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Sample records for general co-expression network-based

  1. Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules.

    Science.gov (United States)

    Xiao, Xiaolin; Moreno-Moral, Aida; Rotival, Maxime; Bottolo, Leonardo; Petretto, Enrico

    2014-01-01

    Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co-expressed

  2. Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules.

    Directory of Open Access Journals (Sweden)

    Xiaolin Xiao

    2014-01-01

    Full Text Available Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states. Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based and humans (mRNA-sequencing-based and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi

  3. Learning in neural networks based on a generalized fluctuation theorem

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    Hayakawa, Takashi; Aoyagi, Toshio

    2015-11-01

    Information maximization has been investigated as a possible mechanism of learning governing the self-organization that occurs within the neural systems of animals. Within the general context of models of neural systems bidirectionally interacting with environments, however, the role of information maximization remains to be elucidated. For bidirectionally interacting physical systems, universal laws describing the fluctuation they exhibit and the information they possess have recently been discovered. These laws are termed fluctuation theorems. In the present study, we formulate a theory of learning in neural networks bidirectionally interacting with environments based on the principle of information maximization. Our formulation begins with the introduction of a generalized fluctuation theorem, employing an interpretation appropriate for the present application, which differs from the original thermodynamic interpretation. We analytically and numerically demonstrate that the learning mechanism presented in our theory allows neural networks to efficiently explore their environments and optimally encode information about them.

  4. A recurrent neural network based on projection operator for extended general variational inequalities.

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    Liu, Qingshan; Cao, Jinde

    2010-06-01

    Based on the projection operator, a recurrent neural network is proposed for solving extended general variational inequalities (EGVIs). Sufficient conditions are provided to ensure the global convergence of the proposed neural network based on Lyapunov methods. Compared with the existing neural networks for variational inequalities, the proposed neural network is a modified version of the general projection neural network existing in the literature and capable of solving the EGVI problems. In addition, simulation results on numerical examples show the effectiveness and performance of the proposed neural network.

  5. Structural Behavioral Study on the General Aviation Network Based on Complex Network

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    Zhang, Liang; Lu, Na

    2017-12-01

    The general aviation system is an open and dissipative system with complex structures and behavioral features. This paper has established the system model and network model for general aviation. We have analyzed integral attributes and individual attributes by applying the complex network theory and concluded that the general aviation network has influential enterprise factors and node relations. We have checked whether the network has small world effect, scale-free property and network centrality property which a complex network should have by applying degree distribution of functions and proved that the general aviation network system is a complex network. Therefore, we propose to achieve the evolution process of the general aviation industrial chain to collaborative innovation cluster of advanced-form industries by strengthening network multiplication effect, stimulating innovation performance and spanning the structural hole path.

  6. Speech Intelligibility Potential of General and Specialized Deep Neural Network Based Speech Enhancement Systems

    DEFF Research Database (Denmark)

    Kolbæk, Morten; Tan, Zheng-Hua; Jensen, Jesper

    2017-01-01

    In this paper, we study aspects of single microphone speech enhancement (SE) based on deep neural networks (DNNs). Specifically, we explore the generalizability capabilities of state-of-the-art DNN-based SE systems with respect to the background noise type, the gender of the target speaker...... general. Finally, we compare how a DNN-based SE system trained to be noise type general, speaker general, and SNR general performs relative to a state-of-the-art short-time spectral amplitude minimum mean square error (STSA-MMSE) based SE algorithm. We show that DNN-based SE systems, when trained...... a state-of-the-art STSA-MMSE based SE method, when tested using a range of unseen speakers and noise types. Finally, a listening test using several DNN-based SE systems tested in unseen speaker conditions show that these systems can improve SI for some SNR and noise type configurations but degrade SI...

  7. A general framework for global asymptotic stability analysis of delayed neural networks based on LMI approach

    International Nuclear Information System (INIS)

    Cao Jinde; Ho, Daniel W.C.

    2005-01-01

    In this paper, global asymptotic stability is discussed for neural networks with time-varying delay. Several new criteria in matrix inequality form are given to ascertain the uniqueness and global asymptotic stability of equilibrium point for neural networks with time-varying delay based on Lyapunov method and Linear Matrix Inequality (LMI) technique. The proposed LMI approach has the advantage of considering the difference of neuronal excitatory and inhibitory efforts, which is also computationally efficient as it can be solved numerically using recently developed interior-point algorithm. In addition, the proposed results generalize and improve previous works. The obtained criteria also combine two existing conditions into one generalized condition in matrix form. An illustrative example is also given to demonstrate the effectiveness of the proposed results

  8. Optimal Allocation of Generalized Power Sources in Distribution Network Based on Multi-Objective Particle Swarm Optimization Algorithm

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    Li Ran

    2017-01-01

    Full Text Available Optimal allocation of generalized power sources in distribution network is researched. A simple index of voltage stability is put forward. Considering the investment and operation benefit, the stability of voltage and the pollution emissions of generalized power sources in distribution network, a multi-objective optimization planning model is established. A multi-objective particle swarm optimization algorithm is proposed to solve the optimal model. In order to improve the global search ability, the strategies of fast non-dominated sorting, elitism and crowding distance are adopted in this algorithm. Finally, tested the model and algorithm by IEEE-33 node system to find the best configuration of GP, the computed result shows that with the generalized power reasonable access to the active distribution network, the investment benefit and the voltage stability of the system is improved, and the proposed algorithm has better global search capability.

  9. Network-Based Effectiveness

    National Research Council Canada - National Science Library

    Friman, Henrik

    2006-01-01

    ...) to increase competitive advantage, innovation, and mission effectiveness. Network-based effectiveness occurs due to the influence of various factors such as people, procedures, technology, and organizations...

  10. Network-Based Effectiveness

    National Research Council Canada - National Science Library

    Friman, Henrik

    2006-01-01

    ... (extended from Leavitt, 1965). This text identifies aspects of network-based effectiveness that can benefit from a better understanding of leadership and management development of people, procedures, technology, and organizations...

  11. Multiscale Embedded Gene Co-expression Network Analysis.

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    Won-Min Song

    2015-11-01

    Full Text Available Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3, the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA by: i introducing quality control of co-expression similarities, ii parallelizing embedded network construction, and iii developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs. We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA. MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

  12. Multiscale Embedded Gene Co-expression Network Analysis.

    Science.gov (United States)

    Song, Won-Min; Zhang, Bin

    2015-11-01

    Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

  13. Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy

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    Oyang Yen-Jen

    2010-10-01

    Full Text Available Abstract Background Molecular networks represent the backbone of molecular activity within cells and provide opportunities for understanding the mechanism of diseases. While protein-protein interaction data constitute static network maps, integration of condition-specific co-expression information provides clues to the dynamic features of these networks. Dilated cardiomyopathy is a leading cause of heart failure. Although previous studies have identified putative biomarkers or therapeutic targets for heart failure, the underlying molecular mechanism of dilated cardiomyopathy remains unclear. Results We developed a network-based comparative analysis approach that integrates protein-protein interactions with gene expression profiles and biological function annotations to reveal dynamic functional modules under different biological states. We found that hub proteins in condition-specific co-expressed protein interaction networks tended to be differentially expressed between biological states. Applying this method to a cohort of heart failure patients, we identified two functional modules that significantly emerged from the interaction networks. The dynamics of these modules between normal and disease states further suggest a potential molecular model of dilated cardiomyopathy. Conclusions We propose a novel framework to analyze the interaction networks in different biological states. It successfully reveals network modules closely related to heart failure; more importantly, these network dynamics provide new insights into the cause of dilated cardiomyopathy. The revealed molecular modules might be used as potential drug targets and provide new directions for heart failure therapy.

  14. Comparison of co-expression measures: mutual information, correlation, and model based indices.

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    Song, Lin; Langfelder, Peter; Horvath, Steve

    2012-12-09

    Co-expression measures are often used to define networks among genes. Mutual information (MI) is often used as a generalized correlation measure. It is not clear how much MI adds beyond standard (robust) correlation measures or regression model based association measures. Further, it is important to assess what transformations of these and other co-expression measures lead to biologically meaningful modules (clusters of genes). We provide a comprehensive comparison between mutual information and several correlation measures in 8 empirical data sets and in simulations. We also study different approaches for transforming an adjacency matrix, e.g. using the topological overlap measure. Overall, we confirm close relationships between MI and correlation in all data sets which reflects the fact that most gene pairs satisfy linear or monotonic relationships. We discuss rare situations when the two measures disagree. We also compare correlation and MI based approaches when it comes to defining co-expression network modules. We show that a robust measure of correlation (the biweight midcorrelation transformed via the topological overlap transformation) leads to modules that are superior to MI based modules and maximal information coefficient (MIC) based modules in terms of gene ontology enrichment. We present a function that relates correlation to mutual information which can be used to approximate the mutual information from the corresponding correlation coefficient. We propose the use of polynomial or spline regression models as an alternative to MI for capturing non-linear relationships between quantitative variables. The biweight midcorrelation outperforms MI in terms of elucidating gene pairwise relationships. Coupled with the topological overlap matrix transformation, it often leads to more significantly enriched co-expression modules. Spline and polynomial networks form attractive alternatives to MI in case of non-linear relationships. Our results indicate that MI

  15. Characterization of chemically induced liver injuries using gene co-expression modules.

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    Gregory J Tawa

    Full Text Available Liver injuries due to ingestion or exposure to chemicals and industrial toxicants pose a serious health risk that may be hard to assess due to a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific damage and clinical outcomes via biomarkers or biomarker panels will provide the foundation for highly specific and robust diagnostic tests. Here, we have used DrugMatrix, a toxicogenomics database containing organ-specific gene expression data matched to dose-dependent chemical exposures and adverse clinical pathology assessments in Sprague Dawley rats, to identify groups of co-expressed genes (modules specific to injury endpoints in the liver. We identified 78 such gene co-expression modules associated with 25 diverse injury endpoints categorized from clinical pathology, organ weight changes, and histopathology. Using gene expression data associated with an injury condition, we showed that these modules exhibited different patterns of activation characteristic of each injury. We further showed that specific module genes mapped to 1 known biochemical pathways associated with liver injuries and 2 clinically used diagnostic tests for liver fibrosis. As such, the gene modules have characteristics of both generalized and specific toxic response pathways. Using these results, we proposed three gene signature sets characteristic of liver fibrosis, steatosis, and general liver injury based on genes from the co-expression modules. Out of all 92 identified genes, 18 (20% genes have well-documented relationships with liver disease, whereas the rest are novel and have not previously been associated with liver disease. In conclusion, identifying gene co-expression modules associated with chemically induced liver injuries aids in generating testable hypotheses and has the potential to identify putative biomarkers of adverse health effects.

  16. Identifying key genes in rheumatoid arthritis by weighted gene co-expression network analysis.

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    Ma, Chunhui; Lv, Qi; Teng, Songsong; Yu, Yinxian; Niu, Kerun; Yi, Chengqin

    2017-08-01

    This study aimed to identify rheumatoid arthritis (RA) related genes based on microarray data using the WGCNA (weighted gene co-expression network analysis) method. Two gene expression profile datasets GSE55235 (10 RA samples and 10 healthy controls) and GSE77298 (16 RA samples and seven healthy controls) were downloaded from Gene Expression Omnibus database. Characteristic genes were identified using metaDE package. WGCNA was used to find disease-related networks based on gene expression correlation coefficients, and module significance was defined as the average gene significance of all genes used to assess the correlation between the module and RA status. Genes in the disease-related gene co-expression network were subject to functional annotation and pathway enrichment analysis using Database for Annotation Visualization and Integrated Discovery. Characteristic genes were also mapped to the Connectivity Map to screen small molecules. A total of 599 characteristic genes were identified. For each dataset, characteristic genes in the green, red and turquoise modules were most closely associated with RA, with gene numbers of 54, 43 and 79, respectively. These genes were enriched in totally enriched in 17 Gene Ontology terms, mainly related to immune response (CD97, FYB, CXCL1, IKBKE, CCR1, etc.), inflammatory response (CD97, CXCL1, C3AR1, CCR1, LYZ, etc.) and homeostasis (C3AR1, CCR1, PLN, CCL19, PPT1, etc.). Two small-molecule drugs sanguinarine and papaverine were predicted to have a therapeutic effect against RA. Genes related to immune response, inflammatory response and homeostasis presumably have critical roles in RA pathogenesis. Sanguinarine and papaverine have a potential therapeutic effect against RA. © 2017 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.

  17. GeneCAT--novel webtools that combine BLAST and co-expression analyses

    DEFF Research Database (Denmark)

    Mutwil, Marek; Obro, Jens; Willats, William G T

    2008-01-01

    The gene co-expression analysis toolbox (GeneCAT) introduces several novel microarray data analyzing tools. First, the multigene co-expression analysis, combined with co-expressed gene networks, provides a more powerful data mining technique than standard, single-gene co-expression analysis. Second...... orthologs in the plant model organisms Arabidopsis thaliana and Hordeum vulgare (Barley). GeneCAT is equipped with expression data for the model plant A. thaliana, and first to introduce co-expression mining tools for the monocot Barley. GeneCAT is available at http://genecat.mpg.de....

  18. Improvisation and co-expression in explorative digital music systems

    DEFF Research Database (Denmark)

    Hansen, Anne-Marie Skriver

    relationships. The benefit of the digitally networked electronic musical instruments is that particular patterns of co-expression can be found and mediated by the music system (that also contains all individual instruments) in ways that make players aware of their mutual play and perhaps will encourage players...... other when they are given a number of creative restrictions in the sonic/musical material that they interact with. The benefit with digital musical instruments is that non-musicians and novices can get access to limited musical material that they are immediately able to master without any musical...... be developed in future designs. The Wacom® pen tablet, a simple drawing interface, was turned into an array of digital musical instruments in order to investigate the benefit of networked musical instruments in the context of the genre of casual games. Through qualitative and quantitative studies of player...

  19. [Weighted gene co-expression network analysis in biomedicine research].

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    Liu, Wei; Li, Li; Ye, Hua; Tu, Wei

    2017-11-25

    High-throughput biological technologies are now widely applied in biology and medicine, allowing scientists to monitor thousands of parameters simultaneously in a specific sample. However, it is still an enormous challenge to mine useful information from high-throughput data. The emergence of network biology provides deeper insights into complex bio-system and reveals the modularity in tissue/cellular networks. Correlation networks are increasingly used in bioinformatics applications. Weighted gene co-expression network analysis (WGCNA) tool can detect clusters of highly correlated genes. Therefore, we systematically reviewed the application of WGCNA in the study of disease diagnosis, pathogenesis and other related fields. First, we introduced principle, workflow, advantages and disadvantages of WGCNA. Second, we presented the application of WGCNA in disease, physiology, drug, evolution and genome annotation. Then, we indicated the application of WGCNA in newly developed high-throughput methods. We hope this review will help to promote the application of WGCNA in biomedicine research.

  20. Weighted Gene Co-expression Network Analysis of the Dioscin Rich Medicinal Plant Dioscorea nipponica

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    Wei Sun

    2017-06-01

    Full Text Available Dioscorea contains critically important species which can be used as staple foods or sources of bioactive substances, including Dioscorea nipponica, which has been used to develop highly successful drugs to treat cardiovascular disease. Its major active ingredients are thought to be sterol compounds such as diosgenin, which has been called “medicinal gold” because of its valuable properties. However, reliance on naturally growing plants as a production system limits the potential use of D. nipponica, raising interest in engineering metabolic pathways to enhance the production of secondary metabolites. However, the biosynthetic pathway of diosgenin is still poorly understood, and D. nipponica is poorly characterized at a molecular level, hindering in-depth investigation. In the present work, the RNAs from five organs and seven methyl jasmonate treated D. nipponica rhizomes were sequenced using the Illumina high-throughput sequencing platform, yielding 52 gigabases of data, which were pooled and assembled into a reference transcriptome. Four hundred and eighty two genes were found to be highly expressed in the rhizomes, and these genes are mainly involved in stress response and transcriptional regulation. Based on their expression patterns, 36 genes were selected for further investigation as candidate genes involved in dioscin biosynthesis. Constructing co-expression networks based on significant changes in gene expression revealed 15 gene modules. Of these, four modules with properties correlating to dioscin regulation and biosynthesis, consisting of 4,665 genes in total, were selected for further functional investigation. These results improve our understanding of dioscin biosynthesis in this important medicinal plant and will help guide more intensive investigations.

  1. Co-Expression of Neighboring Genes in the Zebrafish (Danio rerio Genome

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    Daryi Wang

    2009-08-01

    Full Text Available Neighboring genes in the eukaryotic genome have a tendency to express concurrently, and the proximity of two adjacent genes is often considered a possible explanation for their co-expression behavior. However, the actual contribution of the physical distance between two genes to their co-expression behavior has yet to be defined. To further investigate this issue, we studied the co-expression of neighboring genes in zebrafish, which has a compact genome and has experienced a whole genome duplication event. Our analysis shows that the proportion of highly co-expressed neighboring pairs (Pearson’s correlation coefficient R>0.7 is low (0.24% ~ 0.67%; however, it is still significantly higher than that of random pairs. In particular, the statistical result implies that the co-expression tendency of neighboring pairs is negatively correlated with their physical distance. Our findings therefore suggest that physical distance may play an important role in the co-expression of neighboring genes. Possible mechanisms related to the neighboring genes’ co-expression are also discussed.

  2. Characterization of differentially expressed genes using high-dimensional co-expression networks

    DEFF Research Database (Denmark)

    Coelho Goncalves de Abreu, Gabriel; Labouriau, Rodrigo S.

    2010-01-01

    We present a technique to characterize differentially expressed genes in terms of their position in a high-dimensional co-expression network. The set-up of Gaussian graphical models is used to construct representations of the co-expression network in such a way that redundancy and the propagation...... that allow to make effective inference in problems with high degree of complexity (e.g. several thousands of genes) and small number of observations (e.g. 10-100) as typically occurs in high throughput gene expression studies. Taking advantage of the internal structure of decomposable graphical models, we...... construct a compact representation of the co-expression network that allows to identify the regions with high concentration of differentially expressed genes. It is argued that differentially expressed genes located in highly interconnected regions of the co-expression network are less informative than...

  3. Guidance for RNA-seq co-expression network construction and analysis: safety in numbers.

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    Ballouz, S; Verleyen, W; Gillis, J

    2015-07-01

    RNA-seq co-expression analysis is in its infancy and reasonable practices remain poorly defined. We assessed a variety of RNA-seq expression data to determine factors affecting functional connectivity and topology in co-expression networks. We examine RNA-seq co-expression data generated from 1970 RNA-seq samples using a Guilt-By-Association framework, in which genes are assessed for the tendency of co-expression to reflect shared function. Minimal experimental criteria to obtain performance on par with microarrays were >20 samples with read depth >10 M per sample. While the aggregate network constructed shows good performance (area under the receiver operator characteristic curve ∼0.71), the dependency on number of experiments used is nearly identical to that present in microarrays, suggesting thousands of samples are required to obtain 'gold-standard' co-expression. We find a major topological difference between RNA-seq and microarray co-expression in the form of low overlaps between hub-like genes from each network due to changes in the correlation of expression noise within each technology. jgillis@cshl.edu or sballouz@cshl.edu Networks are available at: http://gillislab.labsites.cshl.edu/supplements/rna-seq-networks/ and supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  4. Dissection of regulatory networks that are altered in disease via differential co-expression.

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    David Amar

    Full Text Available Comparing the gene-expression profiles of sick and healthy individuals can help in understanding disease. Such differential expression analysis is a well-established way to find gene sets whose expression is altered in the disease. Recent approaches to gene-expression analysis go a step further and seek differential co-expression patterns, wherein the level of co-expression of a set of genes differs markedly between disease and control samples. Such patterns can arise from a disease-related change in the regulatory mechanism governing that set of genes, and pinpoint dysfunctional regulatory networks. Here we present DICER, a new method for detecting differentially co-expressed gene sets using a novel probabilistic score for differential correlation. DICER goes beyond standard differential co-expression and detects pairs of modules showing differential co-expression. The expression profiles of genes within each module of the pair are correlated across all samples. The correlation between the two modules, however, differs markedly between the disease and normal samples. We show that DICER outperforms the state of the art in terms of significance and interpretability of the detected gene sets. Moreover, the gene sets discovered by DICER manifest regulation by disease-specific microRNA families. In a case study on Alzheimer's disease, DICER dissected biological processes and protein complexes into functional subunits that are differentially co-expressed, thereby revealing inner structures in disease regulatory networks.

  5. Neural network based multiscale image restoration approach

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    de Castro, Ana Paula A.; da Silva, José D. S.

    2007-02-01

    This paper describes a neural network based multiscale image restoration approach. Multilayer perceptrons are trained with artificial images of degraded gray level circles, in an attempt to make the neural network learn inherent space relations of the degraded pixels. The present approach simulates the degradation by a low pass Gaussian filter blurring operation and the addition of noise to the pixels at pre-established rates. The training process considers the degraded image as input and the non-degraded image as output for the supervised learning process. The neural network thus performs an inverse operation by recovering a quasi non-degraded image in terms of least squared. The main difference of the approach to existing ones relies on the fact that the space relations are taken from different scales, thus providing relational space data to the neural network. The approach is an attempt to come up with a simple method that leads to an optimum solution to the problem. Considering different window sizes around a pixel simulates the multiscale operation. In the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the same steps use for the artificial circle image.

  6. Co-expression network analysis of duplicate genes in maize (Zea mays L.) reveals no subgenome bias.

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    Li, Lin; Briskine, Roman; Schaefer, Robert; Schnable, Patrick S; Myers, Chad L; Flagel, Lex E; Springer, Nathan M; Muehlbauer, Gary J

    2016-11-04

    Gene duplication is prevalent in many species and can result in coding and regulatory divergence. Gene duplications can be classified as whole genome duplication (WGD), tandem and inserted (non-syntenic). In maize, WGD resulted in the subgenomes maize1 and maize2, of which maize1 is considered the dominant subgenome. However, the landscape of co-expression network divergence of duplicate genes in maize is still largely uncharacterized. To address the consequence of gene duplication on co-expression network divergence, we developed a gene co-expression network from RNA-seq data derived from 64 different tissues/stages of the maize reference inbred-B73. WGD, tandem and inserted gene duplications exhibited distinct regulatory divergence. Inserted duplicate genes were more likely to be singletons in the co-expression networks, while WGD duplicate genes were likely to be co-expressed with other genes. Tandem duplicate genes were enriched in the co-expression pattern where co-expressed genes were nearly identical for the duplicates in the network. Older gene duplications exhibit more extensive co-expression variation than younger duplications. Overall, non-syntenic genes primarily from inserted duplications show more co-expression divergence. Also, such enlarged co-expression divergence is significantly related to duplication age. Moreover, subgenome dominance was not observed in the co-expression networks - maize1 and maize2 exhibit similar levels of intra subgenome correlations. Intriguingly, the level of inter subgenome co-expression was similar to the level of intra subgenome correlations, and genes from specific subgenomes were not likely to be the enriched in co-expression network modules and the hub genes were not predominantly from any specific subgenomes in maize. Our work provides a comprehensive analysis of maize co-expression network divergence for three different types of gene duplications and identifies potential relationships between duplication types

  7. Discovery of cis-elements between sorghum and rice using co-expression and evolutionary conservation

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    Haberer Georg

    2009-06-01

    Full Text Available Abstract Background The spatiotemporal regulation of gene expression largely depends on the presence and absence of cis-regulatory sites in the promoter. In the economically highly important grass family, our knowledge of transcription factor binding sites and transcriptional networks is still very limited. With the completion of the sorghum genome and the available rice genome sequence, comparative promoter analyses now allow genome-scale detection of conserved cis-elements. Results In this study, we identified thousands of phylogenetic footprints conserved between orthologous rice and sorghum upstream regions that are supported by co-expression information derived from three different rice expression data sets. In a complementary approach, cis-motifs were discovered by their highly conserved co-occurrence in syntenic promoter pairs. Sequence conservation and matches to known plant motifs support our findings. Expression similarities of gene pairs positively correlate with the number of motifs that are shared by gene pairs and corroborate the importance of similar promoter architectures for concerted regulation. This strongly suggests that these motifs function in the regulation of transcript levels in rice and, presumably also in sorghum. Conclusion Our work provides the first large-scale collection of cis-elements for rice and sorghum and can serve as a paradigm for cis-element analysis through comparative genomics in grasses in general.

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

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    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.

  9. Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex

    Science.gov (United States)

    Hulsman, Marc; Lelieveldt, Boudewijn P. F.; de Ridder, Jeroen; Reinders, Marcel

    2015-01-01

    The three dimensional conformation of the genome in the cell nucleus influences important biological processes such as gene expression regulation. Recent studies have shown a strong correlation between chromatin interactions and gene co-expression. However, predicting gene co-expression from frequent long-range chromatin interactions remains challenging. We address this by characterizing the topology of the cortical chromatin interaction network using scale-aware topological measures. We demonstrate that based on these characterizations it is possible to accurately predict spatial co-expression between genes in the mouse cortex. Consistent with previous findings, we find that the chromatin interaction profile of a gene-pair is a good predictor of their spatial co-expression. However, the accuracy of the prediction can be substantially improved when chromatin interactions are described using scale-aware topological measures of the multi-resolution chromatin interaction network. We conclude that, for co-expression prediction, it is necessary to take into account different levels of chromatin interactions ranging from direct interaction between genes (i.e. small-scale) to chromatin compartment interactions (i.e. large-scale). PMID:25965262

  10. 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

  11. Gene co-expression analysis identifies gene clusters associated with isotropic and polarized growth in Aspergillus fumigatus conidia.

    Science.gov (United States)

    Baltussen, Tim J H; Coolen, Jordy P M; Zoll, Jan; Verweij, Paul E; Melchers, Willem J G

    2018-04-26

    Aspergillus fumigatus is a saprophytic fungus that extensively produces conidia. These microscopic asexually reproductive structures are small enough to reach the lungs. Germination of conidia followed by hyphal growth inside human lungs is a key step in the establishment of infection in immunocompromised patients. RNA-Seq was used to analyze the transcriptome of dormant and germinating A. fumigatus conidia. Construction of a gene co-expression network revealed four gene clusters (modules) correlated with a growth phase (dormant, isotropic growth, polarized growth). Transcripts levels of genes encoding for secondary metabolites were high in dormant conidia. During isotropic growth, transcript levels of genes involved in cell wall modifications increased. Two modules encoding for growth and cell cycle/DNA processing were associated with polarized growth. In addition, the co-expression network was used to identify highly connected intermodular hub genes. These genes may have a pivotal role in the respective module and could therefore be compelling therapeutic targets. Generally, cell wall remodeling is an important process during isotropic and polarized growth, characterized by an increase of transcripts coding for hyphal growth and cell cycle/DNA processing when polarized growth is initiated. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

  12. Matrix factorization reveals aging-specific co-expression gene modules in the fat and muscle tissues in nonhuman primates

    Science.gov (United States)

    Wang, Yongcui; Zhao, Weiling; Zhou, Xiaobo

    2016-10-01

    Accurate identification of coherent transcriptional modules (subnetworks) in adipose and muscle tissues is important for revealing the related mechanisms and co-regulated pathways involved in the development of aging-related diseases. Here, we proposed a systematically computational approach, called ICEGM, to Identify the Co-Expression Gene Modules through a novel mathematical framework of Higher-Order Generalized Singular Value Decomposition (HO-GSVD). ICEGM was applied on the adipose, and heart and skeletal muscle tissues in old and young female African green vervet monkeys. The genes associated with the development of inflammation, cardiovascular and skeletal disorder diseases, and cancer were revealed by the ICEGM. Meanwhile, genes in the ICEGM modules were also enriched in the adipocytes, smooth muscle cells, cardiac myocytes, and immune cells. Comprehensive disease annotation and canonical pathway analysis indicated that immune cells, adipocytes, cardiomyocytes, and smooth muscle cells played a synergistic role in cardiac and physical functions in the aged monkeys by regulation of the biological processes associated with metabolism, inflammation, and atherosclerosis. In conclusion, the ICEGM provides an efficiently systematic framework for decoding the co-expression gene modules in multiple tissues. Analysis of genes in the ICEGM module yielded important insights on the cooperative role of multiple tissues in the development of diseases.

  13. Co-expression modules construction by WGCNA and identify potential prognostic markers of uveal melanoma.

    Science.gov (United States)

    Wan, Qi; Tang, Jing; Han, Yu; Wang, Dan

    2018-01-01

    Uveal melanoma is an aggressive cancer which has a high percentage recurrence and with a worse prognosis. Identify the potential prognostic markers of uveal melanoma may provide information for early detection of recurrence and treatment. RNA sequence data of uveal melanoma and patient clinic traits were obtained from The Cancer Genome Atlas (TCGA) database. Co-expression modules were built by weighted gene co -expression network analysis (WGCNA) and applied to investigate the relationship underlying modules and clinic traits. Besides, functional enrichment analysis was performed on these co-expression genes from interested modules. First, using WGCNA, identified 21 co-expression modules were constructed by the 10975 genes from the 80 human uveal melanoma samples. The number of genes in these modules ranged from 42 to 5091. Found four co -expression modules significantly correlated with three clinic traits (status, recurrence and recurrence Time). Module red, and purple positively correlated with patient's life status and recurrence Time. Module green positively correlates with recurrence. The result of functional enrichment analysis showed that the module magenta was mainly enriched genetic material assemble processes, the purple module was mainly enriched in tissue homeostasis and melanosome membrane and the module red was mainly enriched metastasis of cell, suggesting its critical role in the recurrence and development of the disease. Additionally, identified the hug gene (top connectivity with other genes) in each module. The hub gene SLC17A7, NTRK2, ABTB1 and ADPRHL1 might play a vital role in recurrence of uveal melanoma. Our findings provided the framework of co-expression gene modules of uveal melanoma and identified some prognostic markers might be detection of recurrence and treatment for uveal melanoma. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Building gene co-expression networks using transcriptomics data for systems biology investigations

    DEFF Research Database (Denmark)

    Kadarmideen, Haja; Watson-Haigh, Nathan S.

    2012-01-01

    Gene co-expression networks (GCN), built using high-throughput gene expression data are fundamental aspects of systems biology. The main aims of this study were to compare two popular approaches to building and analysing GCN. We use real ovine microarray transcriptomics datasets representing four......) is connected within a network. The two GCN construction methods used were, Weighted Gene Co-expression Network Analysis (WGCNA) and Partial Correlation and Information Theory (PCIT) methods. Nodes were ranked based on their connectivity measures in each of the four different networks created by WGCNA and PCIT...... (with > 20000 genes) access to large computer clusters, particularly those with larger amounts of shared memory is recommended....

  15. Integrative analysis of many weighted co-expression networks using tensor computation.

    Directory of Open Access Journals (Sweden)

    Wenyuan Li

    2011-06-01

    Full Text Available The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks.

  16. Elements of Network-Based Assessment

    Science.gov (United States)

    Gibson, David

    2007-01-01

    Elements of network-based assessment systems are envisioned based on recent advances in knowledge and practice in learning theory, assessment design and delivery, and semantic web interoperability. The architecture takes advantage of the meditating role of technology as well as recent models of assessment systems. This overview of the elements…

  17. A network based covariance test for detecting multivariate eQTL in saccharomyces cerevisiae.

    Science.gov (United States)

    Yuan, Huili; Li, Zhenye; Tang, Nelson L S; Deng, Minghua

    2016-01-11

    Expression quantitative trait locus (eQTL) analysis has been widely used to understand how genetic variations affect gene expressions in the biological systems. Traditional eQTL is investigated in a pair-wise manner in which one SNP affects the expression of one gene. In this way, some associated markers found in GWAS have been related to disease mechanism by eQTL study. However, in real life, biological process is usually performed by a group of genes. Although some methods have been proposed to identify a group of SNPs that affect the mean of gene expressions in the network, the change of co-expression pattern has not been considered. So we propose a process and algorithm to identify the marker which affects the co-expression pattern of a pathway. Considering two genes may have different correlations under different isoforms which is hard to detect by the linear test, we also consider the nonlinear test. When we applied our method to yeast eQTL dataset profiled under both the glucose and ethanol conditions, we identified a total of 166 modules, with each module consisting of a group of genes and one eQTL where the eQTL regulate the co-expression patterns of the group of genes. We found that many of these modules have biological significance. We propose a network based covariance test to identify the SNP which affects the structure of a pathway. We also consider the nonlinear test as considering two genes may have different correlations under different isoforms which is hard to detect by linear test.

  18. Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks.

    Directory of Open Access Journals (Sweden)

    Parameswaran Ramachandran

    Full Text Available Co-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were developed in the microarray era, before high-throughput sequencing-with its unique statistical properties-became the norm for expression measurement. Here we develop Bayesian Relevance Networks, an algorithm that uses Bayesian reasoning about expression levels to account for the differing levels of uncertainty in expression measurements between highly- and lowly-expressed entities, and between samples with different sequencing depths. It combines data from groups of samples (e.g., replicates to estimate group expression levels and confidence ranges. It then computes uncertainty-moderated estimates of cross-group correlations between entities, and uses permutation testing to assess their statistical significance. Using large scale miRNA data from The Cancer Genome Atlas, we show that our Bayesian update of the classical Relevance Networks algorithm provides improved reproducibility in co-expression estimates and lower false discovery rates in the resulting co-expression networks. Software is available at www.perkinslab.ca.

  19. EMMPRIN co-expressed with matrix metalloproteinases predicts poor prognosis in patients with osteosarcoma.

    Science.gov (United States)

    Futamura, Naohisa; Nishida, Yoshihiro; Urakawa, Hiroshi; Kozawa, Eiji; Ikuta, Kunihiro; Hamada, Shunsuke; Ishiguro, Naoki

    2014-06-01

    Several studies have focused on the relationships between the expression of extracellular matrix metalloproteinase inducer (EMMPRIN) and the prognosis of patients with malignant tumors. However, few of these have investigated the expression of EMMPRIN in osteosarcoma. We examined expression levels of EMMPRIN immunohistochemically in 53 cases of high-grade osteosarcoma of the extremities and analyzed the correlation of its expression with patient prognosis. The correlation between matrix metalloproteinases (MMPs) and EMMPRIN expression and the prognostic value of co-expression were also analyzed. Staining positivity for EMMPRIN was negative in 7 cases, low in 17, moderate in 19, and strong in 10. The overall and disease-free survivals (OS and DFS) in patients with higher EMMPRIN expression (strong-moderate) were significantly lower than those in the lower (weak-negative) group (0.037 and 0.024, respectively). In multivariate analysis, age (P=0.004), location (P=0.046), and EMMPRIN expression (P=0.038) were significant prognostic factors for overall survival. EMMPRIN expression (P=0.024) was also a significant prognostic factor for disease-free survival. Co-expression analyses of EMMPRIN and MMPs revealed that strong co-expression of EMMPRIN and membrane-type 1 (MT1)-MMP had a poor prognostic value (P=0.056 for DFS, P=0.006 for OS). EMMPRIN expression and co-expression with MMPs well predict the prognosis of patients with extremity osteosarcoma, making EMMPRIN a possible therapeutic target in these patients.

  20. Gene co-expression networks and profiles reveal potential biomarkers of boar taint in pigs

    DEFF Research Database (Denmark)

    Drag, Markus; Skinkyté-Juskiené, R.; Do, Duy Ngoc

    synthesis. In testis, >80 DE genes were functionally classified by the PANTHER tool to “Gonadotropin releasing hormone receptor” and “Wnt signaling” pathways which play a role in reproductive maturation and proliferation of spermatogonia, respectively. WGCNA was used to build co-expression modules...

  1. Different substrate regimes determine transcriptional profiles and gene co-expression in Methanosarcina barkeri (DSM 800)

    Czech Academy of Sciences Publication Activity Database

    Lin, Qiang; Fang, X.; Ho, A.; Li, J.; Yan, X.; Tu, B.; Li, Ch.; Li, J.; Yao, M.; Li, X.

    2017-01-01

    Roč. 101, č. 19 (2017), s. 7303-7316 ISSN 0175-7598 Institutional support: RVO:60077344 Keywords : Methanosarcina barkeri * substrate regimes * diversity * co-expression * ecological strategies Subject RIV: EH - Ecology, Behaviour OBOR OECD: Ecology Impact factor: 3.420, year: 2016

  2. Co-expression of the C-terminal domain of Yersinia enterocolitica ...

    Indian Academy of Sciences (India)

    Home; Journals; Journal of Biosciences; Volume 40; Issue 1. Co-expression of the C-terminal domain of Yersinia enterocolitica invasin enhances the efficacy of classical swine-fever-vectored vaccine based on human adenovirus. Helin Li Pengbo Ning Zhi Lin Wulong Liang Kai Kang Lei He Yanming Zhang. Articles Volume ...

  3. Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia

    Directory of Open Access Journals (Sweden)

    Borlawsky Tara B

    2010-10-01

    Full Text Available Abstract Background Chronic lymphocytic leukemia (CLL is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgVH mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgVH status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgVH mutational status which can accurately predict the survival outcome are yet to be discovered. Results In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL. Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the co-expression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgVH mutation status from the ZAP70 co-expression network. Conclusions We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgVH mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.

  4. Optical-Correlator Neural Network Based On Neocognitron

    Science.gov (United States)

    Chao, Tien-Hsin; Stoner, William W.

    1994-01-01

    Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

  5. Network-based Approaches in Pharmacology.

    Science.gov (United States)

    Boezio, Baptiste; Audouze, Karine; Ducrot, Pierre; Taboureau, Olivier

    2017-10-01

    In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. VTCdb: a gene co-expression database for the crop species Vitis vinifera (grapevine).

    Science.gov (United States)

    Wong, Darren C J; Sweetman, Crystal; Drew, Damian P; Ford, Christopher M

    2013-12-16

    Gene expression datasets in model plants such as Arabidopsis have contributed to our understanding of gene function and how a single underlying biological process can be governed by a diverse network of genes. The accumulation of publicly available microarray data encompassing a wide range of biological and environmental conditions has enabled the development of additional capabilities including gene co-expression analysis (GCA). GCA is based on the understanding that genes encoding proteins involved in similar and/or related biological processes may exhibit comparable expression patterns over a range of experimental conditions, developmental stages and tissues. We present an open access database for the investigation of gene co-expression networks within the cultivated grapevine, Vitis vinifera. The new gene co-expression database, VTCdb (http://vtcdb.adelaide.edu.au/Home.aspx), offers an online platform for transcriptional regulatory inference in the cultivated grapevine. Using condition-independent and condition-dependent approaches, grapevine co-expression networks were constructed using the latest publicly available microarray datasets from diverse experimental series, utilising the Affymetrix Vitis vinifera GeneChip (16 K) and the NimbleGen Grape Whole-genome microarray chip (29 K), thus making it possible to profile approximately 29,000 genes (95% of the predicted grapevine transcriptome). Applications available with the online platform include the use of gene names, probesets, modules or biological processes to query the co-expression networks, with the option to choose between Affymetrix or Nimblegen datasets and between multiple co-expression measures. Alternatively, the user can browse existing network modules using interactive network visualisation and analysis via CytoscapeWeb. To demonstrate the utility of the database, we present examples from three fundamental biological processes (berry development, photosynthesis and flavonoid biosynthesis

  7. Pathway enrichment and co-expression cluster analysis - FANTOM5 | LSDB Archive [Life Science Database Archive metadata

    Lifescience Database Archive (English)

    Full Text Available switchLanguage; BLAST Search Image Search Home About Archive Update History Data List Contact us FANTOM...lusters File URL: ftp://ftp.biosciencedbc.jp/archive/fantom5/datafiles/phase1.3/extra/Co-expression_clusters...ite Policy | Contact Us Pathway enrichment and co-expression cluster analysis - FANTOM5 | LSDB Archive ...

  8. Co-expression Network Approach to Studying the Effects of Botulinum Neurotoxin-A.

    Science.gov (United States)

    Mukund, Kavitha; Ward, Samuel R; Lieber, Richard L; Subramaniam, Shankar

    2017-10-16

    Botulinum Neurotoxin A (BoNT-A) is a potent neurotoxin with several clinical applications.The goal of this study was to utilize co-expression network theory to analyze temporal transcriptional data from skeletal muscle after BoNT-A treatment. Expression data for 2000 genes (extracted using a ranking heuristic) served as the basis for this analysis. Using weighted gene co-expression network analysis (WGCNA), we identified 19 co-expressed modules, further hierarchically clustered into 5 groups. Quantifying average expression and co-expression patterns across these groups revealed temporal aspects of muscle's response to BoNT-A. Functional analysis revealed enrichment of group 1 with metabolism; group 5 with contradictory functions of atrophy and cellular recovery; and groups 2 and 3 with extracellular matrix (ECM) and non-fast fiber isoforms. Topological positioning of two highly ranked, significantly expressed genes- Dclk1 and Ostalpha within group 5 suggested possible mechanistic roles in recovery from BoNT-A induced atrophy. Phenotypic correlations of groups with titin and myosin protein content further emphasized the effect of BoNT-A on the sarcomeric contraction machinery in early phase of chemodenervation. In summary, our approach revealed a hierarchical functional response to BoNT-A induced paralysis with early metabolic and later ECM responses and identified putative biomarkers associated with chemodenervation. Additionally, our results provide an unbiased validation of the response documented in our previous workBotulinum Neurotoxin A (BoNT-A) is a potent neurotoxin with several clinical applications.The goal of this study was to utilize co-expression network theory to analyze temporal transcriptional data from skeletal muscle after BoNT-A treatment. Expression data for 2000 genes (extracted using a ranking heuristic) served as the basis for this analysis. Using weighted gene co-expression network analysis (WGCNA), we identified 19 co-expressed modules

  9. Co-expression of TIMP-1 and its cell surface binding partner CD63 in glioblastomas

    DEFF Research Database (Denmark)

    Aaberg-Jessen, Charlotte; Sørensen, Mia D.; Matos, Ana L.S.A.

    2018-01-01

    scoring. CD63 expression in tumor-associated microglia/macrophages was examined by double-immunofluorescence with ionized calcium-binding adapter molecule 1 (Iba1). The association between CD63 and TIMP-1 was investigated using previously obtained TIMP-1 data from our astrocytoma cohort. Cellular co-expression...... of CD63 was widely distributed in astrocytomas with a significantly increased level in glioblastomas. CD63 levels did not significantly correlate with patient survival at a protein level, and CD63 did not augment the prognostic significance of TIMP-1. Up to 38% of the CD63+ cells expressed Iba1; however......, Iba1 did not appear to impact the prognostic value of CD63. A significant correlation was found between TIMP-1 and CD63, and the TIMP-1 and CD63 proteins were co-expressed at the cellular level and located in close molecular proximity, suggesting that TIMP-1 and CD63 could be co...

  10. Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

    Directory of Open Access Journals (Sweden)

    Recep Colak

    2010-10-01

    Full Text Available Computational prediction of functionally related groups of genes (functional modules from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented.We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB, by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples.We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely

  11. Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

    Science.gov (United States)

    Colak, Recep; Moser, Flavia; Chu, Jeffrey Shih-Chieh; Schönhuth, Alexander; Chen, Nansheng; Ester, Martin

    2010-10-25

    Computational prediction of functionally related groups of genes (functional modules) from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense) regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented. We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB), by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples. We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely available large

  12. An extensive (co-expression analysis tool for the cytochrome P450 superfamily in Arabidopsis thaliana

    Directory of Open Access Journals (Sweden)

    Provart Nicholas J

    2008-04-01

    Full Text Available Abstract Background Sequencing of the first plant genomes has revealed that cytochromes P450 have evolved to become the largest family of enzymes in secondary metabolism. The proportion of P450 enzymes with characterized biochemical function(s is however very small. If P450 diversification mirrors evolution of chemical diversity, this points to an unexpectedly poor understanding of plant metabolism. We assumed that extensive analysis of gene expression might guide towards the function of P450 enzymes, and highlight overlooked aspects of plant metabolism. Results We have created a comprehensive database, 'CYPedia', describing P450 gene expression in four data sets: organs and tissues, stress response, hormone response, and mutants of Arabidopsis thaliana, based on public Affymetrix ATH1 microarray expression data. P450 expression was then combined with the expression of 4,130 re-annotated genes, predicted to act in plant metabolism, for co-expression analyses. Based on the annotation of co-expressed genes from diverse pathway annotation databases, co-expressed pathways were identified. Predictions were validated for most P450s with known functions. As examples, co-expression results for P450s related to plastidial functions/photosynthesis, and to phenylpropanoid, triterpenoid and jasmonate metabolism are highlighted here. Conclusion The large scale hypothesis generation tools presented here provide leads to new pathways, unexpected functions, and regulatory networks for many P450s in plant metabolism. These can now be exploited by the community to validate the proposed functions experimentally using reverse genetics, biochemistry, and metabolic profiling.

  13. FastGCN: a GPU accelerated tool for fast gene co-expression networks.

    Directory of Open Access Journals (Sweden)

    Meimei Liang

    Full Text Available Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out.

  14. Inferring the transcriptional landscape of bovine skeletal muscle by integrating co-expression networks.

    Directory of Open Access Journals (Sweden)

    Nicholas J Hudson

    Full Text Available BACKGROUND: Despite modern technologies and novel computational approaches, decoding causal transcriptional regulation remains challenging. This is particularly true for less well studied organisms and when only gene expression data is available. In muscle a small number of well characterised transcription factors are proposed to regulate development. Therefore, muscle appears to be a tractable system for proposing new computational approaches. METHODOLOGY/PRINCIPAL FINDINGS: Here we report a simple algorithm that asks "which transcriptional regulator has the highest average absolute co-expression correlation to the genes in a co-expression module?" It correctly infers a number of known causal regulators of fundamental biological processes, including cell cycle activity (E2F1, glycolysis (HLF, mitochondrial transcription (TFB2M, adipogenesis (PIAS1, neuronal development (TLX3, immune function (IRF1 and vasculogenesis (SOX17, within a skeletal muscle context. However, none of the canonical pro-myogenic transcription factors (MYOD1, MYOG, MYF5, MYF6 and MEF2C were linked to muscle structural gene expression modules. Co-expression values were computed using developing bovine muscle from 60 days post conception (early foetal to 30 months post natal (adulthood for two breeds of cattle, in addition to a nutritional comparison with a third breed. A number of transcriptional landscapes were constructed and integrated into an always correlated landscape. One notable feature was a 'metabolic axis' formed from glycolysis genes at one end, nuclear-encoded mitochondrial protein genes at the other, and centrally tethered by mitochondrially-encoded mitochondrial protein genes. CONCLUSIONS/SIGNIFICANCE: The new module-to-regulator algorithm complements our recently described Regulatory Impact Factor analysis. Together with a simple examination of a co-expression module's contents, these three gene expression approaches are starting to illuminate the in vivo

  15. Gene Co-expression Analysis to Characterize Genes Related to Marbling Trait in Hanwoo (Korean) Cattle.

    Science.gov (United States)

    Lim, Dajeong; Lee, Seung-Hwan; Kim, Nam-Kuk; Cho, Yong-Min; Chai, Han-Ha; Seong, Hwan-Hoo; Kim, Heebal

    2013-01-01

    Marbling (intramuscular fat) is an important trait that affects meat quality and is a casual factor determining the price of beef in the Korean beef market. It is a complex trait and has many biological pathways related to muscle and fat. There is a need to identify functional modules or genes related to marbling traits and investigate their relationships through a weighted gene co-expression network analysis based on the system level. Therefore, we investigated the co-expression relationships of genes related to the 'marbling score' trait and systemically analyzed the network topology in Hanwoo (Korean cattle). As a result, we determined 3 modules (gene groups) that showed statistically significant results for marbling score. In particular, one module (denoted as red) has a statistically significant result for marbling score (p = 0.008) and intramuscular fat (p = 0.02) and water capacity (p = 0.006). From functional enrichment and relationship analysis of the red module, the pathway hub genes (IL6, CHRNE, RB1, INHBA and NPPA) have a direct interaction relationship and share the biological functions related to fat or muscle, such as adipogenesis or muscle growth. This is the first gene network study with m.logissimus in Hanwoo to observe co-expression patterns in divergent marbling phenotypes. It may provide insights into the functional mechanisms of the marbling trait.

  16. Large clusters of co-expressed genes in the Drosophila genome.

    Science.gov (United States)

    Boutanaev, Alexander M; Kalmykova, Alla I; Shevelyov, Yuri Y; Nurminsky, Dmitry I

    2002-12-12

    Clustering of co-expressed, non-homologous genes on chromosomes implies their co-regulation. In lower eukaryotes, co-expressed genes are often found in pairs. Clustering of genes that share aspects of transcriptional regulation has also been reported in higher eukaryotes. To advance our understanding of the mode of coordinated gene regulation in multicellular organisms, we performed a genome-wide analysis of the chromosomal distribution of co-expressed genes in Drosophila. We identified a total of 1,661 testes-specific genes, one-third of which are clustered on chromosomes. The number of clusters of three or more genes is much higher than expected by chance. We observed a similar trend for genes upregulated in the embryo and in the adult head, although the expression pattern of individual genes cannot be predicted on the basis of chromosomal position alone. Our data suggest that the prevalent mechanism of transcriptional co-regulation in higher eukaryotes operates with extensive chromatin domains that comprise multiple genes.

  17. Co-expression analysis identifies CRC and AP1 the regulator of Arabidopsis fatty acid biosynthesis.

    Science.gov (United States)

    Han, Xinxin; Yin, Linlin; Xue, Hongwei

    2012-07-01

    Fatty acids (FAs) play crucial rules in signal transduction and plant development, however, the regulation of FA metabolism is still poorly understood. To study the relevant regulatory network, fifty-eight FA biosynthesis genes including de novo synthases, desaturases and elongases were selected as "guide genes" to construct the co-expression network. Calculation of the correlation between all Arabidopsis thaliana (L.) genes with each guide gene by Arabidopsis co-expression dating mining tools (ACT) identifies 797 candidate FA-correlated genes. Gene ontology (GO) analysis of these co-expressed genes showed they are tightly correlated to photosynthesis and carbohydrate metabolism, and function in many processes. Interestingly, 63 transcription factors (TFs) were identified as candidate FA biosynthesis regulators and 8 TF families are enriched. Two TF genes, CRC and AP1, both correlating with 8 FA guide genes, were further characterized. Analyses of the ap1 and crc mutant showed the altered total FA composition of mature seeds. The contents of palmitoleic acid, stearic acid, arachidic acid and eicosadienoic acid are decreased, whereas that of oleic acid is increased in ap1 and crc seeds, which is consistent with the qRT-PCR analysis revealing the suppressed expression of the corresponding guide genes. In addition, yeast one-hybrid analysis and electrophoretic mobility shift assay (EMSA) revealed that CRC can bind to the promoter regions of KCS7 and KCS15, indicating that CRC may directly regulate FA biosynthesis. © 2012 Institute of Botany, Chinese Academy of Sciences.

  18. Gene Co-expression Analysis to Characterize Genes Related to Marbling Trait in Hanwoo (Korean Cattle

    Directory of Open Access Journals (Sweden)

    Dajeong Lim

    2013-01-01

    Full Text Available Marbling (intramuscular fat is an important trait that affects meat quality and is a casual factor determining the price of beef in the Korean beef market. It is a complex trait and has many biological pathways related to muscle and fat. There is a need to identify functional modules or genes related to marbling traits and investigate their relationships through a weighted gene co-expression network analysis based on the system level. Therefore, we investigated the co-expression relationships of genes related to the ‘marbling score’ trait and systemically analyzed the network topology in Hanwoo (Korean cattle. As a result, we determined 3 modules (gene groups that showed statistically significant results for marbling score. In particular, one module (denoted as red has a statistically significant result for marbling score (p = 0.008 and intramuscular fat (p = 0.02 and water capacity (p = 0.006. From functional enrichment and relationship analysis of the red module, the pathway hub genes (IL6, CHRNE, RB1, INHBA and NPPA have a direct interaction relationship and share the biological functions related to fat or muscle, such as adipogenesis or muscle growth. This is the first gene network study with m.logissimus in Hanwoo to observe co-expression patterns in divergent marbling phenotypes. It may provide insights into the functional mechanisms of the marbling trait.

  19. A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression

    Directory of Open Access Journals (Sweden)

    Mao Yu

    2009-07-01

    Full Text Available Abstract Background The identification of gene differential co-expression patterns between cancer stages is a newly developing method to reveal the underlying molecular mechanisms of carcinogenesis. Most researches of this subject lack an algorithm useful for performing a statistical significance assessment involving cancer progression. Lacking this specific algorithm is apparently absent in identifying precise gene pairs correlating to cancer progression. Results In this investigation we studied gene pair co-expression change by using a stochastic process model for approximating the underlying dynamic procedure of the co-expression change during cancer progression. Also, we presented a novel analytical method named 'Stochastic process model for Identifying differentially co-expressed Gene pair' (SIG method. This method has been applied to two well known prostate cancer data sets: hormone sensitive versus hormone resistant, and healthy versus cancerous. From these data sets, 428,582 gene pairs and 303,992 gene pairs were identified respectively. Afterwards, we used two different current statistical methods to the same data sets, which were developed to identify gene pair differential co-expression and did not consider cancer progression in algorithm. We then compared these results from three different perspectives: progression analysis, gene pair identification effectiveness analysis, and pathway enrichment analysis. Statistical methods were used to quantify the quality and performance of these different perspectives. They included: Re-identification Scale (RS and Progression Score (PS in progression analysis, True Positive Rate (TPR in gene pair analysis, and Pathway Enrichment Score (PES in pathway analysis. Our results show small values of RS and large values of PS, TPR, and PES; thus, suggesting that gene pairs identified by the SIG method are highly correlated with cancer progression, and highly enriched in disease-specific pathways. From

  20. Autonomous power networks based power system

    International Nuclear Information System (INIS)

    Jokic, A.; Van den Bosch, P.P.J.

    2006-01-01

    This paper presented the concept of autonomous networks to cope with this increased complexity in power systems while enhancing market-based operation. The operation of future power systems will be more challenging and demanding than present systems because of increased uncertainties, less inertia in the system, replacement of centralized coordinating activities by decentralized parties and the reliance on dynamic markets for both power balancing and system reliability. An autonomous network includes the aggregation of networked producers and consumers in a relatively small area with respect to the overall system. The operation of an autonomous network is coordinated and controlled with one central unit acting as an interface between internal producers/consumers and the rest of the power system. In this study, the power balance problem and system reliability through provision of ancillary services was formulated as an optimization problem for the overall autonomous networks based power system. This paper described the simulation of an optimal autonomous network dispatching in day ahead markets, based on predicted spot prices for real power, and two ancillary services. It was concluded that large changes occur in a power systems structure and operation, most of them adding to the uncertainty and complexity of the system. The introduced concept of an autonomous power network-based power system was shown to be a realistic and consistent approach to formulate and operate a market-based dispatch of both power and ancillary services. 9 refs., 4 figs

  1. Network Based High Speed Product Innovation

    DEFF Research Database (Denmark)

    Lindgren, Peter

    In the first decade of the 21st century, New Product Development has undergone major changes in the way NPD is managed and organised. This is due to changes in technology, market demands, and in the competencies of companies. As a result NPD organised in different forms of networks is predicted...... to be of ever-increasing importance to many different kinds of companies. This happens at the same times as the share of new products of total turnover and earnings is increasing at unprecedented speed in many firms and industries. The latter results in the need for very fast innovation and product development...... - a need that can almost only be resolved by organising NPD in some form of network configuration. The work of Peter Lindgren is on several aspects of network based high speed product innovation and contributes to a descriptive understanding of this phenomenon as well as with normative theory on how NPD...

  2. Quantum networks based on cavity QED

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, Stephan; Bochmann, Joerg; Figueroa, Eden; Hahn, Carolin; Kalb, Norbert; Muecke, Martin; Neuzner, Andreas; Noelleke, Christian; Reiserer, Andreas; Uphoff, Manuel; Rempe, Gerhard [Max-Planck-Institut fuer Quantenoptik, Hans-Kopfermann-Strasse 1, 85748 Garching (Germany)

    2014-07-01

    Quantum repeaters require an efficient interface between stationary quantum memories and flying photons. Single atoms in optical cavities are ideally suited as universal quantum network nodes that are capable of sending, storing, retrieving, and even processing quantum information. We demonstrate this by presenting an elementary version of a quantum network based on two identical nodes in remote, independent laboratories. The reversible exchange of quantum information and the creation of remote entanglement are achieved by exchange of a single photon. Quantum teleportation is implemented using a time-resolved photonic Bell-state measurement. Quantum control over all degrees of freedom of the single atom also allows for the nondestructive detection of flying photons and the implementation of a quantum gate between the spin state of the atom and the polarization of a photon upon its reflection from the cavity. Our approach to quantum networking offers a clear perspective for scalability and provides the essential components for the realization of a quantum repeater.

  3. Network-based recommendation algorithms: A review

    Science.gov (United States)

    Yu, Fei; Zeng, An; Gillard, Sébastien; Medo, Matúš

    2016-06-01

    Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use-such as the possible influence of recommendation on the evolution of systems that use it-and finally discuss open research directions and challenges.

  4. Designing Network-based Business Model Ontology

    DEFF Research Database (Denmark)

    Hashemi Nekoo, Ali Reza; Ashourizadeh, Shayegheh; Zarei, Behrouz

    2015-01-01

    Survival on dynamic environment is not achieved without a map. Scanning and monitoring of the market show business models as a fruitful tool. But scholars believe that old-fashioned business models are dead; as they are not included the effect of internet and network in themselves. This paper...... is going to propose e-business model ontology from the network point of view and its application in real world. The suggested ontology for network-based businesses is composed of individuals` characteristics and what kind of resources they own. also, their connections and pre-conceptions of connections...... such as shared-mental model and trust. However, it mostly covers previous business model elements. To confirm the applicability of this ontology, it has been implemented in business angel network and showed how it works....

  5. Effects of threshold on the topology of gene co-expression networks.

    Science.gov (United States)

    Couto, Cynthia Martins Villar; Comin, César Henrique; Costa, Luciano da Fontoura

    2017-09-26

    Several developments regarding the analysis of gene co-expression profiles using complex network theory have been reported recently. Such approaches usually start with the construction of an unweighted gene co-expression network, therefore requiring the selection of a suitable threshold defining which pairs of vertices will be connected. We aimed at addressing such an important problem by suggesting and comparing five different approaches for threshold selection. Each of the methods considers a respective biologically-motivated criterion for electing a potentially suitable threshold. A set of 21 microarray experiments from different biological groups was used to investigate the effect of applying the five proposed criteria to several biological situations. For each experiment, we used the Pearson correlation coefficient to measure the relationship between each gene pair, and the resulting weight matrices were thresholded considering several values, generating respective adjacency matrices (co-expression networks). Each of the five proposed criteria was then applied in order to select the respective threshold value. The effects of these thresholding approaches on the topology of the resulting networks were compared by using several measurements, and we verified that, depending on the database, the impact on the topological properties can be large. However, a group of databases was verified to be similarly affected by most of the considered criteria. Based on such results, it can be suggested that when the generated networks present similar measurements, the thresholding method can be chosen with greater freedom. If the generated networks are markedly different, the thresholding method that better suits the interests of each specific research study represents a reasonable choice.

  6. Enforcing Co-expression Within a Brain-Imaging Genomics Regression Framework.

    Science.gov (United States)

    Zille, Pascal; Calhoun, Vince D; Wang, Yu-Ping

    2017-06-28

    Among the challenges arising in brain imaging genetic studies, estimating the potential links between neurological and genetic variability within a population is key. In this work, we propose a multivariate, multimodal formulation for variable selection that leverages co-expression patterns across various data modalities. Our approach is based on an intuitive combination of two widely used statistical models: sparse regression and canonical correlation analysis (CCA). While the former seeks multivariate linear relationships between a given phenotype and associated observations, the latter searches to extract co-expression patterns between sets of variables belonging to different modalities. In the following, we propose to rely on a 'CCA-type' formulation in order to regularize the classical multimodal sparse regression problem (essentially incorporating both CCA and regression models within a unified formulation). The underlying motivation is to extract discriminative variables that are also co-expressed across modalities. We first show that the simplest formulation of such model can be expressed as a special case of collaborative learning methods. After discussing its limitation, we propose an extended, more flexible formulation, and introduce a simple and efficient alternating minimization algorithm to solve the associated optimization problem.We explore the parameter space and provide some guidelines regarding parameter selection. Both the original and extended versions are then compared on a simple toy dataset and a more advanced simulated imaging genomics dataset in order to illustrate the benefits of the latter. Finally, we validate the proposed formulation using single nucleotide polymorphisms (SNP) data and functional magnetic resonance imaging (fMRI) data from a population of adolescents (n = 362 subjects, age 16.9 ± 1.9 years from the Philadelphia Neurodevelopmental Cohort) for the study of learning ability. Furthermore, we carry out a significance

  7. Network statistics of genetically-driven gene co-expression modules in mouse crosses

    Directory of Open Access Journals (Sweden)

    Marie-Pier eScott-Boyer

    2013-12-01

    Full Text Available In biology, networks are used in different contexts as ways to represent relationships between entities, such as for instance interactions between genes, proteins or metabolites. Despite progress in the analysis of such networks and their potential to better understand the collective impact of genes on complex traits, one remaining challenge is to establish the biologic validity of gene co-expression networks and to determine what governs their organization. We used WGCNA to construct and analyze seven gene expression datasets from several tissues of mouse recombinant inbred strains (RIS. For six out of the 7 networks, we found that linkage to module QTLs (mQTLs could be established for 29.3% of gene co-expression modules detected in the several mouse RIS. For about 74.6% of such genetically-linked modules, the mQTL was on the same chromosome as the one contributing most genes to the module, with genes originating from that chromosome showing higher connectivity than other genes in the modules. Such modules (that we considered as genetically-driven had network statistic properties (density, centralization and heterogeneity that set them apart from other modules in the network. Altogether, a sizeable portion of gene co-expression modules detected in mouse RIS panels had genetic determinants as their main organizing principle. In addition to providing a biologic interpretation validation for these modules, these genetic determinants imparted on them particular properties that set them apart from other modules in the network, to the point that they can be predicted to a large extent on the basis of their network statistics.

  8. Weighted gene co-expression network analysis of the peripheral blood from Amyotrophic Lateral Sclerosis patients

    Directory of Open Access Journals (Sweden)

    DeYoung Joseph

    2009-08-01

    Full Text Available Abstract Background Amyotrophic Lateral Sclerosis (ALS is a lethal disorder characterized by progressive degeneration of motor neurons in the brain and spinal cord. Diagnosis is mainly based on clinical symptoms, and there is currently no therapy to stop the disease or slow its progression. Since access to spinal cord tissue is not possible at disease onset, we investigated changes in gene expression profiles in whole blood of ALS patients. Results Our transcriptional study showed dramatic changes in blood of ALS patients; 2,300 probes (9.4% showed significant differential expression in a discovery dataset consisting of 30 ALS patients and 30 healthy controls. Weighted gene co-expression network analysis (WGCNA was used to find disease-related networks (modules and disease related hub genes. Two large co-expression modules were found to be associated with ALS. Our findings were replicated in a second (30 patients and 30 controls and third dataset (63 patients and 63 controls, thereby demonstrating a highly significant and consistent association of two large co-expression modules with ALS disease status. Ingenuity Pathway Analysis of the ALS related module genes implicates enrichment of functional categories related to genetic disorders, neurodegeneration of the nervous system and inflammatory disease. The ALS related modules contain a number of candidate genes possibly involved in pathogenesis of ALS. Conclusion This first large-scale blood gene expression study in ALS observed distinct patterns between cases and controls which may provide opportunities for biomarker development as well as new insights into the molecular mechanisms of the disease.

  9. Once for All: A Novel Robust System for Co-expression of Multiple Chimeric Fluorescent Fusion Proteins in Plants

    Directory of Open Access Journals (Sweden)

    Guitao Zhong

    2017-06-01

    Full Text Available Chimeric fluorescent fusion proteins have been employed as a powerful tool to reveal the subcellular localizations and dynamics of proteins in living cells. Co-expression of a fluorescent fusion protein with well-known organelle markers in the same cell is especially useful in revealing its spatial and temporal functions of the protein in question. However, the conventional methods for co-expressing multiple fluorescent tagged proteins in plants have the drawbacks of low expression efficiency, variations in the expression level and time-consuming genetic crossing. Here, we have developed a novel robust system that allows for high-efficient co-expression of multiple chimeric fluorescent fusion proteins in plants in a time-saving fashion. This system takes advantage of employing a single expression vector which consists of multiple semi-independent expressing cassettes for the protein co-expression thereby overcoming the limitations of using multiple independent expressing plasmids. In addition, it is a highly manipulable DNA assembly system, in which modification and recombination of DNA molecules are easily achieved through an optimized one-step assembly reaction. By employing this effective system, we demonstrated that co-expression of two chimeric fluorescent fusion reporter proteins of vacuolar sorting receptor and secretory carrier membrane protein gave rise to their perspective subcellular localizations in plants via both transient expression and stable transformation. Thus, we believed that this technical advance represents a promising approach for multi-color-protein co-expression in plant cells.

  10. A powerful nonparametric method for detecting differentially co-expressed genes: distance correlation screening and edge-count test.

    Science.gov (United States)

    Zhang, Qingyang

    2018-05-16

    Differential co-expression analysis, as a complement of differential expression analysis, offers significant insights into the changes in molecular mechanism of different phenotypes. A prevailing approach to detecting differentially co-expressed genes is to compare Pearson's correlation coefficients in two phenotypes. However, due to the limitations of Pearson's correlation measure, this approach lacks the power to detect nonlinear changes in gene co-expression which is common in gene regulatory networks. In this work, a new nonparametric procedure is proposed to search differentially co-expressed gene pairs in different phenotypes from large-scale data. Our computational pipeline consisted of two main steps, a screening step and a testing step. The screening step is to reduce the search space by filtering out all the independent gene pairs using distance correlation measure. In the testing step, we compare the gene co-expression patterns in different phenotypes by a recently developed edge-count test. Both steps are distribution-free and targeting nonlinear relations. We illustrate the promise of the new approach by analyzing the Cancer Genome Atlas data and the METABRIC data for breast cancer subtypes. Compared with some existing methods, the new method is more powerful in detecting nonlinear type of differential co-expressions. The distance correlation screening can greatly improve computational efficiency, facilitating its application to large data sets.

  11. MPIGeneNet: Parallel Calculation of Gene Co-Expression Networks on Multicore Clusters.

    Science.gov (United States)

    Gonzalez-Dominguez, Jorge; Martin, Maria J

    2017-10-10

    In this work we present MPIGeneNet, a parallel tool that applies Pearson's correlation and Random Matrix Theory to construct gene co-expression networks. It is based on the state-of-the-art sequential tool RMTGeneNet, which provides networks with high robustness and sensitivity at the expenses of relatively long runtimes for large scale input datasets. MPIGeneNet returns the same results as RMTGeneNet but improves the memory management, reduces the I/O cost, and accelerates the two most computationally demanding steps of co-expression network construction by exploiting the compute capabilities of common multicore CPU clusters. Our performance evaluation on two different systems using three typical input datasets shows that MPIGeneNet is significantly faster than RMTGeneNet. As an example, our tool is up to 175.41 times faster on a cluster with eight nodes, each one containing two 12-core Intel Haswell processors. Source code of MPIGeneNet, as well as a reference manual, are available at https://sourceforge.net/projects/mpigenenet/.

  12. Exploring the Diagnostic Potential of Immune Biomarker Co-expression in Gulf War Illness.

    Science.gov (United States)

    Broderick, Gordon; Fletcher, Mary Ann; Gallagher, Michael; Barnes, Zachary; Vernon, Suzanne D; Klimas, Nancy G

    2018-01-01

    Complex disorders like Gulf War illness (GWI) often defy diagnosis on the basis of a single biomarker and may only be distinguishable by considering the co-expression of multiple markers measured in response to a challenge. We demonstrate the practical application of such an approach using an example where blood was collected from 26 GWI, 13 healthy control subjects, and 9 unhealthy controls with chronic fatigue at three points during a graded exercise challenge. A 3-way multivariate projection model based on 12 markers of endocrine and immune function was constructed using a training set of n = 10 GWI and n = 11 healthy controls. These groups were separated almost completely on the basis of two co-expression patterns. In a separate test set these same features allowed for discrimination of new GWI subjects (n = 16) from unhealthy (n = 9) and healthy control subjects with a sensitivity of 70% and a specificity of 90%.

  13. Autocrine CSF-1 and CSF-1 Receptor Co-expression Promotes Renal Cell Carcinoma Growth

    Science.gov (United States)

    Menke, Julia; Kriegsmann, Jörg; Schimanski, Carl Christoph; Schwartz, Melvin M.; Schwarting, Andreas; Kelley, Vicki R.

    2011-01-01

    Renal cell carcinoma is increasing in incidence but the molecular mechanisms regulating its growth remain elusive. Co-expression of the monocytic growth factor CSF-1 and its receptor CSF-1R on renal tubular epithelial cells (TEC) will promote proliferation and anti-apoptosis during regeneration of renal tubules. Here we show that a CSF-1-dependent autocrine pathway is also responsible for the growth of renal cell carcinoma (RCC). CSF-1 and CSF-1R were co-expressed in RCC and TEC proximally adjacent to RCC. CSF-1 engagement of CSF-1R promoted RCC survival and proliferation and reduced apoptosis, in support of the likelihood that CSF-1R effector signals mediate RCC growth. In vivo CSF-1R blockade using a CSF-1R tyrosine kinase inhibitor decreased RCC proliferation and macrophage infiltration in a manner associated with a dramatic reduction in tumor mass. Further mechanistic investigations linked CSF-1 and EGF signaling in RCC. Taken together, our results suggest that budding RCC stimulates the proximal adjacent microenvironment in the kidney to release mediators of CSF-1, CSF-1R and EGF expression in RCC. Further, our findings imply that targeting CSF-1/CSF-1R signaling may be therapeutically effective in RCC. PMID:22052465

  14. A contribution to the study of plant development evolution based on gene co-expression networks

    Directory of Open Access Journals (Sweden)

    Francisco J. Romero-Campero

    2013-08-01

    Full Text Available Phototrophic eukaryotes are among the most successful organisms on Earth due to their unparalleled efficiency at capturing light energy and fixing carbon dioxide to produce organic molecules. A conserved and efficient network of light-dependent regulatory modules could be at the bases of this success. This regulatory system conferred early advantages to phototrophic eukaryotes that allowed for specialization, complex developmental processes and modern plant characteristics. We have studied light-dependent gene regulatory modules from algae to plants employing integrative-omics approaches based on gene co-expression networks. Our study reveals some remarkably conserved ways in which eukaryotic phototrophs deal with day length and light signaling. Here we describe how a family of Arabidopsis transcription factors involved in photoperiod response has evolved from a single algal gene according to the innovation, amplification and divergence theory of gene evolution by duplication. These modifications of the gene co-expression networks from the ancient unicellular green algae Chlamydomonas reinhardtii to the modern brassica Arabidopsis thaliana may hint on the evolution and specialization of plants and other organisms.

  15. Uncovering co-expression gene network modules regulating fruit acidity in diverse apples.

    Science.gov (United States)

    Bai, Yang; Dougherty, Laura; Cheng, Lailiang; Zhong, Gan-Yuan; Xu, Kenong

    2015-08-16

    Acidity is a major contributor to fruit quality. Several organic acids are present in apple fruit, but malic acid is predominant and determines fruit acidity. The trait is largely controlled by the Malic acid (Ma) locus, underpinning which Ma1 that putatively encodes a vacuolar aluminum-activated malate transporter1 (ALMT1)-like protein is a strong candidate gene. We hypothesize that fruit acidity is governed by a gene network in which Ma1 is key member. The goal of this study is to identify the gene network and the potential mechanisms through which the network operates. Guided by Ma1, we analyzed the transcriptomes of mature fruit of contrasting acidity from six apple accessions of genotype Ma_ (MaMa or Mama) and four of mama using RNA-seq and identified 1301 fruit acidity associated genes, among which 18 were most significant acidity genes (MSAGs). Network inferring using weighted gene co-expression network analysis (WGCNA) revealed five co-expression gene network modules of significant (P acidity. Overall, this study provides important insight into the Ma1-mediated gene network controlling acidity in mature apple fruit of diverse genetic background.

  16. Wireless Sensor Network Based Smart Parking System

    Directory of Open Access Journals (Sweden)

    Jeffrey JOSEPH

    2014-01-01

    Full Text Available Ambient Intelligence is a vision in which various devices come together and process information from multiple sources in order to exert control on the physical environment. In addition to computation and control, communication plays a crucial role in the overall functionality of such a system. Wireless Sensor Networks are one such class of networks, which meet these criteria. These networks consist of spatially distributed sensor motes which work in a co-operative manner to sense and control the environment. In this work, an implementation of an energy-efficient and cost-effective, wireless sensor networks based vehicle parking system for a multi-floor indoor parking facility has been introduced. The system monitors the availability of free parking slots and guides the vehicle to the nearest free slot. The amount of time the vehicle has been parked is monitored for billing purposes. The status of the motes (dead/alive is also recorded. Information like slot allocated, directions to the slot and billing data is sent as a message to customer’s mobile phones. This paper extends our previous work 1 with the development of a low cost sensor mote, about one tenth the cost of a commercially available mote, keeping in mind the price sensitive markets of the developing countries.

  17. Network-based analysis of proteomic profiles

    KAUST Repository

    Wong, Limsoon

    2016-01-26

    Mass spectrometry (MS)-based proteomics is a widely used and powerful tool for profiling systems-wide protein expression changes. It can be applied for various purposes, e.g. biomarker discovery in diseases and study of drug responses. Although RNA-based high-throughput methods have been useful in providing glimpses into the underlying molecular processes, the evidences they provide are indirect. Furthermore, RNA and corresponding protein levels have been known to have poor correlation. On the other hand, MS-based proteomics tend to have consistency issues (poor reproducibility and inter-sample agreement) and coverage issues (inability to detect the entire proteome) that need to be urgently addressed. In this talk, I will discuss how these issues can be addressed by proteomic profile analysis techniques that use biological networks (especially protein complexes) as the biological context. In particular, I will describe several techniques that we have been developing for network-based analysis of proteomics profile. And I will present evidence that these techniques are useful in identifying proteomics-profile analysis results that are more consistent, more reproducible, and more biologically coherent, and that these techniques allow expansion of the detected proteome to uncover and/or discover novel proteins.

  18. Network-Based Community Brings forth Sustainable Society

    Science.gov (United States)

    Kikuchi, Toshiko

    It has already been shown that an artificial society based on the three relations of social configuration (market, communal, and obligatory relations) functioning in balance with each other formed a sustainable society which the social reproduction is possible. In this artificial society model, communal relations exist in a network-based community with alternating members rather than a conventional community with cooperative mutual assistance practiced in some agricultural communities. In this paper, using the comparison between network-based communities with alternating members and conventional communities with fixed members, the significance of a network-based community is considered. In concrete terms, the difference in appearance rate for sustainable society, economic activity and asset inequality between network-based communities and conventional communities is analyzed. The appearance rate for a sustainable society of network-based community is higher than that of conventional community. Moreover, most of network-based communities had a larger total number of trade volume than conventional communities. But, the value of Gini coefficient in conventional community is smaller than that of network-based community. These results show that communal relations based on a network-based community is significant for the social reproduction and economic efficiency. However, in such an artificial society, the inequality is sacrificed.

  19. Gene co-expression networks shed light into diseases of brain iron accumulation.

    Science.gov (United States)

    Bettencourt, Conceição; Forabosco, Paola; Wiethoff, Sarah; Heidari, Moones; Johnstone, Daniel M; Botía, Juan A; Collingwood, Joanna F; Hardy, John; Milward, Elizabeth A; Ryten, Mina; Houlden, Henry

    2016-03-01

    Aberrant brain iron deposition is observed in both common and rare neurodegenerative disorders, including those categorized as Neurodegeneration with Brain Iron Accumulation (NBIA), which are characterized by focal iron accumulation in the basal ganglia. Two NBIA genes are directly involved in iron metabolism, but whether other NBIA-related genes also regulate iron homeostasis in the human brain, and whether aberrant iron deposition contributes to neurodegenerative processes remains largely unknown. This study aims to expand our understanding of these iron overload diseases and identify relationships between known NBIA genes and their main interacting partners by using a systems biology approach. We used whole-transcriptome gene expression data from human brain samples originating from 101 neuropathologically normal individuals (10 brain regions) to generate weighted gene co-expression networks and cluster the 10 known NBIA genes in an unsupervised manner. We investigated NBIA-enriched networks for relevant cell types and pathways, and whether they are disrupted by iron loading in NBIA diseased tissue and in an in vivo mouse model. We identified two basal ganglia gene co-expression modules significantly enriched for NBIA genes, which resemble neuronal and oligodendrocytic signatures. These NBIA gene networks are enriched for iron-related genes, and implicate synapse and lipid metabolism related pathways. Our data also indicates that these networks are disrupted by excessive brain iron loading. We identified multiple cell types in the origin of NBIA disorders. We also found unforeseen links between NBIA networks and iron-related processes, and demonstrate convergent pathways connecting NBIAs and phenotypically overlapping diseases. Our results are of further relevance for these diseases by providing candidates for new causative genes and possible points for therapeutic intervention. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  20. Increase in DNA vaccine efficacy by virosome delivery and co-expression of a cytolytic protein.

    Science.gov (United States)

    Gargett, Tessa; Grubor-Bauk, Branka; Miller, Darren; Garrod, Tamsin; Yu, Stanley; Wesselingh, Steve; Suhrbier, Andreas; Gowans, Eric J

    2014-06-01

    The potential of DNA vaccines has not been realised due to suboptimal delivery, poor antigen expression and the lack of localised inflammation, essential for antigen presentation and an effective immune response to the immunogen. Initially, we examined the delivery of a DNA vaccine encoding a model antigen, luciferase (LUC), to the respiratory tract of mice by encapsulation in a virosome. Virosomes that incorporated influenza virus haemagglutinin effectively delivered DNA to cells in the mouse respiratory tract and resulted in antigen expression and systemic and mucosal immune responses to the immunogen after an intranasal (IN) prime/intradermal (ID) boost regimen, whereas a multidose ID regimen only generated systemic immunity. We also examined systemic immune responses to LUC after ID vaccination with a DNA vaccine, which also encoded one of the several cytolytic or toxic proteins. Although the herpes simplex virus thymidine kinase, in the presence of the prodrug, ganciclovir, resulted in cell death, this failed to increase the humoral or cell-mediated immune responses. In contrast, the co-expression of LUC with the rotavirus non-structural protein 4 (NSP4) protein or a mutant form of mouse perforin, proteins which are directly cytolytic, resulted in increased LUC-specific humoral and cell-mediated immunity. On the other hand, co-expression of LUC with diphtheria toxin subunit A or overexpression of perforin or NSP4 resulted in a lower level of immunity. In summary, the efficacy of DNA vaccines can be improved by targeted IN delivery of DNA or by the induction of cell death in vaccine-targeted cells after ID delivery.

  1. A comprehensive analysis on preservation patterns of gene co-expression networks during Alzheimer's disease progression.

    Science.gov (United States)

    Ray, Sumanta; Hossain, Sk Md Mosaddek; Khatun, Lutfunnesa; Mukhopadhyay, Anirban

    2017-12-20

    Alzheimer's disease (AD) is a chronic neuro-degenerative disruption of the brain which involves in large scale transcriptomic variation. The disease does not impact every regions of the brain at the same time, instead it progresses slowly involving somewhat sequential interaction with different regions. Analysis of the expression patterns of the genes in different regions of the brain influenced in AD surely contribute for a enhanced comprehension of AD pathogenesis and shed light on the early characterization of the disease. Here, we have proposed a framework to identify perturbation and preservation characteristics of gene expression patterns across six distinct regions of the brain ("EC", "HIP", "PC", "MTG", "SFG", and "VCX") affected in AD. Co-expression modules were discovered considering a couple of regions at once. These are then analyzed to know the preservation and perturbation characteristics. Different module preservation statistics and a rank aggregation mechanism have been adopted to detect the changes of expression patterns across brain regions. Gene ontology (GO) and pathway based analysis were also carried out to know the biological meaning of preserved and perturbed modules. In this article, we have extensively studied the preservation patterns of co-expressed modules in six distinct brain regions affected in AD. Some modules are emerged as the most preserved while some others are detected as perturbed between a pair of brain regions. Further investigation on the topological properties of preserved and non-preserved modules reveals a substantial association amongst "betweenness centrality" and "degree" of the involved genes. Our findings may render a deeper realization of the preservation characteristics of gene expression patterns in discrete brain regions affected by AD.

  2. Estimation of the proteomic cancer co-expression sub networks by using association estimators.

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    Cihat Erdoğan

    Full Text Available In this study, the association estimators, which have significant influences on the gene network inference methods and used for determining the molecular interactions, were examined within the co-expression network inference concept. By using the proteomic data from five different cancer types, the hub genes/proteins within the disease-associated gene-gene/protein-protein interaction sub networks were identified. Proteomic data from various cancer types is collected from The Cancer Proteome Atlas (TCPA. Correlation and mutual information (MI based nine association estimators that are commonly used in the literature, were compared in this study. As the gold standard to measure the association estimators' performance, a multi-layer data integration platform on gene-disease associations (DisGeNET and the Molecular Signatures Database (MSigDB was used. Fisher's exact test was used to evaluate the performance of the association estimators by comparing the created co-expression networks with the disease-associated pathways. It was observed that the MI based estimators provided more successful results than the Pearson and Spearman correlation approaches, which are used in the estimation of biological networks in the weighted correlation network analysis (WGCNA package. In correlation-based methods, the best average success rate for five cancer types was 60%, while in MI-based methods the average success ratio was 71% for James-Stein Shrinkage (Shrink and 64% for Schurmann-Grassberger (SG association estimator, respectively. Moreover, the hub genes and the inferred sub networks are presented for the consideration of researchers and experimentalists.

  3. Neutralization of Bacterial YoeBSpn Toxicity and Enhanced Plant Growth in Arabidopsis thaliana via Co-Expression of the Toxin-Antitoxin Genes

    Science.gov (United States)

    Abu Bakar, Fauziah; Yeo, Chew Chieng; Harikrishna, Jennifer Ann

    2016-01-01

    Bacterial toxin-antitoxin (TA) systems have various cellular functions, including as part of the general stress response. The genome of the Gram-positive human pathogen Streptococcus pneumoniae harbors several putative TA systems, including yefM-yoeBSpn, which is one of four systems that had been demonstrated to be biologically functional. Overexpression of the yoeBSpn toxin gene resulted in cell stasis and eventually cell death in its native host, as well as in Escherichia coli. Our previous work showed that induced expression of a yoeBSpn toxin-Green Fluorescent Protein (GFP) fusion gene apparently triggered apoptosis and was lethal in the model plant, Arabidopsis thaliana. In this study, we investigated the effects of co-expression of the yefMSpn antitoxin and yoeBSpn toxin-GFP fusion in transgenic A. thaliana. When co-expressed in Arabidopsis, the YefMSpn antitoxin was found to neutralize the toxicity of YoeBSpn-GFP. Interestingly, the inducible expression of both yefMSpn antitoxin and yoeBSpn toxin-GFP fusion in transgenic hybrid Arabidopsis resulted in larger rosette leaves and taller plants with a higher number of inflorescence stems and increased silique production. To our knowledge, this is the first demonstration of a prokaryotic antitoxin neutralizing its cognate toxin in plant cells. PMID:27104531

  4. Neutralization of Bacterial YoeBSpn Toxicity and Enhanced Plant Growth in Arabidopsis thaliana via Co-Expression of the Toxin-Antitoxin Genes

    Directory of Open Access Journals (Sweden)

    Fauziah Abu Bakar

    2016-04-01

    Full Text Available Bacterial toxin-antitoxin (TA systems have various cellular functions, including as part of the general stress response. The genome of the Gram-positive human pathogen Streptococcus pneumoniae harbors several putative TA systems, including yefM-yoeBSpn, which is one of four systems that had been demonstrated to be biologically functional. Overexpression of the yoeBSpn toxin gene resulted in cell stasis and eventually cell death in its native host, as well as in Escherichia coli. Our previous work showed that induced expression of a yoeBSpn toxin-Green Fluorescent Protein (GFP fusion gene apparently triggered apoptosis and was lethal in the model plant, Arabidopsis thaliana. In this study, we investigated the effects of co-expression of the yefMSpn antitoxin and yoeBSpn toxin-GFP fusion in transgenic A. thaliana. When co-expressed in Arabidopsis, the YefMSpn antitoxin was found to neutralize the toxicity of YoeBSpn-GFP. Interestingly, the inducible expression of both yefMSpn antitoxin and yoeBSpn toxin-GFP fusion in transgenic hybrid Arabidopsis resulted in larger rosette leaves and taller plants with a higher number of inflorescence stems and increased silique production. To our knowledge, this is the first demonstration of a prokaryotic antitoxin neutralizing its cognate toxin in plant cells.

  5. Neural Network Based Load Frequency Control for Restructuring ...

    African Journals Online (AJOL)

    Neural Network Based Load Frequency Control for Restructuring Power Industry. ... an artificial neural network (ANN) application of load frequency control (LFC) of a Multi-Area power system by using a neural network controller is presented.

  6. Meta-analysis of inter-species liver co-expression networks elucidates traits associated with common human diseases.

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    Kai Wang

    2009-12-01

    Full Text Available Co-expression networks are routinely used to study human diseases like obesity and diabetes. Systematic comparison of these networks between species has the potential to elucidate common mechanisms that are conserved between human and rodent species, as well as those that are species-specific characterizing evolutionary plasticity. We developed a semi-parametric meta-analysis approach for combining gene-gene co-expression relationships across expression profile datasets from multiple species. The simulation results showed that the semi-parametric method is robust against noise. When applied to human, mouse, and rat liver co-expression networks, our method out-performed existing methods in identifying gene pairs with coherent biological functions. We identified a network conserved across species that highlighted cell-cell signaling, cell-adhesion and sterol biosynthesis as main biological processes represented in genome-wide association study candidate gene sets for blood lipid levels. We further developed a heterogeneity statistic to test for network differences among multiple datasets, and demonstrated that genes with species-specific interactions tend to be under positive selection throughout evolution. Finally, we identified a human-specific sub-network regulated by RXRG, which has been validated to play a different role in hyperlipidemia and Type 2 diabetes between human and mouse. Taken together, our approach represents a novel step forward in integrating gene co-expression networks from multiple large scale datasets to leverage not only common information but also differences that are dataset-specific.

  7. G-NEST: A gene neighborhood scoring tool to identify co-conserved, co-expressed genes

    Science.gov (United States)

    In previous studies, gene neighborhoods--spatial clusters of co-expressed genes in the genome--have been defined using arbitrary rules such as requiring adjacency, a minimum number of genes, a fixed window size, or a minimum expression level. In the current study, we developed a Gene Neighborhood Sc...

  8. MIDBRAIN CATECHOLAMINERGIC NEURONS CO-EXPRESS α-SYNUCLEIN AND TAU IN PROGRESSIVE SUPRANUCLEAR PALSY

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    María Elena eErro Aguirre

    2015-03-01

    Full Text Available Objective: To analyze the frequency and distribution of α-synuclein deposits in progressive supranuclear palsy (PSP.Methods: The brains of 25 cases of pathologically confirmed PSP were evaluated with immunohistochemistry for α-synuclein and tau. Multiple immunofluorescent stains were applied to analyze the expression of tau and α-synuclein aggregates in catecholaminergic neurons. Patients’ clinical symptoms were retrospectively recorded. Results: Deposits α-synuclein in the form of typical Lewy bodies (LBs were only found in two PSP cases (8% that fulfilled the clinical subtype of PSP known as Richardson’s syndrome (RS. LBs were present in the locus ceruleus, substantia nigra pars compacta, basal forebrain, amygdala and cingulated cortex in a distribution mimicking that of Parkinson’s disease. Triple-immunolabeling revealed co-expression of α-synuclein and tau proteins in some tyrosine hydroxilase-positive neurons of the locus ceruleus and substantia nigra pars compacta.Conclusions: There is no apparent clinical correlation between the presence of LBs in PSP. Tau protein co-aggregate with α-synuclein in catecholaminergic neurons of PSP brains suggesting a synergistic interaction between the two proteins. This is in keeping with the current view of neurodegenerative disorders as ‘misfolded protein diseases’.

  9. Construction and comparison of gene co-expression networks shows complex plant immune responses

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    Luis Guillermo Leal

    2014-10-01

    Full Text Available Gene co-expression networks (GCNs are graphic representations that depict the coordinated transcription of genes in response to certain stimuli. GCNs provide functional annotations of genes whose function is unknown and are further used in studies of translational functional genomics among species. In this work, a methodology for the reconstruction and comparison of GCNs is presented. This approach was applied using gene expression data that were obtained from immunity experiments in Arabidopsis thaliana, rice, soybean, tomato and cassava. After the evaluation of diverse similarity metrics for the GCN reconstruction, we recommended the mutual information coefficient measurement and a clustering coefficient-based method for similarity threshold selection. To compare GCNs, we proposed a multivariate approach based on the Principal Component Analysis (PCA. Branches of plant immunity that were exemplified by each experiment were analyzed in conjunction with the PCA results, suggesting both the robustness and the dynamic nature of the cellular responses. The dynamic of molecular plant responses produced networks with different characteristics that are differentiable using our methodology. The comparison of GCNs from plant pathosystems, showed that in response to similar pathogens plants could activate conserved signaling pathways. The results confirmed that the closeness of GCNs projected on the principal component space is an indicative of similarity among GCNs. This also can be used to understand global patterns of events triggered during plant immune responses.

  10. Interactions between co-expressed Arabidopsis sucrose transporters in the split-ubiquitin system

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    Lalonde Sylvie

    2003-03-01

    Full Text Available Abstract Background The Arabidopsis genome contains nine sucrose transporter paralogs falling into three clades: SUT1-like, SUT2 and SUT4. The carriers differ in their kinetic properties. Many transport proteins are known to exist as oligomers. The yeast-based split ubiquitin system can be used to analyze the ability of membrane proteins to interact. Results Promoter-GUS fusions were used to analyze the cellular expression of the three transporter genes in transgenic Arabidopsis plants. All three fusion genes are co-expressed in companion cells. Protein-protein interactions between Arabidopsis sucrose transporters were tested using the split ubiquitin system. Three paralogous sucrose transporters are capable of interacting as either homo- or heteromers. The interactions are specific, since a potassium channel and a glucose transporter did not show interaction with sucrose transporters. Also the biosynthetic and metabolizing enzymes, sucrose phosphate phosphatase and sucrose synthase, which were found to be at least in part bound to the plasma membrane, did not specifically interact with sucrose transporters. Conclusions The split-ubiquitin system provides a powerful tool to detect potential interactions between plant membrane proteins by heterologous expression in yeast, and can be used to screen for interactions with membrane proteins as baits. Like other membrane proteins, the Arabidopsis sucrose transporters are able to form oligomers. The biochemical approaches are required to confirm the in planta interaction.

  11. Co-expression and characterization of enterocin CRL35 and its mutant in Escherichia coli Rosetta

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    Masías Emilse

    2014-01-01

    Full Text Available Even though many sequences and structures of bacteriocins from lactic acid bacteria have been fully characterized so far, little information is currently available about bacteriocins heterologously produced by Escherichia coli. For this purpose, the structural gene of enterocin CRL35, munA, was PCR-amplified using specific primers and cloned downstream of PelB sequence in the pET22b (+ expression vector. E. coli Rosetta (DE3 pLysS was chosen as the host for production and enterocin was purified by an easy two-step protocol. The bacteriocin was correctly expressed with the expected intramolecular disulfide bond. Nevertheless, it was found that a variant of the enterocin, differing by 12 Da from the native polypeptide, was co-expressed by E. coli Rosetta in comparable amount. Indeed, the mutant bacteriocin contained two amino acid substitutions that were characterized by matrix assisted laser desorption ionization-time of flight (MALDI-TOF and HPLCelectrospray (ESI-Q-TOF tandem mass spectrometry (MS/ MS sequencing. This is the first report regarding the production of mutants of pediocin-like bacteriocins in the E. coli expression system.

  12. Analysis of the dynamic co-expression network of heart regeneration in the zebrafish

    Science.gov (United States)

    Rodius, Sophie; Androsova, Ganna; Götz, Lou; Liechti, Robin; Crespo, Isaac; Merz, Susanne; Nazarov, Petr V.; de Klein, Niek; Jeanty, Céline; González-Rosa, Juan M.; Muller, Arnaud; Bernardin, Francois; Niclou, Simone P.; Vallar, Laurent; Mercader, Nadia; Ibberson, Mark; Xenarios, Ioannis; Azuaje, Francisco

    2016-05-01

    The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact to coordinate heart regeneration. To enable systematic insights into this phenomenon, we generated and integrated a dynamic co-expression network of heart regeneration in the zebrafish and linked systems-level properties to the underlying molecular events. Across multiple post-injury time points, the network displays topological attributes of biological relevance. We show that regeneration steps are mediated by modules of transcriptionally coordinated genes, and by genes acting as network hubs. We also established direct associations between hubs and validated drivers of heart regeneration with murine and human orthologs. The resulting models and interactive analysis tools are available at http://infused.vital-it.ch. Using a worked example, we demonstrate the usefulness of this unique open resource for hypothesis generation and in silico screening for genes involved in heart regeneration.

  13. Genetic Network Inference: From Co-Expression Clustering to Reverse Engineering

    Science.gov (United States)

    Dhaeseleer, Patrik; Liang, Shoudan; Somogyi, Roland

    2000-01-01

    Advances in molecular biological, analytical, and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-duster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e., who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting, and bioengineering.

  14. Immunohistochemical Observation of Co-expression of E- and N-cadherins in Rat Organogenesis

    International Nuclear Information System (INIS)

    Sakamoto, Atsushi; Murata, Kazumoto; Suzuki, Hideto; Yatabe, Megumi; Kikuchi, Motoshi

    2008-01-01

    Cadherins are a family of transmembrane glycoproteins that mediate cell-to-cell adhesion. Isoforms, including E- and N-cadherin, have been identified and shown to regulate morphogenesis through homophilic binding. In the ontogeny, the expressions of E- and N-cadherin change spatiotemporally, and the changes in cadherin isoforms, called cadherin switching, impact the mechanical adhesion of cells. Furthermore, cadherin functions as a receptor that transfers information from outside to inside cells, and in terms of switching, it affects cell phenotypes. To observe the expression patterns of E- and N-cadherins during embryogenesis and to identify cells that transiently coexpress both cadherins, we employed a recently developed immunohistochemical double staining technique in rat fetuses. At embryonic day 9, embryonic ectodermal cells more dominantly expressed E-cadherin, while mesodermal cells more dominantly expressed N-cadherin. At embryonic day 10, the expression pattern of E-cadherin in the surface ectoderm and endoderm and that of N-cadherin in the neuroectoderm were established. After embryonic day 10, unique co-expression of E- and N-cadherin was observed in primordia, such as the bulbus cordis, otic pit, notochord, and Rathke’s pouch. In the present study, it was possible to visualize the expression patterns of E- and N-cadherin during early fetal development, which enabled us to morphologically clarify cadherin switching

  15. Application of Weighted Gene Co-expression Network Analysis for Data from Paired Design.

    Science.gov (United States)

    Li, Jianqiang; Zhou, Doudou; Qiu, Weiliang; Shi, Yuliang; Yang, Ji-Jiang; Chen, Shi; Wang, Qing; Pan, Hui

    2018-01-12

    Investigating how genes jointly affect complex human diseases is important, yet challenging. The network approach (e.g., weighted gene co-expression network analysis (WGCNA)) is a powerful tool. However, genomic data usually contain substantial batch effects, which could mask true genomic signals. Paired design is a powerful tool that can reduce batch effects. However, it is currently unclear how to appropriately apply WGCNA to genomic data from paired design. In this paper, we modified the current WGCNA pipeline to analyse high-throughput genomic data from paired design. We illustrated the modified WGCNA pipeline by analysing the miRNA dataset provided by Shiah et al. (2014), which contains forty oral squamous cell carcinoma (OSCC) specimens and their matched non-tumourous epithelial counterparts. OSCC is the sixth most common cancer worldwide. The modified WGCNA pipeline identified two sets of novel miRNAs associated with OSCC, in addition to the existing miRNAs reported by Shiah et al. (2014). Thus, this work will be of great interest to readers of various scientific disciplines, in particular, genetic and genomic scientists as well as medical scientists working on cancer.

  16. In vivo modification of tyrosine residues in recombinant mussel adhesive protein by tyrosinase co-expression in Escherichia coli

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    Choi Yoo

    2012-10-01

    Full Text Available Abstract Background In nature, mussel adhesive proteins (MAPs show remarkable adhesive properties, biocompatibility, and biodegradability. Thus, they have been considered promising adhesive biomaterials for various biomedical and industrial applications. However, limited production of natural MAPs has hampered their practical applications. Recombinant production in bacterial cells could be one alternative to obtain useable amounts of MAPs, although additional post-translational modification of tyrosine residues into 3,4-dihydroxyphenyl-alanine (Dopa and Dopaquinone is required. The superior properties of MAPs are mainly attributed to the introduction of quinone-derived intermolecular cross-links. To solve this problem, we utilized a co-expression strategy of recombinant MAP and tyrosinase in Escherichia coli to successfully modify tyrosine residues in vivo. Results A recombinant hybrid MAP, fp-151, was used as a target for in vivo modification, and a dual vector system of pET and pACYC-Duet provided co-expression of fp-151 and tyrosinase. As a result, fp-151 was over-expressed and mainly obtained from the soluble fraction in the co-expression system. Without tyrosinase co-expression, fp-151 was over-expressed in an insoluble form in inclusion bodies. The modification of tyrosine residues in the soluble-expressed fp-151 was clearly observed from nitroblue tetrazolium staining and liquid-chromatography-mass/mass spectrometry analyses. The purified, in vivo modified, fp-151 from the co-expression system showed approximately 4-fold higher bulk-scale adhesive strength compared to in vitro tyrosinase-treated fp-151. Conclusion Here, we reported a co-expression system to obtain in vivo modified MAP; additional in vitro tyrosinase modification was not needed to obtain adhesive properties and the in vivo modified MAP showed superior adhesive strength compared to in vitro modified protein. It is expected that this co-expression strategy will accelerate

  17. Resistor Networks based on Symmetrical Polytopes

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    Jeremy Moody

    2015-03-01

    Full Text Available This paper shows how a method developed by Van Steenwijk can be generalized to calculate the resistance between any two vertices of a symmetrical polytope all of whose edges are identical resistors. The method is applied to a number of cases that have not been studied earlier such as the Archimedean polyhedra and their duals in three dimensions, the regular polytopes in four dimensions and the hypercube in any number of dimensions.

  18. Co-expression networks reveal the tissue-specific regulation of transcription and splicing.

    Science.gov (United States)

    Saha, Ashis; Kim, Yungil; Gewirtz, Ariel D H; Jo, Brian; Gao, Chuan; McDowell, Ian C; Engelhardt, Barbara E; Battle, Alexis

    2017-11-01

    Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues. © 2017 Saha et al.; Published by Cold Spring Harbor Laboratory Press.

  19. Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes

    Directory of Open Access Journals (Sweden)

    Paules Richard S

    2007-11-01

    Full Text Available Abstract Background A common observation in the analysis of gene expression data is that many genes display similarity in their expression patterns and therefore appear to be co-regulated. However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge. We developed a novel method for Extracting microarray gene expression Patterns and Identifying co-expressed Genes, designated as EPIG. The approach utilizes the underlying structure of gene expression data to extract patterns and identify co-expressed genes that are responsive to experimental conditions. Results Through evaluation of the correlations among profiles, the magnitude of variation in gene expression profiles, and profile signal-to-noise ratio's, EPIG extracts a set of patterns representing co-expressed genes. The method is shown to work well with a simulated data set and microarray data obtained from time-series studies of dauer recovery and L1 starvation in C. elegans and after ultraviolet (UV or ionizing radiation (IR-induced DNA damage in diploid human fibroblasts. With the simulated data set, EPIG extracted the appropriate number of patterns which were more stable and homogeneous than the set of patterns that were determined using the CLICK or CAST clustering algorithms. However, CLICK performed better than EPIG and CAST with respect to the average correlation between clusters/patterns of the simulated data. With real biological data, EPIG extracted more dauer-specific patterns than CLICK. Furthermore, analysis of the IR/UV data revealed 18 unique patterns and 2661 genes out of approximately 17,000 that were identified as significantly expressed and categorized to the patterns by EPIG. The time-dependent patterns displayed similar and dissimilar responses between IR and UV treatments. Gene Ontology analysis applied to each pattern-related subset of co-expressed genes revealed underlying

  20. Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis.

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    Wei Zhang

    2015-12-01

    Full Text Available High-throughput mRNA sequencing (RNA-Seq is widely used for transcript quantification of gene isoforms. Since RNA-Seq data alone is often not sufficient to accurately identify the read origins from the isoforms for quantification, we propose to explore protein domain-domain interactions as prior knowledge for integrative analysis with RNA-Seq data. We introduce a Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ to integrate protein domain-domain interaction network with short read alignments for transcript abundance estimation. Based on our observation that the abundances of the neighboring isoforms by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene. The transcript abundances of all the genes are then jointly estimated with alternating optimization of multiple EM problems. In simulation Net-RSTQ effectively improved isoform transcript quantifications when isoform co-expressions correlate with their interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an ovarian cancer cell line, and a breast cancer cell line also showed that Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA, the transcript abundances estimated by Net-RSTQ are more informative for patient sample classification of ovarian cancer, breast cancer and lung cancer. All experimental results collectively support that Net-RSTQ is a promising approach for isoform quantification. Net-RSTQ toolbox is available at http://compbio.cs.umn.edu/Net-RSTQ/.

  1. Network-based group variable selection for detecting expression quantitative trait loci (eQTL

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    Zhang Xuegong

    2011-06-01

    Full Text Available Abstract Background Analysis of expression quantitative trait loci (eQTL aims to identify the genetic loci associated with the expression level of genes. Penalized regression with a proper penalty is suitable for the high-dimensional biological data. Its performance should be enhanced when we incorporate biological knowledge of gene expression network and linkage disequilibrium (LD structure between loci in high-noise background. Results We propose a network-based group variable selection (NGVS method for QTL detection. Our method simultaneously maps highly correlated expression traits sharing the same biological function to marker sets formed by LD. By grouping markers, complex joint activity of multiple SNPs can be considered and the dimensionality of eQTL problem is reduced dramatically. In order to demonstrate the power and flexibility of our method, we used it to analyze two simulations and a mouse obesity and diabetes dataset. We considered the gene co-expression network, grouped markers into marker sets and treated the additive and dominant effect of each locus as a group: as a consequence, we were able to replicate results previously obtained on the mouse linkage dataset. Furthermore, we observed several possible sex-dependent loci and interactions of multiple SNPs. Conclusions The proposed NGVS method is appropriate for problems with high-dimensional data and high-noise background. On eQTL problem it outperforms the classical Lasso method, which does not consider biological knowledge. Introduction of proper gene expression and loci correlation information makes detecting causal markers more accurate. With reasonable model settings, NGVS can lead to novel biological findings.

  2. MCT1 Modulates Cancer Cell Pyruvate Export and Growth of Tumors that Co-express MCT1 and MCT4

    OpenAIRE

    Hong, Candice Sun; Graham, Nicholas A.; Gu, Wen; Espindola Camacho, Carolina; Mah, Vei; Maresh, Erin L.; Alavi, Mohammed; Bagryanova, Lora; Krotee, Pascal A.L.; Gardner, Brian K.; Behbahan, Iman Saramipoor; Horvath, Steve; Chia, David; Mellinghoff, Ingo K.; Hurvitz, Sara A.

    2016-01-01

    Monocarboxylate Transporter 1 (MCT1) inhibition is thought to block tumor growth through disruption of lactate transport and glycolysis. Here we show MCT1 inhibition impairs proliferation of glycolytic breast cancer cells co-expressing MCT1 and MCT4 via disruption of pyruvate rather than lactate export. MCT1 expression is elevated in glycolytic breast tumors, and high MCT1 expression predicts poor prognosis in breast and lung cancer patients. Acute MCT1 inhibition reduces pyruvate export but ...

  3. A co-expression gene network associated with developmental regulation of apple fruit acidity.

    Science.gov (United States)

    Bai, Yang; Dougherty, Laura; Cheng, Lailiang; Xu, Kenong

    2015-08-01

    Apple fruit acidity, which affects the fruit's overall taste and flavor to a large extent, is primarily determined by the concentration of malic acid. Previous studies demonstrated that the major QTL malic acid (Ma) on chromosome 16 is largely responsible for fruit acidity variations in apple. Recent advances suggested that a natural mutation that gives rise to a premature stop codon in one of the two aluminum-activated malate transporter (ALMT)-like genes (called Ma1) is the genetic causal element underlying Ma. However, the natural mutation does not explain the developmental changes of fruit malate levels in a given genotype. Using RNA-seq data from the fruit of 'Golden Delicious' taken at 14 developmental stages from 1 week after full-bloom (WAF01) to harvest (WAF20), we characterized their transcriptomes in groups of high (12.2 ± 1.6 mg/g fw, WAF03-WAF08), mid (7.4 ± 0.5 mg/g fw, WAF01-WAF02 and WAF10-WAF14) and low (5.4 ± 0.4 mg/g fw, WAF16-WAF20) malate concentrations. Detailed analyses showed that a set of 3,066 genes (including Ma1) were expressed not only differentially (P FDR < 0.05) between the high and low malate groups (or between the early and late developmental stages) but also in significant (P < 0.05) correlation with malate concentrations. The 3,066 genes fell in 648 MapMan (sub-) bins or functional classes, and 19 of them were significantly (P FDR < 0.05) co-enriched or co-suppressed in a malate dependent manner. Network inferring using the 363 genes encompassed in the 19 (sub-) bins, identified a major co-expression network of 239 genes. Since the 239 genes were also differentially expressed between the early (WAF03-WAF08) and late (WAF16-WAF20) developmental stages, the major network was considered to be associated with developmental regulation of apple fruit acidity in 'Golden Delicious'.

  4. Identification of PEG-induced water stress responsive transcripts using co-expression network in Eucalyptus grandis.

    Science.gov (United States)

    Ghosh Dasgupta, Modhumita; Dharanishanthi, Veeramuthu

    2017-09-05

    Ecophysiological studies in Eucalyptus have shown that water is the principal factor limiting stem growth. Effect of water deficit conditions on physiological and biochemical parameters has been extensively reported in Eucalyptus. The present study was conducted to identify major polyethylene glycol induced water stress responsive transcripts in Eucalyptus grandis using gene co-expression network. A customized array representing 3359 water stress responsive genes was designed to document their expression in leaves of E. grandis cuttings subjected to -0.225MPa of PEG treatment. The differentially expressed transcripts were documented and significantly co-expressed transcripts were used for construction of network. The co-expression network was constructed with 915 nodes and 3454 edges with degree ranging from 2 to 45. Ninety four GO categories and 117 functional pathways were identified in the network. MCODE analysis generated 27 modules and module 6 with 479 nodes and 1005 edges was identified as the biologically relevant network. The major water responsive transcripts represented in the module included dehydrin, osmotin, LEA protein, expansin, arabinogalactans, heat shock proteins, major facilitator proteins, ARM repeat proteins, raffinose synthase, tonoplast intrinsic protein and transcription factors like DREB2A, ARF9, AGL24, UNE12, WLIM1 and MYB66, MYB70, MYB 55, MYB 16 and MYB 103. The coordinated analysis of gene expression patterns and coexpression networks developed in this study identified an array of transcripts that may regulate PEG induced water stress responses in E. grandis. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. Systematic comparison of co-expression of multiple recombinant thermophilic enzymes in Escherichia coli BL21(DE3).

    Science.gov (United States)

    Chen, Hui; Huang, Rui; Zhang, Y-H Percival

    2017-06-01

    The precise control of multiple heterologous enzyme expression levels in one Escherichia coli strain is important for cascade biocatalysis, metabolic engineering, synthetic biology, natural product synthesis, and studies of complexed proteins. We systematically investigated the co-expression of up to four thermophilic enzymes (i.e., α-glucan phosphorylase (αGP), phosphoglucomutase (PGM), glucose 6-phosphate dehydrogenase (G6PDH), and 6-phosphogluconate dehydrogenase (6PGDH)) in E. coli BL21(DE3) by adding T7 promoter or T7 terminator of each gene for multiple genes in tandem, changing gene alignment, and comparing one or two plasmid systems. It was found that the addition of T7 terminator after each gene was useful to decrease the influence of the upstream gene. The co-expression of the four enzymes in E. coli BL21(DE3) was demonstrated to generate two NADPH molecules from one glucose unit of maltodextrin, where NADPH was oxidized to convert xylose to xylitol. The best four-gene co-expression system was based on two plasmids (pET and pACYC) which harbored two genes. As a result, apparent enzymatic activities of the four enzymes were regulated to be at similar levels and the overall four-enzyme activity was the highest based on the formation of xylitol. This study provides useful information for the precise control of multi-enzyme-coordinated expression in E. coli BL21(DE3).

  6. An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks.

    Science.gov (United States)

    Botía, Juan A; Vandrovcova, Jana; Forabosco, Paola; Guelfi, Sebastian; D'Sa, Karishma; Hardy, John; Lewis, Cathryn M; Ryten, Mina; Weale, Michael E

    2017-04-12

    Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn ). We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices. The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful.

  7. Phenotypic characterization of neurotensin messenger RNA-expressing cells in the neuroleptic-treated rat striatum: a detailed cellular co-expression study

    International Nuclear Information System (INIS)

    Emson, P.C.; Westmore, K.; Augood, S.J.

    1996-01-01

    , demonstrating clearly that these dual-labelled cells expressed both messenger RNAs. By contrast, the hybridization signals for proneurotensin and enkephalin, and proneurotensin and dopamine and adenylate cyclase phosphoprotein-32 were generally coincident, at least within the neostriatum; most proneurotensin messenger RNA-positive cells expressed enkephalin messenger RNA and were also positive for dopamine and adenylate cyclase phosphoprotein-32 messenger RNA. However, occasional proneurotensin messenger RNA-positive striatal cells were identified that were single-labelled and did not express enkephalin messenger RNA. Within the septal nucleus, enkephalin messenger RNA and substance P messenger RNA were expressed essentially within segregated cell populations.These studies illustrate further the utility of co-expression techniques for investigating the chemical phenotype of cells within the CNS and demonstrate that the distribution of neuropeptide co-expressing cells is different within different brain regions. That several populations of proneurotensin messenger RNA-positive striatal cells may exist, of which one population is sensitive to haloperidol, co-expresses enkephalin messenger RNA and is positive for dopamine and adenylate cyclase phosphoprotein-32 messenger RNA may be of some significance in neuropsychiatric/neurological disorders given that the translated peptide, neurotensin, is known to influence and interact closely with the dopamine systems. (Copyright (c) 1996 Elsevier Science B.V., Amsterdam. All rights reserved.)

  8. Phenotypic characterization of neurotensin messenger RNA-expressing cells in the neuroleptic-treated rat striatum: a detailed cellular co-expression study

    Energy Technology Data Exchange (ETDEWEB)

    Emson, P C; Westmore, K; Augood, S J [MRC Molecular Neuroscience Group, The Department of Neurobiology, The Babraham Institute, Babraham, Cambridge (United Kingdom)

    1996-12-11

    -positive cells, demonstrating clearly that these dual-labelled cells expressed both messenger RNAs. By contrast, the hybridization signals for proneurotensin and enkephalin, and proneurotensin and dopamine and adenylate cyclase phosphoprotein-32 were generally coincident, at least within the neostriatum; most proneurotensin messenger RNA-positive cells expressed enkephalin messenger RNA and were also positive for dopamine and adenylate cyclase phosphoprotein-32 messenger RNA. However, occasional proneurotensin messenger RNA-positive striatal cells were identified that were single-labelled and did not express enkephalin messenger RNA. Within the septal nucleus, enkephalin messenger RNA and substance P messenger RNA were expressed essentially within segregated cell populations.These studies illustrate further the utility of co-expression techniques for investigating the chemical phenotype of cells within the CNS and demonstrate that the distribution of neuropeptide co-expressing cells is different within different brain regions. That several populations of proneurotensin messenger RNA-positive striatal cells may exist, of which one population is sensitive to haloperidol, co-expresses enkephalin messenger RNA and is positive for dopamine and adenylate cyclase phosphoprotein-32 messenger RNA may be of some significance in neuropsychiatric/neurological disorders given that the translated peptide, neurotensin, is known to influence and interact closely with the dopamine systems. (Copyright (c) 1996 Elsevier Science B.V., Amsterdam. All rights reserved.)

  9. Wireless Sensor Network Based Subsurface Contaminant Plume Monitoring

    Science.gov (United States)

    2012-04-16

    Sensor Network (WSN) to monitor contaminant plume movement in naturally heterogeneous subsurface formations to advance the sensor networking based...time to assess the source and predict future plume behavior. This proof-of-concept research aimed at demonstrating the use of an intelligent Wireless

  10. Artificial Neural Network Based State Estimators Integrated into Kalmtool

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Ravn, Ole; Poulsen, Niels Kjølstad

    2012-01-01

    In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation as...

  11. Network-Based Material Requirements Planning (NBMRP) in ...

    African Journals Online (AJOL)

    Network-Based Material Requirements Planning (NBMRP) in Product Development Project. ... International Journal of Development and Management Review ... To address the problems, this study evaluated the existing material planning practice, and formulated a NBMRP model out of the variables of the existing MRP and ...

  12. IPTV inter-destination synchronization: A network-based approach

    NARCIS (Netherlands)

    Stokking, H.M.; Deventer, M.O. van; Niamut, O.A.; Walraven, F.A.; Mekuria, R.N.

    2010-01-01

    This paper introduces a novel network-based approach to inter-destination media synchronization. The approach meets the need for synchronization in advanced TV concepts like social TV and offers high scalability, unlike conventional end-point based approaches. The solution for interdestination media

  13. Neural network based system for script identification in Indian ...

    Indian Academy of Sciences (India)

    2016-08-26

    Aug 26, 2016 ... The paper describes a neural network-based script identification system which can be used in the machine reading of documents written in English, Hindi and Kannada language scripts. Script identification is a basic requirement in automation of document processing, in multi-script, multi-lingual ...

  14. The Arabidopsis co-expression tool (act): a WWW-based tool and database for microarray-based gene expression analysis

    DEFF Research Database (Denmark)

    Jen, C. H.; Manfield, I. W.; Michalopoulos, D. W.

    2006-01-01

    be examined using the novel clique finder tool to determine the sets of genes most likely to be regulated in a similar manner. In combination, these tools offer three levels of analysis: creation of correlation lists of co-expressed genes, refinement of these lists using two-dimensional scatter plots......We present a new WWW-based tool for plant gene analysis, the Arabidopsis Co-Expression Tool (act) , based on a large Arabidopsis thaliana microarray data set obtained from the Nottingham Arabidopsis Stock Centre. The co-expression analysis tool allows users to identify genes whose expression...

  15. Reprogramming energy metabolism and inducing angiogenesis: co-expression of monocarboxylate transporters with VEGF family members in cervical adenocarcinomas

    International Nuclear Information System (INIS)

    Pinheiro, Céline; Garcia, Eduardo A.; Morais-Santos, Filipa; Moreira, Marise A. R.; Almeida, Fábio M.; Jubé, Luiz F.; Queiroz, Geraldo S.; Paula, Élbio C.; Andreoli, Maria A.; Villa, Luisa L.; Longatto-Filho, Adhemar; Baltazar, Fátima

    2015-01-01

    Deregulation of cellular energetic metabolism was recently pointed out as a hallmark of cancer cells. This deregulation involves a metabolic reprogramming that leads to a high production of lactate. Lactate efflux, besides contributing for the glycolytic flux, also acts in the extracellular matrix, contributing for cancer malignancy, by, among other effects, induction of angiogenesis. However, studies on the interplay between cancer metabolism and angiogenesis are scarce. Therefore, the aim of the present study was to evaluate the metabolic and vascular molecular profiles of cervical adenocarcinomas, their co-expression, and their relation to the clinical and pathological behavior. The immunohistochemical expression of metabolism-related proteins (MCT1, MCT4, CD147, GLUT1 and CAIX) as well as VEGF family members (VEGF-A, VEGF-C, VEGF-D, VEGFR-1, VEGFR-2 and VEGFR-3) was assessed in a series of 232 cervical adenocarcinomas. The co-expression among proteins was assessed and the expression profiles were associated with patients’ clinicopathological parameters. Among the metabolism-related proteins, MCT4 and CAIX were the most frequently expressed in cervical adenocarcinomas while CD147 was the less frequently expressed protein. Overall, VEGF family members showed a strong and extended expression with VEGF-C and VEGFR-2 as the most frequently expressed and VEGFR-1 as the less expressed member. Co-expression of MCT isoforms with VEGF family members was demonstrated. Finally, MCT4 was associated with parametrial invasion and HPV18 infection, CD147 and GLUT1 with distant metastasis, CAIX with tumor size and HPV18 infection, and VEGFR-1 with local and lymphnode metastasis. The results herein presented provide additional evidence for a crosstalk between deregulating cellular energetics and inducing angiogenesis. Also, the metabolic remodeling and angiogenic switch are relevant to cancer progression and aggressiveness in adenocarcinomas

  16. Co-expression of CD147 and GLUT-1 indicates radiation resistance and poor prognosis in cervical squamous cell carcinoma.

    Science.gov (United States)

    Huang, Xin-Qiong; Chen, Xiang; Xie, Xiao-Xue; Zhou, Qin; Li, Kai; Li, Shan; Shen, Liang-Fang; Su, Juan

    2014-01-01

    The aim of this study was to investigate the association of CD147 and GLUT-1, which play important roles in glycolysis in response to radiotherapy and clinical outcomes in patients with locally advanced cervical squamous cell carcinoma (LACSCC). The records of 132 female patients who received primary radiation therapy to treat LACSCC at FIGO stages IB-IVA were retrospectively reviewed. Forty-seven patients with PFS (progression-free survival) of less than 36 months were regarded as radiation-resistant. Eighty-five patients with PFS longer than 36 months were regarded as radiation-sensitive. Using pretreatment paraffin-embedded tissues, we evaluated CD147 and GLUT-1 expression by immunohistochemistry. Overexpression of CD147, GLUT-1, and CD147 and GLUT-1 combined were 44.7%, 52.9% and 36.5%, respectively, in the radiation-sensitive group, and 91.5%, 89.4% and 83.0%, respectively, in the radiation-resistant group. The 5-year progress free survival (PFS) rates in the CD147-low, CD147-high, GLUT-1-low, GLUT-1-high, CD147- and/or GLUT-1-low and CD147- and GLUT-1- dual high expression groups were 66.79%, 87.10%, 52.78%, 85.82%, 55.94%, 82.90% and 50.82%, respectively. CD147 and GLUT-1 co-expression, FIGO stage and tumor diameter were independent poor prognostic factors for patients with LACSCC in multivariate Cox regression analysis. Patients with high expression of CD147 alone, GLUT-1 alone or co-expression of CD147 and GLUT-1 showed greater resistance to radiotherapy and a shorter PFS than those with low expression. In particular, co-expression of CD147 and GLUT-1 can be considered as a negative independent prognostic factor.

  17. In-silico gene co-expression network analysis in Paracoccidioides brasiliensis with reference to haloacid dehalogenase superfamily hydrolase gene

    Directory of Open Access Journals (Sweden)

    Raghunath Satpathy

    2015-01-01

    Full Text Available Context: Paracoccidioides brasiliensis, a dimorphic fungus is the causative agent of paracoccidioidomycosis, a disease globally affecting millions of people. The haloacid dehalogenase (HAD superfamily hydrolases enzyme in the fungi, in particular, is known to be responsible in the pathogenesis by adhering to the tissue. Hence, identification of novel drug targets is essential. Aims: In-silico based identification of co-expressed genes along with HAD superfamily hydrolase in P. brasiliensis during the morphogenesis from mycelium to yeast to identify possible genes as drug targets. Materials and Methods: In total, four datasets were retrieved from the NCBI-gene expression omnibus (GEO database, each containing 4340 genes, followed by gene filtration expression of the data set. Further co-expression (CE study was performed individually and then a combination these genes were visualized in the Cytoscape 2. 8.3. Statistical Analysis Used: Mean and standard deviation value of the HAD superfamily hydrolase gene was obtained from the expression data and this value was subsequently used for the CE calculation purpose by selecting specific correlation power and filtering threshold. Results: The 23 genes that were thus obtained are common with respect to the HAD superfamily hydrolase gene. A significant network was selected from the Cytoscape network visualization that contains total 7 genes out of which 5 genes, which do not have significant protein hits, obtained from gene annotation of the expressed sequence tags by BLAST X. For all the protein PSI-BLAST was performed against human genome to find the homology. Conclusions: The gene co-expression network was obtained with respect to HAD superfamily dehalogenase gene in P. Brasiliensis.

  18. Genes and co-expression modules common to drought and bacterial stress responses in Arabidopsis and rice.

    Directory of Open Access Journals (Sweden)

    Rafi Shaik

    Full Text Available Plants are simultaneously exposed to multiple stresses resulting in enormous changes in the molecular landscape within the cell. Identification and characterization of the synergistic and antagonistic components of stress response mechanisms contributing to the cross talk between stresses is of high priority to explore and enhance multiple stress responses. To this end, we performed meta-analysis of drought (abiotic, bacterial (biotic stress response in rice and Arabidopsis by analyzing a total of 386 microarray samples belonging to 20 microarray studies and identified approximately 3100 and 900 DEGs in rice and Arabidopsis, respectively. About 38.5% (1214 and 28.7% (272 DEGs were common to drought and bacterial stresses in rice and Arabidopsis, respectively. A majority of these common DEGs showed conserved expression status in both stresses. Gene ontology enrichment analysis clearly demarcated the response and regulation of various plant hormones and related biological processes. Fatty acid metabolism and biosynthesis of alkaloids were upregulated and, nitrogen metabolism and photosynthesis was downregulated in both stress conditions. WRKY transcription family genes were highly enriched in all upregulated gene sets while 'CO-like' TF family showed inverse relationship of expression between drought and bacterial stresses. Weighted gene co-expression network analysis divided DEG sets into multiple modules that show high co-expression and identified stress specific hub genes with high connectivity. Detection of consensus modules based on DEGs common to drought and bacterial stress revealed 9 and 4 modules in rice and Arabidopsis, respectively, with conserved and reversed co-expression patterns.

  19. Integrating Genetic and Gene Co-expression Analysis Identifies Gene Networks Involved in Alcohol and Stress Responses.

    Science.gov (United States)

    Luo, Jie; Xu, Pei; Cao, Peijian; Wan, Hongjian; Lv, Xiaonan; Xu, Shengchun; Wang, Gangjun; Cook, Melloni N; Jones, Byron C; Lu, Lu; Wang, Xusheng

    2018-01-01

    Although the link between stress and alcohol is well recognized, the underlying mechanisms of how they interplay at the molecular level remain unclear. The purpose of this study is to identify molecular networks underlying the effects of alcohol and stress responses, as well as their interaction on anxiety behaviors in the hippocampus of mice using a systems genetics approach. Here, we applied a gene co-expression network approach to transcriptomes of 41 BXD mouse strains under four conditions: stress, alcohol, stress-induced alcohol and control. The co-expression analysis identified 14 modules and characterized four expression patterns across the four conditions. The four expression patterns include up-regulation in no restraint stress and given an ethanol injection (NOE) but restoration in restraint stress followed by an ethanol injection (RSE; pattern 1), down-regulation in NOE but rescue in RSE (pattern 2), up-regulation in both restraint stress followed by a saline injection (RSS) and NOE, and further amplification in RSE (pattern 3), and up-regulation in RSS but reduction in both NOE and RSE (pattern 4). We further identified four functional subnetworks by superimposing protein-protein interactions (PPIs) to the 14 co-expression modules, including γ-aminobutyric acid receptor (GABA) signaling, glutamate signaling, neuropeptide signaling, cAMP-dependent signaling. We further performed module specificity analysis to identify modules that are specific to stress, alcohol, or stress-induced alcohol responses. Finally, we conducted causality analysis to link genetic variation to these identified modules, and anxiety behaviors after stress and alcohol treatments. This study underscores the importance of integrative analysis and offers new insights into the molecular networks underlying stress and alcohol responses.

  20. Integrating Genetic and Gene Co-expression Analysis Identifies Gene Networks Involved in Alcohol and Stress Responses

    Directory of Open Access Journals (Sweden)

    Jie Luo

    2018-04-01

    Full Text Available Although the link between stress and alcohol is well recognized, the underlying mechanisms of how they interplay at the molecular level remain unclear. The purpose of this study is to identify molecular networks underlying the effects of alcohol and stress responses, as well as their interaction on anxiety behaviors in the hippocampus of mice using a systems genetics approach. Here, we applied a gene co-expression network approach to transcriptomes of 41 BXD mouse strains under four conditions: stress, alcohol, stress-induced alcohol and control. The co-expression analysis identified 14 modules and characterized four expression patterns across the four conditions. The four expression patterns include up-regulation in no restraint stress and given an ethanol injection (NOE but restoration in restraint stress followed by an ethanol injection (RSE; pattern 1, down-regulation in NOE but rescue in RSE (pattern 2, up-regulation in both restraint stress followed by a saline injection (RSS and NOE, and further amplification in RSE (pattern 3, and up-regulation in RSS but reduction in both NOE and RSE (pattern 4. We further identified four functional subnetworks by superimposing protein-protein interactions (PPIs to the 14 co-expression modules, including γ-aminobutyric acid receptor (GABA signaling, glutamate signaling, neuropeptide signaling, cAMP-dependent signaling. We further performed module specificity analysis to identify modules that are specific to stress, alcohol, or stress-induced alcohol responses. Finally, we conducted causality analysis to link genetic variation to these identified modules, and anxiety behaviors after stress and alcohol treatments. This study underscores the importance of integrative analysis and offers new insights into the molecular networks underlying stress and alcohol responses.

  1. Human Immunodeficiency Virus Type-1 Elite Controllers Maintain Low Co-Expression of Inhibitory Receptors on CD4+ T Cells.

    Science.gov (United States)

    Noyan, Kajsa; Nguyen, Son; Betts, Michael R; Sönnerborg, Anders; Buggert, Marcus

    2018-01-01

    Human immunodeficiency virus type-1 (HIV-1) elite controllers (ELCs) represent a unique population that control viral replication in the absence of antiretroviral therapy (cART). It is well established that expression of multiple inhibitory receptors on CD8+ T cells is associated with HIV-1 disease progression. However, whether reduced co-expression of inhibitory receptors on CD4+ T cells is linked to natural viral control and slow HIV-1 disease progression remains undefined. Here, we report on the expression pattern of numerous measurable inhibitory receptors, associated with T cell exhaustion (programmed cell death-1, CTLA-4, and TIGIT), on different CD4+ T cell memory populations in ELCs and HIV-infected subjects with or without long-term cART. We found that the co-expression pattern of inhibitory receptors was significantly reduced in ELCs compared with HIV-1 cART-treated and viremic subjects, and similar to healthy controls. Markers associated with T cell exhaustion varied among different memory CD4+ T cell subsets and highest levels were found mainly on transitional memory T cells. CD4+ T cells co-expressing all inhibitory markers were positively correlated to T cell activation (CD38+ HLA-DR+) as well as the transcription factors Helios and FoxP3. Finally, clinical parameters such as CD4 count, HIV-1 viral load, and the CD4/CD8 ratio all showed significant associations with CD4+ T cell exhaustion. We demonstrate that ELCs are able to maintain lower levels of CD4+ T cell exhaustion despite years of ongoing viral replication compared with successfully cART-treated subjects. Our findings suggest that ELCs harbor a "healthy" state of inhibitory receptor expression on CD4+ T cells that might play part in maintenance of their control status.

  2. Improved production of biohydrogen in light-powered Escherichia coli by co-expression of proteorhodopsin and heterologous hydrogenase

    Directory of Open Access Journals (Sweden)

    Kim Jaoon YH

    2012-01-01

    Full Text Available Abstract Background Solar energy is the ultimate energy source on the Earth. The conversion of solar energy into fuels and energy sources can be an ideal solution to address energy problems. The recent discovery of proteorhodopsin in uncultured marine γ-proteobacteria has made it possible to construct recombinant Escherichia coli with the function of light-driven proton pumps. Protons that translocate across membranes by proteorhodopsin generate a proton motive force for ATP synthesis by ATPase. Excess protons can also be substrates for hydrogen (H2 production by hydrogenase in the periplasmic space. In the present work, we investigated the effect of the co-expression of proteorhodopsin and hydrogenase on H2 production yield under light conditions. Results Recombinant E. coli BL21(DE3 co-expressing proteorhodopsin and [NiFe]-hydrogenase from Hydrogenovibrio marinus produced ~1.3-fold more H2 in the presence of exogenous retinal than in the absence of retinal under light conditions (70 μmole photon/(m2·s. We also observed the synergistic effect of proteorhodopsin with endogenous retinal on H2 production (~1.3-fold more with a dual plasmid system compared to the strain with a single plasmid for the sole expression of hydrogenase. The increase of light intensity from 70 to 130 μmole photon/(m2·s led to an increase (~1.8-fold in H2 production from 287.3 to 525.7 mL H2/L-culture in the culture of recombinant E. coli co-expressing hydrogenase and proteorhodopsin in conjunction with endogenous retinal. The conversion efficiency of light energy to H2 achieved in this study was ~3.4%. Conclusion Here, we report for the first time the potential application of proteorhodopsin for the production of biohydrogen, a promising alternative fuel. We showed that H2 production was enhanced by the co-expression of proteorhodopsin and [NiFe]-hydrogenase in recombinant E. coli BL21(DE3 in a light intensity-dependent manner. These results demonstrate that E. coli

  3. Automation tools for accelerator control a network based sequencer

    International Nuclear Information System (INIS)

    Clout, P.; Geib, M.; Westervelt, R.

    1991-01-01

    In conjunction with a major client, Vista Control Systems has developed a sequencer for control systems which works in conjunction with its realtime, distributed Vsystem database. Vsystem is a network-based data acquisition, monitoring and control system which has been applied successfully to both accelerator projects and projects outside this realm of research. The network-based sequencer allows a user to simply define a thread of execution in any supported computer on the network. The script defining a sequence has a simple syntax designed for non-programmers, with facilities for selectively abbreviating the channel names for easy reference. The semantics of the script contains most of the familiar capabilities of conventional programming languages, including standard stream I/O and the ability to start other processes with parameters passed. The script is compiled to threaded code for execution efficiency. The implementation is described in some detail and examples are given of applications for which the sequencer has been used

  4. A network-based dynamical ranking system for competitive sports

    Science.gov (United States)

    Motegi, Shun; Masuda, Naoki

    2012-12-01

    From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.

  5. A Neural Network-Based Interval Pattern Matcher

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2015-07-01

    Full Text Available One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising.

  6. Strengthening regional innovation through network-based innovation brokering

    OpenAIRE

    Svare, Helge; Gausdal, Anne Haugen

    2015-01-01

    The primary objective of this paper is to demonstrate how regional innovation system theory may be translated into manageable micro-level methods with the potential for strengthening the productive dynamics of a regional innovation system. The paper meets this objective by presenting network-based innovation brokering (NBIB), a practical method designed using insights from regional innovation system theory and trust theory. Five cases from two Norwegian regional innovation networks show that ...

  7. MCT1 modulates cancer cell pyruvate export and growth of tumors that co-express MCT1 and MCT4

    Science.gov (United States)

    Hong, Candice Sun; Graham, Nicholas A.; Gu, Wen; Camacho, Carolina Espindola; Mah, Vei; Maresh, Erin L.; Alavi, Mohammed; Bagryanova, Lora; Krotee, Pascal A. L.; Gardner, Brian K.; Behbahan, Iman Saramipoor; Horvath, Steve; Chia, David; Mellinghoff, Ingo K.; Hurvitz, Sara A.; Dubinett, Steven M.; Critchlow, Susan E.; Kurdistani, Siavash K.; Goodglick, Lee; Braas, Daniel; Graeber, Thomas G.; Christofk, Heather R.

    2016-01-01

    SUMMARY Monocarboxylate Transporter 1 (MCT1) inhibition is thought to block tumor growth through disruption of lactate transport and glycolysis. Here we show MCT1 inhibition impairs proliferation of glycolytic breast cancer cells co-expressing MCT1 and MCT4 via disruption of pyruvate rather than lactate export. MCT1 expression is elevated in glycolytic breast tumors, and high MCT1 expression predicts poor prognosis in breast and lung cancer patients. Acute MCT1 inhibition reduces pyruvate export but does not consistently alter lactate transport or glycolytic flux in breast cancer cells that co-express MCT1 and MCT4. Despite the lack of glycolysis impairment, MCT1 loss-of-function decreases breast cancer cell proliferation and blocks growth of mammary fat pad xenograft tumors. Our data suggest MCT1 expression is elevated in glycolytic cancers to promote pyruvate export, which when inhibited enhances oxidative metabolism and reduces proliferation. This study presents an alternative molecular consequence of MCT1 inhibitors further supporting their use as anti-cancer therapeutics. PMID:26876179

  8. Genetic architecture of wood properties based on association analysis and co-expression networks in white spruce.

    Science.gov (United States)

    Lamara, Mebarek; Raherison, Elie; Lenz, Patrick; Beaulieu, Jean; Bousquet, Jean; MacKay, John

    2016-04-01

    Association studies are widely utilized to analyze complex traits but their ability to disclose genetic architectures is often limited by statistical constraints, and functional insights are usually minimal in nonmodel organisms like forest trees. We developed an approach to integrate association mapping results with co-expression networks. We tested single nucleotide polymorphisms (SNPs) in 2652 candidate genes for statistical associations with wood density, stiffness, microfibril angle and ring width in a population of 1694 white spruce trees (Picea glauca). Associations mapping identified 229-292 genes per wood trait using a statistical significance level of P wood associated genes and several known MYB and NAC regulators were identified as network hubs. The network revealed a link between the gene PgNAC8, wood stiffness and microfibril angle, as well as considerable within-season variation for both genetic control of wood traits and gene expression. Trait associations were distributed throughout the network suggesting complex interactions and pleiotropic effects. Our findings indicate that integration of association mapping and co-expression networks enhances our understanding of complex wood traits. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

  9. Step-by-Step Construction of Gene Co-expression Networks from High-Throughput Arabidopsis RNA Sequencing Data.

    Science.gov (United States)

    Contreras-López, Orlando; Moyano, Tomás C; Soto, Daniela C; Gutiérrez, Rodrigo A

    2018-01-01

    The rapid increase in the availability of transcriptomics data generated by RNA sequencing represents both a challenge and an opportunity for biologists without bioinformatics training. The challenge is handling, integrating, and interpreting these data sets. The opportunity is to use this information to generate testable hypothesis to understand molecular mechanisms controlling gene expression and biological processes (Fig. 1). A successful strategy to generate tractable hypotheses from transcriptomics data has been to build undirected network graphs based on patterns of gene co-expression. Many examples of new hypothesis derived from network analyses can be found in the literature, spanning different organisms including plants and specific fields such as root developmental biology.In order to make the process of constructing a gene co-expression network more accessible to biologists, here we provide step-by-step instructions using published RNA-seq experimental data obtained from a public database. Similar strategies have been used in previous studies to advance root developmental biology. This guide includes basic instructions for the operation of widely used open source platforms such as Bio-Linux, R, and Cytoscape. Even though the data we used in this example was obtained from Arabidopsis thaliana, the workflow developed in this guide can be easily adapted to work with RNA-seq data from any organism.

  10. Modulating secretory pathway pH by proton channel co-expression can increase recombinant protein stability in plants.

    Science.gov (United States)

    Jutras, Philippe V; D'Aoust, Marc-André; Couture, Manon M-J; Vézina, Louis-Philippe; Goulet, Marie-Claire; Michaud, Dominique; Sainsbury, Frank

    2015-09-01

    Eukaryotic expression systems are used for the production of complex secreted proteins. However, recombinant proteins face considerable biochemical challenges along the secretory pathway, including proteolysis and pH variation between organelles. As the use of synthetic biology matures into solutions for protein production, various host-cell engineering approaches are being developed to ameliorate host-cell factors that can limit recombinant protein quality and yield. We report the potential of the influenza M2 ion channel as a novel tool to neutralize the pH in acidic subcellular compartments. Using transient expression in the plant host, Nicotiana benthamiana, we show that ion channel expression can significantly raise pH in the Golgi apparatus and that this can have a strong stabilizing effect on a fusion protein separated by an acid-susceptible linker peptide. We exemplify the utility of this effect in recombinant protein production using influenza hemagglutinin subtypes differentially stable at low pH; the expression of hemagglutinins prone to conformational change in mildly acidic conditions is considerably enhanced by M2 co-expression. The co-expression of a heterologous ion channel to stabilize acid-labile proteins and peptides represents a novel approach to increasing the yield and quality of secreted recombinant proteins in plants and, possibly, in other eukaryotic expression hosts. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Differential co-expression and regulation analyses reveal different mechanisms underlying major depressive disorder and subsyndromal symptomatic depression.

    Science.gov (United States)

    Xu, Fan; Yang, Jing; Chen, Jin; Wu, Qingyuan; Gong, Wei; Zhang, Jianguo; Shao, Weihua; Mu, Jun; Yang, Deyu; Yang, Yongtao; Li, Zhiwei; Xie, Peng

    2015-04-03

    Recent depression research has revealed a growing awareness of how to best classify depression into depressive subtypes. Appropriately subtyping depression can lead to identification of subtypes that are more responsive to current pharmacological treatment and aid in separating out depressed patients in which current antidepressants are not particularly effective. Differential co-expression analysis (DCEA) and differential regulation analysis (DRA) were applied to compare the transcriptomic profiles of peripheral blood lymphocytes from patients with two depressive subtypes: major depressive disorder (MDD) and subsyndromal symptomatic depression (SSD). Six differentially regulated genes (DRGs) (FOSL1, SRF, JUN, TFAP4, SOX9, and HLF) and 16 transcription factor-to-target differentially co-expressed gene links or pairs (TF2target DCLs) appear to be the key differential factors in MDD; in contrast, one DRG (PATZ1) and eight TF2target DCLs appear to be the key differential factors in SSD. There was no overlap between the MDD target genes and SSD target genes. Venlafaxine (Efexor™, Effexor™) appears to have a significant effect on the gene expression profile of MDD patients but no significant effect on the gene expression profile of SSD patients. DCEA and DRA revealed no apparent similarities between the differential regulatory processes underlying MDD and SSD. This bioinformatic analysis may provide novel insights that can support future antidepressant R&D efforts.

  12. Seismic network based detection, classification and location of volcanic tremors

    Science.gov (United States)

    Nikolai, S.; Soubestre, J.; Seydoux, L.; de Rosny, J.; Droznin, D.; Droznina, S.; Senyukov, S.; Gordeev, E.

    2017-12-01

    Volcanic tremors constitute an important attribute of volcanic unrest in many volcanoes, and their detection and characterization is a challenging issue of volcano monitoring. The main goal of the present work is to develop a network-based method to automatically classify volcanic tremors, to locate their sources and to estimate the associated wave speed. The method is applied to four and a half years of seismic data continuously recorded by 19 permanent seismic stations in the vicinity of the Klyuchevskoy volcanic group (KVG) in Kamchatka (Russia), where five volcanoes were erupting during the considered time period. The method is based on the analysis of eigenvalues and eigenvectors of the daily array covariance matrix. As a first step, following Seydoux et al. (2016), most coherent signals corresponding to dominating tremor sources are detected based on the width of the covariance matrix eigenvalues distribution. With this approach, the volcanic tremors of the two volcanoes known as most active during the considered period, Klyuchevskoy and Tolbachik, are efficiently detected. As a next step, we consider the array covariance matrix's first eigenvectors computed every day. The main hypothesis of our analysis is that these eigenvectors represent the principal component of the daily seismic wavefield and, for days with tremor activity, characterize the dominant tremor sources. Those first eigenvectors can therefore be used as network-based fingerprints of tremor sources. A clustering process is developed to analyze this collection of first eigenvectors, using correlation coefficient as a measure of their similarity. Then, we locate tremor sources based on cross-correlations amplitudes. We characterize seven tremor sources associated with different periods of activity of four volcanoes: Tolbachik, Klyuchevskoy, Shiveluch, and Kizimen. The developed method does not require a priori knowledge, is fully automatic and the database of network-based tremor fingerprints

  13. Weighted gene co-expression network analysis reveals potential genes involved in early metamorphosis process in sea cucumber Apostichopus japonicus.

    Science.gov (United States)

    Li, Yongxin; Kikuchi, Mani; Li, Xueyan; Gao, Qionghua; Xiong, Zijun; Ren, Yandong; Zhao, Ruoping; Mao, Bingyu; Kondo, Mariko; Irie, Naoki; Wang, Wen

    2018-01-01

    Sea cucumbers, one main class of Echinoderms, have a very fast and drastic metamorphosis process during their development. However, the molecular basis under this process remains largely unknown. Here we systematically examined the gene expression profiles of Japanese common sea cucumber (Apostichopus japonicus) for the first time by RNA sequencing across 16 developmental time points from fertilized egg to juvenile stage. Based on the weighted gene co-expression network analysis (WGCNA), we identified 21 modules. Among them, MEdarkmagenta was highly expressed and correlated with the early metamorphosis process from late auricularia to doliolaria larva. Furthermore, gene enrichment and differentially expressed gene analysis identified several genes in the module that may play key roles in the metamorphosis process. Our results not only provide a molecular basis for experimentally studying the development and morphological complexity of sea cucumber, but also lay a foundation for improving its emergence rate. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis

    Directory of Open Access Journals (Sweden)

    Saurav Mallik

    2017-12-01

    Full Text Available For transcriptomic analysis, there are numerous microarray-based genomic data, especially those generated for cancer research. The typical analysis measures the difference between a cancer sample-group and a matched control group for each transcript or gene. Association rule mining is used to discover interesting item sets through rule-based methodology. Thus, it has advantages to find causal effect relationships between the transcripts. In this work, we introduce two new rule-based similarity measures—weighted rank-based Jaccard and Cosine measures—and then propose a novel computational framework to detect condensed gene co-expression modules ( C o n G E M s through the association rule-based learning system and the weighted similarity scores. In practice, the list of evolved condensed markers that consists of both singular and complex markers in nature depends on the corresponding condensed gene sets in either antecedent or consequent of the rules of the resultant modules. In our evaluation, these markers could be supported by literature evidence, KEGG (Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology annotations. Specifically, we preliminarily identified differentially expressed genes using an empirical Bayes test. A recently developed algorithm—RANWAR—was then utilized to determine the association rules from these genes. Based on that, we computed the integrated similarity scores of these rule-based similarity measures between each rule-pair, and the resultant scores were used for clustering to identify the co-expressed rule-modules. We applied our method to a gene expression dataset for lung squamous cell carcinoma and a genome methylation dataset for uterine cervical carcinogenesis. Our proposed module discovery method produced better results than the traditional gene-module discovery measures. In summary, our proposed rule-based method is useful for exploring biomarker modules from transcriptomic data.

  15. ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis.

    Science.gov (United States)

    Mallik, Saurav; Zhao, Zhongming

    2017-12-28

    For transcriptomic analysis, there are numerous microarray-based genomic data, especially those generated for cancer research. The typical analysis measures the difference between a cancer sample-group and a matched control group for each transcript or gene. Association rule mining is used to discover interesting item sets through rule-based methodology. Thus, it has advantages to find causal effect relationships between the transcripts. In this work, we introduce two new rule-based similarity measures-weighted rank-based Jaccard and Cosine measures-and then propose a novel computational framework to detect condensed gene co-expression modules ( C o n G E M s) through the association rule-based learning system and the weighted similarity scores. In practice, the list of evolved condensed markers that consists of both singular and complex markers in nature depends on the corresponding condensed gene sets in either antecedent or consequent of the rules of the resultant modules. In our evaluation, these markers could be supported by literature evidence, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and Gene Ontology annotations. Specifically, we preliminarily identified differentially expressed genes using an empirical Bayes test. A recently developed algorithm-RANWAR-was then utilized to determine the association rules from these genes. Based on that, we computed the integrated similarity scores of these rule-based similarity measures between each rule-pair, and the resultant scores were used for clustering to identify the co-expressed rule-modules. We applied our method to a gene expression dataset for lung squamous cell carcinoma and a genome methylation dataset for uterine cervical carcinogenesis. Our proposed module discovery method produced better results than the traditional gene-module discovery measures. In summary, our proposed rule-based method is useful for exploring biomarker modules from transcriptomic data.

  16. Assessing pathogenicity of MLH1 variants by co-expression of human MLH1 and PMS2 genes in yeast

    Energy Technology Data Exchange (ETDEWEB)

    Vogelsang, Matjaz; Comino, Aleksandra; Zupanec, Neja [Department for Biosynthesis and Biotransformation, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana (Slovenia); Hudler, Petra [Medical Center for Molecular Biology, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, SI-1000 Ljubljana (Slovenia); Komel, Radovan [Department for Biosynthesis and Biotransformation, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana (Slovenia); Medical Center for Molecular Biology, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, SI-1000 Ljubljana (Slovenia)

    2009-10-28

    Loss of DNA mismatch repair (MMR) in humans, mainly due to mutations in the hMLH1 gene, is linked to hereditary nonpolyposis colorectal cancer (HNPCC). Because not all MLH1 alterations result in loss of MMR function, accurate characterization of variants and their classification in terms of their effect on MMR function is essential for reliable genetic testing and effective treatment. To date, in vivo assays for functional characterization of MLH1 mutations performed in various model systems have used episomal expression of the modified MMR genes. We describe here a novel approach to determine accurately the functional significance of hMLH1 mutations in vivo, based on co-expression of human MLH1 and PMS2 in yeast cells. Yeast MLH1 and PMS1 genes, whose protein products form the MutLα complex, were replaced by human orthologs directly on yeast chromosomes by homologous recombination, and the resulting MMR activity was tested. The yeast strain co-expressing hMLH1 and hPMS2 exhibited the same mutation rate as the wild-type. Eight cancer-related MLH1 variants were introduced, using the same approach, into the prepared yeast model, and their effect on MMR function was determined. Five variants (A92P, S93G, I219V, K618R and K618T) were classified as non-pathogenic, whereas variants T117M, Y646C and R659Q were characterized as pathogenic. Results of our in vivo yeast-based approach correlate well with clinical data in five out of seven hMLH1 variants and the described model was thus shown to be useful for functional characterization of MLH1 variants in cancer patients found throughout the entire coding region of the gene.

  17. Assessing pathogenicity of MLH1 variants by co-expression of human MLH1 and PMS2 genes in yeast

    Directory of Open Access Journals (Sweden)

    Hudler Petra

    2009-10-01

    Full Text Available Abstract Background Loss of DNA mismatch repair (MMR in humans, mainly due to mutations in the hMLH1 gene, is linked to hereditary nonpolyposis colorectal cancer (HNPCC. Because not all MLH1 alterations result in loss of MMR function, accurate characterization of variants and their classification in terms of their effect on MMR function is essential for reliable genetic testing and effective treatment. To date, in vivo assays for functional characterization of MLH1 mutations performed in various model systems have used episomal expression of the modified MMR genes. We describe here a novel approach to determine accurately the functional significance of hMLH1 mutations in vivo, based on co-expression of human MLH1 and PMS2 in yeast cells. Methods Yeast MLH1 and PMS1 genes, whose protein products form the MutLα complex, were replaced by human orthologs directly on yeast chromosomes by homologous recombination, and the resulting MMR activity was tested. Results The yeast strain co-expressing hMLH1 and hPMS2 exhibited the same mutation rate as the wild-type. Eight cancer-related MLH1 variants were introduced, using the same approach, into the prepared yeast model, and their effect on MMR function was determined. Five variants (A92P, S93G, I219V, K618R and K618T were classified as non-pathogenic, whereas variants T117M, Y646C and R659Q were characterized as pathogenic. Conclusion Results of our in vivo yeast-based approach correlate well with clinical data in five out of seven hMLH1 variants and the described model was thus shown to be useful for functional characterization of MLH1 variants in cancer patients found throughout the entire coding region of the gene.

  18. Assessing pathogenicity of MLH1 variants by co-expression of human MLH1 and PMS2 genes in yeast

    International Nuclear Information System (INIS)

    Vogelsang, Matjaz; Comino, Aleksandra; Zupanec, Neja; Hudler, Petra; Komel, Radovan

    2009-01-01

    Loss of DNA mismatch repair (MMR) in humans, mainly due to mutations in the hMLH1 gene, is linked to hereditary nonpolyposis colorectal cancer (HNPCC). Because not all MLH1 alterations result in loss of MMR function, accurate characterization of variants and their classification in terms of their effect on MMR function is essential for reliable genetic testing and effective treatment. To date, in vivo assays for functional characterization of MLH1 mutations performed in various model systems have used episomal expression of the modified MMR genes. We describe here a novel approach to determine accurately the functional significance of hMLH1 mutations in vivo, based on co-expression of human MLH1 and PMS2 in yeast cells. Yeast MLH1 and PMS1 genes, whose protein products form the MutLα complex, were replaced by human orthologs directly on yeast chromosomes by homologous recombination, and the resulting MMR activity was tested. The yeast strain co-expressing hMLH1 and hPMS2 exhibited the same mutation rate as the wild-type. Eight cancer-related MLH1 variants were introduced, using the same approach, into the prepared yeast model, and their effect on MMR function was determined. Five variants (A92P, S93G, I219V, K618R and K618T) were classified as non-pathogenic, whereas variants T117M, Y646C and R659Q were characterized as pathogenic. Results of our in vivo yeast-based approach correlate well with clinical data in five out of seven hMLH1 variants and the described model was thus shown to be useful for functional characterization of MLH1 variants in cancer patients found throughout the entire coding region of the gene

  19. Comprehensive analysis of coding-lncRNA gene co-expression network uncovers conserved functional lncRNAs in zebrafish.

    Science.gov (United States)

    Chen, Wen; Zhang, Xuan; Li, Jing; Huang, Shulan; Xiang, Shuanglin; Hu, Xiang; Liu, Changning

    2018-05-09

    Zebrafish is a full-developed model system for studying development processes and human disease. Recent studies of deep sequencing had discovered a large number of long non-coding RNAs (lncRNAs) in zebrafish. However, only few of them had been functionally characterized. Therefore, how to take advantage of the mature zebrafish system to deeply investigate the lncRNAs' function and conservation is really intriguing. We systematically collected and analyzed a series of zebrafish RNA-seq data, then combined them with resources from known database and literatures. As a result, we obtained by far the most complete dataset of zebrafish lncRNAs, containing 13,604 lncRNA genes (21,128 transcripts) in total. Based on that, a co-expression network upon zebrafish coding and lncRNA genes was constructed and analyzed, and used to predict the Gene Ontology (GO) and the KEGG annotation of lncRNA. Meanwhile, we made a conservation analysis on zebrafish lncRNA, identifying 1828 conserved zebrafish lncRNA genes (1890 transcripts) that have their putative mammalian orthologs. We also found that zebrafish lncRNAs play important roles in regulation of the development and function of nervous system; these conserved lncRNAs present a significant sequential and functional conservation, with their mammalian counterparts. By integrative data analysis and construction of coding-lncRNA gene co-expression network, we gained the most comprehensive dataset of zebrafish lncRNAs up to present, as well as their systematic annotations and comprehensive analyses on function and conservation. Our study provides a reliable zebrafish-based platform to deeply explore lncRNA function and mechanism, as well as the lncRNA commonality between zebrafish and human.

  20. Localization and distribution of neurons that co-express xeroderma pigmentosum-A and epidermal growth factor receptor within Rosenthal's canal.

    Science.gov (United States)

    Guthrie, O'neil W

    2015-10-01

    Xeroderma pigmentosum-A (XPA) is a C4-type zinc-finger scaffolding protein that regulates the removal of bulky-helix distorting DNA damage products from the genome. Phosphorylation of serine residues within the XPA protein is associated with improved protection of genomic DNA and cell death resistance. Therefore, kinase signaling is one important mechanism for regulating the protective function of XPA. Previous experiments have shown that spiral ganglion neurons (SGNs) may mobilize XPA as a general stress response to chemical and physical ototoxicants. Therapeutic optimization of XPA via kinase signaling could serve as a means to improve DNA repair capacity within neurons following injury. The kinase signaling activity of the epidermal growth factor receptor (EGFR) has been shown in tumor cell lines to increase the repair of DNA damage products that are primarily repaired by XPA. Such observations suggest that EGFR may regulate the protective function of XPA. However, it is not known whether SGNs in particular or neurons in general could co-express XPA and EGFR. In the current study gene and protein expression of XPA and EGFR were determined from cochlear homogenates. Immunofluorescence assays were then employed to localize neurons expressing both EGFR and XPA within the ganglion. This work was then confirmed with double-immunohistochemistry. Rosenthal's canal served as the reference space in these experiments and design-based stereology was employed in first-order stereology quantification of immunoreactive neurons. The results confirmed that a population of SGNs that constitutively express XPA may also express the EGFR. These results provide the basis for future experiments designed to therapeutically manipulate the EGFR in order to regulate XPA activity and restore gene function in neurons following DNA damage. Copyright © 2015 Elsevier GmbH. All rights reserved.

  1. Using codon optimization, chaperone co-expression, and rational mutagenesis for production and NMR assignments of human eIF2α

    International Nuclear Information System (INIS)

    Ito, Takuhiro; Wagner, Gerhard

    2004-01-01

    Producing a well behaved sample at high concentration is one of the main hurdles when starting a new project on an interesting protein. Especially when one attempts to overexpress a eukaryotic protein in bacteria, some difficulties are encountered, such as low expression level, low solubility, or even lack of folded structure. Overexpression in prokaryotic systems is highly desirable for cost-effective production of different isotope-labeled samples needed for NMR studies. Here we describe generally applicable methods for obtaining highly concentrated protein samples efficiently. This approach was developed as we tried to produce a NMR-suitable sample of the 35 kDa human translation initiation factor eIF2α, a protein that expresses poorly in E. coli and has very low solubility. First, an E. coli codon-optimized gene was synthesized on a thermal cycler, which increased the expression level by a factor of two. Second, we used co-expression of bacterial chaperone proteins, which largely increased the fraction of correctly folded protein found in the soluble phase. Third, we used rational mutagenesis guided by both the sequence alignment among homologues and the homology of one domain to a known fold for improving solubility and stability of the target protein by tenfold. Combining all these methods made it possible to produce from a one-liter preparation a 0.5 mM sample of human eIF2α that showed well-resolved NMR spectra and enabled nearly complete assignment of the protein. These methods may be generally useful for studies of other eukaryotic proteins that are otherwise difficult to express and exhibit poor solubility

  2. Feasibility of a Friendship Network-Based Pediatric Obesity Intervention.

    Science.gov (United States)

    Giannini, Courtney M; Irby, Megan B; Skelton, Joseph A; Gesell, Sabina B

    2017-02-01

    There is growing evidence supporting social network-based interventions for adolescents with obesity. This study's aim was to determine the feasibility of a social network-based intervention by assessing adolescents' friendship networks, willingness to involve friends in treatment, and how these factors influence enjoyment. Adolescents (N = 42) were recruited from a tertiary care obesity clinic. Participants gave a list of closest friends, friendship characteristics, and which of their friends they would involve in treatment. A subset (N = 14) participated in group treatment, were encouraged to bring friends, and invited to a second interview. Participants nominated a mean of 4.0 (standard deviation [SD] = 1.6) friends and were more likely to nominate closer friends (p = 0.003). Friends who attended group sessions were more likely to have multiple friendships in common with the participant's own network (p = 0.04). Involving friends in treatment is feasible and desired by adolescents and may be a novel approach for augmenting obesity treatment outcomes.

  3. Deep Neural Network-Based Chinese Semantic Role Labeling

    Institute of Scientific and Technical Information of China (English)

    ZHENG Xiaoqing; CHEN Jun; SHANG Guoqiang

    2017-01-01

    A recent trend in machine learning is to use deep architec-tures to discover multiple levels of features from data, which has achieved impressive results on various natural language processing (NLP) tasks. We propose a deep neural network-based solution to Chinese semantic role labeling (SRL) with its application on message analysis. The solution adopts a six-step strategy: text normalization, named entity recognition (NER), Chinese word segmentation and part-of-speech (POS) tagging, theme classification, SRL, and slot filling. For each step, a novel deep neural network - based model is designed and optimized, particularly for smart phone applications. Ex-periment results on all the NLP sub - tasks of the solution show that the proposed neural networks achieve state-of-the-art performance with the minimal computational cost. The speed advantage of deep neural networks makes them more competitive for large-scale applications or applications requir-ing real-time response, highlighting the potential of the pro-posed solution for practical NLP systems.

  4. Reconstruction of biological networks based on life science data integration

    Directory of Open Access Journals (Sweden)

    Kormeier Benjamin

    2010-06-01

    Full Text Available For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH - an integration toolkit for building life science data warehouses, CardioVINEdb - a information system for biological data in cardiovascular-disease and VANESA- a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

  5. Reconstruction of biological networks based on life science data integration.

    Science.gov (United States)

    Kormeier, Benjamin; Hippe, Klaus; Arrigo, Patrizio; Töpel, Thoralf; Janowski, Sebastian; Hofestädt, Ralf

    2010-10-27

    For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH--an integration toolkit for building life science data warehouses, CardioVINEdb--a information system for biological data in cardiovascular-disease and VANESA--a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

  6. S187. SEARCHING FOR BRAIN CO-EXPRESSION MODULES THAT CONTRIBUTE DISPROPORTIONATELY TO THE COMMON POLYGENIC RISK FOR SCHIZOPHRENIA

    Science.gov (United States)

    Costas, Javier; Paramo, Mario; Arrojo, Manuel

    2018-01-01

    Abstract Background Genomic research has revealed that schizophrenia is a highly polygenic disease. Recent estimates indicate that at least 71% of genomic segments of 1 Mb include one or more risk loci for schizophrenia (Loh et al., Nature Genet 2015). This extremely high polygenicity represents a challenge to decipher the biological basis of schizophrenia, as it is expected that any set of SNPs with enough size will be associated with the disorder. Among the different gene sets available for study (such as those from Gene Ontology, KEGG pathway, Reactome pathways or protein protein interaction datasets), those based on brain co-expression networks represent putative functional relationships in the relevant tissue. The aim of this work was to identify brain co-expression networks that contribute disproportionately to the common polygenic risk for schizophrenia to get more insight on schizophrenia etiopathology. Methods We analyzed a case -control dataset consisting of 582 schizophrenia patients from Galicia, NW Spain, and 591 ancestrally matched controls, genotyped with the Illumina PsychArray. Using as discovery sample the summary results from the largest GWAS of schizophrenia to date (Psychiatric Genomics Consortium, SCZ2), we generated polygenic risk scores (PRS) in our sample based on SNPs located at genes belonging to brain co-expression modules determined by the CommonMind Consortium (Fromer et al., Nature Neurosci 2016). PRS were generated using the clumping procedure of PLINK, considering several different thresholds to select SNPs from the discovery sample. In order to test if any specific module increased risk to schizophrenia more than expected by their size, we generated up to 10,000 random permutations of the same number of SNPs, matched by frequency, distance to nearest gene, number of SNPs in LD and gene density, using SNPsnap. Results As expected, most modules with enough number of independent SNPs belonging to them showed a significant increase in

  7. Identification of Gene Modules Associated with Low Temperatures Response in Bambara Groundnut by Network-Based Analysis.

    Directory of Open Access Journals (Sweden)

    Venkata Suresh Bonthala

    Full Text Available Bambara groundnut (Vigna subterranea (L. Verdc. is an African legume and is a promising underutilized crop with good seed nutritional values. Low temperature stress in a number of African countries at night, such as Botswana, can effect the growth and development of bambara groundnut, leading to losses in potential crop yield. Therefore, in this study we developed a computational pipeline to identify and analyze the genes and gene modules associated with low temperature stress responses in bambara groundnut using the cross-species microarray technique (as bambara groundnut has no microarray chip coupled with network-based analysis. Analyses of the bambara groundnut transcriptome using cross-species gene expression data resulted in the identification of 375 and 659 differentially expressed genes (p<0.01 under the sub-optimal (23°C and very sub-optimal (18°C temperatures, respectively, of which 110 genes are commonly shared between the two stress conditions. The construction of a Highest Reciprocal Rank-based gene co-expression network, followed by its partition using a Heuristic Cluster Chiseling Algorithm resulted in 6 and 7 gene modules in sub-optimal and very sub-optimal temperature stresses being identified, respectively. Modules of sub-optimal temperature stress are principally enriched with carbohydrate and lipid metabolic processes, while most of the modules of very sub-optimal temperature stress are significantly enriched with responses to stimuli and various metabolic processes. Several transcription factors (from MYB, NAC, WRKY, WHIRLY & GATA classes that may regulate the downstream genes involved in response to stimulus in order for the plant to withstand very sub-optimal temperature stress were highlighted. The identified gene modules could be useful in breeding for low-temperature stress tolerant bambara groundnut varieties.

  8. An efficient co-expression and purification system for the complex of Stx4 and C-terminal domain of Synip

    International Nuclear Information System (INIS)

    Tian Wei; Ma Cong; Liu Yingfang; Xu Tao

    2008-01-01

    Synip and Stx4 complex plays a key role in GLUT4 vesicle trafficking and fusion with plasma membrane. The interaction of Synip with Stx4 prevents interaction of VAMP2 located in GLUT4 vesicle with Stx4 in basal state. Insulin induces the dissociation of the Synip and Stx4 complex, and then triggers VAMP2 to interact with Stx4 to form the SNARE complex, thus promoting the vesicle fusion. In this report, we adopt a novel system for co-expression of the Synip and Stx4 by using two common vectors pGEX6p-1 and pET28a(+) to investigate their expression, purification, and interaction. Through this co-expression system, we successfully co-expressed the Synip and Stx4 complex with high yield, and co-purified at an approximate 1:1 molar ratio with high purity (95%). We also demonstrate that the 1-28 residues of Stx4 are dispensable for interaction with Synip using this co-expression system

  9. Co-expression of the Follicle Stimulating Hormone Receptor and Stem Cell Markers: A Novel Approach to Target Ovarian Cancer Stem Cells

    Science.gov (United States)

    2012-09-01

    ovarian cancer stem cell markers to consider it as a new experimental target for novel nanotechnology approaches capable of destroying ovarian cancer stem...FSHR mRNA after several generations in an amount consistent with stem cell characteristics. Nude mouse experiments to confirm co-expression in vivoare

  10. Neurotensin Is Co-Expressed, Co-Released And Acts Together With Glp-1 And Pyy In Enteroendocrine Control Of Metabolism

    DEFF Research Database (Denmark)

    Grunddal, Kaare Villum; Ratner, Cecilia F; Svendsen, Berit

    2016-01-01

    increasingly multi-hormonal, i.e. co-expressing PYY and neurotensin as they move up the villus. Pro-glucagon promoter and pertussis toxin receptor driven cell ablation and reappearance studies indicated that although all the cells die, the GLP-1 cells reappear more quickly than PYY and neurotensin positive...

  11. Co-Expression of Bmi-1 and Podoplanin Predicts Overall Survival in Patients With Squamous Cell Carcinoma of the Head and Neck Treated With Radio(chemo)therapy

    International Nuclear Information System (INIS)

    Vormittag, Laurenz; Thurnher, Dietmar; Geleff, Silvana; Pammer, Johannes; Heiduschka, Gregor; Brunner, Markus; Grasl, Matthaeus Ch.; Erovic, Boban M.

    2009-01-01

    Purpose: This study was conducted to determine the expression of Bmi-1 and podoplanin in healthy oral mucosa and in untreated tumor tissues samples of patients with squamous cell carcinomas of the head and neck. All patients were treated by primary radio(chemo)therapy. Methods and Materials: The expression of Bmi-1 and podoplanin was immunohistochemically evaluated in 12 normal oral mucosa and 63 tumor specimens and correlated with patients' clinical data. Results: In healthy mucosa expression of Bmi-1 and podoplanin was restricted to the basal cell layer. Expression of both proteins was found in 79% and 86% of our tumor samples, respectively. In 17 and 8 samples, Bmi-1 and podoplanin were co-expressed at the invasive border or diffuse in the bulk of the tumor, respectively. Univariate analysis showed that the co-expression of Bmi-1 and podoplanin correlated to decreased overall survival (p = 0.044). Moreover, multivariate testing identified high expression of podoplanin (p = 0.044), co-expression of Bmi-1 and podoplanin (p = 0.007) and lack of response to therapy (p < 0.0001) as predictors of shortened overall survival in patients treated with primary radio(chemo)therapy. Conclusions: Bmi-1 and podoplanin are expressed at the invasive front of squamous cell carcinomas of the head and neck. Co-expression of Bmi-1 and podoplanin predicts significantly overall survival of patients treated with primary radio(chemo)therapy

  12. Co-expression of G2-EPSPS and glyphosate acetyltransferase GAT genes conferring high tolerance to glyphosate in soybean

    Directory of Open Access Journals (Sweden)

    Bingfu eGuo

    2015-10-01

    Full Text Available Glyphosate is a widely used non-selective herbicide with broad spectrum of weed control around the world. At present, most of the commercial glyphosate tolerant soybeans utilize glyphosate tolerant gene CP4-EPSPS or glyphosate acetyltransferase gene GAT separately. In this study, both glyphosate tolerant gene G2-EPSPS and glyphosate degraded gene GAT were co-transferred into soybean and transgenic plants showed high tolerance to glyphosate. Molecular analysis including PCR, Sothern blot, qRT-PCR and Western blot revealed that target genes have been integrated into genome and expressed effectively at both mRNA and protein levels. Furthermore, the glyphosate tolerance analysis showed that no typical symptom was observed when compared with a glyphosate tolerant line HJ06-698 derived from GR1 transgenic soybean even at four-fold labeled rate of Roundup. Chlorophyll and shikimic acid content analysis of transgenic plant also revealed that these two indexes were not significantly altered after glyphosate application. These results indicated that co-expression of G2-EPSPS and GAT conferred high tolerance to the herbicide glyphosate in soybean. Therefore, combination of tolerant and degraded genes provides a new strategy for developing glyphosate tolerant transgenic crops.

  13. Development of stable Vibrio cholerae O1 Hikojima type vaccine strains co-expressing the Inaba and Ogawa lipopolysaccharide antigens.

    Directory of Open Access Journals (Sweden)

    Stefan L Karlsson

    Full Text Available We describe here the development of stable classical and El Tor V. cholerae O1 strains of the Hikojima serotype that co-express the Inaba and Ogawa antigens of O1 lipopolysaccharide (LPS. Mutation of the wbeT gene reduced LPS perosamine methylation and thereby gave only partial transformation into Ogawa LPS on the cell surface. The strains express approximately equal amounts of Inaba- and Ogawa-LPS antigens which are preserved after formalin-inactivation of the bacteria. Oral immunizations of both inbred and outbred mice with formalin-inactivated whole-cell vaccine preparations of these strains elicited strong intestinal IgA anti-LPS as well as serum vibriocidal antibody responses against both Inaba and Ogawa that were fully comparable to the responses induced by the licensed Dukoral vaccine. Passive protection studies in infant mice showed that immune sera raised against either of the novel Hikojima vaccine strains protected baby mice against infection with virulent strains of both serotypes. This study illustrates the power of using genetic manipulation to improve the properties of bacteria strains for use in killed whole-cell vaccines.

  14. VSNL1 Co-expression networks in aging include calcium signaling, synaptic plasticity, and Alzheimer’s disease pathways

    Directory of Open Access Journals (Sweden)

    C W Lin

    2015-03-01

    Full Text Available The Visinin-like 1 (VSNL1 gene encodes Visinin-like protein 1, a peripheral biomarker for Alzheimer disease (AD. Little is known, however, about normal VSNL1 expression in brain and the biologic networks in which it participates. Frontal cortex gray matter from 209 subjects without neurodegenerative or psychiatric illness, ranging in age from 16–91, were processed on Affymetrix GeneChip 1.1 ST and Human SNP Array 6.0. VSNL1 expression was unaffected by age and sex, and not significantly associated with SNPs in cis or trans. VSNL1 was significantly co-expressed with genes in pathways for Calcium Signaling, AD, Long Term Potentiation, Long Term Depression, and Trafficking of AMPA Receptors. The association with AD was driven, in part, by correlation with amyloid precursor protein (APP expression. These findings provide an unbiased link between VSNL1 and molecular mechanisms of AD, including pathways implicated in synaptic pathology in AD. Whether APP may drive increased VSNL1 expression, VSNL1 drives increased APP expression, or both are downstream of common pathogenic regulators will need to be evaluated in model systems.

  15. Co-expression of two subtypes of melatonin receptor on rat M1-type intrinsically photosensitive retinal ganglion cells.

    Directory of Open Access Journals (Sweden)

    Wen-Long Sheng

    Full Text Available Intrinsically photosensitive retinal ganglion cells (ipRGCs are involved in circadian and other non-image forming visual responses. An open question is whether the activity of these neurons may also be under the regulation mediated by the neurohormone melatonin. In the present work, by double-staining immunohistochemical technique, we studied the expression of MT1 and MT2, two known subtypes of mammalian melatonin receptors, in rat ipRGCs. A single subset of retinal ganglion cells labeled by the specific antibody against melanopsin exhibited the morphology typical of M1-type ipRGCs. Immunoreactivity for both MT1 and MT2 receptors was clearly seen in the cytoplasm of all labeled ipRGCs, indicating that these two receptors were co-expressed in each of these neurons. Furthermore, labeling for both the receptors were found in neonatal M1 cells as early as the day of birth. It is therefore highly plausible that retinal melatonin may directly modulate the activity of ipRGCs, thus regulating non-image forming visual functions.

  16. Construction and identification of double-gene co-expression vector with radiation-inducible human TRAIL and endostatin

    International Nuclear Information System (INIS)

    Li Yanbo; Guo Caixia; Gong Pingsheng; Liu Yang; Liangshuo; Wang Hongfang; Wang Jianfeng; Gong Shouliang

    2010-01-01

    Objective: To construct a recombinant plasmid pshuttle-Egr1-shTRAIL-shES containing tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and endostatin double genes. Methods: The secretary endostatin gene (shES) fragment was amplified from the pMD19T-endostatin vector by PCR. The shES gene was ligated to pMD19Tand sequenced. Finally, using the gene recombinant technique, the recombinant plasmid pshuttle-Egr1- shTRAIL-shES with radiation-inducible Egr1 promoter, secretary TRAIL and endostatin double-gene was constructed. Results: The sequence of the shES gene was in concordance with that anticipated indicating shES gene was acquired successfully.Moreover, the results acquired by PCR and restrictive digestion identification of the recombinant plasmid pshuttle-Egr1-shTRAIL-shES and all the vectors refered to its construction confirmed that pshuttle-Egr1-shTRAIL-shES was constructed correctly. Conclusion: The radiation-inducible double-gene co-expression vector pshuttle-Egr1-shTRAIL-shES is constructed successfully, which would set the experimental foundation for further study on the anti-tumor effect of TRAIL and endostatin double-gene-radiotherapy and its related mechanisms. (authors)

  17. Resilient protein co-expression network in male orbitofrontal cortex layer 2/3 during human aging.

    Science.gov (United States)

    Pabba, Mohan; Scifo, Enzo; Kapadia, Fenika; Nikolova, Yuliya S; Ma, Tianzhou; Mechawar, Naguib; Tseng, George C; Sibille, Etienne

    2017-10-01

    The orbitofrontal cortex (OFC) is vulnerable to normal and pathologic aging. Currently, layer resolution large-scale proteomic studies describing "normal" age-related alterations at OFC are not available. Here, we performed a large-scale exploratory high-throughput mass spectrometry-based protein analysis on OFC layer 2/3 from 15 "young" (15-43 years) and 18 "old" (62-88 years) human male subjects. We detected 4193 proteins and identified 127 differentially expressed (DE) proteins (p-value ≤0.05; effect size >20%), including 65 up- and 62 downregulated proteins (e.g., GFAP, CALB1). Using a previously described categorization of biological aging based on somatic tissues, that is, peripheral "hallmarks of aging," and considering overlap in protein function, we show the highest representation of altered cell-cell communication (54%), deregulated nutrient sensing (39%), and loss of proteostasis (35%) in the set of OFC layer 2/3 DE proteins. DE proteins also showed a significant association with several neurologic disorders; for example, Alzheimer's disease and schizophrenia. Notably, despite age-related changes in individual protein levels, protein co-expression modules were remarkably conserved across age groups, suggesting robust functional homeostasis. Collectively, these results provide biological insight into aging and associated homeostatic mechanisms that maintain normal brain function with advancing age. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. The order of expression is a key factor in the production of active transglutaminase in Escherichia coli by co-expression with its pro-peptide

    Directory of Open Access Journals (Sweden)

    Liu Song

    2011-12-01

    Full Text Available Abstract Background Streptomyces transglutaminase (TGase is naturally synthesized as zymogen (pro-TGase, which is then processed to produce active enzyme by the removal of its N-terminal pro-peptide. This pro-peptide is found to be essential for overexpression of soluble TGase in E. coli. However, expression of pro-TGase by E. coli requires protease-mediated activation in vitro. In this study, we developed a novel co- expression method for the direct production of active TGase in E. coli. Results A TGase from S. hygroscopicus was expressed in E. coli only after fusing with the pelB signal peptide, but fusion with the signal peptide induced insoluble enzyme. Therefore, alternative protocol was designed by co-expressing the TGase and its pro-peptide as independent polypeptides under a single T7 promoter using vector pET-22b(+. Although the pro-peptide was co-expressed, the TGase fused without the signal peptide was undetectable in both soluble and insoluble fractions of the recombinant cells. Similarly, when both genes were expressed in the order of the TGase and the pro-peptide, the solubility of TGase fused with the signal peptide was not improved by the co-expression with its pro-peptide. Interestingly, active TGase was only produced by the cells in which the pro-peptide and the TGase were fused with the signal peptide and sequentially expressed. The purified recombinant and native TGase shared the similar catalytic properties. Conclusions Our results indicated that the pro-peptide can assist correct folding of the TGase inter-molecularly in E. coli, and expression of pro-peptide prior to that of TGase was essential for the production of active TGase. The co-expression strategy based on optimizing the order of gene expression could be useful for the expression of other functional proteins that are synthesized as a precursor.

  19. Identification of estrogen receptor dimer selective ligands reveals growth-inhibitory effects on cells that co-express ERα and ERβ.

    Directory of Open Access Journals (Sweden)

    Emily Powell

    Full Text Available Estrogens play essential roles in the progression of mammary and prostatic diseases. The transcriptional effects of estrogens are transduced by two estrogen receptors, ERα and ERβ, which elicit opposing roles in regulating proliferation: ERα is proliferative while ERβ is anti-proliferative. Exogenous expression of ERβ in ERα-positive cancer cell lines inhibits cell proliferation in response to estrogen and reduces xenografted tumor growth in vivo, suggesting that ERβ might oppose ERα's proliferative effects via formation of ERα/β heterodimers. Despite biochemical and cellular evidence of ERα/β heterodimer formation in cells co-expressing both receptors, the biological roles of the ERα/β heterodimer remain to be elucidated. Here we report the identification of two phytoestrogens that selectively activate ERα/β heterodimers at specific concentrations using a cell-based, two-step high throughput small molecule screen for ER transcriptional activity and ER dimer selectivity. Using ERα/β heterodimer-selective ligands at defined concentrations, we demonstrate that ERα/β heterodimers are growth inhibitory in breast and prostate cells which co-express the two ER isoforms. Furthermore, using Automated Quantitative Analysis (AQUA to examine nuclear expression of ERα and ERβ in human breast tissue microarrays, we demonstrate that ERα and ERβ are co-expressed in the same cells in breast tumors. The co-expression of ERα and ERβ in the same cells supports the possibility of ERα/β heterodimer formation at physio- and pathological conditions, further suggesting that targeting ERα/β heterodimers might be a novel therapeutic approach to the treatment of cancers which co-express ERα and ERβ.

  20. Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery.

    Science.gov (United States)

    Jia, Zhilong; Liu, Ying; Guan, Naiyang; Bo, Xiaochen; Luo, Zhigang; Barnes, Michael R

    2016-05-27

    Drug repositioning, finding new indications for existing drugs, has gained much recent attention as a potentially efficient and economical strategy for accelerating new therapies into the clinic. Although improvement in the sensitivity of computational drug repositioning methods has identified numerous credible repositioning opportunities, few have been progressed. Arguably the "black box" nature of drug action in a new indication is one of the main blocks to progression, highlighting the need for methods that inform on the broader target mechanism in the disease context. We demonstrate that the analysis of co-expressed genes may be a critical first step towards illumination of both disease pathology and mode of drug action. We achieve this using a novel framework, co-expressed gene-set enrichment analysis (cogena) for co-expression analysis of gene expression signatures and gene set enrichment analysis of co-expressed genes. The cogena framework enables simultaneous, pathway driven, disease and drug repositioning analysis. Cogena can be used to illuminate coordinated changes within disease transcriptomes and identify drugs acting mechanistically within this framework. We illustrate this using a psoriatic skin transcriptome, as an exemplar, and recover two widely used Psoriasis drugs (Methotrexate and Ciclosporin) with distinct modes of action. Cogena out-performs the results of Connectivity Map and NFFinder webservers in similar disease transcriptome analyses. Furthermore, we investigated the literature support for the other top-ranked compounds to treat psoriasis and showed how the outputs of cogena analysis can contribute new insight to support the progression of drugs into the clinic. We have made cogena freely available within Bioconductor or https://github.com/zhilongjia/cogena . In conclusion, by targeting co-expressed genes within disease transcriptomes, cogena offers novel biological insight, which can be effectively harnessed for drug discovery and

  1. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

    Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

  2. Neural network-based model reference adaptive control system.

    Science.gov (United States)

    Patino, H D; Liu, D

    2000-01-01

    In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

  3. Network-based analysis of software change propagation.

    Science.gov (United States)

    Wang, Rongcun; Huang, Rubing; Qu, Binbin

    2014-01-01

    The object-oriented software systems frequently evolve to meet new change requirements. Understanding the characteristics of changes aids testers and system designers to improve the quality of softwares. Identifying important modules becomes a key issue in the process of evolution. In this context, a novel network-based approach is proposed to comprehensively investigate change distributions and the correlation between centrality measures and the scope of change propagation. First, software dependency networks are constructed at class level. And then, the number of times of cochanges among classes is minded from software repositories. According to the dependency relationships and the number of times of cochanges among classes, the scope of change propagation is calculated. Using Spearman rank correlation analyzes the correlation between centrality measures and the scope of change propagation. Three case studies on java open source software projects Findbugs, Hibernate, and Spring are conducted to research the characteristics of change propagation. Experimental results show that (i) change distribution is very uneven; (ii) PageRank, Degree, and CIRank are significantly correlated to the scope of change propagation. Particularly, CIRank shows higher correlation coefficient, which suggests it can be a more useful indicator for measuring the scope of change propagation of classes in object-oriented software system.

  4. A Quantum Cryptography Communication Network Based on Software Defined Network

    Directory of Open Access Journals (Sweden)

    Zhang Hongliang

    2018-01-01

    Full Text Available With the development of the Internet, information security has attracted great attention in today’s society, and quantum cryptography communication network based on quantum key distribution (QKD is a very important part of this field, since the quantum key distribution combined with one-time-pad encryption scheme can guarantee the unconditional security of the information. The secret key generated by quantum key distribution protocols is a very valuable resource, so making full use of key resources is particularly important. Software definition network (SDN is a new type of network architecture, and it separates the control plane and the data plane of network devices through OpenFlow technology, thus it realizes the flexible control of the network resources. In this paper, a quantum cryptography communication network model based on SDN is proposed to realize the flexible control of quantum key resources in the whole cryptography communication network. Moreover, we propose a routing algorithm which takes into account both the hops and the end-to-end availible keys, so that the secret key generated by QKD can be used effectively. We also simulate this quantum cryptography communication network, and the result shows that based on SDN and the proposed routing algorithm the performance of this network is improved since the effective use of the quantum key resources.

  5. G-NEST: a gene neighborhood scoring tool to identify co-conserved, co-expressed genes

    Directory of Open Access Journals (Sweden)

    Lemay Danielle G

    2012-09-01

    Full Text Available Abstract Background In previous studies, gene neighborhoods—spatial clusters of co-expressed genes in the genome—have been defined using arbitrary rules such as requiring adjacency, a minimum number of genes, a fixed window size, or a minimum expression level. In the current study, we developed a Gene Neighborhood Scoring Tool (G-NEST which combines genomic location, gene expression, and evolutionary sequence conservation data to score putative gene neighborhoods across all possible window sizes simultaneously. Results Using G-NEST on atlases of mouse and human tissue expression data, we found that large neighborhoods of ten or more genes are extremely rare in mammalian genomes. When they do occur, neighborhoods are typically composed of families of related genes. Both the highest scoring and the largest neighborhoods in mammalian genomes are formed by tandem gene duplication. Mammalian gene neighborhoods contain highly and variably expressed genes. Co-localized noisy gene pairs exhibit lower evolutionary conservation of their adjacent genome locations, suggesting that their shared transcriptional background may be disadvantageous. Genes that are essential to mammalian survival and reproduction are less likely to occur in neighborhoods, although neighborhoods are enriched with genes that function in mitosis. We also found that gene orientation and protein-protein interactions are partially responsible for maintenance of gene neighborhoods. Conclusions Our experiments using G-NEST confirm that tandem gene duplication is the primary driver of non-random gene order in mammalian genomes. Non-essentiality, co-functionality, gene orientation, and protein-protein interactions are additional forces that maintain gene neighborhoods, especially those formed by tandem duplicates. We expect G-NEST to be useful for other applications such as the identification of core regulatory modules, common transcriptional backgrounds, and chromatin domains. The

  6. Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

    Directory of Open Access Journals (Sweden)

    Vandepoele Klaas

    2009-06-01

    Full Text Available Abstract Background Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome. Results In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization and components (e.g. ARPs, actin-related proteins exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively. Conclusion We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

  7. A neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine

    Science.gov (United States)

    Guo, T. H.; Musgrave, J.

    1992-11-01

    In order to properly utilize the available fuel and oxidizer of a liquid propellant rocket engine, the mixture ratio is closed loop controlled during main stage (65 percent - 109 percent power) operation. However, because of the lack of flight-capable instrumentation for measuring mixture ratio, the value of mixture ratio in the control loop is estimated using available sensor measurements such as the combustion chamber pressure and the volumetric flow, and the temperature and pressure at the exit duct on the low pressure fuel pump. This estimation scheme has two limitations. First, the estimation formula is based on an empirical curve fitting which is accurate only within a narrow operating range. Second, the mixture ratio estimate relies on a few sensor measurements and loss of any of these measurements will make the estimate invalid. In this paper, we propose a neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine. The estimator is an extension of a previously developed neural network based sensor failure detection and recovery algorithm (sensor validation). This neural network uses an auto associative structure which utilizes the redundant information of dissimilar sensors to detect inconsistent measurements. Two approaches have been identified for synthesizing mixture ratio from measurement data using a neural network. The first approach uses an auto associative neural network for sensor validation which is modified to include the mixture ratio as an additional output. The second uses a new network for the mixture ratio estimation in addition to the sensor validation network. Although mixture ratio is not directly measured in flight, it is generally available in simulation and in test bed firing data from facility measurements of fuel and oxidizer volumetric flows. The pros and cons of these two approaches will be discussed in terms of robustness to sensor failures and accuracy of the estimate during typical transients using

  8. Optimization of Multiresonant Wireless Power Transfer Network Based on Generalized Coupled Matrix

    Directory of Open Access Journals (Sweden)

    Qiang Zhao

    2017-01-01

    Full Text Available Magnetic coupling resonant wireless power transfer network (MCRWPTN system can realize wireless power transfer for some electrical equipment real-time and high efficiency in a certain spatial scale, which resolves the contradiction between power transfer efficiency and the power transfer distance of the wireless power transfer. A fully coupled resonant energy transfer model for multirelay coils and ports is established. A dynamic adaptive impedance matching control based on fully coupling matrix and particle swarm optimization algorithm based on annealing is developed for the MCRWPTN. Furthermore, as an example, the network which has twenty nodes is analyzed, and the best transmission coefficient which has the highest power transfer efficiency is found using the optimization algorithm, and the coupling constraints are considered simultaneously. Finally, the effectiveness of the proposed method is proved by the simulation results.

  9. General Data Acquisition Platform for Wireless Sensor Network Based on CC2538

    Directory of Open Access Journals (Sweden)

    Yang Zhi-Jun

    2017-01-01

    Full Text Available Wireless sensor networks are the hotspots of current research and have very wide application prospects. Its front end is a sensor that can sense and check the external world. This paper takes temperature and humidity as the research object, and builds a wireless sensor network data acquisition platform by combining the Internet of things and the WeChat public platform. The platform uses DHT11 temperature and humidity sensors and CC2538 sensor nodes to obtain the relevant data, through the server and database for data access. The combination with WeChat public platform not only allows us to view the temperature and humidity in the WeChat public, but also allows us to understand the environmental changes of the relevant detection area more conveniently and quickly. The effectiveness of the platform is also demonstrated by the collection of temperature and humidity data.

  10. Deep Neural Network Based Supervised Speech Segregation Generalizes to Novel Noises through Large-scale Training

    Science.gov (United States)

    2015-01-01

    Table 2: Segregation results in terms of STOI on a variety of novel noises (SNR=-2 dB) Babble-20 Cafeteria Factory Babble-100 Living Room Cafe Park...NOISEX-92 corpus [13], and a living room, a cafe and a park noise from the DEMAND corpus [12]. To put the performance of the noise-independent model in

  11. Combining Host-based and network-based intrusion detection system

    African Journals Online (AJOL)

    These attacks were simulated using hping. The proposed system is implemented in Java. The results show that the proposed system is able to detect attacks both from within (host-based) and outside sources (network-based). Key Words: Intrusion Detection System (IDS), Host-based, Network-based, Signature, Security log.

  12. Telemetry Standards, IRIG Standard 106-17, Chapter 22, Network Based Protocol Suite

    Science.gov (United States)

    2017-07-01

    requirements. 22.2 Network Access Layer 22.2.1 Physical Layer Connectors and cable media should meet the electrical or optical properties required by the...Telemetry Standards, IRIG Standard 106-17 Chapter 22, July 2017 i CHAPTER 22 Network -Based Protocol Suite Acronyms...iii Chapter 22. Network -Based Protocol Suite

  13. Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines

    Science.gov (United States)

    Sidelnikov, O. S.; Redyuk, A. A.; Sygletos, S.

    2017-12-01

    We consider neural network-based schemes of digital signal processing. It is shown that the use of a dynamic neural network-based scheme of signal processing ensures an increase in the optical signal transmission quality in comparison with that provided by other methods for nonlinear distortion compensation.

  14. Bayesian-network-based safety risk analysis in construction projects

    International Nuclear Information System (INIS)

    Zhang, Limao; Wu, Xianguo; Skibniewski, Miroslaw J.; Zhong, Jingbing; Lu, Yujie

    2014-01-01

    This paper presents a systemic decision support approach for safety risk analysis under uncertainty in tunnel construction. Fuzzy Bayesian Networks (FBN) is used to investigate causal relationships between tunnel-induced damage and its influential variables based upon the risk/hazard mechanism analysis. Aiming to overcome limitations on the current probability estimation, an expert confidence indicator is proposed to ensure the reliability of the surveyed data for fuzzy probability assessment of basic risk factors. A detailed fuzzy-based inference procedure is developed, which has a capacity of implementing deductive reasoning, sensitivity analysis and abductive reasoning. The “3σ criterion” is adopted to calculate the characteristic values of a triangular fuzzy number in the probability fuzzification process, and the α-weighted valuation method is adopted for defuzzification. The construction safety analysis progress is extended to the entire life cycle of risk-prone events, including the pre-accident, during-construction continuous and post-accident control. A typical hazard concerning the tunnel leakage in the construction of Wuhan Yangtze Metro Tunnel in China is presented as a case study, in order to verify the applicability of the proposed approach. The results demonstrate the feasibility of the proposed approach and its application potential. A comparison of advantages and disadvantages between FBN and fuzzy fault tree analysis (FFTA) as risk analysis tools is also conducted. The proposed approach can be used to provide guidelines for safety analysis and management in construction projects, and thus increase the likelihood of a successful project in a complex environment. - Highlights: • A systemic Bayesian network based approach for safety risk analysis is developed. • An expert confidence indicator for probability fuzzification is proposed. • Safety risk analysis progress is extended to entire life cycle of risk-prone events. • A typical

  15. A subpopulation of dopaminergic neurons co-expresses serotonin in ventral mesencephalic cultures but not after intrastriatal transplantation in a rat model of Parkinsons disease

    DEFF Research Database (Denmark)

    Di Santo, Stefano; Seiler, Stefanie; Ducray, Angélique

    2017-01-01

    Cell replacement therapy is a promising avenue into the investigation and treatment of Parkinson’s disease (PD) and in some cases significant long-term motor improvements have been demonstrated. The main source of donor tissue is the human fetal ventral mesencephalon (VM), which consists...... 30% of the dopaminergic neurons in the donor tissue co-expressed serotonin, no co-localization could be detected in grafts one month after intrastriatal transplantation into hemi-parkinsonian rats. In conclusion, a significant and susceptible sub-population of dopaminergic neurons in fetal VM tissues...... both fetal rat and human dissociated, organotypic and neurosphere VM cultures as well as an animal model of PD were investigated. In dissociated rat VM cultures approximately 30% of the TH positive neurons co-expressed serotonin, while no co-localization with GABA was observed. Interestingly, co...

  16. Cis-Natural Antisense Transcripts Are Mainly Co-expressed with Their Sense Transcripts and Primarily Related to Energy Metabolic Pathways during Muscle Development.

    Science.gov (United States)

    Zhao, Yunxia; Hou, Ye; Zhao, Changzhi; Liu, Fei; Luan, Yu; Jing, Lu; Li, Xinyun; Zhu, Mengjin; Zhao, Shuhong

    2016-01-01

    Cis-natural antisense transcripts (cis-NATs) are a new class of RNAs identified in various species. However, the biological functions of cis-NATs are largely unknown. In this study, we investigated the transcriptional characteristics and functions of cis-NATs in the muscle tissue of lean Landrace and indigenous fatty Lantang pigs. In total, 3,306 cis-NATs of 2,469 annotated genes were identified in the muscle tissue of pigs. More than 1,300 cis-NATs correlated with their sense genes at the transcriptional level, and approximately 80% of them were co-expressed in the two breeds. Furthermore, over 1,200 differentially expressed cis-NATs were identified during muscle development. Function annotation showed that the cis-NATs participated in muscle development mainly by co-expressing with genes involved in energy metabolic pathways, including citrate cycle (TCA cycle), glycolysis or gluconeogenesis, mitochondrial activation and so on. Moreover, these cis-NATs and their sense genes abruptly increased at the transition from the late fetal stages to the early postnatal stages and then decreased along with muscle development. In conclusion, the cis-NATs in the muscle tissue of pigs were identified and determined to be mainly co-expressed with their sense genes. The co-expressed cis-NATs and their sense gene were primarily related to energy metabolic pathways during muscle development in pigs. Our results offered novel evidence on the roles of cis-NATs during the muscle development of pigs.

  17. Co-expression of NCED and ALO improves vitamin C level and tolerance to drought and chilling in transgenic tobacco and stylo plants.

    Science.gov (United States)

    Bao, Gegen; Zhuo, Chunliu; Qian, Chunmei; Xiao, Ting; Guo, Zhenfei; Lu, Shaoyun

    2016-01-01

    Abscisic acid (ABA) regulates plant adaptive responses to various environmental stresses, while L-ascorbic acid (AsA) that is also named vitamin C is an important antioxidant and involves in plant stress tolerance and the immune system in domestic animals. Transgenic tobacco (Nicotiana tabacum L.) and stylo [Stylosanthes guianensis (Aublet) Swartz], a forage legume, plants co-expressing stylo 9-cis-epoxycarotenoid dioxygenase (SgNCED1) and yeast D-arabinono-1,4-lactone oxidase (ALO) genes were generated in this study, and tolerance to drought and chilling was analysed in comparison with transgenic tobacco overexpressing SgNCED1 or ALO and the wild-type plants. Compared to the SgNCED1 or ALO transgenic plants, in which only ABA or AsA levels were increased, both ABA and AsA levels were increased in transgenic tobacco and stylo plants co-expressing SgNCED1 and ALO genes. Compared to the wild type, an enhanced drought tolerance was observed in SgNCED1 transgenic tobacco plants with induced expression of drought-responsive genes, but not in ALO plants, while an enhanced chilling tolerance was observed in ALO transgenic tobaccos with induced expression of cold-responsive genes, but not in SgNCED1 plants. Co-expression of SgNCED1 and ALO genes resulted in elevated tolerance to both drought and chilling in transgenic tobacco and stylo plants with induced expression of both drought and cold-responsive genes. Our result suggests that co-expression of SgNCED1 and ALO genes is an effective way for use in forage plant improvement for increased tolerance to drought and chilling and nutrition quality. © 2015 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.

  18. Keratitis-Ichthyosis-Deafness syndrome-associated Cx26 mutants produce nonfunctional gap junctions but hyperactive hemichannels when co-expressed with wild type Cx43

    Science.gov (United States)

    García, Isaac E.; Maripillán, Jaime; Jara, Oscar; Ceriani, Ricardo; Palacios-Muñoz, Angelina; Ramachandran, Jayalakshimi; Olivero, Pablo; Pérez-Acle, Tomás; González, Carlos; Sáez, Juan C.; Contreras, Jorge E.; Martínez, Agustín D.

    2015-01-01

    Mutations in Cx26 gene are found in most cases of human genetic deafness. Some mutations produce syndromic deafness associated with skin disorders, like Keratitis Ichthyosis Deafness syndrome (KID). Because in the human skin Cx26 is co-expressed with other connexins, like Cx43 and Cx30, and since KID syndrome is inherited as autosomal dominant condition, it is possible that KID mutations change the way Cx26 interacts with other co-expressed connexins. Indeed, some Cx26 syndromic mutations showed gap junction dominant negative effect when co-expressed with wild type connexins, including Cx26 and Cx43. The nature of these interactions and the consequences on hemichannels and gap junction channels functions remain unknown. In this study we demonstrate that syndromic mutations at the N-terminus segment of Cx26, change connexin oligomerization compatibility, allowing aberrant interactions with Cx43. Strikingly, heteromeric oligomer formed by Cx43/Cx26 (syndromic mutants) show exacerbated hemichannel activity, but nonfunctional gap junction channels; this also occurs for those Cx26 KID mutants that do not show functional homomeric hemichannels. Heterologous expression of these hyperactive heteromeric hemichannels increases cell membrane permeability, favoring ATP release and Ca2+ overload. The functional paradox produced by oligomerization of Cx43 and Cx26 KID mutants could underlie the severe syndromic phenotype in human skin. PMID:25625422

  19. From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data.

    Science.gov (United States)

    Vella, Danila; Zoppis, Italo; Mauri, Giancarlo; Mauri, Pierluigi; Di Silvestre, Dario

    2017-12-01

    The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

  20. Co-expression of D-glucose isomerase and D-psicose 3-epimerase: development of an efficient one-step production of D-psicose.

    Science.gov (United States)

    Men, Yan; Zhu, Yueming; Zeng, Yan; Izumori, Ken; Sun, Yuanxia; Ma, Yanhe

    2014-10-01

    D-Psicose has been attracting attention in recent years because of its alimentary activities and is used as an ingredient in a range of foods and dietary supplements. To develop a one-step enzymatic process of D-psicose production, thermoactive D-glucose isomerase and the D-psicose 3-epimerase obtained from Bacillus sp. and Ruminococcus sp., respectively, were successfully co-expressed in Escherichia coli BL21 strain. The substrate of one-step enzymatic process was D-glucose. The co-expression system exhibited maximum activity at 65 °C and pH 7.0. Mg(2+) could enhance the output of D-psicose by 2.32 fold to 1.6 g/L from 10 g/L of D-glucose. When using high-fructose corn syrup (HFCS) as substrate, 135 g/L D-psicose was produced under optimum conditions. The mass ratio of D-glucose, D-fructose, and D-psicose was almost 3.0:2.7:1.0, when the reaction reached equilibrium after an 8h incubation time. This co-expression system approaching to produce D-psicose has potential application in food and beverage products, especially softdrinks. Copyright © 2014 Elsevier Inc. All rights reserved.

  1. A neural network based methodology to predict site-specific spectral acceleration values

    Science.gov (United States)

    Kamatchi, P.; Rajasankar, J.; Ramana, G. V.; Nagpal, A. K.

    2010-12-01

    A general neural network based methodology that has the potential to replace the computationally-intensive site-specific seismic analysis of structures is proposed in this paper. The basic framework of the methodology consists of a feed forward back propagation neural network algorithm with one hidden layer to represent the seismic potential of a region and soil amplification effects. The methodology is implemented and verified with parameters corresponding to Delhi city in India. For this purpose, strong ground motions are generated at bedrock level for a chosen site in Delhi due to earthquakes considered to originate from the central seismic gap of the Himalayan belt using necessary geological as well as geotechnical data. Surface level ground motions and corresponding site-specific response spectra are obtained by using a one-dimensional equivalent linear wave propagation model. Spectral acceleration values are considered as a target parameter to verify the performance of the methodology. Numerical studies carried out to validate the proposed methodology show that the errors in predicted spectral acceleration values are within acceptable limits for design purposes. The methodology is general in the sense that it can be applied to other seismically vulnerable regions and also can be updated by including more parameters depending on the state-of-the-art in the subject.

  2. Acceleration of spiking neural network based pattern recognition on NVIDIA graphics processors.

    Science.gov (United States)

    Han, Bing; Taha, Tarek M

    2010-04-01

    There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and generally utilize more accurate neuron models, such as the Izhikevich and the Hodgkin-Huxley models, in favor of the more popular integrate and fire model. We examine the feasibility of using graphics processing units (GPUs) to accelerate a spiking neural network based character recognition network to enable such large scale systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA general-purpose (GP) GPU platforms are examined, including the GeForce 9800 GX2, the Tesla C1060, and the Tesla S1070. Our results show that the GPGPUs can provide significant speedup over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided a speedup of 5.6 and 84.4 over highly optimized implementations on the fastest central processing unit (CPU) tested, a quadcore 2.67 GHz Xeon processor, for the Izhikevich and the Hodgkin-Huxley models, respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPUs are well suited for this application domain.

  3. Effector/memory CD4 T cells making either Th1 or Th2 cytokines commonly co-express T-bet and GATA-3.

    Directory of Open Access Journals (Sweden)

    Arundhoti Das

    Full Text Available Naïve CD4 T (NCD4T cells post-activation undergo programming for inducible production of cytokines leading to generation of memory cells with various functions. Based on cytokine based polarization of NCD4T cells in vitro, programming for either 'Th1' (interferon-gamma [IFNg] or 'Th2' (interleukin [IL]-4/5/13 cytokines is thought to occur via mutually exclusive expression and functioning of T-bet or GATA-3 transcription factors (TFs. However, we show that a high proportion of mouse and human memory-phenotype CD4 T (MCD4T cells generated in vivo which expressed either Th1 or Th2 cytokines commonly co-expressed T-bet and GATA-3. While T-bet levels did not differ between IFNg-expressing and IL-4/5/13-expressing MCD4T cells, GATA-3 levels were higher in the latter. These observations were also confirmed in MCD4T cells from FVB/NJ or aged C57BL/6 or IFNg-deficient mice. While MCD4T cells from these strains showed greater Th2 commitment than those from young C57BL/6 mice, pattern of co-expression of TF was similar. Effector T cells generated in vivo following immunization also showed TF co-expression in Th1 or Th2 cytokine producing cells. We speculated that the difference in TF expression pattern of MCD4T cells generated in vivo and those generated in cytokine polarized cultures in vitro could be due to relative absence of polarizing conditions during activation in vivo. We tested this by NCD4T cell activation in non-polarizing conditions in vitro. Anti-CD3 and anti-CD28-mediated priming of polyclonal NCD4T cells in vitro without polarizing milieu generated cells that expressed either IFNg or IL-4/5/13 but not both, yet both IFNg- and IL-4/5/13-expressing cells showed upregulation of both TFs. We also tested monoclonal T cell populations activated in non-polarizing conditions. TCR-transgenic NCD4T cells primed in vitro by cognate peptide in non-polarizing conditions which expressed either IFNg or IL-4/5/13 also showed a high proportion of cells co-expressing

  4. Comprehensive analysis of differential co-expression patterns reveal transcriptional dysregulation mechanism and identify novel prognostic lncRNAs in esophageal squamous cell carcinoma

    Directory of Open Access Journals (Sweden)

    Li Z

    2017-06-01

    Full Text Available Zhen Li,1 Qianlan Yao,1 Songjian Zhao,1 Yin Wang,2,3 Yixue Li,1,4 Zhen Wang4 1School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 2Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 3Collaborative Innovation Center for Genetics and Development, Fudan University, 4Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China Abstract: Esophageal squamous cell carcinoma (ESCC is one of the most common malignancies worldwide and occurs at a relatively high frequency in People’s Republic of China. However, the molecular mechanism underlying ESCC is still unclear. In this study, the mRNA and long non-coding RNA (lncRNA expression profiles of ESCC were downloaded from the Gene Expression Omnibus database, and then differential co-expression analysis was used to reveal the altered co-expression relationship of gene pairs in ESCC tumors. A total of 3,709 mRNAs and 923 lncRNAs were differentially co-expressed between normal and tumor tissues, and we found that most of the gene pairs lost associations in the tumor tissues. The differential regulatory networking approach deciphered that transcriptional dysregulation was ubiquitous in ESCC, and most of the differentially regulated links were modulated by 37 TFs. Our study also found that two novel lncRNAs (ADAMTS9-AS1 and AP000696.2 might be essential in the development of ectoderm and epithelial cells, which could significantly stratify ESCC patients into high-risk and low-risk groups, and were much better than traditional clinical tumor markers. Further inspection of two risk groups showed that the changes in TF-target regulation in the high-risk patients were significantly higher than those in the low-risk patients. In addition, four signal transduction-related DCmRNAs (ERBB3, ENSA, KCNK7, MFSD5

  5. Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

    Directory of Open Access Journals (Sweden)

    Ahcene Farah

    2002-06-01

    Full Text Available This paper proposes a Neural-Network-Based Fuzzy logic system for navigation control of intelligent vehicles. First, the use of Neural Networks and Fuzzy Logic to provide intelligent vehicles  with more autonomy and intelligence is discussed. Second, the system  for the obstacle avoidance behavior is developed. Fuzzy Logic improves Neural Networks (NN obstacle avoidance approach by handling imprecision and rule-based approximate reasoning. This system must make the vehicle able, after supervised learning, to achieve two tasks: 1- to make one’s way towards its target by a NN, and 2- to avoid static or dynamic obstacles by a Fuzzy NN capturing the behavior of a human expert. Afterwards, two association phases between each task and the appropriate actions are carried out by Trial and Error learning and their coordination allows to decide the appropriate action. Finally, the simulation results display the generalization and adaptation abilities of the system by testing it in new unexplored environments.

  6. Failure Analysis of Network Based Accessible Pedestrian Signals in Closed-Loop Operation

    Science.gov (United States)

    2011-03-01

    The potential failure modes of a network based accessible pedestrian system were analyzed to determine the limitations and benefits of closed-loop operation. The vulnerabilities of the system are accessed using the industry standard process known as ...

  7. Implementation of neural network based non-linear predictive

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1998-01-01

    The paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems including open loop unstable and non-minimum phase systems, but has also been proposed extended for the control of non......-linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi......-Newton optimization algorithm. The performance is demonstrated on a pneumatic servo system....

  8. Efficient multiuser quantum cryptography network based on entanglement.

    Science.gov (United States)

    Xue, Peng; Wang, Kunkun; Wang, Xiaoping

    2017-04-04

    We present an efficient quantum key distribution protocol with a certain entangled state to solve a special cryptographic task. Also, we provide a proof of security of this protocol by generalizing the proof of modified of Lo-Chau scheme. Based on this two-user scheme, a quantum cryptography network protocol is proposed without any quantum memory.

  9. A network-based biomarker approach for molecular investigation and diagnosis of lung cancer

    Directory of Open Access Journals (Sweden)

    Chen Bor-Sen

    2011-01-01

    Full Text Available Abstract Background Lung cancer is the leading cause of cancer deaths worldwide. Many studies have investigated the carcinogenic process and identified the biomarkers for signature classification. However, based on the research dedicated to this field, there is no highly sensitive network-based method for carcinogenesis characterization and diagnosis from the systems perspective. Methods In this study, a systems biology approach integrating microarray gene expression profiles and protein-protein interaction information was proposed to develop a network-based biomarker for molecular investigation into the network mechanism of lung carcinogenesis and diagnosis of lung cancer. The network-based biomarker consists of two protein association networks constructed for cancer samples and non-cancer samples. Results Based on the network-based biomarker, a total of 40 significant proteins in lung carcinogenesis were identified with carcinogenesis relevance values (CRVs. In addition, the network-based biomarker, acting as the screening test, proved to be effective in diagnosing smokers with signs of lung cancer. Conclusions A network-based biomarker using constructed protein association networks is a useful tool to highlight the pathways and mechanisms of the lung carcinogenic process and, more importantly, provides potential therapeutic targets to combat cancer.

  10. Mct8 and trh co-expression throughout the hypothalamic paraventricular nucleus is modified by dehydration-induced anorexia in rats.

    Science.gov (United States)

    Alvarez-Salas, Elena; Mengod, Guadalupe; García-Luna, Cinthia; Soberanes-Chávez, Paulina; Matamoros-Trejo, Gilberto; de Gortari, Patricia

    2016-04-01

    Thyrotropin-releasing hormone (TRH) is a neuropeptide with endocrine and neuromodulatory effects. TRH from the paraventricular hypothalamic nucleus (PVN) participates in the control of energy homeostasis; as a neuromodulator TRH has anorexigenic effects. Negative energy balance decreases PVN TRH expression and TSH concentration; in contrast, a particular model of anorexia (dehydration) induces in rats a paradoxical increase in TRH expression in hypophysiotropic cells from caudal PVN and high TSH serum levels, despite their apparent hypothalamic hyperthyroidism and low body weight. We compared here the mRNA co-expression pattern of one of the brain thyroid hormones' transporters, the monocarboxylate transporter-8 (MCT8) with that of TRH in PVN subdivisions of dehydration-induced anorexic (DIA) and control rats. Our aim was to identify whether a low MCT8 expression in anorexic rats could contribute to their high TRH mRNA content.We registered daily food intake and body weight of 7-day DIA and control rats and analyzed TRH and MCT8 mRNA co-expression throughout the PVN by double in situ hybridization assays. We found that DIA rats showed increased number of TRHergic cells in caudal PVN, as well as a decreased percentage of TRH-expressing neurons that co-expressed MCT8 mRNA signal. Results suggest that the reduced proportion of double TRH/MCT8 expressing cells may be limiting the entry of hypothalamic triiodothyronine to the greater number of TRH-expressing neurons from caudal PVN and be in part responsible for the high TRH expression in anorexia rats and for the lack of adaptation of their hypothalamic-pituitary-thyroid axis to their low food intake.

  11. Profiling and Co-expression Network Analysis of Learned Helplessness Regulated mRNAs and lncRNAs in the Mouse Hippocampus

    Directory of Open Access Journals (Sweden)

    Chaoqun Li

    2018-01-01

    Full Text Available Although studies provide insights into the neurobiology of stress and depression, the exact molecular mechanisms underlying their pathologies remain largely unknown. Long non-coding RNA (lncRNA has been implicated in brain functions and behavior. A potential link between lncRNA and psychiatric disorders has been proposed. However, it remains undetermined whether IncRNA regulation, in the brain, contributes to stress or depression pathologies. In this study, we used a valid animal model of depression-like symptoms; namely learned helplessness, RNA-seq, Gene Ontology and co-expression network analyses to profile the expression pattern of lncRNA and mRNA in the hippocampus of mice. We identified 6346 differentially expressed transcripts. Among them, 340 lncRNAs and 3559 protein coding mRNAs were differentially expressed in helpless mice in comparison with control and/or non-helpless mice (inescapable stress resilient mice. Gene Ontology and pathway enrichment analyses indicated that induction of helplessness altered expression of mRNAs enriched in fundamental biological functions implicated in stress/depression neurobiology such as synaptic, metabolic, cell survival and proliferation, developmental and chromatin modification functions. To explore the possible regulatory roles of the altered lncRNAs, we constructed co-expression networks composed of the lncRNAs and mRNAs. Among our differentially expressed lncRNAs, 17% showed significant correlation with genes. Functional co-expression analysis linked the identified lncRNAs to several cellular mechanisms implicated in stress/depression neurobiology. Importantly, 57% of the identified regulatory lncRNAs significantly correlated with 18 different synapse-related functions. Thus, the current study identifies for the first time distinct groups of lncRNAs regulated by induction of learned helplessness in the mouse brain. Our results suggest that lncRNA-directed regulatory mechanisms might contribute to

  12. Profiling and Co-expression Network Analysis of Learned Helplessness Regulated mRNAs and lncRNAs in the Mouse Hippocampus.

    Science.gov (United States)

    Li, Chaoqun; Cao, Feifei; Li, Shengli; Huang, Shenglin; Li, Wei; Abumaria, Nashat

    2017-01-01

    Although studies provide insights into the neurobiology of stress and depression, the exact molecular mechanisms underlying their pathologies remain largely unknown. Long non-coding RNA (lncRNA) has been implicated in brain functions and behavior. A potential link between lncRNA and psychiatric disorders has been proposed. However, it remains undetermined whether IncRNA regulation, in the brain, contributes to stress or depression pathologies. In this study, we used a valid animal model of depression-like symptoms; namely learned helplessness, RNA-seq, Gene Ontology and co-expression network analyses to profile the expression pattern of lncRNA and mRNA in the hippocampus of mice. We identified 6346 differentially expressed transcripts. Among them, 340 lncRNAs and 3559 protein coding mRNAs were differentially expressed in helpless mice in comparison with control and/or non-helpless mice (inescapable stress resilient mice). Gene Ontology and pathway enrichment analyses indicated that induction of helplessness altered expression of mRNAs enriched in fundamental biological functions implicated in stress/depression neurobiology such as synaptic, metabolic, cell survival and proliferation, developmental and chromatin modification functions. To explore the possible regulatory roles of the altered lncRNAs, we constructed co-expression networks composed of the lncRNAs and mRNAs. Among our differentially expressed lncRNAs, 17% showed significant correlation with genes. Functional co-expression analysis linked the identified lncRNAs to several cellular mechanisms implicated in stress/depression neurobiology. Importantly, 57% of the identified regulatory lncRNAs significantly correlated with 18 different synapse-related functions. Thus, the current study identifies for the first time distinct groups of lncRNAs regulated by induction of learned helplessness in the mouse brain. Our results suggest that lncRNA-directed regulatory mechanisms might contribute to stress

  13. Transient Co-Expression of Post-Transcriptional Gene Silencing Suppressors for Increased in Planta Expression of a Recombinant Anthrax Receptor Fusion Protein

    Directory of Open Access Journals (Sweden)

    Kittipong Rattanaporn

    2011-08-01

    Full Text Available Potential epidemics of infectious diseases and the constant threat of bioterrorism demand rapid, scalable, and cost-efficient manufacturing of therapeutic proteins. Molecular farming of tobacco plants provides an alternative for the recombinant production of therapeutics. We have developed a transient production platform that uses Agrobacterium infiltration of Nicotiana benthamiana plants to express a novel anthrax receptor decoy protein (immunoadhesin, CMG2-Fc. This chimeric fusion protein, designed to protect against the deadly anthrax toxins, is composed of the von Willebrand factor A (VWA domain of human capillary morphogenesis 2 (CMG2, an effective anthrax toxin receptor, and the Fc region of human immunoglobulin G (IgG. We evaluated, in N. benthamiana intact plants and detached leaves, the expression of CMG2-Fc under the control of the constitutive CaMV 35S promoter, and the co-expression of CMG2-Fc with nine different viral suppressors of post-transcriptional gene silencing (PTGS: p1, p10, p19, p21, p24, p25, p38, 2b, and HCPro. Overall, transient CMG2-Fc expression was higher on intact plants than detached leaves. Maximum expression was observed with p1 co-expression at 3.5 days post-infiltration (DPI, with a level of 0.56 g CMG2-Fc per kg of leaf fresh weight and 1.5% of the total soluble protein, a ten-fold increase in expression when compared to absence of suppression. Co-expression with the p25 PTGS suppressor also significantly increased the CMG2-Fc expression level after just 3.5 DPI.

  14. Transient co-expression of post-transcriptional gene silencing suppressors for increased in planta expression of a recombinant anthrax receptor fusion protein.

    Science.gov (United States)

    Arzola, Lucas; Chen, Junxing; Rattanaporn, Kittipong; Maclean, James M; McDonald, Karen A

    2011-01-01

    Potential epidemics of infectious diseases and the constant threat of bioterrorism demand rapid, scalable, and cost-efficient manufacturing of therapeutic proteins. Molecular farming of tobacco plants provides an alternative for the recombinant production of therapeutics. We have developed a transient production platform that uses Agrobacterium infiltration of Nicotiana benthamiana plants to express a novel anthrax receptor decoy protein (immunoadhesin), CMG2-Fc. This chimeric fusion protein, designed to protect against the deadly anthrax toxins, is composed of the von Willebrand factor A (VWA) domain of human capillary morphogenesis 2 (CMG2), an effective anthrax toxin receptor, and the Fc region of human immunoglobulin G (IgG). We evaluated, in N. benthamiana intact plants and detached leaves, the expression of CMG2-Fc under the control of the constitutive CaMV 35S promoter, and the co-expression of CMG2-Fc with nine different viral suppressors of post-transcriptional gene silencing (PTGS): p1, p10, p19, p21, p24, p25, p38, 2b, and HCPro. Overall, transient CMG2-Fc expression was higher on intact plants than detached leaves. Maximum expression was observed with p1 co-expression at 3.5 days post-infiltration (DPI), with a level of 0.56 g CMG2-Fc per kg of leaf fresh weight and 1.5% of the total soluble protein, a ten-fold increase in expression when compared to absence of suppression. Co-expression with the p25 PTGS suppressor also significantly increased the CMG2-Fc expression level after just 3.5 DPI.

  15. GnRH neurons of young and aged female rhesus monkeys co-express GPER but are unaffected by long-term hormone replacement.

    Science.gov (United States)

    Naugle, Michelle M; Gore, Andrea C

    2014-01-01

    Menopause is caused by changes in the function of the hypothalamic-pituitary-gonadal axis that controls reproduction. Hypophysiotropic gonadotropin-releasing hormone (GnRH) neurons in the hypothalamus orchestrate the activity of this axis and are regulated by hormonal feedback loops. The mechanisms by which GnRH responds to the primary regulatory sex steroid hormone, estradiol (E2), are still poorly understood in the context of menopause. Our goal was to determine whether the G protein-coupled estrogen receptor (GPER) is co-expressed in adult primate GnRH neurons and whether this changes with aging and/or E2 treatment. We used immunofluorescence double-labeling to characterize the co-expression of GPER in GnRH perikarya and terminals in the hypothalamus. Young and aged rhesus macaques were ovariectomized and given long-term (~2-year) hormone treatments (E2, E2 + progesterone, or vehicle) selected to mimic currently prescribed hormone replacement therapies used for the alleviation of menopausal symptoms in women. We found that about half of GnRH perikarya co-expressed GPER, while only about 12% of GnRH processes and terminals in the median eminence (ME) were double-labeled. Additionally, many GPER-labeled processes were in direct contact with GnRH neurons, often wrapped around the perikarya and processes and in close proximity in the ME. These results extend prior work by showing robust co-localization of GPER in GnRH in a clinically relevant model, and they support the possibility that GPER-mediated E2 regulation of GnRH occurs both in the soma and terminals in nonhuman primates.

  16. Enhancement of γ-aminobutyric acid production in recombinant Corynebacterium glutamicum by co-expressing two glutamate decarboxylase genes from Lactobacillus brevis.

    Science.gov (United States)

    Shi, Feng; Jiang, Junjun; Li, Yongfu; Li, Youxin; Xie, Yilong

    2013-11-01

    γ-Aminobutyric acid (GABA), a non-protein amino acid, is a bioactive component in the food, feed and pharmaceutical fields. To establish an effective single-step production system for GABA, a recombinant Corynebacterium glutamicum strain co-expressing two glutamate decarboxylase (GAD) genes (gadB1 and gadB2) derived from Lactobacillus brevis Lb85 was constructed. Compared with the GABA production of the gadB1 or gadB2 single-expressing strains, GABA production by the gadB1-gadB2 co-expressing strain increased more than twofold. By optimising urea supplementation, the total production of L-glutamate and GABA increased from 22.57 ± 1.24 to 30.18 ± 1.33 g L⁻¹, and GABA production increased from 4.02 ± 0.95 to 18.66 ± 2.11 g L⁻¹ after 84-h cultivation. Under optimal urea supplementation, L-glutamate continued to be consumed, GABA continued to accumulate after 36 h of fermentation, and the pH level fluctuated. GABA production increased to a maximum level of 27.13 ± 0.54 g L⁻¹ after 120-h flask cultivation and 26.32 g L⁻¹ after 60-h fed-batch fermentation. The conversion ratio of L-glutamate to GABA reached 0.60-0.74 mol mol⁻¹. By co-expressing gadB1 and gadB2 and optimising the urea addition method, C. glutamicum was genetically improved for de novo biosynthesis of GABA from its own accumulated L-glutamate.

  17. A DNA vaccine co-expressing Trichinella spiralis MIF and MCD-1 with murine ubiquitin induces partial protective immunity in mice.

    Science.gov (United States)

    Tang, F; Xu, L; Yan, R; Song, X; Li, X

    2013-03-01

    Co-expression of Trichinella spiralis macrophage migration inhibitory factor (TsMIF) with T. spiralis cystatin-like domain protein (TsMCD-1) in a DNA vaccine induces a Th1 immune response and partial protection against T. spiralis infection. The present study evaluated whether co-expression of mouse ubiquitin (Ub) with TsMIF and TsMCD-1 might improve the immune response against T. spiralis infection. Groups of BALB/c mice were immunized twice at 2-week intervals with 100 μg of plasmid DNA encoding either a TsMIF-TsMCD-1 fusion protein (pVAX1-Tsmif-Tsmcd-1) or an Ub-co-expressing triple fusion protein Ub-TsMIF-TsMCD-1 (pVAX1-Ub-Tsmif-Tsmcd-1). Control animals were immunized with pVAX1-Ub or blank vector plasmid. Specific antibody levels (IgG, IgG1, IgG2a, IgG2b, IgM, IgA, IgE) against the recombinant protein TsMIF-TsMCD-1, serum cytokines (interferon (IFN)-γ, interleukin (IL)-4, IL-5, transforming growth factor (TGF)-β1 and IL-17), CD4+/CD8+ T cells and cytotoxic T lymphocyte (CTL) responses were monitored. Challenge infection was performed 2 weeks after the second immunization and worm burden was assayed at 35 days post-challenge. Antibody responses induced by pVAX1-Ub-Tsmif-Tsmcd-1 were significantly lower than for TsMIF-TsMCD-1, but the vaccine induced increased levels of Th1 cytokine (IFN-γ) and increased T-cell cytotoxicity. The reduction of worm burden (37.95%) following immunization with pVAX1-Ub-Tsmif-Tsmcd-1 was significantly greater than that induced by the pVAX1-Tsmif-Tsmcd-1 vaccine (23.17%; P< 0.05).

  18. Functional enhancement of AT1R potency in the presence of the TPαR is revealed by a comprehensive 7TM receptor co-expression screen.

    Directory of Open Access Journals (Sweden)

    Jonas Tind Hansen

    Full Text Available BACKGROUND: Functional cross-talk between seven transmembrane (7TM receptors can dramatically alter their pharmacological properties, both in vitro and in vivo. This represents an opportunity for the development of novel therapeutics that potentially target more specific biological effects while causing fewer adverse events. Although several studies convincingly have established the existence of 7TM receptor cross-talk, little is known about the frequencey and biological significance of this phenomenon. METHODOLOGY/PRINCIPAL FINDINGS: To evaluate the extent of synergism in 7TM receptor signaling, we took a comprehensive approach and co-expressed 123 different 7TM receptors together with the angiotensin II type 1 receptor (AT1R and analyzed how each receptor affected the angiotensin II (AngII response. To monitor the effect we used integrative receptor activation/signaling assay called Receptor Selection and Amplification Technology (R-SAT. In this screen the thromboxane A2α receptor (TPαR was the only receptor which significantly enhanced the AngII-mediated response. The TPαR-mediated enhancement of AngII signaling was significantly reduced when a signaling deficient receptor mutant (TPαR R130V was co-expressed instead of the wild-type TPαR, and was completely blocked both by TPαR antagonists and COX inhibitors inhibiting formation of thromboxane A2 (TXA2. CONCLUSIONS/SIGNIFICANCE: We found a functional enhancement of AT1R only when co-expressed with TPαR, but not with 122 other 7TM receptors. In addition, the TPαR must be functionally active, indicating the AT1R enhancement is mediated by a paracrine mechanism. Since we only found one receptor enhancing AT1R potency, our results suggest that functional augmentation through 7TM receptor cross-talk is a rare event that may require specific conditions to occur.

  19. A Network Approach of Gene Co-expression in the Zea mays/Aspergillus flavus Pathosystem to Map Host/Pathogen Interaction Pathways

    OpenAIRE

    Musungu, Bryan M.; Bhatnagar, Deepak; Brown, Robert L.; Payne, Gary A.; OBrian, Greg; Fakhoury, Ahmad M.; Geisler, Matt

    2016-01-01

    A gene co-expression network (GEN) was generated using a dual RNA-seq study with the fungal pathogen Aspergillus flavus and its plant host Zea mays during the initial 3 days of infection. The analysis deciphered novel pathways and mapped genes of interest in both organisms during the infection. This network revealed a high degree of connectivity in many of the previously recognized pathways in Z. mays such as jasmonic acid, ethylene, and reactive oxygen species (ROS). For the pathogen A. flav...

  20. Hydroxylation of recombinant human collagen type I alpha 1 in transgenic maize co-expressed with a recombinant human prolyl 4-hydroxylase

    Directory of Open Access Journals (Sweden)

    Pappu Kameshwari M

    2011-06-01

    Full Text Available Abstract Background Collagens require the hydroxylation of proline (Pro residues in their triple-helical domain repeating sequence Xaa-Pro-Gly to function properly as a main structural component of the extracellular matrix in animals at physiologically relevant conditions. The regioselective proline hydroxylation is catalyzed by a specific prolyl 4-hydroxylase (P4H as a posttranslational processing step. Results A recombinant human collagen type I α-1 (rCIα1 with high percentage of hydroxylated prolines (Hyp was produced in transgenic maize seeds when co-expressed with both the α- and β- subunits of a recombinant human P4H (rP4H. Germ-specific expression of rCIα1 using maize globulin-1 gene promoter resulted in an average yield of 12 mg/kg seed for the full-length rCIα1 in seeds without co-expression of rP4H and 4 mg/kg seed for the rCIα1 (rCIα1-OH in seeds with co-expression of rP4H. High-resolution mass spectrometry (HRMS analysis revealed that nearly half of the collagenous repeating triplets in rCIα1 isolated from rP4H co-expressing maize line had the Pro residues changed to Hyp residues. The HRMS analysis determined the Hyp content of maize-derived rCIα1-OH as 18.11%, which is comparable to the Hyp level of yeast-derived rCIα1-OH (17.47% and the native human CIa1 (14.59%, respectively. The increased Hyp percentage was correlated with a markedly enhanced thermal stability of maize-derived rCIα1-OH when compared to the non-hydroxylated rCIα1. Conclusions This work shows that maize has potential to produce adequately modified exogenous proteins with mammalian-like post-translational modifications that may be require for their use as pharmaceutical and industrial products.

  1. Implementation of neural network based non-linear predictive control

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1999-01-01

    This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control...... of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...... on an efficient quasi-Newton algorithm. The performance is demonstrated on a pneumatic servo system....

  2. Dynamic sporulation gene co-expression networks for Bacillus subtilis 168 and the food-borne isolate Bacillus amyloliquefaciens: a transcriptomic model.

    Science.gov (United States)

    Omony, Jimmy; de Jong, Anne; Krawczyk, Antonina O; Eijlander, Robyn T; Kuipers, Oscar P

    2018-02-09

    Sporulation is a survival strategy, adapted by bacterial cells in response to harsh environmental adversities. The adaptation potential differs between strains and the variations may arise from differences in gene regulation. Gene networks are a valuable way of studying such regulation processes and establishing associations between genes. We reconstructed and compared sporulation gene co-expression networks (GCNs) of the model laboratory strain Bacillus subtilis 168 and the food-borne industrial isolate Bacillus amyloliquefaciens. Transcriptome data obtained from samples of six stages during the sporulation process were used for network inference. Subsequently, a gene set enrichment analysis was performed to compare the reconstructed GCNs of B. subtilis 168 and B. amyloliquefaciens with respect to biological functions, which showed the enriched modules with coherent functional groups associated with sporulation. On basis of the GCNs and time-evolution of differentially expressed genes, we could identify novel candidate genes strongly associated with sporulation in B. subtilis 168 and B. amyloliquefaciens. The GCNs offer a framework for exploring transcription factors, their targets, and co-expressed genes during sporulation. Furthermore, the methodology described here can conveniently be applied to other species or biological processes.

  3. Co-expression of interleukin 12 enhances antitumor effects of a novel chimeric promoter-mediated suicide gene therapy in an immunocompetent mouse model

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Yu, E-mail: xuyu1001@gmail.com [Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan 430071 (China); Hubei Key Laboratory of Tumor Biological Behaviors and Hubei Cancer Clinical Study Center, 169 Donghu Road, Wuhan 430071 (China); Liu, Zhengchun, E-mail: l135027@126.com [Hubei Key Laboratory of Tumor Biological Behaviors and Hubei Cancer Clinical Study Center, 169 Donghu Road, Wuhan 430071 (China); Kong, Haiyan, E-mail: suppleant@163.com [Hubei Key Laboratory of Tumor Biological Behaviors and Hubei Cancer Clinical Study Center, 169 Donghu Road, Wuhan 430071 (China); Sun, Wenjie, E-mail: wendy11240325@163.com [Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan 430071 (China); Hubei Key Laboratory of Tumor Biological Behaviors and Hubei Cancer Clinical Study Center, 169 Donghu Road, Wuhan 430071 (China); Liao, Zhengkai, E-mail: fastbeta@gmail.com [Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan 430071 (China); Hubei Key Laboratory of Tumor Biological Behaviors and Hubei Cancer Clinical Study Center, 169 Donghu Road, Wuhan 430071 (China); Zhou, Fuxiang, E-mail: happyzhoufx@sina.com [Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan 430071 (China); Hubei Key Laboratory of Tumor Biological Behaviors and Hubei Cancer Clinical Study Center, 169 Donghu Road, Wuhan 430071 (China); Xie, Conghua, E-mail: chxie_65@hotmail.com [Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan 430071 (China); Hubei Key Laboratory of Tumor Biological Behaviors and Hubei Cancer Clinical Study Center, 169 Donghu Road, Wuhan 430071 (China); and others

    2011-09-09

    Highlights: {yields} A novel chimeric promoter consisting of CArG element and hTERT promoter was developed. {yields} The promoter was characterized with radiation-inducibility and tumor-specificity. {yields} Suicide gene system driven by the promoter showed remarkable cytotoxicity in vitro. {yields} Co-expression of IL12 enhanced the promoter mediated suicide gene therapy in vivo. -- Abstract: The human telomerase reverse transcriptase (hTERT) promoter has been widely used in target gene therapy of cancer. However, low transcriptional activity limited its clinical application. Here, we designed a novel dual radiation-inducible and tumor-specific promoter system consisting of CArG elements and the hTERT promoter, resulting in increased expression of reporter genes after gamma-irradiation. Therapeutic and side effects of adenovirus-mediated horseradish peroxidase (HRP)/indole-3-acetic (IAA) system downstream of the chimeric promoter were evaluated in mice bearing Lewis lung carcinoma, combining with or without adenovirus-mediated interleukin 12 (IL12) gene driven by the cytomegalovirus promoter. The combination treatment showed more effective suppression of tumor growth than those with single agent alone, being associated with pronounced intratumoral T-lymphocyte infiltration and minor side effects. Our results suggest that the combination treatment with HRP/IAA system driven by the novel chimeric promoter and the co-expression of IL12 might be an effective and safe target gene therapy strategy of cancer.

  4. Co-expression of interleukin 12 enhances antitumor effects of a novel chimeric promoter-mediated suicide gene therapy in an immunocompetent mouse model

    International Nuclear Information System (INIS)

    Xu, Yu; Liu, Zhengchun; Kong, Haiyan; Sun, Wenjie; Liao, Zhengkai; Zhou, Fuxiang; Xie, Conghua

    2011-01-01

    Highlights: → A novel chimeric promoter consisting of CArG element and hTERT promoter was developed. → The promoter was characterized with radiation-inducibility and tumor-specificity. → Suicide gene system driven by the promoter showed remarkable cytotoxicity in vitro. → Co-expression of IL12 enhanced the promoter mediated suicide gene therapy in vivo. -- Abstract: The human telomerase reverse transcriptase (hTERT) promoter has been widely used in target gene therapy of cancer. However, low transcriptional activity limited its clinical application. Here, we designed a novel dual radiation-inducible and tumor-specific promoter system consisting of CArG elements and the hTERT promoter, resulting in increased expression of reporter genes after gamma-irradiation. Therapeutic and side effects of adenovirus-mediated horseradish peroxidase (HRP)/indole-3-acetic (IAA) system downstream of the chimeric promoter were evaluated in mice bearing Lewis lung carcinoma, combining with or without adenovirus-mediated interleukin 12 (IL12) gene driven by the cytomegalovirus promoter. The combination treatment showed more effective suppression of tumor growth than those with single agent alone, being associated with pronounced intratumoral T-lymphocyte infiltration and minor side effects. Our results suggest that the combination treatment with HRP/IAA system driven by the novel chimeric promoter and the co-expression of IL12 might be an effective and safe target gene therapy strategy of cancer.

  5. Genome-wide expression of transcriptomes and their co-expression pattern in subtropical maize (Zea mays L. under waterlogging stress.

    Directory of Open Access Journals (Sweden)

    Nepolean Thirunavukkarasu

    Full Text Available Waterlogging causes extensive damage to maize crops in tropical and subtropical regions. The identification of tolerance genes and their interactions at the molecular level will be helpful to engineer tolerant genotypes. A whole-genome transcriptome assay revealed the specific role of genes in response to waterlogging stress in susceptible and tolerant genotypes. Genes involved in the synthesis of ethylene and auxin, cell wall metabolism, activation of G-proteins and formation of aerenchyma and adventitious roots, were upregulated in the tolerant genotype. Many transcription factors, particularly ERFs, MYB, HSPs, MAPK, and LOB-domain protein were involved in regulation of these traits. Genes responsible for scavenging of ROS generated under stress were expressed along with those involved in carbohydrate metabolism. The physical locations of 21 genes expressed in the tolerant genotype were found to correspond with the marker intervals of known QTLs responsible for development of adaptive traits. Among the candidate genes, most showed synteny with genes of sorghum and foxtail millet. Co-expression analysis of 528 microarray samples including 16 samples from the present study generated seven functional modules each in the two genotypes, with differing characteristics. In the tolerant genotype, stress genes were co-expressed along with peroxidase and fermentation pathway genes.

  6. Enhanced phytoremediation of mixed heavy metal (mercury)-organic pollutants (trichloroethylene) with transgenic alfalfa co-expressing glutathione S-transferase and human P450 2E1.

    Science.gov (United States)

    Zhang, Yuanyuan; Liu, Junhong; Zhou, Yuanming; Gong, Tingyun; Wang, Jing; Ge, Yinlin

    2013-09-15

    Soil contamination is a global environmental problem and many efforts have been made to find efficient remediation methods over the last decade. Moreover, remediation of mixed contaminated soils are more difficult. In the present study, transgenic alfalfa plants pKHCG co-expressing glutathione S-transferase (GST) and human P450 2E1 (CYP2E1) genes were used for phytoremediation of mixed mercury (Hg)-trichloroethylene (TCE) contaminants. Simultaneous expression of GST and CYP2E1 may produce a significant synergistic effect, and leads to improved resistance and accumulation to heavy metal-organic complex contaminants. Based on the tolerance and accumulation assays, pKHCG transgenic plants were more resistant to Hg/TCE complex pollutants and many folds higher in Hg/TCE-accumulation than the non-transgenic control plants in mixed contaminated soil. It is confirmed that GST and CYP2E1 co-expression may be a useful strategy to help achieve mixed heavy metal-organic pollutants phytoremediation. Copyright © 2013 Elsevier B.V. All rights reserved.

  7. Co-expression of HIV-1 virus-like particles and granulocyte-macrophage colony stimulating factor by GEO-D03 DNA vaccine

    Science.gov (United States)

    Hellerstein, Michael; Xu, Yongxian; Marino, Tracie; Lu, Shan; Yi, Hong; Wright, Elizabeth R.; Robinson, Harriet L.

    2012-01-01

    Here, we report on GEO-D03, a DNA vaccine that co-expresses non-infectious HIV-1 virus-like particles (VLPs) and the human cytokine, granulocyte-macrophage colony-stimulating factor (GM-CSF). The virus-like particles display the native gp160 form of the HIV-1 Envelope glycoprotein (Env) and are designed to elicit antibody against the natural form of Env on virus and virus-infected cells. The DNA-expressed HIV Gag, Pol and Env proteins also have the potential to elicit virus-specific CD4 and CD8 T cells. The purpose of the co-expressed GM-CSF is to target a cytokine that recruits, expands and differentiates macrophages and dendritic cells to the site of VLP expression. The GEO-D03 DNA vaccine is currently entered into human trials as a prime for a recombinant modified vaccinia Ankara (MVA) boost. In preclinical studies in macaques using an SIV prototype vaccine, this vaccination regimen elicited both anti-viral T cells and antibody, and provided 70% protection against acquisition during 12 weekly rectal exposures with a heterologous SIV. Higher avidity of the Env-specific Ab for the native form of the Env in the challenge virus correlated with lower likelihood of SIV infection. PMID:23111169

  8. A random network based, node attraction facilitated network evolution method

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2016-03-01

    Full Text Available In present study, I present a method of network evolution that based on random network, and facilitated by node attraction. In this method, I assume that the initial network is a random network, or a given initial network. When a node is ready to connect, it tends to link to the node already owning the most connections, which coincides with the general rule (Barabasi and Albert, 1999 of node connecting. In addition, a node may randomly disconnect a connection i.e., the addition of connections in the network is accompanied by the pruning of some connections. The dynamics of network evolution is determined of the attraction factor Lamda of nodes, the probability of node connection, the probability of node disconnection, and the expected initial connectance. The attraction factor of nodes, the probability of node connection, and the probability of node disconnection are time and node varying. Various dynamics can be achieved by adjusting these parameters. Effects of simplified parameters on network evolution are analyzed. The changes of attraction factor Lamda can reflect various effects of the node degree on connection mechanism. Even the changes of Lamda only will generate various networks from the random to the complex. Therefore, the present algorithm can be treated as a general model for network evolution. Modeling results show that to generate a power-law type of network, the likelihood of a node attracting connections is dependent upon the power function of the node's degree with a higher-order power. Matlab codes for simplified version of the method are provided.

  9. Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

    Science.gov (United States)

    Xia, Youshen; Wang, Jun

    2015-07-01

    This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Neural network based method for conversion of solar radiation data

    International Nuclear Information System (INIS)

    Celik, Ali N.; Muneer, Tariq

    2013-01-01

    Highlights: ► Generalized regression neural network is used to predict the solar radiation on tilted surfaces. ► The above network, amongst many such as multilayer perceptron, is the most successful one. ► The present neural network returns a relative mean absolute error value of 9.1%. ► The present model leads to a mean absolute error value of estimate of 14.9 Wh/m 2 . - Abstract: The receiving ends of the solar energy conversion systems that generate heat or electricity from radiation is usually tilted at an optimum angle to increase the solar incident on the surface. Solar irradiation data measured on horizontal surfaces is readily available for many locations where such solar energy conversion systems are installed. Various equations have been developed to convert solar irradiation data measured on horizontal surface to that on tilted one. These equations constitute the conventional approach. In this article, an alternative approach, generalized regression type of neural network, is used to predict the solar irradiation on tilted surfaces, using the minimum number of variables involved in the physical process, namely the global solar irradiation on horizontal surface, declination and hour angles. Artificial neural networks have been successfully used in recent years for optimization, prediction and modeling in energy systems as alternative to conventional modeling approaches. To show the merit of the presently developed neural network, the solar irradiation data predicted from the novel model was compared to that from the conventional approach (isotropic and anisotropic models), with strict reference to the irradiation data measured in the same location. The present neural network model was found to provide closer solar irradiation values to the measured than the conventional approach, with a mean absolute error value of 14.9 Wh/m 2 . The other statistical values of coefficient of determination and relative mean absolute error also indicate the

  11. ENERGY AWARE NETWORK: BAYESIAN BELIEF NETWORKS BASED DECISION MANAGEMENT SYSTEM

    Directory of Open Access Journals (Sweden)

    Santosh Kumar Chaudhari

    2011-06-01

    Full Text Available A Network Management System (NMS plays a very important role in managing an ever-evolving telecommunication network. Generally an NMS monitors & maintains the health of network elements. The growing size of the network warrants extra functionalities from the NMS. An NMS provides all kinds of information about networks which can be used for other purposes apart from monitoring & maintaining networks like improving QoS & saving energy in the network. In this paper, we add another dimension to NMS services, namely, making an NMS energy aware. We propose a Decision Management System (DMS framework which uses a machine learning technique called Bayesian Belief Networks (BBN, to make the NMS energy aware. The DMS is capable of analysing and making control decisions based on network traffic. We factor in the cost of rerouting and power saving per port. Simulations are performed on standard network topologies, namely, ARPANet and IndiaNet. It is found that ~2.5-6.5% power can be saved.

  12. The network-based energy management system for convenience stores

    Energy Technology Data Exchange (ETDEWEB)

    Wang, An-Ping; Hsu, Pau-Lo [Department of Electrical and Control Engineering, National Chiao-Tung University, 1001 Ta-Hsiue Road, Hsinchu City 310 (China)

    2008-07-01

    Convenience stores generally consume energy higher than other retailing merchants. As the problem of energy shortage becomes more serious during summer, almost all convenience stores sign a contract with power plants, which provides for fines if demand limiting occurs in Taiwan and many other countries. Therefore, a reliable and effective method to reduce their utility consumption is required for modern business and industry. This research integrates the remote sensors, the control network, and the embedded system technologies to construct a distributed energy management control system for dedicated convenience stores. Energy consumption can thus be reasonably managed with demand limits by measuring and analyzing the power consumption sources in four major subsystems of convenience stores, namely, (1) air-conditioning, (2) lighting, (3) heating, and (4) refrigeration. By applying the proposed demand prediction and control method, the demand limiting condition can be properly predicted, and the possible peak load can thus be eliminated via the network control mechanism. Moreover, by integrating the LonWork fieldbus and the WinCE operating system (OS), the proposed system has been successfully applied to a convenience store. The experimental results indicate that the proposed distributed energy management system suitably predicts the peak loading condition and successfully prevents its occurrence by switching the air-conditioning system without affecting the indoor temperature regulation. (author)

  13. Network-based prediction and analysis of HIV dependency factors.

    Directory of Open Access Journals (Sweden)

    T M Murali

    2011-09-01

    Full Text Available HIV Dependency Factors (HDFs are a class of human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three previous genome-wide RNAi experiments identified HDF sets with little overlap. We combine data from these three studies with a human protein interaction network to predict new HDFs, using an intuitive algorithm called SinkSource and four other algorithms published in the literature. Our algorithm achieves high precision and recall upon cross validation, as do the other methods. A number of HDFs that we predict are known to interact with HIV proteins. They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV. We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. More generally, if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon, our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other.

  14. A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

    Directory of Open Access Journals (Sweden)

    Qian Li

    Full Text Available BACKGROUND: Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. METHODOLOGY: We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671 between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. CONCLUSIONS: This article proposes a network-based multi-target computational estimation

  15. A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

    Science.gov (United States)

    Li, Qian; Li, Xudong; Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

    2011-03-22

    Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by

  16. Deep Belief Networks Based Toponym Recognition for Chinese Text

    Directory of Open Access Journals (Sweden)

    Shu Wang

    2018-06-01

    Full Text Available In Geographical Information Systems, geo-coding is used for the task of mapping from implicitly geo-referenced data to explicitly geo-referenced coordinates. At present, an enormous amount of implicitly geo-referenced information is hidden in unstructured text, e.g., Wikipedia, social data and news. Toponym recognition is the foundation of mining this useful geo-referenced information by identifying words as toponyms in text. In this paper, we propose an adapted toponym recognition approach based on deep belief network (DBN by exploring two key issues: word representation and model interpretation. A Skip-Gram model is used in the word representation process to represent words with contextual information that are ignored by current word representation models. We then determine the core hyper-parameters of the DBN model by illustrating the relationship between the performance and the hyper-parameters, e.g., vector dimensionality, DBN structures and probability thresholds. The experiments evaluate the performance of the Skip-Gram model implemented by the Word2Vec open-source tool, determine stable hyper-parameters and compare our approach with a conditional random field (CRF based approach. The experimental results show that the DBN model outperforms the CRF model with smaller corpus. When the corpus size is large enough, their statistical metrics become approaching. However, their recognition results express differences and complementarity on different kinds of toponyms. More importantly, combining their results can directly improve the performance of toponym recognition relative to their individual performances. It seems that the scale of the corpus has an obvious effect on the performance of toponym recognition. Generally, there is no adequate tagged corpus on specific toponym recognition tasks, especially in the era of Big Data. In conclusion, we believe that the DBN-based approach is a promising and powerful method to extract geo

  17. A network-base analysis of CMIP5 "historical" experiments

    Science.gov (United States)

    Bracco, A.; Foudalis, I.; Dovrolis, C.

    2012-12-01

    In computer science, "complex network analysis" refers to a set of metrics, modeling tools and algorithms commonly used in the study of complex nonlinear dynamical systems. Its main premise is that the underlying topology or network structure of a system has a strong impact on its dynamics and evolution. By allowing to investigate local and non-local statistical interaction, network analysis provides a powerful, but only marginally explored, framework to validate climate models and investigate teleconnections, assessing their strength, range, and impacts on the climate system. In this work we propose a new, fast, robust and scalable methodology to examine, quantify, and visualize climate sensitivity, while constraining general circulation models (GCMs) outputs with observations. The goal of our novel approach is to uncover relations in the climate system that are not (or not fully) captured by more traditional methodologies used in climate science and often adopted from nonlinear dynamical systems analysis, and to explain known climate phenomena in terms of the network structure or its metrics. Our methodology is based on a solid theoretical framework and employs mathematical and statistical tools, exploited only tentatively in climate research so far. Suitably adapted to the climate problem, these tools can assist in visualizing the trade-offs in representing global links and teleconnections among different data sets. Here we present the methodology, and compare network properties for different reanalysis data sets and a suite of CMIP5 coupled GCM outputs. With an extensive model intercomparison in terms of the climate network that each model leads to, we quantify how each model reproduces major teleconnections, rank model performances, and identify common or specific errors in comparing model outputs and observations.

  18. Co-expression of putative stemness and epithelial-to-mesenchymal transition markers on single circulating tumour cells from patients with early and metastatic breast cancer.

    Science.gov (United States)

    Papadaki, Maria A; Kallergi, Galatea; Zafeiriou, Zafeiris; Manouras, Lefteris; Theodoropoulos, Panayiotis A; Mavroudis, Dimitris; Georgoulias, Vassilis; Agelaki, Sofia

    2014-09-03

    The detection of circulating tumor cells (CTCs) in peripheral blood (PB) of patients with breast cancer predicts poor clinical outcome. Cancer cells with stemness and epithelial-to-mesenchymal transition (EMT) features display enhanced malignant and metastatic potential. A new methodology was developed in order to investigate the co-expression of a stemness and an EMT marker (ALDH1 and TWIST, respectively) on single CTCs of patients with early and metastatic breast cancer. Triple immunofluorescence using anti-pancytokeratin (A45-B/B3), anti-ALDH1 and anti-TWIST antibodies was performed in cytospins prepared from hepatocellular carcinoma HepG2 cells and SKBR-3, MCF-7 and MDA.MB.231 breast cancer cell lines. Evaluation of ALDH1 expression levels (high, low or absent) and TWIST subcellular localization (nuclear, cytoplasmic or absent) was performed using the ARIOL system. Cytospins prepared from peripheral blood of patients with early (n = 80) and metastatic (n = 50) breast cancer were analyzed for CTC detection (based on pan-cytokeratin expression and cytomorphological criteria) and characterized according to ALDH1 and TWIST. CTCs were detected in 13 (16%) and 25 (50%) patients with early and metastatic disease, respectively. High ALDH1 expression (ALDH1high) and nuclear TWIST localization (TWISTnuc) on CTCs was confirmed in more patients with metastatic than early breast cancer (80% vs. 30.8%, respectively; p = 0.009). In early disease, ALDH1low/neg CTCs (p = 0.006) and TWISTcyt/neg CTCs (p = 0.040) were mainly observed. Regarding co-expression of these markers, ALDH1high/TWISTnuc CTCs were more frequently evident in the metastatic setting (76% vs. 15.4% of patients, p = 0.001; 61.5% vs. 12.9% of total CTCs), whereas in early disease ALDH1low/neg/TWISTcyt/neg CTCs were mainly detected (61.5% vs. 20% of patients, p = 0.078; 41.9% vs. 7.7% of total CTCs). A new assay is provided for the evaluation of ALDH1 and TWIST co-expression at the

  19. A moth pheromone brewery: production of (Z)-11-hexadecenol by heterologous co-expression of two biosynthetic genes from a noctuid moth in a yeast cell factory.

    Science.gov (United States)

    Hagström, Åsa K; Wang, Hong-Lei; Liénard, Marjorie A; Lassance, Jean-Marc; Johansson, Tomas; Löfstedt, Christer

    2013-12-13

    Moths (Lepidoptera) are highly dependent on chemical communication to find a mate. Compared to conventional unselective insecticides, synthetic pheromones have successfully served to lure male moths as a specific and environmentally friendly way to control important pest species. However, the chemical synthesis and purification of the sex pheromone components in large amounts is a difficult and costly task. The repertoire of enzymes involved in moth pheromone biosynthesis in insecta can be seen as a library of specific catalysts that can be used to facilitate the synthesis of a particular chemical component. In this study, we present a novel approach to effectively aid in the preparation of semi-synthetic pheromone components using an engineered vector co-expressing two key biosynthetic enzymes in a simple yeast cell factory. We first identified and functionally characterized a ∆11 Fatty-Acyl Desaturase and a Fatty-Acyl Reductase from the Turnip moth, Agrotis segetum. The ∆11-desaturase produced predominantly Z11-16:acyl, a common pheromone component precursor, from the abundant yeast palmitic acid and the FAR transformed a series of saturated and unsaturated fatty acids into their corresponding alcohols which may serve as pheromone components in many moth species. Secondly, when we co-expressed the genes in the Brewer's yeast Saccharomyces cerevisiae, a set of long-chain fatty acids and alcohols that are not naturally occurring in yeast were produced from inherent yeast fatty acids, and the presence of (Z)-11-hexadecenol (Z11-16:OH), demonstrated that both heterologous enzymes were active in concert. A 100 ml batch yeast culture produced on average 19.5 μg Z11-16:OH. Finally, we demonstrated that oxidized extracts from the yeast cells containing (Z)-11-hexadecenal and other aldehyde pheromone compounds elicited specific electrophysiological activity from male antennae of the Tobacco budworm, Heliothis virescens, supporting the idea that genes from different

  20. Cellular immunotherapy using irradiated lung cancer cell vaccine co-expressing GM-CSF and IL-18 can induce significant antitumor effects

    International Nuclear Information System (INIS)

    Tian, Hongwei; Zhang, Xiaomei; Dai, Lei; Chen, Xiaolei; Zhang, Shuang; Yang, Yang; Yu, Dechao; Wei, Yuquan; Deng, Hongxin; Shi, Gang; Yang, Guoyou; Zhang, Junfeng; Li, Yiming; Du, Tao; Wang, Jianzhou; Xu, Fen; Cheng, Lin

    2014-01-01

    Although the whole tumor cell vaccine can provide the best source of immunizing antigens, there is still a limitation that most tumors are not naturally immunogenic. Tumor cells genetically modified to secrete immune activating cytokines have been proved to be more immunogenic. IL-18 could augment proliferation of T cells and cytotoxicity of NK cells. GM-CSF could stimulate dendritic cells, macrophages and enhance presentation of tumor antigens. In our study, we used mouse GM-CSF combined with IL-18 to modify Lewis lung cancer LL/2, then investigated whether vaccination could suppress tumor growth and promote survival. The Lewis lung cancer LL/2 was transfected with co-expressing mouse GM-CSF and IL-18 plasmid by cationic liposome, then irradiated with a sublethal dose X ray (100 Gy) to prepare vaccines. Mice were subcutaneously immunized with this inactivated vaccine and then inoculated with autologous LL/2 to estimate the antitumor efficacy. The studies reported here showed that LL/2 tumor cell vaccine modified by a co-expressing mouse GM-CSF and IL-18 plasmid could significantly inhibit tumor growth and increased survival of the mice bearing LL/2 tumor whether prophylactic or adoptive immunotherapy in vivo. A significant reduction of proliferation and increase of apoptosis were also observed in the tumor treated with vaccine of co-expressing GM-CSF and IL-18. The potent antitumor effect correlated with higher secretion levels of pro-inflammatory cytokines such as IL-18, GM-CSF, interferon-γ in serum, the proliferation of CD4 + IFN-γ + , CD8 + IFN-γ + T lymphocytes in spleen and the infiltration of CD4 + , CD8 + T in tumor. Furthermore, the mechanism of tumor-specific immune response was further proved by 51 Cr cytotoxicity assay in vitro and depletion of CD4, CD8, NK immune cell subsets in vivo. The results suggested that the antitumor mechanism was mainly depended on CD4 + , CD8 + T lymphocytes. These results provide a new insight into therapeutic mechanisms

  1. Membrane and envelope virus proteins co-expressed as lysosome associated membrane protein (LAMP fused antigens: a potential tool to develop DNA vaccines against flaviviruses

    Directory of Open Access Journals (Sweden)

    Rafael Dhalia

    2009-12-01

    Full Text Available Vaccination is the most practical and cost-effective strategy to prevent the majority of the flavivirus infection to which there is an available vaccine. However, vaccines based on attenuated virus can potentially promote collateral side effects and even rare fatal reactions. Given this scenario, the developent of alternative vaccination strategies such as DNA-based vaccines encoding specific flavivirus sequences are being considered. Endogenous cytoplasmic antigens, characteristically plasmid DNA-vaccine encoded, are mainly presented to the immune system through Major Histocompatibility Complex class I - MHC I molecules. The MHC I presentation via is mostly associated with a cellular cytotoxic response and often do not elicit a satisfactory humoral response. One of the main strategies to target DNA-encoded antigens to the MHC II compartment is expressing the antigen within the Lysosome-Associated Membrane Protein (LAMP. The flavivirus envelope protein is recognized as the major virus surface protein and the main target for neutralizing antibodies. Different groups have demonstrated that co-expression of flavivirus membrane and envelope proteins in mammalian cells, fused with the carboxyl-terminal of LAMP, is able to induce satisfactory levels of neutralizing antibodies. Here we reviewed the use of the envelope flavivirus protein co-expression strategy as LAMP chimeras with the aim of developing DNA vaccines for dengue, West Nile and yellow fever viruses.A vacinação é a estratégia mais prática e o melhor custo-benefício para prevenir a maioria das infecções dos flavivirus, para os quais existe vacina disponível. Entretanto, as vacinas baseadas em vírus atenuados podem potencialmente promover efeitos colaterais e, mais raramente, reações fatais. Diante deste cenário, o desenvolvimento de estratégias alternativas de vacinação, como vacinas baseadas em DNA codificando seqüências específicas dos flavivirus, está sendo considerado

  2. Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model

    DEFF Research Database (Denmark)

    Kogelman, Lisette; Cirera Salicio, Susanna; Zhernakova, Daria V.

    2014-01-01

    interactions. Identification of co-expressed and regulatory genes in RNA extracted from relevant tissues representing lean and obese individuals provides an entry point for the identification of genes and pathways of importance to the development of obesity. The pig, an omnivorous animal, is an excellent model...... (modules). Additionally, regulator genes were detected using Lemon-Tree algorithms. Results WGCNA revealed five modules which were strongly correlated with at least one obesity-related phenotype (correlations ranging from -0.54 to 0.72, P ... the association between obesity and other diseases, like osteoporosis (osteoclast differentiation, P = 1.4E-7), and immune-related complications (e.g. Natural killer cell mediated cytotoxity, P = 3.8E-5; B cell receptor signaling pathway, P = 7.2E-5). Lemon-Tree identified three potential regulator genes, using...

  3. Whole-cell bioreduction of aromatic α-keto esters using Candida tenuis xylose reductase and Candida boidinii formate dehydrogenase co-expressed in Escherichia coli

    Directory of Open Access Journals (Sweden)

    Egger Sigrid

    2008-12-01

    Full Text Available Abstract Background Whole cell-catalyzed biotransformation is a clear process option for the production of chiral alcohols via enantioselective reduction of precursor ketones. A wide variety of synthetically useful reductases are expressed heterologously in Escherichia coli to a high level of activity. Therefore, this microbe has become a prime system for carrying out whole-cell bioreductions at different scales. The limited capacity of central metabolic pathways in E. coli usually requires that reductase coenzyme in the form of NADPH or NADH be regenerated through a suitable oxidation reaction catalyzed by a second NADP+ or NAD+ dependent dehydrogenase that is co-expressed. Candida tenuis xylose reductase (CtXR was previously shown to promote NADH dependent reduction of aromatic α-keto esters with high Prelog-type stereoselectivity. We describe here the development of a new whole-cell biocatalyst that is based on an E. coli strain co-expressing CtXR and formate dehydrogenase from Candida boidinii (CbFDH. The bacterial system was evaluated for the synthesis of ethyl R-4-cyanomandelate under different process conditions and benchmarked against a previously described catalyst derived from Saccharomyces cerevisiae expressing CtXR. Results Gene co-expression from a pETDuet-1 vector yielded about 260 and 90 units of intracellular CtXR and CbFDH activity per gram of dry E. coli cell mass (gCDW. The maximum conversion rate (rS for ethyl 4-cyanobenzoylformate by intact or polymyxin B sulphate-permeabilized cells was similar (2 mmol/gCDWh, suggesting that the activity of CbFDH was partly rate-limiting overall. Uncatalyzed ester hydrolysis in substrate as well as inactivation of CtXR and CbFDH in the presence of the α-keto ester constituted major restrictions to the yield of alcohol product. Using optimized reaction conditions (100 mM substrate; 40 gCDW/L, we obtained ethyl R-4-cyanomandelate with an enantiomeric excess (e.e. of 97.2% in a yield of 82

  4. A recombinant pseudorabies virus co-expressing capsid proteins precursor P1-2A of FMDV and VP2 protein of porcine parvovirus: a trivalent vaccine candidate.

    Science.gov (United States)

    Hong, Qi; Qian, Ping; Li, Xiang-Min; Yu, Xiao-Lan; Chen, Huan-Chun

    2007-11-01

    Pseudorabies (PR), foot-and-mouth disease (FMD), and porcine parvovirus disease are three important infectious diseases in swine worldwide. The gene-deleted pseudorabies virus (PRV) has been used as a live-viral vector to develop multivalent genetic engineering vaccine. In this study, a recombinant PRV, which could co-express protein precursor P1-2A of FMDV and VP2 protein of PPV, was constructed using PRV TK(-)/gE(-)/LacZ(+) mutant as the vector. After homologous recombination and plaque purification, recombinant virus PRV TK(-)/gE(-)/P1-2A-VP2 was acquired and identified. Immunogenicity, safety of the recombinant PRV and its protection against PRV were confirmed in a mouse model by indirect ELISA and serum neutralization test. The results show that the recombinant PRV is a candidate vaccine strain to develop a novel trivalent vaccine against PRV, FMDV and PPV in swine.

  5. Genome-wide targeted prediction of ABA responsive genes in rice based on over-represented cis-motif in co-expressed genes.

    Science.gov (United States)

    Lenka, Sangram K; Lohia, Bikash; Kumar, Abhay; Chinnusamy, Viswanathan; Bansal, Kailash C

    2009-02-01

    Abscisic acid (ABA), the popular plant stress hormone, plays a key role in regulation of sub-set of stress responsive genes. These genes respond to ABA through specific transcription factors which bind to cis-regulatory elements present in their promoters. We discovered the ABA Responsive Element (ABRE) core (ACGT) containing CGMCACGTGB motif as over-represented motif among the promoters of ABA responsive co-expressed genes in rice. Targeted gene prediction strategy using this motif led to the identification of 402 protein coding genes potentially regulated by ABA-dependent molecular genetic network. RT-PCR analysis of arbitrarily chosen 45 genes from the predicted 402 genes confirmed 80% accuracy of our prediction. Plant Gene Ontology (GO) analysis of ABA responsive genes showed enrichment of signal transduction and stress related genes among diverse functional categories.

  6. Co-expression of Exo-inulinase and Endo-inulinase Genes in the Oleaginous Yeast Yarrowia lipolytica for Efficient Single Cell Oil Production from Inulin.

    Science.gov (United States)

    Shi, Nianci; Mao, Weian; He, Xiaoxia; Chi, Zhe; Chi, Zhenming; Liu, Guanglei

    2018-05-01

    Yarrowia lipolytica is a promising platform for the single cell oil (SCO) production. In this study, a transformant X+N8 in which exo- and endo-inulinase genes were co-expressed could produce an inulinase activity of 124.33 U/mL within 72 h. However, the inulinase activity of a transformant X2 carrying a single exo-inulinase gene was only 47.33 U/mL within 72 h. Moreover, the transformant X+N8 could accumulate 48.13% (w/w) SCO from inulin and the cell dry weight reached 13.63 g/L within 78 h, which were significantly higher than those of the transformant X2 (41.87% (w/w) and 11.23 g/L) under the same conditions. In addition, inulin hydrolysis and utilization of the transformant X+N8 were also more efficient than those of the transformant X2 during the fermentation process. These results demonstrated that the co-expression of the exo- and endo-inulinase genes significantly enhanced the SCO production from inulin due to the improvement of the inulinase activity and the synergistic action of exo- and endo-inulinase. Besides, over 95.01% of the fatty acids from the transformant X+N8 were C16-C18, especially C18:1 (53.10%), suggesting that the fatty acids could be used as feedstock for biodiesel production.

  7. Comparative transcriptome and gene co-expression network analysis reveal genes and signaling pathways adaptively responsive to varied adverse stresses in the insect fungal pathogen, Beauveria bassiana.

    Science.gov (United States)

    He, Zhangjiang; Zhao, Xin; Lu, Zhuoyue; Wang, Huifang; Liu, Pengfei; Zeng, Fanqin; Zhang, Yongjun

    2018-01-01

    Sensing, responding, and adapting to the surrounding environment are crucial for all living organisms to survive, proliferate, and differentiate in their biological niches. Beauveria bassiana is an economically important insect-pathogenic fungus which is widely used as a biocontrol agent to control a variety of insect pests. The fungal pathogen unavoidably encounters a variety of adverse environmental stresses and defense response from the host insects during application of the fungal agents. However, few are known about the transcription response of the fungus to respond or adapt varied adverse stresses. Here, we comparatively analyzed the transcriptome of B. bassiana in globe genome under the varied stationary-phase stresses including osmotic agent (0.8 M NaCl), high temperature (32 °C), cell wall-perturbing agent (Congo red), and oxidative agents (H 2 O 2 or menadione). Total of 12,412 reads were obtained, and mapped to the 6767 genes of the B. bassiana. All of these stresses caused transcription responses involved in basal metabolism, cell wall construction, stress response or cell rescue/detoxification, signaling transduction and gene transcription regulation, and likely other cellular processes. An array of genes displayed similar transcription patterns in response to at least two of the five stresses, suggesting a shared transcription response to varied adverse stresses. Gene co-expression network analysis revealed that mTOR signaling pathway, but not HOG1 MAP kinase pathway, played a central role in regulation the varied adverse stress responses, which was verified by RNAi-mediated knockdown of TOR1. Our findings provided an insight of transcription response and gene co-expression network of B. bassiana in adaptation to varied environments. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Mapping of brain lipid binding protein (Blbp) in the brain of adult zebrafish, co-expression with aromatase B and links with proliferation.

    Science.gov (United States)

    Diotel, Nicolas; Vaillant, Colette; Kah, Olivier; Pellegrini, Elisabeth

    2016-01-01

    Adult fish exhibit a strong neurogenic capacity due to the persistence of radial glial cells. In zebrafish, radial glial cells display well-established markers such as the estrogen-synthesizing enzyme (AroB) and the brain lipid binding protein (Blbp), which is known to strongly bind omega-3 polyunsaturated fatty acids such as docosahexaenoic acid (DHA). While Blpb is mainly described in the telencephalon of adult zebrafish, its expression in the remaining regions of the brain is poorly documented. The present study was designed to further investigate Blbp expression in the brain, its co-expression with AroB, and its link with radial glial cells proliferation in zebrafish. We generated a complete and detailed mapping of Blbp expression in the whole brain and show its complete co-expression with AroB, except in some tectal and hypothalamic regions. By performing PCNA and Blbp immunohistochemistry on cyp19a1b-GFP (AroB-GFP) fish, we also demonstrated preferential Blbp expression in proliferative radial glial cells in almost all regions studied. To our knowledge, this is the first complete and detailed mapping of Blbp-expressing cells showing strong association between Blbp and radial glial cell proliferation in the adult brain of fish. Given that zebrafish is now recognized models for studying neurogenesis and brain repair, our data provide detailed characterization of Blbp in the entire brain and open up a broad field of research investigating the role of omega-3 polyunsaturated fatty acids in neural stem cell activity in fish. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. Microarray profiling and co-expression network analysis of circulating lncRNAs and mRNAs associated with major depressive disorder.

    Directory of Open Access Journals (Sweden)

    Zhifen Liu

    Full Text Available LncRNAs, which represent one of the most highly expressed classes of ncRNAs in the brain, are becoming increasingly interesting with regard to brain functions and disorders. However, changes in the expression of regulatory lncRNAs in Major Depressive Disorder (MDD have not yet been reported. Using microarrays, we profiled the expression of 34834 lncRNAs and 39224 mRNAs in peripheral blood sampled from MDD patients as well as demographically-matched controls. Among these, we found that 2007 lncRNAs and 1667 mRNAs were differentially expressed, 17 of which were documented as depression-related gene in previous studies. Gene Ontology (GO and pathway analyses indicated that the biological functions of differentially expressed mRNAs were related to fundamental metabolic processes and neurodevelopment diseases. To investigate the potential regulatory roles of the differentially expressed lncRNAs on the mRNAs, we also constructed co-expression networks composed of the lncRNAs and mRNAs, which shows significant correlated patterns of expression. In the MDD-derived network, there were a greater number of nodes and connections than that in the control-derived network. The lncRNAs located at chr10:874695-874794, chr10:75873456-75873642, and chr3:47048304-47048512 may be important factors regulating the expression of mRNAs as they have previously been reported associations with MDD. This study is the first to explore genome-wide lncRNA expression and co-expression with mRNA patterns in MDD using microarray technology. We identified circulating lncRNAs that are aberrantly expressed in MDD and the results suggest that lncRNAs may contribute to the molecular pathogenesis of MDD.

  10. Efficient androst-1,4-diene-3,17-dione production by co-expressing 3-ketosteroid-Δ1 -dehydrogenase and catalase in Bacillus subtilis.

    Science.gov (United States)

    Shao, M; Sha, Z; Zhang, X; Rao, Z; Xu, M; Yang, T; Xu, Z; Yang, S

    2017-01-01

    3-ketosteroid-Δ 1 -dehydrogenase (KSDD), a flavin adenine dinucleotide (FAD)-dependent enzyme involved in sterol metabolism, specifically catalyses the conversion of androst-4-ene-3,17-dione (AD) to androst-1,4-diene-3,17-dione (ADD). However, the low KSDD activity and the toxic effects of hydrogen peroxide (H 2 O 2 ) generated during the biotransformation of AD to ADD with FAD regeneration hinder its application on AD conversion. The aim of this work was to improve KSDD activity and eliminate the toxic effects of the generated H 2 O 2 to enhance ADD production. The ksdd gene obtained from Mycobacterium neoaurum JC-12 was codon-optimized to increase its expression level in Bacillus subtilis, and the KSDD activity reached 12·3 U mg -1 , which was sevenfold of that of codon-unoptimized gene. To improve AD conversion, catalase was co-expressed with KSDD in B. subtilis 168/pMA5-ksdd opt -katA to eliminate the toxic effects of H 2 O 2 generated during AD conversion. Finally, under optimized bioconversion conditions, fed-batch strategy was carried out and the ADD yield improved to 8·76 g l -1 . This work demonstrates the potential to improve enzyme activity by codon-optimization and eliminate the toxic effects of H 2 O 2 by co-expressing catalase. This study showed the highest ADD productivity ever reported and provides a promising strain for efficient ADD production in the pharmaceutical industry. © 2016 The Society for Applied Microbiology.

  11. In silico identification of miRNAs and their target genes and analysis of gene co-expression network in saffron (Crocus sativus L.) stigma

    Science.gov (United States)

    Zinati, Zahra; Shamloo-Dashtpagerdi, Roohollah; Behpouri, Ali

    2016-01-01

    As an aromatic and colorful plant of substantive taste, saffron (Crocus sativus L.) owes such properties of matter to growing class of the secondary metabolites derived from the carotenoids, apocarotenoids. Regarding the critical role of microRNAs in secondary metabolic synthesis and the limited number of identified miRNAs in C. sativus, on the other hand, one may see the point how the characterization of miRNAs along with the corresponding target genes in C. sativus might expand our perspectives on the roles of miRNAs in carotenoid/apocarotenoid biosynthetic pathway. A computational analysis was used to identify miRNAs and their targets using EST (Expressed Sequence Tag) library from mature saffron stigmas. Then, a gene co- expression network was constructed to identify genes which are potentially involved in carotenoid/apocarotenoid biosynthetic pathways. EST analysis led to the identification of two putative miRNAs (miR414 and miR837-5p) along with the corresponding stem- looped precursors. To our knowledge, this is the first report on miR414 and miR837-5p in C. sativus. Co-expression network analysis indicated that miR414 and miR837-5p may play roles in C. sativus metabolic pathways and led to identification of candidate genes including six transcription factors and one protein kinase probably involved in carotenoid/apocarotenoid biosynthetic pathway. Presence of transcription factors, miRNAs and protein kinase in the network indicated multiple layers of regulation in saffron stigma. The candidate genes from this study may help unraveling regulatory networks underlying the carotenoid/apocarotenoid biosynthesis in saffron and designing metabolic engineering for enhanced secondary metabolites. PMID:28261627

  12. FERAL : Network-based classifier with application to breast cancer outcome prediction

    NARCIS (Netherlands)

    Allahyar, A.; De Ridder, J.

    2015-01-01

    Motivation: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed.

  13. Modelisation et simulation d'un PON (Passive Optical Network) base ...

    African Journals Online (AJOL)

    English Title: Modeling and simulation of a PON (Passive Optical Network) Based on hybrid technology WDM/TDM. English Abstract. This development is part of dynamism of design for a model combining WDM and TDM multiplexing in the optical network of PON (Passive Optical Network) type, in order to satisfy the high bit ...

  14. A robust neural network-based approach for microseismic event detection

    KAUST Repository

    Akram, Jubran; Ovcharenko, Oleg; Peter, Daniel

    2017-01-01

    We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset

  15. Development of the Social Network-Based Intervention "Powerful Together with Diabetes" Using Intervention Mapping

    NARCIS (Netherlands)

    Vissenberg, Charlotte; Nierkens, Vera; Uitewaal, Paul J. M.; Middelkoop, Barend J. C.; Nijpels, Giel; Stronks, Karien

    2017-01-01

    This article describes the development of the social network-based intervention Powerful Together with Diabetes which aims to improve diabetes self-management (DSM) among patients with type 2 diabetes living in socioeconomically deprived neighborhoods by stimulating social support for DSM and

  16. Enhancing Lifelong Competence Development and Management Systems with Social Network-based Concepts and Tools

    NARCIS (Netherlands)

    Cheak, Alicia; Angehrn, Albert; Sloep, Peter

    2006-01-01

    This paper addresses the challenge of enhancing the social dimension of lifelong Competence Development and Management Systems with social network-based concepts and tools. Our premise is that through a combination of social network visualization tools, simulations, stimulus agents and management

  17. Analysis of the robustness of network-based disease-gene prioritization methods reveals redundancy in the human interactome and functional diversity of disease-genes.

    Directory of Open Access Journals (Sweden)

    Emre Guney

    Full Text Available Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO analysis highlighted the role of functional diversity for such diseases.

  18. Weighted gene co-expression network analysis of expression data of monozygotic twins identifies specific modules and hub genes related to BMI.

    Science.gov (United States)

    Wang, Weijing; Jiang, Wenjie; Hou, Lin; Duan, Haiping; Wu, Yili; Xu, Chunsheng; Tan, Qihua; Li, Shuxia; Zhang, Dongfeng

    2017-11-13

    The therapeutic management of obesity is challenging, hence further elucidating the underlying mechanisms of obesity development and identifying new diagnostic biomarkers and therapeutic targets are urgent and necessary. Here, we performed differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) to identify significant genes and specific modules related to BMI based on gene expression profile data of 7 discordant monozygotic twins. In the differential gene expression analysis, it appeared that 32 differentially expressed genes (DEGs) were with a trend of up-regulation in twins with higher BMI when compared to their siblings. Categories of positive regulation of nitric-oxide synthase biosynthetic process, positive regulation of NF-kappa B import into nucleus, and peroxidase activity were significantly enriched within GO database and NF-kappa B signaling pathway within KEGG database. DEGs of NAMPT, TLR9, PTGS2, HBD, and PCSK1N might be associated with obesity. In the WGCNA, among the total 20 distinct co-expression modules identified, coral1 module (68 genes) had the strongest positive correlation with BMI (r = 0.56, P = 0.04) and disease status (r = 0.56, P = 0.04). Categories of positive regulation of phospholipase activity, high-density lipoprotein particle clearance, chylomicron remnant clearance, reverse cholesterol transport, intermediate-density lipoprotein particle, chylomicron, low-density lipoprotein particle, very-low-density lipoprotein particle, voltage-gated potassium channel complex, cholesterol transporter activity, and neuropeptide hormone activity were significantly enriched within GO database for this module. And alcoholism and cell adhesion molecules pathways were significantly enriched within KEGG database. Several hub genes, such as GAL, ASB9, NPPB, TBX2, IL17C, APOE, ABCG4, and APOC2 were also identified. The module eigengene of saddlebrown module (212 genes) was also significantly

  19. ESR1 Is Co-Expressed with Closely Adjacent Uncharacterised Genes Spanning a Breast Cancer Susceptibility Locus at 6q25.1

    Science.gov (United States)

    Dunbier, Anita K.; Anderson, Helen; Ghazoui, Zara; Lopez-Knowles, Elena; Pancholi, Sunil; Ribas, Ricardo; Drury, Suzanne; Sidhu, Kally; Leary, Alexandra; Martin, Lesley-Ann; Dowsett, Mitch

    2011-01-01

    Approximately 80% of human breast carcinomas present as oestrogen receptor α-positive (ER+ve) disease, and ER status is a critical factor in treatment decision-making. Recently, single nucleotide polymorphisms (SNPs) in the region immediately upstream of the ER gene (ESR1) on 6q25.1 have been associated with breast cancer risk. Our investigation of factors associated with the level of expression of ESR1 in ER+ve tumours has revealed unexpected associations between genes in this region and ESR1 expression that are important to consider in studies of the genetic causes of breast cancer risk. RNA from tumour biopsies taken from 104 postmenopausal women before and after 2 weeks treatment with an aromatase (oestrogen synthase) inhibitor was analyzed on Illumina 48K microarrays. Multiple-testing corrected Spearman correlation revealed that three previously uncharacterized open reading frames (ORFs) located immediately upstream of ESR1, C6ORF96, C6ORF97, and C6ORF211 were highly correlated with ESR1 (Rs = 0.67, 0.64, and 0.55 respectively, FDRaccount for the correlations. The correlations were maintained in cultured cells. An ERα antagonist did not affect the ORFs' expression or their correlation with ESR1, suggesting their transcriptional co-activation is not directly mediated by ERα. siRNA inhibition of C6ORF211 suppressed proliferation in MCF7 cells, and C6ORF211 positively correlated with a proliferation metagene in tumours. In contrast, C6ORF97 expression correlated negatively with the metagene and predicted for improved disease-free survival in a tamoxifen-treated published dataset, independently of ESR1. Our observations suggest that some of the biological effects previously attributed to ER could be mediated and/or modified by these co-expressed genes. The co-expression and function of these genes may be important influences on the recently identified relationship between SNPs in this region and breast cancer risk. PMID:21552322

  20. ESR1 is co-expressed with closely adjacent uncharacterised genes spanning a breast cancer susceptibility locus at 6q25.1.

    Directory of Open Access Journals (Sweden)

    Anita K Dunbier

    2011-04-01

    Full Text Available Approximately 80% of human breast carcinomas present as oestrogen receptor α-positive (ER+ve disease, and ER status is a critical factor in treatment decision-making. Recently, single nucleotide polymorphisms (SNPs in the region immediately upstream of the ER gene (ESR1 on 6q25.1 have been associated with breast cancer risk. Our investigation of factors associated with the level of expression of ESR1 in ER+ve tumours has revealed unexpected associations between genes in this region and ESR1 expression that are important to consider in studies of the genetic causes of breast cancer risk. RNA from tumour biopsies taken from 104 postmenopausal women before and after 2 weeks treatment with an aromatase (oestrogen synthase inhibitor was analyzed on Illumina 48K microarrays. Multiple-testing corrected Spearman correlation revealed that three previously uncharacterized open reading frames (ORFs located immediately upstream of ESR1, C6ORF96, C6ORF97, and C6ORF211 were highly correlated with ESR1 (Rs =  0.67, 0.64, and 0.55 respectively, FDR<1 × 10(-7. Publicly available datasets confirmed this relationship in other groups of ER+ve tumours. DNA copy number changes did not account for the correlations. The correlations were maintained in cultured cells. An ERα antagonist did not affect the ORFs' expression or their correlation with ESR1, suggesting their transcriptional co-activation is not directly mediated by ERα. siRNA inhibition of C6ORF211 suppressed proliferation in MCF7 cells, and C6ORF211 positively correlated with a proliferation metagene in tumours. In contrast, C6ORF97 expression correlated negatively with the metagene and predicted for improved disease-free survival in a tamoxifen-treated published dataset, independently of ESR1. Our observations suggest that some of the biological effects previously attributed to ER could be mediated and/or modified by these co-expressed genes. The co-expression and function of these genes may be

  1. Neural Network-Based Resistance Spot Welding Control and Quality Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Allen, J.D., Jr.; Ivezic, N.D.; Zacharia, T.

    1999-07-10

    This paper describes the development and evaluation of neural network-based systems for industrial resistance spot welding process control and weld quality assessment. The developed systems utilize recurrent neural networks for process control and both recurrent networks and static networks for quality prediction. The first section describes a system capable of both welding process control and real-time weld quality assessment, The second describes the development and evaluation of a static neural network-based weld quality assessment system that relied on experimental design to limit the influence of environmental variability. Relevant data analysis methods are also discussed. The weld classifier resulting from the analysis successfldly balances predictive power and simplicity of interpretation. The results presented for both systems demonstrate clearly that neural networks can be employed to address two significant problems common to the resistance spot welding industry, control of the process itself, and non-destructive determination of resulting weld quality.

  2. MEDUSA - An overset grid flow solver for network-based parallel computer systems

    Science.gov (United States)

    Smith, Merritt H.; Pallis, Jani M.

    1993-01-01

    Continuing improvement in processing speed has made it feasible to solve the Reynolds-Averaged Navier-Stokes equations for simple three-dimensional flows on advanced workstations. Combining multiple workstations into a network-based heterogeneous parallel computer allows the application of programming principles learned on MIMD (Multiple Instruction Multiple Data) distributed memory parallel computers to the solution of larger problems. An overset-grid flow solution code has been developed which uses a cluster of workstations as a network-based parallel computer. Inter-process communication is provided by the Parallel Virtual Machine (PVM) software. Solution speed equivalent to one-third of a Cray-YMP processor has been achieved from a cluster of nine commonly used engineering workstation processors. Load imbalance and communication overhead are the principal impediments to parallel efficiency in this application.

  3. Realization of Intelligent Household Appliance Wireless Monitoring Network Based on LEACH Protocol

    Directory of Open Access Journals (Sweden)

    Weilong ZHOU

    2014-06-01

    Full Text Available The intelligent household appliance wireless monitoring network can real-time monitor the apparent power and power factor of various household appliances in different indoor regions, and can realize the real-time monitoring on the household appliance working status and performance. The household appliance wireless monitoring network based on LEACH protocol is designed in the paper. Firstly, the basic idea of LEACH routing algorithm is proposed. Aiming at the node-distribution feature of intelligent home, the selection of cluster head in the routing algorithm and the data transmission method at the stable communication phase is modified. Moreover, the hardware circuit of power acquisition and power factor measurement is designed. The realization of wireless monitoring network based on CC2530 is described, each module and the whole system were conducted the on-line debugging. Finally, the system is proved to meet the practical requirement through the networking test.

  4. Design of Artificial Neural Network-Based pH Estimator

    Directory of Open Access Journals (Sweden)

    Shebel A. Alsabbah

    2010-10-01

    Full Text Available Taking into consideration the cost, size and drawbacks might be found with real hardware instrument for measuring pH values such that the complications of the wiring, installing, calibrating and troubleshooting the system, would make a person look for a cheaper, accurate, and alternative choice to perform the measuring operation, Where’s hereby, a feedforward artificial neural network-based pH estimator has to be proposed. The proposed estimator has been designed with multi- layer perceptrons. One input which is a measured base stream and two outputs represent pH values at strong base and strong/weak acids for a titration process. The created data base has been obtained with consideration of temperature variation. The final numerical results ensure the effectiveness and robustness of the design neural network-based pH estimator.

  5. A Bootstrap Neural Network Based Heterogeneous Panel Unit Root Test: Application to Exchange Rates

    OpenAIRE

    Christian de Peretti; Carole Siani; Mario Cerrato

    2010-01-01

    This paper proposes a bootstrap artificial neural network based panel unit root test in a dynamic heterogeneous panel context. An application to a panel of bilateral real exchange rate series with the US Dollar from the 20 major OECD countries is provided to investigate the Purchase Power Parity (PPP). The combination of neural network and bootstrapping significantly changes the findings of the economic study in favour of PPP.

  6. Machine learning of radial basis function neural network based on Kalman filter: Introduction

    Directory of Open Access Journals (Sweden)

    Vuković Najdan L.

    2014-01-01

    Full Text Available This paper analyzes machine learning of radial basis function neural network based on Kalman filtering. Three algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. We emphasize basic properties of these estimation algorithms, demonstrate how their advantages can be used for optimization of network parameters, derive mathematical models and show how they can be applied to model problems in engineering practice.

  7. Centralized light-source optical access network based on polarization multiplexing.

    Science.gov (United States)

    Grassi, Fulvio; Mora, José; Ortega, Beatriz; Capmany, José

    2010-03-01

    This paper presents and demonstrates a centralized light source optical access network based on optical polarization multiplexing technique. By using two optical sources emitting light orthogonally polarized in the Central Node for downstream and upstream operations, the Remote Node is kept source-free. EVM values below telecommunication standard requirements have been measured experimentally when bidirectional digital signals have been transmitted over 10 km of SMF employing subcarrier multiplexing technique in the electrical domain.

  8. Research on trust calculation of wireless sensor networks based on time segmentation

    Science.gov (United States)

    Su, Yaoxin; Gao, Xiufeng; Qiao, Wenxin

    2017-05-01

    Because the wireless sensor network is different from the traditional network characteristics, it is easy to accept the intrusion from the compromise node. The trust mechanism is the most effective way to defend against internal attacks. Aiming at the shortcomings of the existing trust mechanism, a method of calculating the trust of wireless sensor networks based on time segmentation is proposed. It improves the security of the network and extends the life of the network

  9. Neural Network based Minimization of BER in Multi-User Detection in SDMA

    OpenAIRE

    VENKATA REDDY METTU; KRISHAN KUMAR,; SRIKANTH PULLABHATLA

    2011-01-01

    In this paper we investigate the use of neural network based minimization of BER in MUD. Neural networks can be used for linear design, Adaptive prediction, Amplitude detection, Character Recognition and many other applications. Adaptive prediction is used in detecting the errors caused in AWGN channel. These errors are rectified by using Widrow-Hoff algorithm by updating their weights andAdaptive prediction methods. Both Widrow-Hoff and Adaptive prediction have been used for rectifying the e...

  10. A Network Approach of Gene Co-expression in the Zea mays/Aspergillus flavus Pathosystem to Map Host/Pathogen Interaction Pathways

    Science.gov (United States)

    Musungu, Bryan M.; Bhatnagar, Deepak; Brown, Robert L.; Payne, Gary A.; OBrian, Greg; Fakhoury, Ahmad M.; Geisler, Matt

    2016-01-01

    A gene co-expression network (GEN) was generated using a dual RNA-seq study with the fungal pathogen Aspergillus flavus and its plant host Zea mays during the initial 3 days of infection. The analysis deciphered novel pathways and mapped genes of interest in both organisms during the infection. This network revealed a high degree of connectivity in many of the previously recognized pathways in Z. mays such as jasmonic acid, ethylene, and reactive oxygen species (ROS). For the pathogen A. flavus, a link between aflatoxin production and vesicular transport was identified within the network. There was significant interspecies correlation of expression between Z. mays and A. flavus for a subset of 104 Z. mays, and 1942 A. flavus genes. This resulted in an interspecies subnetwork enriched in multiple Z. mays genes involved in the production of ROS. In addition to the ROS from Z. mays, there was enrichment in the vesicular transport pathways and the aflatoxin pathway for A. flavus. Included in these genes, a key aflatoxin cluster regulator, AflS, was found to be co-regulated with multiple Z. mays ROS producing genes within the network, suggesting AflS may be monitoring host ROS levels. The entire GEN for both host and pathogen, and the subset of interspecies correlations, is presented as a tool for hypothesis generation and discovery for events in the early stages of fungal infection of Z. mays by A. flavus. PMID:27917194

  11. A gene co-expression network in whole blood of schizophrenia patients is independent of antipsychotic-use and enriched for brain-expressed genes.

    Directory of Open Access Journals (Sweden)

    Simone de Jong

    Full Text Available Despite large-scale genome-wide association studies (GWAS, the underlying genes for schizophrenia are largely unknown. Additional approaches are therefore required to identify the genetic background of this disorder. Here we report findings from a large gene expression study in peripheral blood of schizophrenia patients and controls. We applied a systems biology approach to genome-wide expression data from whole blood of 92 medicated and 29 antipsychotic-free schizophrenia patients and 118 healthy controls. We show that gene expression profiling in whole blood can identify twelve large gene co-expression modules associated with schizophrenia. Several of these disease related modules are likely to reflect expression changes due to antipsychotic medication. However, two of the disease modules could be replicated in an independent second data set involving antipsychotic-free patients and controls. One of these robustly defined disease modules is significantly enriched with brain-expressed genes and with genetic variants that were implicated in a GWAS study, which could imply a causal role in schizophrenia etiology. The most highly connected intramodular hub gene in this module (ABCF1, is located in, and regulated by the major histocompatibility (MHC complex, which is intriguing in light of the fact that common allelic variants from the MHC region have been implicated in schizophrenia. This suggests that the MHC increases schizophrenia susceptibility via altered gene expression of regulatory genes in this network.

  12. Lignin, mitochondrial family and photorespiratory transporter classification as case studies in using co-expression, co-response and protein locations to aid in identifying transport functions

    Directory of Open Access Journals (Sweden)

    Takayuki eTohge

    2014-03-01

    Full Text Available Whole genome sequencing and the relative ease of transcript profiling have facilitated the collection and data warehousing of immense quantities of expression data. However, a substantial proportion of genes are not yet functionally annotated a problem which is particularly acute for transport proteins. In Arabidopsis, for example, only a minor fraction of the estimated 700 intracellular transporters have been identified at the molecular genetic level. Furthermore it is only within the last couple of years that critical genes such as those encoding the final transport step required for the long distance transport of sucrose and the first transporter of the core photorespiratory pathway have been identified. Here we will describe how transcriptional coordination between genes of known function and non-annotated genes allows the identification of putative transporters on the premise that such co-expressed genes tend to be functionally related. We will additionally extend this to include the expansion of this approach to include phenotypic information from other levels of cellular organization such as proteomic and metabolomic data and provide case studies wherein this approach has successfully been used to fill knowledge gaps in important metabolic pathways and physiological processes.

  13. Heterologous co-expression of accA, fabD, and thioesterase genes for improving long-chain fatty acid production in Pseudomonas aeruginosa and Escherichia coli.

    Science.gov (United States)

    Lee, Sunhee; Jeon, Eunyoung; Jung, Yeontae; Lee, Jinwon

    2012-05-01

    The goal of the present study was to increase the content of intracellular long-chain fatty acids in two bacterial strains, Pseudomonas aeruginosa PA14 and Escherichia coli K-12 MG1655, by co-overexpressing essential enzymes that are involved in the fatty acid synthesis metabolic pathway. Recently, microbial fatty acids and their derivatives have been receiving increasing attention as an alternative source of fuel. By introducing two genes (accA and fabD) of P. aeruginosa into the two bacterial strains and by co-expressing with them the fatty acyl-acyl carrier protein thioesterase gene of Streptococcus pyogenes (strain MGAS10270), we have engineered recombinant strains that are efficient producers of long-chain fatty acids (C16 and C18). The recombinant strains exhibit a 1.3-1.7-fold increase in the production of long-chain fatty acids over the wild-type strains. To enhance the production of total long-chain fatty acids, we researched the carbon sources for optimized culture conditions and results were used for post-culture incubation period. E. coli SGJS17 (containing the accA, fabD, and thioesterase genes) produced the highest content of intracellular total fatty acids; in particular, the unsaturated fatty acid content was about 20-fold higher than that in the wild-type E. coli.

  14. The arabidopsis wall associated kinase-like 10 gene encodes a functional guanylyl cyclase and is co-expressed with pathogen defense related genes

    KAUST Repository

    Meier, Stuart; Ruzvidzo, Oziniel; Morse, Monique; Donaldson, Lara; Kwezi, Lusisizwe; Gehring, Christoph A

    2010-01-01

    Background: Second messengers have a key role in linking environmental stimuli to physiological responses. One such messenger, guanosine 3?,5?-cyclic monophosphate (cGMP), has long been known to be an essential signaling molecule in many different physiological processes in higher plants, including biotic stress responses. To date, however, the guanylyl cyclase (GC) enzymes that catalyze the formation of cGMP from GTP have largely remained elusive in higher plants. Principal Findings: We have identified an Arabidopsis receptor type wall associated kinase-like molecule (AtWAKL10) as a candidate GC and provide experimental evidence to show that the intracellular domain of AtWAKL10431-700 can generate cGMP in vitro. Further, we also demonstrate that the molecule has kinase activity indicating that AtWAKL10 is a twin-domain catalytic protein. A co-expression and stimulus-specific expression analysis revealed that AtWAKL10 is consistently coexpressed with well characterized pathogen defense related genes and along with these genes is induced early and sharply in response to a range of pathogens and their elicitors. Conclusions: We demonstrate that AtWAKL10 is a twin-domain, kinase-GC signaling molecule that may function in biotic stress responses that are critically dependent on the second messenger cGMP. © 2010 Meier et al.

  15. A Network Approach of Gene Co-expression in the Zea mays/Aspergillus flavus Pathosystem to Map Host/Pathogen Interaction Pathways.

    Science.gov (United States)

    Musungu, Bryan M; Bhatnagar, Deepak; Brown, Robert L; Payne, Gary A; OBrian, Greg; Fakhoury, Ahmad M; Geisler, Matt

    2016-01-01

    A gene co-expression network (GEN) was generated using a dual RNA-seq study with the fungal pathogen Aspergillus flavus and its plant host Zea mays during the initial 3 days of infection. The analysis deciphered novel pathways and mapped genes of interest in both organisms during the infection. This network revealed a high degree of connectivity in many of the previously recognized pathways in Z. mays such as jasmonic acid, ethylene, and reactive oxygen species (ROS). For the pathogen A. flavus , a link between aflatoxin production and vesicular transport was identified within the network. There was significant interspecies correlation of expression between Z. mays and A. flavus for a subset of 104 Z. mays , and 1942 A. flavus genes. This resulted in an interspecies subnetwork enriched in multiple Z. mays genes involved in the production of ROS. In addition to the ROS from Z. mays , there was enrichment in the vesicular transport pathways and the aflatoxin pathway for A. flavus . Included in these genes, a key aflatoxin cluster regulator, AflS, was found to be co-regulated with multiple Z. mays ROS producing genes within the network, suggesting AflS may be monitoring host ROS levels. The entire GEN for both host and pathogen, and the subset of interspecies correlations, is presented as a tool for hypothesis generation and discovery for events in the early stages of fungal infection of Z. mays by A. flavus .

  16. A bioinformatics prediction approach towards analyzing the glycosylation, co-expression and interaction patterns of epithelial membrane antigen (EMA/MUC1)

    Energy Technology Data Exchange (ETDEWEB)

    Kalra, Rajkumar S., E-mail: renu-wadhwa@aist.go.jp; Wadhwa, Renu, E-mail: renu-wadhwa@aist.go.jp [Cell Proliferation Research Group and DBT-AIST International Laboratory for Advanced Biomedicine, National Institute of Advanced Industrial Science and Technology (AIST Central 4), 1-1-1 Higashi, Tsukuba, Ibaraki 305-8562 (Japan)

    2015-02-27

    Epithelial membrane antigen (EMA or MUC1) is a heavily glycosylated, type I transmembrane glycoprotein commonly expressed by epithelial cells of duct organs. It has been shown to be aberrantly glycosylated in several diseases including cancer. Protein sequence based annotation and analysis of glycosylation profile of glycoproteins by robust computational and comprehensive algorithms provides possible insights to the mechanism(s) of anomalous glycosylation. In present report, by using a number of bioinformatics applications we studied EMA/MUC1 and explored its trans-membrane structural domain sequence that is widely subjected to glycosylation. Exploration of different extracellular motifs led to prediction of N and O-linked glycosylation target sites. Based on the putative O-linked target sites, glycosylated moieties and pathways were envisaged. Furthermore, Protein network analysis demonstrated physical interaction of EMA with a number of proteins and confirmed its functional involvement in cell growth and proliferation pathways. Gene Ontology analysis suggested an involvement of EMA in a number of functions including signal transduction, protein binding, processing and transport along with glycosylation. Thus, present study explored potential of bioinformatics prediction approach in analyzing glycosylation, co-expression and interaction patterns of EMA/MUC1 glycoprotein.

  17. Circumvention of chaperone requirement for aggregate formation of a short polyglutamine tract by the co-expression of a long polyglutamine tract.

    Science.gov (United States)

    Kimura, Yoko; Koitabashi, Sumiko; Kakizuka, Akira; Fujita, Takashi

    2002-10-04

    Polyglutamine disease is now recognized as one of the conformational, amyloid-related diseases. In this disease, polyglutamine expansion in proteins has toxic effects on cells and also results in the formation of aggregates. Polyglutamine aggregate formation is accompanied by conversion of the polyglutamine from a soluble to an insoluble form. In yeast, the efficiency of the aggregate formation is determined by the balance of various parameters, including the length of the polyglutamine tract, the function of Hsp104, and the level of polyglutamine expression. In this study, we found that the co-expression of a long polyglutamine tract, which formed aggregates independently of the function of Hsp104, enhanced the formation of aggregates of a short polyglutamine tract in wild-type cells as well as in Deltahsp104 mutant cells. Thus, the expression of a long polyglutamine tract would be an additional parameter determining the efficiency of aggregate formation of a short polyglutamine tract. The co-localization of aggregates of long and short polyglutamine tracts suggests the possibility that the enhancement occurs due to the seeding of aggregates of the long polyglutamine tracts.

  18. A bioinformatics prediction approach towards analyzing the glycosylation, co-expression and interaction patterns of epithelial membrane antigen (EMA/MUC1)

    International Nuclear Information System (INIS)

    Kalra, Rajkumar S.; Wadhwa, Renu

    2015-01-01

    Epithelial membrane antigen (EMA or MUC1) is a heavily glycosylated, type I transmembrane glycoprotein commonly expressed by epithelial cells of duct organs. It has been shown to be aberrantly glycosylated in several diseases including cancer. Protein sequence based annotation and analysis of glycosylation profile of glycoproteins by robust computational and comprehensive algorithms provides possible insights to the mechanism(s) of anomalous glycosylation. In present report, by using a number of bioinformatics applications we studied EMA/MUC1 and explored its trans-membrane structural domain sequence that is widely subjected to glycosylation. Exploration of different extracellular motifs led to prediction of N and O-linked glycosylation target sites. Based on the putative O-linked target sites, glycosylated moieties and pathways were envisaged. Furthermore, Protein network analysis demonstrated physical interaction of EMA with a number of proteins and confirmed its functional involvement in cell growth and proliferation pathways. Gene Ontology analysis suggested an involvement of EMA in a number of functions including signal transduction, protein binding, processing and transport along with glycosylation. Thus, present study explored potential of bioinformatics prediction approach in analyzing glycosylation, co-expression and interaction patterns of EMA/MUC1 glycoprotein

  19. Soluble polymorphic bank vole prion proteins induced by co-expression of quiescin sulfhydryl oxidase in E. coli and their aggregation behaviors.

    Science.gov (United States)

    Abskharon, Romany; Dang, Johnny; Elfarash, Ameer; Wang, Zerui; Shen, Pingping; Zou, Lewis S; Hassan, Sedky; Wang, Fei; Fujioka, Hisashi; Steyaert, Jan; Mulaj, Mentor; Surewicz, Witold K; Castilla, Joaquín; Wohlkonig, Alexandre; Zou, Wen-Quan

    2017-10-04

    The infectious prion protein (PrP Sc or prion) is derived from its cellular form (PrP C ) through a conformational transition in animal and human prion diseases. Studies have shown that the interspecies conversion of PrP C to PrP Sc is largely swayed by species barriers, which is mainly deciphered by the sequence and conformation of the proteins among species. However, the bank vole PrP C (BVPrP) is highly susceptible to PrP Sc from different species. Transgenic mice expressing BVPrP with the polymorphic isoleucine (109I) but methionine (109M) at residue 109 spontaneously develop prion disease. To explore the mechanism underlying the unique susceptibility and convertibility, we generated soluble BVPrP by co-expression of BVPrP with Quiescin sulfhydryl oxidase (QSOX) in Escherichia coli. Interestingly, rBVPrP-109M and rBVPrP-109I exhibited distinct seeded aggregation pathways and aggregate morphologies upon seeding of mouse recombinant PrP fibrils, as monitored by thioflavin T fluorescence and electron microscopy. Moreover, they displayed different aggregation behaviors induced by seeding of hamster and mouse prion strains under real-time quaking-induced conversion. Our results suggest that QSOX facilitates the formation of soluble prion protein and provide further evidence that the polymorphism at residue 109 of QSOX-induced BVPrP may be a determinant in mediating its distinct convertibility and susceptibility.

  20. Integration of liver gene co-expression networks and eGWAs analyses highlighted candidate regulators implicated in lipid metabolism in pigs.

    Science.gov (United States)

    Ballester, Maria; Ramayo-Caldas, Yuliaxis; Revilla, Manuel; Corominas, Jordi; Castelló, Anna; Estellé, Jordi; Fernández, Ana I; Folch, Josep M

    2017-04-19

    In the present study, liver co-expression networks and expression Genome Wide Association Study (eGWAS) were performed to identify DNA variants and molecular pathways implicated in the functional regulatory mechanisms of meat quality traits in pigs. With this purpose, the liver mRNA expression of 44 candidates genes related with lipid metabolism was analysed in 111 Iberian x Landrace backcross animals. The eGWAS identified 92 eSNPs located in seven chromosomal regions and associated with eight genes: CROT, CYP2U1, DGAT1, EGF, FABP1, FABP5, PLA2G12A, and PPARA. Remarkably, cis-eSNPs associated with FABP1 gene expression which may be determining the C18:2(n-6)/C18:3(n-3) ratio in backfat through the multiple interaction of DNA variants and genes were identified. Furthermore, a hotspot on SSC8 associated with the gene expression of eight genes was identified and the TBCK gene was pointed out as candidate gene regulating it. Our results also suggested that the PI3K-Akt-mTOR pathway plays an important role in the control of the analysed genes highlighting nuclear receptors as the NR3C1 or PPARA. Finally, sex-dimorphism associated with hepatic lipid metabolism was identified with over-representation of female-biased genes. These results increase our knowledge of the genetic architecture underlying fat composition traits.

  1. Co-Expression and Co-Localization of Cartilage Glycoproteins CHI3L1 and Lubricin in Osteoarthritic Cartilage: Morphological, Immunohistochemical and Gene Expression Profiles

    Directory of Open Access Journals (Sweden)

    Marta Anna Szychlinska

    2016-03-01

    Full Text Available Osteoarthritis is the most common human arthritis characterized by degeneration of articular cartilage. Several studies reported that levels of human cartilage glycoprotein chitinase 3-like-1 (CHI3L1 are known as a potential marker for the activation of chondrocytes and the progression of Osteoarthritis (OA, whereas lubricin appears to be chondroprotective. The aim of this study was to investigate the co-expression and co-localization of CHI3L1 and lubricin in normal and osteoarthritic rat articular cartilage to correlate their modified expression to a specific grade of OA. Samples of normal and osteoarthritic rat articular cartilage were analyzed by the Kellgren–Lawrence OA severity scores, the Kraus’ modified Mankin score and the Histopathology Osteoarthritis Research Society International (OARSI system for histomorphometric evaluations, and through CHI3L1 and lubricin gene expression, immunohistochemistry and double immuno-staining analysis. The immunoexpression and the mRNA levels of lubricin increased in normal cartilage and decreased in OA cartilage (normal vs. OA, p < 0.01. By contrast, the immunoexpression and the mRNA levels of CHI3L1 increased in OA cartilage and decreased in normal cartilage (normal vs. OA, p < 0.01. Our findings are consistent with reports suggesting that these two glycoproteins are functionally associated with the development of OA and in particular with grade 2/3 of OA, suggesting that in the future they could be helpful to stage the severity and progression of the disease.

  2. The arabidopsis wall associated kinase-like 10 gene encodes a functional guanylyl cyclase and is co-expressed with pathogen defense related genes

    KAUST Repository

    Meier, Stuart

    2010-01-26

    Background: Second messengers have a key role in linking environmental stimuli to physiological responses. One such messenger, guanosine 3?,5?-cyclic monophosphate (cGMP), has long been known to be an essential signaling molecule in many different physiological processes in higher plants, including biotic stress responses. To date, however, the guanylyl cyclase (GC) enzymes that catalyze the formation of cGMP from GTP have largely remained elusive in higher plants. Principal Findings: We have identified an Arabidopsis receptor type wall associated kinase-like molecule (AtWAKL10) as a candidate GC and provide experimental evidence to show that the intracellular domain of AtWAKL10431-700 can generate cGMP in vitro. Further, we also demonstrate that the molecule has kinase activity indicating that AtWAKL10 is a twin-domain catalytic protein. A co-expression and stimulus-specific expression analysis revealed that AtWAKL10 is consistently coexpressed with well characterized pathogen defense related genes and along with these genes is induced early and sharply in response to a range of pathogens and their elicitors. Conclusions: We demonstrate that AtWAKL10 is a twin-domain, kinase-GC signaling molecule that may function in biotic stress responses that are critically dependent on the second messenger cGMP. © 2010 Meier et al.

  3. Identifying miRNA and gene modules of colon cancer associated with pathological stage by weighted gene co-expression network analysis

    Directory of Open Access Journals (Sweden)

    Zhou X

    2018-05-01

    Full Text Available Xian-guo Zhou,1,2,* Xiao-liang Huang,1,2,* Si-yuan Liang,1–3 Shao-mei Tang,1,2 Si-kao Wu,1,2 Tong-tong Huang,1,2 Zeng-nan Mo,1,2,4 Qiu-yan Wang1,2,5 1Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China; 2Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China; 3Department of Colorectal Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China; 4Department of Urology and Nephrology, The First Affiliated Hospital of Guangxi, Medical University, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China; 5Guangxi Colleges and Universities Key Laboratory of Biological Molecular Medicine Research, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China *These authors contributed equally to this work Introduction: Colorectal cancer (CRC is the fourth most common cause of cancer-related mortality worldwide. The tumor, node, metastasis (TNM stage remains the standard for CRC prognostication. Identification of meaningful microRNA (miRNA and gene modules or representative biomarkers related to the pathological stage of colon cancer helps to predict prognosis and reveal the mechanisms behind cancer progression.Materials and methods: We applied a systems biology approach by combining differential expression analysis and weighted gene co-expression network analysis (WGCNA to detect the pathological stage-related miRNA and gene modules and construct a miRNA–gene network. The Cancer Genome Atlas (TCGA colon adenocarcinoma (CAC RNA-sequencing data and miRNA-sequencing data were subjected to WGCNA analysis, and the GSE29623, GSE35602 and GSE39396 were utilized to validate and

  4. Pathways of Lipid Metabolism in Marine Algae, Co-Expression Network, Bottlenecks and Candidate Genes for Enhanced Production of EPA and DHA in Species of Chromista

    Directory of Open Access Journals (Sweden)

    Alice Mühlroth

    2013-11-01

    Full Text Available The importance of n-3 long chain polyunsaturated fatty acids (LC-PUFAs for human health has received more focus the last decades, and the global consumption of n-3 LC-PUFA has increased. Seafood, the natural n-3 LC-PUFA source, is harvested beyond a sustainable capacity, and it is therefore imperative to develop alternative n-3 LC-PUFA sources for both eicosapentaenoic acid (EPA, 20:5n-3 and docosahexaenoic acid (DHA, 22:6n-3. Genera of algae such as Nannochloropsis, Schizochytrium, Isochrysis and Phaedactylum within the kingdom Chromista have received attention due to their ability to produce n-3 LC-PUFAs. Knowledge of LC-PUFA synthesis and its regulation in algae at the molecular level is fragmentary and represents a bottleneck for attempts to enhance the n-3 LC-PUFA levels for industrial production. In the present review, Phaeodactylum tricornutum has been used to exemplify the synthesis and compartmentalization of n-3 LC-PUFAs. Based on recent transcriptome data a co-expression network of 106 genes involved in lipid metabolism has been created. Together with recent molecular biological and metabolic studies, a model pathway for n-3 LC-PUFA synthesis in P. tricornutum has been proposed, and is compared to industrialized species of Chromista. Limitations of the n-3 LC-PUFA synthesis by enzymes such as thioesterases, elongases, acyl-CoA synthetases and acyltransferases are discussed and metabolic bottlenecks are hypothesized such as the supply of the acetyl-CoA and NADPH. A future industrialization will depend on optimization of chemical compositions and increased biomass production, which can be achieved by exploitation of the physiological potential, by selective breeding and by genetic engineering.

  5. Co-expression of an Erwinia chrysanthemi pectate lyase-encoding gene (pelE) and an E. carotovora polygalacturonase-encoding gene (peh1) in Saccharomyces cerevisiae.

    Science.gov (United States)

    Laing, E; Pretorius, I S

    1993-05-01

    A pectate lyase (PL)-encoding gene (pelE) from Erwinia chrysanthemi and a polygalacturonase (PG)-encoding gene (peh1) from E. carotovora were each inserted between a novel yeast expression-secretion cassette and a yeast gene terminator, and cloned separately into a yeast-centromeric shuttle vector (YCp50), generating recombinant plasmids pAMS12 and pAMS13. Transcription initiation signals present in the expression-secretion cassette were derived from the yeast alcohol dehydrogenase gene promoter (ADC1P), whereas the transcription termination signals were derived from the yeast tryptophan synthase gene terminator (TRP5T). Secretion of PL and PG was directed by the signal sequence of the yeast mating pheromone alpha-factor (MF alpha 1s). A pectinase cassette comprising ADC1P-MF alpha 1s-pelE-TRP5T and ADC1P-MF alpha 1s-peh1-TRP5T was subcloned into YCp50, generating plasmid pAMS14. Subsequently, the dominant selectable Geneticin G418-resistance (GtR) marker, APH1, inserted between the yeast uridine diphosphoglucose 4-epimerase gene promoter (GAL10P) and yeast orotidine-5'-phosphate carboxylase gene terminator (URA3T), was cloned into pAMS14, resulting in plasmid pAMS15. Plasmids pAMS12, pAMS13 and pAMS14 were transformed into a laboratory strain of Saccharomyces cerevisiae, whereas pAMS15 was stably introduced into two commercial wine yeast strains. DNA-DNA and DNA-RNA hybridization analyses revealed the presence of these plasmids, and the pelE and peh1 transcripts in the yeast transformants, respectively. A polypectate agarose assay indicated the extracellular production of biologically active PL and PG by the S. cerevisiae transformants and confirmed that co-expression of the pelE and peh1 genes synergistically enhanced pectate degradation.

  6. Divergent and convergent modes of interaction between wheat and Puccinia graminis f. sp. tritici isolates revealed by the comparative gene co-expression network and genome analyses.

    Science.gov (United States)

    Rutter, William B; Salcedo, Andres; Akhunova, Alina; He, Fei; Wang, Shichen; Liang, Hanquan; Bowden, Robert L; Akhunov, Eduard

    2017-04-12

    Two opposing evolutionary constraints exert pressure on plant pathogens: one to diversify virulence factors in order to evade plant defenses, and the other to retain virulence factors critical for maintaining a compatible interaction with the plant host. To better understand how the diversified arsenals of fungal genes promote interaction with the same compatible wheat line, we performed a comparative genomic analysis of two North American isolates of Puccinia graminis f. sp. tritici (Pgt). The patterns of inter-isolate divergence in the secreted candidate effector genes were compared with the levels of conservation and divergence of plant-pathogen gene co-expression networks (GCN) developed for each isolate. Comprative genomic analyses revealed substantial level of interisolate divergence in effector gene complement and sequence divergence. Gene Ontology (GO) analyses of the conserved and unique parts of the isolate-specific GCNs identified a number of conserved host pathways targeted by both isolates. Interestingly, the degree of inter-isolate sub-network conservation varied widely for the different host pathways and was positively associated with the proportion of conserved effector candidates associated with each sub-network. While different Pgt isolates tended to exploit similar wheat pathways for infection, the mode of plant-pathogen interaction varied for different pathways with some pathways being associated with the conserved set of effectors and others being linked with the diverged or isolate-specific effectors. Our data suggest that at the intra-species level pathogen populations likely maintain divergent sets of effectors capable of targeting the same plant host pathways. This functional redundancy may play an important role in the dynamic of the "arms-race" between host and pathogen serving as the basis for diverse virulence strategies and creating conditions where mutations in certain effector groups will not have a major effect on the pathogen

  7. Evolution, functional differentiation, and co-expression of the RLK gene family revealed in Jilin ginseng, Panax ginseng C.A. Meyer.

    Science.gov (United States)

    Lin, Yanping; Wang, Kangyu; Li, Xiangyu; Sun, Chunyu; Yin, Rui; Wang, Yanfang; Wang, Yi; Zhang, Meiping

    2018-02-21

    Most genes in a genome exist in the form of a gene family; therefore, it is necessary to have knowledge of how a gene family functions to comprehensively understand organismal biology. The receptor-like kinase (RLK)-encoding gene family is one of the most important gene families in plants. It plays important roles in biotic and abiotic stress tolerances, and growth and development. However, little is known about the functional differentiation and relationships among the gene members within a gene family in plants. This study has isolated 563 RLK genes (designated as PgRLK genes) expressed in Jilin ginseng (Panax ginseng C.A. Meyer), investigated their evolution, and deciphered their functional diversification and relationships. The PgRLK gene family is highly diverged and formed into eight types. The LRR type is the earliest and most prevalent, while only the Lec type originated after P. ginseng evolved. Furthermore, although the members of the PgRLK gene family all encode receptor-like protein kinases and share conservative domains, they are functionally very diverse, participating in numerous biological processes. The expressions of different members of the PgRLK gene family are extremely variable within a tissue, at a developmental stage and in the same cultivar, but most of the genes tend to express correlatively, forming a co-expression network. These results not only provide a deeper and comprehensive understanding of the evolution, functional differentiation and correlation of a gene family in plants, but also an RLK genic resource useful for enhanced ginseng genetic improvement.

  8. Connections between EM2-containing terminals and GABA/μ-opioid receptor co-expressing neurons in the rat spinal trigeminal caudal nucleus

    Science.gov (United States)

    Li, Meng-Ying; Wu, Zhen-Yu; Lu, Ya-Cheng; Yin, Jun-Bin; Wang, Jian; Zhang, Ting; Dong, Yu-Lin; Wang, Feng

    2014-01-01

    Endomorphin-2 (EM2) demonstrates a potent antinociceptive effect via the μ-opioid receptor (MOR). To provide morphological evidence for the pain control effect of EM2, the synaptic connections between EM2-immunoreactive (IR) axonal terminals and γ-amino butyric acid (GABA)/MOR co-expressing neurons in lamina II of the spinal trigeminal caudal nucleus (Vc) were investigated in the rat. Dense EM2-, MOR- and GABA-IR fibers and terminals were mainly observed in lamina II of the Vc. Within lamina II, GABA- and MOR-neuronal cell bodies were also encountered. The results of immunofluorescent histochemical triple-staining showed that approximately 14.2 or 18.9% of GABA-IR or MOR-IR neurons also showed MOR- or GABA-immunopositive staining in lamina II; approximately 45.2 and 36.1% of the GABA-IR and MOR-IR neurons, respectively, expressed FOS protein in their nuclei induced by injecting formalin into the left lower lip of the mouth. Most of the GABA/MOR, GABA/FOS, and MOR/FOS double-labeled neurons made close contacts with EM2-IR fibers and terminals. Immuno-electron microscopy confirmed that the EM2-IR terminals formed synapses with GABA-IR or MOR-IR dendritic processes and neuronal cell bodies in lamina II of the Vc. These results suggest that EM2 might participate in pain transmission and modulation by binding to MOR-IR and GABAergic inhibitory interneuron in lamina II of the Vc to exert inhibitory effect on the excitatory interneuron in lamina II and projection neurons in laminae I and III. PMID:25386121

  9. Co-expression of TAL1 and ADH1 in recombinant xylose-fermenting Saccharomyces cerevisiae improves ethanol production from lignocellulosic hydrolysates in the presence of furfural.

    Science.gov (United States)

    Hasunuma, Tomohisa; Ismail, Ku Syahidah Ku; Nambu, Yumiko; Kondo, Akihiko

    2014-02-01

    Lignocellulosic biomass dedicated to bioethanol production usually contains pentoses and inhibitory compounds such as furfural that are not well tolerated by Saccharomyces cerevisiae. Thus, S. cerevisiae strains with the capability of utilizing both glucose and xylose in the presence of inhibitors such as furfural are very important in industrial ethanol production. Under the synergistic conditions of transaldolase (TAL) and alcohol dehydrogenase (ADH) overexpression, S. cerevisiae MT8-1X/TAL-ADH was able to produce 1.3-fold and 2.3-fold more ethanol in the presence of 70 mM furfural than a TAL-expressing strain and a control strain, respectively. We also tested the strains' ability by mimicking industrial ethanol production from hemicellulosic hydrolysate containing fermentation inhibitors, and ethanol production was further improved by 16% when using MT8-1X/TAL-ADH compared to the control strain. Transcript analysis further revealed that besides the pentose phosphate pathway genes TKL1 and TAL1, ADH7 was also upregulated in response to furfural stress, which resulted in higher ethanol production compared to the TAL-expressing strain. The improved capability of our modified strain was based on its capacity to more quickly reduce furfural in situ resulting in higher ethanol production. The co-expression of TAL/ADH genes is one crucial strategy to fully utilize undetoxified lignocellulosic hydrolysate, leading to cost-competitive ethanol production. Copyright © 2013 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

  10. Squamous cell carcinomas escape immune surveillance via inducing chronic activation and exhaustion of CD8+ T Cells co-expressing PD-1 and LAG-3 inhibitory receptors.

    Science.gov (United States)

    Mishra, Ameet K; Kadoishi, Tanya; Wang, Xiaoguang; Driver, Emily; Chen, Zhangguo; Wang, Xiao-Jing; Wang, Jing H

    2016-12-06

    Squamous cell carcinoma (SCC) is the second commonest type of skin cancer. Moreover, about 90% of head and neck cancers are SCCs. SCCs develop at a significantly higher rate under chronic immunosuppressive conditions, implicating a role of immune surveillance in controlling SCCs. It remains largely unknown how SCCs evade immune recognition. Here, we established a mouse model by injecting tumor cells derived from primary SCCs harboring KrasG12D mutation and Smad4 deletion into wild-type (wt) or CD8-/- recipients. We found comparable tumor growth between wt and CD8-/- recipients, indicating a complete escape of CD8+ T cell-mediated anti-tumor responses by these SCCs. Mechanistically, CD8+ T cells apparently were not defective in infiltrating tumors given their relatively increased percentage among tumor infiltrating lymphocytes (TILs). CD8+ TILs exhibited phenotypes of chronic activation and exhaustion, including overexpression of activation markers, co-expression of programmed cell death 1 (PD-1) and lymphocyte activation gene-3 (LAG-3), as well as TCRβ downregulation. Among CD4+ TILs, T regulatory cells (Tregs) were preferentially expanded. Contradictory to prior findings in melanoma, Treg expansion was independent of CD8+ T cells in our SCC model. Unexpectedly, CD8+ T cells were required for promoting NK cell infiltration within SCCs. Furthermore, we uncovered AKT-dependent lymphocyte-induced PD-L1 upregulation on SCCs, which was contributed greatly by combinatorial effects of CD8+ T and NK cells. Lastly, dual blockade of PD-1 and LAG-3 inhibited the tumor growth of SCCs. Thus, our findings identify novel immune evasion mechanisms of SCCs and suggest that immunosuppressive mechanisms operate in a cancer-type specific and context-dependent manner.

  11. Co-expression of Nisin Z and Leucocin C as a Basis for Effective Protection Against Listeria monocytogenes in Pasteurized Milk

    Directory of Open Access Journals (Sweden)

    Yuxin Fu

    2018-03-01

    Full Text Available Nisin, an important bacteriocin from Lactococcus lactis subsp., is primarily active against various Gram-positive bacteria. Leucocin C, produced by Leuconostoc carnosum 4010, is a class IIa bacteriocin used to inhibit the growth of Listeria monocytogenes. Because two bacteriocins have different modes of action, the combined use of them could be a potential strategy for effective inhibition of foodborne pathogens. In this study, L. lactis N8-r-lecCI (N8 harboring lecCI gene coexpressing nisin–leucocin C was constructed based on the food-grade carrier L. lactis N8. Production of both bacteriocins was stably maintained. Antimicrobial measurements showed that the recombinant strain is effectively against Listeria monocytogenes and Staphylococcus aureus and moderately against Salmonella enterica serovar Enteritidis and Escherichia coli because of its stronger antibacterial activity than the parental strain, this result first demonstrated that the co-expression of nisin and leucocin C results in highly efficient antimicrobial activity. The checkerboard assay showed that the antibacterial activity of L. lactis N8-r-lecCI supernatant was enhanced in the presence of low concentration of EDTA. Analysis of the scanning electron microscope image showed the biggest cellular morphology change in L. monocytogenes treated with a mixture of EDTA and L. lactis N8-r-lecCI supernatant. The practical effect was verified in pasteurized milk through time-kill assay. The L. lactis N8-r-lecCI strain expressing both nisin and leucocin C has a promising application prospect in pasteurized milk processing and preservation because of its strong antibacterial activity.

  12. Co-expression of the transcription factors CEH-14 and TTX-1 regulates AFD neuron-specific genes gcy-8 and gcy-18 in C. elegans.

    Science.gov (United States)

    Kagoshima, Hiroshi; Kohara, Yuji

    2015-03-15

    A wide variety of cells are generated by the expression of characteristic sets of genes, primarily those regulated by cell-specific transcription. To elucidate the mechanism regulating cell-specific gene expression in a highly specialized cell, AFD thermosensory neuron in Caenorhabditis elegans, we analyzed the promoter sequences of guanylyl cyclase genes, gcy-8 and gcy-18, exclusively expressed in AFD. In this study, we showed that AFD-specific expression of gcy-8 and gcy-18 requires the co-expression of homeodomain proteins, CEH-14/LHX3 and TTX-1/OTX1. We observed that mutation of ttx-1 or ceh-14 caused a reduction in the expression of gcy-8 and gcy-18 and that the expression was completely lost in double mutants. This synergy effect was also observed with other AFD marker genes, such as ntc-1, nlp-21and cng-3. Electrophoretic mobility shift assays revealed direct interaction of CEH-14 and TTX-1 proteins with gcy-8 and gcy-18 promoters in vitro. The binding sites of CEH-14 and TTX-1 proteins were confirmed to be essential for AFD-specific expression of gcy-8 and gcy-18 in vivo. We also demonstrated that forced expression of CEH-14 and TTX-1 in AWB chemosensory neurons induced ectopic expression of gcy-8 and gcy-18 reporters in this neuron. Finally, we showed that the regulation of gcy-8 and gcy-18 expression by ceh-14 and ttx-1 is evolutionally conserved in five Caenorhabditis species. Taken together, ceh-14 and ttx-1 expression determines the fate of AFD as terminal selector genes at the final step of cell specification. Copyright © 2015 Elsevier Inc. All rights reserved.

  13. Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes.

    Science.gov (United States)

    Wada, Masayoshi; Takahashi, Hiroki; Altaf-Ul-Amin, Md; Nakamura, Kensuke; Hirai, Masami Y; Ohta, Daisaku; Kanaya, Shigehiko

    2012-07-15

    Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of EOperon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system. Copyright © 2012 Elsevier B.V. All rights reserved.

  14. Construction of a plasmid for co-expression of mouse membrane-bound form of IL-15 and RAE-1ε and its biological activity.

    Science.gov (United States)

    Qian, Li; Ji, Ming-Chun; Pan, Xin-Yuan; Gong, Wei-Juan; Tian, Fang; Duan, Qiu-Fang

    2011-05-01

    Interleukin 15 (IL-15) is a pivotal cytokine for the proliferation and activation of a specific group of immune cells such as natural killer (NK), IFN-producing killer dendritic cells (IKDC) and CD8 T cells. RAE-1ε, the ligand for the activating NKG2D receptor, which also play an important role in the proliferation and activation of NK cells and IKDCs. In this study, a membrane-bound form of IL-15 (termed mb15) encoding sequence and RAE-1ε gene were obtained by SOE-PCR or PCR amplification. The amplified mb15 and RAE-1ε gene were then digested and inserted into the multiple cloning site1 (MCS1) and MCS2 of pVITRO2-mcs vector, respectively. A recombinant eukaryotic expression vector for co-expression of mb15 and RAE-1ε was successfully constructed. After it was transfected to BaF3 cells, the expression of IL-15 and RAE-1ε in recombinant BaF3/mb15/RAE-1ε cells were verified by RT-PCR, western blot and FCM analysis. Furthermore, BaF3/mb15/RAE-1ε cells had the ability of promoting NK cells proliferation and IFN-γ secretion. In conclusion, BaF3/mb15/RAE-1ε cells were successfully constructed, which is very useful for further studies, especially for the expansion and activation of certain subsets of immune cells such as NK cells and IKDCs. Copyright © 2011 Elsevier Inc. All rights reserved.

  15. Protection of chickens against infectious bronchitis by a recombinant fowlpox virus co-expressing IBV-S1 and chicken IFNgamma.

    Science.gov (United States)

    Wang, Yun-Feng; Sun, Yong-Ke; Tian, Zhan-Cheng; Shi, Xing-Ming; Tong, Guang-Zhi; Liu, Sheng-Wang; Zhi, Hai-Dong; Kong, Xian-Gang; Wang, Mei

    2009-11-23

    A fowlpox virus expressing the chicken infectious bronchitis virus (IBV) S1 gene of the LX4 strain (rFPV-IBVS1) and a fowlpox virus co-expressing the S1 gene and the chicken type II interferon gene (rFPV-IBVS1-ChIFNgamma) were constructed. These viruses were assessed for their immunological efficacy on specific-pathogen-free (SPF) chickens challenged with a virulent IBV. Although the antibody levels in the rFPV-IBVS1-ChIFNgamma-vaccinated group were lower than those in the attenuated live IB vaccine H120 group and the rFPV-IBVS1 group, the rFPV-IBVS1-ChIFNgamma provided the strongest protection against an IBV LX4 virus challenge (15 out of 16 chickens immunized with rFPV-IBVS1-ChIFNgamma were protected), followed by the attenuated live IB vaccine (13/16 protected) and the rFPV-IBVS1 (12/16 protected). Compared to those of the rFPV-IBVS1 and the attenuated live IB vaccine groups, chickens in the rFPV-IBVS1-ChIFNgamma group eliminated virus more quickly and decreased the presence of viral antigen more significantly in renal tissue. Examination of affected tissues revealed abnormalities in the liver, spleen, kidney, lung and trachea of chickens vaccinated with the attenuated live IB vaccine and the rFPV-IBVS1 vaccine. In rFPV-IBVS1-ChIFNgamma-vaccinated chickens, pathological changes were also observed in those organs, but were milder and lasted shorter. The lesions in the mock control group were the most severe and lasted for at least 20 days. This study demonstrated that chicken type II interferon increased the immunoprotective efficacy of rFPV-IBVS1-ChIFNgamma and normal weight gain in vaccinated chickens although it inhibited serum antibody production.

  16. CoLIde: a bioinformatics tool for CO-expression-based small RNA Loci Identification using high-throughput sequencing data.

    Science.gov (United States)

    Mohorianu, Irina; Stocks, Matthew Benedict; Wood, John; Dalmay, Tamas; Moulton, Vincent

    2013-07-01

    Small RNAs (sRNAs) are 20-25 nt non-coding RNAs that act as guides for the highly sequence-specific regulatory mechanism known as RNA silencing. Due to the recent increase in sequencing depth, a highly complex and diverse population of sRNAs in both plants and animals has been revealed. However, the exponential increase in sequencing data has also made the identification of individual sRNA transcripts corresponding to biological units (sRNA loci) more challenging when based exclusively on the genomic location of the constituent sRNAs, hindering existing approaches to identify sRNA loci. To infer the location of significant biological units, we propose an approach for sRNA loci detection called CoLIde (Co-expression based sRNA Loci Identification) that combines genomic location with the analysis of other information such as variation in expression levels (expression pattern) and size class distribution. For CoLIde, we define a locus as a union of regions sharing the same pattern and located in close proximity on the genome. Biological relevance, detected through the analysis of size class distribution, is also calculated for each locus. CoLIde can be applied on ordered (e.g., time-dependent) or un-ordered (e.g., organ, mutant) series of samples both with or without biological/technical replicates. The method reliably identifies known types of loci and shows improved performance on sequencing data from both plants (e.g., A. thaliana, S. lycopersicum) and animals (e.g., D. melanogaster) when compared with existing locus detection techniques. CoLIde is available for use within the UEA Small RNA Workbench which can be downloaded from: http://srna-workbench.cmp.uea.ac.uk.

  17. Extracellular NGFR Spacers Allow Efficient Tracking and Enrichment of Fully Functional CAR-T Cells Co-Expressing a Suicide Gene.

    Science.gov (United States)

    Casucci, Monica; Falcone, Laura; Camisa, Barbara; Norelli, Margherita; Porcellini, Simona; Stornaiuolo, Anna; Ciceri, Fabio; Traversari, Catia; Bordignon, Claudio; Bonini, Chiara; Bondanza, Attilio

    2018-01-01

    Chimeric antigen receptor (CAR)-T cell immunotherapy is at the forefront of innovative cancer therapeutics. However, lack of standardization of cellular products within the same clinical trial and lack of harmonization between different trials have hindered the clear identification of efficacy and safety determinants that should be unveiled in order to advance the field. With the aim of facilitating the isolation and in vivo tracking of CAR-T cells, we here propose the inclusion within the CAR molecule of a novel extracellular spacer based on the low-affinity nerve-growth-factor receptor (NGFR). We screened four different spacer designs using as target antigen the CD44 isoform variant 6 (CD44v6). We successfully generated NGFR-spaced CD44v6 CAR-T cells that could be efficiently enriched with clinical-grade immuno-magnetic beads without negative consequences on subsequent expansion, immuno-phenotype, in vitro antitumor reactivity, and conditional ablation when co-expressing a suicide gene. Most importantly, these cells could be tracked with anti-NGFR monoclonal antibodies in NSG mice, where they expanded, persisted, and exerted potent antitumor effects against both high leukemia and myeloma burdens. Similar results were obtained with NGFR-enriched CAR-T cells specific for CD19 or CEA, suggesting the universality of this strategy. In conclusion, we have demonstrated that the incorporation of the NGFR marker gene within the CAR sequence allows for a single molecule to simultaneously work as a therapeutic and selection/tracking gene. Looking ahead, NGFR spacer enrichment might allow good manufacturing procedures-manufacturing of standardized CAR-T cell products with high therapeutic potential, which could be harmonized in different clinical trials and used in combination with a suicide gene for future application in the allogeneic setting.

  18. Extracellular NGFR Spacers Allow Efficient Tracking and Enrichment of Fully Functional CAR-T Cells Co-Expressing a Suicide Gene

    Directory of Open Access Journals (Sweden)

    Monica Casucci

    2018-03-01

    Full Text Available Chimeric antigen receptor (CAR-T cell immunotherapy is at the forefront of innovative cancer therapeutics. However, lack of standardization of cellular products within the same clinical trial and lack of harmonization between different trials have hindered the clear identification of efficacy and safety determinants that should be unveiled in order to advance the field. With the aim of facilitating the isolation and in vivo tracking of CAR-T cells, we here propose the inclusion within the CAR molecule of a novel extracellular spacer based on the low-affinity nerve-growth-factor receptor (NGFR. We screened four different spacer designs using as target antigen the CD44 isoform variant 6 (CD44v6. We successfully generated NGFR-spaced CD44v6 CAR-T cells that could be efficiently enriched with clinical-grade immuno-magnetic beads without negative consequences on subsequent expansion, immuno-phenotype, in vitro antitumor reactivity, and conditional ablation when co-expressing a suicide gene. Most importantly, these cells could be tracked with anti-NGFR monoclonal antibodies in NSG mice, where they expanded, persisted, and exerted potent antitumor effects against both high leukemia and myeloma burdens. Similar results were obtained with NGFR-enriched CAR-T cells specific for CD19 or CEA, suggesting the universality of this strategy. In conclusion, we have demonstrated that the incorporation of the NGFR marker gene within the CAR sequence allows for a single molecule to simultaneously work as a therapeutic and selection/tracking gene. Looking ahead, NGFR spacer enrichment might allow good manufacturing procedures-manufacturing of standardized CAR-T cell products with high therapeutic potential, which could be harmonized in different clinical trials and used in combination with a suicide gene for future application in the allogeneic setting.

  19. Extracellular NGFR Spacers Allow Efficient Tracking and Enrichment of Fully Functional CAR-T Cells Co-Expressing a Suicide Gene

    Science.gov (United States)

    Casucci, Monica; Falcone, Laura; Camisa, Barbara; Norelli, Margherita; Porcellini, Simona; Stornaiuolo, Anna; Ciceri, Fabio; Traversari, Catia; Bordignon, Claudio; Bonini, Chiara; Bondanza, Attilio

    2018-01-01

    Chimeric antigen receptor (CAR)-T cell immunotherapy is at the forefront of innovative cancer therapeutics. However, lack of standardization of cellular products within the same clinical trial and lack of harmonization between different trials have hindered the clear identification of efficacy and safety determinants that should be unveiled in order to advance the field. With the aim of facilitating the isolation and in vivo tracking of CAR-T cells, we here propose the inclusion within the CAR molecule of a novel extracellular spacer based on the low-affinity nerve-growth-factor receptor (NGFR). We screened four different spacer designs using as target antigen the CD44 isoform variant 6 (CD44v6). We successfully generated NGFR-spaced CD44v6 CAR-T cells that could be efficiently enriched with clinical-grade immuno-magnetic beads without negative consequences on subsequent expansion, immuno-phenotype, in vitro antitumor reactivity, and conditional ablation when co-expressing a suicide gene. Most importantly, these cells could be tracked with anti-NGFR monoclonal antibodies in NSG mice, where they expanded, persisted, and exerted potent antitumor effects against both high leukemia and myeloma burdens. Similar results were obtained with NGFR-enriched CAR-T cells specific for CD19 or CEA, suggesting the universality of this strategy. In conclusion, we have demonstrated that the incorporation of the NGFR marker gene within the CAR sequence allows for a single molecule to simultaneously work as a therapeutic and selection/tracking gene. Looking ahead, NGFR spacer enrichment might allow good manufacturing procedures-manufacturing of standardized CAR-T cell products with high therapeutic potential, which could be harmonized in different clinical trials and used in combination with a suicide gene for future application in the allogeneic setting. PMID:29619024

  20. Co-Expression of ORFCma with PHB Depolymerase (PhaZCma ) in Escherichia coli Induces Efficient Whole-Cell Biodegradation of Polyesters.

    Science.gov (United States)

    Lee, Ming-Chieh; Liu, En-Jung; Yang, Cheng-Han; Hsiao, Li-Jung; Wu, Tzong-Ming; Li, Si-Yu

    2018-04-01

    Whole-cell degradation of polyesters not only avoids the tedious process of enzyme separation, but also allows the degraded product to be reused as a carbon source. In this study, Escherichia coli BL21(DE3) harboring phaZ Cma , a gene encoding poly(3-hydroxybutyrate) (PHB) depolymerase from Caldimonas manganoxidans, is constructed. The extra-cellular fraction of E. coli/pPHAZ exhibits a fast PHB degradation rate where it only took 35 h to completely degrade PHB films, while C. manganoxidans takes 81 h to do the same. The co-expression of ORF Cma (a putative periplasmic substrate binding protein that is within the same operon of phaZ Cma ) further improves the PHB degradation. While 28 h is needed for E. coli/pPHAZ to cause an 80% weight loss in PHB films, E. coli/pORFPHAZ needs only 21 h. Furthermore, it is able to degrade at-least four different polyesters, PHB, poly(lactic acid) (PLA), polycaprolactone (PCL), and poly(butylene succinate-co-adipate) (PBSA). Testing of the time course of 3-hydroxybutyrate concentration and the turbidity of the degradation solutions over time shows that PhaZ Cma has both exo- and endo-enzymatic activity. The whole-cell E. coli/pORFPHAZ can be used for recycling various polyesters while ORF Cma can potentially be a universal element for enhancing the secretion of recombinant protein. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. Network-based Modeling of Mesoscale Catchments - The Hydrology Perspective of Glowa-danube

    Science.gov (United States)

    Ludwig, R.; Escher-Vetter, H.; Hennicker, R.; Mauser, W.; Niemeyer, S.; Reichstein, M.; Tenhunen, J.

    Within the GLOWA initiative of the German Ministry for Research and Educa- tion (BMBF), the project GLOWA-Danube is funded to establish a transdisciplinary network-based decision support tool for water related issues in the Upper Danube wa- tershed. It aims to develop and validate integration techniques, integrated models and integrated monitoring procedures and to implement them in the network-based De- cision Support System DANUBIA. An accurate description of processes involved in energy, water and matter fluxes and turnovers requires an intense collaboration and exchange of water related expertise of different scientific disciplines. DANUBIA is conceived as a distributed expert network and is developed on the basis of re-useable, refineable, and documented sub-models. In order to synthesize a common understand- ing between the project partners, a standardized notation of parameters and functions and a platform-independent structure of computational methods and interfaces has been established using the Unified Modeling Language UML. DANUBIA is object- oriented, spatially distributed and raster-based at its core. It applies the concept of "proxels" (Process Pixel) as its basic object, which has different dimensions depend- ing on the viewing scale and connects to its environment through fluxes. The presented study excerpts the hydrological view point of GLOWA-Danube, its approach of model coupling and network based communication (using the Remote Method Invocation RMI), the object-oriented technology to simulate physical processes and interactions at the land surface and the methodology to treat the issue of spatial and temporal scal- ing in large, heterogeneous catchments. The mechanisms applied to communicate data and model parameters across the typical discipline borders will be demonstrated from the perspective of a land-surface object, which comprises the capabilities of interde- pendent expert models for snowmelt, soil water movement, runoff formation, plant

  2. Network based multi-channel digital flash X-ray imaging system

    International Nuclear Information System (INIS)

    Wang Jingjin; Yuan Jie; Liu Yaqiang; Lin Yong; Song Zheng; Liu Keyin; Zhang Qi; Zheng Futang

    2000-01-01

    A network based multi-channel digital flash X-ray imaging system has been developed. It can be used to acquire and digitize orthogonal flash X-ray images in multi-interval, and to distribute the images on the network. There is no need of films and chemical process, no anxiety of waiting and no trouble of film archiving. This system is useful for testing ballistics, jet, explode, armour-piercing and fast running machines. The system composing and acquired images are presented. The software for object separating, mass calculating, 3D positioning, speed determining and cavity reconstruction are described

  3. Network based multi-channel digital flash X-ray imaging system

    International Nuclear Information System (INIS)

    Wang Jingjin; Yuan Jie; Liu Yaqiang; Lin Yong; Song Zheng; Liu Keyin

    2003-01-01

    A network based multi-channel digital flash X-ray imaging system has been developed. It can be used to acquire and digitize orthogonal flash X-ray images in multi-interval, and to distribute the images on the network. There is no need of films and chemical process, no anxiety of waiting and no trouble of film archiving. This system is useful for testing ballistics, jet, explode, armour-piercing and fast running machines. The system composing and acquired images of terminal ballistics are presented. The software for object separating, profile calculating and 3D cavity reconstruction are described

  4. A New Optimization Strategy to Improve Design of Hydrogen Network Based Formulation of Hydrogen Consumers

    Directory of Open Access Journals (Sweden)

    M. R. S. Birjandi

    2018-03-01

    Full Text Available This paper describes a shortcut model for formulating hydrogen consumers in hydrogen network based on inlet/outlet flow rate and inlet/outlet hydrogen purity. The formulation procedure is obtained using nonlinear regression of industrial data and represents the relationship between the flow rate and purity of outlet and inlet streams. The proposed model can estimate outlet flow rate and purity of hydrogen by changing inlet flow rate and purity of hydrogen. The shortcut model is used to achieve optimal operation of consumers and it optimizes hydrogen network design.

  5. Synchronization of uncertain time-varying network based on sliding mode control technique

    Science.gov (United States)

    Lü, Ling; Li, Chengren; Bai, Suyuan; Li, Gang; Rong, Tingting; Gao, Yan; Yan, Zhe

    2017-09-01

    We research synchronization of uncertain time-varying network based on sliding mode control technique. The sliding mode control technique is first modified so that it can be applied to network synchronization. Further, by choosing the appropriate sliding surface, the identification law of uncertain parameter, the adaptive law of the time-varying coupling matrix element and the control input of network are designed, it is sure that the uncertain time-varying network can synchronize effectively the synchronization target. At last, we perform some numerical simulations to demonstrate the effectiveness of the proposed results.

  6. Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic System

    Directory of Open Access Journals (Sweden)

    A. A. Salama

    2015-03-01

    Full Text Available In this paper, we present a review of different recommender system algorithms that are utilized in social networks based e-Learning systems. Future research will include our proposed our e-Learning system that utilizes Recommender System and Social Network. Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache in [21, 22, 23] and Salama et al. in [24-66].The purpose of this paper is to utilize a neutrosophic set to analyze social networks data conducted through learning activities.

  7. Network Based Building Lighting Design and Fuzzy Logic via Remote Control

    Directory of Open Access Journals (Sweden)

    Cemal YILMAZ

    2009-02-01

    Full Text Available In this paper, a network based building lighting system is implemented. Profibus-DP network structure is used in the design and Fuzzy Logic Controller (FLC is used on control of the building lighting. Informations received from sensors which measures level of the building illumination is used on FLC and they are transferred to the system by Profibus-DP network. Control of lighting luminaries are made via Profibus-DP network. The illuminance inside the bulding is fitted required level. Energy saving and healthy lighting facilities have been obtained by the design.

  8. Data Collection Method for Mobile Control Sink Node in Wireless Sensor Network Based on Compressive Sensing

    Directory of Open Access Journals (Sweden)

    Ling Yongfa

    2016-01-01

    Full Text Available The paper proposes a mobile control sink node data collection method in the wireless sensor network based on compressive sensing. This method, with regular track, selects the optimal data collection points in the monitoring area via the disc method, calcu-lates the shortest path by using the quantum genetic algorithm, and hence determines the data collection route. Simulation results show that this method has higher network throughput and better energy efficiency, capable of collecting a huge amount of data with balanced energy consumption in the network.

  9. A novel wavelet neural network based pathological stage detection technique for an oral precancerous condition

    Science.gov (United States)

    Paul, R R; Mukherjee, A; Dutta, P K; Banerjee, S; Pal, M; Chatterjee, J; Chaudhuri, K; Mukkerjee, K

    2005-01-01

    Aim: To describe a novel neural network based oral precancer (oral submucous fibrosis; OSF) stage detection method. Method: The wavelet coefficients of transmission electron microscopy images of collagen fibres from normal oral submucosa and OSF tissues were used to choose the feature vector which, in turn, was used to train the artificial neural network. Results: The trained network was able to classify normal and oral precancer stages (less advanced and advanced) after obtaining the image as an input. Conclusions: The results obtained from this proposed technique were promising and suggest that with further optimisation this method could be used to detect and stage OSF, and could be adapted for other conditions. PMID:16126873

  10. Network-Based Coordination of Civil-Service Training: Lessons from the Case of Estonia

    Directory of Open Access Journals (Sweden)

    Metsma Merilin

    2017-06-01

    Full Text Available The focus of this article is on the coordination of civil-service training in a decentralized civil-service system. The Estonian case is studied. The article investigates network-based coordination, analyzes the power sources of the central coordinator and discusses the opportunities and limitations of creating coherence through network-type cooperation. The article concludes that the key power sources for the central coordinator are financial, human and technical resources paired with knowledge, leadership and commitment. The case study shows that, in a decentralized civil service system, a common understanding on training and development can be fostered by intense collaboration through networks.

  11. Neural substrates of motor and cognitive dysfunctions in SCA2 patients: A network based statistics analysis

    Directory of Open Access Journals (Sweden)

    G. Olivito

    2017-01-01

    In the present study, the network-based statistics (NBS approach was used to assess differences in functional connectivity between specific cerebellar and cerebral “nodes” in SCA2 patients. Altered inter-nodal connectivity was found between more posterior regions in the cerebellum and regions in the cerebral cortex clearly related to cognition and emotion. Furthermore, more anterior cerebellar lobules showed altered inter-nodal connectivity with motor and somatosensory cerebral regions. The present data suggest that in SCA2 a cerebellar dysfunction affects long-distance cerebral regions and that the clinical symptoms may be specifically related with connectivity changes between motor and non-motor cerebello-cortical nodes.

  12. Extended neural network-based scheme for real-time force tracking with magnetorheological dampers

    DEFF Research Database (Denmark)

    Weber, Felix; Bhowmik, Subrata; Høgsberg, Jan Becker

    2014-01-01

    This paper validates numerically and experimentally a new neural network-based real-time force tracking scheme for magnetorheological (MR) dampers on a five-storey shear frame with MR damper. The inverse model is trained with absolute values of measured velocity and force because the targeted...... the pre-yield to the post-yield region. A control-oriented approach is presented to compensate for these drawbacks. The resulting control force tracking scheme is validated for the emulation of viscous damping, clipped viscous damping with negative stiffness, and friction damping with negative stiffness...

  13. Design and development of the network based system for the supervision of radioactive sources

    International Nuclear Information System (INIS)

    Yang Yaoyun; Su Genghua; Zhang Hui; Li Junli; Zhu Li

    2010-01-01

    Objective: To help the environmental protection authorities to upgrade the management of the related organizations and radioactive sources and improve the information level of nuclear technology utilization's supervision. Methods: On the basis of investigation of requirements, the network based system for the supervision of radioactive sources was divided into application system and supervision system, based on MYSQL and SQL Server2005 respectively. Results: The system satisfied the current requirements of the nuclear technology utilization's supervision and is in nationwide operation. Conclusion: The system achieved the dynamic tracking management of radioactive sources and improved the efficiency and level of radiation safety supervision in nuclear technology utilizations. (authors)

  14. Network-based differential gene expression analysis suggests cell cycle related genes regulated by E2F1 underlie the molecular difference between smoker and non-smoker lung adenocarcinoma

    Science.gov (United States)

    2013-01-01

    Background Differential gene expression (DGE) analysis is commonly used to reveal the deregulated molecular mechanisms of complex diseases. However, traditional DGE analysis (e.g., the t test or the rank sum test) tests each gene independently without considering interactions between them. Top-ranked differentially regulated genes prioritized by the analysis may not directly relate to the coherent molecular changes underlying complex diseases. Joint analyses of co-expression and DGE have been applied to reveal the deregulated molecular modules underlying complex diseases. Most of these methods consist of separate steps: first to identify gene-gene relationships under the studied phenotype then to integrate them with gene expression changes for prioritizing signature genes, or vice versa. It is warrant a method that can simultaneously consider gene-gene co-expression strength and corresponding expression level changes so that both types of information can be leveraged optimally. Results In this paper, we develop a gene module based method for differential gene expression analysis, named network-based differential gene expression (nDGE) analysis, a one-step integrative process for prioritizing deregulated genes and grouping them into gene modules. We demonstrate that nDGE outperforms existing methods in prioritizing deregulated genes and discovering deregulated gene modules using simulated data sets. When tested on a series of smoker and non-smoker lung adenocarcinoma data sets, we show that top differentially regulated genes identified by the rank sum test in different sets are not consistent while top ranked genes defined by nDGE in different data sets significantly overlap. nDGE results suggest that a differentially regulated gene module, which is enriched for cell cycle related genes and E2F1 targeted genes, plays a role in the molecular differences between smoker and non-smoker lung adenocarcinoma. Conclusions In this paper, we develop nDGE to prioritize

  15. Design and Evaluation of a Wireless Sensor Network Based Aircraft Strength Testing System

    Science.gov (United States)

    Wu, Jian; Yuan, Shenfang; Zhou, Genyuan; Ji, Sai; Wang, Zilong; Wang, Yang

    2009-01-01

    The verification of aerospace structures, including full-scale fatigue and static test programs, is essential for structure strength design and evaluation. However, the current overall ground strength testing systems employ a large number of wires for communication among sensors and data acquisition facilities. The centralized data processing makes test programs lack efficiency and intelligence. Wireless sensor network (WSN) technology might be expected to address the limitations of cable-based aeronautical ground testing systems. This paper presents a wireless sensor network based aircraft strength testing (AST) system design and its evaluation on a real aircraft specimen. In this paper, a miniature, high-precision, and shock-proof wireless sensor node is designed for multi-channel strain gauge signal conditioning and monitoring. A cluster-star network topology protocol and application layer interface are designed in detail. To verify the functionality of the designed wireless sensor network for strength testing capability, a multi-point WSN based AST system is developed for static testing of a real aircraft undercarriage. Based on the designed wireless sensor nodes, the wireless sensor network is deployed to gather, process, and transmit strain gauge signals and monitor results under different static test loads. This paper shows the efficiency of the wireless sensor network based AST system, compared to a conventional AST system. PMID:22408521

  16. Comparisons of complex network based models and real train flow model to analyze Chinese railway vulnerability

    International Nuclear Information System (INIS)

    Ouyang, Min; Zhao, Lijing; Hong, Liu; Pan, Zhezhe

    2014-01-01

    Recently numerous studies have applied complex network based models to study the performance and vulnerability of infrastructure systems under various types of attacks and hazards. But how effective are these models to capture their real performance response is still a question worthy of research. Taking the Chinese railway system as an example, this paper selects three typical complex network based models, including purely topological model (PTM), purely shortest path model (PSPM), and weight (link length) based shortest path model (WBSPM), to analyze railway accessibility and flow-based vulnerability and compare their results with those from the real train flow model (RTFM). The results show that the WBSPM can produce the train routines with 83% stations and 77% railway links identical to the real routines and can approach the RTFM the best for railway vulnerability under both single and multiple component failures. The correlation coefficient for accessibility vulnerability from WBSPM and RTFM under single station failures is 0.96 while it is 0.92 for flow-based vulnerability; under multiple station failures, where each station has the same failure probability fp, the WBSPM can produce almost identical vulnerability results with those from the RTFM under almost all failures scenarios when fp is larger than 0.62 for accessibility vulnerability and 0.86 for flow-based vulnerability

  17. Network-based modeling and intelligent data mining of social media for improving care.

    Science.gov (United States)

    Akay, Altug; Dragomir, Andrei; Erlandsson, Bjorn-Erik

    2015-01-01

    Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. We propose a two-step analysis framework that focuses on positive and negative sentiment, as well as the side effects of treatment, in users' forum posts, and identifies user communities (modules) and influential users for the purpose of ascertaining user opinion of cancer treatment. We used a self-organizing map to analyze word frequency data derived from users' forum posts. We then introduced a novel network-based approach for modeling users' forum interactions and employed a network partitioning method based on optimizing a stability quality measure. This allowed us to determine consumer opinion and identify influential users within the retrieved modules using information derived from both word-frequency data and network-based properties. Our approach can expand research into intelligently mining social media data for consumer opinion of various treatments to provide rapid, up-to-date information for the pharmaceutical industry, hospitals, and medical staff, on the effectiveness (or ineffectiveness) of future treatments.

  18. Network-Based Method for Identifying Co- Regeneration Genes in Bone, Dentin, Nerve and Vessel Tissues.

    Science.gov (United States)

    Chen, Lei; Pan, Hongying; Zhang, Yu-Hang; Feng, Kaiyan; Kong, XiangYin; Huang, Tao; Cai, Yu-Dong

    2017-10-02

    Bone and dental diseases are serious public health problems. Most current clinical treatments for these diseases can produce side effects. Regeneration is a promising therapy for bone and dental diseases, yielding natural tissue recovery with few side effects. Because soft tissues inside the bone and dentin are densely populated with nerves and vessels, the study of bone and dentin regeneration should also consider the co-regeneration of nerves and vessels. In this study, a network-based method to identify co-regeneration genes for bone, dentin, nerve and vessel was constructed based on an extensive network of protein-protein interactions. Three procedures were applied in the network-based method. The first procedure, searching, sought the shortest paths connecting regeneration genes of one tissue type with regeneration genes of other tissues, thereby extracting possible co-regeneration genes. The second procedure, testing, employed a permutation test to evaluate whether possible genes were false discoveries; these genes were excluded by the testing procedure. The last procedure, screening, employed two rules, the betweenness ratio rule and interaction score rule, to select the most essential genes. A total of seventeen genes were inferred by the method, which were deemed to contribute to co-regeneration of at least two tissues. All these seventeen genes were extensively discussed to validate the utility of the method.

  19. Is Congenital Amusia a Disconnection Syndrome? A Study Combining Tract- and Network-Based Analysis

    Directory of Open Access Journals (Sweden)

    Jieqiong Wang

    2017-09-01

    Full Text Available Previous studies on congenital amusia mainly focused on the impaired fronto-temporal pathway. It is possible that neural pathways of amusia patients on a larger scale are affected. In this study, we investigated changes in structural connections by applying both tract-based and network-based analysis to DTI data of 12 subjects with congenital amusia and 20 demographic-matched normal controls. TBSS (tract-based spatial statistics was used to detect microstructural changes. The results showed that amusics had higher diffusivity indices in the corpus callosum, the right inferior/superior longitudinal fasciculus, and the right inferior frontal-occipital fasciculus (IFOF. The axial diffusivity values of the right IFOF were negatively correlated with musical scores in the amusia group. Network-based analysis showed that the efficiency of the brain network was reduced in amusics. The impairments of WM tracts were also found to be correlated with reduced network efficiency in amusics. This suggests that impaired WM tracts may lead to the reduced network efficiency seen in amusics. Our findings suggest that congenital amusia is a disconnection syndrome.

  20. Is Congenital Amusia a Disconnection Syndrome? A Study Combining Tract- and Network-Based Analysis.

    Science.gov (United States)

    Wang, Jieqiong; Zhang, Caicai; Wan, Shibiao; Peng, Gang

    2017-01-01

    Previous studies on congenital amusia mainly focused on the impaired fronto-temporal pathway. It is possible that neural pathways of amusia patients on a larger scale are affected. In this study, we investigated changes in structural connections by applying both tract-based and network-based analysis to DTI data of 12 subjects with congenital amusia and 20 demographic-matched normal controls. TBSS (tract-based spatial statistics) was used to detect microstructural changes. The results showed that amusics had higher diffusivity indices in the corpus callosum, the right inferior/superior longitudinal fasciculus, and the right inferior frontal-occipital fasciculus (IFOF). The axial diffusivity values of the right IFOF were negatively correlated with musical scores in the amusia group. Network-based analysis showed that the efficiency of the brain network was reduced in amusics. The impairments of WM tracts were also found to be correlated with reduced network efficiency in amusics. This suggests that impaired WM tracts may lead to the reduced network efficiency seen in amusics. Our findings suggest that congenital amusia is a disconnection syndrome.

  1. Design and evaluation of a wireless sensor network based aircraft strength testing system.

    Science.gov (United States)

    Wu, Jian; Yuan, Shenfang; Zhou, Genyuan; Ji, Sai; Wang, Zilong; Wang, Yang

    2009-01-01

    The verification of aerospace structures, including full-scale fatigue and static test programs, is essential for structure strength design and evaluation. However, the current overall ground strength testing systems employ a large number of wires for communication among sensors and data acquisition facilities. The centralized data processing makes test programs lack efficiency and intelligence. Wireless sensor network (WSN) technology might be expected to address the limitations of cable-based aeronautical ground testing systems. This paper presents a wireless sensor network based aircraft strength testing (AST) system design and its evaluation on a real aircraft specimen. In this paper, a miniature, high-precision, and shock-proof wireless sensor node is designed for multi-channel strain gauge signal conditioning and monitoring. A cluster-star network topology protocol and application layer interface are designed in detail. To verify the functionality of the designed wireless sensor network for strength testing capability, a multi-point WSN based AST system is developed for static testing of a real aircraft undercarriage. Based on the designed wireless sensor nodes, the wireless sensor network is deployed to gather, process, and transmit strain gauge signals and monitor results under different static test loads. This paper shows the efficiency of the wireless sensor network based AST system, compared to a conventional AST system.

  2. Development of network-based multichannel neuromuscular electrical stimulation system for stroke rehabilitation.

    Science.gov (United States)

    Qu, Hongen; Xie, Yongji; Liu, Xiaoxuan; He, Xin; Hao, Manzhao; Bao, Yong; Xie, Qing; Lan, Ning

    2016-01-01

    Neuromuscular electrical stimulation (NMES) is a promising assistive technology for stroke rehabilitation. Here we present the design and development of a multimuscle stimulation system as an emerging therapy for people with paretic stroke. A network-based multichannel NMES system was integrated based on dual bus architecture of communication and an H-bridge current regulator with a power booster. The structure of the system was a body area network embedded with multiple stimulators and a communication protocol of controlled area network to transmit muscle stimulation parameter information to individual stimulators. A graphical user interface was designed to allow clinicians to specify temporal patterns and muscle stimulation parameters. We completed and tested a prototype of the hardware and communication software modules of the multichannel NMES system. The prototype system was first verified in nondisabled subjects for safety, and then tested in subjects with stroke for feasibility with assisting multijoint movements. Results showed that synergistic stimulation of multiple muscles in subjects with stroke improved performance of multijoint movements with more natural velocity profiles at elbow and shoulder and reduced acromion excursion due to compensatory trunk rotation. The network-based NMES system may provide an innovative solution that allows more physiological activation of multiple muscles in multijoint task training for patients with stroke.

  3. Exposure to depleted uranium does not alter the co-expression of HER-2/neu and p53 in breast cancer patients

    Directory of Open Access Journals (Sweden)

    Al-Toriahi Kaswer M

    2011-03-01

    Full Text Available Abstract Background Amongst the extensive literature on immunohistochemical profile of breast cancer, very little is found on populations exposed to a potential risk factor such as depleted uranium. This study looked at the immunohistochemical expression of HER-2/neu (c-erbB2 and p53 in different histological types of breast cancer found in the middle Euphrates region of Iraq, where the population has been exposed to high levels of depleted uranium. Findings The present investigation was performed over a period starting from September 2008 to April 2009. Formalin-fixed, paraffin-embedded blocks from 70 patients with breast cancer (62 ductal and 8 lobular carcinoma were included in this study. A group of 25 patients with fibroadenoma was included as a comparative group, and 20 samples of normal breast tissue sections were used as controls. Labeled streptavidin-biotin (LSAB+ complex method was employed for immunohistochemical detection of HER-2/neu and p53. The detection rate of HER-2/neu and p53 immunohistochemical expression were 47.14% and 35.71% respectively in malignant tumors; expression was negative in the comparative and control groups (p HER-2/neu immunostaining was significantly associated with histological type, tumor size, nodal involvement, and recurrence of breast carcinoma (p p Both biomarkers were positively correlated with each other. Furthermore, all the cases that co-expressed both HER-2/neu and p53 showed the most unfavorable biopathological profile. Conclusion P53 and HER-2/neu over-expression play an important role in pathogenesis of breast carcinoma. The findings indicate that in regions exposed to high levels of depleted uranium, although p53 and HER-2/neu overexpression are both high, correlation of their expression with age, grade, tumor size, recurrence and lymph node involvement is similar to studies that have been conducted on populations not exposed to depleted uranium. HER-2/neu expression in breast cancer was higher

  4. Construction of a recombinant adenovirus co-expressing truncated human prostate-specific membrane antigen and mouse 4-1BBL genes and its effect on dendritic cells

    Directory of Open Access Journals (Sweden)

    Xiaodong Weng

    2011-03-01

    Full Text Available Our aim was to construct a recombinant adenovirus co-expressing truncated human prostate-specific membrane antigen (tPSMA and mouse 4-1BBL genes and to determine its effect on dendritic cells (DCs generated from bone marrow suspensions harvested from C57BL/6 mice for which the effect of 4-1BBL on DCs is not clear, especially during DCs processing tumor-associated antigen. Replication deficient adenovirus AdMaxTM Expression System was used to construct recombinant adenovirus Ad-tPSMA-internal ribosome entry site-mouse 4-1BBL (Ad-tPSMA-IRES-m4-1BBL and Ad-enhanced green fluorescent protein. Day 7 proliferating DC aggregates generated from C57BL/6 mice were collected as immature DCs and further mature DCs were obtained by lipopolysaccharide activated immature DCs. After DCs were exposed to the recombinant adenovirus with 250 multiplicity of infection, the expression of tPSMA and m4-1BBL proteins were detected by Western blot, and the apoptosis and phenotype of DCs were analyzed by flow cytometry. Cytokines (IL-6 and IL-12 in the supernatant were detected by enzyme-linked immunosorbent assay (ELISA. Proliferation of T cells was detected by allogeneic mixed lymphocyte reactions. The tPSMA and m4-1BBL proteins were expressed correctly. The apoptosis rate of DCs transfected with Ad-tPSMA-IRES-m4-1BBL was 14.6%, lower than that of control DCs. The expression of co-stimulatory molecules [CD80 (81.6 ± 5.4% and CD86 (80.13 ± 2.81%] up-regulated in Ad-tPSMA-IRES-m4-1BBL-pulsed DCs, and the level of IL-6 (3960.2 ± 50.54 pg/mL and IL-12 (249.57 ± 12.51 pg/mL production in Ad-tPSMA-IRES-m4-1BBL-transduced DCs were significantly higher (P < 0.05 than those in control DCs. Ad-tPSMA-IRES-m4-1BBL induced higher T-cell proliferation (OD450 = 0.614 ± 0.018, indicating that this recombinant adenovirus can effectively enhance the activity of DCs.

  5. A Genome-Wide Association Study for Culm Cellulose Content in Barley Reveals Candidate Genes Co-Expressed with Members of the CELLULOSE SYNTHASE A Gene Family

    Science.gov (United States)

    Houston, Kelly; Burton, Rachel A.; Sznajder, Beata; Rafalski, Antoni J.; Dhugga, Kanwarpal S.; Mather, Diane E.; Taylor, Jillian; Steffenson, Brian J.; Waugh, Robbie; Fincher, Geoffrey B.

    2015-01-01

    Cellulose is a fundamentally important component of cell walls of higher plants. It provides a scaffold that allows the development and growth of the plant to occur in an ordered fashion. Cellulose also provides mechanical strength, which is crucial for both normal development and to enable the plant to withstand both abiotic and biotic stresses. We quantified the cellulose concentration in the culm of 288 two – rowed and 288 six – rowed spring type barley accessions that were part of the USDA funded barley Coordinated Agricultural Project (CAP) program in the USA. When the population structure of these accessions was analysed we identified six distinct populations, four of which we considered to be comprised of a sufficient number of accessions to be suitable for genome-wide association studies (GWAS). These lines had been genotyped with 3072 SNPs so we combined the trait and genetic data to carry out GWAS. The analysis allowed us to identify regions of the genome containing significant associations between molecular markers and cellulose concentration data, including one region cross-validated in multiple populations. To identify candidate genes we assembled the gene content of these regions and used these to query a comprehensive RNA-seq based gene expression atlas. This provided us with gene annotations and associated expression data across multiple tissues, which allowed us to formulate a supported list of candidate genes that regulate cellulose biosynthesis. Several regions identified by our analysis contain genes that are co-expressed with CELLULOSE SYNTHASE A (HvCesA) across a range of tissues and developmental stages. These genes are involved in both primary and secondary cell wall development. In addition, genes that have been previously linked with cellulose synthesis by biochemical methods, such as HvCOBRA, a gene of unknown function, were also associated with cellulose levels in the association panel. Our analyses provide new insights into the

  6. Large Scale Proteomic Data and Network-Based Systems Biology Approaches to Explore the Plant World.

    Science.gov (United States)

    Di Silvestre, Dario; Bergamaschi, Andrea; Bellini, Edoardo; Mauri, PierLuigi

    2018-06-03

    The investigation of plant organisms by means of data-derived systems biology approaches based on network modeling is mainly characterized by genomic data, while the potential of proteomics is largely unexplored. This delay is mainly caused by the paucity of plant genomic/proteomic sequences and annotations which are fundamental to perform mass-spectrometry (MS) data interpretation. However, Next Generation Sequencing (NGS) techniques are contributing to filling this gap and an increasing number of studies are focusing on plant proteome profiling and protein-protein interactions (PPIs) identification. Interesting results were obtained by evaluating the topology of PPI networks in the context of organ-associated biological processes as well as plant-pathogen relationships. These examples foreshadow well the benefits that these approaches may provide to plant research. Thus, in addition to providing an overview of the main-omic technologies recently used on plant organisms, we will focus on studies that rely on concepts of module, hub and shortest path, and how they can contribute to the plant discovery processes. In this scenario, we will also consider gene co-expression networks, and some examples of integration with metabolomic data and genome-wide association studies (GWAS) to select candidate genes will be mentioned.

  7. Network-based ranking methods for prediction of novel disease associated microRNAs.

    Science.gov (United States)

    Le, Duc-Hau

    2015-10-01

    Many studies have shown roles of microRNAs on human disease and a number of computational methods have been proposed to predict such associations by ranking candidate microRNAs according to their relevance to a disease. Among them, machine learning-based methods usually have a limitation in specifying non-disease microRNAs as negative training samples. Meanwhile, network-based methods are becoming dominant since they well exploit a "disease module" principle in microRNA functional similarity networks. Of which, random walk with restart (RWR) algorithm-based method is currently state-of-the-art. The use of this algorithm was inspired from its success in predicting disease gene because the "disease module" principle also exists in protein interaction networks. Besides, many algorithms designed for webpage ranking have been successfully applied in ranking disease candidate genes because web networks share topological properties with protein interaction networks. However, these algorithms have not yet been utilized for disease microRNA prediction. We constructed microRNA functional similarity networks based on shared targets of microRNAs, and then we integrated them with a microRNA functional synergistic network, which was recently identified. After analyzing topological properties of these networks, in addition to RWR, we assessed the performance of (i) PRINCE (PRIoritizatioN and Complex Elucidation), which was proposed for disease gene prediction; (ii) PageRank with Priors (PRP) and K-Step Markov (KSM), which were used for studying web networks; and (iii) a neighborhood-based algorithm. Analyses on topological properties showed that all microRNA functional similarity networks are small-worldness and scale-free. The performance of each algorithm was assessed based on average AUC values on 35 disease phenotypes and average rankings of newly discovered disease microRNAs. As a result, the performance on the integrated network was better than that on individual ones. In

  8. Using smart mobile devices in social-network-based health education practice: a learning behavior analysis.

    Science.gov (United States)

    Wu, Ting-Ting

    2014-06-01

    Virtual communities provide numerous resources, immediate feedback, and information sharing, enabling people to rapidly acquire information and knowledge and supporting diverse applications that facilitate interpersonal interactions, communication, and sharing. Moreover, incorporating highly mobile and convenient devices into practice-based courses can be advantageous in learning situations. Therefore, in this study, a tablet PC and Google+ were introduced to a health education practice course to elucidate satisfaction of learning module and conditions and analyze the sequence and frequency of learning behaviors during the social-network-based learning process. According to the analytical results, social networks can improve interaction among peers and between educators and students, particularly when these networks are used to search for data, post articles, engage in discussions, and communicate. In addition, most nursing students and nursing educators expressed a positive attitude and satisfaction toward these innovative teaching methods, and looked forward to continuing the use of this learning approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Wireless sensor network-based greenhouse environment monitoring and automatic control system for dew condensation prevention.

    Science.gov (United States)

    Park, Dae-Heon; Park, Jang-Woo

    2011-01-01

    Dew condensation on the leaf surface of greenhouse crops can promote diseases caused by fungus and bacteria, affecting the growth of the crops. In this paper, we present a WSN (Wireless Sensor Network)-based automatic monitoring system to prevent dew condensation in a greenhouse environment. The system is composed of sensor nodes for collecting data, base nodes for processing collected data, relay nodes for driving devices for adjusting the environment inside greenhouse and an environment server for data storage and processing. Using the Barenbrug formula for calculating the dew point on the leaves, this system is realized to prevent dew condensation phenomena on the crop's surface acting as an important element for prevention of diseases infections. We also constructed a physical model resembling the typical greenhouse in order to verify the performance of our system with regard to dew condensation control.

  10. REMOTE, a Wireless Sensor Network Based System to Monitor Rowing Performance

    Directory of Open Access Journals (Sweden)

    Jordi Llosa

    2009-09-01

    Full Text Available In this paper, we take a hard look at the performance of REMOTE, a sensor network based application that provides a detailed picture of a boat movement, individual rower performance, or his/her performance compared with other crew members. The application analyzes data gathered with a WSN strategically deployed over a boat to obtain information on the boat and oar movements. Functionalities of REMOTE are compared to those of RowX [1] outdoor instrument, a commercial wired sensor instrument designed for similar purposes. This study demonstrates that with smart geometrical configuration of the sensors, rotation and translation of the oars and boat can be obtained. Three different tests are performed: laboratory calibration allows us to become familiar with the accelerometer readings and validate the theory, ergometer tests which help us to set the acquisition parameters, and on boat tests shows the application potential of this technologies in sports.

  11. Neural network based cluster creation in the ATLAS silicon Pixel Detector

    CERN Document Server

    Andreazza, A; The ATLAS collaboration

    2013-01-01

    The read-out from individual pixels on planar semi-conductor sensors are grouped into clusters to reconstruct the location where a charged particle passed through the sensor. The resolution given by individual pixel sizes is significantly improved by using the information from the charge sharing between pixels. Such analog cluster creation techniques have been used by the ATLAS experiment for many years to obtain an excellent performance. However, in dense environments, such as those inside high-energy jets, clusters have an increased probability of merging the charge deposited by multiple particles. Recently, a neural network based algorithm which estimates both the cluster position and whether a cluster should be split has been developed for the ATLAS Pixel Detector. The algorithm significantly reduces ambiguities in the assignment of pixel detector measurement to tracks within jets and improves the position accuracy with respect to standard interpolation techniques by taking into account the 2-dimensional ...

  12. Network Based Educational Environment How Libraries and Librarians Become Organizers of Knowledge Access and Resources

    CERN Document Server

    Pettenati, M C; Pettenati, Corrado

    2000-01-01

    In this paper we will highlight some important issues which will influence the redefinition of roles and duties of libraries and librarians in a networked based educational environment. Although librarians will also keep their traditional roles of faculty support services as well as reference service and research assistance, we identify the participation in the instructional design process, the support in the evaluation, development and use of a proper authoring system and the customization of information access, as being the domains where libraries and librarians should mainly involve themselves in the next future and make profit of their expertise in information and knowledge organization in order to properly and effectively support the institutions in the use of Information Technology in education.

  13. Topology control algorithm for wireless sensor networks based on Link forwarding

    Science.gov (United States)

    Pucuo, Cairen; Qi, Ai-qin

    2018-03-01

    The research of topology control could effectively save energy and increase the service life of network based on wireless sensor. In this paper, a arithmetic called LTHC (link transmit hybrid clustering) based on link transmit is proposed. It decreases expenditure of energy by changing the way of cluster-node’s communication. The idea is to establish a link between cluster and SINK node when the cluster is formed, and link-node must be non-cluster. Through the link, cluster sends information to SINK nodes. For the sake of achieving the uniform distribution of energy on the network, prolongate the network survival time, and improve the purpose of communication, the communication will cut down much more expenditure of energy for cluster which away from SINK node. In the two aspects of improving the traffic and network survival time, we find that the LTCH is far superior to the traditional LEACH by experiments.

  14. REMOTE, a Wireless Sensor Network Based System to Monitor Rowing Performance

    Science.gov (United States)

    Llosa, Jordi; Vilajosana, Ignasi; Vilajosana, Xavier; Navarro, Nacho; Suriñach, Emma; Marquès, Joan Manuel

    2009-01-01

    In this paper, we take a hard look at the performance of REMOTE, a sensor network based application that provides a detailed picture of a boat movement, individual rower performance, or his/her performance compared with other crew members. The application analyzes data gathered with a WSN strategically deployed over a boat to obtain information on the boat and oar movements. Functionalities of REMOTE are compared to those of RowX [1] outdoor instrument, a commercial wired sensor instrument designed for similar purposes. This study demonstrates that with smart geometrical configuration of the sensors, rotation and translation of the oars and boat can be obtained. Three different tests are performed: laboratory calibration allows us to become familiar with the accelerometer readings and validate the theory, ergometer tests which help us to set the acquisition parameters, and on boat tests shows the application potential of this technologies in sports. PMID:22423204

  15. Wireless Sensor Network-Based Greenhouse Environment Monitoring and Automatic Control System for Dew Condensation Prevention

    Science.gov (United States)

    Park, Dae-Heon; Park, Jang-Woo

    2011-01-01

    Dew condensation on the leaf surface of greenhouse crops can promote diseases caused by fungus and bacteria, affecting the growth of the crops. In this paper, we present a WSN (Wireless Sensor Network)-based automatic monitoring system to prevent dew condensation in a greenhouse environment. The system is composed of sensor nodes for collecting data, base nodes for processing collected data, relay nodes for driving devices for adjusting the environment inside greenhouse and an environment server for data storage and processing. Using the Barenbrug formula for calculating the dew point on the leaves, this system is realized to prevent dew condensation phenomena on the crop’s surface acting as an important element for prevention of diseases infections. We also constructed a physical model resembling the typical greenhouse in order to verify the performance of our system with regard to dew condensation control. PMID:22163813

  16. Advanced neural network-based computational schemes for robust fault diagnosis

    CERN Document Server

    Mrugalski, Marcin

    2014-01-01

    The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

  17. Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor

    Directory of Open Access Journals (Sweden)

    Dong Seop Kim

    2018-03-01

    Full Text Available Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR open database, show that our method outperforms previous works.

  18. Y-source impedance-network-based isolated boost DC/DC converter

    DEFF Research Database (Denmark)

    Siwakoti, Yam P.; Town, Graham; Loh, Poh Chiang

    2014-01-01

    A dc-dc converter with very high voltage gain is proposed in this paper for any medium-power application requiring a high voltage boost with galvanic isolation. The proposed converter topology can be realized using only two switches. With this topology a very high voltage boost can be achieved even...... with a relatively low duty cycle of the switches, and the gain obtainable is presently not matched by any existing impedance network based converter operated at the same duty ratio. The proposed converter has a Y-source impedance network to boost the voltage at the intermediate dc-link side and a push......-pull transformer for square-wave AC inversion and isolation. The voltage-doubler rectifier provides a constant dc voltage at the output stage. A theoretical analysis of the converter is presented, supported by simulation and experimental results. A 250 W down-scaled prototype was implemented in the laboratory...

  19. A key heterogeneous structure of fractal networks based on inverse renormalization scheme

    Science.gov (United States)

    Bai, Yanan; Huang, Ning; Sun, Lina

    2018-06-01

    Self-similarity property of complex networks was found by the application of renormalization group theory. Based on this theory, network topologies can be classified into universality classes in the space of configurations. In return, through inverse renormalization scheme, a given primitive structure can grow into a pure fractal network, then adding different types of shortcuts, it exhibits different characteristics of complex networks. However, the effect of primitive structure on networks structural property has received less attention. In this paper, we introduce a degree variance index to measure the dispersion of nodes degree in the primitive structure, and investigate the effect of the primitive structure on network structural property quantified by network efficiency. Numerical simulations and theoretical analysis show a primitive structure is a key heterogeneous structure of generated networks based on inverse renormalization scheme, whether or not adding shortcuts, and the network efficiency is positively correlated with degree variance of the primitive structure.

  20. Artificial neural Network-Based modeling and monitoring of photovoltaic generator

    Directory of Open Access Journals (Sweden)

    H. MEKKI

    2015-03-01

    Full Text Available In this paper, an artificial neural network based-model (ANNBM is introduced for partial shading detection losses in photovoltaic (PV panel. A Multilayer Perceptron (MLP is used to estimate the electrical outputs (current and voltage of the photovoltaic module using the external meteorological data: solar irradiation G (W/m2 and the module temperature T (°C. Firstly, a database of the BP150SX photovoltaic module operating without any defect has been used to train the considered MLP. Subsequently, in the first case of this study, the developed model is used to estimate the output current and voltage of the PV module considering the partial shading effect. Results confirm the good ability of the ANNBM to detect the partial shading effect in the photovoltaic module with logical accuracy. The proposed strategy could also be used for the online monitoring and supervision of PV modules.

  1. A Neural Networks Based Operation Guidance System for Procedure Presentation and Validation

    International Nuclear Information System (INIS)

    Seung, Kun Mo; Lee, Seung Jun; Seong, Poong Hyun

    2006-01-01

    In this paper, a neural network based operator support system is proposed to reduce operator's errors in abnormal situations in nuclear power plants (NPPs). There are many complicated situations, in which regular and suitable operations should be done by operators accordingly. In order to regulate and validate operators' operations, it is necessary to develop an operator support system which includes computer based procedures with the functions for operation validation. Many computerized procedures systems (CPS) have been recently developed. Focusing on the human machine interface (HMI) design and procedures' computerization, most of CPSs used various methodologies to enhance system's convenience, reliability and accessibility. Other than only showing procedures, the proposed system integrates a simple CPS and an operation validation system (OVS) by using artificial neural network (ANN) for operational permission and quantitative evaluation

  2. The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

    Science.gov (United States)

    Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

    2018-01-01

    Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

  3. Estimation of tool wear during CNC milling using neural network-based sensor fusion

    Science.gov (United States)

    Ghosh, N.; Ravi, Y. B.; Patra, A.; Mukhopadhyay, S.; Paul, S.; Mohanty, A. R.; Chattopadhyay, A. B.

    2007-01-01

    Cutting tool wear degrades the product quality in manufacturing processes. Monitoring tool wear value online is therefore needed to prevent degradation in machining quality. Unfortunately there is no direct way of measuring the tool wear online. Therefore one has to adopt an indirect method wherein the tool wear is estimated from several sensors measuring related process variables. In this work, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM). Features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindle current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge. Novel strategies such as, signal level segmentation for temporal registration, feature space filtering, outlier removal, and estimation space filtering have been proposed. The proposed approach has been validated by both laboratory and industrial implementations.

  4. Evolution of an artificial neural network based autonomous land vehicle controller.

    Science.gov (United States)

    Baluja, S

    1996-01-01

    This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail. Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored. Error metrics specific to the task of autonomous vehicle control are introduced; the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented. The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks.

  5. Network based control point for UPnP QoS architecture

    DEFF Research Database (Denmark)

    Brewka, Lukasz Jerzy; Wessing, Henrik; Rossello Busquet, Ana

    2011-01-01

    Enabling coexistence of non-UPnP Devices in an UPnP QoS Architecture is an important issue that might have a major impact on the deployment and usability of UPnP in future home networks. The work presented here shows potential issues of placing non-UPnP Device in the network managed by UPnP QoS. We...... address this issue by extensions to the UPnP QoS Architecture that can prevent non-UPnP Devices from degrading the overall QoS level. The obtained results show that deploying Network Based Control Point service with efficient traffic classifier, improves significantly the end-to-end packet delay...

  6. Decoherence and Entanglement Simulation in a Model of Quantum Neural Network Based on Quantum Dots

    Directory of Open Access Journals (Sweden)

    Altaisky Mikhail V.

    2016-01-01

    Full Text Available We present the results of the simulation of a quantum neural network based on quantum dots using numerical method of path integral calculation. In the proposed implementation of the quantum neural network using an array of single-electron quantum dots with dipole-dipole interaction, the coherence is shown to survive up to 0.1 nanosecond in time and up to the liquid nitrogen temperature of 77K.We study the quantum correlations between the quantum dots by means of calculation of the entanglement of formation in a pair of quantum dots on the GaAs based substrate with dot size of 100 ÷ 101 nanometer and interdot distance of 101 ÷ 102 nanometers order.

  7. Supervisory Adaptive Network-Based Fuzzy Inference System (SANFIS Design for Empirical Test of Mobile Robot

    Directory of Open Access Journals (Sweden)

    Yi-Jen Mon

    2012-10-01

    Full Text Available A supervisory Adaptive Network-based Fuzzy Inference System (SANFIS is proposed for the empirical control of a mobile robot. This controller includes an ANFIS controller and a supervisory controller. The ANFIS controller is off-line tuned by an adaptive fuzzy inference system, the supervisory controller is designed to compensate for the approximation error between the ANFIS controller and the ideal controller, and drive the trajectory of the system onto a specified surface (called the sliding surface or switching surface while maintaining the trajectory onto this switching surface continuously to guarantee the system stability. This SANFIS controller can achieve favourable empirical control performance of the mobile robot in the empirical tests of driving the mobile robot with a square path. Practical experimental results demonstrate that the proposed SANFIS can achieve better control performance than that achieved using an ANFIS controller for empirical control of the mobile robot.

  8. Using adaptive network based fuzzy inference system to forecast regional electricity loads

    International Nuclear Information System (INIS)

    Ying, L.-C.; Pan, M.-C.

    2008-01-01

    Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads

  9. Neural network based cluster creation in the ATLAS silicon pixel detector

    CERN Document Server

    Selbach, K E; The ATLAS collaboration

    2012-01-01

    The read-out from individual pixels on planar semi-conductor sensors are grouped into clusters to reconstruct the location where a charged particle passed through the sensor. The resolution given by individual pixel sizes is significantly improved by using the information from the charge sharing between pixels. Such analog cluster creation techniques have been used by the ATLAS experiment for many years to obtain an excellent performance. However, in dense environments, such as those inside high-energy jets, clusters have an increased probability of merging the charge deposited by multiple particles. Recently, a neural network based algorithm which estimates both the cluster position and whether a cluster should be split has been developed for the ATLAS pixel detector. The algorithm significantly reduces ambiguities in the assignment of pixel detector measurement to tracks within jets and improves the position accuracy with respect to standard interpolation techniques by taking into account the 2-dimensional ...

  10. Comparison analysis on vulnerability of metro networks based on complex network

    Science.gov (United States)

    Zhang, Jianhua; Wang, Shuliang; Wang, Xiaoyuan

    2018-04-01

    This paper analyzes the networked characteristics of three metro networks, and two malicious attacks are employed to investigate the vulnerability of metro networks based on connectivity vulnerability and functionality vulnerability. Meanwhile, the networked characteristics and vulnerability of three metro networks are compared with each other. The results show that Shanghai metro network has the largest transport capacity, Beijing metro network has the best local connectivity and Guangzhou metro network has the best global connectivity, moreover Beijing metro network has the best homogeneous degree distribution. Furthermore, we find that metro networks are very vulnerable subjected to malicious attacks, and Guangzhou metro network has the best topological structure and reliability among three metro networks. The results indicate that the proposed methodology is feasible and effective to investigate the vulnerability and to explore better topological structure of metro networks.

  11. Towards a dynamic social-network-based approach for service composition in the Internet of Things

    Science.gov (United States)

    Xu, Wen; Hu, Zheng; Gong, Tao; Zhao, Zhengzheng

    2011-12-01

    The User-Generated Service (UGS) concept allows end-users to create their own services as well as to share and manage the lifecycles of these services. The current development of the Internet-of-Things (IoT) has brought new challenges to the UGS area. Creating smart services in the IoT environment requires a dynamic social network that considers the relationship between people and things. In this paper, we consider the know-how required to best organize exchanges between users and things to enhance service composition. By surveying relevant aspects including service composition technology, social networks and a recommendation system, we present the first concept of our framework to provide recommendations for a dynamic social network-based means to organize UGSs in the IoT.

  12. Spectral network based on component cells under the SOPHIA European project

    Energy Technology Data Exchange (ETDEWEB)

    Núñez, Rubén, E-mail: ruben.nunez@ies-def.upm.es; Antón, Ignacio; Askins, Steve; Sala, Gabriel [Instituto de Energía Solar - Universidad Politécnica de Madrid, Ciudad Universitaria, 28040 Madrid (Spain); Domínguez, César; Voarino, Philippe [CEA-INES, 50 avenue du Lac Léman, 73375 Le Bourget-du-Lac (France); Steiner, Marc; Siefer, Gerald [Fraunhofer ISE, Heidenhofstr. 2, 79110 Freiburg (Germany); Fucci, Rafaelle; Roca, Franco [ENEA, P.le E.Fermi 1, Località Granatello, 80055 Portici (Italy); Minuto, Alessandro; Morabito, Paolo [RSE, Via Rubattino 54, 20134 Milan (Italy)

    2015-09-28

    In the frame of the European project SOPHIA, a spectral network based on component (also called isotypes) cells has been created. Among the members of this project, several spectral sensors based on component cells and collimating tubes, so-called spectroheliometers, were installed in the last years, allowing the collection of minute-resolution spectral data useful for CPV systems characterization across Europe. The use of spectroheliometers has been proved useful to establish the necessary spectral conditions to perform power rating of CPV modules and systems. If enough data in a given period of time is collected, ideally a year, it is possible to characterize spectrally the place where measurements are taken, in the same way that hours of annual irradiation can be estimated using a pyrheliometer.

  13. A robust neural network-based approach for microseismic event detection

    KAUST Repository

    Akram, Jubran

    2017-08-17

    We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset for event detection. The input features used include the average of absolute amplitudes, variance, energy-ratio and polarization rectilinearity. These features are calculated in a moving-window of same length for the entire waveform. The output is set as a user-specified relative probability curve, which provides a robust way of distinguishing between weak and strong events. An optimal network is selected by studying the weight-based saliency and effect of number of neurons on the predicted results. Using synthetic data examples, we demonstrate that this approach is effective in detecting weaker events and reduces the number of false positives.

  14. A hybrid network-based method for the detection of disease-related genes

    Science.gov (United States)

    Cui, Ying; Cai, Meng; Dai, Yang; Stanley, H. Eugene

    2018-02-01

    Detecting disease-related genes is crucial in disease diagnosis and drug design. The accepted view is that neighbors of a disease-causing gene in a molecular network tend to cause the same or similar diseases, and network-based methods have been recently developed to identify novel hereditary disease-genes in available biomedical networks. Despite the steady increase in the discovery of disease-associated genes, there is still a large fraction of disease genes that remains under the tip of the iceberg. In this paper we exploit the topological properties of the protein-protein interaction (PPI) network to detect disease-related genes. We compute, analyze, and compare the topological properties of disease genes with non-disease genes in PPI networks. We also design an improved random forest classifier based on these network topological features, and a cross-validation test confirms that our method performs better than previous similar studies.

  15. Determination of Optimal Opening Scheme for Electromagnetic Loop Networks Based on Fuzzy Analytic Hierarchy Process

    Directory of Open Access Journals (Sweden)

    Yang Li

    2016-01-01

    Full Text Available Studying optimization and decision for opening electromagnetic loop networks plays an important role in planning and operation of power grids. First, the basic principle of fuzzy analytic hierarchy process (FAHP is introduced, and then an improved FAHP-based scheme evaluation method is proposed for decoupling electromagnetic loop networks based on a set of indicators reflecting the performance of the candidate schemes. The proposed method combines the advantages of analytic hierarchy process (AHP and fuzzy comprehensive evaluation. On the one hand, AHP effectively combines qualitative and quantitative analysis to ensure the rationality of the evaluation model; on the other hand, the judgment matrix and qualitative indicators are expressed with trapezoidal fuzzy numbers to make decision-making more realistic. The effectiveness of the proposed method is validated by the application results on the real power system of Liaoning province of China.

  16. A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

    Science.gov (United States)

    Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

    2018-02-01

    Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

  17. Using adaptive network based fuzzy inference system to forecast regional electricity loads

    Energy Technology Data Exchange (ETDEWEB)

    Ying, Li-Chih [Department of Marketing Management, Central Taiwan University of Science and Technology, 11, Pu-tzu Lane, Peitun, Taichung City 406 (China); Pan, Mei-Chiu [Graduate Institute of Management Sciences, Nanhua University, 32, Chung Keng Li, Dalin, Chiayi 622 (China)

    2008-02-15

    Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads. (author)

  18. Activities in a social networking-based discussion group by endoscopic retrograde cholangiopancreatography doctors.

    Science.gov (United States)

    Kang, Xiaoyu; Zhao, Lina; Liu, Na; Wang, Xiangping; Zhang, Rongchun; Liu, Zhiguo; Liang, Shuhui; Yao, Shaowei; Tao, Qin; Jia, Hui; Pan, Yanglin; Guo, Xuegang

    2017-10-01

    Online social networking is increasingly being used among medical practitioners. However, few studies have evaluated its use in therapeutic endoscopy. Here, we aimed to analyze the shared topics and activities of a group of endoscopic retrograde cholangiopancreatography (ERCP) doctors in a social networking-based endoscopic retrograde cholangiopancreatography discussion group (EDG). Six ERCP trainers working in Xijing Hospital and 48 graduated endoscopists who had finished ERCP training in the same hospital were invited to join in EDG. All group members were informed not to divulge any private information of patients when using EDG. The activities of group members on EDG were retrospectively extracted. The individual data of the graduated endoscopists were collected by a questionnaire. From June 2014 to May 2015, 6924 messages were posted on EDG, half of which were ERCP related. In total, 214 ERCP-related topics were shared, which could be categorized into three types: sharing experience/cases (52.3%), asking questions (38.3%), and sharing literatures/advances (9.3%). Among the 48 graduated endoscopists, 21 had a low case volume of less than 50 per year and 27 had a high volume case volume of 50 or more. High-volume graduated endoscopists posted more ERCP-related messages (P=0.008) and shared more discussion topics (P=0.003) compared with low-volume graduated endoscopists. A survey showed that EDG was useful for graduated endoscopists in ERCP performance and management of post-ERCP complications, etc. A wide range of ERCP-related topics were shared on the social networking-based EDG. The ERCP-related behaviors on EDG were more active in graduated endoscopists with an ERCP case volume of more than 50 per year.

  19. Network-based landscape of research strengths of universities in Mainland China

    Science.gov (United States)

    Liu, Zihua; Xiao, Qin; Zhan, Qian; Gu, Changgui; Yang, Huijie

    2017-07-01

    A landscape of a complex system presents a quantitative measure of its global state. The profile of research strength in Mainland China is investigated in detail, by which we illustrate a complex network based framework to extract a landscape from detailed records. First, a measure analogous to the Jaccard similarity is proposed to calculate from the presided funds similarities between the top-ranked universities. The neighbor threshold method is employed to reconstruct the similarity network of the universities. Second, the network is divided into communities. In each community the node with the largest degree and the smallest average shortest path length is taken as the representative of the community, called central node. The node bridging each pair of communities is defined to be a boundary. The central nodes and boundaries cooperatively give us a picture of the research strength landscape. Third, the evolutionary behavior is monitored by the fission and fusion probability matrices, elements of which are the percentage of a community at present time that joins into every community at the next time, and the percentage of a community at next time that comes from every present community, respectively. The landscapes in three successive 4-year durations are identified. It was found that some types of universities, such as the medicine&pharmacy and the finance&economy, conserve in single communities in the more than ten years, respectively. The agriculture&forest universities tend to cluster into one community. Meanwhile the engineering type distributes in different communities and tends to mix with the comprehension type. This framework can be used straightforwardly to analyze temporal networks. It provides also a new network-based method for multivariate time series analysis.

  20. Recurrent-neural-network-based Boolean factor analysis and its application to word clustering.

    Science.gov (United States)

    Frolov, Alexander A; Husek, Dusan; Polyakov, Pavel Yu

    2009-07-01

    The objective of this paper is to introduce a neural-network-based algorithm for word clustering as an extension of the neural-network-based Boolean factor analysis algorithm (Frolov , 2007). It is shown that this extended algorithm supports even the more complex model of signals that are supposed to be related to textual documents. It is hypothesized that every topic in textual data is characterized by a set of words which coherently appear in documents dedicated to a given topic. The appearance of each word in a document is coded by the activity of a particular neuron. In accordance with the Hebbian learning rule implemented in the network, sets of coherently appearing words (treated as factors) create tightly connected groups of neurons, hence, revealing them as attractors of the network dynamics. The found factors are eliminated from the network memory by the Hebbian unlearning rule facilitating the search of other factors. Topics related to the found sets of words can be identified based on the words' semantics. To make the method complete, a special technique based on a Bayesian procedure has been developed for the following purposes: first, to provide a complete description of factors in terms of component probability, and second, to enhance the accuracy of classification of signals to determine whether it contains the factor. Since it is assumed that every word may possibly contribute to several topics, the proposed method might be related to the method of fuzzy clustering. In this paper, we show that the results of Boolean factor analysis and fuzzy clustering are not contradictory, but complementary. To demonstrate the capabilities of this attempt, the method is applied to two types of textual data on neural networks in two different languages. The obtained topics and corresponding words are at a good level of agreement despite the fact that identical topics in Russian and English conferences contain different sets of keywords.

  1. SNRFCB: sub-network based random forest classifier for predicting chemotherapy benefit on survival for cancer treatment.

    Science.gov (United States)

    Shi, Mingguang; He, Jianmin

    2016-04-01

    Adjuvant chemotherapy (CTX) should be individualized to provide potential survival benefit and avoid potential harm to cancer patients. Our goal was to establish a computational approach for making personalized estimates of the survival benefit from adjuvant CTX. We developed Sub-Network based Random Forest classifier for predicting Chemotherapy Benefit (SNRFCB) based gene expression datasets of lung cancer. The SNRFCB approach was then validated in independent test cohorts for identifying chemotherapy responder cohorts and chemotherapy non-responder cohorts. SNRFCB involved the pre-selection of gene sub-network signatures based on the mutations and on protein-protein interaction data as well as the application of the random forest algorithm to gene expression datasets. Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer patients in the chemotherapy responder group (P = 0.008), but it was not beneficial to patients in the chemotherapy non-responder group (P = 0.657). Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer squamous cell carcinoma (SQCC) subtype patients in the chemotherapy responder cohorts (P = 0.024), but it was not beneficial to patients in the chemotherapy non-responder cohorts (P = 0.383). SNRFCB improved prediction performance as compared to the machine learning method, support vector machine (SVM). To test the general applicability of the predictive model, we further applied the SNRFCB approach to human breast cancer datasets and also observed superior performance. SNRFCB could provide recurrent probability for individual patients and identify which patients may benefit from adjuvant CTX in clinical trials.

  2. A neural network-based approach to noise identification of interferometric GW antennas: the case of the 40 m Caltech laser interferometer

    Energy Technology Data Exchange (ETDEWEB)

    Acernese, F [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Barone, F [Istituto Nazionale di Fisica Nucleare, sez. Napoli, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Rosa, M de [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Rosa, R De [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Eleuteri, A [Istituto Nazionale di Fisica Nucleare, sez. Napoli, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Milano, L [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Tagliaferri, R [Dipartimento di Matematica ed Informatica, Universita di Salerno, via S Allende, I-84081 Baronissi (Salerno) (Italy)

    2002-06-21

    In this paper, a neural network-based approach is presented for the real time noise identification of a GW laser interferometric antenna. The 40 m Caltech laser interferometer output data provide a realistic test bed for noise identification algorithms because of the presence of many relevant effects: violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronic noises, glitches and so on. These effects can be assumed to be present in all the first interferometric long baseline GW antennas such as VIRGO, LIGO, GEO and TAMA. For noise identification, we used the Caltech-40 m laser interferometer data. The results we obtained are pretty good notwithstanding the high initial computational cost. The algorithm we propose is general and robust, taking into account that it does not require a priori information on the data, nor a precise model, and it constitutes a powerful tool for time series data analysis.

  3. A neural network-based approach to noise identification of interferometric GW antennas: the case of the 40 m Caltech laser interferometer

    International Nuclear Information System (INIS)

    Acernese, F; Barone, F; Rosa, M de; Rosa, R De; Eleuteri, A; Milano, L; Tagliaferri, R

    2002-01-01

    In this paper, a neural network-based approach is presented for the real time noise identification of a GW laser interferometric antenna. The 40 m Caltech laser interferometer output data provide a realistic test bed for noise identification algorithms because of the presence of many relevant effects: violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronic noises, glitches and so on. These effects can be assumed to be present in all the first interferometric long baseline GW antennas such as VIRGO, LIGO, GEO and TAMA. For noise identification, we used the Caltech-40 m laser interferometer data. The results we obtained are pretty good notwithstanding the high initial computational cost. The algorithm we propose is general and robust, taking into account that it does not require a priori information on the data, nor a precise model, and it constitutes a powerful tool for time series data analysis

  4. A neural network-based approach to noise identification of interferometric GW antennas: the case of the 40 m Caltech laser interferometer

    CERN Document Server

    Acernese, F; Rosa, M D; Rosa, R D; Eleuteri, A; Milano, L; Tagliaferri, R

    2002-01-01

    In this paper, a neural network-based approach is presented for the real time noise identification of a GW laser interferometric antenna. The 40 m Caltech laser interferometer output data provide a realistic test bed for noise identification algorithms because of the presence of many relevant effects: violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronic noises, glitches and so on. These effects can be assumed to be present in all the first interferometric long baseline GW antennas such as VIRGO, LIGO, GEO and TAMA. For noise identification, we used the Caltech-40 m laser interferometer data. The results we obtained are pretty good notwithstanding the high initial computational cost. The algorithm we propose is general and robust, taking into account that it does not require a priori information on the data, nor a precise model, and it constitutes a powerful tool for time series data analysis.

  5. Generalized Superconductivity. Generalized Levitation

    International Nuclear Information System (INIS)

    Ciobanu, B.; Agop, M.

    2004-01-01

    In the recent papers, the gravitational superconductivity is described. We introduce the concept of generalized superconductivity observing that any nongeodesic motion and, in particular, the motion in an electromagnetic field, can be transformed in a geodesic motion by a suitable choice of the connection. In the present paper, the gravitoelectromagnetic London equations have been obtained from the generalized Helmholtz vortex theorem using the generalized local equivalence principle. In this context, the gravitoelectromagnetic Meissner effect and, implicitly, the gravitoelectromagnetic levitation are given. (authors)

  6. Co-expression of nuclear and cytoplasmic HMGB1 is inversely associated with infiltration of CD45RO+ T cells and prognosis in patients with stage IIIB colon cancer

    International Nuclear Information System (INIS)

    Peng, Rui-Qing; Zeng, Yi-Xin; Zhang, Xiao-Shi; Wu, Xiao-Jun; Ding, Ya; Li, Chun-Yan; Yu, Xing-Juan; Zhang, Xing; Pan, Zhi-Zhong; Wan, De-Sen; Zheng, Li-Ming

    2010-01-01

    The intratumoral infiltration of T cells, especially memory T cells, is associated with a favorable prognosis in early colorectal cancers. However, the mechanism underlying this process remains elusive. This study examined whether high-mobility group box 1 (HMGB1), a damage-associated molecular pattern (DAMP) molecule, is involved in the infiltration of T cells and disease progression in locally advanced colon cancer. Seventy-two cases of pathologically-confirmed specimens were obtained from patients with stage IIIB (T3N1M0) colon cancer who underwent radical resection between January 1999 and May 2002 at the Cancer Center of Sun Yat-Sen University. The density of tumor-infiltrating lymphocytes (TILs) within the tumor tissue and the expression of HMGB1 in the cancer cells were examined via immunohistochemical analysis. The phenotype of CD45RO+ cells was confirmed using a flow cytometric assay. The association between HMGB1 expression, the density of TILs, and the 5-year survival rate were analyzed. The density of CD45RO+ T cells within the tumor was independently prognostic, although a higher density of CD3+ T cells was also associated with a favorable prognosis. More importantly, the expression of HMGB1 was observed in both the nucleus and the cytoplasm (co-expression pattern) in a subset of colon cancer tissues, whereas nuclear-only expression of HMGB1 (nuclear expression pattern) existed in most of the cancer tissues and normal mucosa. The co-expression pattern of HMGB1 in colon cancer cells was inversely associated with the infiltration of both CD3+ and CD45RO+ T cells and 5-year survival rates. This study revealed that the co-expression of HMGB1 is inversely associated with the infiltration of CD45RO+ T cells and prognosis in patients with stage IIIB colon cancer, indicating that the distribution patterns of HMGB1 might contribute to the progression of colon cancer via modulation of the local immune response

  7. Network-based integration of molecular and physiological data elucidates regulatory mechanisms underlying adaptation to high-fat diet

    NARCIS (Netherlands)

    Derous, D.; Kelder, T.; Schothorst, E.M. van; Erk, M. van; Voigt, A.; Klaus, S.; Keijer, J.; Radonjic, M.

    2015-01-01

    Health is influenced by interplay of molecular, physiological and environmental factors. To effectively maintain health and prevent disease, health-relevant relations need to be understood at multiple levels of biological complexity. Network-based methods provide a powerful platform for integration

  8. A Process Perspective on Regulation: A Grounded Theory Study into Regulatory Practice in Newly Liberalized Network-Based Markets

    NARCIS (Netherlands)

    Ubacht, J.

    The transition from a former monopolistic towards a more competitive market in
    newly liberalized network-based markets raises regulatory issues. National Regulatory Authorities (NRA) face the challenge to deal with these issues in order to guide the transition process. Although this transition

  9. Analysing the Correlation between Social Network Analysis Measures and Performance of Students in Social Network-Based Engineering Education

    Science.gov (United States)

    Putnik, Goran; Costa, Eric; Alves, Cátia; Castro, Hélio; Varela, Leonilde; Shah, Vaibhav

    2016-01-01

    Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment,…

  10. Phylogeny and evolutionary histories of Pyrus L. revealed by phylogenetic trees and networks based on data from multiple DNA sequences

    Science.gov (United States)

    Reconstructing the phylogeny of Pyrus has been difficult due to the wide distribution of the genus and lack of informative data. In this study, we collected 110 accessions representing 25 Pyrus species and constructed both phylogenetic trees and phylogenetic networks based on multiple DNA sequence d...

  11. How network-based incubation helps start-up performance : a systematic review against the background of management theories

    NARCIS (Netherlands)

    Eveleens, Chris P.|info:eu-repo/dai/nl/369284704; van Rijnsoever, Frank J.|info:eu-repo/dai/nl/314100334; Niesten, Eva M M I

    2017-01-01

    The literature on how network-based incubation influences the performance of technology-based start-ups has recently grown considerably and provided valuable insights. However, at the same time this literature has become quite fragmented, inconsistently conceptualised, and theoretically

  12. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lund, Ole

    2009-01-01

    this binding event. RESULTS: Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data...

  13. General general game AI

    OpenAIRE

    Togelius, Julian; Yannakakis, Georgios N.; 2016 IEEE Conference on Computational Intelligence and Games (CIG)

    2016-01-01

    Arguably the grand goal of artificial intelligence research is to produce machines with general intelligence: the capacity to solve multiple problems, not just one. Artificial intelligence (AI) has investigated the general intelligence capacity of machines within the domain of games more than any other domain given the ideal properties of games for that purpose: controlled yet interesting and computationally hard problems. This line of research, however, has so far focuse...

  14. Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

    Science.gov (United States)

    Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

    2018-05-01

    Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

  15. A neural network-based exploratory learning and motor planning system for co-robots

    Directory of Open Access Journals (Sweden)

    Byron V Galbraith

    2015-07-01

    Full Text Available Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or learning by doing, an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.

  16. Reconstruction and Analysis of Human Kidney-Specific Metabolic Network Based on Omics Data

    Directory of Open Access Journals (Sweden)

    Ai-Di Zhang

    2013-01-01

    Full Text Available With the advent of the high-throughput data production, recent studies of tissue-specific metabolic networks have largely advanced our understanding of the metabolic basis of various physiological and pathological processes. However, for kidney, which plays an essential role in the body, the available kidney-specific model remains incomplete. This paper reports the reconstruction and characterization of the human kidney metabolic network based on transcriptome and proteome data. In silico simulations revealed that house-keeping genes were more essential than kidney-specific genes in maintaining kidney metabolism. Importantly, a total of 267 potential metabolic biomarkers for kidney-related diseases were successfully explored using this model. Furthermore, we found that the discrepancies in metabolic processes of different tissues are directly corresponding to tissue's functions. Finally, the phenotypes of the differentially expressed genes in diabetic kidney disease were characterized, suggesting that these genes may affect disease development through altering kidney metabolism. Thus, the human kidney-specific model constructed in this study may provide valuable information for the metabolism of kidney and offer excellent insights into complex kidney diseases.

  17. Neural Networks-Based Forecasting Regarding the Convergence Process of CEE Countries to the Eurozone

    Directory of Open Access Journals (Sweden)

    Magdalena RĂDULESCU

    2014-06-01

    Full Text Available In the crisis frame, many forecasts failed to provide well determined ratios. What we tried to explain in this paper is how some selected Central and Eastern European countries will perform in the near future: Romania, Bulgaria, Hungary, Poland and Czech Republic, using neural networks- based forecasting model which we created for the nominal and real convergence ratios. As a methodology, we propose the forecasting based on artificial neural network (ANN, using the well-known software tool GMDH Shell. For each output variable, we obtain a forecast model, according to previous values and other input related variables, and we applied the model to all countries. Our forecasts are much closer to the partial results of 2013 in the analyzed countries than the European Commission’s or other international organizations’ forecasts. The results of the forecast are important both for governments to design their financial strategies and for the investors in these selected countries. According to our results, the Czech Republic seems to be closer to achieve its nominal convergence in the next two years, but it faces great difficulties in the real convergence area, because it did not overpass the recession.

  18. Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection

    Directory of Open Access Journals (Sweden)

    Erik Marchi

    2017-01-01

    Full Text Available In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these approaches, auditory spectral features of the next short term frame are predicted from the previous frames by means of Long-Short Term Memory recurrent denoising autoencoders. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. There is no evidence of studies focused on comparing previous efforts to automatically recognize novel events from audio signals and giving a broad and in depth evaluation of recurrent neural network-based autoencoders. The present contribution aims to consistently evaluate our recent novel approaches to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases: A3Novelty, PASCAL CHiME, and PROMETHEUS. Besides providing an extensive analysis of novel and state-of-the-art methods, the article shows how RNN-based autoencoders outperform statistical approaches up to an absolute improvement of 16.4% average F-measure over the three databases.

  19. Neural networks based identification and compensation of rate-dependent hysteresis in piezoelectric actuators

    International Nuclear Information System (INIS)

    Zhang, Xinliang; Tan, Yonghong; Su, Miyong; Xie, Yangqiu

    2010-01-01

    This paper presents a method of the identification for the rate-dependent hysteresis in the piezoelectric actuator (PEA) by use of neural networks. In this method, a special hysteretic operator is constructed from the Prandtl-Ishlinskii (PI) model to extract the changing tendency of the static hysteresis. Then, an expanded input space is constructed by introducing the proposed hysteretic operator to transform the multi-valued mapping of the hysteresis into a one-to-one mapping. Thus, a feedforward neural network is applied to the approximation of the rate-independent hysteresis on the constructed expanded input space. Moreover, in order to describe the rate-dependent performance of the hysteresis, a special hybrid model, which is constructed by a linear auto-regressive exogenous input (ARX) sub-model preceded with the previously obtained neural network based rate-independent hysteresis sub-model, is proposed. For the compensation of the effect of the hysteresis in PEA, the PID feedback controller with a feedforward hysteresis compensator is developed for the tracking control of the PEA. Thus, a corresponding inverse model based on the proposed modeling method is developed for the feedforward hysteresis compensator. Finally, both simulations and experimental results on piezoelectric actuator are presented to verify the effectiveness of the proposed approach for the rate-dependent hysteresis.

  20. Analysis of a phase synchronized functional network based on the rhythm of brain activities

    International Nuclear Information System (INIS)

    Li Ling; Jin Zhen-Lan; Li Bin

    2011-01-01

    Rhythm of brain activities represents oscillations of postsynaptic potentials in neocortex, therefore it can serve as an indicator of the brain activity state. In order to check the connectivity of brain rhythm, this paper develops a new method of constructing functional network based on phase synchronization. Electroencephalogram (EEG) data were collected while subjects looking at a green cross in two states, performing an attention task and relaxing with eyes-open. The EEG from these two states was filtered by three band-pass filters to obtain signals of theta (4–7 Hz), alpha (8–13 Hz) and beta (14–30 Hz) bands. Mean resultant length was used to estimate strength of phase synchronization in three bands to construct networks of both states, and mean degree K and cluster coefficient C of networks were calculated as a function of threshold. The result shows higher cluster coefficient in the attention state than in the eyes-open state in all three bands, suggesting that cluster coefficient reflects brain state. In addition, an obvious fronto-parietal network is found in the attention state, which is a well-known attention network. These results indicate that attention modulates the fronto-parietal connectivity in different modes as compared with the eyes-open state. Taken together this method is an objective and important tool to study the properties of neural networks of brain rhythm. (interdisciplinary physics and related areas of science and technology)

  1. Network-based analysis reveals functional connectivity related to internet addiction tendency

    Directory of Open Access Journals (Sweden)

    Tanya eWen

    2016-02-01

    Full Text Available IntroductionPreoccupation and compulsive use of the internet can have negative psychological effects, such that it is increasingly being recognized as a mental disorder. The present study employed network-based statistics to explore how whole-brain functional connections at rest is related to the extent of individual’s level of internet addiction, indexed by a self-rated questionnaire. We identified two topologically significant networks, one with connections that are positively correlated with internet addiction tendency, and one with connections negatively correlated with internet addiction tendency. The two networks are interconnected mostly at frontal regions, which might reflect alterations in the frontal region for different aspects of cognitive control (i.e., for control of internet usage and gaming skills. Next, we categorized the brain into several large regional subgroupings, and found that the majority of proportions of connections in the two networks correspond to the cerebellar model of addiction which encompasses the four-circuit model. Lastly, we observed that the brain regions with the most inter-regional connections associated with internet addiction tendency replicate those often seen in addiction literature, and is corroborated by our meta-analysis of internet addiction studies. This research provides a better understanding of large-scale networks involved in internet addiction tendency and shows that pre-clinical levels of internet addiction are associated with similar regions and connections as clinical cases of addiction.

  2. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    Science.gov (United States)

    Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

    1997-01-01

    One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

  3. A network-based frequency analysis of Inclusive Wealth to track sustainable development in world countries.

    Science.gov (United States)

    Ahmad, Nasir; Derrible, Sybil; Managi, Shunsuke

    2018-07-15

    Using human (HC), natural (NC), and produced (PC) capital from Inclusive Wealth as representatives of the triple bottom line of sustainability and utilizing elements of network science, we introduce a Network-based Frequency Analysis (NFA) method to track sustainable development in world countries from 1990 to 2014. The method compares every country with every other and links them when values are close. The country with the most links becomes the main trend, and the performance of every other country is assessed based on its 'orbital' distance from the main trend. Orbital speeds are then calculated to evaluate country-specific dynamic trends. Overall, we find an optimistic trend for HC only, indicating positive impacts of global initiatives aiming towards socio-economic development in developing countries like the Millennium Development Goals and 'Agenda 21'. However, we also find that the relative performance of most countries has not changed significantly in this period, regardless of their gradual development. Specifically, we measure a decrease in produced and natural capital for most countries, despite an increase in GDP, suggesting unsustainable development. Furthermore, we develop a technique to cluster countries and project the results to 2050, and we find a significant decrease in NC for nearly all countries, suggesting an alarming depletion of natural resources worldwide. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. Structural design principles of complex bird songs: a network-based approach.

    Directory of Open Access Journals (Sweden)

    Kazutoshi Sasahara

    Full Text Available Bird songs are acoustic communication signals primarily used in male-male aggression and in male-female attraction. These are often monotonous patterns composed of a few phrases, yet some birds have extremely complex songs with a large phrase repertoire, organized in non-random fashion with discernible patterns. Since structure is typically associated with function, the structures of complex bird songs provide important clues to the evolution of animal communication systems. Here we propose an efficient network-based approach to explore structural design principles of complex bird songs, in which the song networks--transition relationships among different phrases and the related structural measures--are employed. We demonstrate how this approach works with an example using California Thrasher songs, which are sequences of highly varied phrases delivered in succession over several minutes. These songs display two distinct features: a large phrase repertoire with a 'small-world' architecture, in which subsets of phrases are highly grouped and linked with a short average path length; and a balanced transition diversity amongst phrases, in which deterministic and non-deterministic transition patterns are moderately mixed. We explore the robustness of this approach with variations in sample size and the amount of noise. Our approach enables a more quantitative study of global and local structural properties of complex bird songs than has been possible to date.

  5. Cross-layer restoration with software defined networking based on IP over optical transport networks

    Science.gov (United States)

    Yang, Hui; Cheng, Lei; Deng, Junni; Zhao, Yongli; Zhang, Jie; Lee, Young

    2015-10-01

    The IP over optical transport network is a very promising networking architecture applied to the interconnection of geographically distributed data centers due to the performance guarantee of low delay, huge bandwidth and high reliability at a low cost. It can enable efficient resource utilization and support heterogeneous bandwidth demands in highly-available, cost-effective and energy-effective manner. In case of cross-layer link failure, to ensure a high-level quality of service (QoS) for user request after the failure becomes a research focus. In this paper, we propose a novel cross-layer restoration scheme for data center services with software defined networking based on IP over optical network. The cross-layer restoration scheme can enable joint optimization of IP network and optical network resources, and enhance the data center service restoration responsiveness to the dynamic end-to-end service demands. We quantitatively evaluate the feasibility and performances through the simulation under heavy traffic load scenario in terms of path blocking probability and path restoration latency. Numeric results show that the cross-layer restoration scheme improves the recovery success rate and minimizes the overall recovery time.

  6. Pedagogy framework design in social networked-based learning: Focus on children with learning difficulties

    Directory of Open Access Journals (Sweden)

    Samira Sadat Sajadi

    2014-09-01

    Full Text Available This paper presents an investigation on the theory of constructivism applicable for learners with learning difficulties, specifically learners with Attention Deficit Hyperactivity Disorder (ADHD. The primary objective of this paper is to determine whether a constructivist technology enhanced learning pedagogy could be used to help ADHD learners cope with their educational needs within a social-media learning environment. Preliminary work is stated here, in which we are seeking evidence to determine the viability of a constructivist approach for learners with ADHD. The novelty of this research lies in the proposals to support ADHD learners to overcome their weaknesses with appropriate pedagogically sound interventions. As a result, a framework has been designed to illuminate areas in which constructivist pedagogies require to address the limitations of ADHD learners. An analytical framework addressing the suitability of a constructivist learning for ADHD is developed from a combination of literature and expert advice from those involved in the education of learners with ADHD. This analytical framework is married to a new model of pedagogy, which the authors have derived from literature analysis. Future work will expand this model to develop a constructivist social network-based learning and eventually test it in specialist schools with ADHD learners.

  7. Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

    Science.gov (United States)

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2015-09-01

    Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

  8. 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.

  9. Limitations of a metabolic network-based reverse ecology method for inferring host-pathogen interactions.

    Science.gov (United States)

    Takemoto, Kazuhiro; Aie, Kazuki

    2017-05-25

    Host-pathogen interactions are important in a wide range of research fields. Given the importance of metabolic crosstalk between hosts and pathogens, a metabolic network-based reverse ecology method was proposed to infer these interactions. However, the validity of this method remains unclear because of the various explanations presented and the influence of potentially confounding factors that have thus far been neglected. We re-evaluated the importance of the reverse ecology method for evaluating host-pathogen interactions while statistically controlling for confounding effects using oxygen requirement, genome, metabolic network, and phylogeny data. Our data analyses showed that host-pathogen interactions were more strongly influenced by genome size, primary network parameters (e.g., number of edges), oxygen requirement, and phylogeny than the reserve ecology-based measures. These results indicate the limitations of the reverse ecology method; however, they do not discount the importance of adopting reverse ecology approaches altogether. Rather, we highlight the need for developing more suitable methods for inferring host-pathogen interactions and conducting more careful examinations of the relationships between metabolic networks and host-pathogen interactions.

  10. Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection.

    Science.gov (United States)

    Marchi, Erik; Vesperini, Fabio; Squartini, Stefano; Schuller, Björn

    2017-01-01

    In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these approaches, auditory spectral features of the next short term frame are predicted from the previous frames by means of Long-Short Term Memory recurrent denoising autoencoders. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. There is no evidence of studies focused on comparing previous efforts to automatically recognize novel events from audio signals and giving a broad and in depth evaluation of recurrent neural network-based autoencoders. The present contribution aims to consistently evaluate our recent novel approaches to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases: A3Novelty, PASCAL CHiME, and PROMETHEUS. Besides providing an extensive analysis of novel and state-of-the-art methods, the article shows how RNN-based autoencoders outperform statistical approaches up to an absolute improvement of 16.4% average F -measure over the three databases.

  11. Understanding structure of urban traffic network based on spatial-temporal correlation analysis

    Science.gov (United States)

    Yang, Yanfang; Jia, Limin; Qin, Yong; Han, Shixiu; Dong, Honghui

    2017-08-01

    Understanding the structural characteristics of urban traffic network comprehensively can provide references for improving road utilization rate and alleviating traffic congestion. This paper focuses on the spatial-temporal correlations between different pairs of traffic series and proposes a complex network-based method of constructing the urban traffic network. In the network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding spatial-temporal correlation. Further, a modified PageRank algorithm, named the geographical weight-based PageRank algorithm (GWPA), is proposed to analyze the spatial distribution of important segments in the road network. Finally, experiments are conducted by using three kinds of traffic series collected from the urban road network in Beijing. Experimental results show that the urban traffic networks constructed by three traffic variables all indicate both small-world and scale-free characteristics. Compared with the results of PageRank algorithm, GWPA is proved to be valid in evaluating the importance of segments and identifying the important segments with small degree.

  12. Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

    Directory of Open Access Journals (Sweden)

    Manjunath Patel Gowdru Chandrashekarappa

    2014-01-01

    Full Text Available The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN and genetic algorithm neural network (GA-NN. The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs.

  13. Sensor Network-Based and User-Friendly User Location Discovery for Future Smart Homes.

    Science.gov (United States)

    Ahvar, Ehsan; Lee, Gyu Myoung; Han, Son N; Crespi, Noel; Khan, Imran

    2016-06-27

    User location is crucial context information for future smart homes where many location based services will be proposed. This location necessarily means that User Location Discovery (ULD) will play an important role in future smart homes. Concerns about privacy and the need to carry a mobile or a tag device within a smart home currently make conventional ULD systems uncomfortable for users. Future smart homes will need a ULD system to consider these challenges. This paper addresses the design of such a ULD system for context-aware services in future smart homes stressing the following challenges: (i) users' privacy; (ii) device-/tag-free; and (iii) fault tolerance and accuracy. On the other hand, emerging new technologies, such as the Internet of Things, embedded systems, intelligent devices and machine-to-machine communication, are penetrating into our daily life with more and more sensors available for use in our homes. Considering this opportunity, we propose a ULD system that is capitalizing on the prevalence of sensors for the home while satisfying the aforementioned challenges. The proposed sensor network-based and user-friendly ULD system relies on different types of inexpensive sensors, as well as a context broker with a fuzzy-based decision-maker. The context broker receives context information from different types of sensors and evaluates that data using the fuzzy set theory. We demonstrate the performance of the proposed system by illustrating a use case, utilizing both an analytical model and simulation.

  14. Unveiling network-based functional features through integration of gene expression into protein networks.

    Science.gov (United States)

    Jalili, Mahdi; Gebhardt, Tom; Wolkenhauer, Olaf; Salehzadeh-Yazdi, Ali

    2018-06-01

    Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers. Copyright © 2018 Elsevier B.V. All rights reserved.

  15. Resilient Disaster Network Based on Software Defined Cognitive Wireless Network Technology

    Directory of Open Access Journals (Sweden)

    Goshi Sato

    2015-01-01

    Full Text Available In order to temporally recover the information network infrastructure in disaster areas from the Great East Japan Earthquake in 2011, various wireless network technologies such as satellite IP network, 3G, and Wi-Fi were effectively used. However, since those wireless networks are individually introduced and installed but not totally integrated, some of networks were congested due to the sudden network traffic generation and unbalanced traffic distribution, and eventually the total network could not effectively function. In this paper, we propose a disaster resilient network which integrates various wireless networks into a cognitive wireless network that users can use as an access network to the Internet at the serious disaster occurrence. We designed and developed the disaster resilient network based on software defined network (SDN technology to automatically select the best network link and route among the possible access networks to the Internet by periodically monitoring their network states and evaluate those using extended AHP method. In order to verify the usefulness of our proposed system, a prototype system is constructed and its performance is evaluated.

  16. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

    Science.gov (United States)

    Williams-Hayes, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

  17. Alerts Analysis and Visualization in Network-based Intrusion Detection Systems

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Dr. Li [University of Tennessee

    2010-08-01

    The alerts produced by network-based intrusion detection systems, e.g. Snort, can be difficult for network administrators to efficiently review and respond to due to the enormous number of alerts generated in a short time frame. This work describes how the visualization of raw IDS alert data assists network administrators in understanding the current state of a network and quickens the process of reviewing and responding to intrusion attempts. The project presented in this work consists of three primary components. The first component provides a visual mapping of the network topology that allows the end-user to easily browse clustered alerts. The second component is based on the flocking behavior of birds such that birds tend to follow other birds with similar behaviors. This component allows the end-user to see the clustering process and provides an efficient means for reviewing alert data. The third component discovers and visualizes patterns of multistage attacks by profiling the attacker s behaviors.

  18. Authorship attribution of source code by using back propagation neural network based on particle swarm optimization.

    Science.gov (United States)

    Yang, Xinyu; Xu, Guoai; Li, Qi; Guo, Yanhui; Zhang, Miao

    2017-01-01

    Authorship attribution is to identify the most likely author of a given sample among a set of candidate known authors. It can be not only applied to discover the original author of plain text, such as novels, blogs, emails, posts etc., but also used to identify source code programmers. Authorship attribution of source code is required in diverse applications, ranging from malicious code tracking to solving authorship dispute or software plagiarism detection. This paper aims to propose a new method to identify the programmer of Java source code samples with a higher accuracy. To this end, it first introduces back propagation (BP) neural network based on particle swarm optimization (PSO) into authorship attribution of source code. It begins by computing a set of defined feature metrics, including lexical and layout metrics, structure and syntax metrics, totally 19 dimensions. Then these metrics are input to neural network for supervised learning, the weights of which are output by PSO and BP hybrid algorithm. The effectiveness of the proposed method is evaluated on a collected dataset with 3,022 Java files belong to 40 authors. Experiment results show that the proposed method achieves 91.060% accuracy. And a comparison with previous work on authorship attribution of source code for Java language illustrates that this proposed method outperforms others overall, also with an acceptable overhead.

  19. Novel spider-web-like nanoporous networks based on jute cellulose nanowhiskers.

    Science.gov (United States)

    Cao, Xinwang; Wang, Xianfeng; Ding, Bin; Yu, Jianyong; Sun, Gang

    2013-02-15

    Cellulose nanowhiskers as a kind of renewable and biocompatible nanomaterials evoke much interest because of its versatility in various applications. Herein, for the first time, a novel controllable fabrication of spider-web-like nanoporous networks based on jute cellulose nanowhiskers (JCNs) deposited on the electrospun (ES) nanofibrous membrane by simple directly immersion-drying method is reported. Jute cellulose nanowhiskers were extracted from jute fibers with a high yield (over 80%) via a 2,2,6,6-tetramethylpiperidine-1-oxyl radical (TEMPO)/NaBr/NaClO system selective oxidization combined with mechanical homogenization. The morphology of JCNs nanoporous networks/ES nanofibrous membrane architecture, including coverage rate, pore-width and layer-by-layer packing structure of the nanoporous networks, can be finely controlled by regulating the JCNs dispersions properties and drying conditions. The versatile nanoporous network composites based on jute cellulose nanowhiskers with ultrathin diameters (3-10 nm) and nanofibrous membrane supports with diameters of 100-300 nm, would be particularly useful for filter applications. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

  20. A neural network based artificial vision system for licence plate recognition.

    Science.gov (United States)

    Draghici, S

    1997-02-01

    This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%.

  1. A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations

    Directory of Open Access Journals (Sweden)

    Zhenghui Sha

    2018-01-01

    Full Text Available Understanding customer preferences in consideration decisions is critical to choice modeling in engineering design. While existing literature has shown that the exogenous effects (e.g., product and customer attributes are deciding factors in customers’ consideration decisions, it is not clear how the endogenous effects (e.g., the intercompetition among products would influence such decisions. This paper presents a network-based approach based on Exponential Random Graph Models to study customers’ consideration behaviors according to engineering design. Our proposed approach is capable of modeling the endogenous effects among products through various network structures (e.g., stars and triangles besides the exogenous effects and predicting whether two products would be conisdered together. To assess the proposed model, we compare it against the dyadic network model that only considers exogenous effects. Using buyer survey data from the China automarket in 2013 and 2014, we evaluate the goodness of fit and the predictive power of the two models. The results show that our model has a better fit and predictive accuracy than the dyadic network model. This underscores the importance of the endogenous effects on customers’ consideration decisions. The insights gained from this research help explain how endogenous effects interact with exogeous effects in affecting customers’ decision-making.

  2. A Security Assessment Mechanism for Software-Defined Networking-Based Mobile Networks

    Directory of Open Access Journals (Sweden)

    Shibo Luo

    2015-12-01

    Full Text Available Software-Defined Networking-based Mobile Networks (SDN-MNs are considered the future of 5G mobile network architecture. With the evolving cyber-attack threat, security assessments need to be performed in the network management. Due to the distinctive features of SDN-MNs, such as their dynamic nature and complexity, traditional network security assessment methodologies cannot be applied directly to SDN-MNs, and a novel security assessment methodology is needed. In this paper, an effective security assessment mechanism based on attack graphs and an Analytic Hierarchy Process (AHP is proposed for SDN-MNs. Firstly, this paper discusses the security assessment problem of SDN-MNs and proposes a methodology using attack graphs and AHP. Secondly, to address the diversity and complexity of SDN-MNs, a novel attack graph definition and attack graph generation algorithm are proposed. In order to quantify security levels, the Node Minimal Effort (NME is defined to quantify attack cost and derive system security levels based on NME. Thirdly, to calculate the NME of an attack graph that takes the dynamic factors of SDN-MN into consideration, we use AHP integrated with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS as the methodology. Finally, we offer a case study to validate the proposed methodology. The case study and evaluation show the advantages of the proposed security assessment mechanism.

  3. A Security Assessment Mechanism for Software-Defined Networking-Based Mobile Networks.

    Science.gov (United States)

    Luo, Shibo; Dong, Mianxiong; Ota, Kaoru; Wu, Jun; Li, Jianhua

    2015-12-17

    Software-Defined Networking-based Mobile Networks (SDN-MNs) are considered the future of 5G mobile network architecture. With the evolving cyber-attack threat, security assessments need to be performed in the network management. Due to the distinctive features of SDN-MNs, such as their dynamic nature and complexity, traditional network security assessment methodologies cannot be applied directly to SDN-MNs, and a novel security assessment methodology is needed. In this paper, an effective security assessment mechanism based on attack graphs and an Analytic Hierarchy Process (AHP) is proposed for SDN-MNs. Firstly, this paper discusses the security assessment problem of SDN-MNs and proposes a methodology using attack graphs and AHP. Secondly, to address the diversity and complexity of SDN-MNs, a novel attack graph definition and attack graph generation algorithm are proposed. In order to quantify security levels, the Node Minimal Effort (NME) is defined to quantify attack cost and derive system security levels based on NME. Thirdly, to calculate the NME of an attack graph that takes the dynamic factors of SDN-MN into consideration, we use AHP integrated with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) as the methodology. Finally, we offer a case study to validate the proposed methodology. The case study and evaluation show the advantages of the proposed security assessment mechanism.

  4. Network Based Integrated Analysis of Phenotype-Genotype Data for Prioritization of Candidate Symptom Genes

    Directory of Open Access Journals (Sweden)

    Xing Li

    2014-01-01

    Full Text Available Background. Symptoms and signs (symptoms in brief are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM. To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. Methods. This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. Results. The proposed method gets reliable gene rank list with AUC (area under curve 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. Conclusions. Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms.

  5. A neural network-based exploratory learning and motor planning system for co-robots.

    Science.gov (United States)

    Galbraith, Byron V; Guenther, Frank H; Versace, Massimiliano

    2015-01-01

    Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or "learning by doing," an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.

  6. Ethernet access network based on free-space optic deployment technology

    Science.gov (United States)

    Gebhart, Michael; Leitgeb, Erich; Birnbacher, Ulla; Schrotter, Peter

    2004-06-01

    The satisfaction of all communication needs from single households and business companies over a single access infrastructure is probably the most challenging topic in communications technology today. But even though the so-called "Last Mile Access Bottleneck" is well known since more than ten years and many distribution technologies have been tried out, the optimal solution has not yet been found and paying commercial access networks offering all service classes are still rare today. Conventional services like telephone, radio and TV, as well as new and emerging services like email, web browsing, online-gaming, video conferences, business data transfer or external data storage can all be transmitted over the well known and cost effective Ethernet networking protocol standard. Key requirements for the deployment technology driven by the different services are high data rates to the single customer, security, moderate deployment costs and good scalability to number and density of users, quick and flexible deployment without legal impediments and high availability, referring to the properties of optical and wireless communication. We demonstrate all elements of an Ethernet Access Network based on Free Space Optic distribution technology. Main physical parts are Central Office, Distribution Network and Customer Equipment. Transmission of different services, as well as configuration, service upgrades and remote control of the network are handled by networking features over one FSO connection. All parts of the network are proven, the latest commercially available technology. The set up is flexible and can be adapted to any more specific need if required.

  7. A network-based system of simulation, control and online assistance for HTR-10

    Energy Technology Data Exchange (ETDEWEB)

    Zhu Shutang [Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084 (China)], E-mail: zhust@tsinghua.edu.cn; Luo Shaojie; Shi Lei [Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084 (China)

    2008-07-15

    A network-based computer system has been developed for HTR-10. This system integrates three subsystems: the simulation subsystem (SIMUSUB), the visualized control designed subsystem (VCDSUB) and the online assistance subsystem (OASUB). The SIMUSUB consists of four functional elements: the simulation calculating server (SCS), the main control client (MCC), the data disposal client (DDC) and the results graphic display client (RGDC), all of which can communicate with each other via network. It is intended to analyze and calculate physical processes of the reactor core, the main loop system and the steam generator, etc., as well as to simulate the normal operational and transient accidents. The result data can be dynamically displayed through the RGDC. The VCDSUB provides a platform for control system modeling where the control flow systems can be automatically generated and graphically simulated. Based on the data from the field bus, the OASUB provides some of the reactor core parameters, which are difficult to measure. This integrated system can be used as an educational tool to understand the design and operational characteristics of the HTR-10, and can also provide online support for operators in the main control room, or as a convenient powerful tool for the control system design.

  8. A network-based system of simulation, control and online assistance for HTR-10

    International Nuclear Information System (INIS)

    Zhu Shutang; Luo Shaojie; Shi Lei

    2008-01-01

    A network-based computer system has been developed for HTR-10. This system integrates three subsystems: the simulation subsystem (SIMUSUB), the visualized control designed subsystem (VCDSUB) and the online assistance subsystem (OASUB). The SIMUSUB consists of four functional elements: the simulation calculating server (SCS), the main control client (MCC), the data disposal client (DDC) and the results graphic display client (RGDC), all of which can communicate with each other via network. It is intended to analyze and calculate physical processes of the reactor core, the main loop system and the steam generator, etc., as well as to simulate the normal operational and transient accidents. The result data can be dynamically displayed through the RGDC. The VCDSUB provides a platform for control system modeling where the control flow systems can be automatically generated and graphically simulated. Based on the data from the field bus, the OASUB provides some of the reactor core parameters, which are difficult to measure. This integrated system can be used as an educational tool to understand the design and operational characteristics of the HTR-10, and can also provide online support for operators in the main control room, or as a convenient powerful tool for the control system design

  9. Research on evaluation of degree of complexity of mining fault network based on GIS

    Energy Technology Data Exchange (ETDEWEB)

    Hua Zhang; Yun-jia Wang; Chuan-zhi Liu [China University of Mining and Technology, Jiangsu (China). School of Environment Science and Spatial Informatics

    2007-03-15

    A large number of spatial and attribute data are involved in coal resource evaluation. Databases are a relatively advanced data management technology, but their major defects are the poor graphic and spatial data functions, from which it is difficult to realize scientific management of evaluation data with spatial characteristics and evaluation result maps. On account of these deficiencies, the evaluation of degree of complexity of mining fault network based on a geographic information system (GIS) is proposed which integrates management of spatial and attribute data. A fractal is an index which can reflect the comprehensive information of faults' number, density, size, composition and dynamics mechanism. A fractal dimension is used as the quantitative evaluation index. Evaluation software has been developed based on a component GIS-MapX, with which the degree of complexity of fault network is evaluated quantitatively using the quantitative index of fractal dimensions in Liuqiao No.2 coal mine as an example. Results show that it is effective in acquiring model parameters and enhancing the definition of data and evaluation results with the application of GIS technology. The fault network is a system with fractal structure and its complexity can be described reasonably and accurately by fractal dimension, which provides an effective method for coal resource evaluation. 9 refs., 6 figs., 2 tabs.

  10. A Self-Organizing Incremental Neural Network based on local distribution learning.

    Science.gov (United States)

    Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

    2016-12-01

    In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Sensor Network-Based and User-Friendly User Location Discovery for Future Smart Homes

    Directory of Open Access Journals (Sweden)

    Ehsan Ahvar

    2016-06-01

    Full Text Available User location is crucial context information for future smart homes where many location based services will be proposed. This location necessarily means that User Location Discovery (ULD will play an important role in future smart homes. Concerns about privacy and the need to carry a mobile or a tag device within a smart home currently make conventional ULD systems uncomfortable for users. Future smart homes will need a ULD system to consider these challenges. This paper addresses the design of such a ULD system for context-aware services in future smart homes stressing the following challenges: (i users’ privacy; (ii device-/tag-free; and (iii fault tolerance and accuracy. On the other hand, emerging new technologies, such as the Internet of Things, embedded systems, intelligent devices and machine-to-machine communication, are penetrating into our daily life with more and more sensors available for use in our homes. Considering this opportunity, we propose a ULD system that is capitalizing on the prevalence of sensors for the home while satisfying the aforementioned challenges. The proposed sensor network-based and user-friendly ULD system relies on different types of inexpensive sensors, as well as a context broker with a fuzzy-based decision-maker. The context broker receives context information from different types of sensors and evaluates that data using the fuzzy set theory. We demonstrate the performance of the proposed system by illustrating a use case, utilizing both an analytical model and simulation.

  12. Development of the Social Network-Based Intervention "Powerful Together with Diabetes" Using Intervention Mapping.

    Science.gov (United States)

    Vissenberg, Charlotte; Nierkens, Vera; Uitewaal, Paul J M; Middelkoop, Barend J C; Nijpels, Giel; Stronks, Karien

    2017-01-01

    This article describes the development of the social network-based intervention Powerful Together with Diabetes which aims to improve diabetes self-management (DSM) among patients with type 2 diabetes living in socioeconomically deprived neighborhoods by stimulating social support for DSM and diminishing social influences hindering DSM (e.g., peer pressure and social norms). The intervention was specifically developed for patients with Dutch, Turkish, Moroccan, and Surinamese backgrounds. The intervention was developed according to Intervention Mapping. This article describes the first four steps of Intervention Mapping: (1) the needs assessment; (2) development of performance and change objectives; (3) selection of theory-based methods and strategies; and (4) the translation of these into an organized program. These four steps resulted in Powerful Together with Diabetes , a 10-month group-based intervention consisting of 24 meetings, 6 meetings for significant others, and 2 meetings for participants and their spouses. The IM method resulted in a tailored approach with a specific focus on the social networks of its participants. This article concludes that the IM method helped our planning team to tailor the intervention to the needs of our target population and facilitated our evaluation design. However, in hindsight, the intervention could have been improved by investing more in participatory planning and community involvement.

  13. A Network-Based Algorithm for Clustering Multivariate Repeated Measures Data

    Science.gov (United States)

    Koslovsky, Matthew; Arellano, John; Schaefer, Caroline; Feiveson, Alan; Young, Millennia; Lee, Stuart

    2017-01-01

    The National Aeronautics and Space Administration (NASA) Astronaut Corps is a unique occupational cohort for which vast amounts of measures data have been collected repeatedly in research or operational studies pre-, in-, and post-flight, as well as during multiple clinical care visits. In exploratory analyses aimed at generating hypotheses regarding physiological changes associated with spaceflight exposure, such as impaired vision, it is of interest to identify anomalies and trends across these expansive datasets. Multivariate clustering algorithms for repeated measures data may help parse the data to identify homogeneous groups of astronauts that have higher risks for a particular physiological change. However, available clustering methods may not be able to accommodate the complex data structures found in NASA data, since the methods often rely on strict model assumptions, require equally-spaced and balanced assessment times, cannot accommodate missing data or differing time scales across variables, and cannot process continuous and discrete data simultaneously. To fill this gap, we propose a network-based, multivariate clustering algorithm for repeated measures data that can be tailored to fit various research settings. Using simulated data, we demonstrate how our method can be used to identify patterns in complex data structures found in practice.

  14. High-Speed Rail Train Timetabling Problem: A Time-Space Network Based Method with an Improved Branch-and-Price Algorithm

    Directory of Open Access Journals (Sweden)

    Bisheng He

    2014-01-01

    Full Text Available A time-space network based optimization method is designed for high-speed rail train timetabling problem to improve the service level of the high-speed rail. The general time-space path cost is presented which considers both the train travel time and the high-speed rail operation requirements: (1 service frequency requirement; (2 stopping plan adjustment; and (3 priority of train types. Train timetabling problem based on time-space path aims to minimize the total general time-space path cost of all trains. An improved branch-and-price algorithm is applied to solve the large scale integer programming problem. When dealing with the algorithm, a rapid branching and node selection for branch-and-price tree and a heuristic train time-space path generation for column generation are adopted to speed up the algorithm computation time. The computational results of a set of experiments on China’s high-speed rail system are presented with the discussions about the model validation, the effectiveness of the general time-space path cost, and the improved branch-and-price algorithm.

  15. A network-based approach to prioritize results from genome-wide association studies.

    Directory of Open Access Journals (Sweden)

    Nirmala Akula

    Full Text Available Genome-wide association studies (GWAS are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI, a network-based method that combines GWAS data with human protein-protein interaction data (PPI. NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call 'trait prioritized sub-networks.' As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn's disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn's disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses.

  16. A Network-Based Approach to Prioritize Results from Genome-Wide Association Studies

    Science.gov (United States)

    Akula, Nirmala; Baranova, Ancha; Seto, Donald; Solka, Jeffrey; Nalls, Michael A.; Singleton, Andrew; Ferrucci, Luigi; Tanaka, Toshiko; Bandinelli, Stefania; Cho, Yoon Shin; Kim, Young Jin; Lee, Jong-Young; Han, Bok-Ghee; McMahon, Francis J.

    2011-01-01

    Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call ‘trait prioritized sub-networks.’ As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn’s disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn’s disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses. PMID:21915301

  17. Pathway Relevance Ranking for Tumor Samples through Network-Based Data Integration.

    Directory of Open Access Journals (Sweden)

    Lieven P C Verbeke

    Full Text Available The study of cancer, a highly heterogeneous disease with different causes and clinical outcomes, requires a multi-angle approach and the collection of large multi-omics datasets that, ideally, should be analyzed simultaneously. We present a new pathway relevance ranking method that is able to prioritize pathways according to the information contained in any combination of tumor related omics datasets. Key to the method is the conversion of all available data into a single comprehensive network representation containing not only genes but also individual patient samples. Additionally, all data are linked through a network of previously identified molecular interactions. We demonstrate the performance of the new method by applying it to breast and ovarian cancer datasets from The Cancer Genome Atlas. By integrating gene expression, copy number, mutation and methylation data, the method's potential to identify key pathways involved in breast cancer development shared by different molecular subtypes is illustrated. Interestingly, certain pathways were ranked equally important for different subtypes, even when the underlying (epi-genetic disturbances were diverse. Next to prioritizing universally high-scoring pathways, the pathway ranking method was able to identify subtype-specific pathways. Often the score of a pathway could not be motivated by a single mutation, copy number or methylation alteration, but rather by a combination of genetic and epi-genetic disturbances, stressing the need for a network-based data integration approach. The analysis of ovarian tumors, as a function of survival-based subtypes, demonstrated the method's ability to correctly identify key pathways, irrespective of tumor subtype. A differential analysis of survival-based subtypes revealed several pathways with higher importance for the bad-outcome patient group than for the good-outcome patient group. Many of the pathways exhibiting higher importance for the bad

  18. Dynamic neural networks based on-line identification and control of high performance motor drives

    Science.gov (United States)

    Rubaai, Ahmed; Kotaru, Raj

    1995-01-01

    In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

  19. ICS logging solution for network-based attacks using Gumistix technology

    Science.gov (United States)

    Otis, Jeremy R.; Berman, Dustin; Butts, Jonathan; Lopez, Juan

    2013-05-01

    Industrial Control Systems (ICS) monitor and control operations associated with the national critical infrastructure (e.g., electric power grid, oil and gas pipelines and water treatment facilities). These systems rely on technologies and architectures that were designed for system reliability and availability. Security associated with ICS was never an inherent concern, primarily due to the protections afforded by network isolation. However, a trend in ICS operations is to migrate to commercial networks via TCP/IP in order to leverage commodity benefits and cost savings. As a result, system vulnerabilities are now exposed to the online community. Indeed, recent research has demonstrated that many exposed ICS devices are being discovered using readily available applications (e.g., ShodanHQ search engine and Google-esque queries). Due to the lack of security and logging capabilities for ICS, most knowledge about attacks are derived from real world incidents after an attack has already been carried out and the damage has been done. This research provides a method for introducing sensors into the ICS environment that collect information about network-based attacks. The sensors are developed using an inexpensive Gumstix platform that can be deployed and incorporated with production systems. Data obtained from the sensors provide insight into attack tactics (e.g., port scans, Nessus scans, Metasploit modules, and zero-day exploits) and characteristics (e.g., attack origin, frequency, and level of persistence). Findings enable security professionals to draw an accurate, real-time awareness of the threats against ICS devices and help shift the security posture from reactionary to preventative.

  20. Molecular Network-Based Identification of Competing Endogenous RNAs in Thyroid Carcinoma

    Directory of Open Access Journals (Sweden)

    Minjia Lu

    2018-01-01

    Full Text Available RNAs may act as competing endogenous RNAs (ceRNAs, a critical mechanism in determining gene expression regulations in many cancers. However, the roles of ceRNAs in thyroid carcinoma remains elusive. In this study, we have developed a novel pipeline called Molecular Network-based Identification of ceRNA (MNIceRNA to identify ceRNAs in thyroid carcinoma. MNIceRNA first constructs micro RNA (miRNA–messenger RNA (mRNAlong non-coding RNA (lncRNA networks from miRcode database and weighted correlation network analysis (WGCNA, based on which to identify key drivers of differentially expressed RNAs between normal and tumor samples. It then infers ceRNAs of the identified key drivers using the long non-coding competing endogenous database (lnCeDB. We applied the pipeline into The Cancer Genome Atlas (TCGA thyroid carcinoma data. As a result, 598 lncRNAs, 1025 mRNAs, and 90 microRNA (miRNAs were inferred to be differentially expressed between normal and thyroid cancer samples. We then obtained eight key driver miRNAs, among which hsa-mir-221 and hsa-mir-222 were key driver RNAs identified by both miRNA–mRNA–lncRNA and WGCNA network. In addition, hsa-mir-375 was inferred to be significant for patients’ survival with 34 associated ceRNAs, among which RUNX2, DUSP6 and SEMA3D are known oncogenes regulating cellular proliferation and differentiation in thyroid cancer. These ceRNAs are critical in revealing the secrets behind thyroid cancer progression and may serve as future therapeutic biomarkers.

  1. Semi-interpenetrating networks based on POLY(N-isopropyl acrilamide and POLY(N-vinylpyrrolidone

    Directory of Open Access Journals (Sweden)

    Žugić Dragana

    2007-01-01

    Full Text Available Three series of semi-interpenetrating polymer networks based on cross-linked poly(N-isopropylacrylamide, PNIPA, and 1, 2 and 3 wt% of linear poly(N-vinylpyrrolidone, PVP, were synthesized in order to improve the mechanical properties of PNIPA gels. The effect of the incorporation of the linear PVP polymer into the temperature responsive networks on the phase transition temperature, swelling behavior and mechanical properties was studied. Polymer networks with four different crosslinking densities were prepared with various molar ratios (25/1 to 100/1 of the monomer (N-isopropylacrylamide to the cross linker (N,N'-methylene-bisacrylamide. The hydrogels were characterized by determination of the equilibrium degree of swelling at 25 °C, the dynamic shear modulus and the effective crosslinking density, as well as the ultimate hydrogel properties, such as the tensile strength and elongation at break. Furthermore, the deswelling kinetics of the hydrogels was also studied by measuring their water retention capacity. The inclusion of the linear hydrophilic PVP in the PNIPA networks increased the equilibrium degree of swelling, the highest values of which were obtained for samples with 2 and 3 wt% of PVP and the NIPA/MBA molar ratio of 75/1 and 100/1. The highest reinforcement effect, evaluated from the ratio of G'red(semi-IPN to G'red(PNIPA, was obtained by incorporation of 2 wt% PVP. The tensile strength of the semi-IPNs reinforced with linear PVP was higher than that of the PNIPA networks. The elongation at break of these semi-IPNs varied between 22 and 55%, which are 22^11% larger than those for single PNIPA networks. The tensile measurements confirmed that the presence of 2 wt% of the linear polymer significantly reinforced the PNIPA network.

  2. Training and operation of an integrated neuromorphic network based on metal-oxide memristors

    Science.gov (United States)

    Prezioso, M.; Merrikh-Bayat, F.; Hoskins, B. D.; Adam, G. C.; Likharev, K. K.; Strukov, D. B.

    2015-05-01

    Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex, with its approximately 1014 synapses, makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally challenging. To provide comparable complexity while operating much faster and with manageable power dissipation, networks based on circuits combining complementary metal-oxide-semiconductors (CMOSs) and adjustable two-terminal resistive devices (memristors) have been developed. In such circuits, the usual CMOS stack is augmented with one or several crossbar layers, with memristors at each crosspoint. There have recently been notable improvements in the fabrication of such memristive crossbars and their integration with CMOS circuits, including first demonstrations of their vertical integration. Separately, discrete memristors have been used as artificial synapses in neuromorphic networks. Very recently, such experiments have been extended to crossbar arrays of phase-change memristive devices. The adjustment of such devices, however, requires an additional transistor at each crosspoint, and hence these devices are much harder to scale than metal-oxide memristors, whose nonlinear current-voltage curves enable transistor-free operation. Here we report the experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification). The network can be taught in situ using a coarse-grain variety of the delta rule algorithm to perform the perfect classification of 3 × 3-pixel black/white images into three classes (representing letters). This demonstration is an important step towards much larger and more complex memristive neuromorphic networks.

  3. Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics

    Science.gov (United States)

    2014-01-01

    Background The demand for novel molecularly targeted drugs will continue to rise as we move forward toward the goal of personalizing cancer treatment to the molecular signature of individual tumors. However, the identification of targets and combinations of targets that can be safely and effectively modulated is one of the greatest challenges facing the drug discovery process. A promising approach is to use biological networks to prioritize targets based on their relative positions to one another, a property that affects their ability to maintain network integrity and propagate information-flow. Here, we introduce influence networks and demonstrate how they can be used to generate influence scores as a network-based metric to rank genes as potential drug targets. Results We use this approach to prioritize genes as drug target candidates in a set of ER + breast tumor samples collected during the course of neoadjuvant treatment with the aromatase inhibitor letrozole. We show that influential genes, those with high influence scores, tend to be essential and include a higher proportion of essential genes than those prioritized based on their position (i.e. hubs or bottlenecks) within the same network. Additionally, we show that influential genes represent novel biologically relevant drug targets for the treatment of ER + breast cancers. Moreover, we demonstrate that gene influence differs between untreated tumors and residual tumors that have adapted to drug treatment. In this way, influence scores capture the context-dependent functions of genes and present the opportunity to design combination treatment strategies that take advantage of the tumor adaptation process. Conclusions Influence networks efficiently find essential genes as promising drug targets and combinations of targets to inform the development of molecularly targeted drugs and their use. PMID:24495353

  4. Network-based association of hypoxia-responsive genes with cardiovascular diseases

    International Nuclear Information System (INIS)

    Wang, Rui-Sheng; Oldham, William M; Loscalzo, Joseph

    2014-01-01

    Molecular oxygen is indispensable for cellular viability and function. Hypoxia is a stress condition in which oxygen demand exceeds supply. Low cellular oxygen content induces a number of molecular changes to activate regulatory pathways responsible for increasing the oxygen supply and optimizing cellular metabolism under limited oxygen conditions. Hypoxia plays critical roles in the pathobiology of many diseases, such as cancer, heart failure, myocardial ischemia, stroke, and chronic lung diseases. Although the complicated associations between hypoxia and cardiovascular (and cerebrovascular) diseases (CVD) have been recognized for some time, there are few studies that investigate their biological link from a systems biology perspective. In this study, we integrate hypoxia genes, CVD genes, and the human protein interactome in order to explore the relationship between hypoxia and cardiovascular diseases at a systems level. We show that hypoxia genes are much closer to CVD genes in the human protein interactome than that expected by chance. We also find that hypoxia genes play significant bridging roles in connecting different cardiovascular diseases. We construct a hypoxia-CVD bipartite network and find several interesting hypoxia-CVD modules with significant gene ontology similarity. Finally, we show that hypoxia genes tend to have more CVD interactors in the human interactome than in random networks of matching topology. Based on these observations, we can predict novel genes that may be associated with CVD. This network-based association study gives us a broad view of the relationships between hypoxia and cardiovascular diseases and provides new insights into the role of hypoxia in cardiovascular biology. (paper)

  5. Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer.

    Directory of Open Access Journals (Sweden)

    Matthew Ruffalo

    2015-12-01

    Full Text Available Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these "silent players". For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation.

  6. MSD-MAP: A Network-Based Systems Biology Platform for Predicting Disease-Metabolite Links.

    Science.gov (United States)

    Wathieu, Henri; Issa, Naiem T; Mohandoss, Manisha; Byers, Stephen W; Dakshanamurthy, Sivanesan

    2017-01-01

    Cancer-associated metabolites result from cell-wide mechanisms of dysregulation. The field of metabolomics has sought to identify these aberrant metabolites as disease biomarkers, clues to understanding disease mechanisms, or even as therapeutic agents. This study was undertaken to reliably predict metabolites associated with colorectal, esophageal, and prostate cancers. Metabolite and disease biological action networks were compared in a computational platform called MSD-MAP (Multi Scale Disease-Metabolite Association Platform). Using differential gene expression analysis with patient-based RNAseq data from The Cancer Genome Atlas, genes up- or down-regulated in cancer compared to normal tissue were identified. Relational databases were used to map biological entities including pathways, functions, and interacting proteins, to those differential disease genes. Similar relational maps were built for metabolites, stemming from known and in silico predicted metabolite-protein associations. The hypergeometric test was used to find statistically significant relationships between disease and metabolite biological signatures at each tier, and metabolites were assessed for multi-scale association with each cancer. Metabolite networks were also directly associated with various other diseases using a disease functional perturbation database. Our platform recapitulated metabolite-disease links that have been empirically verified in the scientific literature, with network-based mapping of jointly-associated biological activity also matching known disease mechanisms. This was true for colorectal, esophageal, and prostate cancers, using metabolite action networks stemming from both predicted and known functional protein associations. By employing systems biology concepts, MSD-MAP reliably predicted known cancermetabolite links, and may serve as a predictive tool to streamline conventional metabolomic profiling methodologies. Copyright© Bentham Science Publishers; For any

  7. A network-based gene expression signature informs prognosis and treatment for colorectal cancer patients.

    Directory of Open Access Journals (Sweden)

    Mingguang Shi

    Full Text Available Several studies have reported gene expression signatures that predict recurrence risk in stage II and III colorectal cancer (CRC patients with minimal gene membership overlap and undefined biological relevance. The goal of this study was to investigate biological themes underlying these signatures, to infer genes of potential mechanistic importance to the CRC recurrence phenotype and to test whether accurate prognostic models can be developed using mechanistically important genes.We investigated eight published CRC gene expression signatures and found no functional convergence in Gene Ontology enrichment analysis. Using a random walk-based approach, we integrated these signatures and publicly available somatic mutation data on a protein-protein interaction network and inferred 487 genes that were plausible candidate molecular underpinnings for the CRC recurrence phenotype. We named the list of 487 genes a NEM signature because it integrated information from Network, Expression, and Mutation. The signature showed significant enrichment in four biological processes closely related to cancer pathophysiology and provided good coverage of known oncogenes, tumor suppressors, and CRC-related signaling pathways. A NEM signature-based Survival Support Vector Machine prognostic model was trained using a microarray gene expression dataset and tested on an independent dataset. The model-based scores showed a 75.7% concordance with the real survival data and separated patients into two groups with significantly different relapse-free survival (p = 0.002. Similar results were obtained with reversed training and testing datasets (p = 0.007. Furthermore, adjuvant chemotherapy was significantly associated with prolonged survival of the high-risk patients (p = 0.006, but not beneficial to the low-risk patients (p = 0.491.The NEM signature not only reflects CRC biology but also informs patient prognosis and treatment response. Thus, the network-based

  8. The evolution of network-based business models illustrated through the case study of an entrepreneurship project

    DEFF Research Database (Denmark)

    Lund, Morten; Nielsen, Christian

    2014-01-01

    can gain insight into barriers and enablers relating to different types of loose organisations and how to best manage such relationships and interactions Originality/value: This study adds value to the existing literature by reflecting the dynamics created in the interactions between a business model......-based business model that generates additional value for the core business model and for both the partners and the customers. Research limitations/implications: The results should be taken with caution as they are based on the case study of a single network-based business model. Practical implications: Managers......Purpose: Existing frameworks for understanding and analyzing the value configuration and structuring of partnerships in relation such network-based business models are found to be inferior. The purpose of this paper is therefore to broaden our understanding of how business models may change over...

  9. How network-based incubation helps start-up performance : a systematic review against the background of management theories

    OpenAIRE

    Eveleens, Chris P.; van Rijnsoever, Frank J.; Niesten, Eva M M I

    2017-01-01

    The literature on how network-based incubation influences the performance of technology-based start-ups has recently grown considerably and provided valuable insights. However, at the same time this literature has become quite fragmented, inconsistently conceptualised, and theoretically underdeveloped. Therefore, this article uses three management theories to structure the literature, improve the theoretical underpinning and develop an agenda for further research. The management theories are ...

  10. The Evolution of Network-based Business Models Illustrated Through the Case Study of an Entrepreneurship Project

    Directory of Open Access Journals (Sweden)

    Morten Lund

    2014-08-01

    Full Text Available Purpose: Existing frameworks for understanding and analyzing the value configuration and structuring of partnerships in relation such network-based business models are found to be inferior. The purpose of this paper is therefore to broaden our understanding of how business models may change over time and how the role of strategic partners may differ over time too. Design/methodology/approach: A longitudinal case study spanning over years and mobilising multiple qualitative methods such as interviews, observation and participative observation forms the basis of the data collection. Findings: This paper illustrates how a network-based business model arises and evolves and how the forces of a network structure impact the development of its partner relationships. The contribution of this article is to understanding how partners positioned around a business model can be organized into a network-based business model that generates additional value for the core business model and for both the partners and the customers. Research limitations/implications: The results should be taken with caution as they are based on the case study of a single network-based business model. Practical implications: Managers can gain insight into barriers and enablers relating to different types of loose organisations and how to best manage such relationships and interactions Originality/value: This study adds value to the existing literature by reflecting the dynamics created in the interactions between a business model’s strategic partners and how a how a business model can evolve in a series of distinct phases

  11. Co-expression of human cytochrome P4501A1 (CYP1A1) variants and human NADPH-cytochrome P450 reductase in the baculovirus/insect cell system.

    Science.gov (United States)

    Schwarz, D; Kisselev, P; Honeck, H; Cascorbi, I; Schunck, W H; Roots, I

    2001-06-01

    1. Three human cytochrome P4501A1 (CYP1A1) variants, wild-type (CYP1A1.1), CYP1A1.2 (1462V) and CYP1A1.4 (T461N), were co-expressed with human NADPH-P450 reductase (OR) in Spodoptera frugiperda (Sf9) insect cells by baculovirus co-infection to elaborate a suitable system for studying the role of CYPA1 polymorphism in the metabolism of exogenous and endogenous substrates. 2. A wide range of conditions was examined to optimize co-expression with regard to such parameters as relative multiplicity of infection (MOI), time of harvest, haem precursor supplementation and post-translational stabilization. tinder optimized conditions, almost identical expression levels and molar OR/CYP1A1 ratios (20:1) were attained for all CYP1A1 variants. 3. Microsomes isolated from co-infected cells demonstrated ethoxyresorufin deethlylase activities (nmol/min(-1) nmol(-1) CYP1A1) of 16.0 (CYP1A1.1), 20.5 (CYP1A1.2) and 22.5 (CYP1A1.4). Pentoxyresorufin was dealkylated approximately 10-20 times slower with all enzyme variants. 4. All three CYP1A1 variants were active in metabolizing the precarcinogen benzo[a]pyrene (B[a]P), with wild-type enzyme showing the highest activity, followed by CYP1A1.4 (60%) and CYP1A1.2 (40%). Each variant produced all major metabolites including B[a]P-7,8-dihydrodiol, the precursor of the ultimate carcinogenic species. 5. These studies demonstrate that the baculovirus-mediated co-expression-by-co-infection approach all CYP1A1 variants yields functionally active enzyme systems with similar molar OR/CYP1A1 ratios, thus providing suitable preconditions to examine the metabolism of and environmental chemicals by the different CY1A1 variants.

  12. Network-Based Detection and Classification of Seismovolcanic Tremors: Example From the Klyuchevskoy Volcanic Group in Kamchatka

    Science.gov (United States)

    Soubestre, Jean; Shapiro, Nikolai M.; Seydoux, Léonard; de Rosny, Julien; Droznin, Dmitry V.; Droznina, Svetlana Ya.; Senyukov, Sergey L.; Gordeev, Evgeniy I.

    2018-01-01

    We develop a network-based method for detecting and classifying seismovolcanic tremors. The proposed approach exploits the coherence of tremor signals across the network that is estimated from the array covariance matrix. The method is applied to four and a half years of continuous seismic data recorded by 19 permanent seismic stations in the vicinity of the Klyuchevskoy volcanic group in Kamchatka (Russia), where five volcanoes were erupting during the considered time period. We compute and analyze daily covariance matrices together with their eigenvalues and eigenvectors. As a first step, most coherent signals corresponding to dominating tremor sources are detected based on the width of the covariance matrix eigenvalues distribution. Thus, volcanic tremors of the two volcanoes known as most active during the considered period, Klyuchevskoy and Tolbachik, are efficiently detected. As a next step, we consider the daily array covariance matrix's first eigenvector. Our main hypothesis is that these eigenvectors represent the principal components of the daily seismic wavefield and, for days with tremor activity, characterize dominant tremor sources. Those daily first eigenvectors, which can be used as network-based fingerprints of tremor sources, are then grouped into clusters using correlation coefficient as a measure of the vector similarity. As a result, we identify seven clusters associated with different periods of activity of four volcanoes: Tolbachik, Klyuchevskoy, Shiveluch, and Kizimen. The developed method does not require a priori knowledge and is fully automatic; and the database of the network-based tremor fingerprints can be continuously enriched with newly available data.

  13. Comparison of home and away-from-home physical activity using accelerometers and cellular network-based tracking devices.

    Science.gov (United States)

    Ramulu, Pradeep Y; Chan, Emilie S; Loyd, Tara L; Ferrucci, Luigi; Friedman, David S

    2012-08-01

    Measuring physical at home and away from home is essential for assessing health and well-being, and could help design interventions to increase physical activity. Here, we describe how physical activity at home and away from home can be quantified by combining information from cellular network-based tracking devices and accelerometers. Thirty-five working adults wore a cellular network-based tracking device and an accelerometer for 6 consecutive days and logged their travel away from home. Performance of the tracking device was determined using the travel log for reference. Tracking device and accelerometer data were merged to compare physical activity at home and away from home. The tracking device detected 98.6% of all away-from-home excursions, accurately measured time away from home and demonstrated few prolonged signal drop-out periods. Most physical activity took place away from home on weekdays, but not on weekends. Subjects were more physically active per unit of time while away from home, particularly on weekends. Cellular network-based tracking devices represent an alternative to global positioning systems for tracking location, and provide information easily integrated with accelerometers to determine where physical activity takes place. Promoting greater time spent away from home may increase physical activity.

  14. A web-based system for neural network based classification in temporomandibular joint osteoarthritis.

    Science.gov (United States)

    de Dumast, Priscille; Mirabel, Clément; Cevidanes, Lucia; Ruellas, Antonio; Yatabe, Marilia; Ioshida, Marcos; Ribera, Nina Tubau; Michoud, Loic; Gomes, Liliane; Huang, Chao; Zhu, Hongtu; Muniz, Luciana; Shoukri, Brandon; Paniagua, Beatriz; Styner, Martin; Pieper, Steve; Budin, Francois; Vimort, Jean-Baptiste; Pascal, Laura; Prieto, Juan Carlos

    2018-07-01

    study demonstrate a comprehensive phenotypic characterization of TMJ health and disease at clinical, imaging and biological levels, using novel flexible and versatile open-source tools for a web-based system that provides advanced shape statistical analysis and a neural network based classification of temporomandibular joint osteoarthritis. Published by Elsevier Ltd.

  15. Designing Dietary Recommendations Using System Level Interactomics Analysis and Network-Based Inference

    Directory of Open Access Journals (Sweden)

    Tingting Zheng

    2017-09-01

    diet in disease development. Due to the complexity of analyzing the food composition and eating patterns of individuals our in silico analysis, using large-scale gene expression datasets and network-based topological features, may serve as a proof-of-concept in nutritional systems biology for identifying diet-disease relationships and subsequently designing dietary recommendations.

  16. Optical Calibration Process Developed for Neural-Network-Based Optical Nondestructive Evaluation Method

    Science.gov (United States)

    Decker, Arthur J.

    2004-01-01

    A completely optical calibration process has been developed at Glenn for calibrating a neural-network-based nondestructive evaluation (NDE) method. The NDE method itself detects very small changes in the characteristic patterns or vibration mode shapes of vibrating structures as discussed in many references. The mode shapes or characteristic patterns are recorded using television or electronic holography and change when a structure experiences, for example, cracking, debonds, or variations in fastener properties. An artificial neural network can be trained to be very sensitive to changes in the mode shapes, but quantifying or calibrating that sensitivity in a consistent, meaningful, and deliverable manner has been challenging. The standard calibration approach has been difficult to implement, where the response to damage of the trained neural network is compared with the responses of vibration-measurement sensors. In particular, the vibration-measurement sensors are intrusive, insufficiently sensitive, and not numerous enough. In response to these difficulties, a completely optical alternative to the standard calibration approach was proposed and tested successfully. Specifically, the vibration mode to be monitored for structural damage was intentionally contaminated with known amounts of another mode, and the response of the trained neural network was measured as a function of the peak-to-peak amplitude of the contaminating mode. The neural network calibration technique essentially uses the vibration mode shapes of the undamaged structure as standards against which the changed mode shapes are compared. The published response of the network can be made nearly independent of the contaminating mode, if enough vibration modes are used to train the net. The sensitivity of the neural network can be adjusted for the environment in which the test is to be conducted. The response of a neural network trained with measured vibration patterns for use on a vibration isolation

  17. A high-yield co-expression system for the purification of an intact drs2p-cdc50p lipid flippase complex, critically dependent on and stabilized by phosphatidylinositol-4-phosphate

    DEFF Research Database (Denmark)

    Azouaoui, Hassina; Montigny, Cédric; Ash, Miriam-Rose

    2014-01-01

    P-type ATPases from the P4 subfamily (P4-ATPases) are energy-dependent transporters, which are thought to establish lipid asymmetry in eukaryotic cell membranes. Together with their Cdc50 accessory subunits, P4-ATPases couple ATP hydrolysis to lipid transport from the exoplasmic to the cytoplasmic...... leaflet of plasma membranes, late Golgi membranes, and endosomes. To gain insights into the structure and function of these important membrane pumps, robust protocols for expression and purification are required. In this report, we present a procedure for high-yield co-expression of a yeast flippase......, the Drs2p-Cdc50p complex. After recovery of yeast membranes expressing both proteins, efficient purification was achieved in a single step by affinity chromatography on streptavidin beads, yielding ∼1-2 mg purified Drs2p-Cdc50p complex per liter of culture. Importantly, the procedure enabled us to recover...

  18. Opsin expression in Limulus eyes: a UV opsin is expressed in each eye type and co-expressed with a visible light-sensitive opsin in ventral larval eyes.

    Science.gov (United States)

    Battelle, Barbara-Anne; Kempler, Karen E; Harrison, Alexandra; Dugger, Donald R; Payne, Richard

    2014-09-01

    The eyes of the horseshoe crab, Limulus polyphemus, are a model for studies of visual function and the visual systems of euarthropods. Much is known about the structure and function of L. polyphemus photoreceptors, much less about their photopigments. Three visible-light-sensitive L. polyphemus opsins were characterized previously (LpOps1, 2 and 5). Here we characterize a UV opsin (LpUVOps1) that is expressed in all three types of L. polyphemus eyes. It is expressed in most photoreceptors in median ocelli, the only L. polyphemus eyes in which UV sensitivity was previously detected, and in the dendrite of eccentric cells in lateral compound eyes. Therefore, eccentric cells, previously thought to be non-photosensitive second-order neurons, may actually be UV-sensitive photoreceptors. LpUVOps1 is also expressed in small photoreceptors in L. polyphemus ventral larval eyes, and intracellular recordings from these photoreceptors confirm that LpUVOps1 is an active, UV-sensitive photopigment. These photoreceptors also express LpOps5, which we demonstrate is an active, long-wavelength-sensitive photopigment. Thus small photoreceptors in ventral larval eyes, and probably those of the other larval eyes, have dual sensitivity to UV and visible light. Interestingly, the spectral tuning of small ventral photoreceptors may change day to night, because the level of LpOps5 in their rhabdoms is lower during the day than during the night, whereas LpUVOps1 levels show no diurnal change. These and previous findings show that opsin co-expression and the differential regulation of co-expressed opsins in rhabdoms is a common feature of L. polyphemus photoreceptors. © 2014. Published by The Company of Biologists Ltd.

  19. In vivo production of non-glycosylated recombinant proteins in Nicotiana benthamiana plants by co-expression with Endo-β-N-acetylglucosaminidase H (Endo H) of Streptomyces plicatus

    Science.gov (United States)

    Cicek, Kader; Gulec, Burcu; Ungor, Rifat; Hasanova, Gulnara

    2017-01-01

    A plant transient expression system, with eukaryotic post-translational modification machinery, offers superior efficiency, scalability, safety, and lower cost over other expression systems. However, due to aberrant N-glycosylation, this expression system may not be a suitable expression platform for proteins not carrying N-linked glycans in the native hosts. Therefore, it is crucial to develop a strategy to produce target proteins in a non-glycosylated form while preserving their native sequence, conformation and biological activity. Previously, we developed a strategy for enzymatic deglycosylation of proteins in planta by co-expressing bacterial peptide-N-glycosidase F (PNGase F). Though PNGase F removes oligosaccharides from glycosylated proteins, in so doing it causes an amino acid change due to the deamidation of asparagine to aspartate in the N-X-S/T site. Endo-β-N-acetylglucosaminidase (EC3.2.1.96, Endo H), another deglycosylating enzyme, catalyzes cleavage between two N-Acetyl-D-glucosamine residues of the chitobiose core of N-linked glycans, leaving a single N-Acetyl-D-glucosamine residue without the concomitant deamidation of asparagine. In this study, a method for in vivo deglycosylation of recombinant proteins in plants by transient co-expression with bacterial Endo H is described for the first time. Endo H was fully active in vivo. and successfully cleaved N-linked glycans from glycoproteins were tested. In addition, unlike the glycosylated form, in vivo Endo H deglycosylated Pfs48/45 was recognized by conformational specific Pfs48/45 monoclonal antibody, in a manner similar to its PNGase F deglycosylated counterpart. Furthermore, the deglycosylated PA83 molecule produced by Endo H showed better stability than a PNGase F deglycosylated counterpart. Thus, an Endo H in vivo deglycosylation approach provides another opportunity to develop vaccine antigens, therapeutic proteins, antibodies, and industrial enzymes. PMID:28827815

  20. Genome-Scale Co-Expression Network Comparison across Escherichia coli and Salmonella enterica Serovar Typhimurium Reveals Significant Conservation at the Regulon Level of Local Regulators Despite Their Dissimilar Lifestyles

    Science.gov (United States)

    Zarrineh, Peyman; Sánchez-Rodríguez, Aminael; Hosseinkhan, Nazanin; Narimani, Zahra; Marchal, Kathleen; Masoudi-Nejad, Ali

    2014-01-01

    Availability of genome-wide gene expression datasets provides the opportunity to study gene expression across different organisms under a plethora of experimental conditions. In our previous work, we developed an algorithm called COMODO (COnserved MODules across Organisms) that identifies conserved expression modules between two species. In the present study, we expanded COMODO to detect the co-expression conservation across three organisms by adapting the statistics behind it. We applied COMODO to study expression conservation/divergence between Escherichia coli, Salmonella enterica, and Bacillus subtilis. We observed that some parts of the regulatory interaction networks were conserved between E. coli and S. enterica especially in the regulon of local regulators. However, such conservation was not observed between the regulatory interaction networks of B. subtilis and the two other species. We found co-expression conservation on a number of genes involved in quorum sensing, but almost no conservation for genes involved in pathogenicity across E. coli and S. enterica which could partially explain their different lifestyles. We concluded that despite their different lifestyles, no significant rewiring have occurred at the level of local regulons involved for instance, and notable conservation can be detected in signaling pathways and stress sensing in the phylogenetically close species S. enterica and E. coli. Moreover, conservation of local regulons seems to depend on the evolutionary time of divergence across species disappearing at larger distances as shown by the comparison with B. subtilis. Global regulons follow a different trend and show major rewiring even at the limited evolutionary distance that separates E. coli and S. enterica. PMID:25101984

  1. Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis

    Directory of Open Access Journals (Sweden)

    Zhongrong Zhang

    2016-01-01

    Full Text Available Wind energy has increasingly played a vital role in mitigating conventional resource shortages. Nevertheless, the stochastic nature of wind poses a great challenge when attempting to find an accurate forecasting model for wind power. Therefore, precise wind power forecasts are of primary importance to solve operational, planning and economic problems in the growing wind power scenario. Previous research has focused efforts on the deterministic forecast of wind power values, but less attention has been paid to providing information about wind energy. Based on an optimal Adaptive-Network-Based Fuzzy Inference System (ANFIS and Singular Spectrum Analysis (SSA, this paper develops a hybrid uncertainty forecasting model, IFASF (Interval Forecast-ANFIS-SSA-Firefly Alogorithm, to obtain the upper and lower bounds of daily average wind power, which is beneficial for the practical operation of both the grid company and independent power producers. To strengthen the practical ability of this developed model, this paper presents a comparison between IFASF and other benchmarks, which provides a general reference for this aspect for statistical or artificially intelligent interval forecast methods. The comparison results show that the developed model outperforms eight benchmarks and has a satisfactory forecasting effectiveness in three different wind farms with two time horizons.

  2. ChloroP, a neural network-based method for predicting chloroplast transitpeptides and their cleavage sites

    DEFF Research Database (Denmark)

    Emanuelsson, O.; Nielsen, Henrik; von Heijne, Gunnar

    1999-01-01

    the cleavage sites given in SWISS-PROT. An analysis of 715 Arabidopsis thaliana sequences from SWISS-PROT suggests that the ChloroP method should be useful for the identification of putative transit peptides in genome-wide sequence data. The ChloroP predictor is available as a web-server at http......We present a neural network based method (ChloroP) for identifying chloroplast transit peptides and their cleavage sites. Using cross-validation, 88% of the sequences in our homology reduced training set were correctly classified as transit peptides or nontransit peptides. This performance level...

  3. Controlling Chaos and Voltage Collapse using Layered Recurrent Network-based PID-SVC in Power Systems

    Directory of Open Access Journals (Sweden)

    I Made Ginarsa

    2013-11-01

    Full Text Available Chaos and voltage collapse occurred in critical power systems due to disturbing of energy. PID-SVC layered reccurrent neural network-based (LRN-based PID-SVC was proposed to solve this problem. A PID was used to control chaos and voltage collapse. Then, an SVC LRN-based to maintan the load voltage. By using the proposed controller, chaos and voltage collapse were able to suppress and maintain the load voltage around the setting value. Furthemore, the proposed controller gives better response than PI-SVC controller.

  4. QuakeUp: An advanced tool for a network-based Earthquake Early Warning system

    Science.gov (United States)

    Zollo, Aldo; Colombelli, Simona; Caruso, Alessandro; Elia, Luca; Brondi, Piero; Emolo, Antonio; Festa, Gaetano; Martino, Claudio; Picozzi, Matteo

    2017-04-01

    The currently developed and operational Earthquake Early warning, regional systems ground on the assumption of a point-like earthquake source model and 1-D ground motion prediction equations to estimate the earthquake impact. Here we propose a new network-based method which allows for issuing an alert based upon the real-time mapping of the Potential Damage Zone (PDZ), e.g. the epicentral area where the peak ground velocity is expected to exceed the damaging or strong shaking levels with no assumption about the earthquake rupture extent and spatial variability of ground motion. The platform includes the most advanced techniques for a refined estimation of the main source parameters (earthquake location and magnitude) and for an accurate prediction of the expected ground shaking level. The new software platform (QuakeUp) is under development at the Seismological Laboratory (RISSC-Lab) of the Department of Physics at the University of Naples Federico II, in collaboration with the academic spin-off company RISS s.r.l., recently gemmated by the research group. The system processes the 3-component, real-time ground acceleration and velocity data streams at each station. The signal quality is preliminary assessed by checking the signal-to-noise ratio both in acceleration, velocity and displacement and through dedicated filtering algorithms. For stations providing high quality data, the characteristic P-wave period (τ_c) and the P-wave displacement, velocity and acceleration amplitudes (P_d, Pv and P_a) are jointly measured on a progressively expanded P-wave time window. The evolutionary measurements of the early P-wave amplitude and characteristic period at stations around the source allow to predict the geometry and extent of PDZ, but also of the lower shaking intensity regions at larger epicentral distances. This is done by correlating the measured P-wave amplitude with the Peak Ground Velocity (PGV) and Instrumental Intensity (I_MM) and by mapping the measured and

  5. Significance of Co-expression of Epidermal Growth Factor Receptor and Ki67 on Clinical Outcome in Patients With Anal Cancer Treated With Chemoradiotherapy: An Analysis of NRG Oncology RTOG 9811.

    Science.gov (United States)

    Doll, Corinne M; Moughan, Jennifer; Klimowicz, Alexander; Ho, Clement K; Kornaga, Elizabeth N; Lees-Miller, Susan P; Ajani, Jaffer A; Crane, Christopher H; Kachnic, Lisa A; Okawara, Gordon S; Berk, Lawrence B; Roof, Kevin S; Becker, Mark J; Grisell, David L; Ellis, Robert J; Sperduto, Paul W; Marsa, Gerald W; Guha, Chandan; Magliocco, Anthony M

    2017-03-01

    To measure co-expression of EGFR and Ki67 proteins in pretreatment tumor biopsies of anal cancer patients enrolled on NRG Oncology RTOG 9811, a phase III trial comparing 5-fluorouracil/mitomycin-C/radiation therapy (Arm A) versus 5-fluorouracil/cisplatin/radiation therapy (Arm B), and to correlate expression with clinical outcome. EGFR and Ki67 co-expression was measured after constructing a tissue microarray using fluorescence immunohistochemistry and automated quantitative image analysis. The Ki67 score within EGFR high versus low areas (Ki67ratio in EGFR high:low ) in each tumor core was analyzed at the median, quartiles, and as a continuous variable. Associations between the tumor markers and clinical endpoints (overall and disease-free survival, locoregional and colostomy failure, and distant metastases) were explored. A total of 282 pretreatment tumors were analyzed from NRG Oncology RTOG 9811. Of evaluated specimens, 183 (65%, n=89, Arm A; n=94, Arm B) were eligible and analyzable. There were no significant differences in baseline characteristics or outcomes between analyzable and unanalyzable patient cases. Median follow-up was 6.0 years. On multivariate analysis, after adjusting for gender, patients with Ki67ratio in EGFR high:low  ≥median had worse overall survival (hazard ratio 2.41, 95% confidence interval 1.38-4.19, P=.0019). After adjusting for N stage and largest tumor dimension, patients with Ki67ratio in EGFR high:low  ≥ median had a higher risk of a disease-free failure (hazard ratio 1.85, 95% confidence interval 1.18-2.92, P=.0078). Technical validation with an independent anal cancer patient cohort was performed and shows a very similar biomarker score distribution. High Ki67ratio in EGFR high:low is associated with worse clinical outcome in this subset of patients with anal cancer treated with chemoradiation on NRG Oncology RTOG 9811. Evaluation within a clinical trial will be required to determine whether patients with these tumor

  6. Prognostic value of the MicroRNA regulators Dicer and Drosha in non-small-cell lung cancer: co-expression of Drosha and miR-126 predicts poor survival.

    Science.gov (United States)

    Lønvik, Kenneth; Sørbye, Sveinung W; Nilsen, Marit N; Paulssen, Ruth H

    2014-01-01

    Dicer and Drosha are important enzymes for processing microRNAs. Recent studies have exhibited possible links between expression of different miRNAs, levels of miRNA processing enzymes, and cancer prognosis. We have investigated the prognostic impact of Dicer and Drosha and their correlation with miR-126 expression in a large cohort of non-small cell lung cancer (NSCLC) patients. We aimed to find patient groups within the cohort that might have an advantage of receiving adjunctive therapies. Dicer expression in the cytoplasm and Drosha expression in the nucleus were evaluated by manual immunohistochemistry of tissue microarrays (TMAs), including tumor tissue samples from 335 patients with resected stages I to IIIA NSCLC. In addition, in situ hybridizations of TMAs for visualization of miR-126 were performed. Kaplan-Meier analysis was performed, and the log-rank test via SPSS v.22 was used for estimating significance levels. In patients with normal performance status (ECOG = 0, n = 197), high Dicer expression entailed a significantly better prognosis than low Dicer expression (P = 0.024). Dicer had no significant prognostic value in patients with reduced performance status (ECOG = 1-2, n = 138). High Drosha expression was significantly correlated with high levels of the microRNA 126 (miR-126) (P = 0.004). Drosha/miR-126 co-expression had a significant negative impact on the disease-specific survival (DSS) rate (P < 0.001). Multivariate analyses revealed that the interaction Dicer*Histology (P = 0.049) and Drosha/miR-126 co-expression (P = 0.033) were independent prognostic factors. In NSCLC patients with normal performance status, Dicer is a positive prognostic factor. The importance of Drosha as a prognostic factor in our material seems to be related to miR-126 and possibly other microRNAs.

  7. A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions

    Directory of Open Access Journals (Sweden)

    Mengqu Ge

    2016-02-01

    Full Text Available As one large class of non-coding RNAs (ncRNAs, long ncRNAs (lncRNAs have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA–protein bipartite network inference (LPBNI. LPBNI aims to identify potential lncRNA–interacting proteins, by making full use of the known lncRNA–protein interactions. Leave-one-out cross validation (LOOCV test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR and protein-based collaborative filtering (ProCF. Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA–interacting proteins.

  8. Synchronization of Hierarchical Time-Varying Neural Networks Based on Asynchronous and Intermittent Sampled-Data Control.

    Science.gov (United States)

    Xiong, Wenjun; Patel, Ragini; Cao, Jinde; Zheng, Wei Xing

    In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.

  9. LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.

    Science.gov (United States)

    Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

    2016-01-11

    Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.

  10. Modeling of the pyruvate production with Escherichia coli: comparison of mechanistic and neural networks-based models.

    Science.gov (United States)

    Zelić, B; Bolf, N; Vasić-Racki, D

    2006-06-01

    Three different models: the unstructured mechanistic black-box model, the input-output neural network-based model and the externally recurrent neural network model were used to describe the pyruvate production process from glucose and acetate using the genetically modified Escherichia coli YYC202 ldhA::Kan strain. The experimental data were used from the recently described batch and fed-batch experiments [ Zelić B, Study of the process development for Escherichia coli-based pyruvate production. PhD Thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, Zagreb, Croatia, July 2003. (In English); Zelić et al. Bioproc Biosyst Eng 26:249-258 (2004); Zelić et al. Eng Life Sci 3:299-305 (2003); Zelić et al Biotechnol Bioeng 85:638-646 (2004)]. The neural networks were built out of the experimental data obtained in the fed-batch pyruvate production experiments with the constant glucose feed rate. The model validation was performed using the experimental results obtained from the batch and fed-batch pyruvate production experiments with the constant acetate feed rate. Dynamics of the substrate and product concentration changes was estimated using two neural network-based models for biomass and pyruvate. It was shown that neural networks could be used for the modeling of complex microbial fermentation processes, even in conditions in which mechanistic unstructured models cannot be applied.

  11. An overview of structurally complex network-based modeling of public opinion in the “We the Media” era

    Science.gov (United States)

    Wang, Guanghui; Wang, Yufei; Liu, Yijun; Chi, Yuxue

    2018-05-01

    As the transmission of public opinion on the Internet in the “We the Media” era tends to be supraterritorial, concealed and complex, the traditional “point-to-surface” transmission of information has been transformed into “point-to-point” reciprocal transmission. A foundation for studies of the evolution of public opinion and its transmission on the Internet in the “We the Media” era can be laid by converting the massive amounts of fragmented information on public opinion that exists on “We the Media” platforms into structurally complex networks of information. This paper describes studies of structurally complex network-based modeling of public opinion on the Internet in the “We the Media” era from the perspective of the development and evolution of complex networks. The progress that has been made in research projects relevant to the structural modeling of public opinion on the Internet is comprehensively summarized. The review considers aspects such as regular grid-based modeling of the rules that describe the propagation of public opinion on the Internet in the “We the Media” era, social network modeling, dynamic network modeling, and supernetwork modeling. Moreover, an outlook for future studies that address complex network-based modeling of public opinion on the Internet is put forward as a summary from the perspective of modeling conducted using the techniques mentioned above.

  12. Complementary Network-Based Approaches for Exploring Genetic Structure and Functional Connectivity in Two Vulnerable, Endemic Ground Squirrels

    Directory of Open Access Journals (Sweden)

    Victoria H. Zero

    2017-06-01

    Full Text Available The persistence of small populations is influenced by genetic structure and functional connectivity. We used two network-based approaches to understand the persistence of the northern Idaho ground squirrel (Urocitellus brunneus and the southern Idaho ground squirrel (U. endemicus, two congeners of conservation concern. These graph theoretic approaches are conventionally applied to social or transportation networks, but here are used to study population persistence and connectivity. Population graph analyses revealed that local extinction rapidly reduced connectivity for the southern species, while connectivity for the northern species could be maintained following local extinction. Results from gravity models complemented those of population graph analyses, and indicated that potential vegetation productivity and topography drove connectivity in the northern species. For the southern species, development (roads and small-scale topography reduced connectivity, while greater potential vegetation productivity increased connectivity. Taken together, the results of the two network-based methods (population graph analyses and gravity models suggest the need for increased conservation action for the southern species, and that management efforts have been effective at maintaining habitat quality throughout the current range of the northern species. To prevent further declines, we encourage the continuation of management efforts for the northern species, whereas conservation of the southern species requires active management and additional measures to curtail habitat fragmentation. Our combination of population graph analyses and gravity models can inform conservation strategies of other species exhibiting patchy distributions.

  13. Generalized functions

    CERN Document Server

    Gelfand, I M; Graev, M I; Vilenkin, N Y; Pyatetskii-Shapiro, I I

    Volume 1 is devoted to basics of the theory of generalized functions. The first chapter contains main definitions and most important properties of generalized functions as functional on the space of smooth functions with compact support. The second chapter talks about the Fourier transform of generalized functions. In Chapter 3, definitions and properties of some important classes of generalized functions are discussed; in particular, generalized functions supported on submanifolds of lower dimension, generalized functions associated with quadratic forms, and homogeneous generalized functions are studied in detail. Many simple basic examples make this book an excellent place for a novice to get acquainted with the theory of generalized functions. A long appendix presents basics of generalized functions of complex variables.

  14. N-Glycosylation of an IgG antibody secreted by Nicotiana tabacum BY-2 cells can be modulated through co-expression of human β-1,4-galactosyltransferase.

    Science.gov (United States)

    Navarre, Catherine; Smargiasso, Nicolas; Duvivier, Laurent; Nader, Joseph; Far, Johann; De Pauw, Edwin; Boutry, Marc

    2017-06-01

    Nicotiana tabacum BY-2 suspension cells have several advantages that make them suitable for the production of full-size monoclonal antibodies which can be purified directly from the culture medium. Carbohydrate characterization of an antibody (Lo-BM2) expressed in N. tabacum BY-2 cells showed that the purified Lo-BM2 displays N-glycan homogeneity with a high proportion (>70%) of the complex GnGnXF glycoform. The stable co-expression of a human β-1,4-galactosyltransferase targeted to different Golgi sub-compartments altered Lo-BM2N-glycosylation and resulted in the production of an antibody that exhibited either hybrid structures containing a low abundance of the plant epitopes (α-1,3-fucose and β-1,2-xylose), or a large amount of galactose-extended N-glycan structures. These results demonstrate the suitability of stable N-glycoengineered N. tabacum BY-2 cell lines for the production of human-like antibodies.

  15. Co-expression of the Thermotoga neapolitana aglB gene with an upstream 3'-coding fragment of the malG gene improves enzymatic characteristics of recombinant AglB cyclomaltodextrinase.

    Science.gov (United States)

    Lunina, Natalia A; Agafonova, Elena V; Chekanovskaya, Lyudmila A; Dvortsov, Igor A; Berezina, Oksana V; Shedova, Ekaterina N; Kostrov, Sergey V; Velikodvorskaya, Galina A

    2007-07-01

    A cluster of Thermotoga neapolitana genes participating in starch degradation includes the malG gene of sugar transport protein and the aglB gene of cyclomaltodextrinase. The start and stop codons of these genes share a common overlapping sequence, aTGAtg. Here, we compared properties of expression products of three different constructs with aglB from T. neapolitana. The first expression vector contained the aglB gene linked to an upstream 90-bp 3'-terminal region of the malG gene with the stop codon overlapping with the start codon of aglB. The second construct included the isolated coding sequence of aglB with two tandem potential start codons. The expression product of this construct in Escherichia coli had two tandem Met residues at its N terminus and was characterized by low thermostability and high tendency to aggregate. In contrast, co-expression of aglB and the 3'-terminal region of malG (the first construct) resulted in AglB with only one N-terminal Met residue and a much higher specific activity of cyclomaltodextrinase. Moreover, the enzyme expressed by such a construct was more thermostable and less prone to aggregation. The third construct was the same as the second one except that it contained only one ATG start codon. The product of its expression had kinetic and other properties similar to those of the enzyme with only one N-terminal Met residue.

  16. Co-expression of cytokeratins and vimentin by highly invasive trophoblast in the white-winged vampire bat, Diaemus youngi, and the black mastiff bat, Molossus ater, with observations on intermediate filament proteins in the decidua and intraplacental trophoblast.

    Science.gov (United States)

    Badwaik, N K; Rasweiler, J J; Muradali, F

    1998-11-01

    Histological and immunocytochemical studies of gravid reproductive tracts obtained from the white-winged vampire bat (Diaemus youngi) and the black mastiff bat (Molossus ater) have established that both species develop unusually invasive trophoblast. This is released by the developing discoidal haemochorial placenta, expresses both cytokeratins and vimentin, and invades the myometrium and adjacent tissues (including the ovaries) via interstitial migration within the walls of maternal blood vessels. Hence, this trophoblast is noteworthy for the extent to which it undergoes an epithelial-mesenchymal transformation. In Molossus, it originates from the cytotrophoblastic shell running along the base of the placenta, is mononuclear, and preferentially invades maternal arterial vessels serving the discoidal placenta. This trophoblast may have a role in dilatation of these vessels when the discoidal placenta becomes functional. In Diaemus, the highly invasive trophoblast appears to originate instead from a layer of syncytiotrophoblast on the periphery of the placenta is multinucleated, and vigorously invades both arterial and venous vessels. During late pregnancy, it becomes extensively branched and sends attenuated processes around many of the myometrial smooth muscle fibres. In view of its distribution, this trophoblast could have important influences upon myometrial contractility and the function of blood vessels serving the gravid tract. Other aspects of intermediate filament expression in the uteri and placentae of these bats are also noteworthy. Many of the decidual giant cells in Molossus co-express cytokeratins and vimentin, while the syncytiotrophoblast lining the placental labyrinth in Diaemus late in pregnancy expresses little cytokeratin.

  17. Improved soluble expression and characterization of the Hc domain of Clostridium botulinum neurotoxin serotype A in Escherichia coli by using a PCR-synthesized gene and a Trx co-expression strain.

    Science.gov (United States)

    Chen, Rongchang; Shi, Jing; Cai, Kun; Tu, Wei; Hou, Xiaojun; Liu, Hao; Xiao, Le; Wang, Qin; Tang, Yunming; Wang, Hui

    2010-05-01

    Botulinum neurotoxin serotype A (BoNT/A) is an extremely potent bacterial protein toxin. The Hc fragment of BoNT/A (AHc) was shown to be non-toxic, antigenic, and capable of eliciting a protective immunity in animals challenged with homologous BoNT. In this study, we synthesized AHc gene by using T4 DNA ligase and PCR. The AHc was expressed at a high level in Escherichia coli successfully. Because of using the Trx co-expression strain, the expressed AHc is in a soluble and active form. The yield of the purified AHc was about 70mg/L, and its purity was up to 90% through one-step affinity chromatography. The AHc was positively identified by the antibodies raised against BoNT/A using immunological-dot-blot and Western blot assays. AHc was shown to bind with gangliosides and elicit immunity against BoNT/A, indicating that the expressed and purified AHc protein retains a functionally active conformation. Furthermore, the purified AHc has a strong immunogenicity and can be used as a potential subunit candidate vaccine for botulinum toxin serotype A. Copyright (c) 2009 Elsevier Inc. All rights reserved.

  18. Transgenic alfalfa plants co-expressing glutathione S-transferase (GST) and human CYP2E1 show enhanced resistance to mixed contaminates of heavy metals and organic pollutants

    International Nuclear Information System (INIS)

    Zhang, Yuanyuan; Liu, Junhong

    2011-01-01

    Transgenic alfalfa plants simultaneously expressing human CYP2E1 and glutathione S-transferase (GST) were generated from hypocotyl segments by the use of an Agrobacterium transformation system for the phytoremediation of the mixed contaminated soil with heavy metals and organic pollutants. The transgenic alfalfa plants were screened by a combination of kanamycin resistance, PCR, GST and CYP2E1 activity and Western blot analysis. The capabilities of mixed contaminants (heavy metals-organic compounds) resistance of pKHCG transgenic alfalfa plants became markedly increased compared with the transgenic alfalfa plants expressing single gene (GST or CYP2E1) and the non-transgenic control plants. The pKHCG alfalfa plants exhibited strong resistance towards the mixtures of cadmium (Cd) and trichloroethylene (TCE) that were metabolized by the introduced GST and CYP2E1 in combination. Our results show that the pKHCG transgenic alfalfa plants have good potential for phytoremediation because they have cross-tolerance towards the complex contaminants of heavy metals and organic pollutants. Therefore, these transgenic alfalfa plants co-expressing GST and human P450 CDNAs may have a great potential for phytoremediation of mixed environmental contaminants.

  19. Transgenic alfalfa plants co-expressing glutathione S-transferase (GST) and human CYP2E1 show enhanced resistance to mixed contaminates of heavy metals and organic pollutants

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Yuanyuan [Department of Pharmaceutics, Qingdao University of Science and Technology, 53 Zhengzhou Road, P.O. Box 70, Qingdao 266042 (China); Liu, Junhong, E-mail: liujh@qust.edu.cn [Department of Pharmaceutics, Qingdao University of Science and Technology, 53 Zhengzhou Road, P.O. Box 70, Qingdao 266042 (China)

    2011-05-15

    Transgenic alfalfa plants simultaneously expressing human CYP2E1 and glutathione S-transferase (GST) were generated from hypocotyl segments by the use of an Agrobacterium transformation system for the phytoremediation of the mixed contaminated soil with heavy metals and organic pollutants. The transgenic alfalfa plants were screened by a combination of kanamycin resistance, PCR, GST and CYP2E1 activity and Western blot analysis. The capabilities of mixed contaminants (heavy metals-organic compounds) resistance of pKHCG transgenic alfalfa plants became markedly increased compared with the transgenic alfalfa plants expressing single gene (GST or CYP2E1) and the non-transgenic control plants. The pKHCG alfalfa plants exhibited strong resistance towards the mixtures of cadmium (Cd) and trichloroethylene (TCE) that were metabolized by the introduced GST and CYP2E1 in combination. Our results show that the pKHCG transgenic alfalfa plants have good potential for phytoremediation because they have cross-tolerance towards the complex contaminants of heavy metals and organic pollutants. Therefore, these transgenic alfalfa plants co-expressing GST and human P450 CDNAs may have a great potential for phytoremediation of mixed environmental contaminants.

  20. Structure Identification of Uncertain Complex Networks Based on Anticipatory Projective Synchronization.

    Directory of Open Access Journals (Sweden)

    Liu Heng

    Full Text Available This paper investigates a method to identify uncertain system parameters and unknown topological structure in general complex networks with or without time delay. A complex network, which has uncertain topology and unknown parameters, is designed as a drive network, and a known response complex network with an input controller is designed to identify the drive network. Under the proposed input controller, the drive network and the response network can achieve anticipatory projective synchronization when the system is steady. Lyapunov theorem and Barbǎlat's lemma guarantee the stability of synchronization manifold between two networks. When the synchronization is achieved, the system parameters and topology in response network can be changed to equal with the parameters and topology in drive network. A numerical example is given to show the effectiveness of the proposed method.

  1. Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

    International Nuclear Information System (INIS)

    Huang Jin-Wang; Feng Jiu-Chao

    2014-01-01

    For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks (WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter (CKPF) is proposed in this paper. We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter (CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter (UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF. (general)

  2. Block-free optical quantum Banyan network based on quantum state fusion and fission

    International Nuclear Information System (INIS)

    Zhu Chang-Hua; Meng Yan-Hong; Quan Dong-Xiao; Zhao Nan; Pei Chang-Xing

    2014-01-01

    Optical switch fabric plays an important role in building multiple-user optical quantum communication networks. Owing to its self-routing property and low complexity, a banyan network is widely used for building switch fabric. While, there is no efficient way to remove internal blocking in a banyan network in a classical way, quantum state fusion, by which the two-dimensional internal quantum states of two photons could be combined into a four-dimensional internal state of a single photon, makes it possible to solve this problem. In this paper, we convert the output mode of quantum state fusion from spatial-polarization mode into time-polarization mode. By combining modified quantum state fusion and quantum state fission with quantum Fredkin gate, we propose a practical scheme to build an optical quantum switch unit which is block free. The scheme can be extended to building more complex units, four of which are shown in this paper. (general)

  3. General Editorial

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education. General Editorial. Articles in Resonance – Journal of Science Education. Volume 19 Issue 1 January 2014 pp 1-2 General Editorial. General Editorial on Publication Ethics · R Ramaswamy · More Details Fulltext PDF. Volume 19 Issue 1 January 2014 pp 3-3 ...

  4. Accurate detection of hierarchical communities in complex networks based on nonlinear dynamical evolution

    Science.gov (United States)

    Zhuo, Zhao; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

    2018-04-01

    One of the most challenging problems in network science is to accurately detect communities at distinct hierarchical scales. Most existing methods are based on structural analysis and manipulation, which are NP-hard. We articulate an alternative, dynamical evolution-based approach to the problem. The basic principle is to computationally implement a nonlinear dynamical process on all nodes in the network with a general coupling scheme, creating a networked dynamical system. Under a proper system setting and with an adjustable control parameter, the community structure of the network would "come out" or emerge naturally from the dynamical evolution of the system. As the control parameter is systematically varied, the community hierarchies at different scales can be revealed. As a concrete example of this general principle, we exploit clustered synchronization as a dynamical mechanism through which the hierarchical community structure can be uncovered. In particular, for quite arbitrary choices of the nonlinear nodal dynamics and coupling scheme, decreasing the coupling parameter from the global synchronization regime, in which the dynamical states of all nodes are perfectly synchronized, can lead to a weaker type of synchronization organized as clusters. We demonstrate the existence of optimal choices of the coupling parameter for which the synchronization clusters encode accurate information about the hierarchical community structure of the network. We test and validate our method using a standard class of benchmark modular networks with two distinct hierarchies of communities and a number of empirical networks arising from the real world. Our method is computationally extremely efficient, eliminating completely the NP-hard difficulty associated with previous methods. The basic principle of exploiting dynamical evolution to uncover hidden community organizations at different scales represents a "game-change" type of approach to addressing the problem of community

  5. Recurrent neural network based hybrid model for reconstructing gene regulatory network.

    Science.gov (United States)

    Raza, Khalid; Alam, Mansaf

    2016-10-01

    One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Neural Network based Control of SG based Standalone Generating System with Energy Storage for Power Quality Enhancement

    Science.gov (United States)

    Nayar, Priya; Singh, Bhim; Mishra, Sukumar

    2017-08-01

    An artificial intelligence based control algorithm is used in solving power quality problems of a diesel engine driven synchronous generator with automatic voltage regulator and governor based standalone system. A voltage source converter integrated with a battery energy storage system is employed to mitigate the power quality problems. An adaptive neural network based signed regressor control algorithm is used for the estimation of the fundamental component of load currents for control of a standalone system with load leveling as an integral feature. The developed model of the system performs accurately under varying load conditions and provides good dynamic response to the step changes in loads. The real time performance is achieved using MATLAB along with simulink/simpower system toolboxes and results adhere to an IEEE-519 standard for power quality enhancement.

  7. Human Detection System by Fusing Depth Map-Based Method and Convolutional Neural Network-Based Method

    Directory of Open Access Journals (Sweden)

    Anh Vu Le

    2017-01-01

    Full Text Available In this paper, the depth images and the colour images provided by Kinect sensors are used to enhance the accuracy of human detection. The depth-based human detection method is fast but less accurate. On the other hand, the faster region convolutional neural network-based human detection method is accurate but requires a rather complex hardware configuration. To simultaneously leverage the advantages and relieve the drawbacks of each method, one master and one client system is proposed. The final goal is to make a novel Robot Operation System (ROS-based Perception Sensor Network (PSN system, which is more accurate and ready for the real time application. The experimental results demonstrate the outperforming of the proposed method compared with other conventional methods in the challenging scenarios.

  8. Network-based Type-2 Fuzzy System with Water Flow Like Algorithm for System Identification and Signal Processing

    Directory of Open Access Journals (Sweden)

    Che-Ting Kuo

    2015-02-01

    Full Text Available This paper introduces a network-based interval type-2 fuzzy inference system (NT2FIS with a dynamic solution agent algorithm water flow like algorithm (WFA, for nonlinear system identification and blind source separation (BSS problem. The NT2FIS consists of interval type-2 asymmetric fuzzy membership functions and TSK-type consequent parts to enhance the performance. The proposed scheme is optimized by a new heuristic learning algorithm, WFA, with dynamic solution agents. The proposed WFA is inspired by the natural behavior of water flow. Splitting, moving, merging, evaporation, and precipitation have all been introduced for optimization. Some modifications, including new moving strategies, such as the application of tabu searching and gradient-descent techniques, are proposed to enhance the performance of the WFA in training the NT2FIS systems. Simulation and comparison results for nonlinear system identification and blind signal separation are presented to illustrate the performance and effectiveness of the proposed approach.

  9. RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

    Science.gov (United States)

    Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour

    2012-09-01

    In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

  10. Development and Flight Testing of a Neural Network Based Flight Control System on the NF-15B Aircraft

    Science.gov (United States)

    Bomben, Craig R.; Smolka, James W.; Bosworth, John T.; Silliams-Hayes, Peggy S.; Burken, John J.; Larson, Richard R.; Buschbacher, Mark J.; Maliska, Heather A.

    2006-01-01

    The Intelligent Flight Control System (IFCS) project at the NASA Dryden Flight Research Center, Edwards AFB, CA, has been investigating the use of neural network based adaptive control on a unique NF-15B test aircraft. The IFCS neural network is a software processor that stores measured aircraft response information to dynamically alter flight control gains. In 2006, the neural network was engaged and allowed to learn in real time to dynamically alter the aircraft handling qualities characteristics in the presence of actual aerodynamic failure conditions injected into the aircraft through the flight control system. The use of neural network and similar adaptive technologies in the design of highly fault and damage tolerant flight control systems shows promise in making future aircraft far more survivable than current technology allows. This paper will present the results of the IFCS flight test program conducted at the NASA Dryden Flight Research Center in 2006, with emphasis on challenges encountered and lessons learned.

  11. Modeling of a 5-cell direct methanol fuel cell using adaptive-network-based fuzzy inference systems

    Science.gov (United States)

    Wang, Rongrong; Qi, Liang; Xie, Xiaofeng; Ding, Qingqing; Li, Chunwen; Ma, ChenChi M.

    The methanol concentrations, temperature and current were considered as inputs, the cell voltage was taken as output, and the performance of a direct methanol fuel cell (DMFC) was modeled by adaptive-network-based fuzzy inference systems (ANFIS). The artificial neural network (ANN) and polynomial-based models were selected to be compared with the ANFIS in respect of quality and accuracy. Based on the ANFIS model obtained, the characteristics of the DMFC were studied. The results show that temperature and methanol concentration greatly affect the performance of the DMFC. Within a restricted current range, the methanol concentration does not greatly affect the stack voltage. In order to obtain higher fuel utilization efficiency, the methanol concentrations and temperatures should be adjusted according to the load on the system.

  12. Development of network based control and data acquisition systems for diagnostics using CCD detectors. Application to LHD experiments

    International Nuclear Information System (INIS)

    Kado, Shinichiro; Nakanishi, Hideya; Ida, Katsumi; Kojima, Mamoru

    2000-01-01

    The needs of CCD detectors as a plasma diagnostic tool have recently been increased. However, many CCD providers have developed their own controlling systems, and it is difficult to customized the usages in order to make them applicable to the network-based data acquisition clusters which consist of various sorts of diagnostics. This paper presents the development of systems in which CCD detectors are controlled and the data are acquired through networks. By making use of the Client/Server (C/S) model in the Windows NT operating system and block transfer method via shared memory relevant to the model, the dependence on the hardware is hidden by the server service, CCD list sequencer. The client program is designed for the LHD (Large Helical Device) discharge operation sequences and the data acquisition system. (author)

  13. Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Li, Kangji [Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027 (China); School of Electricity Information Engineering, Jiangsu University, Zhenjiang 212013 (China); Su, Hongye [Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027 (China)

    2010-11-15

    There are several ways to forecast building energy consumption, varying from simple regression to models based on physical principles. In this paper, a new method, namely, the hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system (GA-HANFIS) model is developed. In this model, hierarchical structure decreases the rule base dimension. Both clustering and rule base parameters are optimized by GAs and neural networks (NNs). The model is applied to predict a hotel's daily air conditioning consumption for a period over 3 months. The results obtained by the proposed model are presented and compared with regular method of NNs, which indicates that GA-HANFIS model possesses better performance than NNs in terms of their forecasting accuracy. (author)

  14. Modeling of a 5-cell direct methanol fuel cell using adaptive-network-based fuzzy inference systems

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Rongrong; Li, Chunwen [Department of Automation, Tsinghua University, Beijing 100084 (China); Qi, Liang; Xie, Xiaofeng [Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084 (China); Ding, Qingqing [Department of Electrical Engineering, Tsinghua University, Beijing 100084 (China); Ma, ChenChi M. [National Tsing Hua University, Hsinchu 300 (China)

    2008-12-01

    The methanol concentrations, temperature and current were considered as inputs, the cell voltage was taken as output, and the performance of a direct methanol fuel cell (DMFC) was modeled by adaptive-network-based fuzzy inference systems (ANFIS). The artificial neural network (ANN) and polynomial-based models were selected to be compared with the ANFIS in respect of quality and accuracy. Based on the ANFIS model obtained, the characteristics of the DMFC were studied. The results show that temperature and methanol concentration greatly affect the performance of the DMFC. Within a restricted current range, the methanol concentration does not greatly affect the stack voltage. In order to obtain higher fuel utilization efficiency, the methanol concentrations and temperatures should be adjusted according to the load on the system. (author)

  15. Real-time neural network-based self-tuning control of a nonlinear electro-hydraulic servomotor

    Energy Technology Data Exchange (ETDEWEB)

    Canelon, J.I.; Ortega, A.G. [Univ. del Zulia, Maracaibo, Zulia (Venezuela, Bolivarian Republic of). School of Electrical Engineering; Shieh, L.S. [Houston Univ., Houston, TX (United States). Dept. of Electrical and Computer Engineering; Bastidas, J.I. [Univ. del Zulia, Maracaibo, Zulia (Venezuela, Bolivarian Republic of). School of Mechanical Engineering; Zhang, Y.; Akujuobi, C.M. [Prairie View A and M Univ., Prairie View, TX (United States). Center of Excellence for Communication Systems Technology Research and Dept. of Engineering Technology

    2010-08-13

    For high power applications, hydraulic actuators offer many advantages over electromagnetic actuators, including higher torque/mass ratios; smaller control gains; excellent torque capability; filtered high frequency noise; better heat transfer characteristics; smaller size; higher speed of response of the servomechanism; cheaper hardware; and higher reliability. Therefore, any application that requires a large force applied smoothly by an actuator is a candidate for hydraulic power. Examples of such applications include vehicle steering and braking systems; roll mills; drilling rigs; heavy duty crane and presses; and industrial robots and actuators for aircraft control surfaces such as ailerons and flaps. It is extremely important to create effective control strategies for hydraulic systems. This paper outlined the real-time implementation of a neural network-based approach, for self-tuning control of the angular position of a nonlinear electro-hydraulic servomotor. Using an online training algorithm, a neural network autoregressive moving-average model with exogenous input (ARMAX) model of the system was identified and continuously updated and an optimal linear ARMAX model was determined. The paper briefly depicted the neural network-based self-tuning control approach and a description of the experimental equipment (hardware and software) was presented including the implementation details. The experimental results were discussed and conclusions were summarized. It was found that the approach proved to be very effective in the control of this fast dynamics system, outperforming a fine tuned PI controller. Therefore, although the self-tuning approach was computationally demanding, it was feasible for real-time implementation. 22 refs., 6 figs.

  16. A Digital Architecture for a Network-Based Learning Health System: Integrating Chronic Care Management, Quality Improvement, and Research.

    Science.gov (United States)

    Marsolo, Keith; Margolis, Peter A; Forrest, Christopher B; Colletti, Richard B; Hutton, John J

    2015-01-01

    We collaborated with the ImproveCareNow Network to create a proof-of-concept architecture for a network-based Learning Health System. This collaboration involved transitioning an existing registry to one that is linked to the electronic health record (EHR), enabling a "data in once" strategy. We sought to automate a series of reports that support care improvement while also demonstrating the use of observational registry data for comparative effectiveness research. We worked with three leading EHR vendors to create EHR-based data collection forms. We automated many of ImproveCareNow's analytic reports and developed an application for storing protected health information and tracking patient consent. Finally, we deployed a cohort identification tool to support feasibility studies and hypothesis generation. There is ongoing uptake of the system. To date, 31 centers have adopted the EHR-based forms and 21 centers are uploading data to the registry. Usage of the automated reports remains high and investigators have used the cohort identification tools to respond to several clinical trial requests. The current process for creating EHR-based data collection forms requires groups to work individually with each vendor. A vendor-agnostic model would allow for more rapid uptake. We believe that interfacing network-based registries with the EHR would allow them to serve as a source of decision support. Additional standards are needed in order for this vision to be achieved, however. We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build. We have also highlighted opportunities where sponsors could help accelerate progress.

  17. A Flexible Experimental Laboratory for Distributed Generation Networks Based on Power Inverters

    Directory of Open Access Journals (Sweden)

    Jaume Miret

    2017-10-01

    Full Text Available In the recently deregulated electricity market, distributed generation based on renewable sources is becoming more and more relevant. In this area, two main distributed scenarios are focusing the attention of recent research: grid-connected mode, where the generation sources are connected to a grid mainly supplied by big power plants, and islanded mode, where the distributed sources, energy storage devices, and loads compose an autonomous entity that in its general form can be named a microgrid. To conduct a successful research in these two scenarios, it is essential to have a flexible experimental setup. This work deals with the description of a real laboratory setup composed of four nodes that can emulate both scenarios of a distributed generation network. A comprehensive description of the hardware and software setup will be done, focusing especially in the dual-core DSP used for control purposes, which is next to the industry standards and able to emulate real complexities. A complete experimental section will show the main features of the system.

  18. Simulation model for centrifugal pump in flow networks based on internal characteristics

    International Nuclear Information System (INIS)

    Sun, Ji-Lin; Xue, Ruo-Jun; Peng, Min-Jun

    2018-01-01

    For the simulation of centrifugal pump in flow network system, in general three approaches can be used, the fitting model, the numerical method and the internal characteristics model. The fitting model is simple and rapid thus widely used. The numerical method can provide more detailed information in comparison with the fitting model, but increases implementation complexity and computational cost. In real-time simulations of flow networks, to simulate the condition out of the rated condition, especially for the volume flow rate, which the accuracy of fitting model is incredible, a new method for simulating centrifugal pumps was proposed in this research. The method based on the theory head and hydraulic loss in centrifugal pumps, and cavitation is also to be considered. The simulation results are verified with experimental benchmark data from an actual pump. The comparison confirms that the proposed method could fit the flow-head curves well, and the responses of main parameters in dynamic-state operations are consistent with theoretical analyses.

  19. Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

    Science.gov (United States)

    Kesharaju, Manasa; Nagarajah, Romesh

    2015-09-01

    The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.

  20. Optimal Path Choice in Railway Passenger Travel Network Based on Residual Train Capacity

    Directory of Open Access Journals (Sweden)

    Fei Dou

    2014-01-01

    Full Text Available Passenger’s optimal path choice is one of the prominent research topics in the field of railway passenger transport organization. More and more different train types are available, increasing path choices from departure to destination for travelers are unstoppable. However, travelers cannot avoid being confused when they hope to choose a perfect travel plan based on various travel time and cost constraints before departure. In this study, railway passenger travel network is constructed based on train timetable. Both the generalized cost function we developed and the residual train capacity are considered to be the foundation of path searching procedure. The railway passenger travel network topology is analyzed based on residual train capacity. Considering the total travel time, the total travel cost, and the total number of passengers, we propose an optimal path searching algorithm based on residual train capacity in railway passenger travel network. Finally, the rationale of the railway passenger travel network and the optimal path generation algorithm are verified positively by case study.

  1. Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ships Recognition in Infrared Images.

    Science.gov (United States)

    Khellal, Atmane; Ma, Hongbin; Fei, Qing

    2018-05-09

    The success of Deep Learning models, notably convolutional neural networks (CNNs), makes them the favorable solution for object recognition systems in both visible and infrared domains. However, the lack of training data in the case of maritime ships research leads to poor performance due to the problem of overfitting. In addition, the back-propagation algorithm used to train CNN is very slow and requires tuning many hyperparameters. To overcome these weaknesses, we introduce a new approach fully based on Extreme Learning Machine (ELM) to learn useful CNN features and perform a fast and accurate classification, which is suitable for infrared-based recognition systems. The proposed approach combines an ELM based learning algorithm to train CNN for discriminative features extraction and an ELM based ensemble for classification. The experimental results on VAIS dataset, which is the largest dataset of maritime ships, confirm that the proposed approach outperforms the state-of-the-art models in term of generalization performance and training speed. For instance, the proposed model is up to 950 times faster than the traditional back-propagation based training of convolutional neural networks, primarily for low-level features extraction.

  2. An auxiliary optimization method for complex public transit route network based on link prediction

    Science.gov (United States)

    Zhang, Lin; Lu, Jian; Yue, Xianfei; Zhou, Jialin; Li, Yunxuan; Wan, Qian

    2018-02-01

    Inspired by the missing (new) link prediction and the spurious existing link identification in link prediction theory, this paper establishes an auxiliary optimization method for public transit route network (PTRN) based on link prediction. First, link prediction applied to PTRN is described, and based on reviewing the previous studies, the summary indices set and its algorithms set are collected for the link prediction experiment. Second, through analyzing the topological properties of Jinan’s PTRN established by the Space R method, we found that this is a typical small-world network with a relatively large average clustering coefficient. This phenomenon indicates that the structural similarity-based link prediction will show a good performance in this network. Then, based on the link prediction experiment of the summary indices set, three indices with maximum accuracy are selected for auxiliary optimization of Jinan’s PTRN. Furthermore, these link prediction results show that the overall layout of Jinan’s PTRN is stable and orderly, except for a partial area that requires optimization and reconstruction. The above pattern conforms to the general pattern of the optimal development stage of PTRN in China. Finally, based on the missing (new) link prediction and the spurious existing link identification, we propose optimization schemes that can be used not only to optimize current PTRN but also to evaluate PTRN planning.

  3. The study of RMB exchange rate complex networks based on fluctuation mode

    Science.gov (United States)

    Yao, Can-Zhong; Lin, Ji-Nan; Zheng, Xu-Zhou; Liu, Xiao-Feng

    2015-10-01

    In the paper, we research on the characteristics of RMB exchange rate time series fluctuation with methods of symbolization and coarse gaining. First, based on fluctuation features of RMB exchange rate, we define the first type of fluctuation mode as one specific foreign currency against RMB in four days' fluctuating situations, and the second type as four different foreign currencies against RMB in one day's fluctuating situation. With the transforming method, we construct the unique-currency and multi-currency complex networks. Further, through analyzing the topological features including out-degree, betweenness centrality and clustering coefficient of fluctuation-mode complex networks, we find that the out-degree distribution of both types of fluctuation mode basically follows power-law distributions with exponents between 1 and 2. The further analysis reveals that the out-degree and the clustering coefficient generally obey the approximated negative correlation. With this result, we confirm previous observations showing that the RMB exchange rate exhibits a characteristic of long-range memory. Finally, we analyze the most probable transmission route of fluctuation modes, and provide probability prediction matrix. The transmission route for RMB exchange rate fluctuation modes exhibits the characteristics of partially closed loop, repeat and reversibility, which lays a solid foundation for predicting RMB exchange rate fluctuation patterns with large volume of data.

  4. Reconstruction and analysis of the lncRNA-miRNA-mRNA network based on competitive endogenous RNA reveal functional lncRNAs in rheumatoid arthritis.

    Science.gov (United States)

    Jiang, Hui; Ma, Rong; Zou, Shubiao; Wang, Yongzhong; Li, Zhuqing; Li, Weiping

    2017-06-01

    Rheumatoid arthritis (RA) is an autoimmune disease with an unknown etiology, occurring in approximately 1.0% of general population. More and more studies have suggested that long non-coding RNAs (lncRNAs) could play important roles in various biological processes and be associated with the pathogenesis of different kinds of diseases including RA. Although a large number of lncRNAs have been found, our knowledge of their function and physiological/pathological significance is still in its infancy. In order to reveal functional lncRNAs and identify the key lncRNAs in RA, we reconstructed a global triple network based on the competitive endogenous RNA (ceRNA) theory using the data from National Center for Biotechnology Information Gene Expression Omnibus and our previous paper. Meanwhile, Gene Ontology (GO) and pathway analysis were performed using Cytoscape plug-in BinGO and Database for Annotation, Visualization, and Integration Discovery (DAVID), respectively. We found that the lncRNA-miRNA-mRNA network was composed of 7 lncRNA nodes, 90 mRNA nodes, 24 miRNA nodes, and 301 edges. The functional assay showed that 147 GO terms and 23 pathways were enriched. In addition, three lncRNAs (S5645.1, XR_006437.1, J01878) were highly related to RA, and therefore, were selected as key lncRNAs. This study suggests that specific lncRNAs are associated with the development of RA, and three lncRNAs (S5645.1, XR_006437.1, J01878) could be used as potential diagnostic biomarkers and therapeutic targets.

  5. Recruitment and retention in a 10-month social network-based intervention promoting diabetes self-management in socioeconomically deprived patients: a qualitative process evaluation.

    Science.gov (United States)

    Vissenberg, Charlotte; Nierkens, Vera; Uitewaal, Paul J M; Middelkoop, Barend J C; Stronks, Karien

    2017-07-26

    Socioeconomically deprived patients with type 2 diabetes often face challenges with self-management, resulting in more diabetes-related complications. However, these groups are often under-represented in self-management interventions. Evidence on effective recruitment and retention strategies is growing, but lacking for intensive self-management interventions. This study aims to explore recruitment, retention and effective intervention strategies in a 10-month group-based intervention among Dutch, Moroccan, Turkish and Surinamese patients from socioeconomically deprived neighbourhoods. Participants were recruited through general practitioners (GPs) and participated in a 10-month social network-based intervention (10 groups, n=69): Powerful Together with Diabetes . This intervention also targeted the significant others of participants and aimed to increase social support for self-management and to decrease social influences hindering self-management. A qualitative process evaluation was conducted. Retention was measured using log books kept by group leaders. Further, we conducted 17 in-depth interviews with participants (multiethnic sample) and 18 with group leaders. Interviews were transcribed, coded and analysed using framework analyses. The GP's letter and reminder calls, an informational meeting and the intervention's informal nature facilitated recruitment. During the first months, positive group atmosphere, the intervention's perceived usefulness, opportunities to socialise and a reduction in practical barriers facilitated retention. After the first months, conflicting responsibilities and changes in the intervention's nature and planning hindered retention. Calls from group leaders and the prospect of a diploma helped participants overcome these barriers. To promote retention in lengthy self-management interventions, it seems important that patients feel they are going on an outing to a social gathering that is enjoyable, recreational, useful and easy to

  6. Switchgrass genomic diversity, ploidy, and evolution: novel insights from a network-based SNP discovery protocol.

    Directory of Open Access Journals (Sweden)

    Fei Lu

    Full Text Available Switchgrass (Panicum virgatum L. is a perennial grass that has been designated as an herbaceous model biofuel crop for the United States of America. To facilitate accelerated breeding programs of switchgrass, we developed both an association panel and linkage populations for genome-wide association study (GWAS and genomic selection (GS. All of the 840 individuals were then genotyped using genotyping by sequencing (GBS, generating 350 GB of sequence in total. As a highly heterozygous polyploid (tetraploid and octoploid species lacking a reference genome, switchgrass is highly intractable with earlier methodologies of single nucleotide polymorphism (SNP discovery. To access the genetic diversity of species like switchgrass, we developed a SNP discovery pipeline based on a network approach called the Universal Network-Enabled Analysis Kit (UNEAK. Complexities that hinder single nucleotide polymorphism discovery, such as repeats, paralogs, and sequencing errors, are easily resolved with UNEAK. Here, 1.2 million putative SNPs were discovered in a diverse collection of primarily upland, northern-adapted switchgrass populations. Further analysis of this data set revealed the fundamentally diploid nature of tetraploid switchgrass. Taking advantage of the high conservation of genome structure between switchgrass and foxtail millet (Setaria italica (L. P. Beauv., two parent-specific, synteny-based, ultra high-density linkage maps containing a total of 88,217 SNPs were constructed. Also, our results showed clear patterns of isolation-by-distance and isolation-by-ploidy in natural populations of switchgrass. Phylogenetic analysis supported a general south-to-north migration path of switchgrass. In addition, this analysis suggested that upland tetraploid arose from upland octoploid. All together, this study provides unparalleled insights into the diversity, genomic complexity, population structure, phylogeny, phylogeography, ploidy, and evolutionary dynamics

  7. Adaptive control of structural balance for complex dynamical networks based on dynamic coupling of nodes

    Science.gov (United States)

    Gao, Zilin; Wang, Yinhe; Zhang, Lili

    2018-02-01

    In the existing research results of the complex dynamical networks controlled, the controllers are mainly used to guarantee the synchronization or stabilization of the nodes’ state, and the terms coupled with connection relationships may affect the behaviors of nodes, this obviously ignores the dynamic common behavior of the connection relationships between the nodes. In fact, from the point of view of large-scale system, a complex dynamical network can be regarded to be composed of two time-varying dynamic subsystems, which can be called the nodes subsystem and the connection relationships subsystem, respectively. Similar to the synchronization or stabilization of the nodes subsystem, some characteristic phenomena can be also emerged in the connection relationships subsystem. For example, the structural balance in the social networks and the synaptic facilitation in the biological neural networks. This paper focuses on the structural balance in dynamic complex networks. Generally speaking, the state of the connection relationships subsystem is difficult to be measured accurately in practical applications, and thus it is not easy to implant the controller directly into the connection relationships subsystem. It is noted that the nodes subsystem and the relationships subsystem are mutually coupled, which implies that the state of the connection relationships subsystem can be affected by the controllable state of nodes subsystem. Inspired by this observation, by using the structural balance theory of triad, the controller with the parameter adaptive law is proposed for the nodes subsystem in this paper, which may ensure the connection relationship matrix to approximate a given structural balance matrix in the sense of the uniformly ultimately bounded (UUB). That is, the structural balance may be obtained by employing the controlling state of the nodes subsystem. Finally, the simulations are used to show the validity of the method in this paper.

  8. Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction

    Directory of Open Access Journals (Sweden)

    Chengdong Li

    2018-01-01

    Full Text Available To enhance the prediction performance for building energy consumption, this paper presents a modified deep belief network (DBN based hybrid model. The proposed hybrid model combines the outputs from the DBN model with the energy-consuming pattern to yield the final prediction results. The energy-consuming pattern in this study represents the periodicity property of building energy consumption and can be extracted from the observed historical energy consumption data. The residual data generated by removing the energy-consuming pattern from the original data are utilized to train the modified DBN model. The training of the modified DBN includes two steps, the first one of which adopts the contrastive divergence (CD algorithm to optimize the hidden parameters in a pre-train way, while the second one determines the output weighting vector by the least squares method. The proposed hybrid model is applied to two kinds of building energy consumption data sets that have different energy-consuming patterns (daily-periodicity and weekly-periodicity. In order to examine the advantages of the proposed model, four popular artificial intelligence methods—the backward propagation neural network (BPNN, the generalized radial basis function neural network (GRBFNN, the extreme learning machine (ELM, and the support vector regressor (SVR are chosen as the comparative approaches. Experimental results demonstrate that the proposed DBN based hybrid model has the best performance compared with the comparative techniques. Another thing to be mentioned is that all the predictors constructed by utilizing the energy-consuming patterns perform better than those designed only by the original data. This verifies the usefulness of the incorporation of the energy-consuming patterns. The proposed approach can also be extended and applied to some other similar prediction problems that have periodicity patterns, e.g., the traffic flow forecasting and the electricity consumption

  9. Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata.

    Science.gov (United States)

    Chen, Yangzhou; Guo, Yuqi; Wang, Ying

    2017-03-29

    In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research.

  10. Sensor Network-Based Localization for Continuous Tracking Applications: Implementation and Performance Evaluation

    Directory of Open Access Journals (Sweden)

    Franco Davoli

    2008-10-01

    Full Text Available The increasing interest in systems able to provide users with immersive services (e.g., domotics, context-aware applications, and immersive distance learning tools has encouraged the development of cheap and effective platforms aimed at tracking objects and people within a certain space. In this context, wireless sensor networks (WSNs can play a very important role, since specialized sensors can be fruitfully exploited in order to generate/receive signals by means of which the WSN can derive the position of nodes joined to the objects to be tracked. The paper presents an original localization platform that exploits a single-hop WSN, based on a Microchip MCU and a Cypress RF device, to track its moving nodes. Specifically, the nodes of the network are divided into three sets: the first set consists of anchor nodes that, according to the commands from the sink (the central node of the WSN, generate ultrasonic pulses. These pulses are received by the second set of (moving nodes, which estimate the pulse time trip and communicate it to the sink. Finally, the last set is constituted by general purpose nodes that collect any kind of data from the surrounding field. The sink gathers all the data, computes the position of moving nodes, and transfers information to external users on the Internet. The algorithms adopted to manage the network and to localize moving nodes are discussed. A working prototype based upon the hardware platform, software, and protocol described in this paper has been deployed and tested, and some results are shown. Simulation results of the localization system are presented to show system scalability.

  11. Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders.

    Science.gov (United States)

    Liu, Han; Zhou, Jianzhong; Zheng, Yang; Jiang, Wei; Zhang, Yuncheng

    2018-04-19

    As the rolling bearings being the key part of rotary machine, its healthy condition is quite important for safety production. Fault diagnosis of rolling bearing has been research focus for the sake of improving the economic efficiency and guaranteeing the operation security. However, the collected signals are mixed with ambient noise during the operation of rotary machine, which brings great challenge to the exact diagnosis results. Using signals collected from multiple sensors can avoid the loss of local information and extract more helpful characteristics. Recurrent Neural Networks (RNN) is a type of artificial neural network which can deal with multiple time sequence data. The capacity of RNN has been proved outstanding for catching time relevance about time sequence data. This paper proposed a novel method for bearing fault diagnosis with RNN in the form of an autoencoder. In this approach, multiple vibration value of the rolling bearings of the next period are predicted from the previous period by means of Gated Recurrent Unit (GRU)-based denoising autoencoder. These GRU-based non-linear predictive denoising autoencoders (GRU-NP-DAEs) are trained with strong generalization ability for each different fault pattern. Then for the given input data, the reconstruction errors between the next period data and the output data generated by different GRU-NP-DAEs are used to detect anomalous conditions and classify fault type. Classic rotating machinery datasets have been employed to testify the effectiveness of the proposed diagnosis method and its preponderance over some state-of-the-art methods. The experiment results indicate that the proposed method achieves satisfactory performance with strong robustness and high classification accuracy. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  12. Generalized product

    OpenAIRE

    Greco, Salvatore; Mesiar, Radko; Rindone, Fabio

    2014-01-01

    Aggregation functions on [0,1] with annihilator 0 can be seen as a generalized product on [0,1]. We study the generalized product on the bipolar scale [–1,1], stressing the axiomatic point of view. Based on newly introduced bipolar properties, such as the bipolar increasingness, bipolar unit element, bipolar idempotent element, several kinds of generalized bipolar product are introduced and studied. A special stress is put on bipolar semicopulas, bipolar quasi-copulas and bipolar copulas.

  13. Generalized Tavis-Cummings models and quantum networks

    Science.gov (United States)

    Gorokhov, A. V.

    2018-04-01

    The properties of quantum networks based on generalized Tavis-Cummings models are theoretically investigated. We have calculated the information transfer success rate from one node to another in a simple model of a quantum network realized with two-level atoms placed in the cavities and interacting with an external laser field and cavity photons. The method of dynamical group of the Hamiltonian and technique of corresponding coherent states were used for investigation of the temporal dynamics of the two nodes model.

  14. Protection of guinea pigs by vaccination with a recombinant swinepox virus co-expressing HA1 genes of swine H1N1 and H3N2 influenza viruses.

    Science.gov (United States)

    Xu, Jiarong; Yang, Deji; Huang, Dongyan; Xu, Jiaping; Liu, Shichao; Lin, Huixing; Zhu, Haodan; Liu, Bao; Lu, Chengping

    2013-03-01

    Swine influenza (SI) is an acute respiratory infectious disease of swine caused by swine influenza virus (SIV). SIV is not only an important respiratory pathogen in pigs but also a potent threat to human health. Here, we report the construction of a recombinant swinepox virus (rSPV/H3-2A-H1) co-expressing hemagglutinin (HA1) of SIV subtypes H1N1 and H3N2. Immune responses and protection efficacy of the rSPV/H3-2A-H1 were evaluated in guinea pigs. Inoculation of rSPV/H3-2A-H1 yielded neutralizing antibodies against SIV H1N1 and H3N2. The IFN-γ and IL-4 concentrations in the supernatant of lymphocytes stimulated with purified SIV HA1 antigen were significantly higher (P guinea pigs against SIV H1N1 or H3N2 challenge was observed. No SIV shedding was detected from guinea pigs vaccinated with rSPV/H3-2A-H1 after challenge. Most importantly, the guinea pigs immunized with rSPV/H3-2A-H1 did not show gross and micrographic lung lesions. However, the control guinea pigs experienced distinct gross and micrographic lung lesions at 7 days post-challenge. Our data suggest that the recombinant swinepox virus encoding HA1 of SIV H1N1 and H3N2 might serve as a promising candidate vaccine for protection against SIV H1N1 and H3N2 infections.

  15. Nitric oxide synthase-I containing cortical interneurons co-express antioxidative enzymes and anti-apoptotic Bcl-2 following focal ischemia: evidence for direct and indirect mechanisms towards their resistance to neuropathology.

    Science.gov (United States)

    Bidmon, H J; Emde, B; Kowalski, T; Schmitt, M; Mayer, B; Kato, K; Asayama, K; Witte, O W; Zilles, K

    2001-09-01

    Neuronal nitric oxide-I is constitutively expressed in approximately 2% of cortical interneurons and is co-localized with gamma-amino butric acid, somatostatin or neuropeptide Y. These interneurons additionally express high amounts of glutamate receptors which mediate the glutamate-induced hyperexcitation following cerebral injury, under these conditions nitric oxide production increases contributing to a potentiation of oxidative stress. However, perilesional nitric oxide synthase-I containing neurons are known to be resistant to ischemic and excitotoxic injury. In vitro studies show that nitrosonium and nitroxyl ions inactivate N-methyl-D-aspartate receptors, resulting in neuroprotection. The question remains of how these cells are protected against their own high intracellular nitric oxide production after activation. In this study, we investigated immunocytochemically nitric oxide synthase-I containing cortical neurons in rats after unilateral, cortical photothrombosis. In this model of focal ischemia, perilesional, constitutively nitric oxide synthase-I containing neurons survived and co-expressed antioxidative enzymes, such as manganese- and copper-zinc-dependent superoxide dismutases, heme oxygenase-2 and cytosolic glutathione peroxidase. This enhanced antioxidant expression was accompanied by a strong perinuclear presence of the antiapoptotic Bcl-2 protein. No colocalization was detectable with upregulated heme oxygenase-1 in glia and the superoxide and prostaglandin G(2)-producing cyclooxygenase-2 in neurons. These results suggest that nitric oxide synthase-I containing interneurons are protected against intracellular oxidative damage and apoptosis by Bcl-2 and several potent antioxidative enzymes. Since nitric oxide synthase-I positive neurons do not express superoxide-producing enzymes such as cyclooxygenase-1, xanthine oxidase and cyclooxygenase-2 in response to injury, this may additionally contribute to their resistance by reducing their internal

  16. General relativity

    International Nuclear Information System (INIS)

    Kenyon, I.R.

    1990-01-01

    General relativity is discussed in this book at a level appropriate to undergraduate students of physics and astronomy. It describes concepts and experimental results, and provides a succinct account of the formalism. A brief review of special relativity is followed by a discussion of the equivalence principle and its implications. Other topics covered include the concepts of curvature and the Schwarzschild metric, test of the general theory, black holes and their properties, gravitational radiation and methods for its detection, the impact of general relativity on cosmology, and the continuing search for a quantum theory of gravity. (author)

  17. A social network-based intervention stimulating peer influence on children's self-reported water consumption: A randomized control trial.

    Science.gov (United States)

    Smit, Crystal R; de Leeuw, Rebecca N H; Bevelander, Kirsten E; Burk, William J; Buijzen, Moniek

    2016-08-01

    The current pilot study examined the effectiveness of a social network-based intervention using peer influence on self-reported water consumption. A total of 210 children (52% girls; M age = 10.75 ± SD = 0.80) were randomly assigned to either the intervention (n = 106; 52% girls) or control condition (n = 104; 52% girls). In the intervention condition, the most influential children in each classroom were trained to promote water consumption among their peers for eight weeks. The schools in the control condition did not receive any intervention. Water consumption, sugar-sweetened beverage (SSB) consumption, and intentions to drink more water in the near future were assessed by self-report measures before and immediately after the intervention. A repeated measure MANCOVA showed a significant multivariate interaction effect between condition and time (V = 0.07, F(3, 204) = 5.18, p = 0.002, pη(2) = 0.07) on the dependent variables. Further examination revealed significant univariate interaction effects between condition and time on water (p = 0.021) and SSB consumption (p = 0.015) as well as water drinking intentions (p = 0.049). Posthoc analyses showed that children in the intervention condition reported a significant increase in their water consumption (p = 0.018) and a decrease in their SSB consumption (p  0.05). The children who were exposed to the intervention did not report a change in their water drinking intentions over time (p = 0.576) whereas the nonexposed children decreased their intentions (p = 0.026). These findings show promise for a social network-based intervention using peer influence to positively alter consumption behaviors. This RCT was registered in the Australian New Zealand Clinical Trials Registry (ACTRN12614001179628). Study procedures were approved by the Ethics Committee of the Faculty of Social Sciences at Radboud University (ECSW2014-1003-203). Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. General problems

    International Nuclear Information System (INIS)

    2005-01-01

    This article presents the general problems as natural disasters, consequences of global climate change, public health, the danger of criminal actions, the availability to information about problems of environment

  19. Generalized Recovery

    DEFF Research Database (Denmark)

    Jensen, Christian Skov; Lando, David; Pedersen, Lasse Heje

    We characterize when physical probabilities, marginal utilities, and the discount rate can be recovered from observed state prices for several future time periods. Our characterization makes no assumptions of the probability distribution, thus generalizing the time-homogeneous stationary model...

  20. General Conformity

    Science.gov (United States)

    The General Conformity requirements ensure that the actions taken by federal agencies in nonattainment and maintenance areas do not interfere with a state’s plans to meet national standards for air quality.

  1. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    Directory of Open Access Journals (Sweden)

    Lund Ole

    2009-09-01

    Full Text Available Abstract Background The major histocompatibility complex (MHC molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event. Results Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods. Conclusion The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCII-2.0.

  2. The performance evaluation of a new neural network based traffic management scheme for a satellite communication network

    Science.gov (United States)

    Ansari, Nirwan; Liu, Dequan

    1991-01-01

    A neural-network-based traffic management scheme for a satellite communication network is described. The scheme consists of two levels of management. The front end of the scheme is a derivation of Kohonen's self-organization model to configure maps for the satellite communication network dynamically. The model consists of three stages. The first stage is the pattern recognition task, in which an exemplar map that best meets the current network requirements is selected. The second stage is the analysis of the discrepancy between the chosen exemplar map and the state of the network, and the adaptive modification of the chosen exemplar map to conform closely to the network requirement (input data pattern) by means of Kohonen's self-organization. On the basis of certain performance criteria, whether a new map is generated to replace the original chosen map is decided in the third stage. A state-dependent routing algorithm, which arranges the incoming call to some proper path, is used to make the network more efficient and to lower the call block rate. Simulation results demonstrate that the scheme, which combines self-organization and the state-dependent routing mechanism, provides better performance in terms of call block rate than schemes that only have either the self-organization mechanism or the routing mechanism.

  3. Immune system and zinc are associated with recurrent aphthous stomatitis. An assessment using a network-based approach.

    Directory of Open Access Journals (Sweden)

    César Rivera

    2017-09-01

    Full Text Available Objective: The aim of this research was to identify genes, proteins and processes from the biomedical information published on recurrent aphthous stomatitis (RAS using network-based foci. Methods: The clinical context was defined using MeSH terms for RAS and biomarkers, combined with words associated with risk. A set of protein coding genes was prioritized using the Génie web server and classified with PANTHER. For defining biologically relevant proteins, protein-protein interaction networks were constructed using Reactome database and Cytoscape. Top 20 proteins were then subjected to functional enrichment using STRING. Results: From 1,075,576 gene-abstract links, 1,491 genes were prioritized. Proteins were related to signaling molecule proteins (n=221, receptor proteins (n=221 and nucleic acid binding proteins (n=169. The network constructed with these proteins included 3,963 nodes and functional analysis showed that main processes involved immune system and zinc ion binding function. Conclusions: For the first time, bioinformatics tools were used for integrating pathways and networks associated with RAS. Molecules and processes associated with immune system recur robustly in all analyzed information. The molecular zinc ion binding function could be an area for exploring more specific and effective therapeutic interventions.

  4. Two-stage neural-network-based technique for Urdu character two-dimensional shape representation, classification, and recognition

    Science.gov (United States)

    Megherbi, Dalila B.; Lodhi, S. M.; Boulenouar, A. J.

    2001-03-01

    This work is in the field of automated document processing. This work addresses the problem of representation and recognition of Urdu characters using Fourier representation and a Neural Network architecture. In particular, we show that a two-stage Neural Network scheme is used here to make classification of 36 Urdu characters into seven sub-classes namely subclasses characterized by seven proposed and defined fuzzy features specifically related to Urdu characters. We show that here Fourier Descriptors and Neural Network provide a remarkably simple way to draw definite conclusions from vague, ambiguous, noisy or imprecise information. In particular, we illustrate the concept of interest regions and describe a framing method that provides a way to make the proposed technique for Urdu characters recognition robust and invariant to scaling and translation. We also show that a given character rotation is dealt with by using the Hotelling transform. This transform is based upon the eigenvalue decomposition of the covariance matrix of an image, providing a method of determining the orientation of the major axis of an object within an image. Finally experimental results are presented to show the power and robustness of the proposed two-stage Neural Network based technique for Urdu character recognition, its fault tolerance, and high recognition accuracy.

  5. A Fuzzy Neural Network Based on Non-Euclidean Distance Clustering for Quality Index Model in Slashing Process

    Directory of Open Access Journals (Sweden)

    Yuxian Zhang

    2015-01-01

    Full Text Available The quality index model in slashing