

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. Intergroup Joint Scheduling for Mitigating Asymmetric Uplink Interference in Self-Organizing Virtual Cell Networks

    Directory of Open Access Journals (Sweden)

    Ohyun Jo

    2016-01-01

    Full Text Available We introduce the concept of self-organizing VCN (virtual cell network. Here self-organizing VCN topology for efficient operation will be configured, and the functions of the each element will be defined. Also, the operation scenarios of VCN will be described. Then, we propose an efficient scheduling algorithm that considers the asymmetry of interference between downlink and uplink to mitigate intercell interference with little computing overhead. The basic concept is to construct scheduling groups that consist of several users. Each user in a scheduling group is affiliated with a different cell. Then, the intercell groups are managed efficiently in the proposed VCNs. There is no need for the exchange of a lot of information among base stations to schedule the users over the entire network.

  11. 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).

  12. 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.

  13. TET Methylcytosine Oxidases in T Cell and B Cell Development and Function

    Science.gov (United States)

    Tsagaratou, Ageliki; Lio, Chan-Wang J.; Yue, Xiaojing; Rao, Anjana

    2017-01-01

    DNA methylation is established by DNA methyltransferases and is a key epigenetic mark. Ten-eleven translocation (TET) proteins are enzymes that oxidize 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC) and further oxidization products (oxi-mCs), which indirectly promote DNA demethylation. Here, we provide an overview of the effect of TET proteins and altered DNA modification status in T and B cell development and function. We summarize current advances in our understanding of the role of TET proteins and 5hmC in T and B cells in both physiological and pathological contexts. We describe how TET proteins and 5hmC regulate DNA modification, chromatin accessibility, gene expression, and transcriptional networks and discuss potential underlying mechanisms and open questions in the field.

  14. 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.

  15. 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.

  16. Changes in topological organization of functional PET brain network with normal aging.

    Science.gov (United States)

    Liu, Zhiliang; Ke, Lining; Liu, Huafeng; Huang, Wenhua; Hu, Zhenghui

    2014-01-01

    Recent studies about brain network have suggested that normal aging is associated with alterations in coordinated patterns of the large-scale brain functional and structural systems. However, age-related changes in functional networks constructed via positron emission tomography (PET) data are still barely understood. Here, we constructed functional brain networks composed of 90 regions in younger (mean age 36.5 years) and older (mean age 56.3 years) age groups with PET data. 113 younger and 110 older healthy individuals were separately selected for two age groups, from a physical examination database. Corresponding brain functional networks of the two groups were constructed by thresholding average cerebral glucose metabolism correlation matrices of 90 regions and analysed using graph theoretical approaches. Although both groups showed normal small-world architecture in the PET networks, increased clustering and decreased efficiency were found in older subjects, implying a degeneration process that brain system shifts from a small-world network to regular one along with normal aging. Moreover, normal senescence was related to changed nodal centralities predominantly in association and paralimbic cortex regions, e.g. increasing in orbitofrontal cortex (middle) and decreasing in left hippocampus. Additionally, the older networks were about equally as robust to random failures as younger counterpart, but more vulnerable against targeted attacks. Finally, methods in the construction of the PET networks revealed reasonable robustness. Our findings enhanced the understanding about the topological principles of PET networks and changes related to normal aging.

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

    Science.gov (United States)

    Andras, Peter

    2017-01-25

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

  18. 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.

  19. 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

  20. 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.

  1. RNAi screening reveals a large signaling network controlling the Golgi apparatus in human cells.

    Science.gov (United States)

    Chia, Joanne; Goh, Germaine; Racine, Victor; Ng, Susanne; Kumar, Pankaj; Bard, Frederic

    2012-01-01

    The Golgi apparatus has many important physiological functions, including sorting of secretory cargo and biosynthesis of complex glycans. These functions depend on the intricate and compartmentalized organization of the Golgi apparatus. To investigate the mechanisms that regulate Golgi architecture, we developed a quantitative morphological assay using three different Golgi compartment markers and quantitative image analysis, and performed a kinome- and phosphatome-wide RNAi screen in HeLa cells. Depletion of 159 signaling genes, nearly 20% of genes assayed, induced strong and varied perturbations in Golgi morphology. Using bioinformatics data, a large regulatory network could be constructed. Specific subnetworks are involved in phosphoinositides regulation, acto-myosin dynamics and mitogen activated protein kinase signaling. Most gene depletion also affected Golgi functions, in particular glycan biosynthesis, suggesting that signaling cascades can control glycosylation directly at the Golgi level. Our results provide a genetic overview of the signaling pathways that control the Golgi apparatus in human cells.

  2. Altered topological properties of functional network connectivity in schi