WorldWideScience

Sample records for cell network function

  1. [Signaling network-based functional cell design].

    Science.gov (United States)

    Ju, Jianqi; Wei, Ping

    2017-03-25

    Cellular signaling networks act as the central processor to deal with environmental signals and regulate cell function, and determine cell fate. Using synthetic biology approach to engineer cell signaling networks is crucial for ultimately constructing man-made "cell machines". Cellular signaling networks can encode sophisticated cell information by processing quantitatively signaling dynamics, which enables multi-dimensional regulation of functional sub-circuits. Here, we first review the research progresses on the signaling coding mechanisms; and then elaborate the methodologies and applications of cells signaling engineering; finally, we envision that signaling-based cell engineering are important for the increasingly-complicated next generation synthetic biology.

  2. Chromatin states modify network motifs contributing to cell-specific functions

    Science.gov (United States)

    Zhao, Hongying; Liu, Tingting; Liu, Ling; Zhang, Guanxiong; Pang, Lin; Yu, Fulong; Fan, Huihui; Ping, Yanyan; Wang, Li; Xu, Chaohan; Xiao, Yun; Li, Xia

    2015-01-01

    Epigenetic modification can affect many important biological processes, such as cell proliferation and apoptosis. It can alter chromatin conformation and contribute to gene regulation. To investigate how chromatin states associated with network motifs, we assembled chromatin state-modified regulatory networks by combining 269 ChIP-seq data and chromatin states in four cell types. We found that many chromatin states were significantly associated with network motifs, especially for feedforward loops (FFLs). These distinct chromatin state compositions contribute to different expression levels and translational control of targets in FFLs. Strikingly, the chromatin state-modified FFLs were highly cell-specific and, to a large extent, determined cell-selective functions, such as the embryonic stem cell-specific bivalent modification-related FFL with an important role in poising developmentally important genes for expression. Besides, comparisons of chromatin state-modified FFLs between cancerous/stem and primary cell lines revealed specific type of chromatin state alterations that may act together with motif structural changes cooperatively contribute to cell-to-cell functional differences. Combination of these alterations could be helpful in prioritizing candidate genes. Together, this work highlights that a dynamic epigenetic dimension can help network motifs to control cell-specific functions. PMID:26169043

  3. Functional network integration of embryonic stem cell-derived astrocytes in hippocampal slice cultures.

    Science.gov (United States)

    Scheffler, Björn; Schmandt, Tanja; Schröder, Wolfgang; Steinfarz, Barbara; Husseini, Leila; Wellmer, Jörg; Seifert, Gerald; Karram, Khalad; Beck, Heinz; Blümcke, Ingmar; Wiestler, Otmar D; Steinhäuser, Christian; Brüstle, Oliver

    2003-11-01

    Embryonic stem (ES) cells provide attractive prospects for neural transplantation. So far, grafting strategies in the CNS have focused mainly on neuronal replacement. Employing a slice culture model, we found that ES cell-derived glial precursors (ESGPs) possess a remarkable capacity to integrate into the host glial network. Following deposition on the surface of hippocampal slices, ESGPs actively migrate into the recipient tissue and establish extensive cell-cell contacts with recipient glia. Gap junction-mediated coupling between donor and host astrocytes permits widespread delivery of dye from single donor cells. During maturation, engrafted donor cells display morphological, immunochemical and electrophysiological properties that are characteristic of differentiating native glia. Our findings provide the first evidence of functional integration of grafted astrocytes, and depict glial network integration as a potential route for widespread transcellular delivery of small molecules to the CNS.

  4. 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

  5. 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. © 2015. Published by The Company of Biologists Ltd.

  6. Coded Network Function Virtualization

    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...

  7. The regulatory network of B-cell differentiation: a focused view of early B-cell factor 1 function.

    Science.gov (United States)

    Boller, Sören; Grosschedl, Rudolf

    2014-09-01

    During the last decades, many studies have investigated the transcriptional and epigenetic regulation of lineage decision in the hematopoietic system. These efforts led to a model in which extrinsic signals and intrinsic cues establish a permissive chromatin context upon which a regulatory network of transcription factors and epigenetic modifiers act to guide the differentiation of hematopoietic lineages. These networks include lineage-specific factors that further modify the epigenetic landscape and promote the generation of specific cell types. The process of B lymphopoiesis requires a set of transcription factors, including Ikaros, PU.1, E2A, and FoxO1 to 'prime' cis-regulatory regions for subsequent activation by the B-lineage-specific transcription factors EBF1 and Pax-5. The expression of EBF1 is initiated by the combined action of E2A and FoxO1, and it is further enhanced and maintained by several positive feedback loops that include Pax-5 and IL-7 signaling. EBF1 acts in concert with Ikaros, PU.1, Runx1, E2A, FoxO1, and Pax-5 to establish the B cell-specific transcription profile. EBF1 and Pax-5 also collaborate to repress alternative cell fates and lock cells into the B-lineage fate. In addition to the functions of EBF1 in establishing and maintaining B-cell identity, EBF1 is required to coordinate differentiation with cell proliferation and survival. © 2014 The Authors. Immunological Reviews Published by John Wiley & Sons Ltd.

  8. FOXA2 regulates a network of genes involved in critical functions of human intestinal epithelial cells.

    Science.gov (United States)

    Gosalia, Nehal; Yang, Rui; Kerschner, Jenny L; Harris, Ann

    2015-07-01

    The forkhead box A (FOXA) family of pioneer transcription factors is critical for the development of many endoderm-derived tissues. Their importance in regulating biological processes in the lung and liver is extensively characterized, though much less is known about their role in intestine. Here we investigate the contribution of FOXA2 to coordinating intestinal epithelial cell function using postconfluent Caco2 cells, differentiated into an enterocyte-like model. FOXA2 binding sites genome-wide were determined by ChIP-seq and direct targets of the factor were validated by ChIP-qPCR and siRNA-mediated depletion of FOXA1/2 followed by RT-qPCR. Peaks of FOXA2 occupancy were frequent at loci contributing to gene ontology pathways of regulation of cell migration, cell motion, and plasma membrane function. Depletion of both FOXA1 and FOXA2 led to a significant reduction in the expression of multiple transmembrane proteins including ion channels and transporters, which form a network that is essential for maintaining normal ion and solute transport. One of the targets was the adenosine A2B receptor, and reduced receptor mRNA levels were associated with a functional decrease in intracellular cyclic AMP. We also observed that 30% of FOXA2 binding sites contained a GATA motif and that FOXA1/A2 depletion reduced GATA-4, but not GATA-6 protein levels. These data show that FOXA2 plays a pivotal role in regulating intestinal epithelial cell function. Moreover, that the FOXA and GATA families of transcription factors may work cooperatively to regulate gene expression genome-wide in the intestinal epithelium. Copyright © 2015 the American Physiological Society.

  9. Human neural stem cell-derived cultures in three-dimensional substrates form spontaneously functional neuronal networks.

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    Smith, Imogen; Silveirinha, Vasco; Stein, Jason L; de la Torre-Ubieta, Luis; Farrimond, Jonathan A; Williamson, Elizabeth M; Whalley, Benjamin J

    2017-04-01

    Differentiated human neural stem cells were cultured in an inert three-dimensional (3D) scaffold and, unlike two-dimensional (2D) but otherwise comparable monolayer cultures, formed spontaneously active, functional neuronal networks that responded reproducibly and predictably to conventional pharmacological treatments to reveal functional, glutamatergic synapses. Immunocytochemical and electron microscopy analysis revealed a neuronal and glial population, where markers of neuronal maturity were observed in the former. Oligonucleotide microarray analysis revealed substantial differences in gene expression conferred by culturing in a 3D vs a 2D environment. Notable and numerous differences were seen in genes coding for neuronal function, the extracellular matrix and cytoskeleton. In addition to producing functional networks, differentiated human neural stem cells grown in inert scaffolds offer several significant advantages over conventional 2D monolayers. These advantages include cost savings and improved physiological relevance, which make them better suited for use in the pharmacological and toxicological assays required for development of stem cell-based treatments and the reduction of animal use in medical research. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

  10. 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.

  11. Identification of biological functions and gene networks regulated by heat stress in U937 human lymphoma cells.

    Science.gov (United States)

    Furusawa, Yukihiro; Tabuchi, Yoshiaki; Wada, Shigehito; Takasaki, Ichiro; Ohtsuka, Kenzo; Kondo, Takashi

    2011-08-01

    Although cancer cells exposed to temperatures >42.5°C undergo cell death as the temperature rises, exposure of up to 42.5°C induces slight or no cytotoxicity. The temperature of 42.5°C is, therefore, well known to be the inflection point of hyperthermia. To better understand the molecular mechanisms underlying cellular responses to heat stress at temperatures higher and lower than the inflection point, we carried out global scale microarray and computational gene expression analyses. Human leukemia U937 cells were incubated at 42°C or 44°C for 15 min and cultured at 37°C for 0-6 h. Apoptosis accompanied by the activation of caspase-3 and DNA fragmentation was only observed in cells treated with heat stress at 44°C, but not at 42°C. Although a large number of genes were differentially expressed by a factor of 2.0 or greater, we found substantial differences with respect to the biological functions and gene networks of the genes differentially expressed at the two temperatures examined. Interestingly, we identified temperature-specific gene networks that were considered to be mainly associated with cell death or cellular compromise and cellular function and maintenance at 44°C or 42°C, respectively, by using the Ingenuity pathway analysis tools. These findings provide the molecular basis for a further understanding of the mechanisms of the biological changes that are responsive to heat stress in human lymphoma cells.

  12. In Silico Functional Networks Identified in Fish Nucleated Red Blood Cells by Means of Transcriptomic and Proteomic Profiling

    Directory of Open Access Journals (Sweden)

    Sara Puente-Marin

    2018-04-01

    Full Text Available Nucleated red blood cells (RBCs of fish have, in the last decade, been implicated in several immune-related functions, such as antiviral response, phagocytosis or cytokine-mediated signaling. RNA-sequencing (RNA-seq and label-free shotgun proteomic analyses were carried out for in silico functional pathway profiling of rainbow trout RBCs. For RNA-seq, a de novo assembly was conducted, in order to create a transcriptome database for RBCs. For proteome profiling, we developed a proteomic method that combined: (a fractionation into cytosolic and membrane fractions, (b hemoglobin removal of the cytosolic fraction, (c protein digestion, and (d a novel step with pH reversed-phase peptide fractionation and final Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometric (LC ESI-MS/MS analysis of each fraction. Combined transcriptome- and proteome- sequencing data identified, in silico, novel and striking immune functional networks for rainbow trout nucleated RBCs, which are mainly linked to innate and adaptive immunity. Functional pathways related to regulation of hematopoietic cell differentiation, antigen presentation via major histocompatibility complex class II (MHCII, leukocyte differentiation and regulation of leukocyte activation were identified. These preliminary findings further implicate nucleated RBCs in immune function, such as antigen presentation and leukocyte activation.

  13. CD40L induces functional tunneling nanotube networks exclusively in dendritic cells programmed by mediators of type 1 immunity.

    Science.gov (United States)

    Zaccard, Colleen R; Watkins, Simon C; Kalinski, Pawel; Fecek, Ronald J; Yates, Aarika L; Salter, Russell D; Ayyavoo, Velpandi; Rinaldo, Charles R; Mailliard, Robbie B

    2015-02-01

    The ability of dendritic cells (DC) to mediate CD4(+) T cell help for cellular immunity is guided by instructive signals received during DC maturation, as well as the resulting pattern of DC responsiveness to the Th signal, CD40L. Furthermore, the professional transfer of antigenic information from migratory DC to lymph node-residing DC is critical for the effective induction of cellular immune responses. In this study we report that, in addition to their enhanced IL-12p70 producing capacity, human DC matured in the presence of inflammatory mediators of type 1 immunity are uniquely programmed to form networks of tunneling nanotube-like structures in response to CD40L-expressing Th cells or rCD40L. This immunologic process of DC reticulation facilitates intercellular trafficking of endosome-associated vesicles and Ag, but also pathogens such HIV-1, and is regulated by the opposing roles of IFN-γ and IL-4. The initiation of DC reticulation represents a novel helper function of CD40L and a superior mechanism of intercellular communication possessed by type 1 polarized DC, as well as a target for exploitation by pathogens to enhance direct cell-to-cell spread. Copyright © 2015 by The American Association of Immunologists, Inc.

  14. 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-learning......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...

  15. 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

  16. Network-based functional enrichment

    Directory of Open Access Journals (Sweden)

    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

  17. 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.

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

    Directory of Open Access Journals (Sweden)

    David B Rosen

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

  19. 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…

  20. SATWG networked quality function deployment

    Science.gov (United States)

    Brown, Don

    1992-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

    High-throughput screens allow us to understand how transcription factors trigger developmental processes, including cell specification. A major challenge is identification of their binding sites because feedback loops and homeostatic interactions may mask the direct impact of those factors in transcriptome analyses. Moreover, this approach dissects the downstream signaling cascades and facilitates identification of conserved transcriptional programs. Here we show the results and the validation of a DNA adenine methyltransferase identification (DamID) genome-wide screen that identifies the direct targets of Glide/Gcm, a potent transcription factor that controls glia, hemocyte, and tendon cell differentiation in Drosophila. The screen identifies many genes that had not been previously associated with Glide/Gcm and highlights three major signaling pathways interacting with Glide/Gcm: Notch, Hedgehog, and JAK/STAT, which all involve feedback loops. Furthermore, the screen identifies effector molecules that are necessary for cell-cell interactions during late developmental processes and/or in ontogeny. Typically, immunoglobulin (Ig) domain-containing proteins control cell adhesion and axonal navigation. This shows that early and transiently expressed fate determinants not only control other transcription factors that, in turn, implement a specific developmental program but also directly affect late developmental events and cell function. Finally, while the mammalian genome contains two orthologous Gcm genes, their function has been demonstrated in vertebrate-specific tissues, placenta, and parathyroid glands, begging questions on the evolutionary conservation of the Gcm cascade in higher organisms. Here we provide the first evidence for the conservation of Gcm direct targets in humans. In sum, this work uncovers novel aspects of cell specification and sets the basis for further understanding of the role of conserved Gcm gene regulatory cascades. Copyright © 2016

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

    Science.gov (United States)

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

    2016-01-01

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

  3. Network function virtualization concepts and applicability in 5G networks

    CERN Document Server

    Zhang, Ying

    2018-01-01

    A horizontal view of newly emerged technologies in the field of network function virtualization (NFV), introducing the open source implementation efforts that bring NFV from design to reality This book explores the newly emerged technique of network function virtualization (NFV) through use cases, architecture, and challenges, as well as standardization and open source implementations. It is the first systematic source of information about cloud technologies' usage in the cellular network, covering the interplay of different technologies, the discussion of different design choices, and its impact on our future cellular network. Network Function Virtualization: Concepts and Applicability in 5G Networks reviews new technologies that enable NFV, such as Software Defined Networks (SDN), network virtualization, and cloud computing. It also provides an in-depth investigation of the most advanced open source initiatives in this area, including OPNFV, Openstack, and Opendaylight. Finally, this book goes beyond li...

  4. 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

  5. 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.

  6. Mast Cell Function

    Science.gov (United States)

    da Silva, Elaine Zayas Marcelino; Jamur, Maria Célia

    2014-01-01

    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. PMID:25062998

  7. Network Analysis of the Systemic Response to Fasciola hepatica Infection in Sheep Reveals Changes in Fibrosis, Apoptosis, Toll-Like Receptors 3/4, and B Cell Function

    Science.gov (United States)

    Fu, Yan; Browne, John A.; Killick, Kate; Mulcahy, Grace

    2017-01-01

    The Trematode Fasciola hepatica is an important cause of disease in livestock and in man. Modulation of immunity is a critical strategy used by this parasite to facilitate its long-term survival in the host. Understanding the underlying mechanisms at a system level is important for the development of novel control strategies, such as vaccination, as well as for increasing general understanding of helminth-mediated immunoregulation and its consequences. Our previous RNA sequencing work identified a large number of differentially expressed genes (DEG) from ovine peripheral blood mononuclear cells (PBMCs) at acute and chronic stages of F. hepatica infection, and yielded important information on host–parasite interaction, with particular reference to the immune response. To extend our understanding of the immunoregulatory effects of this parasite, we employed InnateDB to further analyze the DEG dataset and identified 2,458 and 224 molecular interactions in the context of innate immunity from the acute and chronic stages of infection, respectively. Notably, 458 interactions at the acute stage of infection were manually curated from studies involving PBMC-related cell-types, which guaranteed confident hypothesis generation. NetworkAnalyst was subsequently used to construct and visualize molecular networks. Two complementary strategies (function-first and connection-first) were conducted to interpret the networks. The function-first approach highlighted subnetworks implicated in regulation of Toll-like receptor 3/4 signaling in both acute and chronic infections. The connection-first approach highlighted regulation of intrinsic apoptosis and B-cell receptor-signaling during acute and chronic infections, respectively. To the best of our knowledge, this study is the first system level analysis of the regulation of host innate immunity during F. hepatica infection. It provides insights into the profound changes induced by F. hepatica infection that not only favors parasite

  8. Network Analysis of the Systemic Response to Fasciola hepatica Infection in Sheep Reveals Changes in Fibrosis, Apoptosis, Toll-Like Receptors 3/4, and B Cell Function

    Directory of Open Access Journals (Sweden)

    Yan Fu

    2017-04-01

    Full Text Available The Trematode Fasciola hepatica is an important cause of disease in livestock and in man. Modulation of immunity is a critical strategy used by this parasite to facilitate its long-term survival in the host. Understanding the underlying mechanisms at a system level is important for the development of novel control strategies, such as vaccination, as well as for increasing general understanding of helminth-mediated immunoregulation and its consequences. Our previous RNA sequencing work identified a large number of differentially expressed genes (DEG from ovine peripheral blood mononuclear cells (PBMCs at acute and chronic stages of F. hepatica infection, and yielded important information on host–parasite interaction, with particular reference to the immune response. To extend our understanding of the immunoregulatory effects of this parasite, we employed InnateDB to further analyze the DEG dataset and identified 2,458 and 224 molecular interactions in the context of innate immunity from the acute and chronic stages of infection, respectively. Notably, 458 interactions at the acute stage of infection were manually curated from studies involving PBMC-related cell-types, which guaranteed confident hypothesis generation. NetworkAnalyst was subsequently used to construct and visualize molecular networks. Two complementary strategies (function-first and connection-first were conducted to interpret the networks. The function-first approach highlighted subnetworks implicated in regulation of Toll-like receptor 3/4 signaling in both acute and chronic infections. The connection-first approach highlighted regulation of intrinsic apoptosis and B-cell receptor-signaling during acute and chronic infections, respectively. To the best of our knowledge, this study is the first system level analysis of the regulation of host innate immunity during F. hepatica infection. It provides insights into the profound changes induced by F. hepatica infection that not only

  9. An adaptive complex network model for brain functional networks.

    Directory of Open Access Journals (Sweden)

    Ignacio J Gomez Portillo

    Full Text Available Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution.

  10. Framework for Ethernet Network Functionality Testing

    OpenAIRE

    Mirza Aamir Mehmood; Ahthasham Sajid; Amir Shehzad

    2011-01-01

    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...

  11. The functional consequences of mutualistic network architecture.

    Directory of Open Access Journals (Sweden)

    José M Gómez

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

  12. 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.

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

    International Nuclear Information System (INIS)

    Feldt, S; Dzakpasu, R; Olariu, E; Żochowski, M; Wang, J X; Shtrahman, E

    2010-01-01

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

  14. 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.

  15. 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.

  16. Capillary network formation from dispersed endothelial cells: Influence of cell traction, cell adhesion, and extracellular matrix rigidity

    Science.gov (United States)

    Ramos, João R. D.; Travasso, Rui; Carvalho, João

    2018-01-01

    The formation of a functional vascular network depends on biological, chemical, and physical processes being extremely well coordinated. Among them, the mechanical properties of the extracellular matrix and cell adhesion are fundamental to achieve a functional network of endothelial cells, able to fully cover a required domain. By the use of a Cellular Potts Model and Finite Element Method it is shown that there exists a range of values of endothelial traction forces, cell-cell adhesion, and matrix rigidities where the network can spontaneously be formed, and its properties are characterized. We obtain the analytical relation that the minimum traction force required for cell network formation must obey. This minimum value for the traction force is approximately independent on the considered cell number and cell-cell adhesion. We quantify how these two parameters influence the morphology of the resulting networks (size and number of meshes).

  17. Interaction network among functional drug groups

    Science.gov (United States)

    2013-01-01

    Background More attention has been being paid to combinatorial effects of drugs to treat complex diseases or to avoid adverse combinations of drug cocktail. Although drug interaction information has been increasingly accumulated, a novel approach like network-based method is needed to analyse that information systematically and intuitively Results Beyond focussing on drug-drug interactions, we examined interactions between functional drug groups. In this work, functional drug groups were defined based on the Anatomical Therapeutic Chemical (ATC) Classification System. We defined criteria whether two functional drug groups are related. Then we constructed the interaction network of drug groups. The resulting network provides intuitive interpretations. We further constructed another network based on interaction sharing ratio of the first network. Subsequent analysis of the networks showed that some features of drugs can be well described by this kind of interaction even for the case of structurally dissimilar drugs. Conclusion Our networks in this work provide intuitive insights into interactions among drug groups rather than those among single drugs. In addition, information on these interactions can be used as a useful source to describe mechanisms and features of drugs. PMID:24555875

  18. 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

  19. Polarization Vision and the Development of Retinal Network Models. Neuronal Information Transfer Functions From Cones and Horizontal Cells to Bipolar Cells

    National Research Council Canada - National Science Library

    Kamermans, Maarten; Hawryshyn, Craig

    2008-01-01

    ... with. Furthermore, the study demonstrated how horizontal cells, that store global stimulus parameters such as spectral composition and e-vector orientation of the global stimulus, adjust the gains...

  20. Fuel cells for telephone networks

    International Nuclear Information System (INIS)

    Wells, J.D.; Scott, D.S.

    1993-01-01

    Critical telephone network systems are currently protected from electric utility power failures by a backup system consisting of lead-acid batteries and an engine-alternator. It is considered here an alternate power system where less expensive off-peak commercial electricity electrolyses water, while fuel cells draw continuously on the stored gas products to provide direct current for the protected equipment. The lead acid batteries are eliminated. The benefits and costs of the existing and alternate systems in scenarios with various system efficiencies, capital costs, and electric utility rates and incentives, are compared. In today's conditions, the alternate system is not economical; however, cost and performance feasibility domains are identified. 2 figs., 4 tabs., 12 refs

  1. 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......) 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....

  2. Parcellating cortical functional networks in individuals.

    Science.gov (United States)

    Wang, Danhong; Buckner, Randy L; Fox, Michael D; Holt, Daphne J; Holmes, Avram J; Stoecklein, Sophia; Langs, Georg; Pan, Ruiqi; Qian, Tianyi; Li, Kuncheng; Baker, Justin T; Stufflebeam, Steven M; Wang, Kai; Wang, Xiaomin; Hong, Bo; Liu, Hesheng

    2015-12-01

    The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and behavior. Here we developed a cortical parcellation approach to accurately map functional organization at the individual level using resting-state functional magnetic resonance imaging (fMRI). A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types, including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting potential for use in clinical applications.

  3. Hierarchical modularity in human brain functional networks

    Directory of Open Access Journals (Sweden)

    David Meunier

    2009-10-01

    Full Text Available 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 the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.

  4. 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

    2015-01-01

    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. PMID:25749296

  5. Direct lifts of coupled cell networks

    Science.gov (United States)

    Dias, A. P. S.; Moreira, C. S.

    2018-04-01

    In networks of dynamical systems, there are spaces defined in terms of equalities of cell coordinates which are flow-invariant under any dynamical system that has a form consistent with the given underlying network structure—the network synchrony subspaces. Given a network and one of its synchrony subspaces, any system with a form consistent with the network, restricted to the synchrony subspace, defines a new system which is consistent with a smaller network, called the quotient network of the original network by the synchrony subspace. Moreover, any system associated with the quotient can be interpreted as the restriction to the synchrony subspace of a system associated with the original network. We call the larger network a lift of the smaller network, and a lift can be interpreted as a result of the cellular splitting of the smaller network. In this paper, we address the question of the uniqueness in this lifting process in terms of the networks’ topologies. A lift G of a given network Q is said to be direct when there are no intermediate lifts of Q between them. We provide necessary and sufficient conditions for a lift of a general network to be direct. Our results characterize direct lifts using the subnetworks of all splitting cells of Q and of all split cells of G. We show that G is a direct lift of Q if and only if either the split subnetwork is a direct lift or consists of two copies of the splitting subnetwork. These results are then applied to the class of regular uniform networks and to the special classes of ring networks and acyclic networks. We also illustrate that one of the applications of our results is to the lifting bifurcation problem.

  6. Small cell networks deployment, management, and optimization

    CERN Document Server

    Claussen, Holger; Ho, Lester; Razavi, Rouzbeh; Kucera, Stepan

    2018-01-01

    Small Cell Networks: Deployment, Management, and Optimization addresses key problems of the cellular network evolution towards HetNets. It focuses on the latest developments in heterogeneous and small cell networks, as well as their deployment, operation, and maintenance. It also covers the full spectrum of the topic, from academic, research, and business to the practice of HetNets in a coherent manner. Additionally, it provides complete and practical guidelines to vendors and operators interested in deploying small cells. The first comprehensive book written by well-known researchers and engineers from Nokia Bell Labs, Small Cell Networks begins with an introduction to the subject--offering chapters on capacity scaling and key requirements of future networks. It then moves on to sections on coverage and capacity optimization, and interference management. From there, the book covers mobility management, energy efficiency, and small cell deployment, ending with a section devoted to future trends and applicat...

  7. Schizophrenia classification using functional network features

    Science.gov (United States)

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

    2012-03-01

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

  8. Distributed Function Calculation over Noisy Networks

    Directory of Open Access Journals (Sweden)

    Zhidun Zeng

    2016-01-01

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

  9. Deep networks for motor control functions

    Directory of Open Access Journals (Sweden)

    Max eBerniker

    2015-03-01

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

  10. Simulation of developing human neuronal cell networks.

    Science.gov (United States)

    Lenk, Kerstin; Priwitzer, Barbara; Ylä-Outinen, Laura; Tietz, Lukas H B; Narkilahti, Susanna; Hyttinen, Jari A K

    2016-08-30

    Microelectrode array (MEA) is a widely used technique to study for example the functional properties of neuronal networks derived from human embryonic stem cells (hESC-NN). With hESC-NN, we can investigate the earliest developmental stages of neuronal network formation in the human brain. In this paper, we propose an in silico model of maturating hESC-NNs based on a phenomenological model called INEX. We focus on simulations of the development of bursts in hESC-NNs, which are the main feature of neuronal activation patterns. The model was developed with data from developing hESC-NN recordings on MEAs which showed increase in the neuronal activity during the investigated six measurement time points in the experimental and simulated data. Our simulations suggest that the maturation process of hESC-NN, resulting in the formation of bursts, can be explained by the development of synapses. Moreover, spike and burst rate both decreased at the last measurement time point suggesting a pruning of synapses as the weak ones are removed. To conclude, our model reflects the assumption that the interaction between excitatory and inhibitory neurons during the maturation of a neuronal network and the spontaneous emergence of bursts are due to increased connectivity caused by the forming of new synapses.

  11. 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...... and followers etc. In this research we analyze and predict the most likely role a particular node can adapt once a member of the network is either killed or caught. The adaptation is based on computing Bayes posteriori probability of each node and the level of the said node in the network structure....

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

    Directory of Open Access Journals (Sweden)

    Menizibeya O. Welcome

    2015-01-01

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

  13. Cell fate reprogramming by control of intracellular network dynamics

    Science.gov (United States)

    Zanudo, Jorge G. T.; Albert, Reka

    Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Although the topic of controlling the dynamics of a system has a long history in control theory, most of this work is not directly applicable to intracellular networks. Here we present a network control method that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our control method takes advantage of certain function-dependent network components and their relation to steady states in order to identify control targets, which are guaranteed to drive any initial state to the target state with 100% effectiveness and need to be applied only transiently for the system to reach and stay in the desired state. We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments. This work was supported by NSF Grant PHY 1205840.

  14. Histone chaperone networks shaping chromatin function

    DEFF Research Database (Denmark)

    Hammond, Colin; Strømme, Caroline Bianchi; Huang, Hongda

    2017-01-01

    The association of histones with specific chaperone complexes is important for their folding, oligomerization, post-translational modification, nuclear import, stability, assembly and genomic localization. In this way, the chaperoning of soluble histones is a key determinant of histone availability...... and fate, which affects all chromosomal processes, including gene expression, chromosome segregation and genome replication and repair. Here, we review the distinct structural and functional properties of the expanding network of histone chaperones. We emphasize how chaperones cooperate in the histone...... chaperone network and via co-chaperone complexes to match histone supply with demand, thereby promoting proper nucleosome assembly and maintaining epigenetic information by recycling modified histones evicted from chromatin....

  15. Contractile network models for adherent cells.

    Science.gov (United States)

    Guthardt Torres, P; Bischofs, I B; Schwarz, U S

    2012-01-01

    Cells sense the geometry and stiffness of their adhesive environment by active contractility. For strong adhesion to flat substrates, two-dimensional contractile network models can be used to understand how force is distributed throughout the cell. Here we compare the shape and force distribution for different variants of such network models. In contrast to Hookean networks, cable networks reflect the asymmetric response of biopolymers to tension versus compression. For passive networks, contractility is modeled by a reduced resting length of the mechanical links. In actively contracting networks, a constant force couple is introduced into each link in order to model contraction by molecular motors. If combined with fixed adhesion sites, all network models lead to invaginated cell shapes, but only actively contracting cable networks lead to the circular arc morphology typical for strongly adhering cells. In this case, shape and force distribution are determined by local rather than global determinants and thus are suited to endow the cell with a robust sense of its environment. We also discuss nonlinear and adaptive linker mechanics as well as the relation to tissue shape. © 2012 American Physical Society

  16. Abnormal functional network connectivity among resting-state networks in children with frontal lobe epilepsy.

    Science.gov (United States)

    Widjaja, E; Zamyadi, M; Raybaud, C; Snead, O C; Smith, M L

    2013-12-01

    Epilepsy is considered a disorder of neural networks. The aims of this study were to assess functional connectivity within resting-state networks and functional network connectivity across resting-state networks by use of resting-state fMRI in children with frontal lobe epilepsy and to relate changes in resting-state networks with neuropsychological function. Fifteen patients with frontal lobe epilepsy and normal MR imaging and 14 healthy control subjects were recruited. Spatial independent component analysis was used to identify the resting-state networks, including frontal, attention, default mode network, sensorimotor, visual, and auditory networks. The Z-maps of resting-state networks were compared between patients and control subjects. The relation between abnormal connectivity and neuropsychological function was assessed. Correlations from all pair-wise combinations of independent components were performed for each group and compared between groups. The frontal network was the only network that showed reduced connectivity in patients relative to control subjects. The remaining 5 networks demonstrated both reduced and increased functional connectivity within resting-state networks in patients. There was a weak association between connectivity in frontal network and executive function (P = .029) and a significant association between sensorimotor network and fine motor function (P = .004). Control subjects had 79 pair-wise independent components that showed significant temporal coherence across all resting-state networks except for default mode network-auditory network. Patients had 66 pairs of independent components that showed significant temporal coherence across all resting-state networks. Group comparison showed reduced functional network connectivity between default mode network-attention, frontal-sensorimotor, and frontal-visual networks and increased functional network connectivity between frontal-attention, default mode network-sensorimotor, and frontal

  17. Intelligent Network Management and Functional Cerebellum Synthesis

    Science.gov (United States)

    Loebner, Egon E.

    1989-01-01

    Transdisciplinary modeling of the cerebellum across histology, physiology, and network engineering provides preliminary results at three organization levels: input/output links to central nervous system networks; links between the six neuron populations in the cerebellum; and computation among the neurons of the populations. Older models probably underestimated the importance and role of climbing fiber input which seems to supply write as well as read signals, not just to Purkinje but also to basket and stellate neurons. The well-known mossy fiber-granule cell-Golgi cell system should also respond to inputs originating from climbing fibers. Corticonuclear microcomplexing might be aided by stellate and basket computation and associate processing. Technological and scientific implications of the proposed cerebellum model are discussed.

  18. Functional brain networks involved in reality monitoring.

    Science.gov (United States)

    Metzak, Paul D; Lavigne, Katie M; Woodward, Todd S

    2015-08-01

    Source monitoring refers to the recollection of variables that specify the context and conditions in which a memory episode was encoded. This process involves using the qualitative and quantitative features of a memory trace to distinguish its source. One specific class of source monitoring is reality monitoring, which involves distinguishing internally generated from externally generated information, that is, memories of imagined events from real events. The purpose of the present study was to identify functional brain networks that underlie reality monitoring, using an alternative type of source monitoring as a control condition. On the basis of previous studies on self-referential thinking, it was expected that a medial prefrontal cortex (mPFC) based network would be more active during reality monitoring than the control condition, due to the requirement to focus on a comparison of internal (self) and external (other) source information. Two functional brain networks emerged from this analysis, one reflecting increasing task-related activity, and one reflecting decreasing task-related activity. The second network was mPFC based, and was characterized by task-related deactivations in areas resembling the default-mode network; namely, the mPFC, middle temporal gyri, lateral parietal regions, and the precuneus, and these deactivations were diminished during reality monitoring relative to source monitoring, resulting in higher activity during reality monitoring. This result supports previous research suggesting that self-referential thinking involves the mPFC, but extends this to a network-level interpretation of reality monitoring. Copyright © 2015 Elsevier Ltd. All rights reserved.

  19. Identification of Functional Information Subgraphs in Complex Networks

    International Nuclear Information System (INIS)

    Bettencourt, Luis M. A.; Gintautas, Vadas; Ham, Michael I.

    2008-01-01

    We present a general information theoretic approach for identifying functional subgraphs in complex networks. We show that the uncertainty in a variable can be written as a sum of information quantities, where each term is generated by successively conditioning mutual informations on new measured variables in a way analogous to a discrete differential calculus. The analogy to a Taylor series suggests efficient optimization algorithms for determining the state of a target variable in terms of functional groups of other nodes. We apply this methodology to electrophysiological recordings of cortical neuronal networks grown in vitro. Each cell's firing is generally explained by the activity of a few neurons. We identify these neuronal subgraphs in terms of their redundant or synergetic character and reconstruct neuronal circuits that account for the state of target cells

  20. VLSI Cells Placement Using the Neural Networks

    International Nuclear Information System (INIS)

    Azizi, Hacene; Zouaoui, Lamri; Mokhnache, Salah

    2008-01-01

    The artificial neural networks have been studied for several years. Their effectiveness makes it possible to expect high performances. The privileged fields of these techniques remain the recognition and classification. Various applications of optimization are also studied under the angle of the artificial neural networks. They make it possible to apply distributed heuristic algorithms. In this article, a solution to placement problem of the various cells at the time of the realization of an integrated circuit is proposed by using the KOHONEN network

  1. 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.

  2. Nano-topography Enhances Communication in Neural Cells Networks.

    Science.gov (United States)

    Onesto, V; Cancedda, L; Coluccio, M L; Nanni, M; Pesce, M; Malara, N; Cesarelli, M; Di Fabrizio, E; Amato, F; Gentile, F

    2017-08-29

    Neural cells are the smallest building blocks of the central and peripheral nervous systems. Information in neural networks and cell-substrate interactions have been heretofore studied separately. Understanding whether surface nano-topography can direct nerve cells assembly into computational efficient networks may provide new tools and criteria for tissue engineering and regenerative medicine. In this work, we used information theory approaches and functional multi calcium imaging (fMCI) techniques to examine how information flows in neural networks cultured on surfaces with controlled topography. We found that substrate roughness S a affects networks topology. In the low nano-meter range, S a  = 0-30 nm, information increases with S a . Moreover, we found that energy density of a network of cells correlates to the topology of that network. This reinforces the view that information, energy and surface nano-topography are tightly inter-connected and should not be neglected when studying cell-cell interaction in neural tissue repair and regeneration.

  3. Nano-topography Enhances Communication in Neural Cells Networks

    KAUST Repository

    Onesto, V.

    2017-08-23

    Neural cells are the smallest building blocks of the central and peripheral nervous systems. Information in neural networks and cell-substrate interactions have been heretofore studied separately. Understanding whether surface nano-topography can direct nerve cells assembly into computational efficient networks may provide new tools and criteria for tissue engineering and regenerative medicine. In this work, we used information theory approaches and functional multi calcium imaging (fMCI) techniques to examine how information flows in neural networks cultured on surfaces with controlled topography. We found that substrate roughness Sa affects networks topology. In the low nano-meter range, S-a = 0-30 nm, information increases with Sa. Moreover, we found that energy density of a network of cells correlates to the topology of that network. This reinforces the view that information, energy and surface nano-topography are tightly inter-connected and should not be neglected when studying cell-cell interaction in neural tissue repair and regeneration.

  4. Learning Methods for Radial Basis Functions Networks

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman; Kudová, Petra

    2005-01-01

    Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005

  5. 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.

  6. Function approximation of tasks by neural networks

    International Nuclear Information System (INIS)

    Gougam, L.A.; Chikhi, A.; Mekideche-Chafa, F.

    2008-01-01

    For several years now, neural network models have enjoyed wide popularity, being applied to problems of regression, classification and time series analysis. Neural networks have been recently seen as attractive tools for developing efficient solutions for many real world problems in function approximation. The latter is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. In a previous contribution, we have used a well known simplified architecture to show that it provides a reasonably efficient, practical and robust, multi-frequency analysis. We have investigated the universal approximation theory of neural networks whose transfer functions are: sigmoid (because of biological relevance), Gaussian and two specified families of wavelets. The latter have been found to be more appropriate to use. The aim of the present contribution is therefore to use a m exican hat wavelet a s transfer function to approximate different tasks relevant and inherent to various applications in physics. The results complement and provide new insights into previously published results on this problem

  7. 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.

  8. Bayesian Joint Modeling of Multiple Brain Functional Networks

    OpenAIRE

    Lukemire, Joshua; Kundu, Suprateek; Pagnoni, Giuseppe; Guo, Ying

    2017-01-01

    Brain function is organized in coordinated modes of spatio-temporal activity (functional networks) exhibiting an intrinsic baseline structure with variations under different experimental conditions. Existing approaches for uncovering such network structures typically do not explicitly model shared and differential patterns across networks, thus potentially reducing the detection power. We develop an integrative modeling approach for jointly modeling multiple brain networks across experimental...

  9. On Functional Module Detection in Metabolic Networks

    Science.gov (United States)

    Koch, Ina; Ackermann, Jörg

    2013-01-01

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

  10. Detecting functional hubs of ictogenic networks.

    Science.gov (United States)

    Zubler, Frederic; Gast, Heidemarie; Abela, Eugenio; Rummel, Christian; Hauf, Martinus; Wiest, Roland; Pollo, Claudio; Schindler, Kaspar

    2015-03-01

    Quantitative EEG (qEEG) has modified our understanding of epileptic seizures, shifting our view from the traditionally accepted hyper-synchrony paradigm toward more complex models based on re-organization of functional networks. However, qEEG measurements are so far rarely considered during the clinical decision-making process. To better understand the dynamics of intracranial EEG signals, we examine a functional network derived from the quantification of information flow between intracranial EEG signals. Using transfer entropy, we analyzed 198 seizures from 27 patients undergoing pre-surgical evaluation for pharmaco-resistant epilepsy. During each seizure we considered for each network the in-, out- and total "hubs", defined respectively as the time and the EEG channels with the maximal incoming, outgoing or total (bidirectional) information flow. In the majority of cases we found that the hubs occur around the middle of seizures, and interestingly not at the beginning or end, where the most dramatic EEG signal changes are found by visual inspection. For the patients who then underwent surgery, good postoperative clinical outcome was on average associated with a higher percentage of out- or total-hubs located in the resected area (for out-hubs p = 0.01, for total-hubs p = 0.04). The location of in-hubs showed no clear predictive value. We conclude that the study of functional networks based on qEEG measurements may help to identify brain areas that are critical for seizure generation and are thus potential targets for focused therapeutic interventions.

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

    Science.gov (United States)

    Yeh, Wei-Chang

    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.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.

  12. Decidable and undecidable arithmetic functions in actin filament networks

    Science.gov (United States)

    Schumann, Andrew

    2018-01-01

    The plasmodium of Physarum polycephalum is very sensitive to its environment, and reacts to stimuli with appropriate motions. Both the sensory and motor stages of these reactions are explained by hydrodynamic processes, based on fluid dynamics, with the participation of actin filament networks. This paper is devoted to actin filament networks as a computational medium. The point is that actin filaments, with contributions from many other proteins like myosin, are sensitive to extracellular stimuli (attractants as well as repellents), and appear and disappear at different places in the cell to change aspects of the cell structure—e.g. its shape. By assembling and disassembling actin filaments, some unicellular organisms, like Amoeba proteus, can move in response to various stimuli. As a result, these organisms can be considered a simple reversible logic gate—extracellular signals being its inputs and motions its outputs. In this way, we can implement various logic gates on amoeboid behaviours. These networks can embody arithmetic functions within p-adic valued logic. Furthermore, within these networks we can define the so-called diagonalization for deducing undecidable arithmetic functions.

  13. Fabrication of microstamps and patterned cell network

    International Nuclear Information System (INIS)

    Seong, Nak Seon; Pak, James Jung Ho; Choi, Ju Hee; Ahn, Dong June; Hwang, Seong Min; Lee, Kyung J.

    2002-01-01

    Elastomeric stamps with micrometer-sized grids are fabricated for building biological cell networks by design. Polymerized polydimethyl-siloxane (PDMS) stamps are cast in a variety of different molds prepared by micro-electro mechanical systems (MEMS) technology. Micro square-grid patterns of 3-aminopropyl triethoxy silane (APS) are successfully imprinted on glass plates, and patterned networks of cardiac cells are obtained as designed. The resulting cellular networks clearly demonstrate that cell attachment and growth are greatly favored on APS-treated thin tracks. Here, we report the technical details related to the fabrication of microstamps, to the stamping procedure, and to the culture method. The potential applications of patterned cellular networks are also discussed

  14. Complex network perspective on structure and function of ...

    Indian Academy of Sciences (India)

    , 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 ...

  15. Network approaches to the functional analysis of microbial proteins.

    Science.gov (United States)

    Hallinan, J S; James, K; Wipat, A

    2011-01-01

    Large amounts of detailed biological data have been generated over the past few decades. Much of these data is freely available in over 1000 online databases; an enticing, but frustrating resource for microbiologists interested in a systems-level view of the structure and function of microbial cells. The frustration engendered by the need to trawl manually through hundreds of databases in order to accumulate information about a gene, protein, pathway, or organism of interest can be alleviated by the use of computational data integration to generated network views of the system of interest. Biological networks can be constructed from a single type of data, such as protein-protein binding information, or from data generated by multiple experimental approaches. In an integrated network, nodes usually represent genes or gene products, while edges represent some form of interaction between the nodes. Edges between nodes may be weighted to represent the probability that the edge exists in vivo. Networks may also be enriched with ontological annotations, facilitating both visual browsing and computational analysis via web service interfaces. In this review, we describe the construction, analysis of both single-data source and integrated networks, and their application to the inference of protein function in microbes. Copyright © 2011 Elsevier Ltd. All rights reserved.

  16. Defining the functional states of Th17 cells

    OpenAIRE

    Lee, Youjin; Kuchroo, Vijay

    2015-01-01

    The molecular mechanisms governing T helper (Th) cell differentiation and function have revealed a complex network of transcriptional and protein regulators. Cytokines not only initiate the differentiation of CD4 Th cells into subsets but also influence the identity, plasticity and effector function of a T cell. Of the subsets, Th17 cells, named for producing interleukin 17 (IL-17) as their signature cytokine, secrete a cohort of other cytokines, including IL-22, IL-21, IL-10, IL-9, IFNγ, and...

  17. Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP

    Directory of Open Access Journals (Sweden)

    Kihara Daisuke

    2010-05-01

    Full Text Available Abstract Background A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance. Results Here we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, Escherichia coli, Saccharomyces cerevisiae, and Plasmodium falciparum (malaria. The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each proteome, one each by considering similarity in a single Gene Ontology (GO category, i.e. Biological Process, Cellular Component, and Molecular Function, and another one by considering overall similarity with the funSim score. The functional similarity networks are shown to have higher modularity than the protein-protein interaction network. Moreover, the funSim score network is distinct from the single GO-score networks by showing a higher clustering degree exponent value and thus has a higher tendency to be hierarchical. In addition, examining function assignments to the protein-protein interaction network and local regions of genomes has identified numerous cases where subnetworks or local regions have functionally coherent proteins. These results will help interpreting interactions of proteins and gene orders in a genome. Several examples of both analyses are highlighted. Conclusion The analyses demonstrate that applying high confidence predictions from PFP

  18. 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/.

  19. Topology-function conservation in protein-protein interaction networks.

    Science.gov (United States)

    Davis, Darren; Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Stojmirovic, Aleksandar; Pržulj, Nataša

    2015-05-15

    Proteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species. To improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms. © The Author 2015. Published by Oxford University Press.

  20. Structural and functional brain networks: from connections to cognition.

    Science.gov (United States)

    Park, Hae-Jeong; Friston, Karl

    2013-11-01

    How rich functionality emerges from the invariant structural architecture of the brain remains a major mystery in neuroscience. Recent applications of network theory and theoretical neuroscience to large-scale brain networks have started to dissolve this mystery. Network analyses suggest that hierarchical modular brain networks are particularly suited to facilitate local (segregated) neuronal operations and the global integration of segregated functions. Although functional networks are constrained by structural connections, context-sensitive integration during cognition tasks necessarily entails a divergence between structural and functional networks. This degenerate (many-to-one) function-structure mapping is crucial for understanding the nature of brain networks. The emergence of dynamic functional networks from static structural connections calls for a formal (computational) approach to neuronal information processing that may resolve this dialectic between structure and function.

  1. 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.

  2. Density functional and neural network analysis

    DEFF Research Database (Denmark)

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

    1997-01-01

    Density functional theory (DFT) calculations have been carried out for hydrated L-alanine, L-alanyl-L-alanine and N-acetyl L-alanine N'-methylamide and examined with respect to the effect of water on the structure, the vibrational frequencies, vibrational absorption (VA) and vibrational circular...... 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...

  3. Deterministic Function Computation with Chemical Reaction Networks*

    Science.gov (United States)

    Chen, Ho-Lin; Doty, David; Soloveichik, David

    2013-01-01

    Chemical reaction networks (CRNs) formally model chemistry in a well-mixed solution. CRNs are widely used to describe information processing occurring in natural cellular regulatory networks, and with upcoming advances in synthetic biology, CRNs are a promising language for the design of artificial molecular control circuitry. Nonetheless, despite the widespread use of CRNs in the natural sciences, the range of computational behaviors exhibited by CRNs is not well understood. CRNs have been shown to be efficiently Turing-universal (i.e., able to simulate arbitrary algorithms) when allowing for a small probability of error. CRNs that are guaranteed to converge on a correct answer, on the other hand, have been shown to decide only the semilinear predicates (a multi-dimensional generalization of “eventually periodic” sets). We introduce the notion of function, rather than predicate, computation by representing the output of a function f : ℕk → ℕl by a count of some molecular species, i.e., if the CRN starts with x1, …, xk molecules of some “input” species X1, …, Xk, the CRN is guaranteed to converge to having f(x1, …, xk) molecules of the “output” species Y1, …, Yl. We show that a function f : ℕk → ℕl is deterministically computed by a CRN if and only if its graph {(x, y) ∈ ℕk × ℕl ∣ f(x) = y} is a semilinear set. Finally, we show that each semilinear function f (a function whose graph is a semilinear set) can be computed by a CRN on input x in expected time O(polylog ∥x∥1). PMID:25383068

  4. Bacterial Networks in Cells and Communities.

    Science.gov (United States)

    Sourjik, Victor; Vorholt, Julia A

    2015-11-20

    Research on the bacterial regulatory networks is currently experiencing a true revival, driven by advances in methodology and by emergence of novel concepts. The biannual conference Bacterial Networks (BacNet15) held in May 2015, in Sant Feliu de Guíxols, Spain, covered progress in the studies of regulatory networks that control bacterial physiology, cell biology, stress responses, metabolism, collective behavior and evolution. It demonstrated how interdisciplinary approaches that combine molecular biology and biochemistry with the latest microscopy developments, whole cell (-omics) approaches and mathematical modeling can help understand design principles relevant in microbiology. It further showed how current biotechnology and medical microbiology could profit from our knowledge of and ability to engineer regulatory networks of bacteria. Copyright © 2015 Elsevier Ltd. All rights reserved.

  5. Deep Neural Networks with Multistate Activation Functions

    Directory of Open Access Journals (Sweden)

    Chenghao Cai

    2015-01-01

    Full Text Available We propose multistate activation functions (MSAFs for deep neural networks (DNNs. These MSAFs are new kinds of activation functions which are capable of representing more than two states, including the N-order MSAFs and the symmetrical MSAF. DNNs with these MSAFs can be trained via conventional Stochastic Gradient Descent (SGD as well as mean-normalised SGD. We also discuss how these MSAFs perform when used to resolve classification problems. Experimental results on the TIMIT corpus reveal that, on speech recognition tasks, DNNs with MSAFs perform better than the conventional DNNs, getting a relative improvement of 5.60% on phoneme error rates. Further experiments also reveal that mean-normalised SGD facilitates the training processes of DNNs with MSAFs, especially when being with large training sets. The models can also be directly trained without pretraining when the training set is sufficiently large, which results in a considerable relative improvement of 5.82% on word error rates.

  6. 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

  7. 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.

  8. Coiled-coil networking shapes cell molecular machinery

    Science.gov (United States)

    Wang, Yongqiang; Zhang, Xinlei; Zhang, Hong; Lu, Yi; Huang, Haolong; Dong, Xiaoxi; Chen, Jinan; Dong, Jiuhong; Yang, Xiao; Hang, Haiying; Jiang, Taijiao

    2012-01-01

    The highly abundant α-helical coiled-coil motif not only mediates crucial protein–protein interactions in the cell but is also an attractive scaffold in synthetic biology and material science and a potential target for disease intervention. Therefore a systematic understanding of the coiled-coil interactions (CCIs) at the organismal level would help unravel the full spectrum of the biological function of this interaction motif and facilitate its application in therapeutics. We report the first identified genome-wide CCI network in Saccharomyces cerevisiae, which consists of 3495 pair-wise interactions among 598 predicted coiled-coil regions. Computational analysis revealed that the CCI network is specifically and functionally organized and extensively involved in the organization of cell machinery. We further show that CCIs play a critical role in the assembly of the kinetochore, and disruption of the CCI network leads to defects in kinetochore assembly and cell division. The CCI network identified in this study is a valuable resource for systematic characterization of coiled coils in the shaping and regulation of a host of cellular machineries and provides a basis for the utilization of coiled coils as domain-based probes for network perturbation and pharmacological applications. PMID:22875988

  9. Learning discriminative functional network features of schizophrenia

    Science.gov (United States)

    Gheiratmand, Mina; Rish, Irina; Cecchi, Guillermo; Brown, Matthew; Greiner, Russell; Bashivan, Pouya; Polosecki, Pablo; Dursun, Serdar

    2017-03-01

    Associating schizophrenia with disrupted functional connectivity is a central idea in schizophrenia research. However, identifying neuroimaging-based features that can serve as reliable "statistical biomarkers" of the disease remains a challenging open problem. We argue that generalization accuracy and stability of candidate features ("biomarkers") must be used as additional criteria on top of standard significance tests in order to discover more robust biomarkers. Generalization accuracy refers to the utility of biomarkers for making predictions about individuals, for example discriminating between patients and controls, in novel datasets. Feature stability refers to the reproducibility of the candidate features across different datasets. Here, we extracted functional connectivity network features from fMRI data at both high-resolution (voxel-level) and a spatially down-sampled lower-resolution ("supervoxel" level). At the supervoxel level, we used whole-brain network links, while at the voxel level, due to the intractably large number of features, we sampled a subset of them. We compared statistical significance, stability and discriminative utility of both feature types in a multi-site fMRI dataset, composed of schizophrenia patients and healthy controls. For both feature types, a considerable fraction of features showed significant differences between the two groups. Also, both feature types were similarly stable across multiple data subsets. However, the whole-brain supervoxel functional connectivity features showed a higher cross-validation classification accuracy of 78.7% vs. 72.4% for the voxel-level features. Cross-site variability and heterogeneity in the patient samples in the multi-site FBIRN dataset made the task more challenging compared to single-site studies. The use of the above methodology in combination with the fully data-driven approach using the whole brain information have the potential to shed light on "biomarker discovery" in schizophrenia.

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

    Science.gov (United States)

    Xu, Jianfeng; Lan, Yueheng

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Jianfeng Xu

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

  12. Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

    Science.gov (United States)

    Xu, Jianfeng; Lan, Yueheng

    2015-01-01

    Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

  13. Soft chitosan microbeads scaffold for 3D functional neuronal networks.

    Science.gov (United States)

    Tedesco, Maria Teresa; Di Lisa, Donatella; Massobrio, Paolo; Colistra, Nicolò; Pesce, Mattia; Catelani, Tiziano; Dellacasa, Elena; Raiteri, Roberto; Martinoia, Sergio; Pastorino, Laura

    2018-02-01

    The availability of 3D biomimetic in vitro neuronal networks of mammalian neurons represents a pivotal step for the development of brain-on-a-chip experimental models to study neuronal (dys)functions and particularly neuronal connectivity. The use of hydrogel-based scaffolds for 3D cell cultures has been extensively studied in the last years. However, limited work on biomimetic 3D neuronal cultures has been carried out to date. In this respect, here we investigated the use of a widely popular polysaccharide, chitosan (CHI), for the fabrication of a microbead based 3D scaffold to be coupled to primary neuronal cells. CHI microbeads were characterized by optical and atomic force microscopies. The cell/scaffold interaction was deeply characterized by transmission electron microscopy and by immunocytochemistry using confocal microscopy. Finally, a preliminary electrophysiological characterization by micro-electrode arrays was carried out. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Virtualized Network Function Orchestration System and Experimental Network Based QR Recognition for a 5G Mobile Access Network

    Directory of Open Access Journals (Sweden)

    Misun Ahn

    2017-12-01

    Full Text Available This paper proposes a virtualized network function orchestration system based on Network Function Virtualization (NFV, one of the main technologies in 5G mobile networks. This system should provide connectivity between network devices and be able to create flexible network function and distribution. This system focuses more on access networks. By experimenting with various scenarios of user service established and activated in a network, we examine whether rapid adoption of new service is possible and whether network resources can be managed efficiently. The proposed method is based on Bluetooth transfer technology and mesh networking to provide automatic connections between network machines and on a Docker flat form, which is a container virtualization technology for setting and managing key functions. Additionally, the system includes a clustering and recovery measure regarding network function based on the Docker platform. We will briefly introduce the QR code perceived service as a user service to examine the proposal and based on this given service, we evaluate the function of the proposal and present analysis. Through the proposed approach, container relocation has been implemented according to a network device’s CPU usage and we confirm successful service through function evaluation on a real test bed. We estimate QR code recognition speed as the amount of network equipment is gradually increased, improving user service and confirm that the speed of recognition is increased as the assigned number of network devices is increased by the user service.

  15. Impulsive generalized function synchronization of complex dynamical networks

    International Nuclear Information System (INIS)

    Zhang, Qunjiao; Chen, Juan; Wan, Li

    2013-01-01

    This Letter investigates generalized function synchronization of continuous and discrete complex networks by impulsive control. By constructing the reasonable corresponding impulsively controlled response networks, some criteria and corollaries are derived for the generalized function synchronization between the impulsively controlled complex networks, continuous and discrete networks are both included. Furthermore, the generalized linear synchronization and nonlinear synchronization are respectively illustrated by several examples. All the numerical simulations demonstrate the correctness of the theoretical results

  16. Cell diversity and network dynamics in photosensitive human brain organoids.

    Science.gov (United States)

    Quadrato, Giorgia; Nguyen, Tuan; Macosko, Evan Z; Sherwood, John L; Min Yang, Sung; Berger, Daniel R; Maria, Natalie; Scholvin, Jorg; Goldman, Melissa; Kinney, Justin P; Boyden, Edward S; Lichtman, Jeff W; Williams, Ziv M; McCarroll, Steven A; Arlotta, Paola

    2017-05-04

    In vitro models of the developing brain such as three-dimensional brain organoids offer an unprecedented opportunity to study aspects of human brain development and disease. However, the cells generated within organoids and the extent to which they recapitulate the regional complexity, cellular diversity and circuit functionality of the brain remain undefined. Here we analyse gene expression in over 80,000 individual cells isolated from 31 human brain organoids. We find that organoids can generate a broad diversity of cells, which are related to endogenous classes, including cells from the cerebral cortex and the retina. Organoids could be developed over extended periods (more than 9 months), allowing for the establishment of relatively mature features, including the formation of dendritic spines and spontaneously active neuronal networks. Finally, neuronal activity within organoids could be controlled using light stimulation of photosensitive cells, which may offer a way to probe the functionality of human neuronal circuits using physiological sensory stimuli.

  17. Exploring candidate biological functions by Boolean Function Networks for Saccharomyces cerevisiae.

    Directory of Open Access Journals (Sweden)

    Maria Simak

    Full Text Available The great amount of gene expression data has brought a big challenge for the discovery of Gene Regulatory Network (GRN. For network reconstruction and the investigation of regulatory relations, it is desirable to ensure directness of links between genes on a map, infer their directionality and explore candidate biological functions from high-throughput transcriptomic data. To address these problems, we introduce a Boolean Function Network (BFN model based on techniques of hidden Markov model (HMM, likelihood ratio test and Boolean logic functions. BFN consists of two consecutive tests to establish links between pairs of genes and check their directness. We evaluate the performance of BFN through the application to S. cerevisiae time course data. BFN produces regulatory relations which show consistency with succession of cell cycle phases. Furthermore, it also improves sensitivity and specificity when compared with alternative methods of genetic network reverse engineering. Moreover, we demonstrate that BFN can provide proper resolution for GO enrichment of gene sets. Finally, the Boolean functions discovered by BFN can provide useful insights for the identification of control mechanisms of regulatory processes, which is the special advantage of the proposed approach. In combination with low computational complexity, BFN can serve as an efficient screening tool to reconstruct genes relations on the whole genome level. In addition, the BFN approach is also feasible to a wide range of time course datasets.

  18. Scale-space measures for graph topology link protein network architecture to function

    NARCIS (Netherlands)

    Hulsman, M.; Dimitrakopoulos, C.; De Ridder, J.

    2014-01-01

    MOTIVATION: The network architecture of physical protein interactions is an important determinant for the molecular functions that are carried out within each cell. To study this relation, the network architecture can be characterized by graph topological characteristics such as shortest paths and

  19. 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. Copyright © 2014 Elsevier Inc. All rights reserved.

  20. Different cell fates from cell-cell interactions: core architectures of two-cell bistable networks.

    Science.gov (United States)

    Rouault, Hervé; Hakim, Vincent

    2012-02-08

    The acquisition of different fates by cells that are initially in the same state is central to development. Here, we investigate the possible structures of bistable genetic networks that can allow two identical cells to acquire different fates through cell-cell interactions. Cell-autonomous bistable networks have been previously sampled using an evolutionary algorithm. We extend this evolutionary procedure to take into account interactions between cells. We obtain a variety of simple bistable networks that we classify into major subtypes. Some have long been proposed in the context of lateral inhibition through the Notch-Delta pathway, some have been more recently considered and others appear to be new and based on mechanisms not previously considered. The results highlight the role of posttranscriptional interactions and particularly of protein complexation and sequestration, which can replace cooperativity in transcriptional interactions. Some bistable networks are entirely based on posttranscriptional interactions and the simplest of these is found to lead, upon a single parameter change, to oscillations in the two cells with opposite phases. We provide qualitative explanations as well as mathematical analyses of the dynamical behaviors of various created networks. The results should help to identify and understand genetic structures implicated in cell-cell interactions and differentiation. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

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

    NARCIS (Netherlands)

    Greff, Klaus; van Damme, Rudolf M.J.; Koutnik, Jan; Broersma, Haitze J.; Mikhal, Julia Olegivna; Lawrence, Celestine Preetham; van der Wiel, Wilfred Gerard; 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,

  2. 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.

  3. 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.

  4. Neurons versus Networks: The Interplay between Individual Neurons and Neural Networks in Cognitive Functions.

    Science.gov (United States)

    Arshavsky, Yuri I

    2016-09-22

    The main paradigm of cognitive neuroscience is the connectionist concept postulating that the higher nervous activity is performed through interactions of neurons forming complex networks, whereas the function of individual neurons is restricted to generating electrical potentials and transmitting signals to other cells. In this article, I describe the observations from three fields-neurolinguistics, physiology of memory, and sensory perception-that can hardly be explained within the constraints of a purely connectionist concept. Rather, these examples suggest that cognitive functions are determined by specific properties of individual neurons and, therefore, are likely to be accomplished primarily at the intracellular level. This view is supported by the recent discovery that the brain's ability to create abstract concepts of particular individuals, animals, or places is performed by neurons ("concept cells") sparsely distributed in the medial temporal lobe. © The Author(s) 2016.

  5. Diabetes and Stem Cell Function

    Directory of Open Access Journals (Sweden)

    Shin Fujimaki

    2015-01-01

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

  6. Analysis of neural networks through base functions

    NARCIS (Netherlands)

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

    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

  7. Association between functional connectivity hubs and brain networks.

    Science.gov (United States)

    Tomasi, Dardo; Volkow, Nora D

    2011-09-01

    Functional networks are usually accessed with "resting-state" functional magnetic resonance imaging using preselected "seeds" regions. Frequently, however, the selection of the seed locations is arbitrary. Recently, we proposed local functional connectivity density mapping (FCDM), an ultrafast data-driven to locate highly connected brain regions (functional hubs). Here, we used the functional hubs obtained from local FCDM to determine the functional networks of the resting state in 979 healthy subjects without a priori hypotheses on seed locations. In addition, we computed the global functional connectivity hubs. Seven networks covering 80% of the gray matter volume were identified. Four major cortical hubs (ventral precuneus/posterior cingulate, inferior parietal cortex, cuneus, and postcentral gyrus) were linked to 4 cortical networks (default mode, dorsal attention, visual, and somatosensory). Three subcortical networks were associated to the major subcortical hubs (cerebellum, thalamus, and amygdala). The networks differed in their resting activity and topology. The higher coupling and overlap of subcortical networks was associated to higher contribution of short-range functional connectivity in thalamus and cerebellum. Whereas cortical local FCD hubs were also hubs of long-range connectivity, which corroborates the key role of cortical hubs in network architecture, subcortical hubs had minimal long-range connectivity. The significant variability among functional networks may underlie their sensitivity/resilience to neuropathology.

  8. 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.

  9. 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.

  10. Dimensionality reduction in conic section function neural network

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    each of the network inputs has on the network output after training a neural network is known, then some inputs can be ... properties of RBF's are attracting a great deal of interest due to their rapid training, generality, and simplicity. In the context of an RBF ... This functional form is pre- selected with the centres ci being some ...

  11. A new chaotic Hopfield network with piecewise linear activation function

    International Nuclear Information System (INIS)

    Peng-Sheng, Zheng; Wan-Sheng, Tang; Jian-Xiong, Zhang

    2010-01-01

    This paper presents a new chaotic Hopfield network with a piecewise linear activation function. The dynamic of the network is studied by virtue of the bifurcation diagram, Lyapunov exponents spectrum and power spectrum. Numerical simulations show that the network displays chaotic behaviours for some well selected parameters

  12. Light Manipulation in Metallic Nanowire Networks with Functional Connectivity

    KAUST Repository

    Galinski, Henning

    2016-12-27

    Guided by ideas from complex systems, a new class of network metamaterials is introduced for light manipulation, which are based on the functional connectivity among heterogeneous subwavelength components arranged in complex networks. The model system is a nanonetwork formed by dealloying a metallic thin film. The connectivity of the network is deterministically controlled, enabling the formation of tunable absorbing states.

  13. PAC learning algorithms for functions approximated by feedforward networks

    Energy Technology Data Exchange (ETDEWEB)

    Rao, N.S.V.; Protopopescu, V. [Oak Ridge National Lab., TN (United States). Center for Engineering Systems Advanced Research

    1996-06-01

    The authors present a class of efficient algorithms for PAC learning continuous functions and regressions that are approximated by feedforward networks. The algorithms are applicable to networks with unknown weights located only in the output layer and are obtained by utilizing the potential function methods of Aizerman et al. Conditions relating the sample sizes to the error bounds are derived using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can be directly adapted to concept learning problems.

  14. 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.

  15. Cell-type-specific predictive network yields novel insights into mouse embryonic stem cell self-renewal and cell fate.

    Directory of Open Access Journals (Sweden)

    Karen G Dowell

    Full Text Available Self-renewal, the ability of a stem cell to divide repeatedly while maintaining an undifferentiated state, is a defining characteristic of all stem cells. Here, we clarify the molecular foundations of mouse embryonic stem cell (mESC self-renewal by applying a proven Bayesian network machine learning approach to integrate high-throughput data for protein function discovery. By focusing on a single stem-cell system, at a specific developmental stage, within the context of well-defined biological processes known to be active in that cell type, we produce a consensus predictive network that reflects biological reality more closely than those made by prior efforts using more generalized, context-independent methods. In addition, we show how machine learning efforts may be misled if the tissue specific role of mammalian proteins is not defined in the training set and circumscribed in the evidential data. For this study, we assembled an extensive compendium of mESC data: ∼2.2 million data points, collected from 60 different studies, under 992 conditions. We then integrated these data into a consensus mESC functional relationship network focused on biological processes associated with embryonic stem cell self-renewal and cell fate determination. Computational evaluations, literature validation, and analyses of predicted functional linkages show that our results are highly accurate and biologically relevant. Our mESC network predicts many novel players involved in self-renewal and serves as the foundation for future pluripotent stem cell studies. This network can be used by stem cell researchers (at http://StemSight.org to explore hypotheses about gene function in the context of self-renewal and to prioritize genes of interest for experimental validation.

  16. Harnessing systems biology approaches to engineer functional microvascular networks.

    Science.gov (United States)

    Sefcik, Lauren S; Wilson, Jennifer L; Papin, Jason A; Botchwey, Edward A

    2010-06-01

    Microvascular remodeling is a complex process that includes many cell types and molecular signals. Despite a continued growth in the understanding of signaling pathways involved in the formation and maturation of new blood vessels, approximately half of all compounds entering clinical trials will fail, resulting in the loss of much time, money, and resources. Most pro-angiogenic clinical trials to date have focused on increasing neovascularization via the delivery of a single growth factor or gene. Alternatively, a focus on the concerted regulation of whole networks of genes may lead to greater insight into the underlying physiology since the coordinated response is greater than the sum of its parts. Systems biology offers a comprehensive network view of the processes of angiogenesis and arteriogenesis that might enable the prediction of drug targets and whether or not activation of the targets elicits the desired outcome. Systems biology integrates complex biological data from a variety of experimental sources (-omics) and analyzes how the interactions of the system components can give rise to the function and behavior of that system. This review focuses on how systems biology approaches have been applied to microvascular growth and remodeling, and how network analysis tools can be utilized to aid novel pro-angiogenic drug discovery.

  17. 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.

  18. Functional network connectivity alterations in schizophrenia and depression.

    Science.gov (United States)

    Wu, Xing-Jie; Zeng, Ling-Li; Shen, Hui; Yuan, Lin; Qin, Jian; Zhang, Peng; Hu, Dewen

    2017-05-30

    There is a high degree of overlap between the symptoms of major depressive disorder (MDD) and schizophrenia, but it remains unclear whether the similar symptoms are derived from convergent alterations in functional network connectivity. In this study, we performed a group independent component analysis on resting-state functional MRI data from 20 MDD patients, 24 schizophrenia patients, and 43 matched healthy controls. The functional network connectivity analysis revealed that, compared to healthy controls, the MDD and schizophrenia patients exhibited convergent decreased positive connectivity between the left and right fronto-parietal control network and decreased negative connectivity between the left control and medial visual networks. Furthermore, the MDD patients showed decreased negative connectivity between the left control and auditory networks, and the schizophrenia patients showed decreased positive connectivity between the bilateral control and language networks and decreased negative connectivity between the right control and dorsal attention networks. The convergent network connectivity alterations may underlie the common primary control and regulation disorders, and the divergent connectivity alterations may enable the distinction between the two disorders. All of the convergent and divergent network connectivity alterations were relevant to the control network, suggesting an important role of the network in the pathophysiology of MDD and schizophrenia. Copyright © 2017. Published by Elsevier B.V.

  19. 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

  20. Scholastic performance and functional connectivity of brain networks in children.

    Directory of Open Access Journals (Sweden)

    Laura Chaddock-Heyman

    Full Text Available One of the keys to understanding scholastic success is to determine the neural processes involved in school performance. The present study is the first to use a whole-brain connectivity approach to explore whether functional connectivity of resting state brain networks is associated with scholastic performance in seventy-four 7- to 9-year-old children. We demonstrate that children with higher scholastic performance across reading, math and language have more integrated and interconnected resting state networks, specifically the default mode network, salience network, and frontoparietal network. To add specificity, core regions of the dorsal attention and visual networks did not relate to scholastic performance. The results extend the cognitive role of brain networks in children as well as suggest the importance of network connectivity in scholastic success.

  1. 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.

  2. Meta-analysis reveals conserved cell cycle transcriptional network across multiple human cell types.

    Science.gov (United States)

    Giotti, Bruno; Joshi, Anagha; Freeman, Tom C

    2017-01-05

    Cell division is central to the physiology and pathology of all eukaryotic organisms. The molecular machinery underpinning the cell cycle has been studied extensively in a number of species and core aspects of it have been found to be highly conserved. Similarly, the transcriptional changes associated with this pathway have been studied in different organisms and different cell types. In each case hundreds of genes have been reported to be regulated, however there seems to be little consensus in the genes identified across different studies. In a recent comparison of transcriptomic studies of the cell cycle in different human cell types, only 96 cell cycle genes were reported to be the same across all studies examined. Here we perform a systematic re-examination of published human cell cycle expression data by using a network-based approach to identify groups of genes with a similar expression profile and therefore function. Two clusters in particular, containing 298 transcripts, showed patterns of expression consistent with cell cycle occurrence across the four human cell types assessed. Our analysis shows that there is a far greater conservation of cell cycle-associated gene expression across human cell types than reported previously, which can be separated into two distinct transcriptional networks associated with the G 1 /S-S and G 2 -M phases of the cell cycle. This work also highlights the benefits of performing a re-analysis on combined datasets.

  3. Genes2FANs: connecting genes through functional association networks

    Science.gov (United States)

    2012-01-01

    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 finding that disease genes in

  4. Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.

    Directory of Open Access Journals (Sweden)

    Yishai Shimoni

    2010-06-01

    Full Text Available Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are

  5. Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.

    Science.gov (United States)

    Shimoni, Yishai; Fink, Marc Y; Choi, Soon-gang; Sealfon, Stuart C

    2010-06-24

    Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any

  6. Functional Topology of Evolving Urban Drainage Networks

    Science.gov (United States)

    Yang, Soohyun; Paik, Kyungrock; McGrath, Gavan S.; Urich, Christian; Krueger, Elisabeth; Kumar, Praveen; Rao, P. Suresh C.

    2017-11-01

    We investigated the scaling and topology of engineered urban drainage networks (UDNs) in two cities, and further examined UDN evolution over decades. UDN scaling was analyzed using two power law scaling characteristics widely employed for river networks: (1) Hack's law of length (L)-area (A) [L∝Ah] and (2) exceedance probability distribution of upstream contributing area (δ) [P>(A≥δ>)˜aδ-ɛ]. For the smallest UDNs ((A≥δ>) plots for river networks are abruptly truncated, those for UDNs display exponential tempering [P>(A≥δ>)=aδ-ɛexp⁡>(-cδ>)]. The tempering parameter c decreases as the UDNs grow, implying that the distribution evolves in time to resemble those for river networks. However, the power law exponent ɛ for large UDNs tends to be greater than the range reported for river networks. Differences in generative processes and engineering design constraints contribute to observed differences in the evolution of UDNs and river networks, including subnet heterogeneity and nonrandom branching.

  7. 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.

  8. Social structures in Russia : cells and networks

    OpenAIRE

    Yefimov, Vladimir

    2001-01-01

    Russian companies heirs of Soviet enterprises are not Western-style companies, a significant difference is that they represent the basic structures of social life in the USSR : cells. The Soviet cellular system itself has deep roots in the history of Russia. The principal social structure of pre-revolutionary Russia was the rural community. In the late 1950s, Soviet society began to move away from the classic model. Cells gradually lose their exclusive role in the functioning of society. New ...

  9. Understanding multicellular function and disease with human tissue-specific networks

    Science.gov (United States)

    Greene, Casey S.; Krishnan, Arjun; Wong, Aaron K.; Ricciotti, Emanuela; Zelaya, Rene A.; Himmelstein, Daniel S.; Zhang, Ran; Hartmann, Boris M.; Zaslavsky, Elena; Sealfon, Stuart C.; Chasman, Daniel I.; FitzGerald, Garret A.; Dolinski, Kara; Grosser, Tilo; Troyanskaya, Olga G.

    2016-01-01

    Tissue and cell-type identity lie at the core of human physiology and disease. Understanding the genetic underpinnings of complex tissues and individual cell lineages is crucial for developing improved diagnostics and therapeutics. We present genome-wide functional interaction networks for 144 human tissues and cell types developed using a data-driven Bayesian methodology that integrates thousands of diverse experiments spanning tissue and disease states. Tissue-specific networks predict lineage-specific responses to perturbation, reveal genes’ changing functional roles across tissues, and illuminate disease-disease relationships. We introduce NetWAS, which combines genes with nominally significant GWAS p-values and tissue-specific networks to identify disease-gene associations more accurately than GWAS alone. Our webserver, GIANT, provides an interface to human tissue networks through multi-gene queries, network visualization, analysis tools including NetWAS, and downloadable networks. GIANT enables systematic exploration of the landscape of interacting genes that shape specialized cellular functions across more than one hundred human tissues and cell types. PMID:25915600

  10. Interference Management with Successive Cancellation for Dense Small Cell Networks

    DEFF Research Database (Denmark)

    Lopez, Victor Fernandez; Pedersen, Klaus I.; Steiner, Jens

    2016-01-01

    Network-Assisted Interference Cancellation and Suppression (NAICS) receivers have appeared as a promising way to curb inter-cell interference in future dense network deployments. This investigation compares the performance of a NAICS receiver with successive interference cancellation capabilities...

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

    Science.gov (United States)

    Zhang, Chao; Yang, Jie; Wu, Wei

    2011-05-01

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

  12. Dimensionality reduction in conic section function neural network

    Indian Academy of Sciences (India)

    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 ...

  13. Dimensionality reduction in conic section function neural network

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    Abstract. 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 consid- ering the hardware design is the high connectivity requirement. If the effect that.

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

    NARCIS (Netherlands)

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

    2015-01-01

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

  15. Radial basis function neural network in fault detection of automotive ...

    African Journals Online (AJOL)

    Radial basis function neural network in fault detection of automotive engines. ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults ... Keywords: Automotive engine, independent RBFNN model, RBF neural network, fault detection

  16. Structure and Function of Task-Oriented Social Networks

    Science.gov (United States)

    2015-01-05

    This is the final report on our AFOSR grant titled Structure and Function of Task-Oriented Social Networks . The goal of this project supported by the grant was to integrate social networks with other empirical data in task oriented projects, in particular open source software projects.

  17. Functional Connectivity Networks in Asymptomatic and Symptomatic DYT1 Carriers.

    Science.gov (United States)

    Premi, Enrico; Diano, Matteo; Gazzina, Stefano; Cauda, Franco; Gualeni, Vera; Tinazzi, Michele; Fiorio, Mirta; Liberini, Paolo; Lazzarini, Clara; Archetti, Silvana; Biasiotto, Giorgio; Turla, Marinella; Bertasi, Valeria; Cotelli, Maria; Gasparotti, Roberto; Padovani, Alessandro; Borroni, Barbara

    2016-11-01

    DYT1 mutation is characterized by focal to generalized dystonia and incomplete penetrance. To explore the complex perturbations in the different neural networks and the mutual interactions among them, we studied symptomatic and asymptomatic DTY1 mutation carriers by resting-state functional MRI. A total of 7 symptomatic DYT1, 10 asymptomatic DYT1, and 26 healthy controls were considered. Resting-state functional MRI (Oxford Centre for Functional MRI of the Brain) [FMRIB] Software Library) (FSL) MELODIC, dual regression, (as a toolbox of FSL, with Nets is referred to "networks") (FSLNets) (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLNets) was performed on 9 resting-state neural networks. DYT1 mutation signature (symptomatic DYT1 and asymptomatic DYT1) was characterized by increased connectivity in the dorsal attention network and in the left fronto-parietal network. Functional correlates of symptomatic DYT1 patients (symptomatic DYT1 vs healthy controls) showed increased connectivity in the sensorimotor network. This study argues that DYT1 dystonia is a network disorder, with crucial nodes in sensory-motor integration of posterior parietal structures. A better characterization of cortical networks involved in dystonia is crucial for possible neurophysiological therapeutic interventions. © 2016 International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.

  18. Methylphenidate Modulates Functional Network Connectivity to Enhance Attention

    Science.gov (United States)

    Zhang, Sheng; Hsu, Wei-Ting; Scheinost, Dustin; Finn, Emily S.; Shen, Xilin; Constable, R. Todd; Li, Chiang-Shan R.; Chun, Marvin M.

    2016-01-01

    Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention. SIGNIFICANCE STATEMENT Recent work identified a promising neuromarker of sustained attention based on whole

  19. Alteration of Motor Network Function Following Injury

    Science.gov (United States)

    2013-10-01

    shown theoretically that this can indeed stabilize specific activity features (Ball et al. 2010; Burdakov 2005; Franklin et al. 2010; MacLean et al...strands of bulking nylon were used to ligate the nerve on both sides of a large cell soma. To impale large cells, each cell was individually...compensation between IA and IKCa. This model summarizes all of the data in the current study and provides a framework for interpretation and future

  20. Omega-Harmonic Functions and Inverse Conductivity Problems on Networks

    National Research Council Canada - National Science Library

    Berenstein, Carlos A; Chung, Soon-Yeong

    2003-01-01

    .... To do this, they introduce an elliptic operator DELTA omega and an omega-harmonic function on the graph, with its physical interpretation being the diffusion equation on the graph, which models an electric network...

  1. Linking species functional roles to their network roles.

    Science.gov (United States)

    Coux, Camille; Rader, Romina; Bartomeus, Ignasi; Tylianakis, Jason M

    2016-07-01

    Species roles in ecological networks combine to generate their architecture, which contributes to their stability. Species trait diversity also affects ecosystem functioning and resilience, yet it remains unknown whether species' contributions to functional diversity relate to their network roles. Here, we use 21 empirical pollen transport networks to characterise this relationship. We found that, apart from a few abundant species, pollinators with original traits either had few interaction partners or interacted most frequently with a subset of these partners. This suggests that narrowing of interactions to a subset of the plant community accompanies pollinator niche specialisation, congruent with our hypothesised trade-off between having unique traits vs. being able to interact with many mutualist partners. Conversely, these effects were not detected in plants, potentially because key aspects of their flowering traits are conserved at a family level. Relating functional and network roles can provide further insight into mechanisms underlying ecosystem functioning. © 2016 John Wiley & Sons Ltd/CNRS.

  2. Diagnosis of Cervical Cancer Using the Median M-Type Radial Basis Function (MMRBF) Neural Network

    Science.gov (United States)

    Gómez-Mayorga, Margarita E.; Gallegos-Funes, Francisco J.; de-La-Rosa-Vázquez, José M.; Cruz-Santiago, Rene; Ponomaryov, Volodymyr

    The automatic analysis of Pap smear microscopic images is one of the most interesting fields in biomedical image processing. In this paper we present the capability of the Median M-Type Radial Basis Function (MMRBF) neural network in the classification of cervical cancer cells. From simulation results we observe that the MMRBF neural network has better classification capabilities in comparison with the Median RBF algorithm used as comparative.

  3. 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.

  4. Automatic Recognition of fMRI-derived Functional Networks using 3D Convolutional Neural Networks.

    Science.gov (United States)

    Zhao, Yu; Dong, Qinglin; Zhang, Shu; Zhang, Wei; Chen, Hanbo; Jiang, Xi; Guo, Lei; Hu, Xintao; Han, Junwei; Liu, Tianming

    2017-06-15

    Current fMRI data modeling techniques such as Independent Component Analysis (ICA) and Sparse Coding methods can effectively reconstruct dozens or hundreds of concurrent interacting functional brain networks simultaneously from the whole brain fMRI signals. However, such reconstructed networks have no correspondences across different subjects. Thus, automatic, effective and accurate classification and recognition of these large numbers of fMRI-derived functional brain networks are very important for subsequent steps of functional brain analysis in cognitive and clinical neuroscience applications. However, this task is still a challenging and open problem due to the tremendous variability of various types of functional brain networks and the presence of various sources of noises. In recognition of the fact that convolutional neural networks (CNN) has superior capability of representing spatial patterns with huge variability and dealing with large noises, in this paper, we design, apply and evaluate a deep 3D CNN framework for automatic, effective and accurate classification and recognition of large number of functional brain networks reconstructed by sparse representation of whole-brain fMRI signals. Our extensive experimental results based on the Human Connectome Project (HCP) fMRI data showed that the proposed deep 3D CNN can effectively and robustly perform functional networks classification and recognition tasks, while maintaining a high tolerance for mistakenly labelled training instances. Our work provides a new deep learning approach for modeling functional connectomes based on fMRI data.

  5. A Mapping Between Structural and Functional Brain Networks.

    Science.gov (United States)

    Meier, Jil; Tewarie, Prejaas; Hillebrand, Arjan; Douw, Linda; van Dijk, Bob W; Stufflebeam, Steven M; Van Mieghem, Piet

    2016-05-01

    The relationship between structural and functional brain networks is still highly debated. Most previous studies have used a single functional imaging modality to analyze this relationship. In this work, we use multimodal data, from functional MRI, magnetoencephalography, and diffusion tensor imaging, and assume that there exists a mapping between the connectivity matrices of the resting-state functional and structural networks. We investigate this mapping employing group averaged as well as individual data. We indeed find a significantly high goodness of fit level for this structure-function mapping. Our analysis suggests that a functional connection is shaped by all walks up to the diameter in the structural network in both modality cases. When analyzing the inverse mapping, from function to structure, longer walks in the functional network also seem to possess minor influence on the structural connection strengths. Even though similar overall properties for the structure-function mapping are found for different functional modalities, our results indicate that the structure-function relationship is modality dependent.

  6. Pharmacological Tools to Study the Role of Astrocytes in Neural Network Functions.

    Science.gov (United States)

    Peña-Ortega, Fernando; Rivera-Angulo, Ana Julia; Lorea-Hernández, Jonathan Julio

    2016-01-01

    Despite that astrocytes and microglia do not communicate by electrical impulses, they can efficiently communicate among them, with each other and with neurons, to participate in complex neural functions requiring broad cell-communication and long-lasting regulation of brain function. Glial cells express many receptors in common with neurons; secrete gliotransmitters as well as neurotrophic and neuroinflammatory factors, which allow them to modulate synaptic transmission and neural excitability. All these properties allow glial cells to influence the activity of neuronal networks. Thus, the incorporation of glial cell function into the understanding of nervous system dynamics will provide a more accurate view of brain function. Our current knowledge of glial cell biology is providing us with experimental tools to explore their participation in neural network modulation. In this chapter, we review some of the classical, as well as some recent, pharmacological tools developed for the study of astrocyte's influence in neural function. We also provide some examples of the use of these pharmacological agents to understand the role of astrocytes in neural network function and dysfunction.

  7. Sex differences in normal age trajectories of functional brain networks.

    Science.gov (United States)

    Scheinost, Dustin; Finn, Emily S; Tokoglu, Fuyuze; Shen, Xilin; Papademetris, Xenophon; Hampson, Michelle; Constable, R Todd

    2015-04-01

    Resting-state functional magnetic resonance image (rs-fMRI) is increasingly used to study functional brain networks. Nevertheless, variability in these networks due to factors such as sex and aging is not fully understood. This study explored sex differences in normal age trajectories of resting-state networks (RSNs) using a novel voxel-wise measure of functional connectivity, the intrinsic connectivity distribution (ICD). Males and females showed differential patterns of changing connectivity in large-scale RSNs during normal aging from early adulthood to late middle-age. In some networks, such as the default-mode network, males and females both showed decreases in connectivity with age, albeit at different rates. In other networks, such as the fronto-parietal network, males and females showed divergent connectivity trajectories with age. Main effects of sex and age were found in many of the same regions showing sex-related differences in aging. Finally, these sex differences in aging trajectories were robust to choice of preprocessing strategy, such as global signal regression. Our findings resolve some discrepancies in the literature, especially with respect to the trajectory of connectivity in the default mode, which can be explained by our observed interactions between sex and aging. Overall, results indicate that RSNs show different aging trajectories for males and females. Characterizing effects of sex and age on RSNs are critical first steps in understanding the functional organization of the human brain. © 2014 Wiley Periodicals, Inc.

  8. 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.

  9. 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

  10. Three-dimensional functional human neuronal networks in uncompressed low-density electrospun fiber scaffolds.

    Science.gov (United States)

    Jakobsson, Albin; Ottosson, Maximilian; Zalis, Marina Castro; O'Carroll, David; Johansson, Ulrica Englund; Johansson, Fredrik

    2017-05-01

    We demonstrate an artificial three-dimensional (3D) electrical active human neuronal network system, by the growth of brain neural progenitors in highly porous low density electrospun poly-ε-caprolactone (PCL) fiber scaffolds. In neuroscience research cell-based assays are important experimental instruments for studying neuronal function in health and disease. Traditional cell culture at 2D-surfaces induces abnormal cell-cell contacts and network formation. Hence, there is a tremendous need to explore in vivo-resembling 3D neural cell culture approaches. We present an improved electrospinning method for fabrication of scaffolds that promote neuronal differentiation into highly 3D integrated networks, formation of inhibitory and excitatory synapses and extensive neurite growth. Notably, in 3D scaffolds in vivo-resembling intermixed neuronal and glial cell network were formed, whereas in parallel 2D cultures a neuronal cell layer grew separated from an underlying glial cell layer. Hence, the use of the 3D cell assay presented will most likely provide more physiological relevant results. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  11. Hyper-connectivity of functional networks for brain disease diagnosis.

    Science.gov (United States)

    Jie, Biao; Wee, Chong-Yaw; Shen, Dinggang; Zhang, Daoqiang

    2016-08-01

    Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help

  12. 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.

  13. Approximation results for neural network operators activated by sigmoidal functions.

    Science.gov (United States)

    Costarelli, Danilo; Spigler, Renato

    2013-08-01

    In this paper, we study pointwise and uniform convergence, as well as the order of approximation, for a family of linear positive neural network operators activated by certain sigmoidal functions. Only the case of functions of one variable is considered, but it can be expected that our results can be generalized to handle multivariate functions as well. Our approach allows us to extend previously existing results. The order of approximation is studied for functions belonging to suitable Lipschitz classes and using a moment-type approach. The special cases of neural network operators activated by logistic, hyperbolic tangent, and ramp sigmoidal functions are considered. In particular, we show that for C(1)-functions, the order of approximation for our operators with logistic and hyperbolic tangent functions here obtained is higher with respect to that established in some previous papers. The case of quasi-interpolation operators constructed with sigmoidal functions is also considered. Copyright © 2013 Elsevier Ltd. All rights reserved.

  14. Cytokine Networks between Innate Lymphoid Cells and Myeloid Cells

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    Arthur Mortha

    2018-02-01

    Full Text Available Innate lymphoid cells (ILCs are an essential component of the innate immune system in vertebrates. They are developmentally rooted in the lymphoid lineage and can diverge into at least three transcriptionally distinct lineages. ILCs seed both lymphoid and non-lymphoid tissues and are locally self-maintained in tissue-resident pools. Tissue-resident ILCs execute important effector functions making them key regulator in tissue homeostasis, repair, remodeling, microbial defense, and anti-tumor immunity. Similar to T lymphocytes, ILCs possess only few sensory elements for the recognition of non-self and thus depend on extrinsic cellular sensory elements residing within the tissue. Myeloid cells, including mononuclear phagocytes (MNPs, are key sentinels of the tissue and are able to translate environmental cues into an effector profile that instructs lymphocyte responses. The adaptation of myeloid cells to the tissue state thus influences the effector program of ILCs and serves as an example of how environmental signals are integrated into the function of ILCs via a tissue-resident immune cell cross talks. This review summarizes our current knowledge on the role of myeloid cells in regulating ILC functions and discusses how feedback communication between ILCs and myeloid cells contribute to stabilize immune homeostasis in order to maintain the healthy state of an organ.

  15. MRI of neuronal network structure, function, and plasticity.

    Science.gov (United States)

    Voss, Henning U; Schiff, Nicholas D

    2009-01-01

    We review two complementary MRI imaging modalities to characterize structure and function of neuronal networks in the human brain, and their application to subjects with severe brain injury. The structural imaging modality, diffusion tensor imaging, is based on imaging the diffusion of water protons in the brain parenchyma. From the diffusion tensor, several quantities characterizing fiber structure in the brain can be derived. The principal direction of the diffusion tensor has been found to depend on the fiber direction of myelinated axons. It can be used for white matter fiber tracking. The anisotropy (or directional dependence) of diffusion has been shown to be sensitive to developmental as well as white matter changes during training and recovery from brain injury. The functional MRI imaging modality, resting state fMRI, concerns the functional connectivity of neuronal networks rather than their anatomical structure. Subjects undergo a conventional fMRI imaging protocol without performing specific tasks. Various resting state network patterns can be computed by algorithms that reveal correlations in the fMRI signal. Often, thalamic structures are involved, suggesting that resting state fMRI could reflect global brain network functionality. Clinical applications of resting state fMRI have been reported, in particular relating signal abnormalities to neurodegenerative processes. To better understand to which degree resting state patterns reflect neuronal network function, we are comparing network patterns of normal subjects with those having severe brain lesions in a small pilot study.

  16. Uncovering Biological Network Function via Graphlet Degree Signatures

    Directory of Open Access Journals (Sweden)

    Nataša Pržulj

    2008-01-01

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

  17. 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.

  18. 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.

  19. Mapping Multiplex Hubs in Human Functional Brain Networks.

    Science.gov (United States)

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

    2016-01-01

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

  20. 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.

  1. Dynamic reconfiguration of human brain functional networks through neurofeedback.

    Science.gov (United States)

    Haller, Sven; Kopel, Rotem; Jhooti, Permi; Haas, Tanja; Scharnowski, Frank; Lovblad, Karl-Olof; Scheffler, Klaus; Van De Ville, Dimitri

    2013-11-01

    Recent fMRI studies demonstrated that functional connectivity is altered following cognitive tasks (e.g., learning) or due to various neurological disorders. We tested whether real-time fMRI-based neurofeedback can be a tool to voluntarily reconfigure brain network interactions. To disentangle learning-related from regulation-related effects, we first trained participants to voluntarily regulate activity in the auditory cortex (training phase) and subsequently asked participants to exert learned voluntary self-regulation in the absence of feedback (transfer phase without learning). Using independent component analysis (ICA), we found network reconfigurations (increases in functional network connectivity) during the neurofeedback training phase between the auditory target region and (1) the auditory pathway; (2) visual regions related to visual feedback processing; (3) insula related to introspection and self-regulation and (4) working memory and high-level visual attention areas related to cognitive effort. Interestingly, the auditory target region was identified as the hub of the reconfigured functional networks without a-priori assumptions. During the transfer phase, we again found specific functional connectivity reconfiguration between auditory and attention network confirming the specific effect of self-regulation on functional connectivity. Functional connectivity to working memory related networks was no longer altered consistent with the absent demand on working memory. We demonstrate that neurofeedback learning is mediated by widespread changes in functional connectivity. In contrast, applying learned self-regulation involves more limited and specific network changes in an auditory setup intended as a model for tinnitus. Hence, neurofeedback training might be used to promote recovery from neurological disorders that are linked to abnormal patterns of brain connectivity. Copyright © 2013 Elsevier Inc. All rights reserved.

  2. 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.

  3. Reorganization of functional networks in mild cognitive impairment.

    Directory of Open Access Journals (Sweden)

    Javier M Buldú

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

  4. FACETS: multi-faceted functional decomposition of protein interaction networks

    Science.gov (United States)

    Seah, Boon-Siew; Bhowmick, Sourav S.; Forbes Dewey, C.

    2012-01-01

    Motivation: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein–protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. Results: We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. Contact: seah0097@ntu.edu.sg or assourav@ntu.edu.sg Supplementary information: Supplementary data are available at the Bioinformatics online. Availability: Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/∼assourav/Facets/ PMID:22908217

  5. On design principles for self-organizing network functions

    NARCIS (Netherlands)

    Altman, Z.; Amirijoo, M.; Gunnarsson, F.; Hoffmann, H.; Kovács, I.Z.; Laselva, D.; Sas, B.; Spaey, K.; Tall, A.; Berg, H. van den; Zetterberg, K.

    2014-01-01

    With an increasing number of SON functions deployed in cellular radio networks, conflicts between the actions proposed by independently-designed and distributed SON functions may arise. The process of minimizing the occurrence, and the consequences, of such conflicts is referred to as SON

  6. 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

  7. Passaged neural stem cell-derived neuronal networks for a portable biosensor.

    Science.gov (United States)

    O'Shaughnessy, Thomas J; Liu, Jinny L; Ma, Wu

    2009-04-15

    We have previously demonstrated a portable biosensor that utilizes networks of mammalian neurons on microelectrode arrays (MEAs) as the sensing element. These neuronal cultures on MEAs are derived from primary neuronal tissues and are short-lived. In order to extend the shelf life of neuronal networks for use in a fieldable sensor technology, a renewable source of networks is needed. Neural stem and progenitor cells are capable of self-renewal and differentiation into functional neuronal networks. The purpose of this study was to develop a strategy for growing passaged neural stem and progenitor cells on MEAs under controlled conditions to produce differentiated neurons and glia comprising functional neuronal networks. Primary and passaged neuroepithelial stem and progenitor cells dissociated from embryonic day 13 rat cortex were seeded on MEAs and maintained with serum-free medium containing basic fibroblast growth factor (bFGF) combined with brain-derived neurotrophic factor (BDNF). These culture conditions lead to abundant neurons, with astrocytes as supportive cells, forming synaptically linked networks of neurons. Spontaneous action potentials were best recorded from networks derived from primary or passaged progenitor cells 4-5 weeks after initial culture. The passaged progenitor cell-derived networks on MEAs responded to the GABA(A) antagonist bicuculline, the NMDA glutamate inhibitor APV, and the non-NMDA glutamate antagonist CNQX indicating active synapses were present. Passaged neural stem and progenitor cell-derived networks on MEAs have properties similar to networks derived from primary neuronal cultures and can serve as a renewable supply of sensor elements for detection of environmental threats.

  8. Scale-space measures for graph topology link protein network architecture to function.

    Science.gov (United States)

    Hulsman, Marc; Dimitrakopoulos, Christos; de Ridder, Jeroen

    2014-06-15

    The network architecture of physical protein interactions is an important determinant for the molecular functions that are carried out within each cell. To study this relation, the network architecture can be characterized by graph topological characteristics such as shortest paths and network hubs. These characteristics have an important shortcoming: they do not take into account that interactions occur across different scales. This is important because some cellular functions may involve a single direct protein interaction (small scale), whereas others require more and/or indirect interactions, such as protein complexes (medium scale) and interactions between large modules of proteins (large scale). In this work, we derive generalized scale-aware versions of known graph topological measures based on diffusion kernels. We apply these to characterize the topology of networks across all scales simultaneously, generating a so-called graph topological scale-space. The comprehensive physical interaction network in yeast is used to show that scale-space based measures consistently give superior performance when distinguishing protein functional categories and three major types of functional interactions-genetic interaction, co-expression and perturbation interactions. Moreover, we demonstrate that graph topological scale spaces capture biologically meaningful features that provide new insights into the link between function and protein network architecture. Matlab(TM) code to calculate the scale-aware topological measures (STMs) is available at http://bioinformatics.tudelft.nl/TSSA © The Author 2014. Published by Oxford University Press.

  9. Functional network integrity presages cognitive decline in preclinical Alzheimer disease.

    Science.gov (United States)

    Buckley, Rachel F; Schultz, Aaron P; Hedden, Trey; Papp, Kathryn V; Hanseeuw, Bernard J; Marshall, Gad; Sepulcre, Jorge; Smith, Emily E; Rentz, Dorene M; Johnson, Keith A; Sperling, Reisa A; Chhatwal, Jasmeer P

    2017-07-04

    To examine the utility of resting-state functional connectivity MRI (rs-fcMRI) measurements of network integrity as a predictor of future cognitive decline in preclinical Alzheimer disease (AD). A total of 237 clinically normal older adults (aged 63-90 years, Clinical Dementia Rating 0) underwent baseline β-amyloid (Aβ) imaging with Pittsburgh compound B PET and structural and rs-fcMRI. We identified 7 networks for analysis, including 4 cognitive networks (default, salience, dorsal attention, and frontoparietal control) and 3 noncognitive networks (primary visual, extrastriate visual, motor). Using linear and curvilinear mixed models, we used baseline connectivity in these networks to predict longitudinal changes in preclinical Alzheimer cognitive composite (PACC) performance, both alone and interacting with Aβ burden. Median neuropsychological follow-up was 3 years. Baseline connectivity in the default, salience, and control networks predicted longitudinal PACC decline, unlike connectivity in the dorsal attention and all noncognitive networks. Default, salience, and control network connectivity was also synergistic with Aβ burden in predicting decline, with combined higher Aβ and lower connectivity predicting the steepest curvilinear decline in PACC performance. In clinically normal older adults, lower functional connectivity predicted more rapid decline in PACC scores over time, particularly when coupled with increased Aβ burden. Among examined networks, default, salience, and control networks were the strongest predictors of rate of change in PACC scores, with the inflection point of greatest decline beyond the fourth year of follow-up. These results suggest that rs-fcMRI may be a useful predictor of early, AD-related cognitive decline in clinical research settings. © 2017 American Academy of Neurology.

  10. Cell type specificity of signaling: view from membrane receptors distribution and their downstream transduction networks.

    Science.gov (United States)

    He, Ying; Yu, Zhonghao; Ge, Dongya; Wang-Sattler, Rui; Thiesen, Hans-Jürgen; Xie, Lu; Li, Yixue

    2012-09-01

    Studies on cell signaling pay more attention to spatial dynamics and how such diverse organization can relate to high order of cellular capabilities. To overview the specificity of cell signaling, we integrated human receptome data with proteome spatial expression profiles to systematically investigate the specificity of receptors and receptor-triggered transduction networks across 62 normal cell types and 14 cancer types. Six percent receptors showed cell-type-specific expression, and 4% signaling networks presented enriched cell-specific proteins induced by the receptors. We introduced a concept of "response context" to annotate the cell-type dependent signaling networks. We found that most cells respond similarly to the same stimulus, as the "response contexts" presented high functional similarity. Despite this, the subtle spatial diversity can be observed from the difference in network architectures. The architecture of the signaling networks in nerve cells displayed less completeness than that in glandular cells, which indicated cellular-context dependent signaling patterns are elaborately spatially organized. Likewise, in cancer cells most signaling networks were generally dysfunctional and less complete than that in normal cells. However, glioma emerged hyper-activated transduction mechanism in malignant state. Receptor ATP6AP2 and TNFRSF21 induced rennin-angiotensin and apoptosis signaling were found likely to explain the glioma-specific mechanism. This work represents an effort to decipher context-specific signaling network from spatial dimension. Our results indicated that although a majority of cells engage general signaling response with subtle differences, the spatial dynamics of cell signaling can not only deepen our insights into different signaling mechanisms, but also help understand cell signaling in disease.

  11. Surrogate-assisted identification of influences of network construction on evolving weighted functional networks

    Science.gov (United States)

    Stahn, Kirsten; Lehnertz, Klaus

    2017-12-01

    We aim at identifying factors that may affect the characteristics of evolving weighted networks derived from empirical observations. To this end, we employ various chains of analysis that are often used in field studies for a data-driven derivation and characterization of such networks. As an example, we consider fully connected, weighted functional brain networks before, during, and after epileptic seizures that we derive from multichannel electroencephalographic data recorded from epilepsy patients. For these evolving networks, we estimate clustering coefficient and average shortest path length in a time-resolved manner. Lastly, we make use of surrogate concepts that we apply at various levels of the chain of analysis to assess to what extent network characteristics are dominated by properties of the electroencephalographic recordings and/or the evolving weighted networks, which may be accessible more easily. We observe that characteristics are differently affected by the unavoidable referencing of the electroencephalographic recording, by the time-series-analysis technique used to derive the properties of network links, and whether or not networks were normalized. Importantly, for the majority of analysis settings, we observe temporal evolutions of network characteristics to merely reflect the temporal evolutions of mean interaction strengths. Such a property of the data may be accessible more easily, which would render the weighted network approach—as used here—as an overly complicated description of simple aspects of the data.

  12. Brookhaven Reactor Experiment Control Facility, a distributed function computer network

    International Nuclear Information System (INIS)

    Dimmler, D.G.; Greenlaw, N.; Kelley, M.A.; Potter, D.W.; Rankowitz, S.; Stubblefield, F.W.

    1975-11-01

    A computer network for real-time data acquisition, monitoring and control of a series of experiments at the Brookhaven High Flux Beam Reactor has been developed and has been set into routine operation. This reactor experiment control facility presently services nine neutron spectrometers and one x-ray diffractometer. Several additional experiment connections are in progress. The architecture of the facility is based on a distributed function network concept. A statement of implementation and results is presented

  13. Multiresolution analysis of functions on directed networks

    Science.gov (United States)

    Sevi, Harry; Rilling, Gabriel; Borgnat, Pierre

    2017-08-01

    We introduce a novel design for analyzing and approximating functions defined on the vertices of a directed graph Γ in a multi-scale fashion. The starting point of our construction is the setting-up of a frequency notion through the study of the Dirichlet energy of random walk operator's eigenfunctions. By this alluring frequency interpretation, the set of random walk's eigenfunctions is considered as the Fourier basis for functions over directed graphs. We are thus able to construct a multi-scale frame based on the bi-orthogonal basis of the random walk on directed graphs. This multi-resolution frame paves thus the way to a generalization of the diffusion wavelet framework to the directed scope.

  14. 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

  15. 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

  16. The structural and functional brain networks that support human social networks.

    Science.gov (United States)

    Noonan, M P; Mars, R B; Sallet, J; Dunbar, R I M; Fellows, L K

    2018-02-20

    Social skills rely on a specific set of cognitive processes, raising the possibility that individual differences in social networks are related to differences in specific brain structural and functional networks. Here, we tested this hypothesis with multimodality neuroimaging. With diffusion MRI (DMRI), we showed that differences in structural integrity of particular white matter (WM) tracts, including cingulum bundle, extreme capsule and arcuate fasciculus were associated with an individual's social network size (SNS). A voxel-based morphology analysis demonstrated correlations between gray matter (GM) volume and SNS in limbic and temporal lobe regions. These structural changes co-occured with functional network differences. As a function of SNS, dorsomedial and dorsolateral prefrontal cortex showed altered resting-state functional connectivity with the default mode network (DMN). Finally, we integrated these three complementary methods, interrogating the relationship between social GM clusters and specific WM and resting-state networks (RSNs). Probabilistic tractography seeded in these GM nodes utilized the SNS-related WM pathways. Further, the spatial and functional overlap between the social GM clusters and the DMN was significantly closer than other control RSNs. These integrative analyses provide convergent evidence of the role of specific circuits in SNS, likely supporting the adaptive behavior necessary for success in extensive social environments. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

  17. Transcriptional and epigenetic networks that drive helper T cell identities

    Science.gov (United States)

    Shih, Han-Yu; Sciumè, Giuseppe; Poholek, Amanda C; Vahedi, Golnaz; Hirahara, Kiyoshi; Villarino, Alejandro V; Bonelli, Michael; Bosselut, Remy; Kanno, Yuka; Muljo, Stefan A; O’Shea, John J.

    2014-01-01

    The discovery of the specification of CD4+ helper T cells to discrete effector “lineages” represented a watershed event in conceptualizing mechanisms of host defense and immunoregulation. However, our appreciation for the actual complexity of helper T cell subsets continues unabated. Just as the Sami language of Scandinavia has 1000 different words for reindeer, the range of fates available for a CD4+ T cell is numerous and may be underestimated. Added to the crowded scene for helper T cell subsets is the continuously growing family of innate lymphoid cells (ILCs), endowed with common effector responses and the previously defined “master regulators” for CD4+ helper T cell subsets are also shared by ILC subsets. Within the context of this extraordinary complexity are concomitant advances in the understanding of transcriptomes and epigenomes. So what do terms like “lineage commitment” and helper T cell “specification” mean in the early 21st century? How do we put all of this together in a coherent conceptual framework? It would be arrogant to assume that we have a sophisticated enough understanding to seriously answer these questions. Instead, we will review the current status of the flexibility of helper T cell responses in relation to their genetic regulatory networks and epigenetic landscapes. Recent data have provided major surprises as to what master regulators can or cannot do, how they interact with other transcription factors and impact global genome-wide changes and how all these factors come together to influence helper cell function. PMID:25123275

  18. Engineering Cell Shape and Function

    Science.gov (United States)

    Singhvi, Rahul; Kumar, Amit; Lopez, Gabriel P.; Stephanopoulos, Gregory N.; Wang, Daniel I. C.; Whitesides, George M.; Ingber, Donald E.

    1994-04-01

    An elastomeric stamp, containing defined features on the micrometer scale, was used to imprint gold surfaces with specific patterns of self-assembled monolayers of alkanethiols and, thereby, to create islands of defined shape and size that support extracellular matrix protein adsorption and cell attachment. Through this technique, it was possible to place cells in predetermined locations and arrays, separated by defined distances, and to dictate their shape. Limiting the degree of cell extension provided control over cell growth and protein secretion. This method is experimentally simple and highly adaptable. It should be useful for applications in biotechnology that require analysis of individual cells cultured at high density or repeated access to cells placed in specified locations.

  19. Human embryonic stem cells form functional thyroid follicles.

    Science.gov (United States)

    Ma, Risheng; Latif, Rauf; Davies, Terry F

    2015-04-01

    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. 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). 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. 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 committed to thyroid cell speciation under

  20. 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.

  1. Ecosystem restoration strengthens pollination network resilience and function.

    Science.gov (United States)

    Kaiser-Bunbury, Christopher N; Mougal, James; Whittington, Andrew E; Valentin, Terence; Gabriel, Ronny; Olesen, Jens M; Blüthgen, Nico

    2017-02-09

    Land degradation results in declining biodiversity and the disruption of ecosystem functioning worldwide, particularly in the tropics. Vegetation restoration is a common tool used to mitigate these impacts and increasingly aims to restore ecosystem functions rather than species diversity. However, evidence from community experiments on the effect of restoration practices on ecosystem functions is scarce. Pollination is an important ecosystem function and the global decline in pollinators attenuates the resistance of natural areas and agro-environments to disturbances. Thus, the ability of pollination functions to resist or recover from disturbance (that is, the functional resilience) may be critical for ensuring a successful restoration process. Here we report the use of a community field experiment to investigate the effects of vegetation restoration, specifically the removal of exotic shrubs, on pollination. We analyse 64 plant-pollinator networks and the reproductive performance of the ten most abundant plant species across four restored and four unrestored, disturbed mountaintop communities. Ecosystem restoration resulted in a marked increase in pollinator species, visits to flowers and interaction diversity. Interactions in restored networks were more generalized than in unrestored networks, indicating a higher functional redundancy in restored communities. Shifts in interaction patterns had direct and positive effects on pollination, especially on the relative and total fruit production of native plants. Pollinator limitation was prevalent at unrestored sites only, where the proportion of flowers producing fruit increased with pollinator visitation, approaching the higher levels seen in restored plant communities. Our results show that vegetation restoration can improve pollination, suggesting that the degradation of ecosystem functions is at least partially reversible. The degree of recovery may depend on the state of degradation before restoration

  2. 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

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

    Directory of Open Access Journals (Sweden)

    Sadra Sadeh

    2015-06-01

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

  4. Method of derivation and differentiation of mouse embryonic stem cells generating synchronous neuronal networks.

    Science.gov (United States)

    Gazina, Elena V; Morrisroe, Emma; Mendis, Gunarathna D C; Michalska, Anna E; Chen, Joseph; Nefzger, Christian M; Rollo, Benjamin N; Reid, Christopher A; Pera, Martin F; Petrou, Steven

    2018-01-01

    Stem cells-derived neuronal cultures hold great promise for in vitro disease modelling and drug screening. However, currently stem cells-derived neuronal cultures do not recapitulate the functional properties of primary neurons, such as network properties. Cultured primary murine neurons develop networks which are synchronised over large fractions of the culture, whereas neurons derived from mouse embryonic stem cells (ESCs) display only partly synchronised network activity and human pluripotent stem cells-derived neurons have mostly asynchronous network properties. Therefore, strategies to improve correspondence of derived neuronal cultures with primary neurons need to be developed to validate the use of stem cell-derived neuronal cultures as in vitro models. By combining serum-free derivation of ESCs from mouse blastocysts with neuronal differentiation of ESCs in morphogen-free adherent culture we generated neuronal networks with properties recapitulating those of mature primary cortical cultures. After 35days of differentiation ESC-derived neurons developed network activity very similar to that of mature primary cortical neurons. Importantly, ESC plating density was critical for network development. Compared to the previously published methods this protocol generated more synchronous neuronal networks, with high similarity to the networks formed in mature primary cortical culture. We have demonstrated that ESC-derived neuronal networks recapitulating key properties of mature primary cortical networks can be generated by optimising both stem cell derivation and differentiation. This validates the approach of using ESC-derived neuronal cultures for disease modelling and in vitro drug screening. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. Signaling by Small GTPases at Cell-Cell junctions: Protein Interactions Building Control and Networks.

    Science.gov (United States)

    Braga, Vania

    2017-09-11

    A number of interesting reports highlight the intricate network of signaling proteins that coordinate formation and maintenance of cell-cell contacts. We have much yet to learn about how the in vitro binding data is translated into protein association inside the cells and whether such interaction modulates the signaling properties of the protein. What emerges from recent studies is the importance to carefully consider small GTPase activation in the context of where its activation occurs, which upstream regulators are involved in the activation/inactivation cycle and the GTPase interacting partners that determine the intracellular niche and extent of signaling. Data discussed here unravel unparalleled cooperation and coordination of functions among GTPases and their regulators in supporting strong adhesion between cells. Copyright © 2017 Cold Spring Harbor Laboratory Press; all rights reserved.

  6. Bisphosphonate-functionalized poly(β-amino ester) network polymers.

    Science.gov (United States)

    Guven, Melek Naz; Seckin Altuncu, Merve; Demir Duman, Fatma; Eren, Tugce Nur; Yagci Acar, Havva; Avci, Duygu

    2017-05-01

    Three novel bisphosphonate-functionalized secondary diamines are synthesized and incorporated into poly(β-amino ester)s (PBAEs) to investigate the effects of bisphosphonates on biodegradation and toxicity of PBAE polymer networks. These three novel amines, BPA1, BPA2, and BPA3, were prepared from the reactions of 1,4-butanediamine, 1,6-hexanediamine, or 4,9-dioxa-1,12-dodecanediamine with tetraethyl vinylidene bisphosphonate, respectively. The PBAE macromers were obtained from the aza-Michael addition reaction of these amines to 1,6-hexane diol diacrylate (HDDA) and poly(ethylene glycol) diacrylate (PEGDA, M n  = 575) and photopolymerized to produce biodegradable gels. These gels with different chemistries exhibited similar degradation behavior with mass loss of 53-73% within 24 h, indicating that degradation is mostly governed by the bisphosphonate group. Based on the in vitro cytotoxicity evaluation against NIH 3T3 mouse embryonic fibroblast cells, the degradation products do not exhibit significant toxicity in most cases. It was also shown that PBAE macromers can be used as cross-linkers for the synthesis of 2-hydroxyethyl methacrylate hydrogels, conferring small and customizable degradation rates upon them. The materials reported have potential to be used as nontoxic degradable biomaterials. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 1412-1421, 2017. © 2017 Wiley Periodicals, Inc.

  7. 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

  8. 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.

  9. Functional module identification in protein interaction networks by interaction patterns

    Science.gov (United States)

    Wang, Yijie; Qian, Xiaoning

    2014-01-01

    Motivation: Identifying functional modules in protein–protein interaction (PPI) networks may shed light on cellular functional organization and thereafter underlying cellular mechanisms. Many existing module identification algorithms aim to detect densely connected groups of proteins as potential modules. However, based on this simple topological criterion of ‘higher than expected connectivity’, those algorithms may miss biologically meaningful modules of functional significance, in which proteins have similar interaction patterns to other proteins in networks but may not be densely connected to each other. A few blockmodel module identification algorithms have been proposed to address the problem but the lack of global optimum guarantee and the prohibitive computational complexity have been the bottleneck of their applications in real-world large-scale PPI networks. Results: In this article, we propose a novel optimization formulation LCP2 (low two-hop conductance sets) using the concept of Markov random walk on graphs, which enables simultaneous identification of both dense and sparse modules based on protein interaction patterns in given networks through searching for LCP2 by random walk. A spectral approximate algorithm SLCP2 is derived to identify non-overlapping functional modules. Based on a bottom-up greedy strategy, we further extend LCP2 to a new algorithm (greedy algorithm for LCP2) GLCP2 to identify overlapping functional modules. We compare SLCP2 and GLCP2 with a range of state-of-the-art algorithms on synthetic networks and real-world PPI networks. The performance evaluation based on several criteria with respect to protein complex prediction, high level Gene Ontology term prediction and especially sparse module detection, has demonstrated that our algorithms based on searching for LCP2 outperform all other compared algorithms. Availability and implementation: All data and code are available at http://www.cse.usf.edu/∼xqian/fmi/slcp2hop

  10. 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...

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

    Science.gov (United States)

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

    2016-03-01

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

  12. Functional Brain Network Mechanism of Hypersensitivity in Chronic Pain.

    Science.gov (United States)

    Lee, UnCheol; Kim, Minkyung; Lee, KyoungEun; Kaplan, Chelsea M; Clauw, Daniel J; Kim, Seunghwan; Mashour, George A; Harris, Richard E

    2018-01-10

    Fibromyalgia (FM) is a chronic widespread pain condition characterized by augmented multi-modal sensory sensitivity. Although the mechanisms underlying this sensitivity are thought to involve an imbalance in excitatory and inhibitory activity throughout the brain, the underlying neural network properties associated with hypersensitivity to pain stimuli are largely unknown. In network science, explosive synchronization (ES) was introduced as a mechanism of hypersensitivity in diverse biological and physical systems that display explosive and global propagations with small perturbations. We hypothesized that ES may also be a mechanism of the hypersensitivity in FM brains. To test this hypothesis, we analyzed resting state electroencephalogram (EEG) of 10 FM patients. First, we examined theoretically well-known ES conditions within functional brain networks reconstructed from EEG, then tested whether a brain network model with ES conditions identified in the EEG data is sensitive to an external perturbation. We demonstrate for the first time that the FM brain displays characteristics of ES conditions, and that these factors significantly correlate with chronic pain intensity. The simulation data support the conclusion that networks with ES conditions are more sensitive to perturbation compared to non-ES network. The model and empirical data analysis provide convergent evidence that ES may be a network mechanism of FM hypersensitivity.

  13. Functional networks inference from rule-based machine learning models.

    Science.gov (United States)

    Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

    2016-01-01

    Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

  14. 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.

  15. Regulation of Satellite Cell Function in Sarcopenia

    Science.gov (United States)

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

    2014-01-01

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

  16. Understanding emergent functions in self-assembled fibrous networks

    Science.gov (United States)

    Sinko, Robert; Keten, Sinan

    2015-09-01

    Understanding self-assembly processes of nanoscale building blocks and characterizing their properties are both imperative for designing new hierarchical, network materials for a wide range of structural, optoelectrical, and transport applications. Although the characterization and choices of these material building blocks have been well studied, our understanding of how to precisely program a specific morphology through self-assembly still must be significantly advanced. In the recent study by Xie et al (2015 Nanotechnology 26 205602), the self-assembly of end-functionalized nanofibres is investigated using a coarse-grained molecular model and offers fundamental insight into how to control the structural morphology of nanofibrous networks. Varying nanoscale networks are observed when the molecular interaction strength is changed and the findings suggest that self-assembly through the tuning of molecular interactions is a key strategy for designing nanostructured networks with specific topologies.

  17. NIRF constitutes a nodal point in the cell cycle network and is a candidate tumor suppressor.

    Science.gov (United States)

    Mori, Tsutomu; Ikeda, Daisuke D; Fukushima, Toshihiko; Takenoshita, Seiichi; Kochi, Hideo

    2011-10-01

    In biological networks, a small number of "hub" proteins play critical roles in the network integrity and functions. The cell cycle network orchestrates versatile cellular functions through interactions between many signaling modules, whose defects impair diverse cellular processes, often leading to cancer. However, the network architecture and molecular basis that ensure proper coordination between distinct modules are unclear. Here, we show that the ubiquitin ligase NIRF (also known as UHRF2), which induces G1 arrest, interacts with multiple cell cycle proteins including cyclins (A2, B1, D1 and E1), p53 and pRB, and ubiquitinates cyclins D1 and E1. Consistent with its versatility, a bioinformatic network analysis demonstrated that NIRF is an intermodular hub protein that is responsible for the coordination of multiple network modules. Notably, intermodular hubs are frequently associated with oncogenesis. Indeed, we detected loss of heterozygosity of the NIRF gene in several kinds of tumors. When a cancer outlier profile analysis was applied to the Oncomine database, loss of the NIRF gene was found at statistically significant levels in diverse tumors. Importantly, a recurrent microdeletion targeting NIRF was observed in non-small cell lung carcinoma. Furthermore, NIRF is immediately adjacent to the single nucleotide polymorphism rs719725, which is reportedly associated with the risk of colorectal cancer. These observations suggest that NIRF occupies a prominent position within the cell cycle network, and is a strong candidate for a tumor suppressor whose aberration contributes to the pathogenesis of diverse malignancies. © 2011 Landes Bioscience

  18. Endothelial cells assemble into a 3-dimensional prevascular network in a bone tissue engineering construct.

    Science.gov (United States)

    Rouwkema, Jeroen; de Boer, Jan; Van Blitterswijk, Clemens A

    2006-09-01

    To engineer tissues with clinically relevant dimensions, one must overcome the challenge of rapidly creating functional blood vessels to supply cells with oxygen and nutrients and to remove waste products. We tested the hypothesis that endothelial cells, cocultured with osteoprogenitor cells, can organize into a prevascular network in vitro. When cultured in a spheroid coculture model with human mesenchymal stem cells, human umbilical vein endothelial cells (HUVECs) form a 3-dimensional prevascular network within 10 days of in vitro culture. The formation of the prevascular network was promoted by seeding 2% or fewer HUVECs. Moreover, the addition of endothelial cells resulted in a 4-fold upregulation of the osteogenic marker alkaline phosphatase. The addition of mouse embryonic fibroblasts did not result in stabilization of the prevascular network. Upon implantation, the prevascular network developed further and structures including lumen could be seen regularly. However, anastomosis with the host vasculature was limited. We conclude that endothelial cells are able to form a 3-dimensional (3D) prevascular network in vitro in a bone tissue engineering setting. This finding is a strong indication that in vitro prevascularization is a promising strategy to improve implant vascularization in bone tissue engineering.

  19. Evidence for hubs in human functional brain networks.

    Science.gov (United States)

    Power, Jonathan D; Schlaggar, Bradley L; Lessov-Schlaggar, Christina N; Petersen, Steven E

    2013-08-21

    Hubs integrate and distribute information in powerful ways due to the number and positioning of their contacts in a network. Several resting-state functional connectivity MRI reports have implicated regions of the default mode system as brain hubs; we demonstrate that previous degree-based approaches to hub identification may have identified portions of large brain systems rather than critical nodes of brain networks. We utilize two methods to identify hub-like brain regions: (1) finding network nodes that participate in multiple subnetworks of the brain, and (2) finding spatial locations in which several systems are represented within a small volume. These methods converge on a distributed set of regions that differ from previous reports on hubs. This work identifies regions that support multiple systems, leading to spatially constrained predictions about brain function that may be tested in terms of lesions, evoked responses, and dynamic patterns of activity. Copyright © 2013 Elsevier Inc. All rights reserved.

  20. Long-distance mechanism of neurotransmitter recycling mediated by glial network facilitates visual function in Drosophila.

    Science.gov (United States)

    Chaturvedi, Ratna; Reddig, Keith; Li, Hong-Sheng

    2014-02-18

    Neurons rely on glia to recycle neurotransmitters such as glutamate and histamine for sustained signaling. Both mammalian and insect glia form intercellular gap-junction networks, but their functional significance underlying neurotransmitter recycling is unknown. Using the Drosophila visual system as a genetic model, here we show that a multicellular glial network transports neurotransmitter metabolites between perisynaptic glia and neuronal cell bodies to mediate long-distance recycling of neurotransmitter. In the first visual neuropil (lamina), which contains a multilayer glial network, photoreceptor axons release histamine to hyperpolarize secondary sensory neurons. Subsequently, the released histamine is taken up by perisynaptic epithelial glia and converted into inactive carcinine through conjugation with β-alanine for transport. In contrast to a previous assumption that epithelial glia deliver carcinine directly back to photoreceptor axons for histamine regeneration within the lamina, we detected both carcinine and β-alanine in the fly retina, where they are found in photoreceptor cell bodies and surrounding pigment glial cells. Downregulating Inx2 gap junctions within the laminar glial network causes β-alanine accumulation in retinal pigment cells and impairs carcinine synthesis, leading to reduced histamine levels and photoreceptor synaptic vesicles. Consequently, visual transmission is impaired and the fly is less responsive in a visual alert analysis compared with wild type. Our results suggest that a gap junction-dependent laminar and retinal glial network transports histamine metabolites between perisynaptic glia and photoreceptor cell bodies to mediate a novel, long-distance mechanism of neurotransmitter recycling, highlighting the importance of glial networks in the regulation of neuronal functions.

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

    Science.gov (United States)

    Giessing, Carsten; Thiel, Christiane M.

    2012-01-01

    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 functional magnetic resonance imaging (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

  2. 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.

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

    Science.gov (United States)

    Wheeler, Kevin R.; Dhawan, Atam P.

    1993-01-01

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

  4. Connectomics and Neuroticism: An Altered Functional Network Organization

    OpenAIRE

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

    2014-01-01

    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 weig...

  5. Functionality for learning networks: lessons learned from social web applications

    NARCIS (Netherlands)

    Berlanga, Adriana; Sloep, Peter; Brouns, Francis; Van Rosmalen, Peter; Bitter-Rijpkema, Marlies; Koper, Rob

    2007-01-01

    Berlanga, A. J., Sloep, P., Brouns, F., Van Rosmalen, P., Bitter-Rijpkema, M., & Koper, R. (2007). Functionality for learning networks: lessons learned from social web applications. Proceedings of the ePortfolio 2007 Conference. October, 18-19, 2007, Maastricht, The Netherlands. [See also

  6. A lateralized functional auditory network is involved in anuran ...

    Indian Academy of Sciences (India)

    2016-10-05

    Oct 5, 2016 ... strate, for the first time, that auditory neural network interconnecting the left and right midbrain and forebrain function ... Finally, these connection changes were sexually dimorphic, revealing sex differences in reproductive roles. [Xue F, Fang G, ... with electroencephalogram (EEG) time series data by imple-.

  7. Kernel Function Tuning for Single-Layer Neural Networks

    Czech Academy of Sciences Publication Activity Database

    Vidnerová, Petra; Neruda, Roman

    -, accepted 28.11. 2017 (2018) ISSN 2278-0149 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : single-layer neural networks * kernel methods * kernel function * optimisation Subject RIV: IN - Informatics, Computer Science http://www.ijmerr.com/

  8. 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

    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

  9. Towards a comprehensive understanding of emerging dynamics and function of pancreatic islets: A complex network approach. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

    Science.gov (United States)

    Loppini, Alessandro

    2018-03-01

    Complex network theory represents a comprehensive mathematical framework to investigate biological systems, ranging from sub-cellular and cellular scales up to large-scale networks describing species interactions and ecological systems. In their exhaustive and comprehensive work [1], Gosak et al. discuss several scenarios in which the network approach was able to uncover general properties and underlying mechanisms of cells organization and regulation, tissue functions and cell/tissue failure in pathology, by the study of chemical reaction networks, structural networks and functional connectivities.

  10. 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.

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

    International Nuclear Information System (INIS)

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

    2013-01-01

    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. (paper)

  12. 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

  13. Intrinsic network activity in tinnitus investigated using functional MRI

    Science.gov (United States)

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

    2016-01-01

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

  14. Naltrexone ameliorates functional network abnormalities in alcohol‐dependent individuals

    Science.gov (United States)

    Baek, Kwangyeol; Tait, Roger; Elliott, Rebecca; Ersche, Karen D.; Flechais, Remy; McGonigle, John; Murphy, Anna; Nestor, Liam J.; Orban, Csaba; Passetti, Filippo; Paterson, Louise M.; Rabiner, Ilan; Reed, Laurence; Smith, Dana; Suckling, John; Taylor, Eleanor M.; Bullmore, Edward T.; Lingford‐Hughes, Anne R.; Deakin, Bill; Nutt, David J.; Sahakian, Barbara J.; Robbins, Trevor W.; Voon, Valerie

    2017-01-01

    Abstract Naltrexone, an opioid receptor antagonist, is commonly used as a relapse prevention medication in alcohol and opiate addiction, but its efficacy and the mechanisms underpinning its clinical usefulness are not well characterized. In the current study, we examined the effects of 50‐mg naltrexone compared with placebo on neural network changes associated with substance dependence in 21 alcohol and 36 poly‐drug‐dependent individuals compared with 36 healthy volunteers. Graph theoretic and network‐based statistical analysis of resting‐state functional magnetic resonance imaging (MRI) data revealed that alcohol‐dependent subjects had reduced functional connectivity of a dispersed network compared with both poly‐drug‐dependent and healthy subjects. Higher local efficiency was observed in both patient groups, indicating clustered and segregated network topology and information processing. Naltrexone normalized heightened local efficiency of the neural network in alcohol‐dependent individuals, to the same levels as healthy volunteers. Naltrexone failed to have an effect on the local efficiency in abstinent poly‐substance‐dependent individuals. Across groups, local efficiency was associated with substance, but no alcohol exposure implicating local efficiency as a potential premorbid risk factor in alcohol use disorders that can be ameliorated by naltrexone. These findings suggest one possible mechanism for the clinical effects of naltrexone, namely, the amelioration of disrupted network topology. PMID:28247526

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

    Directory of Open Access Journals (Sweden)

    Aurélien Naldi

    2017-03-01

    Full Text Available 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.

  16. 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

  17. 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.

  18. Modelling the effects of cell-to-cell variability on the output of interconnected gene networks in bacterial populations.

    Science.gov (United States)

    Politi, Nicolò; Pasotti, Lorenzo; Zucca, Susanna; Magni, Paolo

    2015-01-01

    The interconnection of quantitatively characterized biological devices may lead to composite systems with apparently unpredictable behaviour. Context-dependent variability of biological parts has been investigated in several studies, measuring its entity and identifying the factors contributing to variability. Such studies rely on the experimental analysis of model systems, by quantifying reporter genes via population or single-cell approaches. However, cell-to-cell variability is not commonly included in predictability analyses, thus relying on predictive models trained and tested on central tendency values. This work aims to study in silico the effects of cell-to-cell variability on the population-averaged output of interconnected biological circuits. The steady-state deterministic transfer function of individual devices was described by Hill equations and lognormal synthetic noise was applied to their output. Two- and three-module networks were studied, where individual devices implemented inducible/repressible functions. The single-cell output of such networks was simulated as a function of noise entity; their population-averaged output was computed and used to investigate the expected variability in transfer function identification. The study was extended by testing different noise models, module logic, intrinsic/extrinsic noise proportions and network configurations. First, the transfer function of an individual module was identified from simulated data of a two-module network. The estimated parameter variability among different noise entities was limited (14%), while a larger difference was observed (up to 62%) when estimated and true parameters were compared. Thus, low-variability parameter estimates can be obtained for different noise entities, although deviating from the true parameters, whose measurement requires noise knowledge. Second, the black-box input-output function of a two/three-module network was predicted from the knowledge of the transfer

  19. 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.

  20. Functionally-Specific Changes in Sensorimotor Networks following Motor Learning

    Directory of Open Access Journals (Sweden)

    David J Ostry

    2011-10-01

    Full Text Available The perceptual changes induced by motor learning are important in understanding the adaptive mechanisms and global functions of the human brain. In the present study, we document the neural substrates of this sensory plasticity by combining work on motor learning using a robotic manipulandum with resting-state fMRI measures of learning and psychophysical measures of perceptual function. We show that motor learning results in long-lasting changes to somatosensory areas of the brain. We have developed a new technique for incorporating behavioral measures into resting-state connectivity analyses. The method allows us to identify networks whose functional connectivity changes with learning and specifically to dissociate changes in connectivity that are related to motor learning from those that are related perceptual changes that occur in conjunction with learning. Using this technique we identify a new network in motor learning involving second somatosensory cortex, ventral premotor and supplementary motor cortex whose activation is specifically related to sensory changes that occur in association with learning. The sensory networks that are strengthened in motor learning are similar to those involved in perceptual learning and decision making, which suggests that the process of motor learning engages the perceptual learning network.

  1. Tansig activation function (of MLP network) for cardiac abnormality detection

    Science.gov (United States)

    Adnan, Ja'afar; Daud, Nik Ghazali Nik; Ishak, Mohd Taufiq; Rizman, Zairi Ismael; Rahman, Muhammad Izzuddin Abd

    2018-02-01

    Heart abnormality often occurs regardless of gender, age and races. This problem sometimes does not show any symptoms and it can cause a sudden death to the patient. In general, heart abnormality is the irregular electrical activity of the heart. This paper attempts to develop a program that can detect heart abnormality activity through implementation of Multilayer Perceptron (MLP) network. A certain amount of data of the heartbeat signals from the electrocardiogram (ECG) will be used in this project to train the MLP network by using several training algorithms with Tansig activation function.

  2. 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.

  3. Correlations and functional connections in a population of grid cells.

    Science.gov (United States)

    Dunn, Benjamin; Mørreaunet, Maria; Roudi, Yasser

    2015-02-01

    We study the statistics of spike trains of simultaneously recorded grid cells in freely behaving rats. We evaluate pairwise correlations between these cells and, using a maximum entropy kinetic pairwise model (kinetic Ising model), study their functional connectivity. Even when we account for the covariations in firing rates due to overlapping fields, both the pairwise correlations and functional connections decay as a function of the shortest distance between the vertices of the spatial firing pattern of pairs of grid cells, i.e. their phase difference. They take positive values between cells with nearby phases and approach zero or negative values for larger phase differences. We find similar results also when, in addition to correlations due to overlapping fields, we account for correlations due to theta oscillations and head directional inputs. The inferred connections between neurons in the same module and those from different modules can be both negative and positive, with a mean close to zero, but with the strongest inferred connections found between cells of the same module. Taken together, our results suggest that grid cells in the same module do indeed form a local network of interconnected neurons with a functional connectivity that supports a role for attractor dynamics in the generation of grid pattern.

  4. Role of Polyamines in Immune Cell Functions

    Directory of Open Access Journals (Sweden)

    Rebecca S. Hesterberg

    2018-03-01

    Full Text Available The immune system is remarkably responsive to a myriad of invading microorganisms and provides continuous surveillance against tissue damage and developing tumor cells. To achieve these diverse functions, multiple soluble and cellular components must react in an orchestrated cascade of events to control the specificity, magnitude and persistence of the immune response. Numerous catabolic and anabolic processes are involved in this process, and prominent roles for l-arginine and l-glutamine catabolism have been described, as these amino acids serve as precursors of nitric oxide, creatine, agmatine, tricarboxylic acid cycle intermediates, nucleotides and other amino acids, as well as for ornithine, which is used to synthesize putrescine and the polyamines spermidine and spermine. Polyamines have several purported roles and high levels of polyamines are manifest in tumor cells as well in autoreactive B- and T-cells in autoimmune diseases. In the tumor microenvironment, l-arginine catabolism by both tumor cells and suppressive myeloid cells is known to dampen cytotoxic T-cell functions suggesting there might be links between polyamines and T-cell suppression. Here, we review studies suggesting roles of polyamines in normal immune cell function and highlight their connections to autoimmunity and anti-tumor immune cell function.

  5. 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. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    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%.

  7. Mitochondria, endothelial cell function, and vascular diseases.

    Science.gov (United States)

    Tang, Xiaoqiang; Luo, Yu-Xuan; Chen, Hou-Zao; Liu, De-Pei

    2014-01-01

    Mitochondria are perhaps the most sophisticated and dynamic responsive sensing systems in eukaryotic cells. The role of mitochondria goes beyond their capacity to create molecular fuel and includes the generation of reactive oxygen species, the regulation of calcium, and the activation of cell death. In endothelial cells, mitochondria have a profound impact on cellular function under both healthy and diseased conditions. In this review, we summarize the basic functions of mitochondria in endothelial cells and discuss the roles of mitochondria in endothelial dysfunction and vascular diseases, including atherosclerosis, diabetic vascular dysfunction, pulmonary artery hypertension, and hypertension. Finally, the potential therapeutic strategies to improve mitochondrial function in endothelial cells and vascular diseases are also discussed, with a focus on mitochondrial-targeted antioxidants and calorie restriction.

  8. Mitochondria, Endothelial Cell Function and Vascular Diseases

    Directory of Open Access Journals (Sweden)

    Xiaoqiang eTang

    2014-05-01

    Full Text Available Mitochondria are perhaps the most sophisticated and dynamic responsive sensing systems in eukaryotic cells. The role of mitochondria goes beyond their capacity to create molecular fuel and includes the generation of reactive oxygen species, the regulation of calcium, and the activation of cell death. In endothelial cells, mitochondria have a profound impact on cellular function under both healthy and diseased conditions. In this review, we summarize the basic functions of mitochondria in endothelial cells and discuss the roles of mitochondria in endothelial dysfunction and vascular diseases, including atherosclerosis, diabetic vascular dysfunction, pulmonary artery hypertension and hypertension. Finally, the potential therapeutic strategies to improve mitochondrial function in endothelial cells and vascular diseases are also discussed, with a focus on mitochondrial-targeted antioxidants and calorie restriction.

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

    Science.gov (United States)

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

    2017-10-01

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

  10. Human Pluripotent Stem Cell Differentiation into Functional Epicardial Progenitor Cells

    Directory of Open Access Journals (Sweden)

    Juan Antonio Guadix

    2017-12-01

    Full Text Available Summary: Human pluripotent stem cells (hPSCs are widely used to study cardiovascular cell differentiation and function. Here, we induced differentiation of hPSCs (both embryonic and induced to proepicardial/epicardial progenitor cells that cover the heart during development. Addition of retinoic acid (RA and bone morphogenetic protein 4 (BMP4 promoted expression of the mesodermal marker PDGFRα, upregulated characteristic (proepicardial progenitor cell genes, and downregulated transcription of myocardial genes. We confirmed the (proepicardial-like properties of these cells using in vitro co-culture assays and in ovo grafting of hPSC-epicardial cells into chick embryos. Our data show that RA + BMP4-treated hPSCs differentiate into (proepicardial-like cells displaying functional properties (adhesion and spreading over the myocardium of their in vivo counterpart. The results extend evidence that hPSCs are an excellent model to study (proepicardial differentiation into cardiovascular cells in human development and evaluate their potential for cardiac regeneration. : The authors have shown that hPSCs can be instructed in vitro to differentiate into a specific cardiac embryonic progenitor cell population called the proepicardium. Proepicardial cells are required for normal formation of the heart during development and might contribute to the development of cell-based therapies for heart repair. Keywords: human pluripotent stem cells, proepicardium, progenitor cells, cardiovascular, differentiation

  11. Energy Efficiency Challenges of 5G Small Cell Networks.

    Science.gov (United States)

    Ge, Xiaohu; Yang, Jing; Gharavi, Hamid; Sun, Yang

    2017-05-01

    The deployment of a large number of small cells poses new challenges to energy efficiency, which has often been ignored in fifth generation (5G) cellular networks. While massive multiple-input multiple outputs (MIMO) will reduce the transmission power at the expense of higher computational cost, the question remains as to which computation or transmission power is more important in the energy efficiency of 5G small cell networks. Thus, the main objective in this paper is to investigate the computation power based on the Landauer principle. Simulation results reveal that more than 50% of the energy is consumed by the computation power at 5G small cell base stations (BSs). Moreover, the computation power of 5G small cell BS can approach 800 watt when the massive MIMO (e.g., 128 antennas) is deployed to transmit high volume traffic. This clearly indicates that computation power optimization can play a major role in the energy efficiency of small cell networks.

  12. 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

  13. Edge usage, motifs, and regulatory logic for cell cycling genetic networks

    Science.gov (United States)

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

    2013-01-01

    The cell cycle is a tightly controlled process, yet it shows marked differences across species. Which of its structural features follow solely from the ability to control gene expression? We tackle this question in silico by examining the ensemble of all regulatory networks which satisfy the constraint of producing a given sequence of gene expressions. We focus on three cell cycle profiles coming from baker's yeast, fission yeast, and mammals. First, we show that the networks in each of the ensembles use just a few interactions that are repeatedly reused as building blocks. Second, we find an enrichment in network motifs that is similar in the two yeast cell cycle systems investigated. These motifs do not have autonomous functions, yet they reveal a regulatory logic for cell cycling based on a feed-forward cascade of activating interactions.

  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. 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.

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

    Science.gov (United States)

    Belcher, Annabelle M; Yen, Cecil Chern-Chyi; Notardonato, Lucia; Ross, Thomas J; Volkow, Nora D; Yang, Yihong; Stein, Elliot A; Silva, Afonso C; Tomasi, Dardo

    2016-01-01

    In combination with advances in analytical methods, resting-state fMRI is allowing unprecedented access to 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 FC 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.

  17. Evaluation of the functional status of learning networks based on the dimensions defining communities of practice

    NARCIS (Netherlands)

    Meijs, Celeste; Prinsen, Fleur; De Laat, Maarten

    2017-01-01

    Abstract: Learning in professional networks is gaining popularity in teachers’ professional development. To study how teachers evaluated their networks, we developed a questionnaire called the ‘network barometer’ to inquire functioning according to three dimensions based on communities of

  18. Experimental demonstrations of all-optical networking functions for WDM optical networks

    Science.gov (United States)

    Gurkan, Deniz

    The deployment of optical networks will enable high capacity links between users but will introduce the problems associated with transporting and managing more channels. Many network functions should be implemented in optical domain; main reasons are: to avoid electronic processing bottlenecks, to achieve data-format and data-rate independence, to provide reliable and cost efficient control and management information, to simultaneously process multiple wavelength channel operation for wavelength division multiplexed (WDM) optical networks. The following novel experimental demonstrations of network functions in the optical domain are presented: Variable-bit-rate recognition of the header information in a data packet. The technique is reconfigurable for different header sequences and uses optical correlators as look-up tables. The header is processed and a signal is sent to the switch for a series of incoming data packets at 155 Mb/s, 622 Mb/s, and 2.5 Gb/s in a reconfigurable network. Simultaneous optical time-slot-interchange and wavelength conversion of the bits in a 2.5-Gb/s data stream to achieve a reconfigurable time/wavelength switch. The technique uses difference-frequency-generation (DFG) for wavelength conversion and fiber Bragg gratings (FBG) as wavelength-dependent optical time buffers. The WDM header recognition module simultaneously recognizing two header bits on each of two 2.5-Gbit/s WDM packet streams. The module is tunable to enable reconfigurable look-up tables. Simultaneous and independent label swapping and wavelength conversion of two WDM channels for a multi-protocol label switching (MPLS) network. Demonstration of label swapping of distinct 8-bit-long labels for two WDM data channels is presented. Two-dimensional code conversion module for an optical code-division multiple-access (O-CDMA) local area network (LAN) system. Simultaneous wavelength conversion and time shifting is achieved to enable flexible code conversion and increase code re

  19. Single-Cell Transcriptional Analysis Reveals Novel Neuronal Phenotypes and Interaction Networks Involved in the Central Circadian Clock.

    Science.gov (United States)

    Park, James; Zhu, Haisun; O'Sullivan, Sean; Ogunnaike, Babatunde A; Weaver, David R; Schwaber, James S; Vadigepalli, Rajanikanth

    2016-01-01

    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 toward understanding how a population of heterogeneous single cells organizes into cellular networks that underlie tissue-level function.

  20. 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.

  1. Dissect the Dynamic Molecular Circuits of Cell Cycle Control through Network Evolution Model

    Directory of Open Access Journals (Sweden)

    Yang Peng

    2017-01-01

    Full Text Available The molecular circuits of cell cycle control serve as a key hub to integrate from endogenous and environmental signals into a robust biological decision driving cell growth and division. Dysfunctional cell cycle control is highlighted in a wide spectrum of human cancers. More importantly the mainstay anticancer treatment such as radiation therapy and chemotherapy targets the hallmark of uncontrolled cell proliferation in cancer cells by causing DNA damage, cell cycle arrest, and cell death. Given the functional importance of cell cycle control, the regulatory mechanisms that drive the cell division have been extensively investigated in a huge number of studies by conventional single-gene approaches. However the complexity of cell cycle control renders a significant barrier to understand its function at a network level. In this study, we used mathematical modeling through modern graph theory and differential equation systems. We believe our network evolution model can help us understand the dynamic cell cycle control in tumor evolution and optimizing dosing schedules for radiation therapy and chemotherapy targeting cell cycle.

  2. A Geographic and Functional Network Flow Analysis Tool

    Science.gov (United States)

    2014-06-01

    Additional functionality added to the Arc Maker plugin .................................15 Figure 7. Work flow of running the flow model on a QGIS layer...System PPD-21 Presidential Policy Directive 21 QGIS Quantum GIS XML Extensible Markup Language xiv THIS PAGE INTENTIONALLY LEFT...heavily on Quantum GIS ( QGIS 2012) to model our simulated networks. QGIS is a free, open source Geographic Information System software suite. We ran

  3. Complexity of Gaussian-Radial-Basis Networks Approximating Smooth Functions

    Czech Academy of Sciences Publication Activity Database

    Kainen, P.C.; Kůrková, Věra; Sanguineti, M.

    2009-01-01

    Roč. 25, č. 1 (2009), s. 63-74 ISSN 0885-064X R&D Projects: GA ČR GA201/08/1744 Institutional research plan: CEZ:AV0Z10300504 Keywords : Gaussian-radial-basis-function networks * rates of approximation * model complexity * variation norms * Bessel and Sobolev norms * tractability of approximation Subject RIV: IN - Informatics, Computer Science Impact factor: 1.227, year: 2009

  4. Model Complexities of Shallow Networks Representing Highly Varying Functions

    Czech Academy of Sciences Publication Activity Database

    Kůrková, Věra; Sanguineti, M.

    2016-01-01

    Roč. 171, 1 January (2016), s. 598-604 ISSN 0925-2312 R&D Projects: GA MŠk(CZ) LD13002 Grant - others:grant for Visiting Professors(IT) GNAMPA-INdAM Institutional support: RVO:67985807 Keywords : shallow networks * model complexity * highly varying functions * Chernoff bound * perceptrons * Gaussian kernel units Subject RIV: IN - Informatics, Computer Science Impact factor: 3.317, year: 2016

  5. 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.

  6. 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...

  7. 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...Departamento de Matematica Pura da Faculdade de Ciencias do Porto) As pointed by [1], in the class of coupled cell networks that permits self-coupling

  8. 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.

  9. Modularity-like objective function in annotated networks

    Science.gov (United States)

    Xie, Jia-Rong; Wang, Bing-Hong

    2017-12-01

    We ascertain the modularity-like objective function whose optimization is equivalent to the maximum likelihood in annotated networks. We demonstrate that the modularity-like objective function is a linear combination of modularity and conditional entropy. In contrast with statistical inference methods, in our method, the influence of the metadata is adjustable; when its influence is strong enough, the metadata can be recovered. Conversely, when it is weak, the detection may correspond to another partition. Between the two, there is a transition. This paper provides a concept for expanding the scope of modularity methods.

  10. 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

    2017-05-15

    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. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Functional networks in parallel with cortical development associate with executive functions in children.

    Science.gov (United States)

    Zhong, Jidan; Rifkin-Graboi, Anne; Ta, Anh Tuan; Yap, Kar Lai; Chuang, Kai-Hsiang; Meaney, Michael J; Qiu, Anqi

    2014-07-01

    Children begin performing similarly to adults on tasks requiring executive functions in late childhood, a transition that is probably due to neuroanatomical fine-tuning processes, including myelination and synaptic pruning. In parallel to such structural changes in neuroanatomical organization, development of functional organization may also be associated with cognitive behaviors in children. We examined 6- to 10-year-old children's cortical thickness, functional organization, and cognitive performance. We used structural magnetic resonance imaging (MRI) to identify areas with cortical thinning, resting-state fMRI to identify functional organization in parallel to cortical development, and working memory/response inhibition tasks to assess executive functioning. We found that neuroanatomical changes in the form of cortical thinning spread over bilateral frontal, parietal, and occipital regions. These regions were engaged in 3 functional networks: sensorimotor and auditory, executive control, and default mode network. Furthermore, we found that working memory and response inhibition only associated with regional functional connectivity, but not topological organization (i.e., local and global efficiency of information transfer) of these functional networks. Interestingly, functional connections associated with "bottom-up" as opposed to "top-down" processing were more clearly related to children's performance on working memory and response inhibition, implying an important role for brain systems involved in late childhood. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  12. 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.

  13. Function-Oriented Networking and On-Demand Routing System in Network Using Ant Colony Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Young-Bo Sim

    2017-11-01

    Full Text Available In this paper, we proposed and developed Function-Oriented Networking (FON, a platform for network users. It has a different philosophy as opposed to technologies for network managers of Software-Defined Networking technology, OpenFlow. It is a technology that can immediately reflect the demands of the network users in the network, unlike the existing OpenFlow and Network Functions Virtualization (NFV, which do not reflect directly the needs of the network users. It allows the network user to determine the policy of the direct network, so it can be applied more precisely than the policy applied by the network manager. This is expected to increase the satisfaction of the service users when the network users try to provide new services. We developed FON function that performs on-demand routing for Low-Delay Required service. We analyzed the characteristics of the Ant Colony Optimization (ACO algorithm and found that the algorithm is suitable for low-delay required services. It was also the first in the world to implement the routing software using ACO Algorithm in the real Ethernet network. In order to improve the routing performance, several algorithms of the ACO Algorithm have been developed to enable faster path search-routing and path recovery. The relationship between the network performance index and the ACO routing parameters is derived, and the results are compared and analyzed. Through this, it was possible to develop the ACO algorithm.

  14. Multivariate neural network operators with sigmoidal activation functions.

    Science.gov (United States)

    Costarelli, Danilo; Spigler, Renato

    2013-12-01

    In this paper, we study pointwise and uniform convergence, as well as order of approximation, of a family of linear positive multivariate neural network (NN) operators with sigmoidal activation functions. The order of approximation is studied for functions belonging to suitable Lipschitz classes and using a moment-type approach. The special cases of NN operators, activated by logistic, hyperbolic tangent, and ramp sigmoidal functions are considered. Multivariate NNs approximation finds applications, typically, in neurocomputing processes. Our approach to NN operators allows us to extend previous convergence results and, in some cases, to improve the order of approximation. The case of multivariate quasi-interpolation operators constructed with sigmoidal functions is also considered. Copyright © 2013 Elsevier Ltd. All rights reserved.

  15. 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. Copyright © 2013 Elsevier Inc. All rights reserved.

  16. 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

  17. Accurate path integration in continuous attractor network models of grid cells.

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    Yoram Burak

    2009-02-01

    Full Text Available Grid cells in the rat entorhinal cortex display strikingly regular firing responses to the animal's position in 2-D space and have been hypothesized to form the neural substrate for dead-reckoning. However, errors accumulate rapidly when velocity inputs are integrated in existing models of grid cell activity. To produce grid-cell-like responses, these models would require frequent resets triggered by external sensory cues. Such inadequacies, shared by various models, cast doubt on the dead-reckoning potential of the grid cell system. Here we focus on the question of accurate path integration, specifically in continuous attractor models of grid cell activity. We show, in contrast to previous models, that continuous attractor models can generate regular triangular grid responses, based on inputs that encode only the rat's velocity and heading direction. We consider the role of the network boundary in the integration performance of the network and show that both periodic and aperiodic networks are capable of accurate path integration, despite important differences in their attractor manifolds. We quantify the rate at which errors in the velocity integration accumulate as a function of network size and intrinsic noise within the network. With a plausible range of parameters and the inclusion of spike variability, our model networks can accurately integrate velocity inputs over a maximum of approximately 10-100 meters and approximately 1-10 minutes. These findings form a proof-of-concept that continuous attractor dynamics may underlie velocity integration in the dorsolateral medial entorhinal cortex. The simulations also generate pertinent upper bounds on the accuracy of integration that may be achieved by continuous attractor dynamics in the grid cell network. We suggest experiments to test the continuous attractor model and differentiate it from models in which single cells establish their responses independently of each other.

  18. Axon and dendrite geography predict the specificity of synaptic connections in a functioning spinal cord network

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    Borisyuk Roman

    2007-09-01

    Full Text Available Abstract Background How specific are the synaptic connections formed as neuronal networks develop and can simple rules account for the formation of functioning circuits? These questions are assessed in the spinal circuits controlling swimming in hatchling frog tadpoles. This is possible because detailed information is now available on the identity and synaptic connections of the main types of neuron. Results The probabilities of synapses between 7 types of identified spinal neuron were measured directly by making electrical recordings from 500 pairs of neurons. For the same neuron types, the dorso-ventral distributions of axons and dendrites were measured and then used to calculate the probabilities that axons would encounter particular dendrites and so potentially form synaptic connections. Surprisingly, synapses were found between all types of neuron but contact probabilities could be predicted simply by the anatomical overlap of their axons and dendrites. These results suggested that synapse formation may not require axons to recognise specific, correct dendrites. To test the plausibility of simpler hypotheses, we first made computational models that were able to generate longitudinal axon growth paths and reproduce the axon distribution patterns and synaptic contact probabilities found in the spinal cord. To test if probabilistic rules could produce functioning spinal networks, we then made realistic computational models of spinal cord neurons, giving them established cell-specific properties and connecting them into networks using the contact probabilities we had determined. A majority of these networks produced robust swimming activity. Conclusion Simple factors such as morphogen gradients controlling dorso-ventral soma, dendrite and axon positions may sufficiently constrain the synaptic connections made between different types of neuron as the spinal cord first develops and allow functional networks to form. Our analysis implies that

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

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    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.

  20. A cascade reaction network mimicking the basic functional steps of acquired 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

    2015-01-01

    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 which we call Adaptive Immune Response Simulator (AIRS). DNA and enzymes are used as simple artificial analogues of the components of the AIS to create a system which responds to specific molecular stimuli in vitro. We show that this network of reactions can function in a manner which is superficially similar to the most basic responses of the vertebrate acquired immune system, 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. PMID:26391084

  1. 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.

  2. 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.

  3. Protein networks as logic functions in development and cancer.

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    Janusz Dutkowski

    2011-09-01

    Full Text Available Many biological and clinical outcomes are based not on single proteins, but on modules of proteins embedded in protein networks. A fundamental question is how the proteins within each module contribute to the overall module activity. Here, we study the modules underlying three representative biological programs related to tissue development, breast cancer metastasis, or progression of brain cancer, respectively. For each case we apply a new method, called Network-Guided Forests, to identify predictive modules together with logic functions which tie the activity of each module to the activity of its component genes. The resulting modules implement a diverse repertoire of decision logic which cannot be captured using the simple approximations suggested in previous work such as gene summation or subtraction. We show that in cancer, certain combinations of oncogenes and tumor suppressors exert competing forces on the system, suggesting that medical genetics should move beyond cataloguing individual cancer genes to cataloguing their combinatorial logic.

  4. Efficient VLSI Architecture for Training Radial Basis Function Networks

    Science.gov (United States)

    Fan, Zhe-Cheng; Hwang, Wen-Jyi

    2013-01-01

    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. PMID:23519346

  5. Functional connectivity changes in the language network during stroke recovery.

    Science.gov (United States)

    Nair, Veena A; Young, Brittany M; La, Christian; Reiter, Peter; Nadkarni, Tanvi N; Song, Jie; Vergun, Svyatoslav; Addepally, Naga Saranya; Mylavarapu, Krishna; Swartz, Jennifer L; Jensen, Matthew B; Chacon, Marcus R; Sattin, Justin A; Prabhakaran, Vivek

    2015-02-01

    Several neuroimaging studies have examined language reorganization in stroke patients with aphasia. However, few studies have examined language reorganization in stroke patients without aphasia. Here, we investigated functional connectivity (FC) changes after stroke in the language network using resting-state fMRI and performance on a verbal fluency (VF) task in patients without clinically documented language deficits. Early-stage ischemic stroke patients (N = 26) (average 5 days from onset), 14 of whom were tested at a later stage (average 4.5 months from onset), 26 age-matched healthy control subjects (HCs), and 12 patients with cerebrovascular risk factors (patients at risk, PR) participated in this study. We examined FC of the language network with 23 seed regions based on a previous study. We evaluated patients' behavioral performance on a VF task and correlation between brain resting-state FC (rsFC) and behavior. Compared to HCs, early stroke patients showed significantly decreased rsFC in the language network but no difference with respect to PR. Early stroke patients showed significant differences in performance on the VF task compared to HCs but not PR. Late-stage patients compared to HCs and PR showed no differences in brain rsFC in the language network and significantly stronger connections compared to early-stage patients. Behavioral differences persisted in the late stage compared to HCs. Change in specific connection strengths correlated with changes in behavior from early to late stage. These results show decreased rsFC in the language network and verbal fluency deficits in early stroke patients without clinically documented language deficits.

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

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    Pearl A Campbell

    2007-06-01

    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.

  7. Transcriptional regulatory networks underlying the reprogramming of spermatogonial stem cells to multipotent stem cells.

    Science.gov (United States)

    Jeong, Hoe-Su; Bhin, Jinhyuk; Joon Kim, Hyung; Hwang, Daehee; Ryul Lee, Dong; Kim, Kye-Seong

    2017-04-14

    Spermatogonial stem cells (SSCs) are germline stem cells located along the basement membrane of seminiferous tubules in testes. Recently, SSCs were shown to be reprogrammed into multipotent SSCs (mSSCs). However, both the key factors and biological networks underlying this reprogramming remain elusive. Here, we present transcriptional regulatory networks (TRNs) that control cellular processes related to the SSC-to-mSSC reprogramming. Previously, we established intermediate SSCs (iSSCs) undergoing the transition to mSSCs and generated gene expression profiles of SSCs, iSSCs and mSSCs. By comparing these profiles, we identified 2643 genes that were up-regulated during the reprogramming process and 15 key transcription factors (TFs) that regulate these genes. Using the TF-target relationships, we developed TRNs describing how these TFs regulate three pluripotency-related processes (cell proliferation, stem cell maintenance and epigenetic regulation) during the reprogramming. The TRNs showed that 4 of the 15 TFs (Oct4/Pou5f1, Cux1, Zfp143 and E2f4) regulated cell proliferation during the early stages of reprogramming, whereas 11 TFs (Oct4/Pou5f1, Foxm1, Cux1, Zfp143, Trp53, E2f4, Esrrb, Nfyb, Nanog, Sox2 and Klf4) regulated the three pluripotency-related processes during the late stages of reprogramming. Our TRNs provide a model for the temporally coordinated transcriptional regulation of pluripotency-related processes during the SSC-to-mSSC reprogramming, which can be further tested in detailed functional studies.

  8. 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...... and cytoskeletal organization. Further, cell migration and polarization in are impaired in Invs MEFs. In two-dimensional cell migration, the centrosome is positioned between the nucleus and the leading edge with the primary cilium directed towards the direction of migration. PDGFRα is activated in the primary......, which leads to uncontrolled cell movements. Together, the results obtained from my PhD studies reflect the high level of complexity within signaling systems regulated by the primary cilium that control cellular processes during embryonic development and in tissue homeostasis. As such, this dissertation...

  9. Functions of proteoglycans at the cell surface

    DEFF Research Database (Denmark)

    Höök, M; Woods, A; Johansson, S

    1986-01-01

    sulphate helps to connect the intracellular cytoskeleton to the extracellular matrix in focal adhesions. This evidence includes: the co-localization of actin and heparan sulphate proteoglycan during the process of cell spreading, and in isolated focal adhesions; biochemical analyses of a hydrophobic......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...

  10. Chronic antiepileptic drug use and functional network efficiency: A functional magnetic resonance imaging study.

    Science.gov (United States)

    van Veenendaal, Tamar M; IJff, Dominique M; Aldenkamp, Albert P; Lazeron, Richard H C; Hofman, Paul A M; de Louw, Anton J A; Backes, Walter H; Jansen, Jacobus F A

    2017-06-28

    To increase our insight in the neuronal mechanisms underlying cognitive side-effects of antiepileptic drug (AED) treatment. The relation between functional magnetic resonance-acquired brain network measures, AED use, and cognitive function was investigated. Three groups of patients with epilepsy with a different risk profile for developing cognitive side effects were included: A "low risk" category (lamotrigine or levetiracetam, n = 16), an "intermediate risk" category (carbamazepine, oxcarbazepine, phenytoin, or valproate, n = 34) and a "high risk" category (topiramate, n = 5). Brain connectivity was assessed using resting state functional magnetic resonance imaging and graph theoretical network analysis. The Computerized Visual Searching Task was used to measure central information processing speed, a common cognitive side effect of AED treatment. Central information processing speed was lower in patients taking AEDs from the intermediate and high risk categories, compared with patients from the low risk category. The effect of risk category on global efficiency was significant ( P effect on the clustering coefficient (ANCOVA, P > 0.2). Also no significant associations between information processing speed and global efficiency or the clustering coefficient (linear regression analysis, P > 0.15) were observed. Only the four patients taking topiramate show aberrant network measures, suggesting that alterations in functional brain network organization may be only subtle and measureable in patients with more severe cognitive side effects.

  11. From genomes to in silico cells via metabolic networks

    DEFF Research Database (Denmark)

    Borodina, Irina; Nielsen, Jens

    2005-01-01

    Genome-scale metabolic models are the focal point of systems biology as they allow the collection of various data types in a form suitable for mathematical analysis. High-quality metabolic networks and metabolic networks with incorporated regulation have been successfully used for the analysis of...... approaches to obtain an in silico prediction of cellular function based on the interaction of all of the cellular components....

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

    Directory of Open Access Journals (Sweden)

    Veljkovic Emilija

    2011-07-01

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

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

    Science.gov (United States)

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

    2016-05-24

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

  14. NKT Cell Networks in the Regulation of Tumor Immunity

    Science.gov (United States)

    Robertson, Faith C.; Berzofsky, Jay A.; Terabe, Masaki

    2014-01-01

    CD1d-restricted natural killer T (NKT) cells lie at the interface between the innate and adaptive immune systems and are important mediators of immune responses and tumor immunosurveillance. These NKT cells uniquely recognize lipid antigens, and their rapid yet specific reactions influence both innate and adaptive immunity. In tumor immunity, two NKT subsets (type I and type II) have contrasting roles in which they not only cross-regulate one another, but also impact innate immune cell populations, including natural killer, dendritic, and myeloid lineage cells, as well as adaptive populations, especially CD8+ and CD4+ T cells. The extent to which NKT cells promote or suppress surrounding cells affects the host’s ability to prevent neoplasia and is consequently of great interest for therapeutic development. Data have shown the potential for therapeutic use of NKT cell agonists and synergy with immune response modifiers in both pre-clinical studies and preliminary clinical studies. However, there is room to improve treatment efficacy by further elucidating the biological mechanisms underlying NKT cell networks. Here, we discuss the progress made in understanding NKT cell networks, their consequent role in the regulation of tumor immunity, and the potential to exploit that knowledge in a clinical setting. PMID:25389427

  15. NKT cell networks in the regulation of tumor immunity.

    Science.gov (United States)

    Robertson, Faith C; Berzofsky, Jay A; Terabe, Masaki

    2014-01-01

    CD1d-restricted natural killer T (NKT) cells lie at the interface between the innate and adaptive immune systems and are important mediators of immune responses and tumor immunosurveillance. These NKT cells uniquely recognize lipid antigens, and their rapid yet specific reactions influence both innate and adaptive immunity. In tumor immunity, two NKT subsets (type I and type II) have contrasting roles in which they not only cross-regulate one another, but also impact innate immune cell populations, including natural killer, dendritic, and myeloid lineage cells, as well as adaptive populations, especially CD8(+) and CD4(+) T cells. The extent to which NKT cells promote or suppress surrounding cells affects the host's ability to prevent neoplasia and is consequently of great interest for therapeutic development. Data have shown the potential for therapeutic use of NKT cell agonists and synergy with immune response modifiers in both pre-clinical studies and preliminary clinical studies. However, there is room to improve treatment efficacy by further elucidating the biological mechanisms underlying NKT cell networks. Here, we discuss the progress made in understanding NKT cell networks, their consequent role in the regulation of tumor immunity, and the potential to exploit that knowledge in a clinical setting.

  16. NKT cell networks in the regulation of tumor immunity

    Directory of Open Access Journals (Sweden)

    Faith C Robertson

    2014-10-01

    Full Text Available CD1d-restricted natural killer T (NKT cells lie at the interface between the innate and adaptive immune systems and are important mediators of immune responses and tumor immunosurveillance. These NKT cells uniquely recognize lipid antigens, and their rapid yet specific reactions influence both innate and adaptive immunity. In tumor immunity, two NKT subsets (type I and type II have contrasting roles in which they not only cross-regulate one another, but also impact innate immune cell populations, including natural killer, dendritic and myeloid lineage cells, as well as adaptive populations, especially CD8+ and CD4+ T cells. The extent to which NKT cells promote or suppress surrounding cells affects the host’s ability to prevent neoplasia and is consequently of great interest for therapeutic development. Data have shown the potential for therapeutic use of NKT cell agonists and synergy with immune response modifiers in both pre-clinical studies and preliminary clinical studies. However, there is room to improve treatment efficacy by further elucidating the biological mechanisms underlying NKT cell networks. Here, we discuss the progress made in understanding NKT cell networks, their consequent role in the regulation of tumor immunity, and the potential to exploit that knowledge in a clinical setting.

  17. Model-based design of RNA hybridization networks implemented in living cells.

    Science.gov (United States)

    Rodrigo, Guillermo; Prakash, Satya; Shen, Shensi; Majer, Eszter; Daròs, José-Antonio; Jaramillo, Alfonso

    2017-09-19

    Synthetic gene circuits allow the behavior of living cells to be reprogrammed, and non-coding small RNAs (sRNAs) are increasingly being used as programmable regulators of gene expression. However, sRNAs (natural or synthetic) are generally used to regulate single target genes, while complex dynamic behaviors would require networks of sRNAs regulating each other. Here, we report a strategy for implementing such networks that exploits hybridization reactions carried out exclusively by multifaceted sRNAs that are both targets of and triggers for other sRNAs. These networks are ultimately coupled to the control of gene expression. We relied on a thermodynamic model of the different stable conformational states underlying this system at the nucleotide level. To test our model, we designed five different RNA hybridization networks with a linear architecture, and we implemented them in Escherichia coli. We validated the network architecture at the molecular level by native polyacrylamide gel electrophoresis, as well as the network function at the bacterial population and single-cell levels with a fluorescent reporter. Our results suggest that it is possible to engineer complex cellular programs based on RNA from first principles. Because these networks are mainly based on physical interactions, our designs could be expanded to other organisms as portable regulatory resources or to implement biological computations. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  18. Analysis on Influential Functions in the Weighted Software Network

    Directory of Open Access Journals (Sweden)

    Haitao He

    2018-01-01

    Full Text Available Identifying influential nodes is important for software in terms of understanding the design patterns and controlling the development and the maintenance process. However, there are no efficient methods to discover them so far. Based on the invoking dependency relationships between the nodes, this paper proposes a novel approach to define the node importance for mining the influential software nodes. First, according to the multiple execution information, we construct a weighted software network (WSN to denote the software execution dependency structure. Second, considering the invoking times and outdegree about software nodes, we improve the method PageRank and put forward the targeted algorithm FunctionRank to evaluate the node importance (NI in weighted software network. It has higher influence when the node has lager value of NI. Finally, comparing the NI of nodes, we can obtain the most influential nodes in the software network. In addition, the experimental results show that the proposed approach has good performance in identifying the influential nodes.

  19. 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, Krishna; Goswami, Chirayu; Corselli, Mirko; Traktuev, Dmitry O; Murray, Iain R; Péault, Bruno; March, Keith

    2017-05-01

    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: CD31 - /CD45 - /CD34 + /CD146 - cells (adventitial stromal/stem cells [ASCs]) and CD31 - /CD45 - /CD34 - /CD146 + 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 fluorescence-activated cell sorting. 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 polymerase chain reaction 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 coexpression network reconstruction algorithm. Despite the stochasticity at the single-cell level, covariation of gene expression analysis yielded multiple network connectivity parameters suggesting that these perivascular progenitor cell subclasses possess the following order of maturity: (a) ALDH br ASC (most primitive); (b) ALDH dim ASC; (c) ALDH br PC; (d) 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 coexpression

  20. 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

    2017-03-01

    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.

  1. 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...

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

    Science.gov (United States)

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

    2016-03-04

    Goal-directed human behaviors are driven by motives. Motives are, however, purely mental constructs that are not directly observable. Here, we show that the brain's functional network architecture captures information that predicts different motives behind the same altruistic act with high accuracy. In contrast, mere activity in these regions contains no information about motives. Empathy-based altruism is primarily characterized by a positive connectivity from the anterior cingulate cortex (ACC) to the anterior insula (AI), whereas reciprocity-based altruism additionally invokes strong positive connectivity from the AI to the ACC and even stronger positive connectivity from the AI to the ventral striatum. Moreover, predominantly selfish individuals show distinct functional architectures compared to altruists, and they only increase altruistic behavior in response to empathy inductions, but not reciprocity inductions. Copyright © 2016, American Association for the Advancement of Science.

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

    Science.gov (United States)

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

    2009-05-01

    We formulate a technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulated neural spike train data and real neural data obtained from the mouse hippocampus during exploration and slow-wave sleep. Using the simulated data, we show that our algorithm performs better than existing methods. In the experimental data, we observe state-dependent clustering patterns consistent with known neurophysiological processes involved in memory consolidation.

  4. Shyness and Trajectories of Functional Network Connectivity Over Early Adolescence.

    Science.gov (United States)

    Sylvester, Chad M; Whalen, Diana J; Belden, Andy C; Sanchez, Shana L; Luby, Joan L; Barch, Deanna M

    2017-12-08

    High shyness during early adolescence is associated with impaired peer relationships and risk for psychiatric disorders. Little is known, however, about the relation between shyness and trajectories of brain development over early adolescence. The current study longitudinally examined trajectories of resting-state functional connectivity (rs-fc) within four brain networks in 147 adolescents. Subjects underwent functional magnetic resonance imaging at three different time points, at average ages 10.5 (range = 7.8-13.0), 11.7 (range = 9.3-14.1), and 12.9 years (range = 10.1-15.2). Multilevel linear modeling indicated that high shyness was associated with a less steep negative slope of default mode network (DMN) rs-fc over early adolescence relative to low shyness. Less steep decreases in DMN rs-fc may relate to increased self-focus in adolescents with high shyness. © 2017 The Authors. Child Development © 2017 Society for Research in Child Development, Inc.

  5. The Function Biomedical Informatics Research Network Data Repository.

    Science.gov (United States)

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

    2016-01-01

    The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical data sets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 data set consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4 T scanners. The FBIRN Phase 2 and Phase 3 data sets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN's multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data. Copyright © 2015 Elsevier Inc. All rights reserved.

  6. 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.

  7. 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.

  8. 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 (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.

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

    DEFF Research Database (Denmark)

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

    2016-01-01

    ICIC configuration leads to modest gains, whereas the set of proposed fast dynamic eICIC algorithms result in capacity gains on the order of 35-120% depending on the local environment characteristics. These attractive gains together with the simplicity of the proposed solutions underline the practical relevance...... area. Rather than the classical semi-static and network-wise configuration, the importance of having highly dynamic and distributed mechanisms that are able to adapt to local environment conditions is revealed. We propose two promising cell association algorithms: one aiming at pure load balancing...... 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...

  10. Optical Network as a Service for Service Function Chaining across Datacenters

    DEFF Research Database (Denmark)

    Mehmeri, Victor; Wang, Xi; Zhang, Qiong

    2017-01-01

    We present the SPN OS, a Network-as-a-Service orchestration platform for NFV/SDN integrated service provisioning across multiple datacenters over packet/optical networks. Our prototype showcases template-driven service function chaining and high-level network programming-based optical networking....

  11. Exploring functions of long noncoding RNAs across multiple cancers through co-expression network.

    Science.gov (United States)

    Li, Suqing; Li, Bin; Zheng, Yuanting; Li, Menglong; Shi, Leming; Pu, Xuemei

    2017-04-07

    In contrast to protein-coding genes, long-noncoding RNAs (lncRNAs) are much less well understood, despite increasing evidence indicating a wide range of their biological functions, and possible roles in various cancers. Based on public RNA-seq datasets of four solid cancer types, we here utilize Weighted Correlation Network Analysis (WGCNA) to propose a strategy for exploring the functions of lncRNAs altered in more than two cancer types, which we call onco-lncRNAs. Results indicate that cancer-expressed lncRNAs show high tissue specificity and are weakly expressed, more so than protein-coding genes. Most of the 236 onco-lncRNAs we identified have not been reported to have associations with cancers before. Our analysis exploits co-expression network to reveal that onco-lncRNAs likely play key roles in the multistep development of human cancers, covering a wide range of functions in genome stability maintenance, signaling, cell adhesion and motility, morphogenesis, cell cycle, immune and inflammatory response. These observations contribute to a more comprehensive understanding of cancer-associated lncRNAs, while demonstrating a novel and efficient strategy for subsequent functional studies of lncRNAs.

  12. 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

  13. A synthetic mammalian network to compute population borders based on engineered reciprocal cell-cell communication.

    Science.gov (United States)

    Kolar, Katja; Wischhusen, Hanna M; Müller, Konrad; Karlsson, Maria; Weber, Wilfried; Zurbriggen, Matias D

    2015-12-30

    Multicellular organisms depend on the exchange of information between specialized cells. This communication is often difficult to decipher in its native context, but synthetic biology provides tools to engineer well-defined systems that allow the convenient study and manipulation of intercellular communication networks. Here, we present the first mammalian synthetic network for reciprocal cell-cell communication to compute the border between a sender/receiver and a processing cell population. The two populations communicate via L-tryptophan and interleukin-4 to highlight the population border by the production of a fluorescent protein. The sharpness of that visualized edge can be adjusted by modulating key parameters of the network. We anticipate that this network will on the one hand be a useful tool to gain deeper insights into the mechanisms of tissue formation in nature and will on the other hand contribute to our ability to engineer artificial tissues.

  14. Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases.

    Science.gov (United States)

    Bauer-Mehren, Anna; Bundschus, Markus; Rautschka, Michael; Mayer, Miguel A; Sanz, Ferran; Furlong, Laura I

    2011-01-01

    Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. The

  15. 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

  16. Realizations of highly heterogeneous collagen networks via stochastic reconstruction for micromechanical analysis of tumor cell invasion

    Science.gov (United States)

    Nan, Hanqing; Liang, Long; Chen, Guo; Liu, Liyu; Liu, Ruchuan; Jiao, Yang

    2018-03-01

    Three-dimensional (3D) collective cell migration in a collagen-based extracellular matrix (ECM) is among one of the most significant topics in developmental biology, cancer progression, tissue regeneration, and immune response. Recent studies have suggested that collagen-fiber mediated force transmission in cellularized ECM plays an important role in stress homeostasis and regulation of collective cellular behaviors. Motivated by the recent in vitro observation that oriented collagen can significantly enhance the penetration of migrating breast cancer cells into dense Matrigel which mimics the intravasation process in vivo [Han et al. Proc. Natl. Acad. Sci. USA 113, 11208 (2016), 10.1073/pnas.1610347113], we devise a procedure for generating realizations of highly heterogeneous 3D collagen networks with prescribed microstructural statistics via stochastic optimization. Specifically, a collagen network is represented via the graph (node-bond) model and the microstructural statistics considered include the cross-link (node) density, valence distribution, fiber (bond) length distribution, as well as fiber orientation distribution. An optimization problem is formulated in which the objective function is defined as the squared difference between a set of target microstructural statistics and the corresponding statistics for the simulated network. Simulated annealing is employed to solve the optimization problem by evolving an initial network via random perturbations to generate realizations of homogeneous networks with randomly oriented fibers, homogeneous networks with aligned fibers, heterogeneous networks with a continuous variation of fiber orientation along a prescribed direction, as well as a binary system containing a collagen region with aligned fibers and a dense Matrigel region with randomly oriented fibers. The generation and propagation of active forces in the simulated networks due to polarized contraction of an embedded ellipsoidal cell and a small group

  17. Network Representation of Multi-Cell Accelerating Structures

    CERN Document Server

    Raguin, J Y

    2001-01-01

    The analysis of the electrodynamic properties of a complete multi-cell accelerating structure using electromagnetic numerical simulation codes is presently at the edge of existing computer capabilities. To overcome this limitation, a network representation is proposed which derives the overall scattering transfer matrix of such multi-cell structures from single-cell data calculated using the commercial finite-element code HFSS. For a constant-impedance structure, computation of the eigenvalues of this matrix allows dispersion diagrams to be obtained. In the more general case, this formalism leads to a representation of the coupled-chain of cavities as a set of cascaded non identical multipoles.

  18. A network analysis of the human T-cell activation gene network identifies JAGGED1 as a therapeutic target for autoimmune diseases.

    Directory of Open Access Journals (Sweden)

    Ricardo Palacios

    2007-11-01

    Full Text Available Understanding complex diseases will benefit the recognition of the properties of the gene networks that control biological functions. Here, we set out to model the gene network that controls T-cell activation in humans, which is critical for the development of autoimmune diseases such as Multiple Sclerosis (MS. The network was established on the basis of the quantitative expression from 104 individuals of 20 genes of the immune system, as well as on biological information from the Ingenuity database and Bayesian inference. Of the 31 links (gene interactions identified in the network, 18 were identified in the Ingenuity database and 13 were new and we validated 7 of 8 interactions experimentally. In the MS patients network, we found an increase in the weight of gene interactions related to Th1 function and a decrease in those related to Treg and Th2 function. Indeed, we found that IFN-ss therapy induces changes in gene interactions related to T cell proliferation and adhesion, although these gene interactions were not restored to levels similar to controls. Finally, we identify JAG1 as a new therapeutic target whose differential behaviour in the MS network was not modified by immunomodulatory therapy. In vitro treatment with a Jagged1 agonist peptide modulated the T-cell activation network in PBMCs from patients with MS. Moreover, treatment of mice with experimental autoimmune encephalomyelitis with the Jagged1 agonist ameliorated the disease course, and modulated Th2, Th1 and Treg function. This study illustrates how network analysis can predict therapeutic targets for immune intervention and identified the immunomodulatory properties of Jagged1 making it a new therapeutic target for MS and other autoimmune diseases.

  19. 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. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. Stable functional networks exhibit consistent timing in the human brain.

    Science.gov (United States)

    Chapeton, Julio I; Inati, Sara K; Zaghloul, Kareem A

    2017-03-01

    Despite many advances in the study of large-scale human functional networks, the question of timing, stability, and direction of communication between cortical regions has not been fully addressed. At the cellular level, neuronal communication occurs through axons and dendrites, and the time required for such communication is well defined and preserved. At larger spatial scales, however, the relationship between timing, direction, and communication between brain regions is less clear. Here, we use a measure of effective connectivity to identify connections between brain regions that exhibit communication with consistent timing. We hypothesized that if two brain regions are communicating, then knowledge of the activity in one region should allow an external observer to better predict activity in the other region, and that such communication involves a consistent time delay. We examine this question using intracranial electroencephalography captured from nine human participants with medically refractory epilepsy. We use a coupling measure based on time-lagged mutual information to identify effective connections between brain regions that exhibit a statistically significant increase in average mutual information at a consistent time delay. These identified connections result in sparse, directed functional networks that are stable over minutes, hours, and days. Notably, the time delays associated with these connections are also highly preserved over multiple time scales. We characterize the anatomic locations of these connections, and find that the propagation of activity exhibits a preferred posterior to anterior temporal lobe direction, consistent across participants. Moreover, networks constructed from connections that reliably exhibit consistent timing between anatomic regions demonstrate features of a small-world architecture, with many reliable connections between anatomically neighbouring regions and few long range connections. Together, our results demonstrate

  1. Lateralization effects on functional connectivity of the auditory network in patients with unilateral pulsatile tinnitus as detected by functional MRI.

    Science.gov (United States)

    Lv, Han; Zhao, Pengfei; Liu, Zhaohui; Liu, Xuehuan; Ding, Heyu; Liu, Liheng; Wang, Guopeng; Xie, Jing; Zeng, Rong; Chen, Yuchen; Yang, Zhenghan; Gong, Shusheng; Wang, Zhenchang

    2018-02-02

    Unilateral pulsatile tinnitus (PT) was proved to be a kind of disease with brain functional abnormalities within and beyond the auditory network (AN). However, changes in patterns of the lateralization effects of PT are yet to be established. Relationship between the AN and other brain networks in PT patients is also a scientific question need to be answered. In this study, we recruited 23 left-sided, 23 right-sided PT (LSPT, RSPT) patients and 23 normal controls (NC). We combined applied independent component analysis and seed-based functional connectivity (FC) analysis to investigate alteration feature of the FC of the AN by using resting-state functional magnetic resonance imaging (rs-fMRI). Compared with NC, LSPT patients demonstrated disconnected FC within the AN on both sides. Disrupted network integrity between AN and several brain functional networks, including executive control network, self-perceptual network and the limbic network, was also demonstrated in LSPT patient group bilaterally. In contrast, compared with NC, RSPT demonstrated decreased FC within the AN on the left side, but significant increased FC within the AN on the right side (symptomatic side). Enhanced FC between AN and executive control network, self-perceptual network and limbic network was also found mainly on the right side in patients with RSPT. Positive FC between the auditory network and the limbic network may be a reason to explain why RSPT patients are willing to be in the clinic. Briefly, LSPT exhibit disrupted network integrity in brain functional networks. But RSPT is featured by enhanced FC within AN and between networks, especially on the right (symptomatic) side. Corroboration of featured FC helps to reveal the pathophysiological changing process of the brain in patients with PT, providing imaging-based biomarker to distinguish PT from other kind of tinnitus. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. 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.

  3. 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.

  4. 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.

  5. MicroRNA functional network in pancreatic cancer: From biology to ...

    Indian Academy of Sciences (India)

    2011-06-07

    Jun 7, 2011 ... Cellular pathways; genetic network; microRNA; pancreatic cancer; tumorigenic transformation; 3' untranslated region ... components of the complex functional pathway networks controlling important cellular processes, such as proliferation, development, differentiation, stress response' and apoptosis.

  6. Structurofunctional resting-state networks correlate with motor function in chronic stroke

    Directory of Open Access Journals (Sweden)

    Benjamin T. Kalinosky

    2017-01-01

    Conclusion: The results demonstrate that changes after a stroke in both intrinsic and network-based structurofunctional correlations at rest are correlated with motor function, underscoring the importance of residual structural connectivity in cortical networks.

  7. Architecture and Applications of Functional Three-Dimensional Graphene Networks

    DEFF Research Database (Denmark)

    Dey, Ramendra Sundar; Chi, Qijin

    2015-01-01

    building blocksfor the bottom-up architecture of various graphene based nanomaterials. Th eassembly of functionalized GNS into three-dimensional (3D) porous graphenenetworks represents a novel approach. Resulting 3D porous graphene materialsposses unique physicochemical properties such as large surface......As the fi rst atomic-thick two-dimensional crystalline material, graphene has continuouslycreated a wonder land in materials science within the past decade. Anumber of methods have been developed for preparation and functionalizationof single-layered graphene nanosheets (GNS), which are essential...... areas, goodconductivity and mechanical strength, high thermal stability and desirable fl exibility,which altogether makes this new type of porous materials be highly attractivefor a wide range of applications. In this chapter, we will cover some crucialaspects of porous graphene networked materials...

  8. 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.

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

    OpenAIRE

    Li Xinwu; Guan Pengcheng

    2013-01-01

    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 a...

  10. Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

    Science.gov (United States)

    Zhao, Yu; Dong, Qinglin; Chen, Hanbo; Iraji, Armin; Li, Yujie; Makkie, Milad; Kou, Zhifeng; Liu, Tianming

    2017-12-01

    State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with

  11. Distinctive Correspondence Between Separable Visual Attention Functions and Intrinsic Brain Networks

    Directory of Open Access Journals (Sweden)

    Adriana L. Ruiz-Rizzo

    2018-03-01

    Full Text Available Separable visual attention functions are assumed to rely on distinct but interacting neural mechanisms. Bundesen's “theory of visual attention” (TVA allows the mathematical estimation of independent parameters that characterize individuals' visual attentional capacity (i.e., visual processing speed and visual short-term memory storage capacity and selectivity functions (i.e., top-down control and spatial laterality. However, it is unclear whether these parameters distinctively map onto different brain networks obtained from intrinsic functional connectivity, which organizes slowly fluctuating ongoing brain activity. In our study, 31 demographically homogeneous healthy young participants performed whole- and partial-report tasks and underwent resting-state functional magnetic resonance imaging (rs-fMRI. Report accuracy was modeled using TVA to estimate, individually, the four TVA parameters. Networks encompassing cortical areas relevant for visual attention were derived from independent component analysis of rs-fMRI data: visual, executive control, right and left frontoparietal, and ventral and dorsal attention networks. Two TVA parameters were mapped on particular functional networks. First, participants with higher (vs. lower visual processing speed showed lower functional connectivity within the ventral attention network. Second, participants with more (vs. less efficient top-down control showed higher functional connectivity within the dorsal attention network and lower functional connectivity within the visual network. Additionally, higher performance was associated with higher functional connectivity between networks: specifically, between the ventral attention and right frontoparietal networks for visual processing speed, and between the visual and executive control networks for top-down control. The higher inter-network functional connectivity was related to lower intra-network connectivity. These results demonstrate that separable

  12. Fault diagnosis and performance evaluation for high current LIA based on radial basis function neural network

    International Nuclear Information System (INIS)

    Yang Xinglin; Wang Huacen; Chen Nan; Dai Wenhua; Li Jin

    2006-01-01

    High current linear induction accelerator (LIA) is a complicated experimental physics device. It is difficult to evaluate and predict its performance. this paper presents a method which combines wavelet packet transform and radial basis function (RBF) neural network to build fault diagnosis and performance evaluation in order to improve reliability of high current LIA. The signal characteristics vectors which are extracted based on energy parameters of wavelet packet transform can well present the temporal and steady features of pulsed power signal, and reduce data dimensions effectively. The fault diagnosis system for accelerating cell and the trend classification system for the beam current based on RBF networks can perform fault diagnosis and evaluation, and provide predictive information for precise maintenance of high current LIA. (authors)

  13. 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.

  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. Optimizing Virtual Network Functions Placement in Virtual Data Center Infrastructure Using Machine Learning

    Science.gov (United States)

    Bolodurina, I. P.; Parfenov, D. I.

    2018-01-01

    We have elaborated a neural network model of virtual network flow identification based on the statistical properties of flows circulating in the network of the data center and characteristics that describe the content of packets transmitted through network objects. This enabled us to establish the optimal set of attributes to identify virtual network functions. We have established an algorithm for optimizing the placement of virtual data functions using the data obtained in our research. Our approach uses a hybrid method of visualization using virtual machines and containers, which enables to reduce the infrastructure load and the response time in the network of the virtual data center. The algorithmic solution is based on neural networks, which enables to scale it at any number of the network function copies.

  16. Lumbar Myeloid Cell Trafficking into Locomotor Networks after Thoracic Spinal Cord Injury

    Science.gov (United States)

    Hansen, Christopher N.; Norden, Diana M.; Faw, Timothy D.; Deibert, Rochelle; S.Wohleb, Eric; Sheridan, John F.; P.Godbout, Jonathan; Basso, D. Michele

    2016-01-01

    Spinal cord injury (SCI) promotes inflammation along the neuroaxis that jeopardizes plasticity, intrinsic repair and recovery. While inflammation at the injury site is well-established, less is known within remote spinal networks. The presence of bone marrow-derived immune (myeloid) cells in these areas may further impede functional recovery. Previously, high levels of the gelatinase, matrix metalloproteinase-9 (MMP-9) occurred within the lumbar enlargement after thoracic SCI and impeded activity-dependent recovery. Since SCI-induced MMP-9 potentially increases vascular permeability, myeloid cell infiltration may drive inflammatory toxicity in locomotor networks. Therefore, we examined neurovascular reactivity and myeloid cell infiltration in the lumbar cord after thoracic SCI. We show evidence of region-specific recruitment of myeloid cells into the lumbar but not cervical region. Myeloid infiltration occurred with concomitant increases in chemoattractants (CCL2) and cell adhesion molecules (ICAM-1) around lumbar vasculature 24 hours and 7 days post injury. Bone marrow GFP chimeric mice established robust infiltration of bone marrow-derived myeloid cells into the lumbar gray matter 24 hours after SCI. This cell infiltration occurred when the blood-spinal cord barrier was intact, suggesting active recruitment across the endothelium. Myeloid cells persisted as ramified macrophages at 7 days post injury in parallel with increased inhibitory GAD67 labeling. Importantly, macrophage infiltration required MMP-9. PMID:27191729

  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. Discrete dynamic modeling of T cell survival signaling networks

    Science.gov (United States)

    Zhang, Ranran

    2009-03-01

    Biochemistry-based frameworks are often not applicable for the modeling of heterogeneous regulatory systems that are sparsely documented in terms of quantitative information. As an alternative, qualitative models assuming a small set of discrete states are gaining acceptance. This talk will present a discrete dynamic model of the signaling network responsible for the survival and long-term competence of cytotoxic T cells in the blood cancer T-LGL leukemia. We integrated the signaling pathways involved in normal T cell activation and the known deregulations of survival signaling in leukemic T-LGL, and formulated the regulation of each network element as a Boolean (logic) rule. Our model suggests that the persistence of two signals is sufficient to reproduce all known deregulations in leukemic T-LGL. It also indicates the nodes whose inactivity is necessary and sufficient for the reversal of the T-LGL state. We have experimentally validated several model predictions, including: (i) Inhibiting PDGF signaling induces apoptosis in leukemic T-LGL. (ii) Sphingosine kinase 1 and NFκB are essential for the long-term survival of T cells in T-LGL leukemia. (iii) T box expressed in T cells (T-bet) is constitutively activated in the T-LGL state. The model has identified potential therapeutic targets for T-LGL leukemia and can be used for generating long-term competent CTL necessary for tumor and cancer vaccine development. The success of this model, and of other discrete dynamic models, suggests that the organization of signaling networks has an determining role in their dynamics. Reference: R. Zhang, M. V. Shah, J. Yang, S. B. Nyland, X. Liu, J. K. Yun, R. Albert, T. P. Loughran, Jr., Network Model of Survival Signaling in LGL Leukemia, PNAS 105, 16308-16313 (2008).

  19. EEG classification of emotions using emotion-specific brain functional network.

    Science.gov (United States)

    Gonuguntla, V; Shafiq, G; Wang, Y; Veluvolu, K C

    2015-08-01

    The brain functional network perspective forms the basis to relate mechanisms of brain functions. This work analyzes the network mechanisms related to human emotion based on synchronization measure - phase-locking value in EEG to formulate the emotion specific brain functional network. Based on network dissimilarities between emotion and rest tasks, most reactive channel pairs and the reactive band corresponding to emotions are identified. With the identified most reactive pairs, the subject-specific functional network is formed. The identified subject-specific and emotion-specific dynamic network pattern show significant synchrony variation in line with the experiment protocol. The same network pattern are then employed for classification of emotions. With the study conducted on the 4 subjects, an average classification accuracy of 62 % was obtained with the proposed technique.

  20. 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.

  1. HEp-2 Cell Image Classification With Deep Convolutional Neural Networks.

    Science.gov (United States)

    Gao, Zhimin; Wang, Lei; Zhou, Luping; Zhang, Jianjia

    2017-03-01

    Efficient Human Epithelial-2 cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper proposes an automatic framework for this classification task, by utilizing the deep convolutional neural networks (CNNs) which have recently attracted intensive attention in visual recognition. In addition to describing the proposed classification framework, this paper elaborates several interesting observations and findings obtained by our investigation. They include the important factors that impact network design and training, the role of rotation-based data augmentation for cell images, the effectiveness of cell image masks for classification, and the adaptability of the CNN-based classification system across different datasets. Extensive experimental study is conducted to verify the above findings and compares the proposed framework with the well-established image classification models in the literature. The results on benchmark datasets demonstrate that 1) the proposed framework can effectively outperform existing models by properly applying data augmentation, 2) our CNN-based framework has excellent adaptability across different datasets, which is highly desirable for cell image classification under varying laboratory settings. Our system is ranked high in the cell image classification competition hosted by ICPR 2014.

  2. 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.

  3. Statistical Mechanics of Complex Networks: From the Internet to Cell Biology

    Science.gov (United States)

    Barabási, Albert-László

    2006-03-01

    Networks with complex topology describe systems as diverse as the cell, the World Wide Web or the society. In the past few years we have learned that their evolution is driven by self-organizing processes that are governed by simple but generic scaling laws, leading to the emergence of a vibrant interdisciplinary field that uses the tools of statistical physics to explain the origin and the dynamics of real networks. One of the most surprising finding is that despite their apparent differences, cells and complex man-made networks, such as the Internet or the World Wide Web, and many communication networks share the same large-scale topology, each having a scale-free structure. I will show that the scale-free topology of these complex webs have important consequences on their robustness against failures and attacks, with implications on drug design, the Internet's ability to survive attacks and failures, and our ability to understand the functional role of genes. For further information and papers, see http://www.nd.edu/˜networks

  4. Exploring brain functional plasticity in world class gymnasts: a network analysis.

    Science.gov (United States)

    Wang, Junjing; Lu, Min; Fan, Yuanyuan; Wen, Xue; Zhang, Ruibin; Wang, Bin; Ma, Qing; Song, Zheng; He, Yong; Wang, Jun; Huang, Ruiwang

    2016-09-01

    Long-term motor skill learning can induce plastic structural and functional reorganization of the brain. Our previous studies detected brain structural plasticity related to long-term intensive gymnastic training in world class gymnasts (WCGs). The goal of this study was to investigate brain functional plasticity in WCGs by using network measures of brain functional networks. Specifically, we acquired resting-state fMRI data from 13 WCGs and 14 controls, constructed their brain functional networks, and compared the differences in their network parameters. At the whole brain level, we detected significantly decreased overall functional connectivity (FC) and decreased local and global efficiency in the WCGs compared to the controls. At the modular level, we found intra- and inter-modular reorganization in three modules, the cerebellum, the cingulo-opercular and fronto-parietal networks, in the WCGs. On the nodal level, we revealed significantly decreased nodal strength and efficiency in several non-rich club regions of these three modules in the WCGs. These results suggested that functional plasticity can be detected in the brain functional networks of WCGs, especially in the cerebellum, fronto-parietal network, and cingulo-opercular network. In addition, we found that the FC between the fronto-parietal network and the sensorimotor network was significantly negatively correlated with the number of years of training in the WCGs. These findings may help us to understand the outstanding gymnastic performance of the gymnasts and to reveal the neural mechanisms that distinguish WCGs from controls.

  5. Functional Specialization of Skin Dendritic Cell Subsets in Regulating T Cell Responses

    Science.gov (United States)

    Clausen, Björn E.; Stoitzner, Patrizia

    2015-01-01

    Dendritic cells (DC) are a heterogeneous family of professional antigen-presenting cells classically recognized as most potent inducers of adaptive immune responses. In this respect, Langerhans cells have long been considered to be prototypic immunogenic DC in the skin. More recently this view has considerably changed. The generation of in vivo cell ablation and lineage tracing models revealed the complexity of the skin DC network and, in particular, established the existence of a number of phenotypically distinct Langerin+ and negative DC populations in the dermis. Moreover, by now we appreciate that DC also exert important regulatory functions and are required for the maintenance of tolerance toward harmless foreign and self-antigens. This review summarizes our current understanding of the skin-resident DC system in the mouse and discusses emerging concepts on the functional specialization of the different skin DC subsets in regulating T cell responses. Special consideration is given to antigen cross-presentation as well as immune reactions toward contact sensitizers, cutaneous pathogens, and tumors. These studies form the basis for the manipulation of the human counterparts of the murine DC subsets to promote immunity or tolerance for the treatment of human disease. PMID:26557117

  6. Brain hubs in lesion models: Predicting functional network topology with lesion patterns in patients.

    Science.gov (United States)

    Yuan, Binke; Fang, Yuxing; Han, Zaizhu; Song, Luping; He, Yong; Bi, Yanchao

    2017-12-20

    Various important topological properties of healthy brain connectome have recently been identified. However, the manner in which brain lesion changes the functional network topology is unknown. We examined how critical specific brain areas are in the maintenance of network topology using multivariate support vector regression analysis on brain structural and resting-state functional imaging data in 96 patients with brain damages. Patients' cortical lesion distribution patterns could significantly predict the functional network topology and a set of regions with significant weights in the prediction models were identified as "lesion hubs". Intriguingly, we found two different types of lesion hubs, whose lesions associated with changes of network topology towards relatively different directions, being either more integrated (global) or more segregated (local), and correspond to hubs identified in healthy functional network in complex manners. Our results pose further important questions about the potential dynamics of the functional brain network after brain damage.

  7. 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

  8. Cytotoxic Vibrio T3SS1 Rewires Host Gene Expression to Subvert Cell Death Signaling and Activate Cell Survival Networks

    Science.gov (United States)

    De Nisco, Nicole J.; Kanchwala, Mohammed; Li, Peng; Fernandez, Jessie; Xing, Chao; Orth, Kim

    2017-01-01

    Bacterial effectors are potent manipulators of host signaling pathways. The marine bacterium Vibrio parahaemolyticus (V. para), delivers effectors into host cells through two type three secretion systems (T3SS). The ubiquitous T3SS1 is vital for V. para survival in the environment, whereas T3SS2 causes acute gastroenteritis in human hosts. Although the natural host is undefined, T3SS1 effectors attack highly conserved cellular processes and pathways to orchestrate non-apoptotic cell death. Much is known about how T3SS1 effectors function in isolation, but we wanted to understand how their concerted action globally affects host cell signaling. To assess the host response to T3SS1, we compared gene expression changes over time in primary fibroblasts infected with V. para that have a functional T3SS1 (T3SS1+) to those in cells infected with V. para lacking T3SS1 (T3SS1−). Overall, the host transcriptional response to both T3SS1+ and T3SS1− V. para was rapid, robust, and temporally dynamic. T3SS1 re-wired host gene expression by specifically altering the expression of 398 genes. Although T3SS1 effectors target host cells at the posttranslational level to cause cytotoxicity, network analysis indicated that V. para T3SS1 also precipitates a host transcriptional response that initially activates cell survival and represses cell death networks. The increased expression of several key pro-survival transcripts mediated by T3SS1 was dependent on a host signaling pathway that is silenced later in infection by the posttranslational action of T3SS1. Taken together, our analysis reveals a complex interplay between roles of T3SS1 as both a transcriptional and posttranslational manipulator of host cell signaling. PMID:28512145

  9. Effect of field spread on resting-state MEG functional network analysis: A computational modeling study

    NARCIS (Netherlands)

    Silva Pereira, S.; Hindriks, R.; Mühlberg, S.; Maris, E.G.G.; Ede, F.L. van; Griffa, A.; Hagmann, P.; Deco, G.

    2017-01-01

    A popular way to analyze resting-state EEG and MEG data is to treat them as a functional network in which sensors are identified with nodes and the interaction between channel time-series with the network connections. Although conceptually appealing, the network-theoretical approach to sensor-level

  10. 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, and calcul......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......, 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......, respectively, and 19, 18, and 33% higher serum LH, E(2), and E(2)/T levels, respectively, than the fertile men. Twelve percent of the infertile men had a serum T level that fell below the 2.5 percentile of the fertile levels, and 15% of the infertile men had a LH level that was above the 97.5 percentile...

  11. TMC function in hair cell transduction

    Science.gov (United States)

    Holt, Jeffrey R.; Pan, Bifeng; Koussa, Mounir A.; Asai, Yukako

    2014-01-01

    Transmembrane channel-like (TMC) proteins 1 and 2 are necessary for hair cell mechanotransduction but their precise function is controversial. A growing body of evidence supports a direct role for TMC1 and TMC2 as components of the transduction complex. However, a number of important questions remain and alternate hypotheses have been proposed. Here we present an historical overview of the identification and cloning of Tmc genes, a discussion of mutations in TMC1 that cause deafness in mice and humans and a brief review of other members of the Tmc gene superfamily. We also examine expression of Tmc mRNAs and localization of the protein products. The review focuses on potential functions of TMC proteins and the evidence from Beethoven mice that suggests a direct role for TMC1 in hair cell mechanotransduction. Data that support alternate interpretations are also considered. The article concludes with a discussion of outstanding questions and future directions for TMC research. This article is part of a Special Issue entitled “Annual Reviews 2014”. PMID:24423408

  12. C-element: a new clustering algorithm to find high quality functional modules in PPI networks.

    Science.gov (United States)

    Ghasemi, Mahdieh; Rahgozar, Maseud; Bidkhori, Gholamreza; Masoudi-Nejad, Ali

    2013-01-01

    Graph clustering algorithms are widely used in the analysis of biological networks. Extracting functional modules in protein-protein interaction (PPI) networks is one such use. Most clustering algorithms whose focuses are on finding functional modules try either to find a clique like sub networks or to grow clusters starting from vertices with high degrees as seeds. These algorithms do not make any difference between a biological network and any other networks. In the current research, we present a new procedure to find functional modules in PPI networks. Our main idea is to model a biological concept and to use this concept for finding good functional modules in PPI networks. In order to evaluate the quality of the obtained clusters, we compared the results of our algorithm with those of some other widely used clustering algorithms on three high throughput PPI networks from Sacchromyces Cerevisiae, Homo sapiens and Caenorhabditis elegans as well as on some tissue specific networks. Gene Ontology (GO) analyses were used to compare the results of different algorithms. Each algorithm's result was then compared with GO-term derived functional modules. We also analyzed the effect of using tissue specific networks on the quality of the obtained clusters. The experimental results indicate that the new algorithm outperforms most of the others, and this improvement is more significant when tissue specific networks are used.

  13. Histamine regulates murine primary dendritic cell functions.

    Science.gov (United States)

    Schenk, Heiko; Neumann, Detlef; Kloth, Christina

    2016-10-01

    The modulation of antigen uptake and activation of dendritic cells (DCs) by histamine may function as a regulator of inflammation. Therefore, we sought to determine the impact of histamine on antigen uptake by and activation of murine DCs. DCs from spleen and lung were either identified by flow cytometry or were immunomagnetically enriched. Cells were stimulated with histamine, and the regulation of MHC-II and co-stimulatory molecule expression (CD80, CD86, and ICOS-L) and antigen uptake were quantified by flow cytometry. Individual contributions of the histamine receptor subtypes were determined by using the antagonists mepyramine (histamine H1-receptor: H1R), famotidine (H2R), and JNJ 7777120 (H4R). Histamine accelerated the uptake of soluble antigen via the H1R, H2R, and H4R in splenic DCs. Co-stimulatory molecule expression was enhanced already by enrichment procedures, thus, the analyses were performed in unseparated cell populations. Histamine enhanced the expression of CD86 and ICOS-L while expression of CD80 was unaffected. Antagonism at H1R, H2R, and H4R and at H1R and H4R reduced the histamine-induced enhanced expression of CD86 and ICOS-L, respectively. Histamine contributes to the regulation of the immunological synapse by stimulation of antigen uptake and activation of DCs via H1R, H2R, and H4R.

  14. The synaptic properties of cells define the hallmarks of interval timing in a recurrent neural network.

    Science.gov (United States)

    Pérez, Oswaldo; Merchant, Hugo

    2018-04-03

    Extensive research has described two key features of interval timing. The bias property is associated with accuracy and implies that time is overestimated for short intervals and underestimated for long intervals. The scalar property is linked to precision and states that the variability of interval estimates increases as a function of interval duration. The neural mechanisms behind these properties are not well understood. Here we implemented a recurrent neural network that mimics a cortical ensemble and includes cells that show paired-pulse facilitation and slow inhibitory synaptic currents. The network produces interval selective responses and reproduces both bias and scalar properties when a Bayesian decoder reads its activity. Notably, the interval-selectivity, timing accuracy, and precision of the network showed complex changes as a function of the decay time constants of the modeled synaptic properties and the level of background activity of the cells. These findings suggest that physiological values of the time constants for paired-pulse facilitation and GABAb, as well as the internal state of the network, determine the bias and scalar properties of interval timing. Significant Statement Timing is a fundamental element of complex behavior, including music and language. Temporal processing in a wide variety of contexts shows two primary features: time estimates exhibit a shift towards the mean (the bias property) and are more variable for longer intervals (the scalar property). We implemented a recurrent neural network that includes long-lasting synaptic currents, which can not only produce interval selective responses but also follow the bias and scalar properties. Interestingly, only physiological values of the time constants for paired-pulse facilitation and GABAb, as well as intermediate background activity within the network can reproduce the two key features of interval timing. Copyright © 2018 the authors.

  15. 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

  16. CellWhere: graphical display of interaction networks organized on subcellular localizations.

    Science.gov (United States)

    Zhu, Lu; Malatras, Apostolos; Thorley, Matthew; Aghoghogbe, Idonnya; Mer, Arvind; Duguez, Stéphanie; Butler-Browne, Gillian; Voit, Thomas; Duddy, William

    2015-07-01

    Given a query list of genes or proteins, CellWhere produces an interactive graphical display that mimics the structure of a cell, showing the local interaction network organized into subcellular locations. This user-friendly tool helps in the formulation of mechanistic hypotheses by enabling the experimental biologist to explore simultaneously two elements of functional context: (i) protein subcellular localization and (ii) protein-protein interactions or gene functional associations. Subcellular localization terms are obtained from public sources (the Gene Ontology and UniProt-together containing several thousand such terms) then mapped onto a smaller number of CellWhere localizations. These localizations include all major cell compartments, but the user may modify the mapping as desired. Protein-protein interaction listings, and their associated evidence strength scores, are obtained from the Mentha interactome server, or power-users may upload a pre-made network produced using some other interactomics tool. The Cytoscape.js JavaScript library is used in producing the graphical display. Importantly, for a protein that has been observed at multiple subcellular locations, users may prioritize the visual display of locations that are of special relevance to their research domain. CellWhere is at http://cellwhere-myology.rhcloud.com. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  17. MDD diagnosis based on partial-brain functional connection network

    Science.gov (United States)

    Yan, Gaoliang; Hu, Hailong; Zhao, Xiang; Zhang, Lin; Qu, Zehui; Li, Yantao

    2018-04-01

    Artificial intelligence (AI) is a hotspot in computer science research nowadays. To apply AI technology in all industries has been the developing direction for researchers. Major depressive disorder (MDD) is a common disease of serious mental disorders. The World Health Organization (WHO) reports that MDD is projected to become the second most common cause of death and disability by 2020. At present, the way of MDD diagnosis is single. Applying AI technology to MDD diagnosis and pathophysiological research will speed up the MDD research and improve the efficiency of MDD diagnosis. In this study, we select the higher degree of brain network functional connectivity by statistical methods. And our experiments show that the average accuracy of Logistic Regression (LR) classifier using feature filtering reaches 88.48%. Compared with other classification methods, both the efficiency and accuracy of this method are improved, which will greatly improve the process of MDD diagnose. In these experiments, we also define the brain regions associated with MDD, which plays a vital role in MDD pathophysiological research.

  18. 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.

  19. Metal-Insulator-Semiconductor Nanowire Network Solar Cells.

    Science.gov (United States)

    Oener, Sebastian Z; van de Groep, Jorik; Macco, Bart; Bronsveld, Paula C P; Kessels, W M M; Polman, Albert; Garnett, Erik C

    2016-06-08

    Metal-insulator-semiconductor (MIS) junctions provide the charge separating properties of Schottky junctions while circumventing the direct and detrimental contact of the metal with the semiconductor. A passivating and tunnel dielectric is used as a separation layer to reduce carrier recombination and remove Fermi level pinning. When applied to solar cells, these junctions result in two main advantages over traditional p-n-junction solar cells: a highly simplified fabrication process and excellent passivation properties and hence high open-circuit voltages. However, one major drawback of metal-insulator-semiconductor solar cells is that a continuous metal layer is needed to form a junction at the surface of the silicon, which decreases the optical transmittance and hence short-circuit current density. The decrease of transmittance with increasing metal coverage, however, can be overcome by nanoscale structures. Nanowire networks exhibit precisely the properties that are required for MIS solar cells: closely spaced and conductive metal wires to induce an inversion layer for homogeneous charge carrier extraction and simultaneously a high optical transparency. We experimentally demonstrate the nanowire MIS concept by using it to make silicon solar cells with a measured energy conversion efficiency of 7% (∼11% after correction), an effective open-circuit voltage (Voc) of 560 mV and estimated short-circuit current density (Jsc) of 33 mA/cm(2). Furthermore, we show that the metal nanowire network can serve additionally as an etch mask to pattern inverted nanopyramids, decreasing the reflectivity substantially from 36% to ∼4%. Our extensive analysis points out a path toward nanowire based MIS solar cells that exhibit both high Voc and Jsc values.

  20. Modafinil modulates resting-state functional network connectivity and cognitive control in alcohol-dependent patients

    NARCIS (Netherlands)

    Schmaal, Lianne; Goudriaan, Anna E.; Joos, Leen; Krüse, Anne Maren; Dom, Geert; van den Brink, Wim; Veltman, Dick J.

    2013-01-01

    Chronic alcohol abuse is associated with deficits in cognitive control functions. Cognitive control is likely to be mediated through the interaction between intrinsic large-scale brain networks involved in externally oriented executive functioning and internally focused thought processing. Improving

  1. Modafinil Modulates Resting-State Functional Network Connectivity and Cognitive Control in Alcohol-Dependent Patients

    NARCIS (Netherlands)

    Schmaal, L.; Goudriaan, A.E.; Joos, L.; Kruse, A.M.; Dom, G.; van den Brink, W.; Veltman, D.J.

    2013-01-01

    Background: Chronic alcohol abuse is associated with deficits in cognitive control functions. Cognitive control is likely to be mediated through the interaction between intrinsic large-scale brain networks involved in externally oriented executive functioning and internally focused thought

  2. Integrity of the osteocyte bone cell network in osteoporotic fracture: Implications for mechanical load adaptation

    International Nuclear Information System (INIS)

    Kuliwaba, J S; Truong, L; Codrington, J D; Fazzalari, N L

    2010-01-01

    The human skeleton has the ability to modify its material composition and structure to accommodate loads through adaptive modelling and remodelling. The osteocyte cell network is now considered to be central to the regulation of skeletal homeostasis; however, very little is known of the integrity of the osteocyte cell network in osteoporotic fragility fracture. This study was designed to characterise osteocyte morphology, the extent of osteocyte cell apoptosis and expression of sclerostin protein (a negative regulator of bone formation) in trabecular bone from the intertrochanteric region of the proximal femur, for postmenopausal women with fragility hip fracture compared to age-matched women who had not sustained fragility fracture. Osteocyte morphology (osteocyte, empty lacunar, and total lacunar densities) and the degree of osteocyte apoptosis (percent caspase-3 positive osteocyte lacunae) were similar between the fracture patients and non-fracture women. The fragility hip fracture patients had a lower proportion of sclerostin-positive osteocyte lacunae in comparison to sclerostin-negative osteocyte lacunae, in contrast to similar percent sclerostin-positive/sclerostin-negative lacunae for non-fracture women. The unexpected finding of decreased sclerostin expression in trabecular bone osteocytes from fracture cases may be indicative of elevated bone turnover and under-mineralisation, characteristic of postmenopausal osteoporosis. Further, altered osteocytic expression of sclerostin may be involved in the mechano-responsiveness of bone. Optimal function of the osteocyte cell network is likely to be a critical determinant of bone strength, acting via mechanical load adaptation, and thus contributing to osteoporotic fracture risk.

  3. Integrity of the osteocyte bone cell network in osteoporotic fracture: Implications for mechanical load adaptation

    Science.gov (United States)

    Kuliwaba, J. S.; Truong, L.; Codrington, J. D.; Fazzalari, N. L.

    2010-06-01

    The human skeleton has the ability to modify its material composition and structure to accommodate loads through adaptive modelling and remodelling. The osteocyte cell network is now considered to be central to the regulation of skeletal homeostasis; however, very little is known of the integrity of the osteocyte cell network in osteoporotic fragility fracture. This study was designed to characterise osteocyte morphology, the extent of osteocyte cell apoptosis and expression of sclerostin protein (a negative regulator of bone formation) in trabecular bone from the intertrochanteric region of the proximal femur, for postmenopausal women with fragility hip fracture compared to age-matched women who had not sustained fragility fracture. Osteocyte morphology (osteocyte, empty lacunar, and total lacunar densities) and the degree of osteocyte apoptosis (percent caspase-3 positive osteocyte lacunae) were similar between the fracture patients and non-fracture women. The fragility hip fracture patients had a lower proportion of sclerostin-positive osteocyte lacunae in comparison to sclerostin-negative osteocyte lacunae, in contrast to similar percent sclerostin-positive/sclerostin-negative lacunae for non-fracture women. The unexpected finding of decreased sclerostin expression in trabecular bone osteocytes from fracture cases may be indicative of elevated bone turnover and under-mineralisation, characteristic of postmenopausal osteoporosis. Further, altered osteocytic expression of sclerostin may be involved in the mechano-responsiveness of bone. Optimal function of the osteocyte cell network is likely to be a critical determinant of bone strength, acting via mechanical load adaptation, and thus contributing to osteoporotic fracture risk.

  4. 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

  5. Disrupting Cocaine Trafficking Networks: Interdicting a Combined Social-Functional Network Model

    Science.gov (United States)

    2016-03-01

    LITERATURE REVIEW ...................................................................................29  A.  QUANTITATIVE NETWORK ANALYSIS METHODS...direct sale. Using hypothetical data based on open-source material, we define a social network of three main categories of archetypical DTOs (with...follows this structure: Chapter II is a review of existing literature in the field; Chapter III presents the network specifics, the attacker- defender

  6. A Comprehensive Nuclear Receptor Network for Breast Cancer Cells

    Directory of Open Access Journals (Sweden)

    Ralf Kittler

    2013-02-01

    Full Text Available In breast cancer, nuclear receptors (NRs play a prominent role in governing gene expression, have prognostic utility, and are therapeutic targets. We built a regulatory map for 24 NRs, six chromatin state markers, and 14 breast-cancer-associated transcription factors (TFs that are expressed in the breast cancer cell line MCF-7. The resulting network reveals a highly interconnected regulatory matrix where extensive crosstalk occurs among NRs and other breast -cancer-associated TFs. We show that large numbers of factors are coordinately bound to highly occupied target regions throughout the genome, and these regions are associated with active chromatin state and hormone-responsive gene expression. This network also provides a framework for stratifying and predicting patient outcomes, and we use it to show that the peroxisome proliferator-activated receptor delta binds to a set of genes also regulated by the retinoic acid receptors and whose expression is associated with poor prognosis in breast cancer.

  7. Quantitative measurement of alterations in DNA damage repair (DDR) pathways using single cell network profiling (SCNP).

    Science.gov (United States)

    Rosen, David B; Leung, Ling Y; Louie, Brent; Cordeiro, James A; Conroy, Andrew; Shapira, Iuliana; Fields, Scott Z; Cesano, Alessandra; Hawtin, Rachael E

    2014-06-25

    Homologous recombination repair (HRR) pathway deficiencies have significant implications for cancer predisposition and treatment strategies. Improved quantitative methods for functionally characterizing these deficiencies are required to accurately identify patients at risk of developing cancer and to identify mechanisms of drug resistance or sensitivity. Flow cytometry-based single cell network profiling (SCNP) was used to measure drug-induced activation of DNA damage response (DDR) proteins in cell lines with defined HRR pathway mutations (including ATM-/-, ATM+/-, BRCA1+/-, BRCA2-/-) and in primary acute myeloid leukemia (AML) samples. Both non-homologous end joining (NHEJ) and HRR pathways were examined by measuring changes in intracellular readouts (including p-H2AX, p-ATM, p-DNA-PKcs, p-53BP1, p-RPA2/32, p-BRCA1, p-p53, and p21) in response to exposure to mechanistically distinct genotoxins. The cell cycle S/G2/M phase CyclinA2 marker was used to normalize for proliferation rates. Etoposide induced proliferation-independent DNA damage and activation of multiple DDR proteins in primary AML cells and ATM +/+but not ATM -/- cell lines. Treatment with the PARPi AZD2281 +/- temozolomide induced DNA damage in CyclinA2+ cells in both primary AML cells and cell lines and distngiushed cell lines deficient (BRCA2-/-) or impaired (BRCA1+/-) in HRR activity from BRCA1+/+ cell lines based on p-H2AX induction. Application of this assay to primary AML samples identified heterogeneous patterns of repair activity including muted or proficient activation of NHEJ and HRR pathways and predominant activation of NHEJ in a subset of samples. SCNP identified functional DDR readouts in both NHEJ and HRR pathways, which can be applied to identify cells with BRCA1+/- haploinsuffiency and characterize differential DDR pathway functionality in primary clinical samples.

  8. From in silico astrocyte cell models to neuron-astrocyte network models: A review.

    Science.gov (United States)

    Oschmann, Franziska; Berry, Hugues; Obermayer, Klaus; Lenk, Kerstin

    2018-01-01

    The idea that astrocytes may be active partners in synaptic information processing has recently emerged from abundant experimental reports. Because of their spatial proximity to neurons and their bidirectional communication with them, astrocytes are now considered as an important third element of the synapse. Astrocytes integrate and process synaptic information and by doing so generate cytosolic calcium signals that are believed to reflect neuronal transmitter release. Moreover, they regulate neuronal information transmission by releasing gliotransmitters into the synaptic cleft affecting both pre- and postsynaptic receptors. Concurrent with the first experimental reports of the astrocytic impact on neural network dynamics, computational models describing astrocytic functions have been developed. In this review, we give an overview over the published computational models of astrocytic functions, from single-cell dynamics to the tripartite synapse level and network models of astrocytes and neurons. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. Alternative approach to automated management of load flow in engineering networks considering functional reliability

    Directory of Open Access Journals (Sweden)

    Ирина Александровна Гавриленко

    2016-02-01

    Full Text Available The approach to automated management of load flow in engineering networks considering functional reliability was proposed in the article. The improvement of the concept of operational and strategic management of load flow in engineering networks was considered. The verbal statement of the problem for thesis research is defined, namely, the problem of development of information technology for exact calculation of the functional reliability of the network, or the risk of short delivery of purpose-oriented product for consumers

  10. 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.

  11. Representation of linguistic form and function in recurrent neural networks

    NARCIS (Netherlands)

    Kadar, Akos; Chrupala, Grzegorz; Alishahi, Afra

    2017-01-01

    We present novel methods for analyzing the activation patterns of recurrent neural networks from a linguistic point of view and explore the types of linguistic structure they learn. As a case study, we use a standard standalone language model, and a multi-task gated recurrent network architecture

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

  13. Complex network perspective on structure and function of ...

    Indian Academy of Sciences (India)

    of community social networks, which are dense node–node links within modules, but have sparser links between ... 3.2 Bow tie structure. The whole metabolic network of S. aureus is then decomposed into four parts based on the 'bow tie' structure (figure 2, table 2). It should be noted that most nodes in S, P and IS parts are ...

  14. Energy Efficient Pico Cell Range Expansion and Density Joint Optimization for Heterogeneous Networks with eICIC.

    Science.gov (United States)

    Sun, Yanzan; Xia, Wenqing; Zhang, Shunqing; Wu, Yating; Wang, Tao; Fang, Yong

    2018-03-02

    Heterogeneous networks, constituted by conventional macro cells and overlaying pico cells, have been deemed a promising paradigm to support the deluge of data traffic with higher spectral efficiency and Energy Efficiency (EE). In order to deploy pico cells in reality, the density of Pico Base Stations (PBSs) and the pico Cell Range Expansion (CRE) are two important factors for the network spectral efficiency as well as EE improvement. However, associated with the range and density evolution, the inter-tier interference within the heterogeneous architecture will be challenging, and the time domain Enhanced Inter-cell Interference Coordination (eICIC) technique becomes necessary. Aiming to improve the network EE, the above factors are jointly considered in this paper. More specifically, we first derive the closed-form expression of the network EE as a function of the density of PBSs and pico CRE bias based on stochastic geometry theory, followed by a linear search algorithm to optimize the pico CRE bias and PBS density, respectively. Moreover, in order to realize the pico CRE bias and PBS density joint optimization, a heuristic algorithm is proposed to achieve the network EE maximization. Numerical simulations show that our proposed pico CRE bias and PBS density joint optimization algorithm can improve the network EE significantly with low computational complexity.

  15. Energy Efficient Pico Cell Range Expansion and Density Joint Optimization for Heterogeneous Networks with eICIC

    Directory of Open Access Journals (Sweden)

    Yanzan Sun

    2018-03-01

    Full Text Available Heterogeneous networks, constituted by conventional macro cells and overlaying pico cells, have been deemed a promising paradigm to support the deluge of data traffic with higher spectral efficiency and Energy Efficiency (EE. In order to deploy pico cells in reality, the density of Pico Base Stations (PBSs and the pico Cell Range Expansion (CRE are two important factors for the network spectral efficiency as well as EE improvement. However, associated with the range and density evolution, the inter-tier interference within the heterogeneous architecture will be challenging, and the time domain Enhanced Inter-cell Interference Coordination (eICIC technique becomes necessary. Aiming to improve the network EE, the above factors are jointly considered in this paper. More specifically, we first derive the closed-form expression of the network EE as a function of the density of PBSs and pico CRE bias based on stochastic geometry theory, followed by a linear search algorithm to optimize the pico CRE bias and PBS density, respectively. Moreover, in order to realize the pico CRE bias and PBS density joint optimization, a heuristic algorithm is proposed to achieve the network EE maximization. Numerical simulations show that our proposed pico CRE bias and PBS density joint optimization algorithm can improve the network EE significantly with low computational complexity.

  16. 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.

  17. 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

  18. A flexible ontology for inference of emergent whole cell function from relationships between subcellular processes.

    Science.gov (United States)

    Hansen, Jens; Meretzky, David; Woldesenbet, Simeneh; Stolovitzky, Gustavo; Iyengar, Ravi

    2017-12-18

    Whole cell responses arise from coordinated interactions between diverse human gene products functioning within various pathways underlying sub-cellular processes (SCP). Lower level SCPs interact to form higher level SCPs, often in a context specific manner to give rise to whole cell function. We sought to determine if capturing such relationships enables us to describe the emergence of whole cell functions from interacting SCPs. We developed the Molecular Biology of the Cell Ontology based on standard cell biology and biochemistry textbooks and review articles. Currently, our ontology contains 5,384 genes, 753 SCPs and 19,180 expertly curated gene-SCP associations. Our algorithm to populate the SCPs with genes enables extension of the ontology on demand and the adaption of the ontology to the continuously growing cell biological knowledge. Since whole cell responses most often arise from the coordinated activity of multiple SCPs, we developed a dynamic enrichment algorithm that flexibly predicts SCP-SCP relationships beyond the current taxonomy. This algorithm enables us to identify interactions between SCPs as a basis for higher order function in a context dependent manner, allowing us to provide a detailed description of how SCPs together can give rise to whole cell functions. We conclude that this ontology can, from omics data sets, enable the development of detailed SCP networks for predictive modeling of emergent whole cell functions.

  19. Gene-Disease Network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental Diseases

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    Bauer-Mehren, Anna; Bundschus, Markus; Rautschka, Michael; Mayer, Miguel A.; Sanz, Ferran; Furlong, Laura I.

    2011-01-01

    Background Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. Principal Findings We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. Conclusions For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors

  20. Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases.

    Directory of Open Access Journals (Sweden)

    Anna Bauer-Mehren

    Full Text Available BACKGROUND: Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. PRINCIPAL FINDINGS: We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. CONCLUSIONS: For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and

  1. A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks.

    Science.gov (United States)

    Samarasinghe, S; Ling, H

    In this paper, we show how to extend our previously proposed novel continuous time Recurrent Neural Networks (RNN) approach that retains the advantage of continuous dynamics offered by Ordinary Differential Equations (ODE) while enabling parameter estimation through adaptation, to larger signalling networks using a modular approach. Specifically, the signalling network is decomposed into several sub-models based on important temporal events in the network. Each sub-model is represented by the proposed RNN and trained using data generated from the corresponding ODE model. Trained sub-models are assembled into a whole system RNN which is then subjected to systems dynamics and sensitivity analyses. The concept is illustrated by application to G1/S transition in cell cycle using Iwamoto et al. (2008) ODE model. We decomposed the G1/S network into 3 sub-models: (i) E2F transcription factor release; (ii) E2F and CycE positive feedback loop for elevating cyclin levels; and (iii) E2F and CycA negative feedback to degrade E2F. The trained sub-models accurately represented system dynamics and parameters were in good agreement with the ODE model. The whole system RNN however revealed couple of parameters contributing to compounding errors due to feedback and required refinement to sub-model 2. These related to the reversible reaction between CycE/CDK2 and p27, its inhibitor. The revised whole system RNN model very accurately matched dynamics of the ODE system. Local sensitivity analysis of the whole system model further revealed the most dominant influence of the above two parameters in perturbing G1/S transition, giving support to a recent hypothesis that the release of inhibitor p27 from Cyc/CDK complex triggers cell cycle stage transition. To make the model useful in a practical setting, we modified each RNN sub-model with a time relay switch to facilitate larger interval input data (≈20min) (original model used data for 30s or less) and retrained them that produced

  2. Uncovering Factors Related to Pancreatic Beta-Cell Function

    OpenAIRE

    Curran, Aoife M.; Ryan, Miriam F.; Drummond, Elaine; Gibney, Eileen R.; Gibney, Michael J.; Roche, Helen M.; Brennan, Lorraine

    2016-01-01

    Aim: The incidence of type 2 diabetes has increased rapidly on a global scale. Beta-cell dysfunction contributes to the overall pathogenesis of type 2 diabetes. However, factors contributing to beta-cell function are not clear. The aims of this study were (i) to identify factors related to pancreatic beta-cell function and (ii) to perform mechanistic studies in vitro. Methods: Three specific measures of beta-cell function were assessed for 110 participants who completed an oral glucose tolera...

  3. Expression and function of nicotinic acetylcholine receptors in stem cells

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    Herman S. Cheung

    2016-07-01

    Full Text Available Nicotinic acetylcholine receptors are prototypical ligand gated ion channels typically found in muscular and neuronal tissues. Functional nicotinic acetylcholine receptors, however, have also recently been identified on other cell types, including stem cells. Activation of these receptors by the binding of agonists like choline, acetylcholine, or nicotine has been implicated in many cellular changes. In regards to stem cell function, nicotinic acetylcholine receptor activation leads to changes in stem cell proliferation, migration and differentiation potential. In this review we summarize the expression and function of known nicotinic acetylcholine receptors in different classes of stem cells including: pluripotent stem cells, mesenchymal stem cells, periodontal ligament derived stem cells, and neural progenitor cells and discuss the potential downstream effects of receptor activation on stem cell function.

  4. Functional organization of intrinsic connectivity networks in Chinese-chess experts.

    Science.gov (United States)

    Duan, Xujun; Long, Zhiliang; Chen, Huafu; Liang, Dongmei; Qiu, Lihua; Huang, Xiaoqi; Liu, Timon Cheng-Yi; Gong, Qiyong

    2014-04-16

    The functional architecture of the human brain has been extensively described in terms of functional connectivity networks, detected from the low-frequency coherent neuronal fluctuations during a resting state condition. Accumulating evidence suggests that the overall organization of functional connectivity networks is associated with individual differences in cognitive performance and prior experience. Such an association raises the question of how cognitive expertise exerts an influence on the topological properties of large-scale functional networks. To address this question, we examined the overall organization of brain functional networks in 20 grandmaster and master level Chinese-chess players (GM/M) and twenty novice players, by means of resting-state functional connectivity and graph theoretical analyses. We found that, relative to novices, functional connectivity was increased in GM/Ms between basal ganglia, thalamus, hippocampus, and several parietal and temporal areas, suggesting the influence of cognitive expertise on intrinsic connectivity networks associated with learning and memory. Furthermore, we observed economical small-world topology in the whole-brain functional connectivity networks in both groups, but GM/Ms exhibited significantly increased values of normalized clustering coefficient which resulted in increased small-world topology. These findings suggest an association between the functional organization of brain networks and individual differences in cognitive expertise, which might provide further evidence of the mechanisms underlying expert behavior. Copyright © 2014 Elsevier B.V. All rights reserved.

  5. Properties of healthcare teaming networks as a function of network construction algorithms.

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    Martin S Zand

    Full Text Available Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other, and to map how patients traverse the network of providers. Most healthcare service network models have been constructed from patient claims data, using billing claims to link a patient with a specific provider in time. The data sets can be quite large (106-108 individual claims per year, 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, which as we demonstrate, can be dramatically different. To address this issue, we compared the properties of healthcare networks constructed using different algorithms from 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 find that each algorithm produced networks with substantially different topological properties, as reflected by numbers of edges, network density, assortativity, clustering coefficients and other structural measures. Provider networks adhered to a power law, while organization networks were best fit by a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and network density, and greatly altered measures of vertex prominence such as the betweenness centrality. Data analysis identified patterns in the distance patients travel between network providers, and a striking set of teaming relationships between providers in the Northeast

  6. Network dynamics determine the autocrine and paracrine signaling functions of TNF

    Science.gov (United States)

    Caldwell, Andrew B.; Cheng, Zhang; Vargas, Jesse D.; Birnbaum, Harry A.

    2014-01-01

    A hallmark of the inflammatory response to pathogen exposure is the production of tumor necrosis factor (TNF) that coordinates innate and adaptive immune responses by functioning in an autocrine or paracrine manner. Numerous molecular mechanisms contributing to TNF production have been identified, but how they function together in macrophages remains unclear. Here, we pursued an iterative systems biology approach to develop a quantitative understanding of the regulatory modules that control TNF mRNA synthesis and processing, mRNA half-life and translation, and protein processing and secretion. By linking the resulting model of TNF production to models of the TLR-, the TNFR-, and the NFκB signaling modules, we were able to study TNF’s functions during the inflammatory response to diverse TLR agonists. Contrary to expectation, we predicted and then experimentally confirmed that in response to lipopolysaccaride, TNF does not have an autocrine function in amplifying the NFκB response, although it plays a potent paracrine role in neighboring cells. However, in response to CpG DNA, autocrine TNF extends the duration of NFκB activity and shapes CpG-induced gene expression programs. Our systems biology approach revealed that network dynamics of MyD88 and TRIF signaling and of cytokine production and response govern the stimulus-specific autocrine and paracrine functions of TNF. PMID:25274725

  7. Exploration of the pathways and interaction network involved in bladder cancer cell line with knockdown of Opa interacting protein 5.

    Science.gov (United States)

    He, Xuefeng; Ding, Xiang; Wen, Duangai; Hou, Jianquan; Ping, Jigen; He, Jun

    2017-09-01

    In our previous study, we displayed that knockdown of Opa interacting protein 5 (OIP5) inhibited cell growth, disturbed cell cycle and increased cell apoptosis in bladder cancer (BC) cell line. Our present study aimed to explore the underlying pathways and interaction network involved in the roles of OIP5 in BC. Microarray analysis was conducted to obtain mRNA expression profiling of OIP5 knockdown (shOIP5) and control (shCtrl) BC cell lines. Bioinformatics analyses were performed including differentially expressed mRNAs (DEGs) identification, protein-protein interaction network construction, biological functions of prediction and ingenuity pathways analysis (IPA). Western Blotting (WB) was subjected to validate the protein expression levels of candidate DEGs in shOIP5 BC cell line. Respective 255 up- and 184 down-regulated DEGs were identified in shOIP5 group compared with shCtrl group. In the PPI network, CAND1 and MYC had the highest connectivity with DEGs. 439 DEGs were significantly enriched in inflammatory response, regulation of cell proliferation, Toll-like receptor signaling pathway, cytokine-cytokine receptor interaction and bladder cancer. In the disease and function enrichment, DEGs were obviously involved in cellular movement, cellular growth and proliferation, cancer, inflammatory response, cell death and survival. In the OIP5 regulatory network, CDH2, IRS1, IRAK3, ID1, TNF, IL6, ITGA6, MYC and SOD2 interacted with OIP5. The WB validation results were compatible with our bioinformatics analyses. OIP5 interaction network might function as an oncogene in BC progression based on aberrant inflammatory responses. Our study might provide valuable information for investigation of tumorigenesis mechanism in BC. Copyright © 2017 Elsevier GmbH. All rights reserved.

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

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    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

  9. Absolute exponential stability of recurrent neural networks with Lipschitz-continuous activation functions and time delays.

    Science.gov (United States)

    Cao, Jinde; Wang, Jun

    2004-04-01

    This paper investigates the absolute exponential stability of a general class of delayed neural networks, which require the activation functions to be partially Lipschitz continuous and monotone nondecreasing only, but not necessarily differentiable or bounded. Three new sufficient conditions are derived to ascertain whether or not the equilibrium points of the delayed neural networks with additively diagonally stable interconnection matrices are absolutely exponentially stable by using delay Halanay-type inequality and Lyapunov function. The stability criteria are also suitable for delayed optimization neural networks and delayed cellular neural networks whose activation functions are often nondifferentiable or unbounded. The results herein answer a question: if a neural network without any delay is absolutely exponentially stable, then under what additional conditions, the neural networks with delay is also absolutely exponentially stable.

  10. Functional Organization of the Action Observation Network in Autism: A Graph Theory Approach.

    Science.gov (United States)

    Alaerts, Kaat; Geerlings, Franca; Herremans, Lynn; Swinnen, Stephan P; Verhoeven, Judith; Sunaert, Stefan; Wenderoth, Nicole

    2015-01-01

    The ability to recognize, understand and interpret other's actions and emotions has been linked to the mirror system or action-observation-network (AON). Although variations in these abilities are prevalent in the neuro-typical population, persons diagnosed with autism spectrum disorders (ASD) have deficits in the social domain and exhibit alterations in this neural network. Here, we examined functional network properties of the AON using graph theory measures and region-to-region functional connectivity analyses of resting-state fMRI-data from adolescents and young adults with ASD and typical controls (TC). Overall, our graph theory analyses provided convergent evidence that the network integrity of the AON is altered in ASD, and that reductions in network efficiency relate to reductions in overall network density (i.e., decreased overall connection strength). Compared to TC, individuals with ASD showed significant reductions in network efficiency and increased shortest path lengths and centrality. Importantly, when adjusting for overall differences in network density between ASD and TC groups, participants with ASD continued to display reductions in network integrity, suggesting that also network-level organizational properties of the AON are altered in ASD. While differences in empirical connectivity contributed to reductions in network integrity, graph theoretical analyses provided indications that also changes in the high-level network organization reduced integrity of the AON.

  11. Functional Organization of the Action Observation Network in Autism: A Graph Theory Approach.

    Directory of Open Access Journals (Sweden)

    Kaat Alaerts

    Full Text Available The ability to recognize, understand and interpret other's actions and emotions has been linked to the mirror system or action-observation-network (AON. Although variations in these abilities are prevalent in the neuro-typical population, persons diagnosed with autism spectrum disorders (ASD have deficits in the social domain and exhibit alterations in this neural network.Here, we examined functional network properties of the AON using graph theory measures and region-to-region functional connectivity analyses of resting-state fMRI-data from adolescents and young adults with ASD and typical controls (TC.Overall, our graph theory analyses provided convergent evidence that the network integrity of the AON is altered in ASD, and that reductions in network efficiency relate to reductions in overall network density (i.e., decreased overall connection strength. Compared to TC, individuals with ASD showed significant reductions in network efficiency and increased shortest path lengths and centrality. Importantly, when adjusting for overall differences in network density between ASD and TC groups, participants with ASD continued to display reductions in network integrity, suggesting that also network-level organizational properties of the AON are altered in ASD.While differences in empirical connectivity contributed to reductions in network integrity, graph theoretical analyses provided indications that also changes in the high-level network organization reduced integrity of the AON.

  12. 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. Copyright © 2016 Elsevier B.V. All rights reserved.

  13. Invertebrate diversity classification using self-organizing map neural network: with some special topological functions

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    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.

  14. Verifying cell loss requirements in high-speed communication networks

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    Kerry W. Fendick

    1998-01-01

    Full Text Available In high-speed communication networks it is common to have requirements of very small cell loss probabilities due to buffer overflow. Losses are measured to verify that the cell loss requirements are being met, but it is not clear how to interpret such measurements. We propose methods for determining whether or not cell loss requirements are being met. A key idea is to look at the stream of losses as successive clusters of losses. Often clusters of losses, rather than individual losses, should be regarded as the important “loss events”. Thus we propose modeling the cell loss process by a batch Poisson stochastic process. Successive clusters of losses are assumed to arrive according to a Poisson process. Within each cluster, cell losses do not occur at a single time, but the distance between losses within a cluster should be negligible compared to the distance between clusters. Thus, for the purpose of estimating the cell loss probability, we ignore the spaces between successive cell losses in a cluster of losses. Asymptotic theory suggests that the counting process of losses initiating clusters often should be approximately a Poisson process even though the cell arrival process is not nearly Poisson. The batch Poisson model is relatively easy to test statistically and fit; e.g., the batch-size distribution and the batch arrival rate can readily be estimated from cell loss data. Since batch (cluster sizes may be highly variable, it may be useful to focus on the number of batches instead of the number of cells in a measurement interval. We also propose a method for approximately determining the parameters of a special batch Poisson cell loss with geometric batch-size distribution from a queueing model of the buffer content. For this step, we use a reflected Brownian motion (RBM approximation of a G/D/1/C queueing model. We also use the RBM model to estimate the input burstiness given the cell loss rate. In addition, we use the RBM model to

  15. Affective state-dependent changes in the brain functional network in major depressive disorder.

    Science.gov (United States)

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

    2014-09-01

    In major depressive disorder (MDD), as a network-level disease, the pathophysiology would be displayed to a wide extent over the brain. Moreover, the network-wide changes could be dependent on the context of affective processing. In this study, we sought affective state-dependent changes of the brain functional network by applying a graph-theoretical approach to functional magnetic resonance imaging data acquired in 13 patients with MDD and 12 healthy controls who were exposed to video clips inducing the negative, neutral or positive affective state. For each affective condition, a group-wise brain functional network was constructed based on partial correlation of mean activity across subjects between brain areas. Network parameters, global and local efficiencies, were measured from the brain functional network. Compared with controls', patients' brain functional network shifted to the regular network in the topological architecture, showing decreased global efficiency and increased local efficiency, during negative and neutral affective processing. Further, the shift to the regular network in patients was most evident during negative affective processing. MDD is proposed to provoke widespread changes across the whole brain in an affective state-dependent manner, specifically in the negative affective state. © The Author (2013). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  16. A spiking network model of cerebellar Purkinje cells and molecular layer interneurons exhibiting irregular firing

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    William eLennon

    2014-12-01

    Full Text Available While the anatomy of the cerebellar microcircuit is well studied, how it implements cerebellar function is not understood. A number of models have been proposed to describe this mechanism but few emphasize the role of the vast network Purkinje cells (PKJs form with the molecular layer interneurons (MLIs – the stellate and basket cells. We propose a model of the MLI-PKJ network composed of simple spiking neurons incorporating the major anatomical and physiological features. In computer simulations, the model reproduces the irregular firing patterns observed in PKJs and MLIs in vitro and a shift toward faster, more regular firing patterns when inhibitory synaptic currents are blocked. In the model, the time between PKJ spikes is shown to be proportional to the amount of feedforward inhibition from an MLI on average. The two key elements of the model are: (1 spontaneously active PKJs and MLIs due to an endogenous depolarizing current, and (2 adherence to known anatomical connectivity along a parasagittal strip of cerebellar cortex. We propose this model to extend previous spiking network models of the cerebellum and for further computational investigation into the role of irregular firing and MLIs in cerebellar learning and function.

  17. Complexity functions for networks: Dynamical hubs and complexity clusters

    Science.gov (United States)

    Afraimovich, Valentin; Dmitrichev, Aleksei; Shchapin, Dmitry; Nekorkin, Vladimir

    2018-02-01

    A method for studying the behavior of the elements of dynamical networks is introduced. We measure the amount of instability stored at each element according to the value of the mean complexity related to this element. Elements with close values of the mean complexity can be unified into complexity clusters; elements with the smallest values of complexities form dynamical hubs. The effectiveness of the method is manifested by its successive application to networks of coupled Lorenz systems.

  18. Organisms modeling: The question of radial basis function networks

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    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.

  19. 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. PMID:26161960

  20. 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.

  1. 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.

  2. Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks

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    Anke Meyer-Bäse

    2017-10-01

    Full Text Available Neuroimaging in combination with graph theory has been successful in analyzing the functional connectome. However almost all analysis are performed based on static graph theory. The derived quantitative graph measures can only describe a snap shot of the disease over time. Neurodegenerative disease evolution is poorly understood and treatment strategies are consequently only of limited efficiency. Fusing modern dynamic graph network theory techniques and modeling strategies at different time scales with pinning observability of complex brain networks will lay the foundation for a transformational paradigm in neurodegnerative diseases research regarding disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. We model and analyze brain networks as two-time scale sparse dynamic graph networks with hubs (clusters representing the fast sub-system and the interconnections between hubs the slow sub-system. Alterations in brain function as seen in dementia can be dynamically modeled by determining the clusters in which disturbance inputs have entered and the impact they have on the large-scale dementia dynamic system. Observing a small fraction of specific nodes in dementia networks such that the others can be recovered is accomplished by the novel concept of pinning observability. In addition, how to control this complex network seems to be crucial in understanding the progressive abnormal neural circuits in many neurodegenerative diseases. Detecting the controlling regions in the networks, which serve as key nodes to control the aberrant dynamics of the networks to a desired state and thus influence the progressive abnormal behavior, will have a huge impact in understanding and developing therapeutic solutions and also will provide useful information about the trajectory of the disease. In this paper, we present the theoretical framework and

  3. 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.

  4. 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

  5. Dendritic spread and functional coverage of starburst amacrine cells.

    Science.gov (United States)

    Keeley, Patrick W; Whitney, Irene E; Raven, Mary A; Reese, Benjamin E

    2007-12-10

    The network of starburst amacrine cells plays a fundamental role in the neural circuitry underlying directional selectivity within the retina. Individual sectors of the starburst dendritic field are directionally selective by virtue of a mutually inhibitory relationship between starburst amacrine cells with overlapping dendrites. These features of the starburst amacrine cell network suggest that starburst cells regulate their dendritic overlap to ensure a uniform coverage of the retinal surface. The present study has compared the dendritic morphology of starburst amacrine cells in two different strains of mice that differ in starburst amacrine cell number. The A/J (A) strain contains about one-quarter fewer starburst amacrine cells than does the C57BL/6J (B6) strain, although the mosaics of starburst amacrine cells in both strains are comparably patterned. Dendritic field size, however, does not compensate for the difference in density, the A strain having a slightly smaller dendritic field relative to the B6 strain, yielding a significantly larger dendritic coverage factor for individual cells in the B6 strain. The area of the distal (output) annulus of the dendritic field occupies a comparable proportion of the overall field area in the two strains, but overlapping annuli establish a finer meshwork of co-fasciculating processes in the B6 strain. These results would suggest that the architecture of the dendritic network, rather than the overall size of the dendritic field, is dependent on the density of starburst amacrine cells. (c) 2007 Wiley-Liss, Inc.

  6. The calcium feedback loop and T cell activation: how cytoskeleton networks control intracellular calcium flux.

    Science.gov (United States)

    Joseph, Noah; Reicher, Barak; Barda-Saad, Mira

    2014-02-01

    During T cell activation, the engagement of a T cell with an antigen-presenting cell (APC) results in rapid cytoskeletal rearrangements and a dramatic increase of intracellular calcium (Ca(2+)) concentration, downstream to T cell antigen receptor (TCR) ligation. These events facilitate the organization of an immunological synapse (IS), which supports the redistribution of receptors, signaling molecules and organelles towards the T cell-APC interface to induce downstream signaling events, ultimately supporting T cell effector functions. Thus, Ca(2+) signaling and cytoskeleton rearrangements are essential for T cell activation and T cell-dependent immune response. Rapid release of Ca(2+) from intracellular stores, e.g. the endoplasmic reticulum (ER), triggers the opening of Ca(2+) release-activated Ca(2+) (CRAC) channels, residing in the plasma membrane. These channels facilitate a sustained influx of extracellular Ca(2+) across the plasma membrane in a process termed store-operated Ca(2+) entry (SOCE). Because CRAC channels are themselves inhibited by Ca(2+) ions, additional factors are suggested to enable the sustained Ca(2+) influx required for T cell function. Among these factors, we focus here on the contribution of the actin and microtubule cytoskeleton. The TCR-mediated increase in intracellular Ca(2+) evokes a rapid cytoskeleton-dependent polarization, which involves actin cytoskeleton rearrangements and microtubule-organizing center (MTOC) reorientation. Here, we review the molecular mechanisms of Ca(2+) flux and cytoskeletal rearrangements, and further describe the way by which the cytoskeletal networks feedback to Ca(2+) signaling by controlling the spatial and temporal distribution of Ca(2+) sources and sinks, modulating TCR-dependent Ca(2+) signals, which are required for an appropriate T cell response. This article is part of a Special Issue entitled: Reciprocal influences between cell cytoskeleton and membrane channels, receptors and transporters

  7. Tracking the Reorganization of Module Structure in Time-Varying Weighted Brain Functional Connectivity Networks.

    Science.gov (United States)

    Schmidt, Christoph; Piper, Diana; Pester, Britta; Mierau, Andreas; Witte, Herbert

    2018-05-01

    Identification of module structure in brain functional networks is a promising way to obtain novel insights into neural information processing, as modules correspond to delineated brain regions in which interactions are strongly increased. Tracking of network modules in time-varying brain functional networks is not yet commonly considered in neuroscience despite its potential for gaining an understanding of the time evolution of functional interaction patterns and associated changing degrees of functional segregation and integration. We introduce a general computational framework for extracting consensus partitions from defined time windows in sequences of weighted directed edge-complete networks and show how the temporal reorganization of the module structure can be tracked and visualized. Part of the framework is a new approach for computing edge weight thresholds for individual networks based on multiobjective optimization of module structure quality criteria as well as an approach for matching modules across time steps. By testing our framework using synthetic network sequences and applying it to brain functional networks computed from electroencephalographic recordings of healthy subjects that were exposed to a major balance perturbation, we demonstrate the framework's potential for gaining meaningful insights into dynamic brain function in the form of evolving network modules. The precise chronology of the neural processing inferred with our framework and its interpretation helps to improve the currently incomplete understanding of the cortical contribution for the compensation of such balance perturbations.

  8. Wavelet Neural Network Using Multiple Wavelet Functions in Target Threat Assessment

    Science.gov (United States)

    Guo, Lihong; Duan, Hong

    2013-01-01

    Target threat assessment is a key issue in the collaborative attack. To improve the accuracy and usefulness of target threat assessment in the aerial combat, we propose a variant of wavelet neural networks, MWFWNN network, to solve threat assessment. How to select the appropriate wavelet function is difficult when constructing wavelet neural network. This paper proposes a wavelet mother function selection algorithm with minimum mean squared error and then constructs MWFWNN network using the above algorithm. Firstly, it needs to establish wavelet function library; secondly, wavelet neural network is constructed with each wavelet mother function in the library and wavelet function parameters and the network weights are updated according to the relevant modifying formula. The constructed wavelet neural network is detected with training set, and then optimal wavelet function with minimum mean squared error is chosen to build MWFWNN network. Experimental results show that the mean squared error is 1.23 × 10−3, which is better than WNN, BP, and PSO_SVM. Target threat assessment model based on the MWFWNN has a good predictive ability, so it can quickly and accurately complete target threat assessment. PMID:23509436

  9. CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components.

    Science.gov (United States)

    Heberle, Henry; Carazzolle, Marcelo Falsarella; Telles, Guilherme P; Meirelles, Gabriela Vaz; Minghim, Rosane

    2017-09-13

    The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability. Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB. CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

  10. Intermittent Theta-Burst Stimulation of the Lateral Cerebellum Increases Functional Connectivity of the Default Network

    Science.gov (United States)

    Farzan, Faranak; Eldaief, Mark C.; Schmahmann, Jeremy D.; Pascual-Leone, Alvaro

    2014-01-01

    Cerebral cortical intrinsic connectivity networks share topographically arranged functional connectivity with the cerebellum. However, the contribution of cerebellar nodes to distributed network organization and function remains poorly understood. In humans, we applied theta-burst transcranial magnetic stimulation, guided by subject-specific connectivity, to regions of the cerebellum to evaluate the functional relevance of connections between cerebellar and cerebral cortical nodes in different networks. We demonstrate that changing activity in the human lateral cerebellar Crus I/II modulates the cerebral default mode network, whereas vermal lobule VII stimulation influences the cerebral dorsal attention system. These results provide novel insights into the distributed, but anatomically specific, modulatory impact of cerebellar effects on large-scale neural network function. PMID:25186750

  11. Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Dürr, Oliver; Sick, Beate

    2016-10-01

    Deep learning methods are currently outperforming traditional state-of-the-art computer vision algorithms in diverse applications and recently even surpassed human performance in object recognition. Here we demonstrate the potential of deep learning methods to high-content screening-based phenotype classification. We trained a deep learning classifier in the form of convolutional neural networks with approximately 40,000 publicly available single-cell images from samples treated with compounds from four classes known to lead to different phenotypes. The input data consisted of multichannel images. The construction of appropriate feature definitions was part of the training and carried out by the convolutional network, without the need for expert knowledge or handcrafted features. We compare our results against the recent state-of-the-art pipeline in which predefined features are extracted from each cell using specialized software and then fed into various machine learning algorithms (support vector machine, Fisher linear discriminant, random forest) for classification. The performance of all classification approaches is evaluated on an untouched test image set with known phenotype classes. Compared to the best reference machine learning algorithm, the misclassification rate is reduced from 8.9% to 6.6%. © 2016 Society for Laboratory Automation and Screening.

  12. 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).

  13. Invertebrate diversity classification using self-organizing map neural network: with some special topological functions

    OpenAIRE

    WenJun Zhang; QuHuan Li

    2014-01-01

    In present study we used self-organizing map (SOM) neural network to conduct the non-supervisory clustering of invertebrate orders in rice field. Four topological functions, i.e., cossintopf, sincostopf, acossintopf, and expsintopf, established on the template in toolbox of Matlab, were used in SOM neural network learning. Results showed that clusters were different when using different topological functions because different topological functions will generate different spatial structure of ...

  14. Network pharmacology of medicinal attributes and functions of Chinese herbal medicines: (II Relational networks and pharmacological mechanisms of medicinal attributes and functions of Chinese herbal medicines

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2017-06-01

    Full Text Available In present study, the database, CHM-DATA, with 1127 Chinese herbal medicines mainly having recorded chemical composition, involving 7 taste attributes, 5 medicinal properties, 1 toxicity attribute, 22 chemical composition categories, 12 meridians and collaterals (Gui Jing, and 78 medicinal functions (Gong Xiao, was used to calculate point correlations between these 125 attributes. Totally four relational networks, i.e., the networks for medicinal attributes and functions, for chemical composition and meridians and collaterals, for meridians and collaterals and medicinal functions, and for meridians and collaterals were constructed based on the significant point correlations. Network analysis indicated that the former three ones are scale-free complex networks and the last one tends to be a random network. Node degrees of the four networks follow power-law distribution. Detailed between-attribute relationships and medicinal mechanisms were revealed. For example, concerning chemical composition categories, alkaloids and amines have positive correlation / correspondence. More alkaloids correspond to more amines. Alkaloids negatively correlate with volatile oils / ordinary oils, carbohydrates / starch, ketones / flavonoids, and olefins. Alkaloids mainly function in decrease internal heat, dry dampness, etc. Organic acids and alkaloids have negative correlation. More organic acids mean the less alkaloids. Organic acids mainly act on large intestine meridians and collaterals, and function in moisten dryness. As for meridians and collaterals, kidney meridians and collaterals negatively correlate with lung meridians and collaterals, stomach meridians and collaterals, and large intestine meridians and collaterals. Kidney meridians and collaterals positively function in consolidate or warm kidney, invigorate male impotence (Yang or strengthen male essence, strengthen bones and muscles, stop diarrheal, regulate menstruation or promote blood flow, relieve

  15. Automatic selection of resting-state networks with functional magnetic resonance imaging

    Directory of Open Access Journals (Sweden)

    Silvia Francesca eStorti

    2013-05-01

    Full Text Available Functional magnetic resonance imaging (fMRI during a resting-state condition can reveal the co-activation of specific brain regions in distributed networks, called resting-state networks, which are selected by independent component analysis (ICA of the fMRI data. One of the major difficulties with component analysis is the automatic selection of the ICA features related to brain activity. In this study we describe a method designed to automatically select networks of potential functional relevance, specifically, those regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. To do this, image analysis was based on probabilistic ICA as implemented in FSL software. After decomposition, the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, Pearson's median coefficient of skewness of the spatial maps generated by FSL, followed by clustering, segmentation, and spectral analysis. To evaluate the performance of the approach, we investigated the resting-state networks in 25 subjects. For each subject, three resting-state scans were obtained with a Siemens Allegra 3 T scanner (NYU data set. Comparison of the visually and the automatically identified neuronal networks showed that the algorithm had high accuracy (first scan: 95%, second scan: 95%, third scan: 93% and precision (90%, 90%, 84%. The reproducibility of the networks for visual and automatic selection was very close: it was highly consistent in each subject for the default-mode network (≥ 92% and the occipital network, which includes the medial visual cortical areas (≥ 94%, and consistent for the attention network (≥ 80%, the right and/or left lateralized frontoparietal attention networks, and the temporal-motor network (≥ 80%. The automatic selection method may be used to detect neural networks and reduce subjectivity in ICA

  16. SINCERITIES: Inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles.

    Science.gov (United States)

    Papili Gao, Nan; Ud-Dean, S M Minhaz; Gandrillon, Olivier; Gunawan, Rudiyanto

    2017-09-14

    Single cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired. We developed SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS) for the inference of GRNs from single cell transcriptional profiles. We focused on time-stamped cross-sectional expression data, commonly generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers directed regulatory relationships among genes by employing regularized linear regression (ridge regression), using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and single cell transcriptional profiles of THP-1 monocytic human leukemia cells. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Moreover, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality. Finally, an application of SINCERITIES to single cell expression data of T2EC chicken erythrocytes pointed to BATF as a candidate novel regulator of erythroid development. The MATLAB and R version of SINCERITIES is freely available from the following websites: http://www.cabsel.ethz.ch/tools/sincerities.html and

  17. The highly buffered Arabidopsis immune signaling network conceals the functions of its components.

    Directory of Open Access Journals (Sweden)

    Rachel A Hillmer

    2017-05-01

    Full Text Available Plant immunity protects plants from numerous potentially pathogenic microbes. The biological network that controls plant inducible immunity must function effectively even when network components are targeted and disabled by pathogen effectors. Network buffering could confer this resilience by allowing different parts of the network to compensate for loss of one another's functions. Networks rich in buffering rely on interactions within the network, but these mechanisms are difficult to study by simple genetic means. Through a network reconstitution strategy, in which we disassemble and stepwise reassemble the plant immune network that mediates Pattern-Triggered-Immunity, we have resolved systems-level regulatory mechanisms underlying the Arabidopsis transcriptome response to the immune stimulant flagellin-22 (flg22. These mechanisms show widespread evidence of interactions among major sub-networks-we call these sectors-in the flg22-responsive transcriptome. Many of these interactions result in network buffering. Resolved regulatory mechanisms show unexpected patterns for how the jasmonate (JA, ethylene (ET, phytoalexin-deficient 4 (PAD4, and salicylate (SA signaling sectors control the transcriptional response to flg22. We demonstrate that many of the regulatory mechanisms we resolved are not detectable by the traditional genetic approach of single-gene null-mutant analysis. Similar to potential pathogenic perturbations, null-mutant effects on immune signaling can be buffered by the network.

  18. Learning Errors by Radial Basis Function Neural Networks and Regularization Networks

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman; Vidnerová, Petra

    2009-01-01

    Roč. 1, č. 2 (2009), s. 49-57 ISSN 2005-4262 R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : neural network * RBF networks * regularization * learning Subject RIV: IN - Informatics, Computer Science http://www.sersc.org/journals/IJGDC/vol2_no1/5.pdf

  19. Task-dependent reorganization of functional connectivity networks during visual semantic decision making.

    Science.gov (United States)

    DeSalvo, Matthew N; Douw, Linda; Takaya, Shigetoshi; Liu, Hesheng; Stufflebeam, Steven M

    2014-01-01

    Functional MRI is widely used to study task-related changes in neuronal activity as well as resting-state functional connectivity. In this study, we explore task-related changes in functional connectivity networks using fMRI. Dynamic connectivity may represent a new measure of neural network robustness that would impact both clinical and research efforts. However, prior studies of task-related changes in functional connectivity have shown apparently conflicting results, leading to several competing hypotheses regarding the relationship between task-related and resting-state brain networks. We used a graph theory-based network approach to compare functional connectivity in healthy subjects between the resting state and when performing a clinically used semantic decision task. We analyzed fMRI data from 21 healthy, right-handed subjects. While three nonoverlapping, highly intraconnected functional modules were observed in the resting state, an additional language-related module emerged during the semantic decision task. Both overall and within-module connectivity were greater in default mode network (DMN) and classical language areas during semantic decision making compared to rest, while between-module connectivity was diffusely greater at rest, revealing a more widely distributed pattern of functional connectivity at rest. The results of this study suggest that there are differences in network topology between resting and task states. Specifically, semantic decision making is associated with a reduction in distributed connectivity through hub areas of the DMN as well as an increase in connectivity within both default and language networks.

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

    Science.gov (United States)

    Andras, Peter

    2018-02-01

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

  1. 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. Copyright © 2015 Elsevier Inc. All rights reserved.

  2. Bioinformatic Integration of Molecular Networks and Major Pathways Involved in Mice Cochlear and Vestibular Supporting Cells.

    Science.gov (United States)

    Requena, Teresa; Gallego-Martinez, Alvaro; Lopez-Escamez, Jose A

    2018-01-01

    Background : Cochlear and vestibular epithelial non-hair cells (ENHCs) are the supporting elements of the cellular architecture in the organ of Corti and the vestibular neuroepithelium in the inner ear. Intercellular and cell-extracellular matrix interactions are essential to prevent an abnormal ion redistribution leading to hearing and vestibular loss. The aim of this study is to define the main pathways and molecular networks in the mouse ENHCs. Methods : We retrieved microarray and RNA-seq datasets from mouse epithelial sensory and non-sensory cells from gEAR portal (http://umgear.org/index.html) and obtained gene expression fold-change between ENHCs and non-epithelial cells (NECs) against HCs for each gene. Differentially expressed genes (DEG) with a log2 fold change between 1 and -1 were discarded. The remaining genes were selected to search for interactions using Ingenuity Pathway Analysis and STRING platform. Specific molecular networks for ENHCs in the cochlea and the vestibular organs were generated and significant pathways were identified. Results : Between 1723 and 1559 DEG were found in the mouse cochlear and vestibular tissues, respectively. Six main pathways showed enrichment in the supporting cells in both tissues: (1) "Inhibition of Matrix Metalloproteases"; (2) "Calcium Transport I"; (3) "Calcium Signaling"; (4) "Leukocyte Extravasation Signaling"; (5) "Signaling by Rho Family GTPases"; and (6) "Axonal Guidance Si". In the mouse cochlea, ENHCs showed a significant enrichment in 18 pathways highlighting "axonal guidance signaling (AGS)" ( p = 4.37 × 10 -8 ) and "RhoGDI Signaling" ( p = 3.31 × 10 -8 ). In the vestibular dataset, there were 20 enriched pathways in ENHCs, the most significant being "Leukocyte Extravasation Signaling" ( p = 8.71 × 10 -6 ), "Signaling by Rho Family GTPases" ( p = 1.20 × 10 -5 ) and "Calcium Signaling" ( p = 1.20 × 10 -5 ). Among the top ranked networks, the most biologically significant network contained the

  3. Osteomacs interact with megakaryocytes and osteoblasts to regulate murine hematopoietic stem cell function.

    Science.gov (United States)

    Mohamad, Safa F; Xu, Linlin; Ghosh, Joydeep; Childress, Paul J; Abeysekera, Irushi; Himes, Evan R; Wu, Hao; Alvarez, Marta B; Davis, Korbin M; Aguilar-Perez, Alexandra; Hong, Jung Min; Bruzzaniti, Angela; Kacena, Melissa A; Srour, Edward F

    2017-12-12

    Networking between hematopoietic stem cells (HSCs) and cells of the hematopoietic niche is critical for stem cell function and maintenance of the stem cell pool. We characterized calvariae-resident osteomacs (OMs) and their interaction with megakaryocytes to sustain HSC function and identified distinguishing properties between OMs and bone marrow (BM)-derived macrophages. OMs, identified as CD45 + F4/80 + cells, were easily detectable (3%-5%) in neonatal calvarial cells. Coculture of neonatal calvarial cells with megakaryocytes for 7 days increased OM three- to sixfold, demonstrating that megakaryocytes regulate OM proliferation. OMs were required for the hematopoiesis-enhancing activity of osteoblasts, and this activity was augmented by megakaryocytes. Serial transplantation demonstrated that HSC repopulating potential was best maintained by in vitro cultures containing osteoblasts, OMs, and megakaryocytes. With or without megakaryocytes, BM-derived macrophages were unable to functionally substitute for neonatal calvarial cell-associated OMs. In addition, OMs differentiated into multinucleated, tartrate resistant acid phosphatase-positive osteoclasts capable of bone resorption. Nine-color flow cytometric analysis revealed that although BM-derived macrophages and OMs share many cell surface phenotypic similarities (CD45, F4/80, CD68, CD11b, Mac2, and Gr-1), only a subgroup of OMs coexpressed M-CSFR and CD166, thus providing a unique profile for OMs. CD169 was expressed by both OMs and BM-derived macrophages and therefore was not a distinguishing marker between these 2 cell types. These results demonstrate that OMs support HSC function and illustrate that megakaryocytes significantly augment the synergistic activity of osteoblasts and OMs. Furthermore, this report establishes for the first time that the crosstalk between OMs, osteoblasts, and megakaryocytes is a novel network supporting HSC function.

  4. Disrupted functional connectivity of the default mode network due to acute vestibular deficit

    Directory of Open Access Journals (Sweden)

    Carsten M. Klingner

    2014-01-01

    Here, we employ functional magnetic resonance imaging (fMRI in the resting state to investigate changes in the functional connectivity between the DMN and task-positive networks, in a longitudinal design combined with measurements of caloric function. We demonstrate an initially disturbed connectedness of the DMN after vestibular neuritis. We hypothesize that the disturbed connectivity between the default mode network and particular parts of the task-positive network might be related to a sustained utilization of processing capacity by diverging sensory information. The current results provide some insights into mechanisms of central compensation following an acute vestibular deficit and the importance of the DMN in this disease.

  5. Highly Enhanced Vapor Sensing of Multiwalled Carbon Nanotube Network Sensors by n-Butylamine Functionalization

    Directory of Open Access Journals (Sweden)

    P. Slobodian

    2014-01-01

    Full Text Available The sensing of volatile organic compounds by multiwall carbon nanotube networks of randomly entangled pristine nanotubes or the nanotubes functionalized by n-butylamine, which were deposited on polyurethane supporting electrospinned nonwoven membrane, has been investigated. The results show that the sensing of volatile organic compounds by functionalized nanotubes considerably increases with respect to pristine nanotubes. The increase is highly dependent on used vapor polarity. For the case of highly polar methanol, the functionalized MWCNT network exhibits even more than eightfold higher sensitivity in comparison to the network prepared from pristine nanotubes.

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

    Directory of Open Access Journals (Sweden)

    Fukuda eMegumi

    2015-03-01

    Full Text Available Motor or perceptual learning is known to influence functional connectivity between brain regions and induce short-term changes in the intrinsic functional networks revealed as correlations in slow blood-oxygen-level dependent (BOLD signal fluctuations. However, no cause-and-effect relationship has been elucidated between a specific change in connectivity and a long-term change in global networks. Here, we examine the hypothesis that functional connectivity (i.e. temporal correlation between two regions is increased and preserved for a long time when two regions are simultaneously activated or deactivated. Using the connectivity-neurofeedback training paradigm, subjects successfully learned to increase the correlation of activity between the lateral parietal and primary motor areas, regions that belong to different intrinsic networks and negatively correlated before training under the resting conditions. Furthermore, whole-brain hypothesis-free analysis as well as functional network analyses demonstrated that the correlation in the resting state between these areas as well as the correlation between the intrinsic networks that include the areas increased for at least two months. These findings indicate that the connectivity-neurofeedback training can cause long-term changes in intrinsic connectivity and that intrinsic networks can be shaped by experience-driven modulation of regional correlation.

  7. Selecting radial basis function network centers with recursive orthogonal least squares training.

    Science.gov (United States)

    Gomm, J B; Yu, D L

    2000-01-01

    Recursive orthogonal least squares (ROLS) is a numerically robust method for solving for the output layer weights of a radial basis function (RBF) network, and requires less computer memory than the batch alternative. In this paper, the use of ROLS is extended to selecting the centers of an RBF network. It is shown that the information available in an ROLS algorithm after network training can be used to sequentially select centers to minimize the network output error. This provides efficient methods for network reduction to achieve smaller architectures with acceptable accuracy and without retraining. Two selection methods are developed, forward and backward. The methods are illustrated in applications of RBF networks to modeling a nonlinear time series and a real multiinput-multioutput chemical process. The final network models obtained achieve acceptable accuracy with significant reductions in the number of required centers.

  8. Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks

    Science.gov (United States)

    Burken, John J.

    2005-01-01

    This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.

  9. Ontogeny and function of murine epidermal Langerhans cells.

    Science.gov (United States)

    Kaplan, Daniel H

    2017-09-19

    Langerhans cells (LCs) are epidermis-resident antigen-presenting cells that share a common ontogeny with macrophages but function as dendritic cells (DCs). Their development, recruitment and retention in the epidermis is orchestrated by interactions with keratinocytes through multiple mechanisms. LC and dermal DC subsets often show functional redundancy, but LCs are required for specific types of adaptive immune responses when antigen is concentrated in the epidermis. This Review will focus on those developmental and functional properties that are unique to LCs.

  10. Incidental and intentional learning of verbal episodic material differentially modifies functional brain networks.

    Directory of Open Access Journals (Sweden)

    Marie-Therese Kuhnert

    Full Text Available Learning- and memory-related processes are thought to result from dynamic interactions in large-scale brain networks that include lateral and mesial structures of the temporal lobes. We investigate the impact of incidental and intentional learning of verbal episodic material on functional brain networks that we derive from scalp-EEG recorded continuously from 33 subjects during a neuropsychological test schedule. Analyzing the networks' global statistical properties we observe that intentional but not incidental learning leads to a significantly increased clustering coefficient, and the average shortest path length remains unaffected. Moreover, network modifications correlate with subsequent recall performance: the more pronounced the modifications of the clustering coefficient, the higher the recall performance. Our findings provide novel insights into the relationship between topological aspects of functional brain networks and higher cognitive functions.

  11. Radial basis function neural network for power system load-flow

    International Nuclear Information System (INIS)

    Karami, A.; Mohammadi, M.S.

    2008-01-01

    This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)

  12. Phenotypic and functional plasticity of cells of innate immunity: macrophages, mast cells and neutrophils

    DEFF Research Database (Denmark)

    Galli, Stephen J; Borregaard, Niels; Wynn, Thomas A

    2011-01-01

    Hematopoietic cells, including lymphoid and myeloid cells, can develop into phenotypically distinct 'subpopulations' with different functions. However, evidence indicates that some of these subpopulations can manifest substantial plasticity (that is, undergo changes in their phenotype and function...

  13. Driving and driven architectures of directed small-world human brain functional networks.

    Directory of Open Access Journals (Sweden)

    Chaogan Yan

    Full Text Available Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86 to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule. Further split-half analyses indicated that our results were highly reproducible between two

  14. The European ALARA network. Development, functioning and main activities

    International Nuclear Information System (INIS)

    Schmitt-Hannig, A.

    2009-01-01

    The new ICRP recommendations (ICRP 103), and in particular the detailed treatment of optimisation in the ICRP Publication 101, define optimisation of protection as a source-related process aimed at keeping the likelihood of incurred exposures, the number of people exposed and the magnitude of their individual doses as low as reasonably achievable, also below constraints, taking into account economic and societal factors. Practical implementation and further development of the ALARA principle has been achieved for many years now by the successful cooperation of experts from different European organisations; first as pioneers by establishing the European ALARA Network and then by enthusiastically supporting the activities of the network itself. This contribution presents the evolution, operation and key activities of the European ALARA Network (EAN) in the last years; the successful cooperation of experts from different professional backgrounds, advocating the ALARA principle in a range of radiation protection areas, and contributing to its further development by trading experience and networking. The interaction between the EAN and international organisations, which support the ALARA principle by including relevant activities in their work programmes, is described. (orig.)

  15. A lateralized functional auditory network is involved in anuran ...

    Indian Academy of Sciences (India)

    2016-10-05

    Oct 5, 2016 ... and thus is a useful method for recognizing central nervous system networks underlying specific cognitive processes, such as processing conspecific communication sounds. Since the changes in the EEG properties can indicate the corresponding changes in cognitive processes (Rugg and Coles 1995; De.

  16. Functional classification of the Gauteng provincial road network ...

    African Journals Online (AJOL)

    At link level it was possible to synchronise the upgraded network with the financial asset registry in order to gauge the current state of the provincial roads within Gauteng, and to identify those that are in need of attention and/or rehabilitation to optimise efficiency and safety. This approach can be standardised throughout the ...

  17. Functional Network Connectivity Patterns between Idiopathic Generalized Epilepsy with Myoclonic and Absence Seizures

    Directory of Open Access Journals (Sweden)

    Qifu Li

    2017-05-01

    Full Text Available The extensive cerebral cortex and subcortical structures are considered as the major regions related to the generalized epileptiform discharges in idiopathic generalized epilepsy. However, various clinical syndromes and electroencephalogram (EEG signs exist across generalized seizures, such as the loss of consciousness during absence seizures (AS and the jerk of limbs during myoclonic seizures (MS. It is presumed that various functional systems affected by discharges lead to the difference in syndromes of these seizures. Twenty epileptic patients with MS, 21 patients with AS, and 21 healthy controls were recruited in this study. The functional network connectivity was analyzed based on the resting-state functional magnetic resonance imaging scans. The statistical analysis was performed in three groups to assess the difference in the functional brain networks in two types of generalized seizures. Twelve resting-state networks were identified in three groups. Both patient groups showed common abnormalities, including decreased functional connectivity in salience network (SN, cerebellum network, and primary perceptional networks and decreased connection between SN and visual network, compared with healthy controls. Interestingly, the frontal part of high-level cognitive resting-state networks showed increased functional connectivity (FC in patients with MS, but decreased FC in patients with AS. Moreover, patients with MS showed decreased negative connections between high-level cognitive networks and primary system. The common alteration in both patient groups, including SN, might reflect a similar mechanism associated with the loss of consciousness during generalized seizures. This study provided the evidence of brain network in generalized epilepsy to understand the difference between MS and AS.

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

    Science.gov (United States)

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

    2015-04-15

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

  19. Transcription factor networks in B-cell differentiation link development to acute lymphoid leukemia.

    Science.gov (United States)

    Somasundaram, Rajesh; Prasad, Mahadesh A J; Ungerbäck, Jonas; Sigvardsson, Mikael

    2015-07-09

    B-lymphocyte development in the bone marrow is controlled by the coordinated action of transcription factors creating regulatory networks ensuring activation of the B-lymphoid program and silencing of alternative cell fates. This process is tightly connected to malignant transformation because B-lineage acute lymphoblastic leukemia cells display a pronounced block in differentiation resulting in the expansion of immature progenitor cells. Over the last few years, high-resolution analysis of genetic changes in leukemia has revealed that several key regulators of normal B-cell development, including IKZF1, TCF3, EBF1, and PAX5, are genetically altered in a large portion of the human B-lineage acute leukemias. This opens the possibility of directly linking the disrupted development as well as aberrant gene expression patterns in leukemic cells to molecular functions of defined transcription factors in normal cell differentiation. This review article focuses on the roles of transcription factors in early B-cell development and their involvement in the formation of human leukemia. © 2015 by The American Society of Hematology.

  20. Reduced integration and improved segregation of functional brain networks in Alzheimer’s disease

    Science.gov (United States)

    Kabbara, A.; Eid, H.; El Falou, W.; Khalil, M.; Wendling, F.; Hassan, M.

    2018-04-01

    Objective. Emerging evidence shows that cognitive deficits in Alzheimer’s disease (AD) are associated with disruptions in brain functional connectivity. Thus, the identification of alterations in AD functional networks has become a topic of increasing interest. However, to what extent AD induces disruption of the balance of local and global information processing in the human brain remains elusive. The main objective of this study is to explore the dynamic topological changes of AD networks in terms of brain network segregation and integration. Approach. We used electroencephalography (EEG) data recorded from 20 participants (10 AD patients and 10 healthy controls) during resting state. Functional brain networks were reconstructed using EEG source connectivity computed in different frequency bands. Graph theoretical analyses were performed assess differences between both groups. Main results. Results revealed that AD networks, compared to networks of age-matched healthy controls, are characterized by lower global information processing (integration) and higher local information processing (segregation). Results showed also significant correlation between the alterations in the AD patients’ functional brain networks and their cognitive scores. Significance. These findings may contribute to the development of EEG network-based test that could strengthen results obtained from currently-used neurophysiological tests in neurodegenerative diseases.

  1. Inferring the physical connectivity of complex networks from their functional dynamics

    Directory of Open Access Journals (Sweden)

    Holm Liisa

    2010-05-01

    Full Text Available Abstract Background Biological networks, such as protein-protein interactions, metabolic, signalling, transcription-regulatory networks and neural synapses, are representations of large-scale dynamic systems. The relationship between the network structure and functions remains one of the central problems in current multidisciplinary research. Significant progress has been made toward understanding the implication of topological features for the network dynamics and functions, especially in biological networks. Given observations of a network system's behaviours or measurements of its functional dynamics, what can we conclude of the details of physical connectivity of the underlying structure? Results We modelled the network system by employing a scale-free network of coupled phase oscillators. Pairwise phase coherence (PPC was calculated for all the pairs of oscillators to present functional dynamics induced by the system. At the regime of global incoherence, we observed a Significant pairwise synchronization only between two nodes that are physically connected. Right after the onset of global synchronization, disconnected nodes begin to oscillate in a correlated fashion and the PPC of two nodes, either connected or disconnected, depends on their degrees. Based on the observation of PPCs, we built a weighted network of synchronization (WNS, an all-to-all functionally connected network where each link is weighted by the PPC of two oscillators at the ends of the link. In the regime of strong coupling, we observed a Significant similarity in the organization of WNSs induced by systems sharing the same substrate network but different configurations of initial phases and intrinsic frequencies of oscillators. We reconstruct physical network from the WNS by choosing the links whose weights are higher than a given threshold. We observed an optimal reconstruction just before the onset of global synchronization. Finally, we correlated the topology of the

  2. Modeling reveals bistability and low-pass filtering in the network module determining blood stem cell fate.

    Directory of Open Access Journals (Sweden)

    Jatin Narula

    2010-05-01

    Full Text Available Combinatorial regulation of gene expression is ubiquitous in eukaryotes with multiple inputs converging on regulatory control elements. The dynamic properties of these elements determine the functionality of genetic networks regulating differentiation and development. Here we propose a method to quantitatively characterize the regulatory output of distant enhancers with a biophysical approach that recursively determines free energies of protein-protein and protein-DNA interactions from experimental analysis of transcriptional reporter libraries. We apply this method to model the Scl-Gata2-Fli1 triad-a network module important for cell fate specification of hematopoietic stem cells. We show that this triad module is inherently bistable with irreversible transitions in response to physiologically relevant signals such as Notch, Bmp4 and Gata1 and we use the model to predict the sensitivity of the network to mutations. We also show that the triad acts as a low-pass filter by switching between steady states only in response to signals that persist for longer than a minimum duration threshold. We have found that the auto-regulation loops connecting the slow-degrading Scl to Gata2 and Fli1 are crucial for this low-pass filtering property. Taken together our analysis not only reveals new insights into hematopoietic stem cell regulatory network functionality but also provides a novel and widely applicable strategy to incorporate experimental measurements into dynamical network models.

  3. Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions.

    Directory of Open Access Journals (Sweden)

    Thomas Wallach

    2013-03-01

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

  4. Honeycomb structural composite polymer network of gelatin and functional cellulose ester for controlled release of omeprazole.

    Science.gov (United States)

    Zhuang, Chen; Shi, Chengmei; Tao, Furong; Cui, Yuezhi

    2017-12-01

    The functionalized cellulose ester MCN was firstly synthesized and used to cross-link gelatin by amidation between -NH 2 in gelatin and active ester groups in MCN to form a composite polymer network Gel-MCN, which was confirmed by Van Slyke method, FTIR, XRD and TGA-DTG spectra. The model drug omeprazole was loaded in Gel-MCN composites mainly by electrostatic interaction and hydrogen bonds, which were certified by FTIR, XRD and TGA-DSC. Thermal stability, anti-biodegradability, mechanical property and surface hydrophobicity of the composites with different cross-linking extents and drug loading were systematically investigated. SEM images demonstrated the honeycomb structural cells of cross-linked gelatin networks and this ensured drug entrapment. The drug release mechanism was dominated by a combined effect of diffusion and degradation, and the release rate decreased with cross-linking degree increased. The developed drug delivery system had profound significance in improving pesticide effect and bioavailability of drugs. Copyright © 2017. Published by Elsevier B.V.

  5. Impaired functional default mode network in patients with mild neurological Wilson's disease.

    Science.gov (United States)

    Han, Yongsheng; Cheng, Hewei; Toledo, Jon B; Wang, Xun; Li, Bo; Han, Yongzhu; Wang, Kai; Fan, Yong

    2016-09-01

    Wilson's disease (WD) is an autosomal recessive metabolic disorder characterized by cognitive, psychiatric and motor signs and symptoms that are associated with structural and pathological brain abnormalities, in addition to liver changes. However, functional brain connectivity pattern of WD patients remains largely unknown. In the present study, we investigated functional brain connectivity pattern of WD patients using resting state functional magnetic resonance imaging. Particularly, we studied default mode network (DMN) using posterior cingulate cortex (PCC) based seed functional connectivity analysis and graph theoretic functional brain network analysis tools, and investigated the relationship between the DMN's functional connectivity pattern of WD patients and their attention functions examined using the attention network test (ANT). Our results demonstrated that WD patients had altered DMN's functional connectivity and lower local and global network efficiency compared with normal controls (NCs). In addition, the functional connectivity between left inferior temporal cortex and right lateral parietal cortex was correlated with altering function, one of the attention functions, across WD and NC subjects. These findings indicated that the DMN's functional connectivity was altered in WD patients, which might be correlated with their attention dysfunction. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. High power fuel cell simulator based on artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Chavez-Ramirez, Abraham U.; Munoz-Guerrero, Roberto [Departamento de Ingenieria Electrica, CINVESTAV-IPN. Av. Instituto Politecnico Nacional No. 2508, D.F. CP 07360 (Mexico); Duron-Torres, S.M. [Unidad Academica de Ciencias Quimicas, Universidad Autonoma de Zacatecas, Campus Siglo XXI, Edif. 6 (Mexico); Ferraro, M.; Brunaccini, G.; Sergi, F.; Antonucci, V. [CNR-ITAE, Via Salita S. Lucia sopra Contesse 5-98126 Messina (Italy); Arriaga, L.G. [Centro de Investigacion y Desarrollo Tecnologico en Electroquimica S.C., Parque Tecnologico Queretaro, Sanfandila, Pedro Escobedo, Queretaro (Mexico)

    2010-11-15

    Artificial Neural Network (ANN) has become a powerful modeling tool for predicting the performance of complex systems with no well-known variable relationships due to the inherent properties. A commercial Polymeric Electrolyte Membrane fuel cell (PEMFC) stack (5 kW) was modeled successfully using this tool, increasing the number of test into the 7 inputs - 2 outputs-dimensional spaces in the shortest time, acquiring only a small amount of experimental data. Some parameters could not be measured easily on the real system in experimental tests; however, by receiving the data from PEMFC, the ANN could be trained to learn the internal relationships that govern this system, and predict its behavior without any physical equations. Confident accuracy was achieved in this work making possible to import this tool to complex systems and applications. (author)

  7. Functional network macroscopes for probing past and present Earth system dynamics (Invited)

    Science.gov (United States)

    Donges, J. F.

    2013-12-01

    The Earth, as viewed from a physicist's perspective, is a dynamical system of great complexity. Functional complex networks are inferred from observational data and model runs or constructed on the basis of theoretical considerations. Representing statistical interdependencies or causal interactions between objects (e.g., Earth system subdomains, processes, or local field variables), functional complex networks are conceptually well-suited for naturally addressing some of the fundamental questions of Earth system analysis concerning, among others, major dynamical patterns, teleconnections, and feedback loops in the planetary machinery, as well as critical elements such as thresholds, bottlenecks, and switches. The first part of this talk concerns complex network theory and network-based time series analysis. Regarding complex network theory, the novel contributions include consistent frameworks for analyzing the topology of (i) general networks of interacting networks and (ii) networks with vertices of heterogeneously distributed weights, as well as (iii) an analytical theory for describing spatial networks. In the realm of time series analysis, (i) recurrence network analysis is put forward as a theoretically founded, nonlinear technique for the study of single, but possibly multivariate time series. (ii) Coupled climate networks are introduced as an exploratory tool of data analysis for quantitatively characterizing the intricate statistical interdependency structure within and between several fields of time series. The second part presents applications for detecting dynamical transitions (tipping points) in time series and studying bottlenecks in the atmosphere's general circulation structure. The analysis of paleoclimate data reveals a possible influence of large-scale shifts in Plio-Pleistocene African climate variability on events in human evolution. This presentation summarizes the contents of the dissertation titled "Functional network macroscopes for

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

    Science.gov (United States)

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

    2016-04-01

    We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology. pyunicorn is available online at https://github.com/pik-copan/pyunicorn. Reference: J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng, L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra, and J. Kurths, Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, Chaos 25, 113101 (2015), DOI: 10.1063/1.4934554, Preprint: arxiv.org:1507.01571 [physics.data-an].

  9. Functional Integration between Salience and Central Executive Networks: A Role for Action Video Game Experience.

    Science.gov (United States)

    Gong, Diankun; He, Hui; Ma, Weiyi; Liu, Dongbo; Huang, Mengting; Dong, Li; Gong, Jinnan; Li, Jianfu; Luo, Cheng; Yao, Dezhong

    2016-01-01

    Action video games (AVGs) have attracted increasing research attention as they offer a unique perspective into the relation between active learning and neural plasticity. However, little research has examined the relation between AVG experience and the plasticity of neural network mechanisms. It has been proposed that AVG experience is related to the integration between Salience Network (SN) and Central Executive Network (CEN), which are responsible for attention and working memory, respectively, two cognitive functions essential for AVG playing. This study initiated a systematic investigation of this proposition by analyzing AVG experts' and amateurs' resting-state brain functions through graph theoretical analyses and functional connectivity. Results reveal enhanced intra- and internetwork functional integrations in AVG experts compared to amateurs. The findings support the possible relation between AVG experience and the neural network plasticity.

  10. Functional Integration between Salience and Central Executive Networks: A Role for Action Video Game Experience

    Directory of Open Access Journals (Sweden)

    Diankun Gong

    2016-01-01

    Full Text Available Action video games (AVGs have attracted increasing research attention as they offer a unique perspective into the relation between active learning and neural plasticity. However, little research has examined the relation between AVG experience and the plasticity of neural network mechanisms. It has been proposed that AVG experience is related to the integration between Salience Network (SN and Central Executive Network (CEN, which are responsible for attention and working memory, respectively, two cognitive functions essential for AVG playing. This study initiated a systematic investigation of this proposition by analyzing AVG experts’ and amateurs’ resting-state brain functions through graph theoretical analyses and functional connectivity. Results reveal enhanced intra- and internetwork functional integrations in AVG experts compared to amateurs. The findings support the possible relation between AVG experience and the neural network plasticity.

  11. The Approach to an Estimation of a Local Area Network Functioning Efficiency

    Directory of Open Access Journals (Sweden)

    M. M. Taraskin

    2010-09-01

    Full Text Available In the article authors call attention to a choice of system of metrics, which permits to take a qualitative assessment of local area network functioning efficiency in condition of computer attacks.

  12. Satisfiability of logic programming based on radial basis function neural networks

    International Nuclear Information System (INIS)

    Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong

    2014-01-01

    In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems

  13. Satisfiability of logic programming based on radial basis function neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)

    2014-07-10

    In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.

  14. Adolescent transgressors: social network and family functioning / Adolescentes infratores: rede social e funcionamento familiar

    Directory of Open Access Journals (Sweden)

    Bianca de Moraes Branco

    2008-01-01

    Full Text Available The aim of this studys is to identify the characteristics of the adolescents as well as their familys social network, regarding socio-bio-demographic aspects and the interns' perception of their familys functioning. The participants were five adolescents in a State institution (FASE-RS who were also allowed to have external activities. A case study method was used through the making of a social network map and the family's functioning was measured using the global assessment of relational functioning scale (GARF. None of the five adolescents assessed used the work/school quadrant of social network. Three of them only filled the family and friends' quadrants. The total members and institutions included in the social network map varied between six and twelve. The scores on family functioning were low.

  15. A network biology approach evaluating the anticancer effects of bortezomib identifies SPARC as a therapeutic target in adult T-cell leukemia cells

    Directory of Open Access Journals (Sweden)

    Yu Zhang

    2008-10-01

    Full Text Available Junko H Ohyashiki1, Ryoko Hamamura2, Chiaki Kobayashi2, Yu Zhang2, Kazuma Ohyashiki21Intractable Immune System Disease Research Center, Tokyo Medical University, Tokyo, Japan; 2First Department of Internal Medicine, Tokyo Medical University, Tokyo, JapanAbstract: There is a need to identify the regulatory gene interaction of anticancer drugs on target cancer cells. Whole genome expression profiling offers promise in this regard, but can be complicated by the challenge of identifying the genes affected by hundreds to thousands of genes that induce changes in expression. A proteasome inhibitor, bortezomib, could be a potential therapeutic agent in treating adult T-cell leukemia (ATL patients, however, the underlying mechanism by which bortezomib induces cell death in ATL cells via gene regulatory network has not been fully elucidated. Here we show that a Bayesian statistical framework by VoyaGene® identified a secreted protein acidic and rich in cysteine (SPARC gene, a tumor-invasiveness related gene, as a possible modulator of bortezomib-induced cell death in ATL cells. Functional analysis using RNAi experiments revealed that inhibition of the expression SPARC by siRNA enhanced the apoptotic effect of bortezomib on ATL cells in accordance with an increase of cleaved caspase 3. Targeting SPARC may help to treat ATL patients in combination with bortezomib. This work shows that a network biology approach can be used advantageously to identify the genetic interaction related to anticancer effects.Keywords: network biology, adult T cell leukemia, bortezomib, SPARC

  16. Similarity in gene-regulatory networks suggests that cancer cells share characteristics of embryonic neural cells.

    Science.gov (United States)

    Zhang, Zan; Lei, Anhua; Xu, Liyang; Chen, Lu; Chen, Yonglong; Zhang, Xuena; Gao, Yan; Yang, Xiaoli; Zhang, Min; Cao, Ying

    2017-08-04

    Cancer cells are immature cells resulting from cellular reprogramming by gene misregulation, and redifferentiation is expected to reduce malignancy. It is unclear, however, whether cancer cells can undergo terminal differentiation. Here, we show that inhibition of the epigenetic modification enzyme enhancer of zeste homolog 2 (EZH2), histone deacetylases 1 and 3 (HDAC1 and -3), lysine demethylase 1A (LSD1), or DNA methyltransferase 1 (DNMT1), which all promote cancer development and progression, leads to postmitotic neuron-like differentiation with loss of malignant features in distinct solid cancer cell lines. The regulatory effect of these enzymes in neuronal differentiation resided in their intrinsic activity in embryonic neural precursor/progenitor cells. We further found that a major part of pan-cancer-promoting genes and the signal transducers of the pan-cancer-promoting signaling pathways, including the epithelial-to-mesenchymal transition (EMT) mesenchymal marker genes, display neural specific expression during embryonic neurulation. In contrast, many tumor suppressor genes, including the EMT epithelial marker gene that encodes cadherin 1 ( CDH1 ), exhibited non-neural or no expression. This correlation indicated that cancer cells and embryonic neural cells share a regulatory network, mediating both tumorigenesis and neural development. This observed similarity in regulatory mechanisms suggests that cancer cells might share characteristics of embryonic neural cells. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  17. Visualizing chemical functionality in plant cell walls

    OpenAIRE

    Zeng, Yining; Himmel, Michael E.; Ding, Shi-You

    2017-01-01

    Understanding plant cell wall cross-linking chemistry and polymeric architecture is key to the efficient utilization of biomass in all prospects from rational genetic modification to downstream chemical and biological conversion to produce fuels and value chemicals. In fact, the bulk properties of cell wall recalcitrance are collectively determined by its chemical features over a wide range of length scales from tissue, cellular to polymeric architectures. Microscopic visualization of cell wa...

  18. Cortical network during deception detection by functional neuroimaging

    International Nuclear Information System (INIS)

    Saito, Keiichi

    2008-01-01

    We examined the coherence of cortical network during deception detection. First, we performed combined EEG-MRI experiments during the Guilty Knowledge Test (GKT) using number cards which has been used to model deception and 5 right-handed healthy participants performed the experiment. The superior frontal gyrus, the anterior cingulate cortex and the inferior parietal lobule were activated and the P 300 event-related brain potential (300-450 ms) was detected at only 'Lie' card. Secondary, we measured magnetoencephalography (MEG) data during GKT and the other 5 right-handed healthy subjects participated in the next experiment. The coherence between the superior frontal gyrus and the inferior parietal lobule showed significant differences between 'Lie' card and 'truth' cards during P 300 emerging. This results indicates that the coherence of cortical network is useful for GKT. (author)

  19. Deficient natural killer cell function in preeclampsia

    Energy Technology Data Exchange (ETDEWEB)

    Alanen, A.; Lassila, O.

    1982-11-01

    Natural killer cell activity of peripheral blood lymphocytes was measured against K-562 target cells with a 4-hour /sup 51/Cr release assay in 15 primigravid women with preeclamptic symptoms. Nineteen primigravid women with an uncomplicated pregnancy and 18 nonpregnant women served as controls. The natural killer cell activity of preeclamptic women was observed to be significantly lower than that of both control groups. Natural killer cells in preeclamptic women responded normally to augmentation caused by interferon. These findings give further evidence for the participation of the maternal immune system in this pregnancy disorder.

  20. Bioinformatic Integration of Molecular Networks and Major Pathways Involved in Mice Cochlear and Vestibular Supporting Cells

    Directory of Open Access Journals (Sweden)

    Teresa Requena

    2018-04-01

    Full Text Available Background: Cochlear and vestibular epithelial non-hair cells (ENHCs are the supporting elements of the cellular architecture in the organ of Corti and the vestibular neuroepithelium in the inner ear. Intercellular and cell-extracellular matrix interactions are essential to prevent an abnormal ion redistribution leading to hearing and vestibular loss. The aim of this study is to define the main pathways and molecular networks in the mouse ENHCs.Methods: We retrieved microarray and RNA-seq datasets from mouse epithelial sensory and non-sensory cells from gEAR portal (http://umgear.org/index.html and obtained gene expression fold-change between ENHCs and non-epithelial cells (NECs against HCs for each gene. Differentially expressed genes (DEG with a log2 fold change between 1 and −1 were discarded. The remaining genes were selected to search for interactions using Ingenuity Pathway Analysis and STRING platform. Specific molecular networks for ENHCs in the cochlea and the vestibular organs were generated and significant pathways were identified.Results: Between 1723 and 1559 DEG were found in the mouse cochlear and vestibular tissues, respectively. Six main pathways showed enrichment in the supporting cells in both tissues: (1 “Inhibition of Matrix Metalloproteases”; (2 “Calcium Transport I”; (3 “Calcium Signaling”; (4 “Leukocyte Extravasation Signaling”; (5 “Signaling by Rho Family GTPases”; and (6 “Axonal Guidance Si”. In the mouse cochlea, ENHCs showed a significant enrichment in 18 pathways highlighting “axonal guidance signaling (AGS” (p = 4.37 × 10−8 and “RhoGDI Signaling” (p = 3.31 × 10−8. In the vestibular dataset, there were 20 enriched pathways in ENHCs, the most significant being “Leukocyte Extravasation Signaling” (p = 8.71 × 10−6, “Signaling by Rho Family GTPases” (p = 1.20 × 10−5 and “Calcium Signaling” (p = 1.20 × 10−5. Among the top ranked networks, the most biologically

  1. Exponential stability of Cohen-Grossberg neural networks with a general class of activation functions

    International Nuclear Information System (INIS)

    Wan Anhua; Wang Miansen; Peng Jigen; Qiao Hong

    2006-01-01

    In this Letter, the dynamics of Cohen-Grossberg neural networks model are investigated. The activation functions are only assumed to be Lipschitz continuous, which provide a much wider application domain for neural networks than the previous results. By means of the extended nonlinear measure approach, new and relaxed sufficient conditions for the existence, uniqueness and global exponential stability of equilibrium of the neural networks are obtained. Moreover, an estimate for the exponential convergence rate of the neural networks is precisely characterized. Our results improve those existing ones

  2. Drp1 guarding of the mitochondrial network is important for glucose-stimulated insulin secretion in pancreatic beta cells

    Energy Technology Data Exchange (ETDEWEB)

    Reinhardt, Florian; Schultz, Julia; Waterstradt, Rica; Baltrusch, Simone, E-mail: simone.baltrusch@med.uni-rostock.de

    2016-06-10

    Mitochondria form a tubular network in mammalian cells, and the mitochondrial life cycle is determined by fission, fusion and autophagy. Dynamin-related protein 1 (Drp1) has a pivotal role in these processes because it alone is able to constrict mitochondria. However, the regulation and function of Drp1 have been shown to vary between cell types. Mitochondrial morphology affects mitochondrial metabolism and function. In pancreatic beta cells mitochondrial metabolism is a key component of the glucose-induced cascade of insulin secretion. The goal of the present study was to investigate the action of Drp1 in pancreatic beta cells. For this purpose Drp1 was down-regulated by means of shDrp1 in insulin-secreting INS1 cells and mouse pancreatic islets. In INS1 cells reduced Drp1 expression resulted in diminished expression of proteins regulating mitochondrial fusion, namely mitofusin 1 and 2, and optic atrophy protein 1. Diminished mitochondrial dynamics can therefore be assumed. After down-regulation of Drp1 in INS1 cells and spread mouse islets the initially homogenous mitochondrial network characterised by a moderate level of interconnections shifted towards high heterogeneity with elongated, clustered and looped mitochondria. These morphological changes were found to correlate directly with functional alterations. Mitochondrial membrane potential and ATP generation were significantly reduced in INS1 cells after Drp1down-regulation. Finally, a significant loss of glucose-stimulated insulin secretion was demonstrated in INS1 cells and mouse pancreatic islets. In conclusion, Drp1 expression is important in pancreatic beta cells to maintain the regulation of insulin secretion. -- Highlights: •Down-regulation of Drp1 in INS1 cells reduces mitochondrial fusion protein expression. •Mitochondrial membrane potential in INS1 cells is diminished after Drp1 down-regulation. •Mitochondria become elongated after down-regulation of Drp1 in beta cells. •Down-regulation of

  3. Insights into brain architectures from the homological scaffolds of functional connectivity networks

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    Louis-David Lord

    2016-11-01

    Full Text Available In recent years, the application of network analysis to neuroimaging data has provided useful insights about the brain’s functional and structural organization in both health and disease. This has proven a significant paradigm shift from the study of individual brain regions in isolation. Graph-based models of the brain consist of vertices, which represent distinct brain areas, and edges which encode the presence (or absence of a structural or functional relationship between each pair of vertices. By definition, any graph metric will be defined upon this dyadic representation of the brain activity. It is however unclear to what extent these dyadic relationships can capture the brain’s complex functional architecture and the encoding of information in distributed networks. Moreover, because network representations of global brain activity are derived from measures that have a continuous response (i.e. interregional BOLD signals, it is methodologically complex to characterize the architecture of functional networks using traditional graph-based approaches. In the present study, we investigate the relationship between standard network metrics computed from dyadic interactions in a functional network, and a metric defined on the persistence homological scaffold of the network, which is a summary of the persistent homology structure of resting-state fMRI data. The persistence homological scaffold is a summary network that differs in important ways from the standard network representations of functional neuroimaging data: i it is constructed using the information from all edge weights comprised in the original network without applying an ad hoc threshold and ii as a summary of persistent homology, it considers the contributions of simplicial structures to the network organization rather than dyadic edge-vertices interactions. We investigated the information domain captured by the persistence homological scaffold by computing the strength of each

  4. Long-term oil contamination alters the molecular ecological networks of soil microbial functional genes

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    Yuting eLiang

    2016-02-01

    Full Text Available With knowledge on microbial composition and diversity, investigation of within-community interactions is a further step to elucidate microbial ecological functions, such as the biodegradation of hazardous contaminants. In this work, microbial functional molecular ecological networks were studied in both contaminated and uncontaminated soils to determine the possible influences of oil contamination on microbial interactions and potential functions. Soil samples were obtained from an oil-exploring site located in South China, and the microbial functional genes were analyzed with GeoChip, a high-throughput functional microarray. By building random networks based on null model, we demonstrated that overall network structures and properties were significantly different between contaminated and uncontaminated soils (P < 0.001. Network connectivity, module numbers, and modularity were all reduced with contamination. Moreover, the topological roles of the genes (module hub and connectors were altered with oil contamination. Subnetworks of genes involved in alkane and polycyclic aromatic hydrocarbon degradation were also constructed. Negative co-occurrence patterns prevailed among functional genes, thereby indicating probable competition relationships. The potential keystone genes, defined as either hubs or genes with highest connectivities in the network, were further identified. The network constructed in this study predicted the potential effects of anthropogenic contamination on microbial community co-occurrence interactions.

  5. Dopaminergic modulation of tracer coupling in a ganglion-amacrine cell network

    OpenAIRE

    MILLS, STEPHEN L.; XIA, XIAO-BO; HOSHI, HIDEO; FIRTH, SALLY I.; RICE, MARGARET E.; FRISHMAN, LAURA J.; MARSHAK, DAVID W.

    2007-01-01

    Many retinal ganglion cells are coupled via gap junctions with neighboring amacrine cells and ganglion cells. We investigated the extent and dynamics of coupling in one such network, the OFF α ganglion cell of rabbit retina and its associated amacrine cells. We also observed the relative spread of Neurobiotin injected into a ganglion cell in the presence of modulators of gap junctional permeability. We found that gap junctions between amacrine cells were closed via stimulation of a D1 dopamin...

  6. Aberrant Resting-State Functional Connectivity in the Salience Network of Adolescent Chronic Fatigue Syndrome.

    Directory of Open Access Journals (Sweden)

    Laura Anne Wortinger

    Full Text Available Neural network investigations are currently absent in adolescent chronic fatigue syndrome (CFS. In this study, we examine whether the core intrinsic connectivity networks (ICNs are altered in adolescent CFS patients. Eighteen adolescent patients with CFS and 18 aged matched healthy adolescent control subjects underwent resting-state functional magnetic resonance imaging (rfMRI. Data was analyzed using dual-regression independent components analysis, which is a data-driven approach for the identification of independent brain networks. Intrinsic connectivity was evaluated in the default mode network (DMN, salience network (SN, and central executive network (CEN. Associations between network characteristics and symptoms of CFS were also explored. Adolescent CFS patients displayed a significant decrease in SN functional connectivity to the right posterior insula compared to healthy comparison participants, which was related to fatigue symptoms. Additionally, there was an association between pain intensity and SN functional connectivity to the left middle insula and caudate that differed between adolescent patients and healthy comparison participants. Our findings of insula dysfunction and its association with fatigue severity and pain intensity in adolescent CFS demonstrate an aberration of the salience network which might play a role in CFS pathophysiology.

  7. Functional network-based statistics in depression: Theory of mind subnetwork and importance of parietal region.

    Science.gov (United States)

    Lai, Chien-Han; Wu, Yu-Te; Hou, Yuh-Ming

    2017-08-01

    The functional network analysis of whole brain is an emerging field for research in depression. We initiated this study to investigate which subnetwork is significantly altered within the functional connectome in major depressive disorder (MDD). The study enrolled 52 first-episode medication-naïve patients with MDD and 40 controls for functional network analysis. All participants received the resting-state functional imaging using a 3-Tesla magnetic resonance scanner. After preprocessing, we calculated the connectivity matrix of functional connectivity in whole brain for each subject. The network-based statistics of connectome was used to perform group comparisons between patients and controls. The correlations between functional connectivity and clinical parameters were also performed. MDD patients had significant alterations in the network involving "theory of mind" regions, such as the left precentral gyrus, left angular gyrus, bilateral rolandic operculums and left inferior frontal gyrus. The center node of significant network was the left angular gyrus. No significant correlations of functional connectivity within the subnetwork and clinical parameters were noted. Functional connectivity of "theory of mind" subnetwork may be the core issue for pathophysiology in MDD. In addition, the center role of parietal region should be emphasized in future study. Copyright © 2017. Published by Elsevier B.V.

  8. Default mode network in the effects of Δ9-Tetrahydrocannabinol (THC on human executive function.

    Directory of Open Access Journals (Sweden)

    Matthijs G Bossong

    Full Text Available Evidence is increasing for involvement of the endocannabinoid system in cognitive functions including attention and executive function, as well as in psychiatric disorders characterized by cognitive deficits, such as schizophrenia. Executive function appears to be associated with both modulation of active networks and inhibition of activity in the default mode network. In the present study, we examined the role of the endocannabinoid system in executive function, focusing on both the associated brain network and the default mode network. A pharmacological functional magnetic resonance imaging (fMRI study was conducted with a placebo-controlled, cross-over design, investigating effects of the endocannabinoid agonist Δ9-tetrahydrocannabinol (THC on executive function in 20 healthy volunteers, using a continuous performance task with identical pairs. Task performance was impaired after THC administration, reflected in both an increase in false alarms and a reduction in detected targets. This was associated with reduced deactivation in a set of brain regions linked to the default mode network, including posterior cingulate cortex and angular gyrus. Less deactivation was significantly correlated with lower performance after THC. Regions that were activated by the continuous performance task, notably bilateral prefrontal and parietal cortex, did not show effects of THC. These findings suggest an important role for the endocannabinoid system in both default mode modulation and executive function. This may be relevant for psychiatric disorders associated with executive function deficits, such as schizophrenia and ADHD.

  9. Increased frontal functional networks in adult survivors of childhood brain tumors

    Directory of Open Access Journals (Sweden)

    Hongbo Chen

    2016-01-01

    Full Text Available Childhood brain tumors and associated treatment have been shown to affect brain development and cognitive outcomes. Understanding the functional connectivity of brain many years after diagnosis and treatment may inform the development of interventions to improve the long-term outcomes of adult survivors of childhood brain tumors. This work investigated the frontal region functional connectivity of 16 adult survivors of childhood cerebellar tumors after an average of 14.9 years from diagnosis and 16 demographically-matched controls using resting state functional MRI (rs-fMRI. Independent component analysis (ICA was applied to identify the resting state activity from rs-fMRI data and to select the specific regions associated with executive functions, followed by the secondary analysis of the functional networks connecting these regions. It was found that survivors exhibited differences in the functional connectivity in executive control network (ECN, default mode network (DMN and salience network (SN compared to demographically-matched controls. More specifically, the number of functional connectivity observed in the survivors is higher than that in the controls, and with increased strength, or stronger correlation coefficient between paired seeds, in survivors compared to the controls. Observed hyperconnectivity in the selected frontal functional network thus is consistent with findings in patients with other neurological injuries and diseases.

  10. In vitro functional gut-like organ formation from mouse embryonic stem cells.

    Science.gov (United States)

    Yamada, Takatsugu; Yoshikawa, Masahide; Takaki, Miyako; Torihashi, Shigeko; Kato, Yoko; Nakajima, Yoshiyuki; Ishizaka, Shigeaki; Tsunoda, Yukio

    2002-01-01

    Embryonic stem (ES) cells have a pluripotent ability to differentiate into a variety of cell lineages in vitro. We have recently found that ES cells can give rise to a functional gut-like unit, which forms a three-dimensional dome-like structure with lumen and exhibits mechanical activity, such as spontaneous contraction and peristalsis. The aim of the present study was to investigate the electrophysiological and morphological properties of ES cell-derived contracting clusters. Electrical activity was examined by an extracellular recording. Morphology and cellular components were investigated by immunohistochemistry and electron microscopy. Clusters with rhythmic contractions displayed electrical slow waves at a regular rhythm, and clusters with highly coordinated peristalsis showed regular slow waves and spontaneous spike action potentials. Immunoreactivity for c-Kit, a marker of interstitial cells of Cajal (ICC), was observed in dense network structures. Neuronal marker PGP9.5 immunoreactivity was observed only in clusters with peristalsis. The topographical structure of the wall was organized by an inner epithelial layer and outer smooth muscle layer. The smooth muscle layer was provided with an ICC network and innervated with enteric neurons. ES cells can differentiate into a functional gut-like organ in vitro that exhibits physiological and morphological properties characteristic of the gastrointestinal (GI) tract. This ES cell-derived gut provides a powerful tool for studying GI motility and gut development in vitro, and has potential for elucidating and treating a variety of motility disorders.

  11. A Novel Re-keying Function Protocol (NRFP) For Wireless Sensor Network Security.

    Science.gov (United States)

    Abdullah, Maan Younis; Hua, Gui Wei; Alsharabi, Naif

    2008-12-04

    This paper describes a novel re-keying function protocol (NRFP) for wireless sensor network security. A re-keying process management system for sensor networks is designed to support in-network processing. The design of the protocol is motivated by decentralization key management for wireless sensor networks (WSNs), covering key deployment, key refreshment, and key establishment. NRFP supports the establishment of novel administrative functions for sensor nodes that derive/re-derive a session key for each communication session. The protocol proposes direct connection, in-direct connection and hybrid connection. NRFP also includes an efficient protocol for local broadcast authentication based on the use of one-way key chains. A salient feature of the authentication protocol is that it supports source authentication without precluding in-network processing. Security and performance analysis shows that it is very efficient in computation, communication and storage and, that NRFP is also effective in defending against many sophisticated attacks.

  12. A Novel Re-keying Function Protocol (NRFP For Wireless Sensor Network Security

    Directory of Open Access Journals (Sweden)

    Naif Alsharabi

    2008-12-01

    Full Text Available This paper describes a novel re-keying function protocol (NRFP for wireless sensor network security. A re-keying process management system for sensor networks is designed to support in-network processing. The design of the protocol is motivated by decentralization key management for wireless sensor networks (WSNs, covering key deployment, key refreshment, and key establishment. NRFP supports the establishment of novel administrative functions for sensor nodes that derive/re-derive a session key for each communication session. The protocol proposes direct connection, in-direct connection and hybrid connection. NRFP also includes an efficient protocol for local broadcast authentication based on the use of one-way key chains. A salient feature of the authentication protocol is that it supports source authentication without precluding in-network processing. Security and performance analysis shows that it is very efficient in computation, communication and storage and, that NRFP is also effective in defending against many sophisticated attacks.

  13. Functional magnetic resonance microscopy at single-cell resolution in Aplysia californica

    Science.gov (United States)

    Radecki, Guillaume; Nargeot, Romuald; Jelescu, Ileana Ozana; Le Bihan, Denis; Ciobanu, Luisa

    2014-01-01

    In this work, we show the feasibility of performing functional MRI studies with single-cell resolution. At ultrahigh magnetic field, manganese-enhanced magnetic resonance microscopy allows the identification of most motor neurons in the buccal network of Aplysia at low, nontoxic Mn2+ concentrations. We establish that Mn2+ accumulates intracellularly on injection into the living Aplysia and that its concentration increases when the animals are presented with a sensory stimulus. We also show that we can distinguish between neuronal activities elicited by different types of stimuli. This method opens up a new avenue into probing the functional organization and plasticity of neuronal networks involved in goal-directed behaviors with single-cell resolution. PMID:24872449

  14. The Anatomy of the Global Football Player Transfer Network: Club Functionalities versus Network Properties.

    Directory of Open Access Journals (Sweden)

    Xiao Fan Liu

    Full Text Available Professional association football is a game of talent. The success of a professional club hinges largely on its ability of assembling the best team. Building on a dataset of player transfer records among more than 400 clubs in 24 world-wide top class leagues from 2011 to 2015, this study aims to relate a club's success to its activities in the player transfer market from a network perspective. We confirm that modern professional football is indeed a money game, in which larger investment spent on the acquisition of talented players generally yields better team performance. However, further investigation shows that professional football clubs can actually play different strategies in surviving or even excelling this game, and the success of strategies is strongly associated to their network properties in the football player transfer network.

  15. The Anatomy of the Global Football Player Transfer Network: Club Functionalities versus Network Properties.

    Science.gov (United States)

    Liu, Xiao Fan; Liu, Yu-Liang; Lu, Xin-Hang; Wang, Qi-Xuan; Wang, Tong-Xing

    2016-01-01

    Professional association football is a game of talent. The success of a professional club hinges largely on its ability of assembling the best team. Building on a dataset of player transfer records among more than 400 clubs in 24 world-wide top class leagues from 2011 to 2015, this study aims to relate a club's success to its activities in the player transfer market from a network perspective. We confirm that modern professional football is indeed a money game, in which larger investment spent on the acquisition of talented players generally yields better team performance. However, further investigation shows that professional football clubs can actually play different strategies in surviving or even excelling this game, and the success of strategies is strongly associated to their network properties in the football player transfer network.

  16. The construction of an amino acid network for understanding protein structure and function.

    Science.gov (United States)

    Yan, Wenying; Zhou, Jianhong; Sun, Maomin; Chen, Jiajia; Hu, Guang; Shen, Bairong

    2014-06-01

    Amino acid networks (AANs) are undirected networks consisting of amino acid residues and their interactions in three-dimensional protein structures. The analysis of AANs provides novel insight into protein science, and several common amino acid network properties have revealed diverse classes of proteins. In this review, we first summarize methods for the construction and characterization of AANs. We then compare software tools for the construction and analysis of AANs. Finally, we review the application of AANs for understanding protein structure and function, including the identification of functional residues, the prediction of protein folding, analyzing protein stability and protein-protein interactions, and for understanding communication within and between proteins.

  17. Hypoxia induces a phase transition within a kinase signaling network in cancer cells.

    Science.gov (United States)

    Wei, Wei; Shi, Qihui; Remacle, Francoise; Qin, Lidong; Shackelford, David B; Shin, Young Shik; Mischel, Paul S; Levine, R D; Heath, James R

    2013-04-09

    Hypoxia is a near-universal feature of cancer, promoting glycolysis, cellular proliferation, and angiogenesis. The molecular mechanisms of hypoxic signaling have been intensively studied, but the impact of changes in oxygen partial pressure (pO2) on the state of signaling networks is less clear. In a glioblastoma multiforme (GBM) cancer cell model, we examined the response of signaling networks to targeted pathway inhibition between 21% and 1% pO2. We used a microchip technology that facilitates quantification of a panel of functional proteins from statistical numbers of single cells. We find that near 1.5% pO2, the signaling network associated with mammalian target of rapamycin (mTOR) complex 1 (mTORC1)--a critical component of hypoxic signaling and a compelling cancer drug target--is deregulated in a manner such that it will be unresponsive to mTOR kinase inhibitors near 1.5% pO2, but will respond at higher or lower pO2 values. These predictions were validated through experiments on bulk GBM cell line cultures and on neurosphere cultures of a human-origin GBM xenograft tumor. We attempt to understand this behavior through the use of a quantitative version of Le Chatelier's principle, as well as through a steady-state kinetic model of protein interactions, both of which indicate that hypoxia can influence mTORC1 signaling as a switch. The Le Chatelier approach also indicates that this switch may be thought of as a type of phase transition. Our analysis indicates that certain biologically complex cell behaviors may be understood using fundamental, thermodynamics-motivated principles.

  18. Hypoxia induces a phase transition within a kinase signaling network in cancer cells

    Science.gov (United States)

    Wei, Wei; Shi, Qihui; Remacle, Francoise; Qin, Lidong; Shackelford, David B.; Shin, Young Shik; Mischel, Paul S.; Levine, R. D.; Heath, James R.

    2013-01-01

    Hypoxia is a near-universal feature of cancer, promoting glycolysis, cellular proliferation, and angiogenesis. The molecular mechanisms of hypoxic signaling have been intensively studied, but the impact of changes in oxygen partial pressure (pO2) on the state of signaling networks is less clear. In a glioblastoma multiforme (GBM) cancer cell model, we examined the response of signaling networks to targeted pathway inhibition between 21% and 1% pO2. We used a microchip technology that facilitates quantification of a panel of functional proteins from statistical numbers of single cells. We find that near 1.5% pO2, the signaling network associated with mammalian target of rapamycin (mTOR) complex 1 (mTORC1)—a critical component of hypoxic signaling and a compelling cancer drug target—is deregulated in a manner such that it will be unresponsive to mTOR kinase inhibitors near 1.5% pO2, but will respond at higher or lower pO2 values. These predictions were validated through experiments on bulk GBM cell line cultures and on neurosphere cultures of a human-origin GBM xenograft tumor. We attempt to understand this behavior through the use of a quantitative version of Le Chatelier’s principle, as well as through a steady-state kinetic model of protein interactions, both of which indicate that hypoxia can influence mTORC1 signaling as a switch. The Le Chatelier approach also indicates that this switch may be thought of as a type of phase transition. Our analysis indicates that certain biologically complex cell behaviors may be understood using fundamental, thermodynamics-motivated principles. PMID:23530221

  19. Network pharmacology of medicinal attributes and functions of Chinese herbal medicines: (IV Classification and network analysis of medicinal functions of Chinese herbal medicines

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2017-09-01

    Full Text Available In present study I used the data from CHM-DATA, the interactive database of 1127 Chinese herbal medicines with 78 medicinal functions (attributes. The relational network for medicinal functions of Chinese herbal medicines was constructed using my earlier data and methods. Results of network analysis showed that the network is a scale-fr