WorldWideScience

Sample records for network response identification

  1. 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.

  2. Identification of novel and salt-responsive miRNAs to explore miRNA-mediated regulatory network of salt stress response in radish (Raphanus sativus L.).

    Science.gov (United States)

    Sun, Xiaochuan; Xu, Liang; Wang, Yan; Yu, Rugang; Zhu, Xianwen; Luo, Xiaobo; Gong, Yiqin; Wang, Ronghua; Limera, Cecilia; Zhang, Keyun; Liu, Liwang

    2015-03-17

    Salt stress is one of the most representative abiotic stresses that severely affect plant growth and development. MicroRNAs (miRNAs) are well known for their significant involvement in plant responses to abiotic stresses. Although miRNAs implicated in salt stress response have been widely reported in numerous plant species, their regulatory roles in the adaptive response to salt stress in radish (Raphanus sativus L.), an important root vegetable crop worldwide, remain largely unknown. Solexa sequencing of two sRNA libraries from NaCl-free (CK) and NaCl-treated (Na200) radish roots were performed for systematical identification of salt-responsive miRNAs and their expression profiling in radish. Totally, 136 known miRNAs (representing 43 miRNA families) and 68 potential novel miRNAs (belonging to 51 miRNA families) were identified. Of these miRNAs, 49 known and 22 novel miRNAs were differentially expressed under salt stress. Target prediction and annotation indicated that these miRNAs exerted a role by regulating specific stress-responsive genes, such as squamosa promoter binding-like proteins (SPLs), auxin response factors (ARFs), nuclear transcription factor Y (NF-Y) and superoxide dismutase [Cu-Zn] (CSD1). Further functional analysis suggested that these target genes were mainly implicated in signal perception and transduction, regulation of ion homeostasis, basic metabolic processes, secondary stress responses, as well as modulation of attenuated plant growth and development under salt stress. Additionally, the expression patterns of ten miRNAs and five corresponding target genes were validated by reverse-transcription quantitative PCR (RT-qPCR). With the sRNA sequencing, salt-responsive miRNAs and their target genes in radish were comprehensively identified. The results provide novel insight into complex miRNA-mediated regulatory network of salt stress response in radish, and facilitate further dissection of molecular mechanism underlying plant adaptive response

  3. Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron.

    Science.gov (United States)

    Costalago Meruelo, Alicia; Simpson, David M; Veres, Sandor M; Newland, Philip L

    2016-03-01

    Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to displacement of a sensory organ, the femoral chordotonal organ, which monitors movements of the tibia relative to the femur of the leg. The aim of the study was threefold: first to determine the potential value of ANNs as tools to model and investigate neural networks, second to understand the generalisation properties of ANNs across individuals and to different input signals and third, to understand individual differences in responses of an identified neuron. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results suggest that ANNs are significantly better than LNL and Wiener models in predicting specific neural responses to Gaussian White Noise, but not significantly different when tested with sinusoidal inputs. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model, although notable differences between some individuals were evident. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  4. Identification and analysis of new proteins involved in the DNA damage response network of Fanconi anemia and Bloom syndrome.

    Science.gov (United States)

    Guo, Rong; Xu, Dongyi; Wang, Weidong

    2009-05-01

    The use of co-immunoprecipitation (co-IP) to purify multi-protein complexes has contributed greatly to our understanding of the DNA damage response network associated with Fanconi anemia (FA), Bloom syndrome (BS) and breast cancer. Four new FA genes and two new protein partners for the Bloom syndrome gene product have been identified by co-IP. Here, we discuss our experience in using co-IP and other techniques to isolate and characterize new FA and BS-related proteins.

  5. Module identification in bipartite and directed networks

    OpenAIRE

    Guimera, R.; Sales-Pardo, M.; Amaral, L. A. N.

    2007-01-01

    Modularity is one of the most prominent properties of real-world complex networks. Here, we address the issue of module identification in two important classes of networks: bipartite networks and directed unipartite networks. Nodes in bipartite networks are divided into two non-overlapping sets, and the links must have one end node from each set. Directed unipartite networks only have one type of nodes, but links have an origin and an end. We show that directed unipartite networks can be conv...

  6. Identification of a regulation network in response to cadmium toxicity using blood clam Tegillarca granosa as model

    Science.gov (United States)

    Bao, Yongbo; Liu, Xiao; Zhang, Weiwei; Cao, Jianping; Li, Wei; Li, Chenghua; Lin, Zhihua

    2016-01-01

    Clam, a filter-feeding lamellibranch mollusk, is capable to accumulate high levels of trace metals and has therefore become a model for investigation the mechanism of heavy metal toxification. In this study, the effects of cadmium were characterized in the gills of Tegillarca granosa during a 96-hour exposure course using integrated metabolomic and proteomic approaches. Neurotoxicity and disturbances in energy metabolism were implicated according to the metabolic responses after Cd exposure, and eventually affected the osmotic function of gill tissue. Proteomic analysis showed that oxidative stress, calcium-binding and sulfur-compound metabolism proteins were key factors responding to Cd challenge. A knowledge-based network regulation model was constructed with both metabolic and proteomic data. The model suggests that Cd stimulation mainly inhibits a core regulation network that is associated with histone function, ribosome processing and tight junctions, with the hub proteins actin, gamma 1 and Calmodulin 1. Moreover, myosin complex inhibition causes abnormal tight junctions and is linked to the irregular synthesis of amino acids. For the first time, this study provides insight into the proteomic and metabolomic changes caused by Cd in the blood clam T. granosa and suggests a potential toxicological pathway for Cd. PMID:27760991

  7. Automatic identification of species with neural networks.

    Science.gov (United States)

    Hernández-Serna, Andrés; Jiménez-Segura, Luz Fernanda

    2014-01-01

    A new automatic identification system using photographic images has been designed to recognize fish, plant, and butterfly species from Europe and South America. The automatic classification system integrates multiple image processing tools to extract the geometry, morphology, and texture of the images. Artificial neural networks (ANNs) were used as the pattern recognition method. We tested a data set that included 740 species and 11,198 individuals. Our results show that the system performed with high accuracy, reaching 91.65% of true positive fish identifications, 92.87% of plants and 93.25% of butterflies. Our results highlight how the neural networks are complementary to species identification.

  8. Automatic identification of species with neural networks

    Directory of Open Access Journals (Sweden)

    Andrés Hernández-Serna

    2014-11-01

    Full Text Available A new automatic identification system using photographic images has been designed to recognize fish, plant, and butterfly species from Europe and South America. The automatic classification system integrates multiple image processing tools to extract the geometry, morphology, and texture of the images. Artificial neural networks (ANNs were used as the pattern recognition method. We tested a data set that included 740 species and 11,198 individuals. Our results show that the system performed with high accuracy, reaching 91.65% of true positive fish identifications, 92.87% of plants and 93.25% of butterflies. Our results highlight how the neural networks are complementary to species identification.

  9. Entrepreneurial Idea Identification through Online Social Networks

    Science.gov (United States)

    Lang, Matthew C.

    2010-01-01

    The increasing use of social network websites may signal a change in the way the next generation of entrepreneurs identify entrepreneurial ideas. An important part of the entrepreneurship literature emphasizes how vital the use of social networks is to entrepreneurial idea identification, opportunity recognition, and ultimately new venture…

  10. SAINT: Supervised Actor Identification for Network Tuning

    Science.gov (United States)

    Farrugia, Michael; Hurley, Neil; Quigley, Aaron

    Whenever the actors of a social network are not uniquely identifiable in the data, then entity resolution in the form of actor identification becomes a critical facet of a social network construction process. Here we develop SAINT, a pipeline for supervised entity resolution that uses relational information to improve, or tune, the quality of the constructed network. The first phase of SAINT uses attribute only based entity resolution to create an initial social network. Relational information between actors, actor network properties and other relational output of the first classification phase, are used in a second phase to improve the results of the original entity resolution. When compared to single phased approaches, the results from this two phased approach are consistently superior in both recall and precision measures. Embedded within SAINT are a series of evaluation checkpoints designed to measure both the quality of the individual classifiers and their impact within the entire pipeline. Our evaluation results provide insight on the potential propagation of error and open research questions for further improvement of the individual classifiers within the entire pipeline. As the main application of the process is to improve actor identification in social networks, we characterise the impact that entity resolution has on the final constructed network. We compare the network constructed using SAINT with a ground truth network using perfect entity resolution and use global and local network measures to study the differences.

  11. Identification of noisy response latency

    DEFF Research Database (Denmark)

    Tamborrino, Massimiliano; Ditlevsen, Susanne; Lansky, Petr

    2012-01-01

    be highly unreliable, unless the background signal is accounted for in the analysis. In fact, if the background signal is ignored, however small it is compared to the response and however large the delay is, the estimate of the time delay will go to zero for any reasonable estimator when increasing......In many physical systems there is a time delay before an applied input (stimulation) has an impact on the output (response), and the quantification of this delay is of paramount interest. If the response can only be observed on top of an indistinguishable background signal, the estimation can...... the number of observations. Here we propose a unified concept of response latency identification in event data corrupted by a background signal. It is done in the context of information transfer within a neural system, more specifically on spike trains from single neurons. The estimators are compared...

  12. Parameter Identification by Bayes Decision and Neural Networks

    DEFF Research Database (Denmark)

    Kulczycki, P.; Schiøler, Henrik

    1994-01-01

    The problem of parameter identification by Bayes point estimation using neural networks is investigated.......The problem of parameter identification by Bayes point estimation using neural networks is investigated....

  13. Systematic identification of statistically significant network measures

    Science.gov (United States)

    Ziv, Etay; Koytcheff, Robin; Middendorf, Manuel; Wiggins, Chris

    2005-01-01

    We present a graph embedding space (i.e., a set of measures on graphs) for performing statistical analyses of networks. Key improvements over existing approaches include discovery of “motif hubs” (multiple overlapping significant subgraphs), computational efficiency relative to subgraph census, and flexibility (the method is easily generalizable to weighted and signed graphs). The embedding space is based on scalars, functionals of the adjacency matrix representing the network. Scalars are global, involving all nodes; although they can be related to subgraph enumeration, there is not a one-to-one mapping between scalars and subgraphs. Improvements in network randomization and significance testing—we learn the distribution rather than assuming Gaussianity—are also presented. The resulting algorithm establishes a systematic approach to the identification of the most significant scalars and suggests machine-learning techniques for network classification.

  14. 23 CFR 658.21 - Identification of National Network.

    Science.gov (United States)

    2010-04-01

    ... 23 Highways 1 2010-04-01 2010-04-01 false Identification of National Network. 658.21 Section 658... Identification of National Network. (a) To identify the National Network, a State may sign the routes or provide maps of lists of highways describing the National Network. (b) Exceptional local conditions on the...

  15. Thrips (Thysanoptera) identification using artificial neural networks.

    Science.gov (United States)

    Fedor, P; Malenovský, I; Vanhara, J; Sierka, W; Havel, J

    2008-10-01

    We studied the use of a supervised artificial neural network (ANN) model for semi-automated identification of 18 common European species of Thysanoptera from four genera: Aeolothrips Haliday (Aeolothripidae), Chirothrips Haliday, Dendrothrips Uzel, and Limothrips Haliday (all Thripidae). As input data, we entered 17 continuous morphometric and two qualitative two-state characters measured or determined on different parts of the thrips body (head, pronotum, forewing and ovipositor) and the sex. Our experimental data set included 498 thrips specimens. A relatively simple ANN architecture (multilayer perceptrons with a single hidden layer) enabled a 97% correct simultaneous identification of both males and females of all the 18 species in an independent test. This high reliability of classification is promising for a wider application of ANN in the practice of Thysanoptera identification.

  16. Identification of functional networks of estrogen- and c-Myc-responsive genes and their relationship to response to tamoxifen therapy in breast cancer.

    Directory of Open Access Journals (Sweden)

    Elizabeth A Musgrove

    Full Text Available BACKGROUND: Estrogen is a pivotal regulator of cell proliferation in the normal breast and breast cancer. Endocrine therapies targeting the estrogen receptor are effective in breast cancer, but their success is limited by intrinsic and acquired resistance. METHODOLOGY/PRINCIPAL FINDINGS: With the goal of gaining mechanistic insights into estrogen action and endocrine resistance, we classified estrogen-regulated genes by function, and determined the relationship between functionally-related genesets and the response to tamoxifen in breast cancer patients. Estrogen-responsive genes were identified by transcript profiling of MCF-7 breast cancer cells. Pathway analysis based on functional annotation of these estrogen-regulated genes identified gene signatures with known or predicted roles in cell cycle control, cell growth (i.e. ribosome biogenesis and protein synthesis, cell death/survival signaling and transcriptional regulation. Since inducible expression of c-Myc in antiestrogen-arrested cells can recapitulate many of the effects of estrogen on molecular endpoints related to cell cycle progression, the estrogen-regulated genes that were also targets of c-Myc were identified using cells inducibly expressing c-Myc. Selected genes classified as estrogen and c-Myc targets displayed similar levels of regulation by estrogen and c-Myc and were not estrogen-regulated in the presence of siMyc. Genes regulated by c-Myc accounted for 50% of all acutely estrogen-regulated genes but comprised 85% (110/129 genes in the cell growth signature. siRNA-mediated inhibition of c-Myc induction impaired estrogen regulation of ribosome biogenesis and protein synthesis, consistent with the prediction that estrogen regulates cell growth principally via c-Myc. The 'cell cycle', 'cell growth' and 'cell death' gene signatures each identified patients with an attenuated response in a cohort of 246 tamoxifen-treated patients. In multivariate analysis the cell death signature

  17. Parametric Identification of Aircraft Loads: An Artificial Neural Network Approach

    Science.gov (United States)

    2016-03-30

    Undergraduate Student Paper Postgraduate Student Paper Parametric Identification of Aircraft Loads: An Artificial Neural Network Approach...monitoring, flight parameter, nonlinear modeling, Artificial Neural Network , typical loadcase. Introduction Aircraft load monitoring is an... Neural Networks (ANN), i.e. the BP network and Kohonen Clustering Network , are applied and revised by Kalman Filter and Genetic Algorithm to build

  18. Deterministic System Identification Using RBF Networks

    Directory of Open Access Journals (Sweden)

    Joilson Batista de Almeida Rego

    2014-01-01

    Full Text Available This paper presents an artificial intelligence application using a nonconventional mathematical tool: the radial basis function (RBF networks, aiming to identify the current plant of an induction motor or other nonlinear systems. Here, the objective is to present the RBF response to different nonlinear systems and analyze the obtained results. A RBF network is trained and simulated in order to obtain the dynamical solution with basin of attraction and equilibrium point for known and unknown system and establish a relationship between these dynamical systems and the RBF response. On the basis of several examples, the results indicating the effectiveness of this approach are demonstrated.

  19. Differential gene network analysis for the identification of asthma-associated therapeutic targets in allergen-specific T-helper memory responses.

    Science.gov (United States)

    Troy, Niamh M; Hollams, Elysia M; Holt, Patrick G; Bosco, Anthony

    2016-02-27

    Asthma is strongly associated with allergic sensitization, but the mechanisms that determine why only a subset of atopics develop asthma are not well understood. The aim of this study was to test the hypothesis that variations in allergen-driven CD4 T cell responses are associated with susceptibility to expression of asthma symptoms. The study population consisted of house dust mite (HDM) sensitized atopics with current asthma (n = 22), HDM-sensitized atopics without current asthma (n = 26), and HDM-nonsensitized controls (n = 24). Peripheral blood mononuclear cells from these groups were cultured in the presence or absence of HDM extract for 24 h. CD4 T cells were then isolated by immunomagnetic separation, and gene expression patterns were profiled on microarrays. Differential network analysis of HDM-induced CD4 T cell responses in sensitized atopics with or without asthma unveiled a cohort of asthma-associated genes that escaped detection by more conventional data analysis techniques. These asthma-associated genes were enriched for targets of STAT6 signaling, and they were nested within a larger coexpression module comprising 406 genes. Upstream regulator analysis suggested that this module was driven primarily by IL-2, IL-4, and TNF signaling; reconstruction of the wiring diagram of the module revealed a series of hub genes involved in inflammation (IL-1B, NFkB, STAT1, STAT3), apoptosis (BCL2, MYC), and regulatory T cells (IL-2Ra, FoxP3). Finally, we identified several negative regulators of asthmatic CD4 T cell responses to allergens (e.g. IL-10, type I interferons, microRNAs, drugs, metabolites), and these represent logical candidates for therapeutic intervention. Differential network analysis of allergen-induced CD4 T cell responses can unmask covert disease-associated genes and pin point novel therapeutic targets.

  20. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    1995-01-01

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  1. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  2. Tropical Timber Identification using Backpropagation Neural Network

    Science.gov (United States)

    Siregar, B.; Andayani, U.; Fatihah, N.; Hakim, L.; Fahmi, F.

    2017-01-01

    Each and every type of wood has different characteristics. Identifying the type of wood properly is important, especially for industries that need to know the type of timber specifically. However, it requires expertise in identifying the type of wood and only limited experts available. In addition, the manual identification even by experts is rather inefficient because it requires a lot of time and possibility of human errors. To overcome these problems, a digital image based method to identify the type of timber automatically is needed. In this study, backpropagation neural network is used as artificial intelligence component. Several stages were developed: a microscope image acquisition, pre-processing, feature extraction using gray level co-occurrence matrix and normalization of data extraction using decimal scaling features. The results showed that the proposed method was able to identify the timber with an accuracy of 94%.

  3. A new application of neural network technique to sensorless speed identification of induction motor

    OpenAIRE

    Mostefai, Mohamed; Miloud, Yahia; Abdullah MILOUDI

    2016-01-01

    A new application of neural network technique to sensorless speed identification of scalar-controlled induction motor is implemented in this paper. The neural network estimates the rotor speed through stator measurements and nominal settings of the motor. By changing the motor parameters, the neural network can estimate the speed of another motor. We evaluated our approach based on the speed response and load disturbance effects on two different motors. The test results demonstrate the feasib...

  4. A new application of neural network technique to sensorless speed identification of induction motor

    Directory of Open Access Journals (Sweden)

    Mohamed MOSTEFAI

    2016-12-01

    Full Text Available A new application of neural network technique to sensorless speed identification of scalar-controlled induction motor is implemented in this paper. The neural network estimates the rotor speed through stator measurements and nominal settings of the motor. By changing the motor parameters, the neural network can estimate the speed of another motor. We evaluated our approach based on the speed response and load disturbance effects on two different motors. The test results demonstrate the feasibility of the method.

  5. Dynamic Object Identification with SOM-based neural networks

    Directory of Open Access Journals (Sweden)

    Aleksey Averkin

    2014-03-01

    Full Text Available In this article a number of neural networks based on self-organizing maps, that can be successfully used for dynamic object identification, is described. Unique SOM-based modular neural networks with vector quantized associative memory and recurrent self-organizing maps as modules are presented. The structured algorithms of learning and operation of such SOM-based neural networks are described in details, also some experimental results and comparison with some other neural networks are given.

  6. NNSYSID - toolbox for system identification with neural networks

    DEFF Research Database (Denmark)

    Norgaard, M.; Ravn, Ole; Poulsen, Niels Kjølstad

    2002-01-01

    The NNSYSID toolset for System Identification has been developed as an add on to MATLAB(R). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms...

  7. Identification of the Bacterial Community Responsible for ...

    African Journals Online (AJOL)

    Identification of bacteria community responsible for decontaminating Eleme petrochemical industrial effluent using 16S PCR denaturing gradient gel electrophoresis (DGGE) was determined. Gene profiles were determined by extracting DNA from bacterial isolates and amplified by polymerase chain reaction (PCR) using ...

  8. Boosted jet identification using particle candidates and deep neural networks

    CERN Document Server

    CMS Collaboration

    2017-01-01

    This note presents developments for the identification of hadronically decaying top quarks using deep neural networks in CMS. A new method that utilizes one dimensional convolutional neural networks based on jet constituent particles is proposed. Alternative methods using boosted decision trees based on jet observables are compared. The new method shows significant improvement in performance.

  9. Neural Networks for Language Identification: A Comparative Study.

    Science.gov (United States)

    MacNamara, Shane; Cunningham, Padraig; Byrne, John

    1998-01-01

    Analyzes a neural network for its ability to perform a task involving identification of the language entries in a 19th-century library catalog containing entries in 14 different languages. Compares the neural network's performance with that of trigrams and a suffix/morphology analysis; the trigrams prove to be superior. (AEF)

  10. Towards shortest path identification on large networks

    National Research Council Canada - National Science Library

    Selim, Haysam; Zhan, Justin

    2016-01-01

    ...) and then finding the shortest path in a quick manner due to the data reduction in the graph. As the number of vertices and edges tend to increase on large networks the aim of this article is to make the reduction of the network that will cause an impact on calculating the shortest path for a faster analysis in a shortest time.

  11. Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification

    Directory of Open Access Journals (Sweden)

    Dhiadeen Mohammed Salih

    2015-01-01

    Full Text Available A single hidden layer feedforward neural network (SLFN with online sequential extreme learning machine (OSELM algorithm has been introduced and applied in many regression problems successfully. However, using SLFN with OSELM as black-box for nonlinear system identification may lead to building models for the identified plant with inconsistency responses from control perspective. The reason can refer to the random initialization procedure of the SLFN hidden node parameters with OSELM algorithm. In this paper, a single hidden layer feedforward wavelet network (WN is introduced with OSELM for nonlinear system identification aimed at getting better generalization performances by reducing the effect of a random initialization procedure.

  12. A NEURAL NETWORK BASED IRIS RECOGNITION SYSTEM FOR PERSONAL IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    Usham Dias

    2010-10-01

    Full Text Available This paper presents biometric personal identification based on iris recognition using artificial neural networks. Personal identification system consists of localization of the iris region, normalization, enhancement and then iris pattern recognition using neural network. In this paper, through results obtained, we have shown that a person’s left and right eye are unique. In this paper, we also show that the network is sensitive to the initial weights and that over-training gives bad results. We also propose a fast algorithm for the localization of the inner and outer boundaries of the iris region. Results of simulations illustrate the effectiveness of the neural system in personal identification. Finally a hardware iris recognition model is proposed and implementation aspects are discussed.

  13. IDENTIFICATION AND CONTROL OF AN ASYNCHRONOUS MACHINE USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    A ZERGAOUI

    2000-06-01

    Full Text Available In this work, we present the application of artificial neural networks to the identification and control of the asynchronous motor, which is a complex nonlinear system with variable internal dynamics.  We show that neural networks can be applied to control the stator currents of the induction motor.  The results of the different simulations are presented to evaluate the performance of the neural controller proposed.

  14. Structural systems identification of genetic regulatory networks.

    Science.gov (United States)

    Xiong, Hao; Choe, Yoonsuck

    2008-02-15

    Reverse engineering of genetic regulatory networks from experimental data is the first step toward the modeling of genetic networks. Linear state-space models, also known as linear dynamical models, have been applied to model genetic networks from gene expression time series data, but existing works have not taken into account available structural information. Without structural constraints, estimated models may contradict biological knowledge and estimation methods may over-fit. In this report, we extended expectation-maximization (EM) algorithms to incorporate prior network structure and to estimate genetic regulatory networks that can track and predict gene expression profiles. We applied our method to synthetic data and to SOS data and showed that our method significantly outperforms the regular EM without structural constraints. The Matlab code is available upon request and the SOS data can be downloaded from http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zak's data is available from his website, http://www.che.udel.edu/systems/people/zak.

  15. Conceptualizing the Dynamics between Bicultural Identification and Personal Social Networks.

    Science.gov (United States)

    Repke, Lydia; Benet-Martínez, Verónica

    2017-01-01

    An adequate understanding of the acculturation processes affecting immigrants and their descendants involves ascertaining the dynamic interplay between the way these individuals manage their multiple (and sometimes conflictual) cultural value systems and identifications and possible changes in their social networks. To fill this gap, the present research examines how key acculturation variables (e.g., strength of ethnic/host cultural identifications, bicultural identity integration or BII) relate to the composition and structure of bicultural individuals' personal social networks. In Study 1, we relied on a generationally and culturally diverse community sample of 123 Latinos residing in the US. Participants nominated eight individuals (i.e., alters) from their habitual social networks and across two relational domains: friendships and colleagues. Results indicated that the interconnection of same ethnicity alters across different relationship domains is linked to cultural identifications, while the amount of coethnic and host individuals in the network is not. In particular, higher interconnection between Latino friends and colleagues was linked to lower levels of U.S. Conversely, the interconnection of non-Latino friends and colleagues was associated with lower levels of Latino identification. This pattern of results suggests that the relational context for each type of cultural identification works in a subtractive and inverse manner. Further, time spent in the US was linked to both Latino and U.S. cultural identifications, but this relationship was moderated by the level of BII. Specifically, the association between time in the US and strength of both cultural identities was stronger for individuals reporting low levels of BII. Taking the findings from Study 1 as departure point, Study 2 used an agent-based model data simulation approach to explore the dynamic ways in which the content and the structure of an immigrant's social network might matter over time in

  16. Identification of host response signatures of infection.

    Energy Technology Data Exchange (ETDEWEB)

    Branda, Steven S.; Sinha, Anupama; Bent, Zachary

    2013-02-01

    Biological weapons of mass destruction and emerging infectious diseases represent a serious and growing threat to our national security. Effective response to a bioattack or disease outbreak critically depends upon efficient and reliable distinguishing between infected vs healthy individuals, to enable rational use of scarce, invasive, and/or costly countermeasures (diagnostics, therapies, quarantine). Screening based on direct detection of the causative pathogen can be problematic, because culture- and probe-based assays are confounded by unanticipated pathogens (e.g., deeply diverged, engineered), and readily-accessible specimens (e.g., blood) often contain little or no pathogen, particularly at pre-symptomatic stages of disease. Thus, in addition to the pathogen itself, one would like to detect infection-specific host response signatures in the specimen, preferably ones comprised of nucleic acids (NA), which can be recovered and amplified from tiny specimens (e.g., fingerstick draws). Proof-of-concept studies have not been definitive, however, largely due to use of sub-optimal sample preparation and detection technologies. For purposes of pathogen detection, Sandia has developed novel molecular biology methods that enable selective isolation of NA unique to, or shared between, complex samples, followed by identification and quantitation via Second Generation Sequencing (SGS). The central hypothesis of the current study is that variations on this approach will support efficient identification and verification of NA-based host response signatures of infectious disease. To test this hypothesis, we re-engineered Sandia's sophisticated sample preparation pipelines, and developed new SGS data analysis tools and strategies, in order to pioneer use of SGS for identification of host NA correlating with infection. Proof-of-concept studies were carried out using specimens drawn from pathogen-infected non-human primates (NHP). This work provides a strong foundation for

  17. System identification algorithms for the analysis of dielectric responses from broadband spectroscopies

    Energy Technology Data Exchange (ETDEWEB)

    Hadjiloucas, S; Walker, G C; Bowen, J W [Cybernetics, School of Systems Engineering, The University of Reading, RG6 6AY (United Kingdom); Galvao, R K H, E-mail: s.hadjiloucas@reading.ac.uk [Divisao de Engenharia Eletronica, Instituto Tecnologico de Aeronautica, Sao Jose dos Campos, SP, 12228-900 Brazil (Brazil)

    2011-08-12

    We discuss the modelling of dielectric responses for an electromagnetically excited network of capacitors and resistors using a systems identification framework. Standard models that assume integral order dynamics are augmented to incorporate fractional order dynamics. This enables us to relate more faithfully the modelled responses to those reported in the Dielectrics literature.

  18. A Bayesian Network for Combat Identification

    Science.gov (United States)

    2004-03-01

    countermeasures and other tactical actions such as avoidance, targeting and homing. ESM is limited as a method of identification since it relies on goniometry to...relate sensed radiation to a specific track. Therefore its readings are less reliable in dense air traffic, because it will be uncertain which...used as a reference. The availability of usable reference material affects the reliability . Another automated way to determine a track’s identity is

  19. On closed loop transient response system identification

    Directory of Open Access Journals (Sweden)

    Christer Dalen

    2016-10-01

    Full Text Available Some methods for transient closed loop step response system identification presented in the literature are reviewed. Interestingly some errors in a method published in the early 80's where propagated into a recently published method. These methods are reviewed and some improved methods are suggested and presented. The methods are compared against each other on some closed loop system examples, e.g. a well pipeline-riser severe-slugging flow regime example, using Monte Carlo simulations for comparison of the methods.

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

    Indian Academy of Sciences (India)

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

    environments. The system developed includes a feature extractor and a modular neural network. The feature extractor consists of two stages. In the first stage ... environments is script/language identification (Muthusamy et al 1994; Hochberg et al 1997). ... In order to take advantage of the learning and generalization abilities ...

  1. Decentralized system identification using stochastic subspace identification for wireless sensor networks.

    Science.gov (United States)

    Cho, Soojin; Park, Jong-Woong; Sim, Sung-Han

    2015-04-08

    Wireless sensor networks (WSNs) facilitate a new paradigm to structural identification and monitoring for civil infrastructure. Conventional structural monitoring systems based on wired sensors and centralized data acquisition systems are costly for installation as well as maintenance. WSNs have emerged as a technology that can overcome such difficulties, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing is common practice, WSNs require decentralized computing algorithms to reduce data transmission due to the limitation associated with wireless communication. In this paper, the stochastic subspace identification (SSI) technique is selected for system identification, and SSI-based decentralized system identification (SDSI) is proposed to be implemented in a WSN composed of Imote2 wireless sensors that measure acceleration. The SDSI is tightly scheduled in the hierarchical WSN, and its performance is experimentally verified in a laboratory test using a 5-story shear building model.

  2. Human Face Identification using KL Transform and Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Yong Joo [LG Electronics Inc. Multimedia Research Lab. (Korea, Republic of); Ji, Seung Hwan [Mi Re Industry Inc. (Korea, Republic of); Yoo, Jae Hyung; Kim, Jung Hwan; Park, Min Yong [Yonsei University (Korea, Republic of)

    1999-01-01

    Machine recognition of faces from still and video images is emerging as an active research area spanning several disciplines such as image processing, pattern recognition, computer vision and neural networks. In addition, human face identification has numerous applications such as human interface based systems and real-time video systems of surveillance and security. In this paper, we propose an algorithm that can identify a particular individual face. We consider human face identification system in color space, which hasn`t often considered in conventional methods. In order to make the algorithm insensitive to luminance, we convert the conventional RGB coordinates into normalized CIE coordinates. The normalized-CIE-based facial images are KL-transformed. The transformed data are used as used as the input of multi-layered neural network and the network are trained using error-backpropagation methods. Finally, we verify the system performance of the proposed algorithm by experiments. (author). 12 refs., 7 figs., 3 tabs.

  3. Identification of important nodes in directed biological networks: a network motif approach.

    Directory of Open Access Journals (Sweden)

    Pei Wang

    Full Text Available Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA, this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.

  4. Communities in Large Networks: Identification and Ranking

    DEFF Research Database (Denmark)

    Olsen, Martin

    2008-01-01

    We study the problem of identifying and ranking the members of a community in a very large network with link analysis only, given a set of representatives of the community. We define the concept of a community justified by a formal analysis of a simple model of the evolution of a directed graph. ...... and its immediate surroundings. The members are ranked with a “local” variant of the PageRank algorithm. Results are reported from successful experiments on identifying and ranking Danish Computer Science sites and Danish Chess pages using only a few representatives....

  5. White blood cells identification system based on convolutional deep neural learning networks.

    Science.gov (United States)

    Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

    2017-11-16

    White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear image analysis. Previous identification systems for WBCs consist of successive dependent stages; pre-processing, segmentation, feature extraction, feature selection, and classification. There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased. Classifying small limited datasets through deep learning systems is a major challenge and should be investigated. In this paper, we propose a novel identification system for WBCs based on deep convolutional neural networks. Two methodologies based on transfer learning are followed: transfer learning based on deep activation features and fine-tuning of existed deep networks. Deep acrivation featues are extracted from several pre-trained networks and employed in a traditional identification system. Moreover, a novel end-to-end convolutional deep architecture called "WBCsNet" is proposed and built from scratch. Finally, a limited balanced WBCs dataset classification is performed through the WBCsNet as a pre-trained network. During our experiments, three different public WBCs datasets (2551 images) have been used which contain 5 healthy WBCs types. The overall system accuracy achieved by the proposed WBCsNet is (96.1%) which is more than different transfer learning approaches or even the previous traditional identification system. We also present features visualization for the WBCsNet activation which reflects higher response than the pre-trained activated one. a novel WBCs identification system based on deep learning theory is proposed and a high performance WBCsNet can be employed as a pre-trained network. Copyright © 2017. Published by Elsevier B.V.

  6. Network Culture, Performance & Corporate Responsibility

    OpenAIRE

    Brondoni, Silvio M

    2003-01-01

    The growth and sustainability of free market economies highlights the need to define rules more suited to the current condition of market globalisation and also encourages firms to adopt more transparent and accountable corporate responsibility (and corporate social responsibility, namely the relationship between the company, environment and social setting). From a managerial perspective, corporate responsibility is linked to ensure the lasting pursuit of the company mission, seeking increasi...

  7. Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems

    CERN Document Server

    Patan, Maciej

    2012-01-01

    Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...

  8. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System...... Identification, Prediction, Simulation and Control of a dynamic, non-linear and noisy process. Further, the difficulties to control a practical non-linear laboratory process in a satisfactory way by using a traditional controller are overcomed by using a trained neural network to perform non-linear System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...

  9. Effect of size heterogeneity on community identification in complex networks

    Energy Technology Data Exchange (ETDEWEB)

    Danon, L.; Diaz-Guilera, A.; Arenas, A.

    2008-01-01

    Identifying community structure can be a potent tool in the analysis and understanding of the structure of complex networks. Up to now, methods for evaluating the performance of identification algorithms use ad-hoc networks with communities of equal size. We show that inhomogeneities in community sizes can and do affect the performance of algorithms considerably, and propose an alternative method which takes these factors into account. Furthermore, we propose a simple modification of the algorithm proposed by Newman for community detection (Phys. Rev. E 69 066133) which treats communities of different sizes on an equal footing, and show that it outperforms the original algorithm while retaining its speed.

  10. Viden: Attacker Identification on In-Vehicle Networks

    OpenAIRE

    Cho, Kyong-Tak; Shin, Kang

    2017-01-01

    Various defense schemes --- which determine the presence of an attack on the in-vehicle network --- have recently been proposed. However, they fail to identify which Electronic Control Unit (ECU) actually mounted the attack. Clearly, pinpointing the attacker ECU is essential for fast/efficient forensic, isolation, security patch, etc. To meet this need, we propose a novel scheme, called Viden (Voltage-based attacker identification), which can identify the attacker ECU by measuring and utilizi...

  11. Data identification for improving gene network inference using computational algebra.

    Science.gov (United States)

    Dimitrova, Elena; Stigler, Brandilyn

    2014-11-01

    Identification of models of gene regulatory networks is sensitive to the amount of data used as input. Considering the substantial costs in conducting experiments, it is of value to have an estimate of the amount of data required to infer the network structure. To minimize wasted resources, it is also beneficial to know which data are necessary to identify the network. Knowledge of the data and knowledge of the terms in polynomial models are often required a priori in model identification. In applications, it is unlikely that the structure of a polynomial model will be known, which may force data sets to be unnecessarily large in order to identify a model. Furthermore, none of the known results provides any strategy for constructing data sets to uniquely identify a model. We provide a specialization of an existing criterion for deciding when a set of data points identifies a minimal polynomial model when its monomial terms have been specified. Then, we relax the requirement of the knowledge of the monomials and present results for model identification given only the data. Finally, we present a method for constructing data sets that identify minimal polynomial models.

  12. Network response synchronization enhanced by synaptic plasticity

    Science.gov (United States)

    Lobov, S.; Simonov, A.; Kastalskiy, I.; Kazantsev, V.

    2016-02-01

    Synchronization of neural network response on spatially localized periodic stimulation was studied. The network consisted of synaptically coupled spiking neurons with spike-timing-dependent synaptic plasticity (STDP). Network connectivity was defined by time evolving matrix of synaptic weights. We found that the steady-state spatial pattern of the weights could be rearranged due to locally applied external periodic stimulation. A method for visualization of synaptic weights as vector field was introduced to monitor the evolving connectivity matrix. We demonstrated that changes in the vector field and associated weight rearrangements underlay an enhancement of synchronization range.

  13. Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

    Science.gov (United States)

    Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.

    2016-01-01

    Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties. PMID:27870845

  14. Identification of influential users by neighbors in online social networks

    Science.gov (United States)

    Sheikhahmadi, Amir; Nematbakhsh, Mohammad Ali; Zareie, Ahmad

    2017-11-01

    Identification and ranking of influential users in social networks for the sake of news spreading and advertising has recently become an attractive field of research. Given the large number of users in social networks and also the various relations that exist among them, providing an effective method to identify influential users has been gradually considered as an essential factor. In most of the already-provided methods, those users who are located in an appropriate structural position of the network are regarded as influential users. These methods do not usually pay attention to the interactions among users, and also consider those relations as being binary in nature. This paper, therefore, proposes a new method to identify influential users in a social network by considering those interactions that exist among the users. Since users tend to act within the frame of communities, the network is initially divided into different communities. Then the amount of interaction among users is used as a parameter to set the weight of relations existing within the network. Afterward, by determining the neighbors' role for each user, a two-level method is proposed for both detecting users' influence and also ranking them. Simulation and experimental results on twitter data shows that those users who are selected by the proposed method, comparing to other existing ones, are distributed in a more appropriate distance. Moreover, the proposed method outperforms the other ones in terms of both the influential speed and capacity of the users it selects.

  15. User Identification Framework in Social Network Services Environment

    Directory of Open Access Journals (Sweden)

    Brijesh BAKARIYA

    2014-01-01

    Full Text Available Social Network Service is a one of the service where people may communicate with one an-other; and may also exchange messages even of any type of audio or video communication. Social Network Service as name suggests a type of network. Such type of web application plays a dominant role in internet technology. In such type of online community, people may share their common interest. Facebook LinkedIn, orkut and many more are the Social Network Service and it is good medium of making link with people having unique or common interest and goals. But the problem of privacy protection is a big issue in today’s world. As social networking sites allows anonymous users to share information of other stuffs. Due to which cybercrime is also increasing to a rapid extent. In this article we preprocessed the web log data of Social Network Services and assemble that data on the basis of image file format like jpg, jpeg, gif, png, bmp etc. and also propose a framework for victim’s identification.

  16. Localization and identification of structural nonlinearities using cascaded optimization and neural networks

    Science.gov (United States)

    Koyuncu, A.; Cigeroglu, E.; Özgüven, H. N.

    2017-10-01

    In this study, a new approach is proposed for identification of structural nonlinearities by employing cascaded optimization and neural networks. Linear finite element model of the system and frequency response functions measured at arbitrary locations of the system are used in this approach. Using the finite element model, a training data set is created, which appropriately spans the possible nonlinear configurations space of the system. A classification neural network trained on these data sets then localizes and determines the types of all nonlinearities associated with the nonlinear degrees of freedom in the system. A new training data set spanning the parametric space associated with the determined nonlinearities is created to facilitate parametric identification. Utilizing this data set, initially, a feed forward regression neural network is trained, which parametrically identifies the classified nonlinearities. Then, the results obtained are further improved by carrying out an optimization which uses network identified values as starting points. Unlike identification methods available in literature, the proposed approach does not require data collection from the degrees of freedoms where nonlinear elements are attached, and furthermore, it is sufficiently accurate even in the presence of measurement noise. The application of the proposed approach is demonstrated on an example system with nonlinear elements and on a real life experimental setup with a local nonlinearity.

  17. Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification

    Directory of Open Access Journals (Sweden)

    Thitaree Tanprasert

    2017-01-01

    Full Text Available This paper proposes an algorithm for real-time driver identification using the combination of unsupervised anomaly detection and neural networks. The proposed algorithm uses nonphysiological signals as input, namely, driving behavior signals from inertial sensors (e.g., accelerometers and geolocation signals from GPS sensors. First anomaly detection is performed to assess if the current driver is whom he/she claims to be. If an anomaly is detected, the algorithm proceeds to find relevant features in the input signals and use neural networks to identify drivers. To assess the proposed algorithm, real-world data are collected from ten drivers who drive different vehicles on several routes in real-world traffic conditions. Driver identification is performed on each of the seven-second-long driving behavior signals and geolocation signals in a streaming manner. It is shown that the proposed algorithm can achieve relatively high accuracy and identify drivers within 13 seconds. The proposed algorithm also outperforms the previously proposed driver identification algorithms. Furthermore, to demonstrate how the proposed algorithm can be deployed in real-world applications, results from real-world data associated with each operation of the proposed algorithm are shown step-by-step.

  18. Decentralized System Identification Using Stochastic Subspace Identification for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Soojin Cho

    2015-04-01

    Full Text Available Wireless sensor networks (WSNs facilitate a new paradigm to structural identification and monitoring for civil infrastructure. Conventional structural monitoring systems based on wired sensors and centralized data acquisition systems are costly for installation as well as maintenance. WSNs have emerged as a technology that can overcome such difficulties, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing is common practice, WSNs require decentralized computing algorithms to reduce data transmission due to the limitation associated with wireless communication. In this paper, the stochastic subspace identification (SSI technique is selected for system identification, and SSI-based decentralized system identification (SDSI is proposed to be implemented in a WSN composed of Imote2 wireless sensors that measure acceleration. The SDSI is tightly scheduled in the hierarchical WSN, and its performance is experimentally verified in a laboratory test using a 5-story shear building model.

  19. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

    Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

  20. Empirical identification of squeeze-film damper bearings using neural networks

    Science.gov (United States)

    Groves, K. H.; Bonello, P.

    2013-02-01

    To date empirically obtained SFD models have been based upon the determination of linearised force coefficients; such models are severely limited in their range of applicability since they are only valid for small perturbations from a mean position. The present research provides the introduction and validation of a nonlinear SFD identification technique that uses neural networks, trained from experimental data, to reproduce the input-output function over the full range of the SFD clearance. Details of the commissioning of a specially designed identification test rig and its associated data acquisition system are presented. The neural network's construction and training process is described and relevant testing is detailed. The empirically identified neural network is progressively validated, culminating in remarkably accurate nonlinear vibration response prediction of an SFD test rig subjected to external dual-frequency orthogonal excitation, as present in twin-spool engines (where the nonlinear vibrations are driven by the unbalance on the two rotors turning at different speeds). When used within the dynamic analysis of the test rig, the trained neural network is shown to be capable of predicting complex nonlinear phenomena with excellent accuracy. By comparison to an advanced theoretical model, the results show that the neural networks are able to capture the effects of features that are difficult to include in a hydrodynamic model or are particular to a given SFD.

  1. Hydraulic response in flooded stream networks

    Science.gov (United States)

    Åkesson, Anna; Wörman, Anders; Bottacin-Busolin, Andrea

    2015-01-01

    Average water travel times through a stream network were determined as a function of stage (discharge) and stream network properties. Contrary to most previous studies on the topic, the present work allowed for streamflow velocities to vary spatially (for most of the analyses) as well as temporally. The results show that different stream network mechanisms and properties interact in a complex and stage-dependent manner, implying that the relative importance of the different hydraulic properties varies in space and over time. Theoretical reasoning, based on the central temporal moments derived from the kinematic-diffusive wave equation in a semi-2-D formulation including the effects of flooded cross sections, shows that the hydraulic properties in contrast to the geomorphological properties will become increasingly important as the discharge increases, stressing the importance of accurately describing the hydraulic mechanisms within stream networks. Using the physically based, stage-dependent response function as a parameterization basis for the streamflow routing routine (a linear reservoir) of a hydrological model, discharge predictions were shown to improve in two Swedish catchments, compared with a conventional, statistically based parameterization scheme. Predictions improved for a wide range of modeled scenarios, for the entire discharge series as well as for peak flow conditions. The foremost novelty of the study lies in that the physically based response function for a streamflow routing routine has successfully been determined independent of calibration, i.e., entirely through process-based hydraulic stream network modeling.

  2. Optimizing radio frequency identification network planning through ring probabilistic logic neurons

    Directory of Open Access Journals (Sweden)

    Aydin Azizi

    2016-08-01

    Full Text Available Radio frequency identification is a developing technology that has recently been adopted in industrial applications for identification and tracking operations. The radio frequency identification network planning problem deals with many criteria like number and positions of the deployed antennas in the networks, transmitted power of antennas, and coverage of network. All these criteria must satisfy a set of objectives, such as load balance, economic efficiency, and interference, in order to obtain accurate and reliable network planning. Achieving the best solution for radio frequency identification network planning has been an area of great interest for many scientists. This article introduces the Ring Probabilistic Logic Neuron as a time-efficient and accurate algorithm to deal with the radio frequency identification network planning problem. To achieve the best results, redundant antenna elimination algorithm is used in addition to the proposed optimization techniques. The aim of proposed algorithm is to solve the radio frequency identification network planning problem and to design a cost-effective radio frequency identification network by minimizing the number of embedded radio frequency identification antennas in the network, minimizing collision of antennas, and maximizing coverage area of the objects. The proposed solution is compared with the evolutionary algorithms, namely genetic algorithm and particle swarm optimization. The simulation results show that the Ring Probabilistic Logic Neuron algorithm obtains a far more superior solution for radio frequency identification network planning problem when compared to genetic algorithm and particle swarm optimization.

  3. Simulation of Stimuli-Responsive Polymer Networks

    Directory of Open Access Journals (Sweden)

    Thomas Gruhn

    2013-11-01

    Full Text Available The structure and material properties of polymer networks can depend sensitively on changes in the environment. There is a great deal of progress in the development of stimuli-responsive hydrogels for applications like sensors, self-repairing materials or actuators. Biocompatible, smart hydrogels can be used for applications, such as controlled drug delivery and release, or for artificial muscles. Numerical studies have been performed on different length scales and levels of details. Macroscopic theories that describe the network systems with the help of continuous fields are suited to study effects like the stimuli-induced deformation of hydrogels on large scales. In this article, we discuss various macroscopic approaches and describe, in more detail, our phase field model, which allows the calculation of the hydrogel dynamics with the help of a free energy that considers physical and chemical impacts. On a mesoscopic level, polymer systems can be modeled with the help of the self-consistent field theory, which includes the interactions, connectivity, and the entropy of the polymer chains, and does not depend on constitutive equations. We present our recent extension of the method that allows the study of the formation of nano domains in reversibly crosslinked block copolymer networks. Molecular simulations of polymer networks allow the investigation of the behavior of specific systems on a microscopic scale. As an example for microscopic modeling of stimuli sensitive polymer networks, we present our Monte Carlo simulations of a filament network system with crosslinkers.

  4. Sensor network based vehicle classification and license plate identification system

    Energy Technology Data Exchange (ETDEWEB)

    Frigo, Janette Rose [Los Alamos National Laboratory; Brennan, Sean M [Los Alamos National Laboratory; Rosten, Edward J [Los Alamos National Laboratory; Raby, Eric Y [Los Alamos National Laboratory; Kulathumani, Vinod K [WEST VIRGINIA UNIV.

    2009-01-01

    Typically, for energy efficiency and scalability purposes, sensor networks have been used in the context of environmental and traffic monitoring applications in which operations at the sensor level are not computationally intensive. But increasingly, sensor network applications require data and compute intensive sensors such video cameras and microphones. In this paper, we describe the design and implementation of two such systems: a vehicle classifier based on acoustic signals and a license plate identification system using a camera. The systems are implemented in an energy-efficient manner to the extent possible using commercially available hardware, the Mica motes and the Stargate platform. Our experience in designing these systems leads us to consider an alternate more flexible, modular, low-power mote architecture that uses a combination of FPGAs, specialized embedded processing units and sensor data acquisition systems.

  5. Event Networks and the Identification of Crime Pattern Motifs.

    Directory of Open Access Journals (Sweden)

    Toby Davies

    Full Text Available In this paper we demonstrate the use of network analysis to characterise patterns of clustering in spatio-temporal events. Such clustering is of both theoretical and practical importance in the study of crime, and forms the basis for a number of preventative strategies. However, existing analytical methods show only that clustering is present in data, while offering little insight into the nature of the patterns present. Here, we show how the classification of pairs of events as close in space and time can be used to define a network, thereby generalising previous approaches. The application of graph-theoretic techniques to these networks can then offer significantly deeper insight into the structure of the data than previously possible. In particular, we focus on the identification of network motifs, which have clear interpretation in terms of spatio-temporal behaviour. Statistical analysis is complicated by the nature of the underlying data, and we provide a method by which appropriate randomised graphs can be generated. Two datasets are used as case studies: maritime piracy at the global scale, and residential burglary in an urban area. In both cases, the same significant 3-vertex motif is found; this result suggests that incidents tend to occur not just in pairs, but in fact in larger groups within a restricted spatio-temporal domain. In the 4-vertex case, different motifs are found to be significant in each case, suggesting that this technique is capable of discriminating between clustering patterns at a finer granularity than previously possible.

  6. Event Networks and the Identification of Crime Pattern Motifs

    Science.gov (United States)

    2015-01-01

    In this paper we demonstrate the use of network analysis to characterise patterns of clustering in spatio-temporal events. Such clustering is of both theoretical and practical importance in the study of crime, and forms the basis for a number of preventative strategies. However, existing analytical methods show only that clustering is present in data, while offering little insight into the nature of the patterns present. Here, we show how the classification of pairs of events as close in space and time can be used to define a network, thereby generalising previous approaches. The application of graph-theoretic techniques to these networks can then offer significantly deeper insight into the structure of the data than previously possible. In particular, we focus on the identification of network motifs, which have clear interpretation in terms of spatio-temporal behaviour. Statistical analysis is complicated by the nature of the underlying data, and we provide a method by which appropriate randomised graphs can be generated. Two datasets are used as case studies: maritime piracy at the global scale, and residential burglary in an urban area. In both cases, the same significant 3-vertex motif is found; this result suggests that incidents tend to occur not just in pairs, but in fact in larger groups within a restricted spatio-temporal domain. In the 4-vertex case, different motifs are found to be significant in each case, suggesting that this technique is capable of discriminating between clustering patterns at a finer granularity than previously possible. PMID:26605544

  7. Response of African eggplants to Fusarium spp. and identification of ...

    African Journals Online (AJOL)

    Response of African eggplants to Fusarium spp. and identification of sources of resistance. Phoebe Kirigo Mwaniki, Mathew Musumbale Abang, Isabel Nyokabi Wagara, Joseph Ngwela Wolukau, Schroers Hans-Josef ...

  8. Village Building Identification Based on Ensemble Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Zhiling Guo

    2017-10-01

    Full Text Available In this study, we present the Ensemble Convolutional Neural Network (ECNN, an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86.

  9. Heavy flavor identification at CMS with deep neural networks

    CERN Document Server

    CMS Collaboration

    2017-01-01

    At the Large Hadron Collider, the identification of jets originating from heavy flavour quarks (b or c-tagging) is important for searches for new physics and for measurements of standard model processes. A variety of b-tagging algorithms has been developed by CMS to select b-quark jets based on variables such as the impact parameters of the charged-particle tracks, the properties of reconstructed decay vertices, and the presence or absence of a lepton, or combinations thereof. These algorithms heavily rely on machine learning tools and are thus natural candidates for advanced tools like deep neural networks. A new algorithm, DeepCSV, uses a deep neural network. The input is the same set of observables used by the existing CSVv2 b-tagger, with the extension that it uses information of more tracks. Also, the training strategy was adapted and about 50 million jets are used for the training of the deep neural network. The new DeepCSV algorithm outperforms the CSVv2 tagger, with an absolute b-tagging efficiency im...

  10. Disease candidate gene identification and prioritization using protein interaction networks

    Directory of Open Access Journals (Sweden)

    Aronow Bruce J

    2009-02-01

    Full Text Available Abstract Background Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN analyses. Results For the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds", and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings – for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method – the three methods achieved a comparable AUC value, suggesting a similar performance. Conclusion Even though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.

  11. Energy Efficient Distributed Fault Identification Algorithm in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Meenakshi Panda

    2014-01-01

    Full Text Available A distributed fault identification algorithm is proposed here to find both hard and soft faulty sensor nodes present in wireless sensor networks. The algorithm is distributed, self-detectable, and can detect the most common byzantine faults such as stuck at zero, stuck at one, and random data. In the proposed approach, each sensor node gathered the observed data from the neighbors and computed the mean to check whether faulty sensor node is present or not. If a node found the presence of faulty sensor node, then compares observed data with the data of the neighbors and predict probable fault status. The final fault status is determined by diffusing the fault information from the neighbors. The accuracy and completeness of the algorithm are verified with the help of statistical model of the sensors data. The performance is evaluated in terms of detection accuracy, false alarm rate, detection latency and message complexity.

  12. System Identification Using Multilayer Differential Neural Networks: A New Result

    Directory of Open Access Journals (Sweden)

    J. Humberto Pérez-Cruz

    2012-01-01

    Full Text Available In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.

  13. Modal Parameter Identification from Responses of General Unknown Random Inputs

    OpenAIRE

    Ibrahim, S. R.; Asmussen, J. C.; Brincker, Rune

    1995-01-01

    Modal parameter identification from ambient responses due to a general unknown random inputs is investigated. Existing identification techniques which are based on assumptions of white noise and or stationary random inputs are utilized even though the inputs conditions are not satisfied. This is accomplished via adding. In cascade. A force cascade conversion to the structures system under consideration. The input to the force conversion system is white noise and the output of which is the act...

  14. Fuzzy stochastic neural network model for structural system identification

    Science.gov (United States)

    Jiang, Xiaomo; Mahadevan, Sankaran; Yuan, Yong

    2017-01-01

    This paper presents a dynamic fuzzy stochastic neural network model for nonparametric system identification using ambient vibration data. The model is developed to handle two types of imprecision in the sensed data: fuzzy information and measurement uncertainties. The dimension of the input vector is determined by using the false nearest neighbor approach. A Bayesian information criterion is applied to obtain the optimum number of stochastic neurons in the model. A fuzzy C-means clustering algorithm is employed as a data mining tool to divide the sensed data into clusters with common features. The fuzzy stochastic model is created by combining the fuzzy clusters of input vectors with the radial basis activation functions in the stochastic neural network. A natural gradient method is developed based on the Kullback-Leibler distance criterion for quick convergence of the model training. The model is validated using a power density pseudospectrum approach and a Bayesian hypothesis testing-based metric. The proposed methodology is investigated with numerically simulated data from a Markov Chain model and a two-story planar frame, and experimentally sensed data from ambient vibration data of a benchmark structure.

  15. Multi-level damage identification with response reconstruction

    Science.gov (United States)

    Zhang, Chao-Dong; Xu, You-Lin

    2017-10-01

    Damage identification through finite element (FE) model updating usually forms an inverse problem. Solving the inverse identification problem for complex civil structures is very challenging since the dimension of potential damage parameters in a complex civil structure is often very large. Aside from enormous computation efforts needed in iterative updating, the ill-condition and non-global identifiability features of the inverse problem probably hinder the realization of model updating based damage identification for large civil structures. Following a divide-and-conquer strategy, a multi-level damage identification method is proposed in this paper. The entire structure is decomposed into several manageable substructures and each substructure is further condensed as a macro element using the component mode synthesis (CMS) technique. The damage identification is performed at two levels: the first is at macro element level to locate the potentially damaged region and the second is over the suspicious substructures to further locate as well as quantify the damage severity. In each level's identification, the damage searching space over which model updating is performed is notably narrowed down, not only reducing the computation amount but also increasing the damage identifiability. Besides, the Kalman filter-based response reconstruction is performed at the second level to reconstruct the response of the suspicious substructure for exact damage quantification. Numerical studies and laboratory tests are both conducted on a simply supported overhanging steel beam for conceptual verification. The results demonstrate that the proposed multi-level damage identification via response reconstruction does improve the identification accuracy of damage localization and quantization considerably.

  16. A Kalman-filter based approach to identification of time-varying gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Jie Xiong

    Full Text Available MOTIVATION: Conventional identification methods for gene regulatory networks (GRNs have overwhelmingly adopted static topology models, which remains unchanged over time to represent the underlying molecular interactions of a biological system. However, GRNs are dynamic in response to physiological and environmental changes. Although there is a rich literature in modeling static or temporally invariant networks, how to systematically recover these temporally changing networks remains a major and significant pressing challenge. The purpose of this study is to suggest a two-step strategy that recovers time-varying GRNs. RESULTS: It is suggested in this paper to utilize a switching auto-regressive model to describe the dynamics of time-varying GRNs, and a two-step strategy is proposed to recover the structure of time-varying GRNs. In the first step, the change points are detected by a Kalman-filter based method. The observed time series are divided into several segments using these detection results; and each time series segment belonging to two successive demarcating change points is associated with an individual static regulatory network. In the second step, conditional network structure identification methods are used to reconstruct the topology for each time interval. This two-step strategy efficiently decouples the change point detection problem and the topology inference problem. Simulation results show that the proposed strategy can detect the change points precisely and recover each individual topology structure effectively. Moreover, computation results with the developmental data of Drosophila Melanogaster show that the proposed change point detection procedure is also able to work effectively in real world applications and the change point estimation accuracy exceeds other existing approaches, which means the suggested strategy may also be helpful in solving actual GRN reconstruction problem.

  17. Reverse engineering biological networks :applications in immune responses to bio-toxins.

    Energy Technology Data Exchange (ETDEWEB)

    Martino, Anthony A.; Sinclair, Michael B.; Davidson, George S.; Haaland, David Michael; Timlin, Jerilyn Ann; Thomas, Edward Victor; Slepoy, Alexander; Zhang, Zhaoduo; May, Elebeoba Eni; Martin, Shawn Bryan; Faulon, Jean-Loup Michel

    2005-12-01

    Our aim is to determine the network of events, or the regulatory network, that defines an immune response to a bio-toxin. As a model system, we are studying T cell regulatory network triggered through tyrosine kinase receptor activation using a combination of pathway stimulation and time-series microarray experiments. Our approach is composed of five steps (1) microarray experiments and data error analysis, (2) data clustering, (3) data smoothing and discretization, (4) network reverse engineering, and (5) network dynamics analysis and fingerprint identification. The technological outcome of this study is a suite of experimental protocols and computational tools that reverse engineer regulatory networks provided gene expression data. The practical biological outcome of this work is an immune response fingerprint in terms of gene expression levels. Inferring regulatory networks from microarray data is a new field of investigation that is no more than five years old. To the best of our knowledge, this work is the first attempt that integrates experiments, error analyses, data clustering, inference, and network analysis to solve a practical problem. Our systematic approach of counting, enumeration, and sampling networks matching experimental data is new to the field of network reverse engineering. The resulting mathematical analyses and computational tools lead to new results on their own and should be useful to others who analyze and infer networks.

  18. Modal Parameter Identification from Responses of General Unknown Random Inputs

    DEFF Research Database (Denmark)

    Ibrahim, S. R.; Asmussen, J. C.; Brincker, Rune

    1996-01-01

    Modal parameter identification from ambient responses due to a general unknown random inputs is investigated. Existing identification techniques which are based on assumptions of white noise and or stationary random inputs are utilized even though the inputs conditions are not satisfied....... This is accomplished via adding. In cascade. A force cascade conversion to the structures system under consideration. The input to the force conversion system is white noise and the output of which is the actual force(s) applied to the structure. The white noise input(s) and the structures responses are then used...... to identify the compined system. Identification results are then sorted as either structural parameters or input force(s) characteristics....

  19. Response to Intervention and the Identification of Specific Learning Disabilities

    Science.gov (United States)

    Reschly, Daniel J.

    2014-01-01

    The use of response-to-intervention (RTI) to identify children and youth with specific learning disabilities (SLDs) is described with multiple illustrations. Essential components of the RTI process are specified at multiple tiers of intervention, each essential to valid SLD identification. The RTI goals are prevention in general education, early…

  20. Modal Identification from Ambient Responses using Frequency Domain Decomposition

    DEFF Research Database (Denmark)

    Brincker, Rune; Zhang, L.; Andersen, P.

    2000-01-01

    In this paper a new frequency domain technique is introduced for the modal identification from ambient responses, ie. in the case where the modal parameters must be estimated without knowing the input exciting the system. By its user friendliness the technique is closely related to the classical...

  1. Identification of differentially expressed proteins in response to Pb ...

    African Journals Online (AJOL)

    use

    oxidative stress. Key words: Antioxidants, chlorophyll, MALDI-TOF-MS, oxidative stress, protein identification. INTRODUCTION. Defensive responses in plants to abiotic stresses like heavy metals have become a major part of the research in plant sciences which mainly concentrate on the elucidation of mechanisms playing ...

  2. Gearbox Fault Identification and Classification with Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    ZhiQiang Chen

    2015-01-01

    Full Text Available Vibration signals of gearbox are sensitive to the existence of the fault. Based on vibration signals, this paper presents an implementation of deep learning algorithm convolutional neural network (CNN used for fault identification and classification in gearboxes. Different combinations of condition patterns based on some basic fault conditions are considered. 20 test cases with different combinations of condition patterns are used, where each test case includes 12 combinations of different basic condition patterns. Vibration signals are preprocessed using statistical measures from the time domain signal such as standard deviation, skewness, and kurtosis. In the frequency domain, the spectrum obtained with FFT is divided into multiple bands, and the root mean square (RMS value is calculated for each one so the energy maintains its shape at the spectrum peaks. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery. Comparing with peer algorithms, the present method exhibits the best performance in the gearbox fault diagnosis.

  3. Guest editorial - Networked collaboration, sharing and response

    Directory of Open Access Journals (Sweden)

    Olav Skundberg

    2008-11-01

    Full Text Available  This issue of Seminar.net contains three articles that were written in connection with a Norwegian e-learning conference titled “Networked collaboration, sharing and response”. The conference was held in Mars 2008 in Trondheim, and the presentations from the conference is available (in norwegian language at http://www.nvu.no. Networked collaboration was chosen as a theme because collaboration is important to achieve learning, according to the social-constructivistic pedagogy that has a strong standing in Norway, but how should this occur on the net? Sharing of content, as in digital learning resources, is a phenomenon with increasing popularity as described in the OECD-report “Giving Knowledge for Free”. But to achieve reuse of content, not only publishing it, it is important with a networked community where the plethora of information can be sorted with relevance to specific topics. Response is about guiding, coaching and tutoring. In what ways may resources and tools be used to move in the direction of solving Bloom’s two sigma problem/challenge? The first article, by Morten Flate Paulsen, shows how cooperative learning can be implemented successfully so that students have optimal individual freedom within online learning communities. The second article, by Carl F. Dons, shows how student teachers can be prepared to deal with pupils who have a wide range of experiences of the digital world. The third and last article, by Kristin Dale, is sharing experiences with multiple choice-tests to give midterm responses to students. In addition, this issue has a commentary article by Rune Krumsvik discussing the need to develop new practices for teachers and students on the background of the digital developments. The conference and articles covers three big themes. It may be difficult to find more important issues, apart from finding money and time to support its development. Olav Skundberg, guest editorAssociate professor

  4. A scalable algorithm for structure identification of complex gene regulatory network from temporal expression data.

    Science.gov (United States)

    Gui, Shupeng; Rice, Andrew P; Chen, Rui; Wu, Liang; Liu, Ji; Miao, Hongyu

    2017-01-31

    Gene regulatory interactions are of fundamental importance to various biological functions and processes. However, only a few previous computational studies have claimed success in revealing genome-wide regulatory landscapes from temporal gene expression data, especially for complex eukaryotes like human. Moreover, recent work suggests that these methods still suffer from the curse of dimensionality if a network size increases to 100 or higher. Here we present a novel scalable algorithm for identifying genome-wide gene regulatory network (GRN) structures, and we have verified the algorithm performances by extensive simulation studies based on the DREAM challenge benchmark data. The highlight of our method is that its superior performance does not degenerate even for a network size on the order of 10(4), and is thus readily applicable to large-scale complex networks. Such a breakthrough is achieved by considering both prior biological knowledge and multiple topological properties (i.e., sparsity and hub gene structure) of complex networks in the regularized formulation. We also validate and illustrate the application of our algorithm in practice using the time-course gene expression data from a study on human respiratory epithelial cells in response to influenza A virus (IAV) infection, as well as the CHIP-seq data from ENCODE on transcription factor (TF) and target gene interactions. An interesting finding, owing to the proposed algorithm, is that the biggest hub structures (e.g., top ten) in the GRN all center at some transcription factors in the context of epithelial cell infection by IAV. The proposed algorithm is the first scalable method for large complex network structure identification. The GRN structure identified by our algorithm could reveal possible biological links and help researchers to choose which gene functions to investigate in a biological event. The algorithm described in this article is implemented in MATLAB (Ⓡ) , and the source code is

  5. Identification of the non-linear systems using internal recurrent neural networks

    Directory of Open Access Journals (Sweden)

    Bogdan CODRES

    2006-12-01

    Full Text Available In the past years utilization of neural networks took a distinct ampleness because of the following properties: distributed representation of information, capacity of generalization in case of uncontained situation in training data set, tolerance to noise, resistance to partial destruction, parallel processing. Another major advantage of neural networks is that they allow us to obtain the model of the investigated system, systems that is not necessarily to be linear. In fact, the true value of neural networks is seen in the case of identification and control of nonlinear systems. In this paper there are presented some identification techniques using neural networks.

  6. Network-assisted target identification for haploinsufficiency and homozygous profiling screens

    Science.gov (United States)

    Wang, Sheng

    2017-01-01

    Chemical genomic screens have recently emerged as a systematic approach to drug discovery on a genome-wide scale. Drug target identification and elucidation of the mechanism of action (MoA) of hits from these noisy high-throughput screens remain difficult. Here, we present GIT (Genetic Interaction Network-Assisted Target Identification), a network analysis method for drug target identification in haploinsufficiency profiling (HIP) and homozygous profiling (HOP) screens. With the drug-induced phenotypic fitness defect of the deletion of a gene, GIT also incorporates the fitness defects of the gene’s neighbors in the genetic interaction network. On three genome-scale yeast chemical genomic screens, GIT substantially outperforms previous scoring methods on target identification on HIP and HOP assays, respectively. Finally, we showed that by combining HIP and HOP assays, GIT further boosts target identification and reveals potential drug’s mechanism of action. PMID:28574983

  7. Rotor Resistance Online Identification of Vector Controlled Induction Motor Based on Neural Network

    OpenAIRE

    Bo Fan; Zhixin Yang; Wei Xu; Xianbo Wang

    2014-01-01

    Rotor resistance identification has been well recognized as one of the most critical factors affecting the theoretical study and applications of AC motor’s control for high performance variable frequency speed adjustment. This paper proposes a novel model for rotor resistance parameters identification based on Elman neural networks. Elman recurrent neural network is capable of performing nonlinear function approximation and possesses the ability of time-variable characteristic adaptation. Tho...

  8. Bayesian networks for victim identification on the basis of DNA profiles

    NARCIS (Netherlands)

    Bruijning-van Dongen, C. J.; Slooten, K.; Burgers, W.; Wiegerinck, W.

    We have developed software to improve screening and matching routine for victim identification based on DNA profiles. The software, called Napoleon/Bonaparte, uses Bayesian networks for the analysis. It is designed for effective handling of the identification process in case of a large disaster with

  9. Reconstructing cancer drug response networks using multitask learning.

    Science.gov (United States)

    Ruffalo, Matthew; Stojanov, Petar; Pillutla, Venkata Krishna; Varma, Rohan; Bar-Joseph, Ziv

    2017-10-10

    Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific response networks in cancer. The reconstructed networks correctly identify several shared key proteins and pathways while simultaneously highlighting many cell type specific proteins. We used top proteins from each drug network to predict survival for patients prescribed the drug. Predictions based on proteins from the in-vitro derived networks significantly outperformed predictions based on known cancer genes indicating that Multi-Task learning can indeed identify accurate drug response networks.

  10. Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks.

    Science.gov (United States)

    Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang

    2011-05-01

    A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.

  11. 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.

  12. A general regression artificial neural network for two-phase flow regime identification

    Energy Technology Data Exchange (ETDEWEB)

    Tambouratzis, Tatiana, E-mail: tatianatambouratzis@gmail.co [Department of Industrial Management and Technology, University of Piraeus, 107 Deligiorgi St., Piraeus 185 34 (Greece); Department of Nuclear Engineering, Chalmers University of Technology, SE-41296 Goeteborg (Sweden); Pazsit, Imre, E-mail: imre@chalmers.s [Department of Nuclear Engineering, Chalmers University of Technology, SE-41296 Goeteborg (Sweden); Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI 48019 (United States)

    2010-05-15

    Supplementing the collection of artificial neural network methodologies devised for monitoring energy producing installations, a general regression artificial neural network is proposed for the identification of the two-phase flow that occurs in the coolant channels of boiling water reactors. The utilization of a limited number of image features derived from radiography images affords the proposed approach with efficiency and non-invasiveness. Additionally, the application of counter-clustering to the input patterns prior to training accomplishes an 80% reduction in network size as well as in training and test time. Cross-validation tests confirm accurate on-line flow regime identification.

  13. Neural Networks for Medical Image Processing: A Study of Feature Identification

    OpenAIRE

    Dayhoff, Ruth E.; Dayhoff, Judith E.

    1988-01-01

    Neural networks, a parallel computing architecture modelled on living nervous systems, are able to “learn” by example. The ability of a simulated neural network to distinguish among simulated microscopic amoebae nuclei images was studied. The neural network was successfully shown to organize feature detectors without the intermediate step of manual identification of salient features. The feature detectors were mapped onto the image format and the issue of redundancy was examined.

  14. Identification of leader and self-organizing communities in complex networks

    OpenAIRE

    Jingcheng Fu; Weixiong Zhang; Jianliang Wu

    2017-01-01

    Community or module structure is a natural property of complex networks. Leader communities and self-organizing communities have been introduced recently to characterize networks and understand how communities arise in complex networks. However, identification of leader and self-organizing communities is technically challenging since no adequate quantification has been developed to properly separate the two types of communities. We introduced a new measure, called ratio of node degree varianc...

  15. Identification and management of distributed data NGN, content-centric networks and the web

    CERN Document Server

    Bartolomeo, Giovanni

    2013-01-01

    Although several books and academic courses discuss data management and networking, few of them focus on the convergence of networking and software technologies for identifying, addressing, and managing distributed data. Focusing on this convergence, Identification and Management of Distributed Data: NGN, Content-Centric Networks and the Web collates and describes the various distributed data management technologies to help readers from various backgrounds understand the common aspects that govern distributed data management. With a focus on the primary problems in identifying, addressing, and

  16. Authority and Responsibilities of a Network Director.

    Science.gov (United States)

    Reynolds, Maryan E.

    A network director is an individual who: is visionary yet practical; possesses understanding of the human animal; has good interpersonal relationships; is committed to the user not the institution; is knowledgeable in regard to the various types of participating institutions; recognizes the network must be built strength on strength; is a skillful…

  17. Identification of hybrid node and link communities in complex networks.

    Science.gov (United States)

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-02

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  18. Identification of hybrid node and link communities in complex networks

    Science.gov (United States)

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-01

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  19. Natural Cubic Spline Regression Modeling Followed by Dynamic Network Reconstruction for the Identification of Radiation-Sensitivity Gene Association Networks from Time-Course Transcriptome Data.

    Science.gov (United States)

    Michna, Agata; Braselmann, Herbert; Selmansberger, Martin; Dietz, Anne; Hess, Julia; Gomolka, Maria; Hornhardt, Sabine; Blüthgen, Nils; Zitzelsberger, Horst; Unger, Kristian

    2016-01-01

    Gene expression time-course experiments allow to study the dynamics of transcriptomic changes in cells exposed to different stimuli. However, most approaches for the reconstruction of gene association networks (GANs) do not propose prior-selection approaches tailored to time-course transcriptome data. Here, we present a workflow for the identification of GANs from time-course data using prior selection of genes differentially expressed over time identified by natural cubic spline regression modeling (NCSRM). The workflow comprises three major steps: 1) the identification of differentially expressed genes from time-course expression data by employing NCSRM, 2) the use of regularized dynamic partial correlation as implemented in GeneNet to infer GANs from differentially expressed genes and 3) the identification and functional characterization of the key nodes in the reconstructed networks. The approach was applied on a time-resolved transcriptome data set of radiation-perturbed cell culture models of non-tumor cells with normal and increased radiation sensitivity. NCSRM detected significantly more genes than another commonly used method for time-course transcriptome analysis (BETR). While most genes detected with BETR were also detected with NCSRM the false-detection rate of NCSRM was low (3%). The GANs reconstructed from genes detected with NCSRM showed a better overlap with the interactome network Reactome compared to GANs derived from BETR detected genes. After exposure to 1 Gy the normal sensitive cells showed only sparse response compared to cells with increased sensitivity, which exhibited a strong response mainly of genes related to the senescence pathway. After exposure to 10 Gy the response of the normal sensitive cells was mainly associated with senescence and that of cells with increased sensitivity with apoptosis. We discuss these results in a clinical context and underline the impact of senescence-associated pathways in acute radiation response of normal

  20. Identification of yeast transcriptional regulation networks using multivariate random forests.

    Directory of Open Access Journals (Sweden)

    Yuanyuan Xiao

    2009-06-01

    Full Text Available The recent availability of whole-genome scale data sets that investigate complementary and diverse aspects of transcriptional regulation has spawned an increased need for new and effective computational approaches to analyze and integrate these large scale assays. Here, we propose a novel algorithm, based on random forest methodology, to relate gene expression (as derived from expression microarrays to sequence features residing in gene promoters (as derived from DNA motif data and transcription factor binding to gene promoters (as derived from tiling microarrays. We extend the random forest approach to model a multivariate response as represented, for example, by time-course gene expression measures. An analysis of the multivariate random forest output reveals complex regulatory networks, which consist of cohesive, condition-dependent regulatory cliques. Each regulatory clique features homogeneous gene expression profiles and common motifs or synergistic motif groups. We apply our method to several yeast physiological processes: cell cycle, sporulation, and various stress conditions. Our technique displays excellent performance with regard to identifying known regulatory motifs, including high order interactions. In addition, we present evidence of the existence of an alternative MCB-binding pathway, which we confirm using data from two independent cell cycle studies and two other physioloigical processes. Finally, we have uncovered elaborate transcription regulation refinement mechanisms involving PAC and mRRPE motifs that govern essential rRNA processing. These include intriguing instances of differing motif dosages and differing combinatorial motif control that promote regulatory specificity in rRNA metabolism under differing physiological processes.

  1. Epidemic spreading on networks based on stress response

    Science.gov (United States)

    Nian, Fuzhong; Yao, Shuanglong

    2017-06-01

    Based on the stress responses of individuals, the susceptible-infected-susceptible epidemic model was improved on the small-world networks and BA scale-free networks and the simulations were implemented and analyzed. Results indicate that the behaviors of individual’s stress responses could induce the epidemic spreading resistance and adaptation at the network level. This phenomenon showed that networks were learning how to adapt to the disease and the evolution process could improve their immunization to future infectious diseases and would effectively prevent the spreading of infectious diseases.

  2. Stress, stress‐induced cortisol responses, and eyewitness identification performance

    Science.gov (United States)

    Raymaekers, Linsey H.C.; Otgaar, Henry; Memon, Amina; Waltjen, Thijs T.; Nivo, Maud; Slegers, Chiel; Broers, Nick J.; Smeets, Tom

    2016-01-01

    Abstract In the eyewitness identification literature, stress and arousal at the time of encoding are considered to adversely influence identification performance. This assumption is in contrast with findings from the neurobiology field of learning and memory, showing that stress and stress hormones are critically involved in forming enduring memories. This discrepancy may be related to methodological differences between the two fields of research, such as the tendency for immediate testing or the use of very short (1–2 hours) retention intervals in eyewitness research, while neurobiology studies insert at least 24 hours. Other differences refer to the extent to which stress‐responsive systems (i.e., the hypothalamic–pituitary–adrenal axis) are stimulated effectively under laboratory conditions. The aim of the current study was to conduct an experiment that accounts for the contemporary state of knowledge in both fields. In all, 123 participants witnessed a live staged theft while being exposed to a laboratory stressor that reliably elicits autonomic and glucocorticoid stress responses or while performing a control task. Salivary cortisol levels were measured to control for the effectiveness of the stress induction. One week later, participants attempted to identify the thief from target‐present and target‐absent line‐ups. According to regression and receiver operating characteristic analyses, stress did not have robust detrimental effects on identification performance. Copyright © 2016 John Wiley & Sons, Ltd. © 2016 The Authors Behavioral Sciences & the Law Published by John Wiley & Sons Ltd PMID:27417874

  3. Home Network Technologies and Automating Demand Response

    Energy Technology Data Exchange (ETDEWEB)

    McParland, Charles

    2009-12-01

    Over the past several years, interest in large-scale control of peak energy demand and total consumption has increased. While motivated by a number of factors, this interest has primarily been spurred on the demand side by the increasing cost of energy and, on the supply side by the limited ability of utilities to build sufficient electricity generation capacity to meet unrestrained future demand. To address peak electricity use Demand Response (DR) systems are being proposed to motivate reductions in electricity use through the use of price incentives. DR systems are also be design to shift or curtail energy demand at critical times when the generation, transmission, and distribution systems (i.e. the 'grid') are threatened with instabilities. To be effectively deployed on a large-scale, these proposed DR systems need to be automated. Automation will require robust and efficient data communications infrastructures across geographically dispersed markets. The present availability of widespread Internet connectivity and inexpensive, reliable computing hardware combined with the growing confidence in the capabilities of distributed, application-level communications protocols suggests that now is the time for designing and deploying practical systems. Centralized computer systems that are capable of providing continuous signals to automate customers reduction of power demand, are known as Demand Response Automation Servers (DRAS). The deployment of prototype DRAS systems has already begun - with most initial deployments targeting large commercial and industrial (C & I) customers. An examination of the current overall energy consumption by economic sector shows that the C & I market is responsible for roughly half of all energy consumption in the US. On a per customer basis, large C & I customers clearly have the most to offer - and to gain - by participating in DR programs to reduce peak demand. And, by concentrating on a small number of relatively

  4. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview...

  5. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

  6. Epileptic neuronal networks: methods of identification and clinical relevance

    NARCIS (Netherlands)

    Stefan, H.; Lopes da Silva, F.H.

    2013-01-01

    The main objective of this paper is to examine evidence for the concept that epileptic activity should be envisaged in terms of functional connectivity and dynamics of neuronal networks. Basic concepts regarding structure and dynamics of neuronal networks are briefly described. Particular attention

  7. Epileptic neuronal networks: methods of identification and clinical relevance.

    Science.gov (United States)

    Stefan, Hermann; Lopes da Silva, Fernando H

    2013-01-01

    The main objective of this paper is to examine evidence for the concept that epileptic activity should be envisaged in terms of functional connectivity and dynamics of neuronal networks. Basic concepts regarding structure and dynamics of neuronal networks are briefly described. Particular attention is given to approaches that are derived, or related, to the concept of causality, as formulated by Granger. Linear and non-linear methodologies aiming at characterizing the dynamics of neuronal networks applied to EEG/MEG and combined EEG/fMRI signals in epilepsy are critically reviewed. The relevance of functional dynamical analysis of neuronal networks with respect to clinical queries in focal cortical dysplasias, temporal lobe epilepsies, and "generalized" epilepsies is emphasized. In the light of the concepts of epileptic neuronal networks, and recent experimental findings, the dichotomic classification in focal and generalized epilepsy is re-evaluated. It is proposed that so-called "generalized epilepsies," such as absence seizures, are actually fast spreading epilepsies, the onset of which can be tracked down to particular neuronal networks using appropriate network analysis. Finally new approaches to delineate epileptogenic networks are discussed.

  8. Identification of Tobacco Topping Responsive Proteins in Roots

    Directory of Open Access Journals (Sweden)

    Hongxiang eGuo

    2016-04-01

    Full Text Available Tobacco plant has many responses to topping, such as the increase in ability of nicotine synthesis and secondary growth of roots. Some topping responsive miRNAs and genes had been identified in our previous work, but it is not enough to elaborate mechanism of tobacco response to topping. Here, topping responsive proteins were screened from tobacco roots with two-dimensional electrophoresis. Of these proteins, calretulin (CRT and Auxin-responsive protein IAA9 were related to the secondary growth of roots, LRR disease resistance, heat shock protein 70 and farnesyl pyrophosphate synthase 1(FPPS)were involved in wounding stress response, and F-box protein played an important role in promoting the ability of nicotine synthesis after topping. In addition, there were five tobacco bHLH proteins (NtbHLH, NtMYC1a, NtMYC1b, NtMYC2a and NtMYC2b related to nicotine synthesis. It was suggested that NtMYC2 might be the main positive transcription factor and NtbHLH protein is a negative regulator in the JA-mediating activation of nicotine synthesis after topping. Tobacco topping activates some comprehensive biology processes involving IAA and JA signaling pathway, and the identification of these proteins will be helpful to understand the process of topping response.

  9. Analysis of Network Topologies Underlying Ethylene Growth Response Kinetics

    Directory of Open Access Journals (Sweden)

    Aaron M. Prescott

    2016-08-01

    Full Text Available Most models for ethylene signaling involve a linear pathway. However, measurements of seedling growth kinetics when ethylene is applied and removed have resulted in more complex network models that include coherent feedforward, negative feedback, and positive feedback motifs. However, the dynamical responses of the proposed networks have not been explored in a quantitative manner. Here, we explore (i whether any of the proposed models are capable of producing growth-response behaviors consistent with experimental observations and (ii what mechanistic roles various parts of the network topologies play in ethylene signaling. To address this, we used computational methods to explore two general network topologies: The first contains a coherent feedforward loop that inhibits growth and a negative feedback from growth onto itself (CFF/NFB. In the second, ethylene promotes the cleavage of EIN2, with the product of the cleavage inhibiting growth and promoting the production of EIN2 through a positive feedback loop (PFB. Since few network parameters for ethylene signaling are known in detail, we used an evolutionary algorithm to explore sets of parameters that produce behaviors similar to experimental growth response kinetics of both wildtype and mutant seedlings. We generated a library of parameter sets by independently running the evolutionary algorithm many times. Both network topologies produce behavior consistent with experimental observations and analysis of the parameter sets allows us to identify important network interactions and parameter constraints. We additionally screened these parameter sets for growth recovery in the presence of sub-saturating ethylene doses, which is an experimentally-observed property that emerges in some of the evolved parameter sets. Finally, we probed simplified networks maintaining key features of the CFF/NFB and PFB topologies. From this, we verified observations drawn from the larger networks about mechanisms

  10. Epileptic neuronal networks: methods of identification andclinical relevance.

    Directory of Open Access Journals (Sweden)

    Hermann eStefan

    2013-03-01

    Full Text Available The main objective of this paper is to examine evidence for the concept that epileptic activityshould be envisaged in terms of functional connectivity and dynamics of neuronal networks,Basic concepts regarding structure and dynamics of neuronal networks are briefly described.Particular attention is given to approaches that are derived, or related, to the concept ofcausality, as formulated by Granger. Linear and non linear methodologies aiming atcharacterizing the dynamics of neuronal networks applied to EEG/MEG and combined EEG/fMRI signals in epilepsy are critically reviewed. The relevance of functional dynamicalanalysis of neuronal networks with respect to clinical queries in focal cortical dysplasias,temporal lobe epilepsies and "generalized epilepsies is emphasized. In the light of theconcepts of epileptic neuronal networks, and recent experimental findings, the dichotomicclassification in focal and generalized epilepsy is re-evaluated. It is proposed that so-called"generalized epilepsies", such as absence seizures, are actually fast spreading epilepsies, theonset of which can be tracked down to particular neuronal networks using appropriatenetwork analysis. Finally new approaches to delineate epileptogenic networks are discussed.

  11. Identification of regulatory modules in genome scale transcription regulatory networks.

    Science.gov (United States)

    Song, Qi; Grene, Ruth; Heath, Lenwood S; Li, Song

    2017-12-15

    In gene regulatory networks, transcription factors often function as co-regulators to synergistically induce or inhibit expression of their target genes. However, most existing module-finding algorithms can only identify densely connected genes but not co-regulators in regulatory networks. We have developed a new computational method, CoReg, to identify transcription co-regulators in large-scale regulatory networks. CoReg calculates gene similarities based on number of common neighbors of any two genes. Using simulated and real networks, we compared the performance of different similarity indices and existing module-finding algorithms and we found CoReg outperforms other published methods in identifying co-regulatory genes. We applied CoReg to a large-scale network of Arabidopsis with more than 2.8 million edges and we analyzed more than 2,300 published gene expression profiles to charaterize co-expression patterns of gene moduled identified by CoReg. We identified three types of modules in the Arabidopsis network: regulator modules, target modules and intermediate modules. Regulator modules include genes with more than 90% edges as out-going edges; Target modules include genes with more than 90% edges as incoming edges. Other modules are classified as intermediate modules. We found that genes in target modules tend to be highly co-expressed under abiotic stress conditions, suggesting this network struture is robust against perturbation. Our analysis shows that the CoReg is an accurate method in identifying co-regulatory genes in large-scale networks. We provide CoReg as an R package, which can be applied in finding co-regulators in any organisms with genome-scale regulatory network data.

  12. Natural semantic networks in the Social Representations of Responsibility

    Directory of Open Access Journals (Sweden)

    Humberto Emilio Aguilera Arévalo

    2010-07-01

    Full Text Available The study of social representations of responsibility is a fundamental construct of the present democratic societies. Different empirical techniques such as natural semantic networks can significantly improve the approach to the object of study than the traditional associationist techniques. The present study examines natural semantic networks of six stimulus words with respect to responsibility and irresponsibility at the individual, in group and out group level in a sample of Guatemalan students.

  13. Identification of network externalities in markets for non-durables

    OpenAIRE

    Grajek, Michal

    2002-01-01

    This paper introduces a structural econometric model of consumer demand for non-durable goods, which exhibits network externalities. The structural model allows us to identify the parameters, which determine the strength of the externalities in the underlying economic model from the empirical estimation results. The estimates of these parameters can then be employed to test the economic significance of the externalities and the compatibility of networks. The identifying assumption that drives...

  14. BMRF-MI: integrative identification of protein interaction network by modeling the gene dependency.

    Science.gov (United States)

    Shi, Xu; Wang, Xiao; Shajahan, Ayesha; Hilakivi-Clarke, Leena; Clarke, Robert; Xuan, Jianhua

    2015-01-01

    Identification of protein interaction network is a very important step for understanding the molecular mechanisms in cancer. Several methods have been developed to integrate protein-protein interaction (PPI) data with gene expression data for network identification. However, they often fail to model the dependency between genes in the network, which makes many important genes, especially the upstream genes, unidentified. It is necessary to develop a method to improve the network identification performance by incorporating the dependency between genes. We proposed an approach for identifying protein interaction network by incorporating mutual information (MI) into a Markov random field (MRF) based framework to model the dependency between genes. MI is widely used in information theory to measure the uncertainty between random variables. Different from traditional Pearson correlation test, MI is capable of capturing both linear and non-linear relationship between random variables. Among all the existing MI estimators, we choose to use k-nearest neighbor MI (kNN-MI) estimator which is proved to have minimum bias. The estimated MI is integrated with an MRF framework to model the gene dependency in the context of network. The maximum a posterior (MAP) estimation is applied on the MRF-based model to estimate the network score. In order to reduce the computational complexity of finding the optimal network, a probabilistic searching algorithm is implemented. We further increase the robustness and reproducibility of the results by applying a non-parametric bootstrapping method to measure the confidence level of the identified genes. To evaluate the performance of the proposed method, we test the method on simulation data under different conditions. The experimental results show an improved accuracy in terms of subnetwork identification compared to existing methods. Furthermore, we applied our method onto real breast cancer patient data; the identified protein interaction

  15. Rotor Resistance Online Identification of Vector Controlled Induction Motor Based on Neural Network

    Directory of Open Access Journals (Sweden)

    Bo Fan

    2014-01-01

    Full Text Available Rotor resistance identification has been well recognized as one of the most critical factors affecting the theoretical study and applications of AC motor’s control for high performance variable frequency speed adjustment. This paper proposes a novel model for rotor resistance parameters identification based on Elman neural networks. Elman recurrent neural network is capable of performing nonlinear function approximation and possesses the ability of time-variable characteristic adaptation. Those influencing factors of specified parameter are analyzed, respectively, and various work states are covered to ensure the completeness of the training samples. Through signal preprocessing on samples and training dataset, different input parameters identifications with one network are compared and analyzed. The trained Elman neural network, applied in the identification model, is able to efficiently predict the rotor resistance in high accuracy. The simulation and experimental results show that the proposed method owns extensive adaptability and performs very well in its application to vector controlled induction motor. This identification method is able to enhance the performance of induction motor’s variable-frequency speed regulation.

  16. Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network

    Directory of Open Access Journals (Sweden)

    Hongshan Yu

    2014-01-01

    Full Text Available Hammerstein model has been popularly applied to identify the nonlinear systems. In this paper, a Hammerstein-type neural network (HTNN is derived to formulate the well-known Hammerstein model. The HTNN consists of a nonlinear static gain in cascade with a linear dynamic part. First, the Lipschitz criterion for order determination is derived. Second, the backpropagation algorithm for updating the network weights is presented, and the stability analysis is also drawn. Finally, simulation results show that HTNN identification approach demonstrated identification performances.

  17. The NNSYSID Toolbox - A MATLAB Toolbox for System Identification with Neural Networks

    DEFF Research Database (Denmark)

    Nørgård, Peter Magnus; Ravn, Ole; Hansen, Lars Kai

    1996-01-01

    To assist the identification of nonlinear dynamic systems, a set of tools has been developed for the MATLAB(R) environment. The tools include a number of different model structures, highly effective training algorithms, functions for validating trained networks, and pruning algorithms for determi......To assist the identification of nonlinear dynamic systems, a set of tools has been developed for the MATLAB(R) environment. The tools include a number of different model structures, highly effective training algorithms, functions for validating trained networks, and pruning algorithms...

  18. Multimodal Neural Network for Overhead Person Re-identification

    DEFF Research Database (Denmark)

    Lejbølle, Aske Rasch; Nasrollahi, Kamal; Krogh, Benjamin

    2017-01-01

    Person re-identification is a topic which has potential to be used for applications within forensics, flow analysis and queue monitoring. It is the process of matching persons across two or more camera views, most often by extracting colour and texture based hand-crafted features, to identify sim...

  19. Stress, stress-induced cortisol responses, and eyewitness identification performance.

    Science.gov (United States)

    Sauerland, Melanie; Raymaekers, Linsey H C; Otgaar, Henry; Memon, Amina; Waltjen, Thijs T; Nivo, Maud; Slegers, Chiel; Broers, Nick J; Smeets, Tom

    2016-07-01

    In the eyewitness identification literature, stress and arousal at the time of encoding are considered to adversely influence identification performance. This assumption is in contrast with findings from the neurobiology field of learning and memory, showing that stress and stress hormones are critically involved in forming enduring memories. This discrepancy may be related to methodological differences between the two fields of research, such as the tendency for immediate testing or the use of very short (1-2 hours) retention intervals in eyewitness research, while neurobiology studies insert at least 24 hours. Other differences refer to the extent to which stress-responsive systems (i.e., the hypothalamic-pituitary-adrenal axis) are stimulated effectively under laboratory conditions. The aim of the current study was to conduct an experiment that accounts for the contemporary state of knowledge in both fields. In all, 123 participants witnessed a live staged theft while being exposed to a laboratory stressor that reliably elicits autonomic and glucocorticoid stress responses or while performing a control task. Salivary cortisol levels were measured to control for the effectiveness of the stress induction. One week later, participants attempted to identify the thief from target-present and target-absent line-ups. According to regression and receiver operating characteristic analyses, stress did not have robust detrimental effects on identification performance. Copyright © 2016 John Wiley & Sons, Ltd. © 2016 The Authors Behavioral Sciences & the Law Published by John Wiley & Sons Ltd. © 2016 The Authors Behavioral Sciences & the Law Published by John Wiley & Sons Ltd.

  20. Identification of Complex Dynamical Systems with Neural Networks (2/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  1. Identification of Complex Dynamical Systems with Neural Networks (1/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  2. Vergence responses to vertical binocular disparity during lexical identification.

    Science.gov (United States)

    Nikolova, M; Jainta, S; Blythe, H I; Jones, M O; Liversedge, S P

    2015-01-01

    Humans typically make use of both eyes during reading, which necessitates precise binocular coordination in order to achieve a unified perceptual representation of written text. A number of studies have explored the magnitude and effects of naturally occurring and induced horizontal fixation disparity during reading and non-reading tasks. However, the literature concerning the processing of disparities in different dimensions, particularly in the context of reading, is considerably limited. We therefore investigated vertical vergence in response to stereoscopically presented linguistic stimuli with varying levels of vertical offset. A lexical decision task was used to explore the ability of participants to fuse binocular image disparity in the vertical direction during word identification. Additionally, a lexical frequency manipulation explored the potential interplay between visual fusion processes and linguistic processes. Results indicated that no significant motor fusional responses were made in the vertical dimension (all p-values>.11), though that did not hinder successful lexical identification. In contrast, horizontal vergence movements were consistently observed on all fixations in the absence of a horizontal disparity manipulation. These findings add to the growing understanding of binocularity and its role in written language processing, and fit neatly with previous literature regarding binocular coordination in non-reading tasks. Copyright © 2014 Elsevier Ltd. All rights reserved.

  3. Flow Pattern Identification of Horizontal Two-Phase Refrigerant Flow Using Neural Networks

    Science.gov (United States)

    2015-12-31

    making classification difficult. Consequently, Table 5 shows neural net - work classification results for nine flow patterns. The number of runs...AFRL-RQ-WP-TP-2016-0079 FLOW PATTERN IDENTIFICATION OF HORIZONTAL TWO-PHASE REFRIGERANT FLOW USING NEURAL NETWORKS (POSTPRINT) Abdeel J... NEURAL NETWORKS (POSTPRINT) 5a. CONTRACT NUMBER In-house 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 62203F 6. AUTHOR(S) Abdeel J. Roman and

  4. Identification of control targets in Boolean molecular network models via computational algebra.

    Science.gov (United States)

    Murrugarra, David; Veliz-Cuba, Alan; Aguilar, Boris; Laubenbacher, Reinhard

    2016-09-23

    Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. This paper presents a method for the identification of potential intervention targets in Boolean molecular network models using algebraic techniques. The approach exploits an algebraic representation of Boolean networks to encode the control candidates in the network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 network and a blood T cell lymphocyte granular leukemia survival signaling network. Supplementary data is available online and our code in Macaulay2 and Matlab are available via http://www.ms.uky.edu/~dmu228/ControlAlg . This paper presents a novel method for the identification of intervention targets in Boolean network models. The results in this paper show that the proposed methods are useful and efficient for moderately large networks.

  5. The graph theoretical analysis of the SSVEP harmonic response networks.

    Science.gov (United States)

    Zhang, Yangsong; Guo, Daqing; Cheng, Kaiwen; Yao, Dezhong; Xu, Peng

    2015-06-01

    Steady-state visually evoked potentials (SSVEP) have been widely used in the neural engineering and cognitive neuroscience researches. Previous studies have indicated that the SSVEP fundamental frequency responses are correlated with the topological properties of the functional networks entrained by the periodic stimuli. Given the different spatial and functional roles of the fundamental frequency and harmonic responses, in this study we further investigated the relation between the harmonic responses and the corresponding functional networks, using the graph theoretical analysis. We found that the second harmonic responses were positively correlated to the mean functional connectivity, clustering coefficient, and global and local efficiencies, while negatively correlated with the characteristic path lengths of the corresponding networks. In addition, similar pattern occurred with the lowest stimulus frequency (6.25 Hz) at the third harmonic responses. These findings demonstrate that more efficient brain networks are related to larger SSVEP responses. Furthermore, we showed that the main connection pattern of the SSVEP harmonic response networks originates from the interactions between the frontal and parietal-occipital regions. Overall, this study may bring new insights into the understanding of the brain mechanisms underlying SSVEP.

  6. Robust network topologies for generating switch-like cellular responses.

    Directory of Open Access Journals (Sweden)

    Najaf A Shah

    2011-06-01

    Full Text Available Signaling networks that convert graded stimuli into binary, all-or-none cellular responses are critical in processes ranging from cell-cycle control to lineage commitment. To exhaustively enumerate topologies that exhibit this switch-like behavior, we simulated all possible two- and three-component networks on random parameter sets, and assessed the resulting response profiles for both steepness (ultrasensitivity and extent of memory (bistability. Simulations were used to study purely enzymatic networks, purely transcriptional networks, and hybrid enzymatic/transcriptional networks, and the topologies in each class were rank ordered by parametric robustness (i.e., the percentage of applied parameter sets exhibiting ultrasensitivity or bistability. Results reveal that the distribution of network robustness is highly skewed, with the most robust topologies clustering into a small number of motifs. Hybrid networks are the most robust in generating ultrasensitivity (up to 28% and bistability (up to 18%; strikingly, a purely transcriptional framework is the most fragile in generating either ultrasensitive (up to 3% or bistable (up to 1% responses. The disparity in robustness among the network classes is due in part to zero-order ultrasensitivity, an enzyme-specific phenomenon, which repeatedly emerges as a particularly robust mechanism for generating nonlinearity and can act as a building block for switch-like responses. We also highlight experimentally studied examples of topologies enabling switching behavior, in both native and synthetic systems, that rank highly in our simulations. This unbiased approach for identifying topologies capable of a given response may be useful in discovering new natural motifs and in designing robust synthetic gene networks.

  7. Nonprofit Organizations in Disaster Response and Management: A Network Analysis

    Directory of Open Access Journals (Sweden)

    NAIM KAPUCU

    2018-01-01

    Full Text Available This paper tracks changes in the national disaster management system with regard to the nonprofit sector by looking at the roles ascribed to nonprofit organizations in the Federal Response Plan (FRP, National Response Plan (NRP, and National Response Framework (NRF. Additionally, the data collected from news reports and organizational after action reports about the inter-organizational interactions of emergency management agencies during the September 11th attacks and Hurricane Katrina are analyzed by using network analysis tools. The findings of the study indicate that there has been an increase in the interactions of the National Voluntary Organizations Active in Disasters (NVOAD network member organizations on par with policy changes in the NRP to involve nonprofit organizations in the national disaster planning process. In addition, those organizations close to the center of the network experienced enhanced communication and resource acquisition allowing them to successfully accomplish their missions, a finding that supports the development of strong network connections.

  8. Distinct Tensile Response of Model Semi-flexible Elastomer Networks

    Science.gov (United States)

    Aguilera-Mercado, Bernardo M.; Cohen, Claude; Escobedo, Fernando A.

    2011-03-01

    Through coarse-grained molecular modeling, we study how the elastic response strongly depends upon nanostructural heterogeneities in model networks made of semi-flexible chains exhibiting both regular and realistic connectivity. Idealized regular polymer networks have been shown to display a peculiar elastic response similar to that of super-tough natural materials (e.g., organic adhesives inside abalone shells). We investigate the impact of chain stiffness, and the effect of including tri-block copolymer chains, on the network's topology and elastic response. We find in some systems a dual tensile response: a liquid-like behavior at small deformations, and a distinct saw-tooth shaped stress-strain curve at moderate to large deformations. Additionally, stiffer regular networks exhibit a marked hysteresis over loading-unloading cycles that can be deleted by heating-cooling cycles or by performing deformations along different axes. Furthermore, small variations of chain stiffness may entirely change the nature of the network's tensile response from an entropic to an enthalpic elastic regime, and micro-phase separation of different blocks within elastomer networks may significantly enhance their mechanical strength. This work was supported by the American Chemical Society.

  9. Identification of leader and self-organizing communities in complex networks.

    Science.gov (United States)

    Fu, Jingcheng; Zhang, Weixiong; Wu, Jianliang

    2017-04-06

    Community or module structure is a natural property of complex networks. Leader communities and self-organizing communities have been introduced recently to characterize networks and understand how communities arise in complex networks. However, identification of leader and self-organizing communities is technically challenging since no adequate quantification has been developed to properly separate the two types of communities. We introduced a new measure, called ratio of node degree variances, to distinguish leader communities from self-organizing communities, and developed a statistical model to quantitatively characterize the two types of communities. We experimentally studied the power and robustness of the new method on several real-world networks in combination of some of the existing community identification methods. Our results revealed that social networks and citation networks contain more leader communities whereas technological networks such as power grid network have more self-organizing communities. Moreover, our results also indicated that self-organizing communities tend to be smaller than leader communities. The results shed new lights on community formation and module structures in complex systems.

  10. Identification and analysis of glutathione S-transferase gene family in sweet potato reveal divergent GST-mediated networks in aboveground and underground tissues in response to abiotic stresses.

    Science.gov (United States)

    Ding, Na; Wang, Aimin; Zhang, Xiaojun; Wu, Yunxiang; Wang, Ruyuan; Cui, Huihui; Huang, Rulin; Luo, Yonghai

    2017-11-28

    Sweet potato, a hexaploid species lacking a reference genome, is one of the most important crops in many developing countries, where abiotic stresses are a primary cause of reduction of crop yield. Glutathione S-transferases (GSTs) are multifunctional enzymes that play important roles in oxidative stress tolerance and cellular detoxification. A total of 42 putative full-length GST genes were identified from two local transcriptome databases and validated by molecular cloning and Sanger sequencing. Sequence and intraspecific phylogenetic analyses revealed extensive differentiation in their coding sequences and divided them into eight subfamilies. Interspecific phylogenetic and comparative analyses indicated that most examined GST paralogs might originate and diverge before the speciation of sweet potato. Results from large-scale RNA-seq and quantitative real-time PCR experiments exhibited extensive variation in gene-expression profiles across different tissues and varieties, which implied strong evolutionary divergence in their gene-expression regulation. Moreover, we performed five manipulated stress experiments and uncovered highly divergent stress-response patterns of sweet potato GST genes in aboveground and underground tissues. Our study identified a large number of sweet potato GST genes, systematically investigated their evolutionary diversification, and provides new insights into the GST-mediated stress-response mechanisms in this worldwide crop.

  11. Particle Identification in Cherenkov Detectors using Convolutional Neural Networks

    CERN Document Server

    Theodore, Tomalty

    2016-01-01

    Cherenkov detectors are used for charged particle identification. When a charged particle moves through a medium faster than light can propagate in that medium, Cherenkov radiation is released in the shape of a cone in the direction of movement. The interior of the Cherenkov detector is instrumented with PMTs to detect this Cherenkov light. Particles, then, can be identified by the shapes of the images on the detector walls.

  12. Social networks : Effects on Identification, Performance and Satisfaction Effects on identification, performance and satisfaction

    NARCIS (Netherlands)

    Stormbroek-Burgers, van R.G.B.M.; Montfort, van K.; Sluis, van der E.C. (Lidewey)

    2011-01-01

    This study contributes to research on the impact of social networks on organizational outcomes in the context of the increasing number of professionals in the Netherlands. The aim of this study was to get insight into the characteristics of professionals’ social networks and to examine the effect of

  13. Response variability in balanced cortical networks

    DEFF Research Database (Denmark)

    Lerchner, Alexander; Ursta, C.; Hertz, J.

    2006-01-01

    We study the spike statistics of neurons in a network with dynamically balanced excitation and inhibition. Our model, intended to represent a generic cortical column, comprises randomly connected excitatory and inhibitory leaky integrate-and-fire neurons, driven by excitatory input from an external...... population. The high connectivity permits a mean field description in which synaptic currents can be treated as gaussian noise, the mean and autocorrelation function of which are calculated self-consistently from the firing statistics of single model neurons. Within this description, a wide range of Fano...

  14. Spatial identification of tributary impacts in river networks

    Science.gov (United States)

    Christian E. Torgersen; Robert E. Gresswell; Douglas S. Bateman; Kelly M. Burnett

    2008-01-01

    The ability to assess spatial patterns of ecological conditions in river networks has been confounded by difficulties of measuring and perceiving features that are essentially invisible to observers on land and to aircraft and satellites from above. The nature of flowing water, which is opaque or at best semi-transparent, makes it difficult to visualize fine-scale...

  15. Inverse parameter identification for a branching 1D arterial network

    CSIR Research Space (South Africa)

    Bogaers, Alfred EJ

    2012-07-01

    Full Text Available In this paper we investigate the invertability of a branching 1D arterial blood flow network. We limit our investigation to a single bifurcating vessel, where the material properties, unloaded areas and variables characterizing the input and output...

  16. Identification of cancer fusion drivers using network fusion centrality

    Science.gov (United States)

    Wu, Chia-Chin; Kannan, Kalpana; Lin, Steven; Yen, Laising; Milosavljevic, Aleksandar

    2013-01-01

    Summary: Gene fusions are being discovered at an increasing rate using massively parallel sequencing technologies. Prioritization of cancer fusion drivers for validation cannot be performed using traditional single-gene based methods because fusions involve portions of two partner genes. To address this problem, we propose a novel network analysis method called fusion centrality that is specifically tailored for prioritizing gene fusions. We first propose a domain-based fusion model built on the theory of exon/domain shuffling. The model leads to a hypothesis that a fusion is more likely to be an oncogenic driver if its partner genes act like hubs in a network because the fusion mutation can deregulate normal functions of many other genes and their pathways. The hypothesis is supported by the observation that for most known cancer fusion genes, at least one of the fusion partners appears to be a hub in a network, and even for many fusions both partners appear to be hubs. Based on this model, we construct fusion centrality, a multi-gene-based network metric, and use it to score fusion drivers. We show that the fusion centrality outperforms other single gene-based methods. Specifically, the method successfully predicts most of 38 newly discovered fusions that had validated oncogenic importance. To our best knowledge, this is the first network-based approach for identifying fusion drivers. Availability: Matlab code implementing the fusion centrality method is available upon request from the corresponding authors. Contact: perwu777@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23505294

  17. Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Xiaofei Yan

    2016-08-01

    Full Text Available Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN-based multi-sensor system and artificial neural network (ANN. Sensors (CO, CO2, smoke, air temperature and relative humidity were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO2 and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO2; smoke and temperature; smoke, CO2 and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5% than single-sensor input (50.9%–92.5%. Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition.

  18. Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network.

    Science.gov (United States)

    Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

    2016-08-04

    Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO₂, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO₂ and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO₂; smoke and temperature; smoke, CO₂ and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%-92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition.

  19. On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

    DEFF Research Database (Denmark)

    Alizadeh, Tohid

    2008-01-01

    This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP-RBF neu...

  20. A network identity authentication protocol of bank account system based on fingerprint identification and mixed encryption

    Science.gov (United States)

    Zhu, Lijuan; Liu, Jingao

    2013-07-01

    This paper describes a network identity authentication protocol of bank account system based on fingerprint identification and mixed encryption. This protocol can provide every bank user a safe and effective way to manage his own bank account, and also can effectively prevent the hacker attacks and bank clerk crime, so that it is absolute to guarantee the legitimate rights and interests of bank users.

  1. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

    DEFF Research Database (Denmark)

    Hjerrild, Majbrit; Stensballe, Allan; Rasmussen, Thomas E

    2011-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...

  2. The Convolutional Visual Network for Identification and Reconstruction of NOvA Events

    Energy Technology Data Exchange (ETDEWEB)

    Psihas, Fernanda [Indiana U.

    2017-11-22

    In 2016 the NOvA experiment released results for the observation of oscillations in the vμ and ve channels as well as ve cross section measurements using neutrinos from Fermilab’s NuMI beam. These and other measurements in progress rely on the accurate identification and reconstruction of the neutrino flavor and energy recorded by our detectors. This presentation describes the first application of convolutional neural network technology for event identification and reconstruction in particle detectors like NOvA. The Convolutional Visual Network (CVN) Algorithm was developed for identification, categorization, and reconstruction of NOvA events. It increased the selection efficiency of the ve appearance signal by 40% and studies show potential impact to the vμ disappearance analysis.

  3. Identification of Industrial Furnace Temperature for Sintering Process in Nuclear Fuel Fabrication Using NARX Neural Networks

    Directory of Open Access Journals (Sweden)

    Dede Sutarya

    2014-01-01

    Full Text Available Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve robust fault-tolerant behavior. Among the different nonlinear identification techniques, methods based on neural network model are gradually becoming established not only in the academia, but also in industrial application. An identification scheme of nonlinear systems for sintering furnace temperature in nuclear fuel fabrication using neural network autoregressive with exogenous inputs (NNARX model investigated in this paper. The main contribution of this paper is to identify the appropriate model and structure to be applied in control temperature in the sintering process in nuclear fuel fabrication, that is, a nonlinear dynamical system. Satisfactory agreement between identified and experimental data is found with normalized sum square error 1.9e-03 for heating step and 6.3859e-08 for soaking step. That result shows the model successfully predict the evolution of the temperature in the furnace.

  4. The Convolutional Visual Network for Identification and Reconstruction of NOvA Events

    Science.gov (United States)

    Psihas, Fernanda; NOvA Collaboration

    2017-10-01

    In 2016 the NOvA experiment released results for the observation of oscillations in the vμ and ve channels as well as ve cross section measurements using neutrinos from Fermilab’s NuMI beam. These and other measurements in progress rely on the accurate identification and reconstruction of the neutrino flavor and energy recorded by our detectors. This presentation describes the first application of convolutional neural network technology for event identification and reconstruction in particle detectors like NOvA. The Convolutional Visual Network (CVN) Algorithm was developed for identification, categorization, and reconstruction of NOvA events. It increased the selection efficiency of the ve appearance signal by 40% and studies show potential impact to the vμ disappearance analysis.

  5. Identification of milestone papers through time-balanced network centrality

    CERN Document Server

    Mariani, Manuel Sebastian; Zhang, Yi-Cheng

    2016-01-01

    Citations between scientific papers and related bibliometric indices, such as the $h$-index for authors and the impact factor for journals, are being increasingly used -- often in controversial ways -- as quantitative tools for research evaluation. Yet, a fundamental research question remains still open: to which extent do quantitative metrics capture the significance of scientific works? We analyze the network of citations among the $449,935$ papers published by the American Physical Society (APS) journals between $1893$ and $2009$, and focus on the comparison of metrics built on the citation count with network-based metrics. We contrast five article-level metrics with respect to the rankings that they assign to a set of fundamental papers, called Milestone Letters, carefully selected by the APS editors for "making long-lived contributions to physics, either by announcing significant discoveries, or by initiating new areas of research". A new metric, which combines PageRank centrality with the explicit requi...

  6. Identification of cancer fusion drivers using network fusion centrality

    OpenAIRE

    Wu, Chia-Chin; Kannan, Kalpana; Lin, Steven; Yen, Laising; Milosavljevic, Aleksandar

    2013-01-01

    Summary: Gene fusions are being discovered at an increasing rate using massively parallel sequencing technologies. Prioritization of cancer fusion drivers for validation cannot be performed using traditional single-gene based methods because fusions involve portions of two partner genes. To address this problem, we propose a novel network analysis method called fusion centrality that is specifically tailored for prioritizing gene fusions. We first propose a domain-based fusion model built on ...

  7. Development of HT-BP nueral network system for the identification of well test interpretation model

    Energy Technology Data Exchange (ETDEWEB)

    Sung, W.; Hanyang, U.; Yoo, I. [and others

    1995-12-31

    The neural network technique that is a field of artificial intelligence (AI) has proved to be a good model classifier in all areas of engineering and especially, it has gained a considerable acceptance in well test interpretation model (WTIM) identification of petroleum engineering. Conventionally, identification of the WTIM has been approached by graphical analysis method that requires an experienced expert. Recently, neural network technique equipped with back propagation (BP) learning algorithm was presented and it differs from the AI technique such as symbolic approach that must be accompanied with the data preparation procedures such as smoothing, segmenting, and symbolic transformation. In this paper, we developed BP neural network with Hough transform (HT) technique to overcome data selection problem and to use single neural network rather sequential nets. The Hough transform method was proved to be a powerful tool for the shape detection in image processing and computer vision technologies. Along these lines, a number of exercises were conducted with the actual well test data in two steps. First, the newly developed AI model, namely, ANNIS (Artificial intelligence Neural Network Identification System) was utilized to identify WTIM. Secondly, we obtained reservoir characteristics with the well test model equipped with modified Levenberg-Marquart method. The results show that ANNIS was proved to be quite reliable model for the data having noisy, missing, and extraneous points. They also demonstrate that reservoir parameters were successfully estimated.

  8. Damage identification in beams by a response surface based technique

    Directory of Open Access Journals (Sweden)

    Teidj S.

    2014-01-01

    Full Text Available In this work, identification of damage in uniform homogeneous metallic beams was considered through the propagation of non dispersive elastic torsional waves. The proposed damage detection procedure consisted of the following sequence. Giving a localized torque excitation, having the form of a short half-sine pulse, the first step was calculating the transient solution of the resulting torsional wave. This torque could be generated in practice by means of asymmetric laser irradiation of the beam surface. Then, a localized defect assumed to be characterized by an abrupt reduction of beam section area with a given height and extent was placed at a known location of the beam. Next, the response in terms of transverse section rotation rate was obtained for a point situated afterwards the defect, where the sensor was positioned. This last could utilize in practice the concept of laser vibrometry. A parametric study has been conducted after that by using a full factorial design of experiments table and numerical simulations based on a finite difference characteristic scheme. This has enabled the derivation of a response surface model that was shown to represent adequately the response of the system in terms of the following factors: defect extent and severity. The final step was performing the inverse problem solution in order to identify the defect characteristics by using measurement.

  9. Network Security Risk Assessment Based on Item Response Theory

    Directory of Open Access Journals (Sweden)

    Fangwei Li

    2015-08-01

    Full Text Available Owing to the traditional risk assessment method has one-sidedness and is difficult to reflect the real network situation, a risk assessment method based on Item Response Theory (IRT is put forward in network security. First of all, the novel algorithms of calculating the threat of attack and the successful probability of attack are proposed by the combination of IRT model and Service Security Level. Secondly, the service weight of importance is calculated by the three-demarcation analytic hierarchy process. Finally, the risk situation graph of service, host and network logic layer could be generated by the improved method. The simulation results show that this method can be more comprehensive consideration of factors which are affecting network security, and a more realistic network risk situation graph in real-time will be obtained.

  10. An integer optimization algorithm for robust identification of non-linear gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Chemmangattuvalappil Nishanth

    2012-09-01

    Full Text Available Abstract Background Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. Results We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters

  11. System Identification

    NARCIS (Netherlands)

    Keesman, K.J.

    2011-01-01

    Summary System Identification Introduction.- Part I: Data-based Identification.- System Response Methods.- Frequency Response Methods.- Correlation Methods.- Part II: Time-invariant Systems Identification.- Static Systems Identification.- Dynamic Systems Identification.- Part III: Time-varying

  12. Glucose responsive hydrogel networks based on protein recognition.

    Science.gov (United States)

    Ehrick, Jason D; Luckett, Matthew R; Khatwani, Santoshkumar; Wei, Yinan; Deo, Sapna K; Bachas, Leonidas G; Daunert, Sylvia

    2009-09-09

    Stimuli-responsive materials capable of manifesting physical changes in response to environmental signals are valuable tools for use in a variety of biomedical applications. Herein we describe one such smart glucose-responsive hydrogel material prepared by immobilizing the glucose/galactose binding protein within an acrylamide hydrogel network. This hydrogel demonstrates a quantitative "accordion"-like dynamic response in the presence of glucose. We further show the feasibility of employing this responsive smart material as a gating agent for controlled drug delivery, thus, demonstrating that these hydrogels may eventually lead to the development of implantable drug delivery systems for diabetes management applications.

  13. A transcription factor hierarchy defines an environmental stress response network.

    Science.gov (United States)

    Song, Liang; Huang, Shao-Shan Carol; Wise, Aaron; Castanon, Rosa; Nery, Joseph R; Chen, Huaming; Watanabe, Marina; Thomas, Jerushah; Bar-Joseph, Ziv; Ecker, Joseph R

    2016-11-04

    Environmental stresses are universally encountered by microbes, plants, and animals. Yet systematic studies of stress-responsive transcription factor (TF) networks in multicellular organisms have been limited. The phytohormone abscisic acid (ABA) influences the expression of thousands of genes, allowing us to characterize complex stress-responsive regulatory networks. Using chromatin immunoprecipitation sequencing, we identified genome-wide targets of 21 ABA-related TFs to construct a comprehensive regulatory network in Arabidopsis thaliana Determinants of dynamic TF binding and a hierarchy among TFs were defined, illuminating the relationship between differential gene expression patterns and ABA pathway feedback regulation. By extrapolating regulatory characteristics of observed canonical ABA pathway components, we identified a new family of transcriptional regulators modulating ABA and salt responsiveness and demonstrated their utility to modulate plant resilience to osmotic stress. Copyright © 2016, American Association for the Advancement of Science.

  14. Network analysis identifies ELF3 as a QTL for the shade avoidance response in Arabidopsis.

    Directory of Open Access Journals (Sweden)

    José M Jiménez-Gómez

    2010-09-01

    Full Text Available Quantitative Trait Loci (QTL analyses in immortal populations are a powerful method for exploring the genetic mechanisms that control interactions of organisms with their environment. However, QTL analyses frequently do not culminate in the identification of a causal gene due to the large chromosomal regions often underlying QTLs. A reasonable approach to inform the process of causal gene identification is to incorporate additional genome-wide information, which is becoming increasingly accessible. In this work, we perform QTL analysis of the shade avoidance response in the Bayreuth-0 (Bay-0, CS954 x Shahdara (Sha, CS929 recombinant inbred line population of Arabidopsis. We take advantage of the complex pleiotropic nature of this trait to perform network analysis using co-expression, eQTL and functional classification from publicly available datasets to help us find good candidate genes for our strongest QTL, SAR2. This novel network analysis detected EARLY FLOWERING 3 (ELF3; AT2G25930 as the most likely candidate gene affecting the shade avoidance response in our population. Further genetic and transgenic experiments confirmed ELF3 as the causative gene for SAR2. The Bay-0 and Sha alleles of ELF3 differentially regulate developmental time and circadian clock period length in Arabidopsis, and the extent of this regulation is dependent on the light environment. This is the first time that ELF3 has been implicated in the shade avoidance response and that different natural alleles of this gene are shown to have phenotypic effects. In summary, we show that development of networks to inform candidate gene identification for QTLs is a promising technique that can significantly accelerate the process of QTL cloning.

  15. Cerebral microvascular network geometry changes in response to functional stimulation.

    Science.gov (United States)

    Lindvere, Liis; Janik, Rafal; Dorr, Adrienne; Chartash, David; Sahota, Bhupinder; Sled, John G; Stefanovic, Bojana

    2013-05-01

    The cortical microvessels are organized in an intricate, hierarchical, three-dimensional network. Superimposed on this anatomical complexity is the highly complicated signaling that drives the focal blood flow adjustments following a rise in the activity of surrounding neurons. The microvascular response to neuronal activation remains incompletely understood. We developed a custom two photon fluorescence microscopy acquisition and analysis to obtain 3D maps of neuronal activation-induced changes in the geometry of the microvascular network of the primary somatosensory cortex of anesthetized rats. An automated, model-based tracking algorithm was employed to reconstruct the 3D microvascular topology and represent it as a graph. The changes in the geometry of this network were then tracked, over time, in the course of electrical stimulation of the contralateral forepaw. Both dilatory and constrictory responses were observed across the network. Early dilatory and late constrictory responses propagated from deeper to more superficial cortical layers while the response of the vertices that showed initial constriction followed by later dilation spread from cortical surface toward increasing cortical depths. Overall, larger caliber adjustments were observed deeper inside the cortex. This work yields the first characterization of the spatiotemporal pattern of geometric changes on the level of the cortical microvascular network as a whole and provides the basis for bottom-up modeling of the hemodynamically-weighted neuroimaging signals. Copyright © 2013 Elsevier Inc. All rights reserved.

  16. Identification and Position Control of Marine Helm using Artificial Neural Network Neural Network

    Directory of Open Access Journals (Sweden)

    Hui ZHU

    2008-02-01

    Full Text Available If nonlinearities such as saturation of the amplifier gain and motor torque, gear backlash, and shaft compliances- just to name a few - are considered in the position control system of marine helm, traditional control methods are no longer sufficient to be used to improve the performance of the system. In this paper an alternative approach to traditional control methods - a neural network reference controller - is proposed to establish an adaptive control of the position of the marine helm to achieve the controlled variable at the command position. This neural network controller comprises of two neural networks. One is the plant model network used to identify the nonlinear system and the other the controller network used to control the output to follow the reference model. The experimental results demonstrate that this adaptive neural network reference controller has much better control performance than is obtained with traditional controllers.

  17. Neural Network Target Identification System for False Alarm Reduction

    Science.gov (United States)

    Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

    2009-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

  18. IP Network Failure Identification Based on the Detailed Analysis of OSPF LSA Flooding

    Science.gov (United States)

    Hei, Yuichiro; Ogishi, Tomohiko; Ano, Shigehiro; Hasegawa, Toru

    It is important to monitor routing protocols to ensure IP networks and their operations can maintain sufficient level of stability and reliability because IP routing is an essential part of such networks. In this paper, we focus on Open Shortest Path First (OSPF), a widely deployed intra-domain routing protocol. Routers running OSPF advertise their link states on Link State Advertisements (LSAs) as soon as they detect changes in their link states. In IP network operations, it is important for operators to ascertain the location and type of a failure in order to deal with failures adequately. We therefore studied IP network failure identification based on the monitoring of OSPF LSAs. There are three issues to consider in regard to identifying network failures by monitoring LSAs. The first is that multiple LSAs are flooded by a single failure. The second is the LSA delay, and the third is that multiple failures may occur simultaneously. In this paper, we propose a method of network failure identification based on a detailed analysis of OSPF LSA flooding that takes into account the above three issues.

  19. Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

    Science.gov (United States)

    Peng, Jinzhu; Dubay, Rickey

    2011-10-01

    In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  20. Corporate Social Responsibility in Online Social Networks

    DEFF Research Database (Denmark)

    Horn, Christian; Brem, Alexander; Wölfl, S.

    2014-01-01

    Considering growing public awareness of social, ethical and ecological responsibility, companies have constantly been increasing their efforts in CSR communications. Social Media as tools of brand communication receive increasing attention and it is expected that the marketing sector...... will experience changes through this phenomenon in the future. This empirical study investigates the types of content that is communicated for different brands and industries in leading Social Media portals on the German market in 2011. It turns out that this kind of CSR communication differs in terms of portals...

  1. Drawing networks of rejection - a systems biological approach to the identification of candidate genes in heart transplantation.

    Science.gov (United States)

    Cadeiras, Martin; von Bayern, Manuel; Sinha, Anshu; Shahzad, Khurram; Latif, Farhana; Lim, Wei Keat; Grenett, Hernan; Tabak, Esteban; Klingler, Tod; Califano, Andrea; Deng, Mario C

    2011-04-01

    Technological development led to an increased interest in systems biological approaches to characterize disease mechanisms and candidate genes relevant to specific diseases. We suggested that the human peripheral blood mononuclear cells (PBMC) network can be delineated by cellular reconstruction to guide identification of candidate genes. Based on 285 microarrays (7370 genes) from 98 heart transplant patients enrolled in the Cardiac Allograft Rejection Gene Expression Observational study, we used an information-theoretic, reverse-engineering algorithm called ARACNe (algorithm for the reconstruction of accurate cellular networks) and chromatin immunoprecipitation assay to reconstruct and validate a putative gene PBMC interaction network. We focused our analysis on transcription factor (TF) genes and developed a priority score to incorporate aspects of network dynamics and information from published literature to supervise gene discovery. ARACNe generated a cellular network and predicted interactions for each TF during rejection and quiescence. Genes ranked highest by priority score included those related to apoptosis, humoural and cellular immune response such as GA binding protein transcription factor (GABP), nuclear factor of κ light polypeptide gene enhancer in B-cells (NFκB), Fas (TNFRSF6)-associated via death domain (FADD) and c-AMP response element binding protein. We used the TF CREB to validate our network. ARACNe predicted 29 putative first-neighbour genes of CREB. Eleven of these (37%) were previously reported. Out of the 18 unknown predicted interactions, 14 primers were identified and 11 could be immunoprecipitated (78.6%). Overall, 75% (n= 22) inferred CREB targets were validated, a significantly higher fraction than randomly expected (P biological approaches to identify possible molecular targets and biomarkers. © 2011 The Authors Journal of Cellular and Molecular Medicine © 2011 Foundation for Cellular and Molecular Medicine/Blackwell Publishing

  2. Identification of Literary Movements Using Complex Networks to Represent Texts

    CERN Document Server

    Amancio, Diego R; Costa, Luciano da F; 10.1088/1367-2630/14/4/043029

    2013-01-01

    The use of statistical methods to analyze large databases of text has been useful to unveil patterns of human behavior and establish historical links between cultures and languages. In this study, we identify literary movements by treating books published from 1590 to 1922 as complex networks, whose metrics were analyzed with multivariate techniques to generate six clusters of books. The latter correspond to time periods coinciding with relevant literary movements over the last 5 centuries. The most important factor contributing to the distinction between different literary styles was {the average shortest path length (particularly, the asymmetry of the distribution)}. Furthermore, over time there has been a trend toward larger average shortest path lengths, which is correlated with increased syntactic complexity, and a more uniform use of the words reflected in a smaller power-law coefficient for the distribution of word frequency. Changes in literary style were also found to be driven by opposition to earli...

  3. Data assimilation for identification of cardiovascular network characteristics.

    Science.gov (United States)

    Lal, Rajnesh; Mohammadi, Bijan; Nicoud, Franck

    2017-05-01

    A method to estimate the hemodynamics parameters of a network of vessels using an Ensemble Kalman filter is presented. The elastic moduli (Young's modulus) of blood vessels and the terminal boundary parameters are estimated as the solution of an inverse problem. Two synthetic test cases and a configuration where experimental data are available are presented. The sensitivity analysis confirms that the proposed method is quite robust even with a few numbers of observations. The simulations with the estimated parameters recovers target pressure or flow rate waveforms at given specific locations, improving the state-of-the-art predictions available in the literature. This shows the effectiveness and efficiency of both the parameter estimation algorithm and the blood flow model. Copyright © 2016 John Wiley & Sons, Ltd.

  4. The salience network is responsible for switching between the default mode network and the central executive network: replication from DCM.

    Science.gov (United States)

    Goulden, Nia; Khusnulina, Aygul; Davis, Nicholas J; Bracewell, Robert M; Bokde, Arun L; McNulty, Jonathan P; Mullins, Paul G

    2014-10-01

    With the advent of new analysis methods in neuroimaging that involve independent component analysis (ICA) and dynamic causal modelling (DCM), investigations have focused on measuring both the activity and connectivity of specific brain networks. In this study we combined DCM with spatial ICA to investigate network switching in the brain. Using time courses determined by ICA in our dynamic causal models, we focused on the dynamics of switching between the default mode network (DMN), the network which is active when the brain is not engaging in a specific task, and the central executive network (CEN), which is active when the brain is engaging in a task requiring attention. Previous work using Granger causality methods has shown that regions of the brain which respond to the degree of subjective salience of a stimulus, the salience network, are responsible for switching between the DMN and the CEN (Sridharan et al., 2008). In this work we apply DCM to ICA time courses representing these networks in resting state data. In order to test the repeatability of our work we applied this to two independent datasets. This work confirms that the salience network drives the switching between default mode and central executive networks and that our novel technique is repeatable. Crown Copyright © 2014. Published by Elsevier Inc. All rights reserved.

  5. Identification and characterization of transcription networks in environmentally significant species

    Energy Technology Data Exchange (ETDEWEB)

    Lawrence, Charles E.; McCue, Lee Ann

    2005-11-30

    Understanding the regulation of gene expression, transcription regulation in particular, is one of the grand challenges of molecular biology. Transcription regulation is arguably the most important foundation of cellular function, since it exerts the most fundamental control of the abundance of virtually all of a cell's functional macromolecules. Nevertheless, this process, perhaps because of its difficulty, has been the subject of only a limited number of genomic level analyses. We have undertaken bioinformatics projects to address this issue by developing (1) a cross-species comparison method (i.e. phylogenetic footprinting) for the identification of transcription factor binding sites, (2) a Bayesian clustering method to identify regulons, (3) an improved scanning algorithm that uses a position weight matrix and several related species sequence data to locate transcription factor binding sites, and (4) a method to predict cognate binding sites for transcription factors of unknown specificity. These bioinformatics methods were developed using the model proteobacterium Escherichia coli, with further applications to the genomes of environmentally significant microbes (Rhodopseudomonas palustris, Shewanella oneidensis) in later years of the grant.

  6. Faba bean drought responsive gene identification and validation.

    Science.gov (United States)

    Ammar, Megahed H; Khan, Altaf M; Migdadi, Hussein M; Abdelkhalek, Samah M; Alghamdi, Salem S

    2017-01-01

    This study was carried out to identify drought-responsive genes in a drought tolerant faba bean variety (Hassawi 2) using a suppressive subtraction hybridization approach (SSH). A total of 913 differentially expressed clones were sequenced from a differential cDNA library that resulted in a total of 225 differentially expressed ESTs. The genes of mitochondrial and chloroplast origin were removed, and the remaining 137 EST sequences were submitted to the gene bank EST database (LIBEST_028448). A sequence analysis identified 35 potentially drought stress-related ESTs that regulate ion channels, kinases, and energy production and utilization and transcription factors. Quantitative PCR on Hassawi 2 genotype confirmed that more than 65% of selected drought-responsive genes were drought-related. Among these induced genes, the expression levels of eight highly up-regulated unigenes were further analyzed across 38 selected faba bean genotypes that differ in their drought tolerance levels. These unigenes included ribulose 1,5-bisphosphate carboxylase (rbcL) gene, non-LTR retroelement reverse related, probable cyclic nucleotide-gated ion channel, polyubiquitin, potassium channel, calcium-dependent protein kinase and putative respiratory burst oxidase-like protein C and a novel unigene. The expression patterns of these unigenes were variable across 38 genotypes however, it was found to be very high in tolerant genotype. The up-regulation of these unigenes in majority of tolerant genotypes suggests their possible role in drought tolerance. The identification of possible drought responsive candidate genes in Vicia faba reported here is an important step toward the development of drought-tolerant genotypes that can cope with arid environments.

  7. Faba bean drought responsive gene identification and validation

    Directory of Open Access Journals (Sweden)

    Megahed H. Ammar

    2017-01-01

    Full Text Available This study was carried out to identify drought-responsive genes in a drought tolerant faba bean variety (Hassawi 2 using a suppressive subtraction hybridization approach (SSH. A total of 913 differentially expressed clones were sequenced from a differential cDNA library that resulted in a total of 225 differentially expressed ESTs. The genes of mitochondrial and chloroplast origin were removed, and the remaining 137 EST sequences were submitted to the gene bank EST database (LIBEST_028448. A sequence analysis identified 35 potentially drought stress-related ESTs that regulate ion channels, kinases, and energy production and utilization and transcription factors. Quantitative PCR on Hassawi 2 genotype confirmed that more than 65% of selected drought-responsive genes were drought-related. Among these induced genes, the expression levels of eight highly up-regulated unigenes were further analyzed across 38 selected faba bean genotypes that differ in their drought tolerance levels. These unigenes included ribulose 1,5-bisphosphate carboxylase (rbcL gene, non-LTR retroelement reverse related, probable cyclic nucleotide-gated ion channel, polyubiquitin, potassium channel, calcium-dependent protein kinase and putative respiratory burst oxidase-like protein C and a novel unigene. The expression patterns of these unigenes were variable across 38 genotypes however, it was found to be very high in tolerant genotype. The up-regulation of these unigenes in majority of tolerant genotypes suggests their possible role in drought tolerance. The identification of possible drought responsive candidate genes in Vicia faba reported here is an important step toward the development of drought-tolerant genotypes that can cope with arid environments.

  8. Emergency Response using Ephemeral Social Communities across Online Social Networks

    Directory of Open Access Journals (Sweden)

    Youna Jung

    2015-12-01

    Full Text Available In an emergency situation, receiving prompt and organized help from nearby people is of critical importance. The growing use of online social networks (OSNs in emergency situations is a clear indication of the natural applicability of online social networking technologies to emergency responses. Despite this intense interest, a number of fundamental limitations still exist, such as lack of conceptual models and limitations on group organization and cooperation. To address existing limitations, we propose Whistle+ – a cooperation framework for OSN users which can 1 dynamically organize an emergency community with nearby eligible users who are distributed in heterogeneous online social networks, and 2 guarantee secure communication, unrestricted cooperation and resource sharing by leveraging the SocialVPN virtual network and the Jitsi communicator. To test the feasibility and applicability of Whistle+, we present a prototype implementation and demonstrate its applicability to an example use case.

  9. A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

    Science.gov (United States)

    Hong, Xia

    2006-07-01

    In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

  10. Local and global responses in complex gene regulation networks

    Science.gov (United States)

    Tsuchiya, Masa; Selvarajoo, Kumar; Piras, Vincent; Tomita, Masaru; Giuliani, Alessandro

    2009-04-01

    An exacerbated sensitivity to apparently minor stimuli and a general resilience of the entire system stay together side-by-side in biological systems. This apparent paradox can be explained by the consideration of biological systems as very strongly interconnected network systems. Some nodes of these networks, thanks to their peculiar location in the network architecture, are responsible for the sensitivity aspects, while the large degree of interconnection is at the basis of the resilience properties of the system. One relevant feature of the high degree of connectivity of gene regulation networks is the emergence of collective ordered phenomena influencing the entire genome and not only a specific portion of transcripts. The great majority of existing gene regulation models give the impression of purely local ‘hard-wired’ mechanisms disregarding the emergence of global ordered behavior encompassing thousands of genes while the general, genome wide, aspects are less known. Here we address, on a data analysis perspective, the discrimination between local and global scale regulations, this goal was achieved by means of the examination of two biological systems: innate immune response in macrophages and oscillating growth dynamics in yeast. Our aim was to reconcile the ‘hard-wired’ local view of gene regulation with a global continuous and scalable one borrowed from statistical physics. This reconciliation is based on the network paradigm in which the local ‘hard-wired’ activities correspond to the activation of specific crucial nodes in the regulation network, while the scalable continuous responses can be equated to the collective oscillations of the network after a perturbation.

  11. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

    Science.gov (United States)

    Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

    2000-01-01

    Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

  12. Treatment of non-response in longitudinal network studies

    NARCIS (Netherlands)

    Huisman, Mark; Steglich, Christian

    2008-01-01

    The collection of longitudinal data on complete social networks often faces the problem of actor non-response. The resulting incomplete data pose a challenge to statistical analysis, as there typically is no natural way to treat the missing cases. This paper examines the problems caused by actors

  13. A sparse sensor network topologized for cylindrical wave-based identification of damage in pipeline structures

    Science.gov (United States)

    Wang, Qiang; Hong, Ming; Su, Zhongqing

    2016-07-01

    A sparse sensor network, based on the concept of semi-decentralized and standardized sensing, is developed, to actively excite and acquire cylindrical waves for damage identification and health monitoring of pipe structures. Differentiating itself from conventional ‘ring-style’ transducer arrays which attempt to steer longitudinal axisymmetric cylindrical waves via transducer synchronism, this sparse sensor network shows advantages in some aspects, including the use of fewer sensors, simpler manipulation, quicker configuration, less mutual dependence among sensors, and an improved signal-to-noise ratio. The sparse network is expanded topologically, aimed at eliminating the presence of ‘blind zones’ and the challenges associated with multi-path propagation of cylindrical waves. Theoretical analysis is implemented to comprehend propagation characteristics of waves guided by a cylindrical structure. A probability-based diagnostic imaging algorithm is introduced to visualize damage in pixelated images in an intuitive, prompt, and automatic manner. A self-contained health monitoring system is configured for experimental validation, via which quantitative identification of mono- and multi-damage in a steel cylinder is demonstrated. The results highlight an expanded sensing coverage of the sparse sensor network and its enhanced capacity of acquiring rich information, avoiding the cost of augmenting the number of sensors in a sensor network.

  14. Optical fingerprint identification using cellular neural network and joint transform correlation

    Science.gov (United States)

    Bal, Abdullah; Alam, Mohammad S.; El-Saba, Aed

    2004-10-01

    An important step in the fingerprint identification system is the extraction of relevant details against distributed complex features. Identification performance is directly related to the enhancement of fingerprint images during or after the enrollment phase. Among the various enhancement algorithms, artificial intelligence based feature extraction techniques are attractive due to their adaptive learning properties. In this paper, we propose a cellular neural network (CNN) based filtering technique due to its ability of parallel processing and generating learnable filtering features. CNN offers high efficient feature extraction and enhancement possibility for fingerprint images. The enhanced fingerprint images are then introduced to joint transform correlator (JTC) architecture to identify unknown fingerprint from the database. Since the fringe-adjusted JTC algorithm has been found to yield significantly better correlation output compared to alternate JTCs, we used it for the identification process. Test results are presented to verify the effectiveness of the proposed algorithm.

  15. EgoNet: identification of human disease ego-network modules

    Science.gov (United States)

    2014-01-01

    Background Mining novel biomarkers from gene expression profiles for accurate disease classification is challenging due to small sample size and high noise in gene expression measurements. Several studies have proposed integrated analyses of microarray data and protein-protein interaction (PPI) networks to find diagnostic subnetwork markers. However, the neighborhood relationship among network member genes has not been fully considered by those methods, leaving many potential gene markers unidentified. The main idea of this study is to take full advantage of the biological observation that genes associated with the same or similar diseases commonly reside in the same neighborhood of molecular networks. Results We present EgoNet, a novel method based on egocentric network-analysis techniques, to exhaustively search and prioritize disease subnetworks and gene markers from a large-scale biological network. When applied to a triple-negative breast cancer (TNBC) microarray dataset, the top selected modules contain both known gene markers in TNBC and novel candidates, such as RAD51 and DOK1, which play a central role in their respective ego-networks by connecting many differentially expressed genes. Conclusions Our results suggest that EgoNet, which is based on the ego network concept, allows the identification of novel biomarkers and provides a deeper understanding of their roles in complex diseases. PMID:24773628

  16. Identification and tracking of vertebrae in ultrasound using deep networks with unsupervised feature learning

    Science.gov (United States)

    Hetherington, Jorden; Pesteie, Mehran; Lessoway, Victoria A.; Abolmaesumi, Purang; Rohling, Robert N.

    2017-03-01

    Percutaneous needle insertion procedures on the spine often require proper identification of the vertebral level in order to effectively deliver anesthetics and analgesic agents to achieve adequate block. For example, in obstetric epidurals, the target is at the L3-L4 intervertebral space. The current clinical method involves "blind" identification of the vertebral level through manual palpation of the spine, which has only 30% accuracy. This implies the need for better anatomical identification prior to needle insertion. A system is proposed to identify the vertebrae, assigning them to their respective levels, and track them in a standard sequence of ultrasound images, when imaged in the paramedian plane. Machine learning techniques are developed to identify discriminative features of the laminae. In particular, a deep network is trained to automatically learn the anatomical features of the lamina peaks, and classify image patches, for pixel-level classification. The chosen network utilizes multiple connected auto-encoders to learn the anatomy. Pre-processing with ultrasound bone enhancement techniques is done to aid the pixel-level classification performance. Once the lamina are identified, vertebrae are assigned levels and tracked in sequential frames. Experimental results were evaluated against an expert sonographer. Based on data acquired from 15 subjects, vertebrae identification with sensitivity of 95% and precision of 95% was achieved within each frame. Between pairs of subsequently analyzed frames, matches of predicted vertebral level labels were correct in 94% of cases, when compared to matches of manually selected labels

  17. System Identification of a Nonlinear Multivariable Steam Generator Power Plant Using Time Delay and Wavelet Neural Networks

    Directory of Open Access Journals (Sweden)

    Laila Khalilzadeh Ganjali-khani

    2013-01-01

    Full Text Available One of the most effective strategies for steam generator efficiency enhancement is to improve the control system. For such an improvement, it is essential to have an accurate model for the steam generator of power plant. In this paper, an industrial steam generator is considered as a nonlinear multivariable system for identification. An important step in nonlinear system identification is the development of a nonlinear model. In recent years, artificial neural networks have been successfully used for identification of nonlinear systems in many researches. Wavelet neural networks (WNNs also are used as a powerful tool for nonlinear system identification. In this paper we present a time delay neural network model and a WNN model in order to identify an industrial steam generator. Simulation results show the effectiveness of the proposed models in the system identification and demonstrate that the WNN model is more precise to estimate the plant outputs.

  18. The Construction of Corporate Social Responsibility in Network Societies

    DEFF Research Database (Denmark)

    Schultz, Friederike; Castello, Itziar; Morsing, Mette

    2013-01-01

    The paper introduces the communication view on Corporate Social Responsibility (CSR), which regards CSR as communicatively constructed in dynamic interaction processes in today's networked societies. Building on the idea that communication constitutes organizations we discuss the potentially......-normative view on CSR, which highlights the societal conditions and role of corporations in creating norms. We argue that both the established views, by not sufficiently acknowledging communication dynamics in networked societies, remain biased in three ways: control-biased, consistency-biased, and consensus......-biased. We discuss implications of these biases and propose a future research agenda for the communication view on CSR....

  19. Upscaling of spectral induced polarization response using random tube networks

    Science.gov (United States)

    Maineult, Alexis; Revil, André; Camerlynck, Christian; Florsch, Nicolas; Titov, Konstantin

    2017-05-01

    In order to upscale the induced polarization (IP) response of porous media, from the pore scale to the sample scale, we implement a procedure to compute the macroscopic complex resistivity response of random tube networks. A network is made of a 2-D square-meshed grid of connected tubes, which obey to a given tube radius distribution. In a simplified approach, the electrical impedance of each tube follows a local Pelton resistivity model, with identical resistivity, chargeability and Cole-Cole exponent values for all the tubes-only the time constant varies, as it depends on the radius of each tube and on a diffusion coefficient also identical for all the tubes. By solving the conservation law for the electrical charge, the macroscopic IP response of the network is obtained. We fit successfully the macroscopic complex resistivity also by a Pelton resistivity model. Simulations on uncorrelated and correlated networks, for which the tube radius distribution is so that the decimal logarithm of the radius is normally distributed, evidence that the local and macroscopic model parameters are the same, except the Cole-Cole exponent: its macroscopic value diminishes with increasing heterogeneity (i.e. with increasing standard deviation of the radius distribution), compared to its local value. The methodology is also applied to six siliciclastic rock samples, for which the pore radius distributions from mercury porosimetry are available. These samples exhibit the same behaviour as synthetic media, that is, the macroscopic Cole-Cole exponent is always lower than the local one. As a conclusion, the pore network method seems to be a promising tool for studying the upscaling of the IP response of porous media.

  20. Character identification by maximizing the difference between target and non-target responses in EEG without sophisticated classifiers.

    Science.gov (United States)

    Toma, Junya; Fukami, Tadanori; Shimada, Takamasa

    2013-01-01

    We propose a simple character identification method demonstrated by using an electroencephalogram (EEG) with a stimulus presentation technique. The method assigns a code maximizing the minimum Hamming distance between character codes. Character identification is achieved by increasing the difference between target and non-target responses without sophisticated classifiers such as neural network or support vector machine. Here, we introduce two kinds of scores reflecting the existence of the P300 component from the point of time and frequency domains. We then applied this method to character identification using a 3 × 3 matrix and compared the results to that of a conventional P300 speller. The accuracy of character identification with our method indicated a performance of 100% character identification from five subjects. In contrast, the correct character was detected in two subjects and a wrong one was detected for one subject. For the remaining two subjects, no character was detected within ten trials. Our method required 4.8 trials on average to detect the correct character.

  1. Nonlinear identification using a B-spline neural network and chaotic immune approaches

    Science.gov (United States)

    dos Santos Coelho, Leandro; Pessôa, Marcelo Wicthoff

    2009-11-01

    One of the important applications of B-spline neural network (BSNN) is to approximate nonlinear functions defined on a compact subset of a Euclidean space in a highly parallel manner. Recently, BSNN, a type of basis function neural network, has received increasing attention and has been applied in the field of nonlinear identification. BSNNs have the potential to "learn" the process model from input-output data or "learn" fault knowledge from past experience. BSNN can be used as function approximators to construct the analytical model for residual generation too. However, BSNN is trained by gradient-based methods that may fall into local minima during the learning procedure. When using feed-forward BSNNs, the quality of approximation depends on the control points (knots) placement of spline functions. This paper describes the application of a modified artificial immune network inspired optimization method - the opt-aiNet - combined with sequences generate by Hénon map to provide a stochastic search to adjust the control points of a BSNN. The numerical results presented here indicate that artificial immune network optimization methods are useful for building good BSNN model for the nonlinear identification of two case studies: (i) the benchmark of Box and Jenkins gas furnace, and (ii) an experimental ball-and-tube system.

  2. Neural network connectivity and response latency modelled by stochastic processes

    DEFF Research Database (Denmark)

    Tamborrino, Massimiliano

    is connected to thousands of other neurons. The rst question is: how to model neural networks through stochastic processes? A multivariate Ornstein-Uhlenbeck process, obtained as a diffusion approximation of a jump process, is the proposed answer. Obviously, dependencies between neurons imply dependencies...... between their spike times. Therefore, the second question is: how to detect neural network connectivity from simultaneously recorded spike trains? Answering this question corresponds to investigate the joint distribution of sequences of rst passage times. A non-parametric method based on copulas...... generation of pikes. When a stimulus is applied to the network, the spontaneous rings may prevail and hamper detection of the effects of the stimulus. Therefore, the spontaneous rings cannot be ignored and the response latency has to be detected on top of a background signal. Everything becomes more dicult...

  3. Response of low voltage networks with high penetration of photovoltaic systems to transmission network faults

    NARCIS (Netherlands)

    Skaloumpakas, K.; Boemer, J.C.; Van Ruitenbeek, E.; Gibescu, M.

    2014-01-01

    The installed capacity of photovoltaic (PV) systems connected to low voltage (LV) networks in Germany has increased to more than 25 GW. Current grid codes still mandate these PV systems to disconnect in case of voltage dips below 0.8 p.u. The resulting response of LV distribution systems with high

  4. Dataset for Testing Contamination Source Identification Methods for Water Distribution Networks

    Science.gov (United States)

    This dataset includes the results of a simulation study using the source inversion techniques available in the Water Security Toolkit. The data was created to test the different techniques for accuracy, specificity, false positive rate, and false negative rate. The tests examined different parameters including measurement error, modeling error, injection characteristics, time horizon, network size, and sensor placement. The water distribution system network models that were used in the study are also included in the dataset. This dataset is associated with the following publication:Seth, A., K. Klise, J. Siirola, T. Haxton , and C. Laird. Testing Contamination Source Identification Methods for Water Distribution Networks. Journal of Environmental Division, Proceedings of American Society of Civil Engineers. American Society of Civil Engineers (ASCE), Reston, VA, USA, ., (2016).

  5. A new approach to the automatic identification of organism evolution using neural networks.

    Science.gov (United States)

    Kasperski, Andrzej; Kasperska, Renata

    2016-01-01

    Automatic identification of organism evolution still remains a challenging task, which is especially exiting, when the evolution of human is considered. The main aim of this work is to present a new idea to allow organism evolution analysis using neural networks. Here we show that it is possible to identify evolution of any organisms in a fully automatic way using the designed EvolutionXXI program, which contains implemented neural network. The neural network has been taught using cytochrome b sequences of selected organisms. Then, analyses have been carried out for the various exemplary organisms in order to demonstrate capabilities of the EvolutionXXI program. It is shown that the presented idea allows supporting existing hypotheses, concerning evolutionary relationships between selected organisms, among others, Sirenia and elephants, hippopotami and whales, scorpions and spiders, dolphins and whales. Moreover, primate (including human), tree shrew and yeast evolution has been reconstructed. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  6. Artificial neural network based fault identification scheme implementation for a three-phase induction motor.

    Science.gov (United States)

    Kolla, Sri R; Altman, Shawn D

    2007-04-01

    This paper presents results from the implementation and testing of a PC based monitoring and fault identification scheme for a three-phase induction motor using artificial neural networks (ANNs). To accomplish the task, a hardware system is designed and built to acquire three-phase voltages and currents from a 1/3 HP squirrel-cage, three-phase induction motor. A software program is written to read the voltages and currents, which are first used to train a feed-forward neural network structure using the JavaNNS program. The trained network is placed in a LabVIEW based program formula node that monitors the voltages and currents online and displays the fault conditions and turns the motor off. The complete system is successfully tested in real time by creating different faults on the motor.

  7. Identification of critical regulatory genes in cancer signaling network using controllability analysis

    Science.gov (United States)

    Ravindran, Vandana; Sunitha, V.; Bagler, Ganesh

    2017-05-01

    Cancer is characterized by a complex web of regulatory mechanisms which makes it difficult to identify features that are central to its control. Molecular integrative models of cancer, generated with the help of data from experimental assays, facilitate use of control theory to probe for ways of controlling the state of such a complex dynamic network. We modeled the human cancer signaling network as a directed graph and analyzed it for its controllability, identification of driver nodes and their characterization. We identified the driver nodes using the maximum matching algorithm and classified them as backbone, peripheral and ordinary based on their role in regulatory interactions and control of the network. We found that the backbone driver nodes were key to driving the regulatory network into cancer phenotype (via mutations) as well as for steering into healthy phenotype (as drug targets). This implies that while backbone genes could lead to cancer by virtue of mutations, they are also therapeutic targets of cancer. Further, based on their impact on the size of the set of driver nodes, genes were characterized as indispensable, dispensable and neutral. Indispensable nodes within backbone of the network emerged as central to regulatory mechanisms of control of cancer. In addition to probing the cancer signaling network from the perspective of control, our findings suggest that indispensable backbone driver nodes could be potentially leveraged as therapeutic targets. This study also illustrates the application of structural controllability for studying the mechanisms underlying the regulation of complex diseases.

  8. Multi-species Identification of Polymorphic Peptide Variants via Propagation in Spectral Networks

    Energy Technology Data Exchange (ETDEWEB)

    Na, Seungjin; Payne, Samuel H.; Bandeira, Nuno

    2016-09-08

    The spectral networks approach enables the detection of pairs of spectra from related peptides and thus allows for the propagation of annotations from identified peptides to unidentified spectra. Beyond allowing for unbiased discovery of unexpected post-translational modifications, spectral networks are also applicable to multi-species comparative proteomics or metaproteomics to identify numerous orthologous versions of a protein. We present algorithmic and statistical advances in spectral networks that have made it possible to rigorously assess the statistical significance of spectral pairs and accurately estimate the error rate of identifications via propagation. In the analysis of three related Cyanothece species, a model organism for biohydrogen production, spectral networks identified peptides with highly divergent sequences with up to dozens of variants per peptide, including many novel peptides in species that lack a sequenced genome. Furthermore, spectral networks strongly suggested the presence of novel peptides even in genomically characterized species (i.e. missing from databases) in that a significant portion of unidentified multi-species networks included at least two polymorphic peptide variants.

  9. Drug target identification using network analysis: Taking active components in Sini decoction as an example

    Science.gov (United States)

    Chen, Si; Jiang, Hailong; Cao, Yan; Wang, Yun; Hu, Ziheng; Zhu, Zhenyu; Chai, Yifeng

    2016-04-01

    Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

  10. Stress, stress?induced cortisol responses, and eyewitness identification performance

    OpenAIRE

    Sauerland, Melanie; Raymaekers, Linsey H.C.; Otgaar, Henry; Memon, Amina; Waltjen, Thijs T.; Nivo, Maud; Slegers, Chiel; Broers, Nick J; Smeets, Tom

    2016-01-01

    Abstract In the eyewitness identification literature, stress and arousal at the time of encoding are considered to adversely influence identification performance. This assumption is in contrast with findings from the neurobiology field of learning and memory, showing that stress and stress hormones are critically involved in forming enduring memories. This discrepancy may be related to methodological differences between the two fields of research, such as the tendency for immediate testing or...

  11. The impact of measurement errors in the identification of regulatory networks

    Directory of Open Access Journals (Sweden)

    Sato João R

    2009-12-01

    Full Text Available Abstract Background There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent and non-time series (independent data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models and dependent (autoregressive models data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error. The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

  12. 5-HTTLPR differentially predicts brain network responses to emotional faces

    DEFF Research Database (Denmark)

    Fisher, Patrick M; Grady, Cheryl L; Madsen, Martin K

    2015-01-01

    The effects of the 5-HTTLPR polymorphism on neural responses to emotionally salient faces have been studied extensively, focusing on amygdala reactivity and amygdala-prefrontal interactions. Despite compelling evidence that emotional face paradigms engage a distributed network of brain regions...... resonance imaging in 76 healthy adults. We observed robust increased response to emotional faces in the amygdala, hippocampus, caudate, fusiform gyrus, superior temporal sulcus and lateral prefrontal and occipito-parietal cortices. We observed dissociation between 5-HTTLPR groups such that LA LA individuals...

  13. Diverse Hormone Response Networks in 41 Independent Drosophila Cell Lines

    Directory of Open Access Journals (Sweden)

    Marcus Stoiber

    2016-03-01

    Full Text Available Steroid hormones induce cascades of gene activation and repression with transformative effects on cell fate . Steroid transduction plays a major role in the development and physiology of nearly all metazoan species, and in the progression of the most common forms of cancer. Despite the paramount importance of steroids in developmental and translational biology, a complete map of transcriptional response has not been developed for any hormone . In the case of 20-hydroxyecdysone (ecdysone in Drosophila melanogaster, these trajectories range from apoptosis to immortalization. We mapped the ecdysone transduction network in a cohort of 41 cell lines, the largest such atlas yet assembled. We found that the early transcriptional response mirrors the distinctiveness of physiological origins: genes respond in restricted patterns, conditional on the expression levels of dozens of transcription factors. Only a small cohort of genes is constitutively modulated independent of initial cell state. Ecdysone-responsive genes tend to organize into directional same-stranded units, with consecutive genes induced from the same strand. Here, we identify half of the ecdysone receptor heterodimer as the primary rate-limiting step in the response, and find that initial receptor isoform levels modulate the activated cohort of target transcription factors. This atlas of steroid response reveals organizing principles of gene regulation by a model type II nuclear receptor and lays the foundation for comprehensive and predictive understanding of the ecdysone transduction network in the fruit fly.

  14. Pathway switching explains the sharp response characteristic of hypoxia response network.

    Directory of Open Access Journals (Sweden)

    Yihai Yu

    2007-08-01

    Full Text Available Hypoxia induces the expression of genes that alter metabolism through the hypoxia-inducible factor (HIF. A theoretical model based on differential equations of the hypoxia response network has been previously proposed in which a sharp response to changes in oxygen concentration was observed but not quantitatively explained. That model consisted of reactions involving 23 molecular species among which the concentrations of HIF and oxygen were linked through a complex set of reactions. In this paper, we analyze this previous model using a combination of mathematical tools to draw out the key components of the network and explain quantitatively how they contribute to the sharp oxygen response. We find that the switch-like behavior is due to pathway-switching wherein HIF degrades rapidly under normoxia in one pathway, while the other pathway accumulates HIF to trigger downstream genes under hypoxia. The analytic technique is potentially useful in studying larger biomedical networks.

  15. 19 CFR 122.184 - Change of identification; change in circumstances of employee; additional employer responsibilities.

    Science.gov (United States)

    2010-04-01

    ... 19 Customs Duties 1 2010-04-01 2010-04-01 false Change of identification; change in circumstances of employee; additional employer responsibilities. 122.184 Section 122.184 Customs Duties U.S... REGULATIONS Access to Customs Security Areas § 122.184 Change of identification; change in circumstances of...

  16. Identification and expression analysis of cold and freezing stress responsive genes of Brassica oleracea.

    Science.gov (United States)

    Ahmed, Nasar Uddin; Jung, Hee-Jeong; Park, Jong-In; Cho, Yong-Gu; Hur, Yoonkang; Nou, Ill-Sup

    2015-01-10

    Cold and freezing stress is a major environmental constraint to the production of Brassica crops. Enhancement of tolerance by exploiting cold and freezing tolerance related genes offers the most efficient approach to address this problem. Cold-induced transcriptional profiling is a promising approach to the identification of potential genes related to cold and freezing stress tolerance. In this study, 99 highly expressed genes were identified from a whole genome microarray dataset of Brassica rapa. Blast search analysis of the Brassica oleracea database revealed the corresponding homologous genes. To validate their expression, pre-selected cold tolerant and susceptible cabbage lines were analyzed. Out of 99 BoCRGs, 43 were differentially expressed in response to varying degrees of cold and freezing stress in the contrasting cabbage lines. Among the differentially expressed genes, 18 were highly up-regulated in the tolerant lines, which is consistent with their microarray expression. Additionally, 12 BoCRGs were expressed differentially after cold stress treatment in two contrasting cabbage lines, and BoCRG54, 56, 59, 62, 70, 72 and 99 were predicted to be involved in cold regulatory pathways. Taken together, the cold-responsive genes identified in this study provide additional direction for elucidating the regulatory network of low temperature stress tolerance and developing cold and freezing stress resistant Brassica crops. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Nonlinear identification of a DIR-SOFC stack using wavelet networks

    Science.gov (United States)

    Li, Jun; Kang, Ying-Wei; Cao, Guang-Yi; Zhu, Xin-Jian; Tu, Heng-Yong; Li, Jian

    2008-05-01

    Application of wavelet networks for identification of a direct internal reforming solid oxide fuel cell (DIR-SOFC) stack is reported in this paper. The SOFC is a complex system particularly when it is directly fueled with hydrocarbons (natural gas, coal gas, etc.). Most of the traditional models of the SOFC, based on the reforming, electrochemical and thermal modeling, are too complicated. To facilitate controller design and analysis of systems, the wavelet network dynamic model of the DIR-SOFC is constructed, avoiding the consideration of the complex processes in the fuel cells. The input and output data are used for initializing and training the wavelet network by a recursive approach. The Gram-Schmidt algorithm, the Cross-Validation method and immune selection principles are applied to optimization of the network. The simulation is performed and comparisons of characteristics under different operating conditions are given. The results show high static and dynamic accuracy of the identified model. Further, the obtained wavelet network model can be used for developing the model-based controllers of DIR-SOFC.

  18. Identification of functional modules by integration of multiple data sources using a Bayesian network classifier.

    Science.gov (United States)

    Wang, Jinlian; Zuo, Yiming; Liu, Lun; Man, Yangao; Tadesse, Mahlet G; Ressom, Habtom W

    2014-04-01

    Prediction of functional modules is indispensable for detecting protein deregulation in human complex diseases such as cancer. Bayesian network is one of the most commonly used models to integrate heterogeneous data from multiple sources such as protein domain, interactome, functional annotation, genome-wide gene expression, and the literature. In this article, we present a Bayesian network classifier that is customized to (1) increase the ability to integrate diverse information from different sources, (2) effectively predict protein-protein interactions, (3) infer aberrant networks with scale-free and small-world properties, and (4) group molecules into functional modules or pathways based on the primary function and biological features. Application of this model in discovering protein biomarkers of hepatocellular carcinoma leads to the identification of functional modules that provide insights into the mechanism of the development and progression of hepatocellular carcinoma. These functional modules include cell cycle deregulation, increased angiogenesis (eg, vascular endothelial growth factor, blood vessel morphogenesis), oxidative metabolic alterations, and aberrant activation of signaling pathways involved in cellular proliferation, survival, and differentiation. The discoveries and conclusions derived from our customized Bayesian network classifier are consistent with previously published results. The proposed approach for determining Bayesian network structure facilitates the integration of heterogeneous data from multiple sources to elucidate the mechanisms of complex diseases.

  19. On the identification of quark and gluon jets using artificial neural network method

    CERN Document Server

    Zhang, Kun Shi

    2004-01-01

    The identification of quark and gluon jets produced in e^{+}e^{-} collisions using the artificial neural network method is addressed. The structure and the learning algorithm of the BP( back propagation) neural network model is studied. Three characteristic parameters-the average multiplicity and the average transverse momentum of jets and the average value of the angles opposite to the quark or gluon jets are taken as training parameters and are input to the BP network for repeated training. The learning process is ended when the output error of the neural network is less than a preset precision( sigma =0.005). The same training routine is repeated in each of the 8 energy bins ranging from 2.5-22.5 GeV, respectively. The finally updated weights and thresholds of the BP neural network are tested using the quark and gluon jet samples, getting from the nonsymmetric three-jet events produced by the Monte Carlo generator JETSET 7.4. Then the pattern recognition of the mixed sample getting from the combination of ...

  20. Signalling network construction for modelling plant defence response.

    Directory of Open Access Journals (Sweden)

    Dragana Miljkovic

    Full Text Available Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2 triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be

  1. Identification from the Natural Response of Vasco Da Gama Bridge

    DEFF Research Database (Denmark)

    Cunha, A.; Caetano, E.; Brincker, Rune

    2004-01-01

    Subspace Identification (SSI) methods. The modal estimates obtained using these alternative approaches are compared, taking also into account the estimates previously obtained with the conventional Peak Picking technique from the free vibration test of the bridge, performed at the end of construction.......This paper describes the reanalysis of the ambient vibration data of Vasco da Gama cable-stayed bridge with the purpose of testing the efficiency and accuracy of two recent and promising identification methods in a large application: the Frequency Domain Decomposition (FDD) and the Stochastic...

  2. A Heterogeneous Wireless Identification Network for the Localization of Animals Based on Stochastic Movements

    Directory of Open Access Journals (Sweden)

    Ivana Raos

    2009-05-01

    Full Text Available The improvement in the transmission range in wireless applications without the use of batteries remains a significant challenge in identification applications. In this paper, we describe a heterogeneous wireless identification network mostly powered by kinetic energy, which allows the localization of animals in open environments. The system relies on radio communications and a global positioning system. It is made up of primary and secondary nodes. Secondary nodes are kinetic-powered and take advantage of animal movements to activate the node and transmit a specific identifier, reducing the number of batteries of the system. Primary nodes are battery-powered and gather secondary-node transmitted information to provide it, along with position and time data, to a final base station in charge of the animal monitoring. The system allows tracking based on contextual information obtained from statistical data.

  3. NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

    Directory of Open Access Journals (Sweden)

    Min Peng

    2016-10-01

    Full Text Available Near-infrared (NIR face recognition has attracted increasing attention because of its advantage of illumination invariance. However, traditional face recognition methods based on NIR are designed for and tested in cooperative-user applications. In this paper, we present a convolutional neural network (CNN for NIR face recognition (specifically face identification in non-cooperative-user applications. The proposed NIRFaceNet is modified from GoogLeNet, but has a more compact structure designed specifically for the Chinese Academy of Sciences Institute of Automation (CASIA NIR database and can achieve higher identification rates with less training time and less processing time. The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present. The performance suggests that the proposed NIRFaceNet method may be more suitable for non-cooperative-user applications.

  4. Feasibility study of system identification of orbit response matrices at FACET

    CERN Document Server

    Pfingstner, Jürgen

    2012-01-01

    Beam-based alignment methods, orbit feedback systems and diagnosis tools rely on good knowledge of the orbit response matrix. In this paper, different on-line system identification algorithms are used to estimate the orbit response matrix of the \\emph{Facility for Advanced Accelerator Experiment Tests} (FACET) at the SLAC National Accelerator Laboratory. The performance of the different algorithms is compared via simulation studies. It is found that an identification of the full orbit response matrix with an acceptable accuracy takes several hours in a parasitic mode (during physics operation). If the identification algorithms do not have to run in a parasitic mode (large emittance increase) the full response matrix can be identified in less than an hour. The adapted algorithm formulations, the performance comparisons and the found algorithm limitations provide important information that can be applied to the system identification of other accelerators.

  5. Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

    Science.gov (United States)

    Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai

    2014-01-01

    Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923

  6. Development of objective flow regime identification method using self-organizing neural network

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jae Young; Kim, Nam Seok; Kwak, Nam Yee [Handong Global Univ., Pohang (Korea, Republic of)

    2004-07-01

    Two-phase flow shows various flow patterns according to the amount of the void and its relative velocity to the liquid flow. This variation directly affect the interfacial transfer which is the key factor for the design or analysis of the phase change systems. Especially the safety analysis of the nuclear power plant has been performed based on the numerical code furnished with the proper constitutive relations depending highly upon the flow regimes. Heavy efforts have been focused to identify the flow regime and at this moment we stand on relative very stable engineering background compare to the other research field. However, the issues related to objectiveness and transient flow regime are still open to study. Lee et al. and Ishii developed the method for the objective and instantaneous flow regime identification based on the neural network and new index of probability distribution of the flow regime which allows just one second observation for the flow regime identification. In the present paper, we developed the self-organized neural network for more objective approach to this problem. Kohonen's Self-Organizing Map (SOM) has been used for clustering, visualization, and abstraction. The SOM is trained through unsupervised competitive learning using a 'winner takes it all' policy. Therefore, its unsupervised training character delete the possible interference of the regime developer to the neural network training. After developing the computer code, we evaluate the performance of the code with the vertically upward two-phase flow in the pipes of 25.4 and 50.4 cmm I.D. Also, the sensitivity of the number of the clusters to the flow regime identification was made.

  7. Remote monitoring of soldier safety through body posture identification using wearable sensor networks

    Science.gov (United States)

    Biswas, Subir; Quwaider, Muhannad

    2008-04-01

    The physical safety and well being of the soldiers in a battlefield is the highest priority of Incident Commanders. Currently, the ability to track and monitor soldiers rely on visual and verbal communication which can be somewhat limited in scenarios where the soldiers are deployed inside buildings and enclosed areas that are out of visual range of the commanders. Also, the need for being stealth can often prevent a battling soldier to send verbal clues to a commander about his or her physical well being. Sensor technologies can remotely provide various data about the soldiers including physiological monitoring and personal alert safety system functionality. This paper presents a networked sensing solution in which a body area wireless network of multi-modal sensors can monitor the body movement and other physiological parameters for statistical identification of a soldier's body posture, which can then be indicative of the physical conditions and safety alerts of the soldier in question. The specific concept is to leverage on-body proximity sensing and a Hidden Markov Model (HMM) based mechanism that can be applied for stochastic identification of human body postures using a wearable sensor network. The key idea is to collect relative proximity information between wireless sensors that are strategically placed over a subject's body to monitor the relative movements of the body segments, and then to process that using HMM in order to identify the subject's body postures. The key novelty of this approach is a departure from the traditional accelerometry based approaches in which the individual body segment movements, rather than their relative proximity, is used for activity monitoring and posture detection. Through experiments with body mounted sensors we demonstrate that while the accelerometry based approaches can be used for differentiating activity intensive postures such as walking and running, they are not very effective for identification and

  8. Direct Photon Identification with Artificial Neural Network in the Photon Spectrometer PHOS

    CERN Document Server

    Bogolyubsky, M Yu; Sadovsky, S A; Kharlov, Yu.V.

    2003-01-01

    A neural network method is developed to discriminate direct photons from the neutral pion background in the PHOS spectrometer of the ALICE experiment at the LHC collider. The neural net has been trained to distinguish different classes of events by analyzing the energy-profile tensor of a cluster in its eigen vector coordinate system. Monte-Carlo simulations show that this method diminishes by an order of magnitude the probability of $\\pi^0$-meson misidentification as a photon with respect to the direct photon identification efficiency in the energy range up to 120 GeV.

  9. Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network.

    Science.gov (United States)

    Li, Ruowang; Dudek, Scott M; Kim, Dokyoon; Hall, Molly A; Bradford, Yuki; Peissig, Peggy L; Brilliant, Murray H; Linneman, James G; McCarty, Catherine A; Bao, Le; Ritchie, Marylyn D

    2016-01-01

    The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analyzing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions among multiple SNPs. In many traits, a large proportion of variation remain unexplained by using main effects alone, leaving the door open for exploring the role of genetic interactions. However, identifying genetic interactions in large-scale genomics data poses a challenge even for modern computing. For this study, we present a new algorithm, Grammatical Evolution Bayesian Network (GEBN) that utilizes Bayesian Networks to identify interactions in the data, and at the same time, uses an evolutionary algorithm to reduce the computational cost associated with network optimization. GEBN excelled in simulation studies where the data contained main effects and interaction effects. We also applied GEBN to a Type 2 diabetes (T2D) dataset obtained from the Marshfield Personalized Medicine Research Project (PMRP). We were able to identify genetic interactions for T2D cases and controls and use information from those interactions to classify T2D samples. We obtained an average testing area under the curve (AUC) of 86.8 %. We also identified several interacting genes such as INADL and LPP that are known to be associated with T2D. Developing the computational tools to explore genetic associations beyond main

  10. An artificial neural network based $b$ jet identification algorithm at the CDF Experiment

    CERN Document Server

    Freeman, J; Ketchum, W; Poprocki, S; Pronko, A; Rusu, V; Wittich, P

    2011-01-01

    We present the development and validation of a new multivariate $b$ jet identification algorithm ("$b$ tagger") used at the CDF experiment at the Fermilab Tevatron. At collider experiments, $b$ taggers allow one to distinguish particle jets containing $B$ hadrons from other jets. Employing feed-forward neural network architectures, this tagger is unique in its emphasis on using information from individual tracks. This tagger not only contains the usual advantages of a multivariate technique such as maximal use of information in a jet and tunable purity/efficiency operating points, but is also capable of evaluating jets with only a single track. To demonstrate the effectiveness of the tagger, we employ a novel method wherein we calculate the false tag rate and tag efficiency as a function of the placement of a lower threshold on a jet's neural network output value in $Z+1$ jet and $t\\bar{t}$ candidate samples, rich in light flavor and $b$ jets, respectively.

  11. Identification of Jets Containing b-Hadrons with Recurrent Neural Networks at the ATLAS Experiment

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    A novel b-jet identification algorithm is constructed with a Recurrent Neural Network (RNN) at the ATLAS Experiment. This talk presents the expected performance of the RNN based b-tagging in simulated $t \\bar t$ events. The RNN based b-tagging processes properties of tracks associated to jets which are represented in sequences. In contrast to traditional impact-parameter-based b-tagging algorithms which assume the tracks of jets are independent from each other, RNN based b-tagging can exploit the spatial and kinematic correlations of tracks which are initiated from the same b-hadrons. The neural network nature of the tagging algorithm also allows the flexibility of extending input features to include more track properties than can be effectively used in traditional algorithms.

  12. Differential Neural Networks for Identification and Filtering in Nonlinear Dynamic Games

    Directory of Open Access Journals (Sweden)

    Emmanuel García

    2014-01-01

    Full Text Available This paper deals with the problem of identifying and filtering a class of continuous-time nonlinear dynamic games (nonlinear differential games subject to additive and undesired deterministic perturbations. Moreover, the mathematical model of this class is completely unknown with the exception of the control actions of each player, and even though the deterministic noises are known, their power (or their effect is not. Therefore, two differential neural networks are designed in order to obtain a feedback (perfect state information pattern for the mentioned class of games. In this way, the stability conditions for two state identification errors and for a filtering error are established, the upper bounds of these errors are obtained, and two new learning laws for each neural network are suggested. Finally, an illustrating example shows the applicability of this approach.

  13. Fault Identification in Distributed Sensor Networks Based on Universal Probabilistic Modeling.

    Science.gov (United States)

    Ntalampiras, Stavros

    2015-09-01

    This paper proposes a holistic modeling scheme for fault identification in distributed sensor networks. The proposed scheme is based on modeling the relationship between two datastreams by means of a hidden Markov model (HMM) trained on the parameters of linear time-invariant dynamic systems, which estimate the specific relationship over consecutive time windows. Every system state, including the nominal one, is represented by an HMM and the novel data are categorized according to the model producing the highest likelihood. The system is able to understand whether the novel data belong to the fault dictionary, are fault-free, or represent a new fault type. We extensively evaluated the discrimination capabilities of the proposed approach and contrasted it with a multilayer perceptron using data coming from the Barcelona water distribution network. Nine system states are present in the dataset and the recognition rates are provided in the confusion matrix form.

  14. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

    DEFF Research Database (Denmark)

    Hjerrild, M.; Stensballe, A.; Rasmussen, T.E.

    2004-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...... kinase A (PKA) phosphorylation sites. The neural network was trained with a positive set of 258 experimentally verified PKA phosphorylation sites. The predictions by NetPhosK were! validated using four novel PKA substrates: Necdin, RFX5, En-2, and Wee 1. The four proteins were phosphorylated by PKA...... in vitro and 13 PKA phosphorylation sites were identified by mass spectrometry. NetPhosK was 100% sensitive and 41% specific in predicting PKA sites in the four proteins. These results demonstrate the potential of using integrated computational and experimental methods for detailed investigations...

  15. Universal Approximation of a Class of Interval Type-2 Fuzzy Neural Networks in Nonlinear Identification

    Directory of Open Access Journals (Sweden)

    Oscar Castillo

    2013-01-01

    Full Text Available Neural networks (NNs, type-1 fuzzy logic systems (T1FLSs, and interval type-2 fuzzy logic systems (IT2FLSs have been shown to be universal approximators, which means that they can approximate any nonlinear continuous function. Recent research shows that embedding an IT2FLS on an NN can be very effective for a wide number of nonlinear complex systems, especially when handling imperfect or incomplete information. In this paper we show, based on the Stone-Weierstrass theorem, that an interval type-2 fuzzy neural network (IT2FNN is a universal approximator, which uses a set of rules and interval type-2 membership functions (IT2MFs for this purpose. Simulation results of nonlinear function identification using the IT2FNN for one and three variables and for the Mackey-Glass chaotic time series prediction are presented to illustrate the concept of universal approximation.

  16. System identification and adaptive control theory and applications of the neurofuzzy and fuzzy cognitive network models

    CERN Document Server

    Boutalis, Yiannis; Kottas, Theodore; Christodoulou, Manolis A

    2014-01-01

    Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented.  Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model  stems  from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering s...

  17. Metastable Features of Economic Networks and Responses to Exogenous Shocks.

    Directory of Open Access Journals (Sweden)

    Ali Hosseiny

    Full Text Available It is well known that a network structure plays an important role in addressing a collective behavior. In this paper we study a network of firms and corporations for addressing metastable features in an Ising based model. In our model we observe that if in a recession the government imposes a demand shock to stimulate the network, metastable features shape its response. Actually we find that there exists a minimum bound where any demand shock with a size below it is unable to trigger the market out of recession. We then investigate the impact of network characteristics on this minimum bound. We surprisingly observe that in a Watts-Strogatz network, although the minimum bound depends on the average of the degrees, when translated into the language of economics, such a bound is independent of the average degrees. This bound is about 0.44ΔGDP, where ΔGDP is the gap of GDP between recession and expansion. We examine our suggestions for the cases of the United States and the European Union in the recent recession, and compare them with the imposed stimulations. While the stimulation in the US has been above our threshold, in the EU it has been far below our threshold. Beside providing a minimum bound for a successful stimulation, our study on the metastable features suggests that in the time of crisis there is a "golden time passage" in which the minimum bound for successful stimulation can be much lower. Hence, our study strongly suggests stimulations to arise within this time passage.

  18. Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Baburaj Karanayil

    2009-01-01

    Full Text Available This paper presents a new method of online estimation of the stator and rotor resistance of the induction motor in the indirect vector-controlled drive, with artificial neural networks. The back propagation algorithm is used for training of the neural networks. The error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the rotor resistance estimation. For the stator resistance estimation, the error between the measured stator current and the estimated stator current using neural network is back propagated to adjust the weights of the neural network. The performance of the stator and rotor resistance estimators and torque and flux responses of the drive, together with these estimators, is investigated with the help of simulations for variations in the stator and rotor resistance from their nominal values. Both types of resistance are estimated experimentally, using the proposed neural network in a vector-controlled induction motor drive. Data on tracking performances of these estimators are presented. With this approach, the rotor resistance estimation was found to be insensitive to the stator resistance variations both in simulation and experiment.

  19. Identification and characterization of the leaf specific networks of inner and rosette leaves in Brassica rapa.

    Science.gov (United States)

    Kim, Man-Sun; Hong, Seongmin; Devaraj, Sangeeth Prasath; Im, Subin; Kim, Jeong-Rae; Lim, Yong Pyo

    2017-08-26

    Inner and rosette leaves of Chinese cabbage (Brassica rapa) have different characteristics in terms of nutritional value, appearance, taste, color and texture. Many researchers have utilized differentially expressed genes for exploring the difference between inner and rosette leaves of Brassica rapa. The functional characteristics of a gene, however, is determined by complex interactions between genes. Hence, a noble network approach is required for elucidating such functional difference that is not captured by gene expression profiles alone. In this study, we measured gene expression in the standard cabbage genome by RNA-Sequencing and constructed rosette and inner leaf networks based on the gene expression profiles. Furthermore, we compared the topological and functional characteristics of these networks. We found significant functional difference between the rosette and inner leaf networks. Specifically, we found that the genes in the rosette leaf network were associated with homeostasis and response to external stimuli whereas the genes in the inner leaf network were mainly related to the glutamine biosynthesis processes and developmental processes with hormones. Overall, the network approach provides an insight into the functional difference of the two leaves. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Optimal network solution for proactive risk assessment and emergency response

    Science.gov (United States)

    Cai, Tianxing

    Coupled with the continuous development in the field industrial operation management, the requirement for operation optimization in large scale manufacturing network has provoked more interest in the research field of engineering. Compared with the traditional way to take the remedial measure after the occurrence of the emergency event or abnormal situation, the current operation control calls for more proactive risk assessment to set up early warning system and comprehensive emergency response planning. Among all the industries, chemical industry and energy industry have higher opportunity to face with the abnormal and emergency situations due to their own industry characterization. Therefore the purpose of the study is to develop methodologies to give aid in emergency response planning and proactive risk assessment in the above two industries. The efficacy of the developed methodologies is demonstrated via two industrial real problems. The first case is to handle energy network dispatch optimization under emergency of local energy shortage under extreme conditions such as earthquake, tsunami, and hurricane, which may cause local areas to suffer from delayed rescues, widespread power outages, tremendous economic losses, and even public safety threats. In such urgent events of local energy shortage, agile energy dispatching through an effective energy transportation network, targeting the minimum energy recovery time, should be a top priority. The second case is a scheduling methodology to coordinate multiple chemical plants' start-ups in order to minimize regional air quality impacts under extreme meteorological conditions. The objective is to reschedule multi-plant start-up sequence to achieve the minimum sum of delay time compared to the expected start-up time of each plant. All these approaches can provide quantitative decision support for multiple stake holders, including government and environment agencies, chemical industry, energy industry and local

  1. Emergency response networks for disaster monitoring and detection from space

    Science.gov (United States)

    Vladimirova, Tanya; Sweeting, Martin N.; Vitanov, Ivan; Vitanov, Valentin I.

    2009-05-01

    Numerous man-made and natural disasters have stricken mankind since the beginning of the new millennium. The scale and impact of such disasters often prevent the collection of sufficient data for an objective assessment and coordination of timely rescue and relief missions on the ground. As a potential solution to this problem, in recent years constellations of Earth observation small satellites and in particular micro-satellites (<100 kg) in low Earth orbit have emerged as an efficient platform for reliable disaster monitoring. The main task of the Earth observation satellites is to capture images of the Earth surface using various techniques. For a large number of applications the resulting delay between image capture and delivery is not acceptable, in particular for rapid response remote sensing aiming at disaster monitoring and detection. In such cases almost instantaneous data availability is a strict requirement to enable an assessment of the situation and instigate an adequate response. Examples include earthquakes, volcanic eruptions, flooding, forest fires and oil spills. The proposed solution to this issue are low-cost networked distributed satellite systems in low Earth orbit capable of connecting to terrestrial networks and geostationary Earth orbit spacecraft in real time. This paper discusses enabling technologies for rapid response disaster monitoring and detection from space such as very small satellite design, intersatellite communication, intelligent on-board processing, distributed computing and bio-inspired routing techniques.

  2. Development of Bioinformatic and Experimental Technologies for Identification of Prokaryotic Regulatory Networks

    Energy Technology Data Exchange (ETDEWEB)

    Lawrence, Charles E. [Brown Univ., Providence, RI (United States); McCue, Lee Ann [Brown Univ., Providence, RI (United States)

    2008-07-31

    The transcription regulatory network is arguably the most important foundation of cellular function, since it exerts the most fundamental control over the abundance of virtually all of a cell’s functional macromolecules. The two major components of a prokaryotic cell’s transcription regulation network are the transcription factors (TFs) and the transcription factor binding sites (TFBS); these components are connected by the binding of TFs to their cognate TFBS under appropriate environmental conditions. Comparative genomics has proven to be a powerful bioinformatics method with which to study transcription regulation on a genome-wide level. We have further extended comparative genomics technologies that we introduced over the last several years. Specifically, we developed and applied statistical approaches to analysis of correlated sequence data (i.e., sequences from closely related species). We also combined these technologies with functional genomic, proteomic and sequence data from multiple species, and developed computational technologies that provide inferences on the regulatory network connections, identifying the cognate transcription factor for predicted regulatory sites. Arguably the most important contribution of this work emerged in the course of the project. Specifically, the development of novel procedures of estimation and prediction in discrete high-D settings has broad implications for biology, genomics and well beyond. We showed that these procedures enjoy advantages over existing technologies in the identification of TBFS. These efforts are aimed toward identifying a cell’s complete transcription regulatory network and underlying molecular mechanisms.

  3. Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure.

    Science.gov (United States)

    El-Nagar, Ahmad M

    2017-10-31

    In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  4. The Effect of Edge Definition of Complex Networks on Protein Structure Identification

    Directory of Open Access Journals (Sweden)

    Jing Sun

    2013-01-01

    Full Text Available The main objective of this study is to explore the contribution of complex network together with its different definitions of vertexes and edges to describe the structure of proteins. Protein folds into a specific conformation for its function depending on interactions between residues. Consequently, in many studies, a protein structure was treated as a complex system comprised of individual components residues, and edges were interactions between residues. What is the proper time for representing a protein structure as a network? To confirm the effect of different definitions of vertexes and edges in constructing the amino acid interaction networks, protein domains and the structural unit of proteins were described using this method. The identification performance of 2847 proteins with domain/domains proved that the structure of proteins was described well when was around 5.0–7.5 Å, and the optimal cutoff value for constructing the protein structure networks was 5.0 Å ( distances while the ideal community division method was community structure detection based on edge betweenness in this study.

  5. Effective identification of essential proteins based on priori knowledge, network topology and gene expressions.

    Science.gov (United States)

    Li, Min; Zheng, Ruiqing; Zhang, Hanhui; Wang, Jianxin; Pan, Yi

    2014-06-01

    Identification of essential proteins is very important for understanding the minimal requirements for cellular life and also necessary for a series of practical applications, such as drug design. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which makes it possible to detect proteins' essentialities from the network level. Considering that most species already have a number of known essential proteins, we proposed a new priori knowledge-based scheme to discover new essential proteins from protein interaction networks. Based on the new scheme, two essential protein discovery algorithms, CPPK and CEPPK, were developed. CPPK predicts new essential proteins based on network topology and CEPPK detects new essential proteins by integrating network topology and gene expressions. The performances of CPPK and CEPPK were validated based on the protein interaction network of Saccharomyces cerevisiae. The experimental results showed that the priori knowledge of known essential proteins was effective for improving the predicted precision. The predicted precisions of CPPK and CEPPK clearly exceeded that of the other 10 previously proposed essential protein discovery methods: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Bottle Neck (BN), Density of Maximum Neighborhood Component (DMNC), Local Average Connectivity-based method (LAC), and Network Centrality (NC). Especially, CPPK achieved 40% improvement in precision over BC, CC, SC, EC, and BN, and CEPPK performed even better. CEPPK was also compared to four other methods (EPC, ORFL, PeC, and CoEWC) which were not node centralities and CEPPK was showed to achieve the best results. Copyright © 2014 Elsevier Inc. All rights reserved.

  6. Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models

    Directory of Open Access Journals (Sweden)

    Tsuda Koji

    2007-11-01

    Full Text Available Abstract Background Identifying large gene regulatory networks is an important task, while the acquisition of data through perturbation experiments (e.g., gene switches, RNAi, heterozygotes is expensive. It is thus desirable to use an identification method that effectively incorporates available prior knowledge – such as sparse connectivity – and that allows to design experiments such that maximal information is gained from each one. Results Our main contributions are twofold: a method for consistent inference of network structure is provided, incorporating prior knowledge about sparse connectivity. The algorithm is time efficient and robust to violations of model assumptions. Moreover, we show how to use it for optimal experimental design, reducing the number of required experiments substantially. We employ sparse linear models, and show how to perform full Bayesian inference for these. We not only estimate a single maximum likelihood network, but compute a posterior distribution over networks, using a novel variant of the expectation propagation method. The representation of uncertainty enables us to do effective experimental design in a standard statistical setting: experiments are selected such that the experiments are maximally informative. Conclusion Few methods have addressed the design issue so far. Compared to the most well-known one, our method is more transparent, and is shown to perform qualitatively superior. In the former, hard and unrealistic constraints have to be placed on the network structure for mere computational tractability, while such are not required in our method. We demonstrate reconstruction and optimal experimental design capabilities on tasks generated from realistic non-linear network simulators. The methods described in the paper are available as a Matlab package at http://www.kyb.tuebingen.mpg.de/sparselinearmodel.

  7. In Vivo Identification of Eugenol-Responsive and Muscone-Responsive Mouse Odorant Receptors

    Science.gov (United States)

    Adipietro, Kaylin; Titlow, William B.; Breheny, Patrick; Walz, Andreas; Mombaerts, Peter; Matsunami, Hiroaki

    2014-01-01

    Our understanding of mammalian olfactory coding has been impeded by the paucity of information about the odorant receptors (ORs) that respond to a given odorant ligand in awake, freely behaving animals. Identifying the ORs that respond in vivo to a given odorant ligand from among the ∼1100 ORs in mice is intrinsically challenging but critical for our understanding of olfactory coding at the periphery. Here, we report an in vivo assay that is based on a novel gene-targeted mouse strain, S100a5–tauGFP, in which a fluorescent reporter selectively marks olfactory sensory neurons that have been activated recently in vivo. Because each olfactory sensory neuron expresses a single OR gene, multiple ORs responding to a given odorant ligand can be identified simultaneously by capturing the population of activated olfactory sensory neurons and using expression profiling methods to screen the repertoire of mouse OR genes. We used this in vivo assay to re-identify known eugenol- and muscone-responsive mouse ORs. We identified additional ORs responsive to eugenol or muscone. Heterologous expression assays confirmed nine eugenol-responsive ORs (Olfr73, Olfr178, Olfr432, Olfr610, Olfr958, Olfr960, Olfr961, Olfr913, and Olfr1234) and four muscone-responsive ORs (Olfr74, Olfr235, Olfr816, and Olfr1440). We found that the human ortholog of Olfr235 and Olfr1440 responds to macrocyclic ketone and lactone musk odorants but not to polycyclic musk odorants or a macrocyclic diester musk odorant. This novel assay, called the Kentucky in vivo odorant ligand–receptor assay, should facilitate the in vivo identification of mouse ORs for a given odorant ligand of interest. PMID:25411495

  8. Local Identification of Voltage Instability from Load Tap Changer Response

    DEFF Research Database (Denmark)

    Weckesser, Johannes Tilman Gabriel; Papangelis, Lampros; Vournas, Costas D.

    2017-01-01

    This paper presents a local long-term voltage instability monitoring method, which is suitable for on-line applications. The proposed extended-time Local Identification of Voltage Emergency Situations (eLIVES) method is a significantly modified version of the previously presented LIVES method. Th...... to acquired distribution voltage measurements and a new set of rules to detect a voltage emergency situation. The effectiveness of the eLIVES method is presented on the IEEE Nordic test system for voltage stability and security assessment.......This paper presents a local long-term voltage instability monitoring method, which is suitable for on-line applications. The proposed extended-time Local Identification of Voltage Emergency Situations (eLIVES) method is a significantly modified version of the previously presented LIVES method...

  9. An automatic microseismic or acoustic emission arrival identification scheme with deep recurrent neural networks

    Science.gov (United States)

    Zheng, Jing; Lu, Jiren; Peng, Suping; Jiang, Tianqi

    2018-02-01

    The conventional arrival pick-up algorithms cannot avoid the manual modification of the parameters for the simultaneous identification of multiple events under different signal-to-noise ratios (SNRs). Therefore, in order to automatically obtain the arrivals of multiple events with high precision under different SNRs, in this study an algorithm was proposed which had the ability to pick up the arrival of microseismic or acoustic emission events based on deep recurrent neural networks. The arrival identification was performed using two important steps, which included a training phase and a testing phase. The training process was mathematically modelled by deep recurrent neural networks using Long Short-Term Memory architecture. During the testing phase, the learned weights were utilized to identify the arrivals through the microseismic/acoustic emission data sets. The data sets were obtained by rock physics experiments of the acoustic emission. In order to obtain the data sets under different SNRs, this study added random noise to the raw experiments' data sets. The results showed that the outcome of the proposed method was able to attain an above 80 per cent hit-rate at SNR 0 dB, and an approximately 70 per cent hit-rate at SNR -5 dB, with an absolute error in 10 sampling points. These results indicated that the proposed method had high selection precision and robustness.

  10. MPINet: Metabolite Pathway Identification via Coupling of Global Metabolite Network Structure and Metabolomic Profile

    Directory of Open Access Journals (Sweden)

    Feng Li

    2014-01-01

    Full Text Available High-throughput metabolomics technology, such as gas chromatography mass spectrometry, allows the analysis of hundreds of metabolites. Understanding that these metabolites dominate the study condition from biological pathway perspective is still a significant challenge. Pathway identification is an invaluable aid to address this issue and, thus, is urgently needed. In this study, we developed a network-based metabolite pathway identification method, MPINet, which considers the global importance of metabolites and the unique character of metabolomic profile. Through integrating the global metabolite functional network structure and the character of metabolomic profile, MPINet provides a more accurate metabolomic pathway analysis. This integrative strategy simultaneously captures the global nonequivalence of metabolites in a pathway and the bias from metabolomic experimental technology. We then applied MPINet to four different types of metabolite datasets. In the analysis of metastatic prostate cancer dataset, we demonstrated the effectiveness of MPINet. With the analysis of the two type 2 diabetes datasets, we show that MPINet has the potentiality for identifying novel pathways related with disease and is reliable for analyzing metabolomic data. Finally, we extensively applied MPINet to identify drug sensitivity related pathways. These results suggest MPINet’s effectiveness and reliability for analyzing metabolomic data across multiple different application fields.

  11. Real-time radionuclide identification in γ-emitter mixtures based on spiking neural network.

    Science.gov (United States)

    Bobin, C; Bichler, O; Lourenço, V; Thiam, C; Thévenin, M

    2016-03-01

    Portal radiation monitors dedicated to the prevention of illegal traffic of nuclear materials at international borders need to deliver as fast as possible a radionuclide identification of a potential radiological threat. Spectrometry techniques applied to identify the radionuclides contributing to γ-emitter mixtures are usually performed using off-line spectrum analysis. As an alternative to these usual methods, a real-time processing based on an artificial neural network and Bayes' rule is proposed for fast radionuclide identification. The validation of this real-time approach was carried out using γ-emitter spectra ((241)Am, (133)Ba, (207)Bi, (60)Co, (137)Cs) obtained with a high-efficiency well-type NaI(Tl). The first tests showed that the proposed algorithm enables a fast identification of each γ-emitting radionuclide using the information given by the whole spectrum. Based on an iterative process, the on-line analysis only needs low-statistics spectra without energy calibration to identify the nature of a radiological threat. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Identification of Hadronic Tau Lepton Decays at the ATLAS Detector Using Artificial Neural Networks

    CERN Document Server

    AUTHOR|(CDS)2093068; Zuber, Kai

    Tau leptons play an important role in a wide range of physics analyses at the LHC, such as the verification of the Standard Model at the TeV scale or the determination of Higgs boson properties. For the identification of hadronically decaying tau leptons with the ATLAS detector, a sophisticated, multi-variate algorithm is required. This is due to the high production cross section for QCD jets, the dominant background. Artificial neural networks (ANNs) have gained much attention in recent years by winning several pattern recognition contests. In this thesis, a survey of ANNs is given with a focus on developments of the past 20 years. Based on this work, a novel, ANN-based tau identification is presented which is competitive to the current BDT-based approach. The influence of various hyperparameters on the identification is studied and optimized. Both stability and performance are enhanced through formation of ANN ensembles. Additionally, a score-flattening algorithm is presented that is beneficial to physics a...

  13. An Introduction to Network Psychometrics: Relating Ising Network Models to Item Response Theory Models.

    Science.gov (United States)

    Marsman, M; Borsboom, D; Kruis, J; Epskamp, S; van Bork, R; Waldorp, L J; Maas, H L J van der; Maris, G

    2017-11-07

    In recent years, network models have been proposed as an alternative representation of psychometric constructs such as depression. In such models, the covariance between observables (e.g., symptoms like depressed mood, feelings of worthlessness, and guilt) is explained in terms of a pattern of causal interactions between these observables, which contrasts with classical interpretations in which the observables are conceptualized as the effects of a reflective latent variable. However, few investigations have been directed at the question how these different models relate to each other. To shed light on this issue, the current paper explores the relation between one of the most important network models-the Ising model from physics-and one of the most important latent variable models-the Item Response Theory (IRT) model from psychometrics. The Ising model describes the interaction between states of particles that are connected in a network, whereas the IRT model describes the probability distribution associated with item responses in a psychometric test as a function of a latent variable. Despite the divergent backgrounds of the models, we show a broad equivalence between them and also illustrate several opportunities that arise from this connection.

  14. Automated identification of copepods using digital image processing and artificial neural network.

    Science.gov (United States)

    Leow, Lee Kien; Chew, Li-Lee; Chong, Ving Ching; Dhillon, Sarinder Kaur

    2015-01-01

    Copepods are planktonic organisms that play a major role in the marine food chain. Studying the community structure and abundance of copepods in relation to the environment is essential to evaluate their contribution to mangrove trophodynamics and coastal fisheries. The routine identification of copepods can be very technical, requiring taxonomic expertise, experience and much effort which can be very time-consuming. Hence, there is an urgent need to introduce novel methods and approaches to automate identification and classification of copepod specimens. This study aims to apply digital image processing and machine learning methods to build an automated identification and classification technique. We developed an automated technique to extract morphological features of copepods' specimen from captured images using digital image processing techniques. An Artificial Neural Network (ANN) was used to classify the copepod specimens from species Acartia spinicauda, Bestiolina similis, Oithona aruensis, Oithona dissimilis, Oithona simplex, Parvocalanus crassirostris, Tortanus barbatus and Tortanus forcipatus based on the extracted features. 60% of the dataset was used for a two-layer feed-forward network training and the remaining 40% was used as testing dataset for system evaluation. Our approach demonstrated an overall classification accuracy of 93.13% (100% for A. spinicauda, B. similis and O. aruensis, 95% for T. barbatus, 90% for O. dissimilis and P. crassirostris, 85% for O. similis and T. forcipatus). The methods presented in this study enable fast classification of copepods to the species level. Future studies should include more classes in the model, improving the selection of features, and reducing the time to capture the copepod images.

  15. Systems level analysis and identification of pathways and networks associated with liver fibrosis.

    Directory of Open Access Journals (Sweden)

    Mohamed Diwan M AbdulHameed

    Full Text Available Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to

  16. SLIDE: automatic spine level identification system using a deep convolutional neural network.

    Science.gov (United States)

    Hetherington, Jorden; Lessoway, Victoria; Gunka, Vit; Abolmaesumi, Purang; Rohling, Robert

    2017-07-01

    Percutaneous spinal needle insertion procedures often require proper identification of the vertebral level to effectively and safely deliver analgesic agents. The current clinical method involves "blind" identification of the vertebral level through manual palpation of the spine, which has only 30% reported accuracy. Therefore, there is a need for better anatomical identification prior to needle insertion. A real-time system was developed to identify the vertebral level from a sequence of ultrasound images, following a clinical imaging protocol. The system uses a deep convolutional neural network (CNN) to classify transverse images of the lower spine. Several existing CNN architectures were implemented, utilizing transfer learning, and compared for adequacy in a real-time system. In the system, the CNN output is processed, using a novel state machine, to automatically identify vertebral levels as the transducer moves up the spine. Additionally, a graphical display was developed and integrated within 3D Slicer. Finally, an augmented reality display, projecting the level onto the patient's back, was also designed. A small feasibility study [Formula: see text] evaluated performance. The proposed CNN successfully discriminates ultrasound images of the sacrum, intervertebral gaps, and vertebral bones, achieving 88% 20-fold cross-validation accuracy. Seventeen of 20 test ultrasound scans had successful identification of all vertebral levels, processed at real-time speed (40 frames/s). A machine learning system is presented that successfully identifies lumbar vertebral levels. The small study on human subjects demonstrated real-time performance. A projection-based augmented reality display was used to show the vertebral level directly on the subject adjacent to the puncture site.

  17. Teaching Number Identification to Students with Severe Disabilities Using Response Cards

    Science.gov (United States)

    Skibo, Holly; Mims, Pamela; Spooner, Fred

    2011-01-01

    Active student responding (ASR) has been shown to be an effective way to improve the mathematical skills of students. One specific method of ASR is the use of response cards. In this study, a system of least prompts combined with response cards was used to increase mathematical knowledge, and number identification, of three elementary students…

  18. Identification and Adjustment of Guide Rail Geometric Errors Based on BP Neural Network

    Science.gov (United States)

    He, Gaiyun; Huang, Can; Guo, Longzhen; Sun, Guangming; Zhang, Dawei

    2017-06-01

    The relative positions between the four slide blocks vary with the movement of the table due to the geometric errors of the guide rail. Consequently, the additional load on the slide blocks is increased. A new method of error measurement and identification by using a self-designed stress test plate was presented. BP neural network model was used to establish the mapping between the stress of key measurement points on the test plate and the displacements of slide blocks. By measuring the stress, the relative displacements of slide blocks were obtained, from which the geometric errors of the guide rails were converted. Firstly, the finite element model was built to find the key measurement points of the test plate. Then the BP neural network was trained by using the samples extracted from the finite element model. The stress at the key measurement points were taken as the input and the relative displacements of the slide blocks were taken as the output. Finally, the geometric errors of the two guide rails were obtained according to the measured stress. The results show that the maximum difference between the measured geometric errors and the output of BP neural network was 5 μm. Therefore, the correctness and feasibility of the method were verified.

  19. Identification of influential nodes in complex networks: Method from spreading probability viewpoint

    Science.gov (United States)

    Bao, Zhong-Kui; Ma, Chuang; Xiang, Bing-Bing; Zhang, Hai-Feng

    2017-02-01

    The problem of identifying influential nodes in complex networks has attracted much attention owing to its wide applications, including how to maximize the information diffusion, boost product promotion in a viral marketing campaign, prevent a large scale epidemic and so on. From spreading viewpoint, the probability of one node propagating its information to one other node is closely related to the shortest distance between them, the number of shortest paths and the transmission rate. However, it is difficult to obtain the values of transmission rates for different cases, to overcome such a difficulty, we use the reciprocal of average degree to approximate the transmission rate. Then a semi-local centrality index is proposed to incorporate the shortest distance, the number of shortest paths and the reciprocal of average degree simultaneously. By implementing simulations in real networks as well as synthetic networks, we verify that our proposed centrality can outperform well-known centralities, such as degree centrality, betweenness centrality, closeness centrality, k-shell centrality, and nonbacktracking centrality. In particular, our findings indicate that the performance of our method is the most significant when the transmission rate nears to the epidemic threshold, which is the most meaningful region for the identification of influential nodes.

  20. Identification of Lactic Acid Bacteria and Propionic Acid Bacteria using FTIR Spectroscopy and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Beata Nalepa

    2012-01-01

    Full Text Available In the present study, lactic acid bacteria and propionic acid bacteria have been identified at the genus level with the use of artificial neural networks (ANNs and Fourier transform infrared spectroscopy (FTIR. Bacterial strains of the genera Lactobacillus, Lactococcus, Leuconostoc, Streptococcus and Propionibacterium were analyzed since they deliver health benefits and are routinely used in the food processing industry. The correctness of bacterial identification by ANNs and FTIR was evaluated at two stages. At first stage, ANNs were tested based on the spectra of 66 reference bacterial strains. At second stage, the evaluation involved 286 spectra of bacterial strains isolated from food products, deposited in our laboratory collection, and identified by genus-specific PCR. ANNs were developed based on the spectra and their first derivatives. The most satisfactory results were reported for the probabilistic neural network, which was built using a combination of W5W4W3 spectral ranges. This network correctly identified the genus of 95 % of the lactic acid bacteria and propionic acid bacteria strains analyzed.

  1. Efficient identification of critical residues based only on protein structure by network analysis.

    Directory of Open Access Journals (Sweden)

    Michael P Cusack

    Full Text Available Despite the increasing number of published protein structures, and the fact that each protein's function relies on its three-dimensional structure, there is limited access to automatic programs used for the identification of critical residues from the protein structure, compared with those based on protein sequence. Here we present a new algorithm based on network analysis applied exclusively on protein structures to identify critical residues. Our results show that this method identifies critical residues for protein function with high reliability and improves automatic sequence-based approaches and previous network-based approaches. The reliability of the method depends on the conformational diversity screened for the protein of interest. We have designed a web site to give access to this software at http://bis.ifc.unam.mx/jamming/. In summary, a new method is presented that relates critical residues for protein function with the most traversed residues in networks derived from protein structures. A unique feature of the method is the inclusion of the conformational diversity of proteins in the prediction, thus reproducing a basic feature of the structure/function relationship of proteins.

  2. [Analysis of different pipe corrosion by ESEM and bacteria identification by API in pilot distribution network].

    Science.gov (United States)

    Wu, Qing; Zhao, Xinhua; Yu, Qing; Li, Jun

    2008-07-01

    To understand the corrosion of different material water supply pipelines and bacterium in drinking water and biofilms. A pilot distribution network was built and water quality detection was made on popular pipelines of galvanized iron pipe, PPR and ABS plastic pipes by ESEM (environmental scanning electron microscopy). Bacterium in drinking water and biofilms were identified by API Bacteria Identification System 10s and 20E (Biomerieux, France), and pathogenicity of bacterium were estimated. Galvanized zinc pipes were seriously corroded; there were thin layers on inner face of PPR and ABS plastic pipes. 10 bacterium (got from water samples) were identified by API10S, in which 7 bacterium were opportunistic pathogens. 21 bacterium (got from water and biofilms samples) were identified by API20E, in which 5 bacterium were pathogens and 11 bacterium were opportunistic pathogens and 5 bacteria were not reported for their pathogenicities to human beings. The bacterial water quality of drinking water distribution networks were not good. Most bacterium in drinking water and biofilms on the inner face of pipeline of the drinking water distribution network were opportunistic pathogens, it could cause serious water supply accident, if bacteria spread in suitable conditions. In the aspect of pipe material, old pipelines should be changed by new material pipes.

  3. EEG signal classification using PSO trained RBF neural network for epilepsy identification

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar Satapathy

    Full Text Available The electroencephalogram (EEG is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT. To classify the EEG signal, we used a radial basis function neural network (RBFNN. As shown herein, the network can be trained to optimize the mean square error (MSE by using a modified particle swarm optimization (PSO algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques. Keywords: Electroencephalography, Radial basis function neural network, Particle swarm optimization, Discrete wavelet transform, Machine learning

  4. Improved Radio Frequency Identification Indoor Localization Method via Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Dongliang Guo

    2014-01-01

    Full Text Available Indoor localization technique has received much attention in recent years. Many techniques have been developed to solve the problem. Among the recent proposed methods, radio frequency identification (RFID indoor localization technology has the advantages of low-cost, noncontact, non-line-of-sight, and high precision. This paper proposed two radial basis function (RBF neural network based indoor localization methods. The RBF neural networks are trained to learn the mapping relationship between received signal strength indication values and position of objects. Traditional method used the received signal strength directly as the input of neural network; we added another input channel by taking the difference of the received signal strength, thus improving the reliability and precision of positioning. Fuzzy clustering is used to determine the center of radial basis function. In order to reduce the impact of signal fading due to non-line-of-sight and multipath transmission in indoor environment, we improved the Gaussian filter to process received signal strength values. The experimental results show that the proposed method outperforms the existing methods as well as improves the reliability and precision of the RFID indoor positioning system.

  5. Identification and Adjustment of Guide Rail Geometric Errors Based on BP Neural Network

    Directory of Open Access Journals (Sweden)

    He Gaiyun

    2017-06-01

    Full Text Available The relative positions between the four slide blocks vary with the movement of the table due to the geometric errors of the guide rail. Consequently, the additional load on the slide blocks is increased. A new method of error measurement and identification by using a self-designed stress test plate was presented. BP neural network model was used to establish the mapping between the stress of key measurement points on the test plate and the displacements of slide blocks. By measuring the stress, the relative displacements of slide blocks were obtained, from which the geometric errors of the guide rails were converted. Firstly, the finite element model was built to find the key measurement points of the test plate. Then the BP neural network was trained by using the samples extracted from the finite element model. The stress at the key measurement points were taken as the input and the relative displacements of the slide blocks were taken as the output. Finally, the geometric errors of the two guide rails were obtained according to the measured stress. The results show that the maximum difference between the measured geometric errors and the output of BP neural network was 5 μm. Therefore, the correctness and feasibility of the method were verified.

  6. Identification and expression analysis of primary auxin-responsive ...

    Indian Academy of Sciences (India)

    2013-12-09

    Dec 9, 2013 ... sion of VvAux/IAA4 in Vitis vinifera was rapidly induced in response to NAA treatment, but was decreased by salt, drought and salicylic acid (SA) treatments which provide evidence of crosstalk between phytohormone and abiotic stresses, and support a role for auxin in stress responses. (Cakir et al. 2013).

  7. A Bayesian Network-Based Approach to Selection of Intervention Points in the Mitogen-Activated Protein Kinase Plant Defense Response Pathway.

    Science.gov (United States)

    Venkat, Priya S; Narayanan, Krishna R; Datta, Aniruddha

    2017-04-01

    An important problem in computational biology is the identification of potential points of intervention that can lead to modified network behavior in a genetic regulatory network. We consider the problem of deducing the effect of individual genes on the behavior of the network in a statistical framework. In this article, we make use of biological information from the literature to develop a Bayesian network and introduce a method to estimate parameters of this network using data that are relevant to the biological phenomena under study. Then, we give a novel approach to select significant nodes in the network using a decision-theoretic approach. The proposed method is applied to the analysis of the mitogen-activated protein kinase pathway in the plant defense response to pathogens. Results from applying the method to experimental data show that the proposed approach is effective in selecting genes that play crucial roles in the biological phenomenon being studied.

  8. An inquiry into the characteristics, applicability and prerequisites of Radio-Frequency Identification (RFID solutions in transport networks and logistics

    Directory of Open Access Journals (Sweden)

    Antoniu Ovidiu Balint

    2013-09-01

    Full Text Available The use of intelligent solution represents the key factor for developing both the logistics sector and the economic environment. This paper analyses the Radio-Frequency Identification (RFID technology and its role in the Supply Chain Management (SCM especially in logistics and transport networks. The main objective is to demonstrate that RFID represents a solution for improving the transport networks and logistics sector by implementing various complex and intelligent solutions that can improve the actual economic environment

  9. Dose response relationship in anti-stress gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Qiang Zhang

    2007-03-01

    Full Text Available To maintain a stable intracellular environment, cells utilize complex and specialized defense systems against a variety of external perturbations, such as electrophilic stress, heat shock, and hypoxia, etc. Irrespective of the type of stress, many adaptive mechanisms contributing to cellular homeostasis appear to operate through gene regulatory networks that are organized into negative feedback loops. In general, the degree of deviation of the controlled variables, such as electrophiles, misfolded proteins, and O2, is first detected by specialized sensor molecules, then the signal is transduced to specific transcription factors. Transcription factors can regulate the expression of a suite of anti-stress genes, many of which encode enzymes functioning to counteract the perturbed variables. The objective of this study was to explore, using control theory and computational approaches, the theoretical basis that underlies the steady-state dose response relationship between cellular stressors and intracellular biochemical species (controlled variables, transcription factors, and gene products in these gene regulatory networks. Our work indicated that the shape of dose response curves (linear, superlinear, or sublinear depends on changes in the specific values of local response coefficients (gains distributed in the feedback loop. Multimerization of anti-stress enzymes and transcription factors into homodimers, homotrimers, or even higher-order multimers, play a significant role in maintaining robust homeostasis. Moreover, our simulation noted that dose response curves for the controlled variables can transition sequentially through four distinct phases as stressor level increases: initial superlinear with lesser control, superlinear more highly controlled, linear uncontrolled, and sublinear catastrophic. Each phase relies on specific gain-changing events that come into play as stressor level increases. The low-dose region is intrinsically nonlinear

  10. A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms

    Directory of Open Access Journals (Sweden)

    Sajad Sabzi

    2018-03-01

    Full Text Available Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely studied. This paper presents a novel approach to automatic fruit identification applied to three common varieties of oranges (Citrus sinensis L., namely Bam, Payvandi and Thomson. A total of 300 color images were used for the experiments, 100 samples for each orange variety, which are publicly available. After segmentation, 263 parameters, including texture, color and shape features, were extracted from each sample using image processing. Among them, the 6 most effective features were automatically selected by using a hybrid approach consisting of an artificial neural network and particle swarm optimization algorithm (ANN-PSO. Then, three different classifiers were applied and compared: hybrid artificial neural network – artificial bee colony (ANN-ABC; hybrid artificial neural network – harmony search (ANN-HS; and k-nearest neighbors (kNN. The experimental results show that the hybrid approaches outperform the results of kNN. The average correct classification rate of ANN-HS was 94.28%, while ANN-ABS achieved 96.70% accuracy with the available data, contrasting with the 70.9% baseline accuracy of kNN. Thus, this new proposed methodology provides a fast and accurate way to classify multiple fruits varieties, which can be easily implemented in processing factories. The main contribution of this work is that the method can be directly adapted to other use cases, since the selection of the optimal features and the configuration of the neural network are performed automatically using metaheuristic algorithms.

  11. A Robust Speaker Identification System Using the Responses from a Model of the Auditory Periphery.

    Science.gov (United States)

    Islam, Md Atiqul; Jassim, Wissam A; Cheok, Ng Siew; Zilany, Muhammad Shamsul Arefeen

    2016-01-01

    Speaker identification under noisy conditions is one of the challenging topics in the field of speech processing applications. Motivated by the fact that the neural responses are robust against noise, this paper proposes a new speaker identification system using 2-D neurograms constructed from the responses of a physiologically-based computational model of the auditory periphery. The responses of auditory-nerve fibers for a wide range of characteristic frequency were simulated to speech signals to construct neurograms. The neurogram coefficients were trained using the well-known Gaussian mixture model-universal background model classification technique to generate an identity model for each speaker. In this study, three text-independent and one text-dependent speaker databases were employed to test the identification performance of the proposed method. Also, the robustness of the proposed method was investigated using speech signals distorted by three types of noise such as the white Gaussian, pink, and street noises with different signal-to-noise ratios. The identification results of the proposed neural-response-based method were compared to the performances of the traditional speaker identification methods using features such as the Mel-frequency cepstral coefficients, Gamma-tone frequency cepstral coefficients and frequency domain linear prediction. Although the classification accuracy achieved by the proposed method was comparable to the performance of those traditional techniques in quiet, the new feature was found to provide lower error rates of classification under noisy environments.

  12. A Robust Speaker Identification System Using the Responses from a Model of the Auditory Periphery.

    Directory of Open Access Journals (Sweden)

    Md Atiqul Islam

    Full Text Available Speaker identification under noisy conditions is one of the challenging topics in the field of speech processing applications. Motivated by the fact that the neural responses are robust against noise, this paper proposes a new speaker identification system using 2-D neurograms constructed from the responses of a physiologically-based computational model of the auditory periphery. The responses of auditory-nerve fibers for a wide range of characteristic frequency were simulated to speech signals to construct neurograms. The neurogram coefficients were trained using the well-known Gaussian mixture model-universal background model classification technique to generate an identity model for each speaker. In this study, three text-independent and one text-dependent speaker databases were employed to test the identification performance of the proposed method. Also, the robustness of the proposed method was investigated using speech signals distorted by three types of noise such as the white Gaussian, pink, and street noises with different signal-to-noise ratios. The identification results of the proposed neural-response-based method were compared to the performances of the traditional speaker identification methods using features such as the Mel-frequency cepstral coefficients, Gamma-tone frequency cepstral coefficients and frequency domain linear prediction. Although the classification accuracy achieved by the proposed method was comparable to the performance of those traditional techniques in quiet, the new feature was found to provide lower error rates of classification under noisy environments.

  13. A Robust Speaker Identification System Using the Responses from a Model of the Auditory Periphery

    Science.gov (United States)

    Islam, Md. Atiqul; Jassim, Wissam A.; Cheok, Ng Siew; Zilany, Muhammad Shamsul Arefeen

    2016-01-01

    Speaker identification under noisy conditions is one of the challenging topics in the field of speech processing applications. Motivated by the fact that the neural responses are robust against noise, this paper proposes a new speaker identification system using 2-D neurograms constructed from the responses of a physiologically-based computational model of the auditory periphery. The responses of auditory-nerve fibers for a wide range of characteristic frequency were simulated to speech signals to construct neurograms. The neurogram coefficients were trained using the well-known Gaussian mixture model-universal background model classification technique to generate an identity model for each speaker. In this study, three text-independent and one text-dependent speaker databases were employed to test the identification performance of the proposed method. Also, the robustness of the proposed method was investigated using speech signals distorted by three types of noise such as the white Gaussian, pink, and street noises with different signal-to-noise ratios. The identification results of the proposed neural-response-based method were compared to the performances of the traditional speaker identification methods using features such as the Mel-frequency cepstral coefficients, Gamma-tone frequency cepstral coefficients and frequency domain linear prediction. Although the classification accuracy achieved by the proposed method was comparable to the performance of those traditional techniques in quiet, the new feature was found to provide lower error rates of classification under noisy environments. PMID:27392046

  14. Image understanding algorithms for segmentation evaluation and region-of-interest identification using Bayesian networks

    Science.gov (United States)

    Jaber, Mustafa; Saber, Eli

    2011-06-01

    A two-fold image understanding algorithm based on Bayesian networks is introduced. The methodology has modules for image segmentation evaluation and region of interest (ROI) identification. The former uses a set of segmentation maps (SMs) of a target image to identify the optimal one. These SMs could be generated from the same segmentation algorithm at different thresholds or from different segmentation techniques. Global and regional low-level image features are extracted from the optimal SM and used along with the original image to identify the ROI. The proposed algorithm was tested on a set of 4000 color images that are publicly available and compared favorably to the state-of-the-art techniques. Applications of the proposed framework include image compression, image summarization, mobile phone imagery, digital photo cropping, and image thumb-nailing.

  15. Identification of Jets Containing $b$-Hadrons with Recurrent Neural Networks at the ATLAS Experiment

    CERN Document Server

    The ATLAS collaboration

    2017-01-01

    A novel $b$-jet identification algorithm is constructed with a Recurrent Neural Network (RNN) at the ATLAS experiment at the CERN Large Hadron Collider. The RNN based $b$-tagging algorithm processes charged particle tracks associated to jets without reliance on secondary vertex finding, and can augment existing secondary-vertex based taggers. In contrast to traditional impact-parameter-based $b$-tagging algorithms which assume that tracks associated to jets are independent from each other, the RNN based $b$-tagging algorithm can exploit the spatial and kinematic correlations between tracks which are initiated from the same $b$-hadrons. This new approach also accommodates an extended set of input variables. This note presents the expected performance of the RNN based $b$-tagging algorithm in simulated $t \\bar t$ events at $\\sqrt{s}=13$ TeV.

  16. Non-invasive on-line two-phase flow regime identification employing artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Tambouratzis, T. [Department of Industrial Management and Technology, University of Piraeus, 107 Deligiorgi St., Piraeus 185 34 (Greece)], E-mail: tatianatambouratzis@gmail.com; Pazsit, I. [Department of Reactor Physics, Chalmers University of Technology, SE-412 Goteborg (Sweden)

    2009-05-01

    A novel non-invasive approach to the on-line identification of BWR two-phase flow regimes is investigated. The proposed approach receives neutron radiography images of coolant flow recordings as its input and performs feature extraction on each image via simple and directly computable statistical operators. The extracted features are subsequently used as inputs to an ensemble of self-organizing maps whose outputs demonstrate swift and accurate classification of each image into its corresponding flow regime. The novelty of the approach lies in the use of the self-organizing map which generates the different classes by itself, according to feature similarity of the corresponding images; this contrasts traditional artificial neural networks where the user has to define both the number of distinct classes as well as to supply separate training vectors for each class.

  17. Preliminary Results of Ocular Artefacts Identification in EEC Series by Neural Network

    Directory of Open Access Journals (Sweden)

    M. Kofronova

    1996-06-01

    Full Text Available The human electroencephalogram (EEG, is record of the electrical activity of the brain and contains useful diagnostic information on a variety of neurological disorders. Normal EEG signal are usually registered from electrodes placed on the scalp, and are often very small in amplitude, of 20 µV. The EEG, like all biomedical signals, is very susceptible to a variety of large signal contamination or artefacts (signals of other than brain activity which reduce its clinical usefulness. For example, blinking or moving eyes produces large electrical potentials around the eyes called the electrooculogram (EOG. The EOG spreads across the scalp to contaminate the EEG, when it is referred to as an ocular artefact (OA. This paper includes method of identification portion of the EEG record where ocular artefact appears and classification its type by neural network.

  18. Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks.

    Science.gov (United States)

    Kalender Atak, Zeynep; Imrichova, Hana; Svetlichnyy, Dmitry; Hulselmans, Gert; Christiaens, Valerie; Reumers, Joke; Ceulemans, Hugo; Aerts, Stein

    2017-08-30

    The identification of functional non-coding mutations is a key challenge in the field of genomics. Here we introduce μ-cisTarget to filter, annotate and prioritize cis-regulatory mutations based on their putative effect on the underlying "personal" gene regulatory network. We validated μ-cisTarget by re-analyzing the TAL1 and LMO1 enhancer mutations in T-ALL, and the TERT promoter mutation in melanoma. Next, we re-sequenced the full genomes of ten cancer cell lines and used matched transcriptome data and motif discovery to identify master regulators with de novo binding sites that result in the up-regulation of nearby oncogenic drivers. μ-cisTarget is available from http://mucistarget.aertslab.org .

  19. Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks.

    Directory of Open Access Journals (Sweden)

    Vinayak S Joshi

    Full Text Available The separation of the retinal vessel network into distinct arterial and venous vessel trees is of high interest. We propose an automated method for identification and separation of retinal vessel trees in a retinal color image by converting a vessel segmentation image into a vessel segment map and identifying the individual vessel trees by graph search. Orientation, width, and intensity of each vessel segment are utilized to find the optimal graph of vessel segments. The separated vessel trees are labeled as primary vessel or branches. We utilize the separated vessel trees for arterial-venous (AV classification, based on the color properties of the vessels in each tree graph. We applied our approach to a dataset of 50 fundus images from 50 subjects. The proposed method resulted in an accuracy of 91.44% correctly classified vessel pixels as either artery or vein. The accuracy of correctly classified major vessel segments was 96.42%.

  20. Identification of drought-responsive universal stress proteins in viridiplantae.

    Science.gov (United States)

    Isokpehi, Raphael D; Simmons, Shaneka S; Cohly, Hari H P; Ekunwe, Stephen I N; Begonia, Gregorio B; Ayensu, Wellington K

    2011-02-07

    Genes encoding proteins that contain the universal stress protein (USP) domain are known to provide bacteria, archaea, fungi, protozoa, and plants with the ability to respond to a plethora of environmental stresses. Specifically in plants, drought tolerance is a desirable phenotype. However, limited focused and organized functional genomic datasets exist on drought-responsive plant USP genes to facilitate their characterization. The overall objective of the investigation was to identify diverse plant universal stress proteins and Expressed Sequence Tags (ESTs) responsive to water-deficit stress. We hypothesize that cross-database mining of functional annotations in protein and gene transcript bioinformatics resources would help identify candidate drought-responsive universal stress proteins and transcripts from multiple plant species. Our bioinformatics approach retrieved, mined and integrated comprehensive functional annotation data on 511 protein and 1561 ESTs sequences from 161 viridiplantae taxa. A total of 32 drought-responsive ESTs from 7 plant genera Glycine, Hordeum, Manihot, Medicago, Oryza, Pinus and Triticum were identified. Two Arabidopsis USP genes At3g62550 and At3g53990 that encode ATP-binding motif were up-regulated in a drought microarray dataset. Further, a dataset of 80 simple sequence repeats (SSRs) linked to 20 singletons and 47 transcript assembles was constructed. Integrating the datasets on SSRs and drought-responsive ESTs identified three drought-responsive ESTs from bread wheat (BE604157), soybean (BM887317) and maritime pine (BX682209). The SSR sequence types were CAG, ATA and AT respectively. The datasets from cross-database mining provide organized resources for the characterization of USP genes as useful targets for engineering plant varieties tolerant to unfavorable environmental conditions.

  1. Hysteresis Nonlinearity Identification Using New Preisach Model-Based Artificial Neural Network Approach

    Directory of Open Access Journals (Sweden)

    Mohammad Reza Zakerzadeh

    2011-01-01

    Full Text Available Preisach model is a well-known hysteresis identification method in which the hysteresis is modeled by linear combination of hysteresis operators. Although Preisach model describes the main features of system with hysteresis behavior, due to its rigorous numerical nature, it is not convenient to use in real-time control applications. Here a novel neural network approach based on the Preisach model is addressed, provides accurate hysteresis nonlinearity modeling in comparison with the classical Preisach model and can be used for many applications such as hysteresis nonlinearity control and identification in SMA and Piezo actuators and performance evaluation in some physical systems such as magnetic materials. To evaluate the proposed approach, an experimental apparatus consisting one-dimensional flexible aluminum beam actuated with an SMA wire is used. It is shown that the proposed ANN-based Preisach model can identify hysteresis nonlinearity more accurately than the classical one. It also has powerful ability to precisely predict the higher-order hysteresis minor loops behavior even though only the first-order reversal data are in use. It is also shown that to get the same precise results in the classical Preisach model, many more data should be used, and this directly increases the experimental cost.

  2. Network-Based Identification of Adaptive Pathways in Evolved Ethanol-Tolerant Bacterial Populations.

    Science.gov (United States)

    Swings, Toon; Weytjens, Bram; Schalck, Thomas; Bonte, Camille; Verstraeten, Natalie; Michiels, Jan; Marchal, Kathleen

    2017-11-01

    Efficient production of ethanol for use as a renewable fuel requires organisms with a high level of ethanol tolerance. However, this trait is complex and increased tolerance therefore requires mutations in multiple genes and pathways. Here, we use experimental evolution for a system-level analysis of adaptation of Escherichia coli to high ethanol stress. As adaptation to extreme stress often results in complex mutational data sets consisting of both causal and noncausal passenger mutations, identifying the true adaptive mutations in these settings is not trivial. Therefore, we developed a novel method named IAMBEE (Identification of Adaptive Mutations in Bacterial Evolution Experiments). IAMBEE exploits the temporal profile of the acquisition of mutations during evolution in combination with the functional implications of each mutation at the protein level. These data are mapped to a genome-wide interaction network to search for adaptive mutations at the level of pathways. The 16 evolved populations in our data set together harbored 2,286 mutated genes with 4,470 unique mutations. Analysis by IAMBEE significantly reduced this number and resulted in identification of 90 mutated genes and 345 unique mutations that are most likely to be adaptive. Moreover, IAMBEE not only enabled the identification of previously known pathways involved in ethanol tolerance, but also identified novel systems such as the AcrAB-TolC efflux pump and fatty acids biosynthesis and even allowed to gain insight into the temporal profile of adaptation to ethanol stress. Furthermore, this method offers a solid framework for identifying the molecular underpinnings of other complex traits as well. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  3. Identification of a CO2 responsive regulon in Bordetella.

    Science.gov (United States)

    Hester, Sara E; Lui, Minghsun; Nicholson, Tracy; Nowacki, Daryl; Harvill, Eric T

    2012-01-01

    Sensing the environment allows pathogenic bacteria to coordinately regulate gene expression to maximize survival within or outside of a host. Here we show that Bordetella species regulate virulence factor expression in response to carbon dioxide levels that mimic in vivo conditions within the respiratory tract. We found strains of Bordetella bronchiseptica that did not produce adenylate cyclase toxin (ACT) when grown in liquid or solid media with ambient air aeration, but produced ACT and additional antigens when grown in air supplemented to 5% CO(2). Transcriptome analysis and quantitative real time-PCR analysis revealed that strain 761, as well as strain RB50, increased transcription of genes encoding ACT, filamentous hemagglutinin (FHA), pertactin, fimbriae and the type III secretion system in 5% CO(2) conditions, relative to ambient air. Furthermore, transcription of cyaA and fhaB in response to 5% CO(2) was increased even in the absence of BvgS. In vitro analysis also revealed increases in cytotoxicity and adherence when strains were grown in 5% CO(2). The human pathogens B. pertussis and B. parapertussis also increased transcription of several virulence factors when grown in 5% CO(2), indicating that this response is conserved among the classical bordetellae. Together, our data indicate that Bordetella species can sense and respond to physiologically relevant changes in CO(2) concentrations by regulating virulence factors important for colonization, persistence and evasion of the host immune response.

  4. Identification of a novel submergence response gene regulated by ...

    African Journals Online (AJOL)

    Tuoyo Aghomotsegin

    2016-12-07

    Dec 7, 2016 ... 3Engineering Research Center of Ecology and Agricultural Use of Wetland, Ministry of Education, Yangzte University,. Jingzhou 434025, P.R. .... intolerance to submergence) and M202(Sub1A) by qRT-. PCR. We identified a novel gene responsive to submergence, called RS1. The expression patterns of.

  5. Identification of a CO2 responsive regulon in Bordetella.

    Directory of Open Access Journals (Sweden)

    Sara E Hester

    Full Text Available Sensing the environment allows pathogenic bacteria to coordinately regulate gene expression to maximize survival within or outside of a host. Here we show that Bordetella species regulate virulence factor expression in response to carbon dioxide levels that mimic in vivo conditions within the respiratory tract. We found strains of Bordetella bronchiseptica that did not produce adenylate cyclase toxin (ACT when grown in liquid or solid media with ambient air aeration, but produced ACT and additional antigens when grown in air supplemented to 5% CO(2. Transcriptome analysis and quantitative real time-PCR analysis revealed that strain 761, as well as strain RB50, increased transcription of genes encoding ACT, filamentous hemagglutinin (FHA, pertactin, fimbriae and the type III secretion system in 5% CO(2 conditions, relative to ambient air. Furthermore, transcription of cyaA and fhaB in response to 5% CO(2 was increased even in the absence of BvgS. In vitro analysis also revealed increases in cytotoxicity and adherence when strains were grown in 5% CO(2. The human pathogens B. pertussis and B. parapertussis also increased transcription of several virulence factors when grown in 5% CO(2, indicating that this response is conserved among the classical bordetellae. Together, our data indicate that Bordetella species can sense and respond to physiologically relevant changes in CO(2 concentrations by regulating virulence factors important for colonization, persistence and evasion of the host immune response.

  6. Identification of vernalization responsive genes in the winter wheat ...

    Indian Academy of Sciences (India)

    of Agricultural Sciences, Beijing 100081, People's Republic of China. 7China Agricultural University, Beijing 100083, People's Republic of China. Abstract. This study aimed to identify vernalization responsive genes in the winter wheat cultivar Jing841 by comparing the transcrip- tome data with that of a spring wheat cultivar ...

  7. Lineup identification accuracy: The effects of alcohol, target presence, confidence ratings, and response time

    Directory of Open Access Journals (Sweden)

    Wendy Kneller

    2016-01-01

    Full Text Available Despite the intoxication of many eyewitnesses at crime scenes, only four published studies to date have investigated the effects of alcohol intoxication on eyewitness identification performance. While one found intoxication significantly increased false identification rates from target absent showups, three found no such effect using the more traditional lineup procedure. The present study sought to further explore the effects of alcohol intoxication on identification performance and examine whether accurate decisions from intoxicated witnesses could be postdicted by confidence and response times. One hundred and twenty participants engaged in a study examining the effects of intoxication (control, placebo, and mild intoxication and target presence on identification performance. Participants viewed a simultaneous lineup one week after watching a mock crime video of a man attempting to steal cars. Ethanol intoxication (0.6 ml/kg was found to make no significant difference to identification accuracy and such identifications from intoxicated individuals were made no less confidently or slowly than those from sober witnesses. These results are discussed with respect to the previous research examining intoxicated witness identification accuracy and the misconceptions the criminal justice system holds about the accuracy of such witnesses.

  8. MYCONET : European network of information sources for an identification system of emerging mycotoxins in wheat based supply chains

    NARCIS (Netherlands)

    Fels-Klerx, van der H.J.; Booij, C.J.H.

    2008-01-01

    This report describes the results of the MYCONET project, an international research project aimed at initiating a sustainable platform (network) of information sources that proactively provides specified information for an emerging risk identification system. As a case study, the project focused on

  9. Identification of gene networks and pathways associated with Guillain-Barre syndrome.

    Directory of Open Access Journals (Sweden)

    Kuo-Hsuan Chang

    Full Text Available BACKGROUND: The underlying change of gene network expression of Guillain-Barré syndrome (GBS remains elusive. We sought to identify GBS-associated gene networks and signaling pathways by analyzing the transcriptional profile of leukocytes in the patients with GBS. METHODS AND FINDINGS: Quantitative global gene expression microarray analysis of peripheral blood leukocytes was performed on 7 patients with GBS and 7 healthy controls. Gene expression profiles were compared between patients and controls after standardization. The set of genes that significantly correlated with GBS was further analyzed by Ingenuity Pathways Analyses. 256 genes and 18 gene networks were significantly associated with GBS (fold change ≥2, P<0.05. FOS, PTGS2, HMGB2 and MMP9 are the top four of 246 significantly up-regulated genes. The most significant disease and altered biological function genes associated with GBS were those involved in inflammatory response, infectious disease, and respiratory disease. Cell death, cellular development and cellular movement were the top significant molecular and cellular functions involved in GBS. Hematological system development and function, immune cell trafficking and organismal survival were the most significant GBS-associated function in physiological development and system category. Several hub genes, such as MMP9, PTGS2 and CREB1 were identified in the associated gene networks. Canonical pathway analysis showed that GnRH, corticotrophin-releasing hormone and ERK/MAPK signaling were the most significant pathways in the up-regulated gene set in GBS. CONCLUSIONS: This study reveals the gene networks and canonical pathways associated with GBS. These data provide not only networks between the genes for understanding the pathogenic properties of GBS but also map significant pathways for the future development of novel therapeutic strategies.

  10. Customising the therapeutic response of signalling networks to promote antitumor responses by drug combinations

    Directory of Open Access Journals (Sweden)

    Alexey eGoltsov

    2014-02-01

    Full Text Available Drug resistance, de novo and acquired, pervades cellular signalling networks from one signalling motif to another as a result of cancer progression and/or drug intervention. This resistance is one of the key determinants of efficacy in targeted anticancer drug therapy. Although poorly understood, drug resistance is already being addressed in combination therapy by selecting drug targets where sensitivity increases due to combination components or as a result of de novo or acquired mutations. Additionally, successive drug combinations have shown low resistance potency. To promote a rational, systematic development of combination therapies, it is necessary to establish the underlying mechanisms that drive the advantages of drug combinations and design methods to determine advanced targets for drug combination therapy. Based on a joint systems analysis of cellular signalling network (SN response and its sensitivity to drug action and oncogenic mutations, we describe an in silico method to analyse the targets of drug combinations. The method explores mechanisms of sensitizing the SN through combination of two drugs targeting vertical signalling pathways. We propose a paradigm of SN response customization by one drug to both maximize the effect of another drug in combination and promote a robust therapeutic response against oncogenic mutations. The method was applied to the customization of the response of the ErbB/PI3K/PTEN/AKT pathway by combination of drugs targeting HER2 receptors and proteins in the downstream pathway. The results of a computational experiment showed that the modification of the SN response from hyperbolic to smooth sigmoid response by manipulation of two drugs in combination leads to greater robustness in therapeutic response against oncogenic mutations determining cancer heterogeneity. The application of this method in drug combination co-development suggests a combined evaluation of inhibition effects along with the

  11. The Motive for Support and the Identification of Responsive Partners.

    Science.gov (United States)

    Turan, Bulent; Horowitz, Leonard M

    2010-06-01

    To obtain support from others, a person must first identify responsive partners. One strategy for doing so is to use indicators of responsive partners. We argue that a person with a strong motive for support should rate all indicators highly useful-the "Elevated Motives Effect." Study 1 confirmed this hypothesis by correlating participants' total ratings with existing measures of motive-strength. Study 2 applied the Elevated Motives Effect to demonstrate that motive-strength (in interaction with knowledge of indicators) predicts performance on a laboratory task in which participants evaluated a person: Superior knowledge led to superior performance only when motive-strength was high. Study 3, an experience-sampling study, showed that in everyday life, motivated people more often seek support from others when distressed.

  12. Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method

    Science.gov (United States)

    Al-garadi, Mohammed Ali; Varathan, Kasturi Dewi; Ravana, Sri Devi

    2017-02-01

    Online social networks (OSNs) have become a vital part of everyday living. OSNs provide researchers and scientists with unique prospects to comprehend individuals on a scale and to analyze human behavioral patterns. Influential spreaders identification is an important subject in understanding the dynamics of information diffusion in OSNs. Targeting these influential spreaders is significant in planning the techniques for accelerating the propagation of information that is useful for various applications, such as viral marketing applications or blocking the diffusion of annoying information (spreading of viruses, rumors, online negative behaviors, and cyberbullying). Existing K-core decomposition methods consider links equally when calculating the influential spreaders for unweighted networks. Alternatively, the proposed link weights are based only on the degree of nodes. Thus, if a node is linked to high-degree nodes, then this node will receive high weight and is treated as an important node. Conversely, the degree of nodes in OSN context does not always provide accurate influence of users. In the present study, we improve the K-core method for OSNs by proposing a novel link-weighting method based on the interaction among users. The proposed method is based on the observation that the interaction of users is a significant factor in quantifying the spreading capability of user in OSNs. The tracking of diffusion links in the real spreading dynamics of information verifies the effectiveness of our proposed method for identifying influential spreaders in OSNs as compared with degree centrality, PageRank, and original K-core.

  13. Computerized Liquid Crystal Phase Identification by Neural Networks Analysis of Polarizing Microscopy Textures

    Science.gov (United States)

    Karaszi, Zoltan; Konya, Andrew; Dragan, Feodor; Jakli, Antal; CPIP/LCI; CS Dept. of Kent State University Collaboration

    Polarizing optical microscopy (POM) is traditionally the best-established method of studying liquid crystals, and using POM started already with Otto Lehman in 1890. An expert, who is familiar with the science of optics of anisotropic materials and typical textures of liquid crystals, can identify phases with relatively large confidence. However, for unambiguous identification usually other expensive and time-consuming experiments are needed. Replacement of the subjective and qualitative human eye-based liquid crystal texture analysis with quantitative computerized image analysis technique started only recently and were used to enhance the detection of smooth phase transitions, determine order parameter and birefringence of specific liquid crystal phases. We investigate if the computer can recognize and name the phase where the texture was taken. To judge the potential of reliable image recognition based on this procedure, we used 871 images of liquid crystal textures belonging to five main categories: Nematic, Smectic A, Smectic C, Cholesteric and Crystal, and used a Neural Network Clustering Technique included in the data mining software package in Java ``WEKA''. A neural network trained on a set of 827 LC textures classified the remaining 44 textures with 80% accuracy.

  14. GPS on Every Roof, GPS Sensor Network for Post-Seismic Building-Wise Damage Identification

    Directory of Open Access Journals (Sweden)

    Kenji Oguni

    2013-12-01

    Full Text Available Development of wireless sensor network equipped with GPS for post-seismic building-wise damage identification is presented in this paper. This system is called GPS on Every Roof. Sensor node equipped with GPS antenna and receiver is installed on the top of the roof of each and every building. The position of this sensor node is measured before and after earthquake. The final goal of this system is to i identify the displacement of the roof of each house and ii collect the information of displacement of the roof of the houses through wireless communication. Superposing this information on GIS, building-wise damage distribution due to earthquake can be obtained. The system overview, hardware and some of the key components of the system such as on-board GPS relative positioning algorithm to achieve the accuracy in the order of several centimeters are described in detail. Also, the results from a field experiment using a wireless sensor network with 39 sensor nodes are presented.

  15. Network of wireless gamma ray sensors for radiological detection and identification

    Science.gov (United States)

    Barzilov, A.; Womble, P.; Novikov, I.; Paschal, J.; Board, J.; Moss, K.

    2007-04-01

    The paper describes the design and development of a network of wireless gamma-ray sensors based on cell phone or WiFi technology. The system is intended for gamma-ray detection and automatic identification of radioactive isotopes and nuclear materials. The sensor is a gamma-ray spectrometer that uses wireless technology to distribute the results. A small-size sensor module contains a scintillation detector along with a small size data acquisition system, PDA, battery, and WiFi radio or a cell phone modem. The PDA with data acquisition and analysis software analyzes the accumulated spectrum on real-time basis and returns results to the screen reporting the isotopic composition and intensity of detected radiation source. The system has been programmed to mitigate false alarms from medical isotopes and naturally occurring radioactive materials. The decision-making software can be "trained" to indicate specific signatures of radiation sources like special nuclear materials. The sensor is supplied with GPS tracker coupling radiological information with geographical coordinates. The sensor is designed for easy use and rapid deployment in common wireless networks.

  16. A network-based method for the identification of putative genes related to infertility.

    Science.gov (United States)

    Wang, ShaoPeng; Huang, GuoHua; Hu, Qinghua; Zou, Quan

    2016-11-01

    Infertility has become one of the major health problems worldwide, with its incidence having risen markedly in recent decades. There is an urgent need to investigate the pathological mechanisms behind infertility and to design effective treatments. However, this is made difficult by the fact that various biological factors have been identified to be related to infertility, including genetic factors. A network-based method was established to identify new genes potentially related to infertility. A network constructed using human protein-protein interactions based on previously validated infertility-related genes enabled the identification of some novel candidate genes. These genes were then filtered by a permutation test and their functional and structural associations with infertility-related genes. Our method identified 23 novel genes, which have strong functional and structural associations with previously validated infertility-related genes. Substantial evidence indicates that the identified genes are strongly related to dysfunction of the four main biological processes of fertility: reproductive development and physiology, gametogenesis, meiosis and recombination, and hormone regulation. The newly discovered genes may provide new directions for investigating infertility. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. Tooth labeling in cone-beam CT using deep convolutional neural network for forensic identification

    Science.gov (United States)

    Miki, Yuma; Muramatsu, Chisako; Hayashi, Tatsuro; Zhou, Xiangrong; Hara, Takeshi; Katsumata, Akitoshi; Fujita, Hiroshi

    2017-03-01

    In large disasters, dental record plays an important role in forensic identification. However, filing dental charts for corpses is not an easy task for general dentists. Moreover, it is laborious and time-consuming work in cases of large scale disasters. We have been investigating a tooth labeling method on dental cone-beam CT images for the purpose of automatic filing of dental charts. In our method, individual tooth in CT images are detected and classified into seven tooth types using deep convolutional neural network. We employed the fully convolutional network using AlexNet architecture for detecting each tooth and applied our previous method using regular AlexNet for classifying the detected teeth into 7 tooth types. From 52 CT volumes obtained by two imaging systems, five images each were randomly selected as test data, and the remaining 42 cases were used as training data. The result showed the tooth detection accuracy of 77.4% with the average false detection of 5.8 per image. The result indicates the potential utility of the proposed method for automatic recording of dental information.

  18. Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

    Directory of Open Access Journals (Sweden)

    Leandro L. S. Linhares

    2015-01-01

    Full Text Available Recent researches have demonstrated that the Fuzzy Wavelet Neural Networks (FWNNs are an efficient tool to identify nonlinear systems. In these structures, features related to fuzzy logic, wavelet functions, and neural networks are combined in an architecture similar to the Adaptive Neurofuzzy Inference Systems (ANFIS. In practical applications, the experimental data set used in the identification task often contains unknown noise and outliers, which decrease the FWNN model reliability. In order to reduce the negative effects of these erroneous measurements, this work proposes the direct use of a similarity measure based on information theory in the FWNN learning procedure. The Mean Squared Error (MSE cost function is replaced by the Maximum Correntropy Criterion (MCC in the traditional error backpropagation (BP algorithm. The input-output maps of a real nonlinear system studied in this work are identified from an experimental data set corrupted by different outliers rates and additive white Gaussian noise. The results demonstrate the advantages of the proposed cost function using the MCC as compared to the MSE. This work also investigates the influence of the kernel size on the performance of the MCC in the BP algorithm, since it is the only free parameter of correntropy.

  19. Fuzzy Chance-constrained Programming Based Security Information Optimization for Low Probability of Identification Enhancement in Radar Network Systems

    Directory of Open Access Journals (Sweden)

    C. G. Shi

    2015-04-01

    Full Text Available In this paper, the problem of low probability of identification (LPID improvement for radar network systems is investigated. Firstly, the security information is derived to evaluate the LPID performance for radar network. Then, without any prior knowledge of hostile intercept receiver, a novel fuzzy chance-constrained programming (FCCP based security information optimization scheme is presented to achieve enhanced LPID performance in radar network systems, which focuses on minimizing the achievable mutual information (MI at interceptor, while the attainable MI outage probability at radar network is enforced to be greater than a specified confidence level. Regarding to the complexity and uncertainty of electromagnetic environment in the modern battlefield, the trapezoidal fuzzy number is used to describe the threshold of achievable MI at radar network based on the credibility theory. Finally, the FCCP model is transformed to a crisp equivalent form with the property of trapezoidal fuzzy number. Numerical simulation results demonstrating the performance of the proposed strategy are provided.

  20. United Complex Centrality for Identification of Essential Proteins from PPI Networks.

    Science.gov (United States)

    Li, Min; Lu, Yu; Niu, Zhibei; Wu, Fang-Xiang

    2017-01-01

    Essential proteins are indispensable for the survival or reproduction of an organism. Identification of essential proteins is not only necessary for the understanding of the minimal requirements for cellular life, but also important for the disease study and drug design. With the development of high-throughput techniques, a large number of protein-protein interaction data are available, which promotes the studies of essential proteins from the network level. Up to now, though a series of computational methods have been proposed, the prediction precision still needs to be improved. In this paper, we propose a new method, United complex Centrality (UC), to identify essential proteins by integrating the protein complexes with the topological features of protein-protein interaction (PPI) networks. By analyzing the relationship between the essential proteins and the known protein complexes of S. cerevisiae and human, we find that the proteins in complexes are more likely to be essential compared with the proteins not included in any complexes and the proteins appeared in multiple complexes are more inclined to be essential compared to those only appeared in a single complex. Considering that some protein complexes generated by computational methods are inaccurate, we also provide a modified version of UC with parameter alpha, named UC-P. The experimental results show that protein complex information can help identify the essential proteins more accurate both for the PPI network of S. cerevisiae and that of human. The proposed method UC performs obviously better than the eight previously proposed methods (DC, IC, EC, SC, BC, CC, NC, and LAC) for identifying essential proteins.

  1. Identification and characterisation of an iron-responsive candidate probiotic.

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    Jennifer R Bailey

    Full Text Available BACKGROUND: Iron is an essential cofactor in almost all biological systems. The lactic acid bacteria (LAB, frequently employed as probiotics, are unusual in having little or no requirement for iron. Iron in the human body is sequestered by transferrins and lactoferrin, limiting bacterial growth. An increase in the availability of iron in the intestine by bleeding, surgery, or under stress leads to an increase in the growth and virulence of many pathogens. Under these high iron conditions, LAB are rapidly out-competed; for the levels of probiotic bacteria to be maintained under high iron conditions they must be able to respond by increasing growth rate to compete with the normal flora. Despite this, iron-responsive genera are poorly characterised as probiotics. METHODOLOGY/PRINCIPAL FINDINGS: Here, we show that a panel of probiotics are not able to respond to increased iron availability, and identify an isolate of Streptococcus thermophilus that can increase growth rate in response to increased iron availability. The isolate of S. thermophilus selected was able to reduce epithelial cell death as well as NF-κB signalling and IL-8 production triggered by pathogens. It was capable of crossing an epithelial cell barrier in conjunction with E. coli and downregulating Th1 and Th17 responses in primary human intestinal leukocytes. CONCLUSIONS/SIGNIFICANCE: We propose that an inability to compete with potential pathogens under conditions of high iron availability such as stress and trauma may contribute to the lack of efficacy of many LAB-based probiotics in treating disease. Therefore, we offer an alternative paradigm which considers that probiotics should be able to be competitive during periods of intestinal bleeding, trauma or stress.

  2. Identification of pulse echo impulse responses for multi source transmission

    DEFF Research Database (Denmark)

    Gran, Fredrik; Jensen, Jørgen Arendt

    2004-01-01

    is a mixture of the information corresponding to several transmitters. There is, thus, no direct way of determining which information corresponds to which transmitter, preventing proper focusing. In this paper we decode the received signal by estimating the pulse echo impulse responses between every....... The method is evaluated using the simulation tool Field II. Three point spread functions are simulated where axial movement of 1 m/s is present. The axial resolution for the moving scatterer is 0.249 mm (-3dB) and 0.291 mm (-6dB), which is compared to a standard STA transmission scheme with sequential...

  3. Parameter identification from frequency response of MEMS energy harvesters

    Science.gov (United States)

    Truong, Binh Duc; Le, Cuong Phu; Halvorsen, Einar

    2017-06-01

    In this study, we present theoretical analysis and numerical results on a simple technique for extracting unknown model parameters for MEMS electrostatic energy harvesters. We show that the frequency response can be utilized in a least-squares minimization scheme to estimate the damping coefficient, mechanical stiffness and transducer/load parasitic capacitances. The accuracy of the method is tested by application to simulated cases of linear and non-linear harvesters. A single data sweep from such a pseudo-experiment suffices to determine the unknown parameters of the electromechanical model with accuracy. The method is shown to work satisfactorily for both linear and nonlinear devices.

  4. Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens.

    Directory of Open Access Journals (Sweden)

    Deborah Chasman

    2016-07-01

    Full Text Available Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection.

  5. Rapid response seismic networks in Europe: lessons learnt from the L'Aquila earthquake emergency

    Directory of Open Access Journals (Sweden)

    Angelo Strollo

    2011-08-01

    Full Text Available

    The largest dataset ever recorded during a normal fault seismic sequence was acquired during the 2009 seismic emergency triggered by the damaging earthquake in L'Aquila (Italy. This was possible through the coordination of different rapid-response seismic networks in Italy, France and Germany. A seismic network of more than 60 stations recorded up to 70,000 earthquakes. Here, we describe the different open-data archives where it is possible to find this unique set of data for studies related to hazard, seismotectonics and earthquake physics. Moreover, we briefly describe some immediate and direct applications of emergency seismic networks. At the same time, we note the absence of communication platforms between the different European networks. Rapid-response networks need to agree on common strategies for network operations. Hopefully, over the next few years, the European Rapid-Response Seismic Network will became a reality.

  6. Unsupervised algorithms for intrusion detection and identification in wireless ad hoc sensor networks

    Science.gov (United States)

    Hortos, William S.

    2009-05-01

    In previous work by the author, parameters across network protocol layers were selected as features in supervised algorithms that detect and identify certain intrusion attacks on wireless ad hoc sensor networks (WSNs) carrying multisensor data. The algorithms improved the residual performance of the intrusion prevention measures provided by any dynamic key-management schemes and trust models implemented among network nodes. The approach of this paper does not train algorithms on the signature of known attack traffic, but, instead, the approach is based on unsupervised anomaly detection techniques that learn the signature of normal network traffic. Unsupervised learning does not require the data to be labeled or to be purely of one type, i.e., normal or attack traffic. The approach can be augmented to add any security attributes and quantified trust levels, established during data exchanges among nodes, to the set of cross-layer features from the WSN protocols. A two-stage framework is introduced for the security algorithms to overcome the problems of input size and resource constraints. The first stage is an unsupervised clustering algorithm which reduces the payload of network data packets to a tractable size. The second stage is a traditional anomaly detection algorithm based on a variation of support vector machines (SVMs), whose efficiency is improved by the availability of data in the packet payload. In the first stage, selected algorithms are adapted to WSN platforms to meet system requirements for simple parallel distributed computation, distributed storage and data robustness. A set of mobile software agents, acting like an ant colony in securing the WSN, are distributed at the nodes to implement the algorithms. The agents move among the layers involved in the network response to the intrusions at each active node and trustworthy neighborhood, collecting parametric values and executing assigned decision tasks. This minimizes the need to move large amounts

  7. Improving the Coefficients of Proportional-Integral Controller Based On System Identification Process on Doosti Irrigation Network

    Directory of Open Access Journals (Sweden)

    S. M. Seyedmousavi

    2016-02-01

    Full Text Available Introduction: The use of automatic control system for managing the conveyance and distribution of water in surface irrigation systems, as a means of improving the management and performance of these systems is essential. Automating networks using the programmable controller provide the implementation of a number of control ways according to different water delivery scenarios. Also the implementation of these systems using sensors and recording water levels by hydraulic devices can supply an accurate data collection of water distribution networks in the long term and can influence the decisions to manage the network. Control systems in irrigation canals include two parts: calculation for adjustment the structures (algorithms or control system software and application the calculated settings on structures (hardware of control systems. The success of these parts is depending on the ability of control algorithm to determine the control parameters. Materials and Methods: Doosti irrigation network in the plains of Sarakhs is located in the northeast of Iran. In the end of the main canal, two canals has branched which named EPC and WPC2. This study was performed on the EPC canal with the discharge and length of 4.43 m3/s and 18.7 km respectively. There are 15 duck-bill check structures along the EPC. Also 14 intake structures and secondary canals are responsible for water distribution between local water users. In this study, the performance of system identification process, SI, in estimation of coefficients of proportional-integral controller and improvement of adjusting the control algorithm was compared with trial and error process. This proportional-integral control algorithm was designed for EPC canal. The efficiency of algorithm was evaluated using the simulation results of several different choices of operating systems with SOBEK hydrodynamic model and computing evaluation indices of control systems. This model can simulate all kinds of

  8. A glutamatergic network mediates lithium response in bipolar disorder as defined by epigenome pathway analysis.

    Science.gov (United States)

    Higgins, Gerald A; Allyn-Feuer, Ari; Barbour, Edward; Athey, Brian D

    2015-01-01

    A regulatory network in the human brain mediating lithium response in bipolar patients was revealed by analysis of functional SNPs from genome-wide association studies (GWAS) and published gene association studies, followed by epigenome mapping. An initial set of 23,312 SNPs in linkage disequilibrium with lead SNPs, and sub-threshold GWAS SNPs rescued by pathway analysis, were studied in the same populations. These were assessed using our workflow and annotation by the epigenome roadmap consortium. Twenty-seven percent of 802 SNPs that were associated with lithium response (13 published studies gene association studies and two GWAS) were shared in common with 1281 SNPs from 18 GWAS examining psychiatric disorders and adverse events associated with lithium treatment. Nineteen SNPs were annotated as active regulatory elements such as enhancers and promoters in a tissue-specific manner. They were located within noncoding regions of ten genes: ANK3, ARNTL, CACNA1C, CACNG2, CDKN1A, CREB1, GRIA2, GSK3B, NR1D1 and SLC1A2. Following gene set enrichment and pathway analysis, these genes were found to be significantly associated (p = 10(-27); Fisher exact test) with an AMPA2 glutamate receptor network in human brain. Our workflow results showed concordance with annotation of regulatory elements from the epigenome roadmap. Analysis of cognate mRNA and enhancer RNA exhibited patterns consistent with an integrated pathway in human brain. This pharmacoepigenomic regulatory pathway is located in the same brain regions that exhibit tissue volume loss in bipolar disorder. Although in silico analysis requires biological validation, the approach provides value for identification of candidate variants that may be used in pharmacogenomic testing to identify bipolar patients likely to respond to lithium.

  9. Transcriptome-Wide Identification and Characterization of Potato Circular RNAs in Response to Pectobacterium carotovorum Subspecies brasiliense Infection.

    Science.gov (United States)

    Zhou, Ran; Zhu, Yongxing; Zhao, Jiao; Fang, Zhengwu; Wang, Shuping; Yin, Junliang; Chu, Zhaohui; Ma, Dongfang

    2017-12-27

    Little information about the roles of circular RNAs (circRNAs) during potato-Pectobacterium carotovorum subsp. brasiliense (Pcb) interaction is currently available. In this study, we conducted the systematic identification of circRNAs from time series samples of potato cultivars Valor (susceptible) and BP1 (disease tolerant) infected by Pcb. A total of 2098 circRNAs were detected and about half (931, 44.38%) were intergenic circRNAs. And differential expression analysis detected 429 significantly regulated circRNAs. circRNAs play roles by regulating parental genes and sponging miRNAs. Gene Ontology (GO) enrichment of parental genes and miRNAs targeted mRNAs revealed that these differentially expressed (DE) circRNAs were involved in defense response (GO:0006952), cell wall (GO:0005199), ADP binding (GO:0043531), phosphorylation (GO:0016310), and kinase activity (GO:0016301), suggesting the roles of circRNAs in regulating potato immune response. Furthermore, weighted gene co-expression network analysis (WGCNA) found that circRNAs were closely related with coding-genes and long intergenic noncoding RNAs (lincRNAs). And together they were cultivar-specifically regulated to strengthen immune response of potato to Pcb infection, implying the roles of circRNAs in reprogramming disease responsive transcriptome. Our results will provide new insights into the potato-Pcb interaction and may lead to novel disease control strategy in the future.

  10. Transcriptome-Wide Identification and Characterization of Potato Circular RNAs in Response to Pectobacterium carotovorum Subspecies brasiliense Infection

    Directory of Open Access Journals (Sweden)

    Ran Zhou

    2017-12-01

    Full Text Available Little information about the roles of circular RNAs (circRNAs during potato-Pectobacterium carotovorum subsp. brasiliense (Pcb interaction is currently available. In this study, we conducted the systematic identification of circRNAs from time series samples of potato cultivars Valor (susceptible and BP1 (disease tolerant infected by Pcb. A total of 2098 circRNAs were detected and about half (931, 44.38% were intergenic circRNAs. And differential expression analysis detected 429 significantly regulated circRNAs. circRNAs play roles by regulating parental genes and sponging miRNAs. Gene Ontology (GO enrichment of parental genes and miRNAs targeted mRNAs revealed that these differentially expressed (DE circRNAs were involved in defense response (GO:0006952, cell wall (GO:0005199, ADP binding (GO:0043531, phosphorylation (GO:0016310, and kinase activity (GO:0016301, suggesting the roles of circRNAs in regulating potato immune response. Furthermore, weighted gene co-expression network analysis (WGCNA found that circRNAs were closely related with coding-genes and long intergenic noncoding RNAs (lincRNAs. And together they were cultivar-specifically regulated to strengthen immune response of potato to Pcb infection, implying the roles of circRNAs in reprogramming disease responsive transcriptome. Our results will provide new insights into the potato-Pcb interaction and may lead to novel disease control strategy in the future.

  11. Distributed reinforcement learning for adaptive and robust network intrusion response

    Science.gov (United States)

    Malialis, Kleanthis; Devlin, Sam; Kudenko, Daniel

    2015-07-01

    Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to rate-limit or throttle traffic towards a victim server. The focus of this paper is on online learning and scalability. We propose an approach that incorporates task decomposition, team rewards and a form of reward shaping called difference rewards. One of the novel characteristics of the proposed system is that it provides a decentralised coordinated response to the DDoS problem, thus being resilient to DDoS attacks themselves. The proposed system learns remarkably fast, thus being suitable for online learning. Furthermore, its scalability is successfully demonstrated in experiments involving 1000 learning agents. We compare our approach against a baseline and a popular state-of-the-art throttling technique from the network security literature and show that the proposed approach is more effective, adaptive to sophisticated attack rate dynamics and robust to agent failures.

  12. Plasmonic response in nanoporous metal: dependence on network topology

    Science.gov (United States)

    Galí, Marc A.; Tai, Matthew C.; Arnold, Matthew D.; Cortie, Michael B.; Gentle, Angus R.; Smith, Geoffrey B.

    2015-12-01

    The optical and electrical responses of open, nanoscale, metal networks are of interest in a variety of technologies including transparent conducting electrodes, charge storage, and surfaces with controlled spectral selectivity. The properties of such nanoporous structures depend on the shape and extent of individual voids and the associated hyper-dimensional connectivity and density of the metal filaments. Unfortunately, a quantitative understanding of this dependence is at present only poorly developed. Here we address this problem using numerical simulations applied to a systematically designed series of prototypical sponges. The sponges are produced by a Monte Carlo simulation of the dealloying of Ag-Al alloys containing from 60% to 85% Al. The result is a series of Ag sponges of realistic morphology. The optical properties of the sponges are then calculated by the discrete dipole approximation and the results used to construct an 'effective medium' model for each sponge. We show how the resulting effective medium can be correlated with the associated morphological characteristics of each target and how the optical properties are primarily controlled by the density of the sponge and its state of percolation.

  13. Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis.

    Science.gov (United States)

    Li, Rudong; Dong, Xiao; Ma, Chengcheng; Liu, Lei

    2014-08-23

    Prostate cancer is one of the most common malignant diseases and is characterized by heterogeneity in the clinical course. To date, there are no efficient morphologic features or genomic biomarkers that can characterize the phenotypes of the cancer, especially with regard to metastasis--the most adverse outcome. Searching for effective surrogate genes out of large quantities of gene expression data is a key to cancer phenotyping and/or understanding molecular mechanisms underlying prostate cancer development. Using the maximum relevance minimum redundancy (mRMR) method on microarray data from normal tissues, primary tumors and metastatic tumors, we identifed four genes that can optimally classify samples of different prostate cancer phases. Moreover, we constructed a molecular interaction network with existing bioinformatic resources and co-identifed eight genes on the shortest-paths among the mRMR-identified genes, which are potential co-acting factors of prostate cancer. Functional analyses show that molecular functions involved in cell communication, hormone-receptor mediated signaling, and transcription regulation play important roles in the development of prostate cancer. We conclude that the surrogate genes we have selected compose an effective classifier of prostate cancer phases, which corresponds to a minimum characterization of cancer phenotypes on the molecular level. Along with their molecular interaction partners, it is fairly to assume that these genes may have important roles in prostate cancer development; particularly, the un-reported genes may bring new insights for the understanding of the molecular mechanisms. Thus our results may serve as a candidate gene set for further functional studies.

  14. Multilevel Bloom Filters for P2P Flows Identification Based on Cluster Analysis in Wireless Mesh Network

    Directory of Open Access Journals (Sweden)

    Xia-an Bi

    2015-01-01

    Full Text Available With the development of wireless mesh networks and distributed computing, lots of new P2P services have been deployed and enrich the Internet contents and applications. The rapid growth of P2P flows brings great pressure to the regular network operation. So the effective flow identification and management of P2P applications become increasingly urgent. In this paper, we build a multilevel bloom filters data structure to identify the P2P flows through researches on the locality characteristics of P2P flows. Different level structure stores different numbers of P2P flow rules. According to the characteristics values of the P2P flows, we adjust the parameters of the data structure of bloom filters. The searching steps of the scheme traverse from the first level to the last level. Compared with the traditional algorithms, our method solves the drawbacks of previous schemes. The simulation results demonstrate that our algorithm effectively enhances the performance of P2P flows identification. Then we deploy our flow identification algorithm in the traffic monitoring sensors which belong to the network traffic monitoring system at the export link in the campus network. In the real environment, the experiment results demonstrate that our algorithm has a fast speed and high accuracy to identify the P2P flows; therefore, it is suitable for actual deployment.

  15. A State Space Method for Modal Identification of Mechanical Systems from Time Domain Responses

    Directory of Open Access Journals (Sweden)

    Xiaobo Liu

    2005-01-01

    Full Text Available A new state space method is presented for modal identification of a mechanical system from its time domain impulse or initial condition responses. A key step in this method is the identification of the characteristic polynomial coefficients of an adjoint system. Once these coefficients are determined, a canonical state space realization of the adjoint system and the system's modal parameters are formulated straightforwardly. This method is conceptually and mathematically simple and is easy to be implemented. Detailed mathematical treatments are demonstrated and numerical examples are provided to illustrate the use and effectiveness of the method.

  16. Identification of putative domain linkers by a neural network – application to a large sequence database

    Directory of Open Access Journals (Sweden)

    Kuroda Yutaka

    2006-06-01

    Full Text Available Background The reliable dissection of large proteins into structural domains represents an important issue for structural genomics/proteomics projects. To provide a practical approach to this issue, we tested the ability of neural network to identify domain linkers from the SWISSPROT database (101602 sequences. Results Our search detected 3009 putative domain linkers adjacent to or overlapping with domains, as defined by sequence similarity to either Protein Data Bank (PDB or Conserved Domain Database (CDD sequences. Among these putative linkers, 75% were "correctly" located within 20 residues of a domain terminus, and the remaining 25% were found in the middle of a domain, and probably represented failed predictions. Moreover, our neural network predicted 5124 putative domain linkers in structurally un-annotated regions without sequence similarity to PDB or CDD sequences, which suggest to the possible existence of novel structural domains. As a comparison, we performed the same analysis by identifying low-complexity regions (LCR, which are known to encode unstructured polypeptide segments, and observed that the fraction of LCRs that correlate with domain termini is similar to that of domain linkers. However, domain linkers and LCRs appeared to identify different types of domain boundary regions, as only 32% of the putative domain linkers overlapped with LCRs. Conclusion Overall, our study indicates that the two methods detect independent and complementary regions, and that the combination of these methods can substantially improve the sensitivity of the domain boundary prediction. This finding should enable the identification of novel structural domains, yielding new targets for large scale protein analyses.

  17. GESTODIFFERENT: a Cytoscape plugin for the generation and the identification of gene regulatory networks describing a stochastic cell differentiation process.

    Science.gov (United States)

    Antoniotti, Marco; Bader, Gary D; Caravagna, Giulio; Crippa, Silvia; Graudenzi, Alex; Mauri, Giancarlo

    2013-02-15

    The characterization of the complex phenomenon of cell differentiation is a key goal of both systems and computational biology. GeStoDifferent is a Cytoscape plugin aimed at the generation and the identification of gene regulatory networks (GRNs) describing an arbitrary stochastic cell differentiation process. The (dynamical) model adopted to describe general GRNs is that of noisy random Boolean networks (NRBNs), with a specific focus on their emergent dynamical behavior. GeStoDifferent explores the space of GRNs by filtering the NRBN instances inconsistent with a stochastic lineage differentiation tree representing the cell lineages that can be obtained by following the fate of a stem cell descendant. Matched networks can then be analyzed by Cytoscape network analysis algorithms or, for instance, used to define (multiscale) models of cellular dynamics. Freely available at http://bimib.disco.unimib.it/index.php/Retronet#GESTODifferent or at the Cytoscape App Store http://apps.cytoscape.org/.

  18. Testing the Identification/Production Hypothesis of Implicit Memory in Schizophrenia: The Role of Response Competition.

    Science.gov (United States)

    Marques, Valéria R S; Spataro, Pietro; Cestari, Vincenzo; Sciarretta, Antonio; Rossi-Arnaud, Clelia

    2016-03-01

    Previous evidence indicates that patients with schizophrenia exhibit reduced repetition priming in production tasks (in which each response cue engenders a competition between alternative responses), but not in identification tasks (in which each response cue allows a unique response). However, cross-task comparisons may lead to inappropriate conclusions, because implicit tests vary on several dimensions in addition to the critical dimension of response competition. The present study sought to isolate the role of response competition, by varying the number of solutions in the context of the same implicit tasks. Two experiments investigated the performance of patients with schizophrenia and healthy controls in the high-competition and low-competition versions of word-stem completion (Exp.1) and verb generation (Exp.2). Response competition affected both the proportions of stems completed (higher to few-solution than to many-solution stems) and the reaction times of verb generation (slower to nouns having no dominant verb associates than to nouns having one dominant verb associate). Patients with schizophrenia showed significant (non-zero) priming in both experiments: crucially, the magnitude of this facilitation was equivalent to that observed in healthy controls and was not reduced in the high-competition versions of the two tasks. These findings suggest that implicit memory is spared in schizophrenia, irrespective of the degree of response competition during the retrieval phase; in addition, they add to the ongoing debate regarding the validity of the identification/production hypothesis of repetition priming.

  19. Brain networks involved in haptic and visual identification of facial expressions of emotion: an fMRI study.

    Science.gov (United States)

    Kitada, Ryo; Johnsrude, Ingrid S; Kochiyama, Takanori; Lederman, Susan J

    2010-01-15

    Previous neurophysiological and neuroimaging studies have shown that a cortical network involving the inferior frontal gyrus (IFG), inferior parietal lobe (IPL) and cortical areas in and around the posterior superior temporal sulcus (pSTS) region is employed in action understanding by vision and audition. However, the brain regions that are involved in action understanding by touch are unknown. Lederman et al. (2007) recently demonstrated that humans can haptically recognize facial expressions of emotion (FEE) surprisingly well. Here, we report a functional magnetic resonance imaging (fMRI) study in which we test the hypothesis that the IFG, IPL and pSTS regions are involved in haptic, as well as visual, FEE identification. Twenty subjects haptically or visually identified facemasks with three different FEEs (disgust, neutral and happiness) and casts of shoes (shoes) of three different types. The left posterior middle temporal gyrus, IPL, IFG and bilateral precentral gyrus were activated by FEE identification relative to that of shoes, regardless of sensory modality. By contrast, an inferomedial part of the left superior parietal lobule was activated by haptic, but not visual, FEE identification. Other brain regions, including the lingual gyrus and superior frontal gyrus, were activated by visual identification of FEEs, relative to haptic identification of FEEs. These results suggest that haptic and visual FEE identification rely on distinct but overlapping neural substrates including the IFG, IPL and pSTS region.

  20. The identification of the relationship between chemical and electrical parameters of honeys using artificial neural networks.

    Science.gov (United States)

    Pentoś, Katarzyna; Luczycka, Deta; Wróbel, Radosław

    2014-10-01

    A number of significant scientific studies have confirmed the health benefits of honey. Due to the high price of natural honey, it is a common target for adulteration which reduces its medicinal value. Adulteration detection methods require specific laboratory equipment and are very expensive. The development of measurement techniques enables the measurement of electrical characteristics of strained honey. Honey electrical parameters can possibly be used for its quality assessment. The identification of the relationship between chemical and electrical parameters of honeys and analysis to determine if there are frequency-dependent changes, can help in developing of that group of methods. The aim of this research was to determine how the chemical parameters of certain honeys influence the dielectric loss factor and the permittivity of strained honey measured in various frequencies. Another aim was to determine whether the percentage influence of certain chemical parameters of honeys on electrical characteristics significantly depends on frequency value. The research was based on neural network models and sensitivity analysis. The percentage influence of certain chemical parameters on electrical characteristics significantly depends on frequency value. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. Identification of geomagnetic current systems from a ground-based network

    Science.gov (United States)

    Pereira, F.; Dudok de Wit, T.; Menvielle, M.

    2003-04-01

    The Earth's total magnetic field is a superposition of magnetic fields from a variety of sources. At the Earth's surface, the most important source is the internal field produced by currents within the Earth's liquid core. At high latitudes of our planet, magnetopheric and ionospheric current systems are other important sources of magnetic field. A large part of research in geomagnetism is devoted to the identification of these internal and external sources. The separation of current systems from ground-based measurements is a source separation problem. In this study, our approach consists in inferring from a statistical analysis of the data set what are the different contributing source terms. Our analysis will be done by a statistical method known as the Singular Value Decomposition. The SVD is widely used in multivariate analysis for reduction of dimensionality, which offers a more concise description of the observed data and helps to extract significant information from the data. From geomagnetic data provided by the INTERMAGNET global network, the results of the SVD analysis can be interpreted in terms of current systems such as the magnetic field declination, the separation of the auroral electrojets into quiet and intermittent components, the seasonal effects, the ring current signature, the observation of the polar cusp and the cross-tail currents, the displacement of the auroral oval (and even the detection of geomagnetic jerks ?).

  2. Identification of lethal cluster of genes in the yeast transcription network

    Science.gov (United States)

    Rho, K.; Jeong, H.; Kahng, B.

    2006-05-01

    Identification of essential or lethal genes would be one of the ultimate goals in drug designs. Here we introduce an in silico method to select the cluster with a high population of lethal genes, called lethal cluster, through microarray assay. We construct a gene transcription network based on the microarray expression level. Links are added one by one in the descending order of the Pearson correlation coefficients between two genes. As the link density p increases, two meaningful link densities pm and ps are observed. At pm, which is smaller than the percolation threshold, the number of disconnected clusters is maximum, and the lethal genes are highly concentrated in a certain cluster that needs to be identified. Thus the deletion of all genes in that cluster could efficiently lead to a lethal inviable mutant. This lethal cluster can be identified by an in silico method. As p increases further beyond the percolation threshold, the power law behavior in the degree distribution of a giant cluster appears at ps. We measure the degree of each gene at ps. With the information pertaining to the degrees of each gene at ps, we return to the point pm and calculate the mean degree of genes of each cluster. We find that the lethal cluster has the largest mean degree.

  3. Environmental Learning in Online Social Networks: Adopting Environmentally Responsible Behaviors

    Science.gov (United States)

    Robelia, Beth A.; Greenhow, Christine; Burton, Lisa

    2011-01-01

    Online social networks are increasingly important information and communication tools for young people and for the environmental movement. Networks may provide the motivation for young adults to increase environmental behaviors by increasing their knowledge of environmental issues and of the specific actions they can take to reduce greenhouse gas…

  4. Progressively expanded neural network for automatic material identification in hyperspectral imagery

    Science.gov (United States)

    Paheding, Sidike

    The science of hyperspectral remote sensing focuses on the exploitation of the spectral signatures of various materials to enhance capabilities including object detection, recognition, and material characterization. Hyperspectral imagery (HSI) has been extensively used for object detection and identification applications since it provides plenty of spectral information to uniquely identify materials by their reflectance spectra. HSI-based object detection algorithms can be generally classified into stochastic and deterministic approaches. Deterministic approaches are comparatively simple to apply since it is usually based on direct spectral similarity such as spectral angles or spectral correlation. In contrast, stochastic algorithms require statistical modeling and estimation for target class and non-target class. Over the decades, many single class object detection methods have been proposed in the literature, however, deterministic multiclass object detection in HSI has not been explored. In this work, we propose a deterministic multiclass object detection scheme, named class-associative spectral fringe-adjusted joint transform correlation. Human brain is capable of simultaneously processing high volumes of multi-modal data received every second of the day. In contrast, a machine sees input data simply as random binary numbers. Although machines are computationally efficient, they are inferior when comes to data abstraction and interpretation. Thus, mimicking the learning strength of human brain has been current trend in artificial intelligence. In this work, we present a biological inspired neural network, named progressively expanded neural network (PEN Net), based on nonlinear transformation of input neurons to a feature space for better pattern differentiation. In PEN Net, discrete fixed excitations are disassembled and scattered in the feature space as a nonlinear line. Each disassembled element on the line corresponds to a pattern with similar features

  5. Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Høgsberg, Jan Becker; Winther, Ole

    2011-01-01

    It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is ab...... to perform accurate response prediction much faster than the corresponding finite element model. Initial result indicate a reduction in cpu time by two orders of magnitude.......It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is able...

  6. Employees' responses to an organizational merger: Intraindividual change in organizational identification, attachment, and turnover.

    Science.gov (United States)

    Sung, Wookje; Woehler, Meredith L; Fagan, Jesse M; Grosser, Travis J; Floyd, Theresa M; Labianca, Giuseppe Joe

    2017-06-01

    The authors used pre-post merger data from 599 employees experiencing a major corporate merger to compare 3 conceptual models based on the logic of social identity theory (SIT) and exchange theory to explain employees' merger responses. At issue is how perceived change in employees' own jobs and roles (i.e., personal valence) and perceived change in their organization's status and merger appropriateness (i.e., organizational valence) affect their changing organizational identification, attachment attitudes, and voluntary turnover. The first model suggests that organizational identification and organizational attachment develop independently and have distinct antecedents. The second model posits that organizational identification mediates the relationships between change in organizational and personal valence and change in attachment and turnover. The third model posits that change in personal valence moderates the relationship between changes in organizational valence and in organizational identification and attachment. Using latent difference score (LDS) modeling in an SEM framework and survival analysis, the results suggest an emergent fourth model that integrates the first and second models: Although change in organizational identification during the merger mediates the relationship between change in personal status and organizational valence and change in attachment, there is a direct and unmediated relationship between change in personal valence and attachment. This integrated model has implications for M&A theory and practice. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  7. Comparison of a spiking neural network and an MLP for robust identification of generator dynamics in a multimachine power system.

    Science.gov (United States)

    Johnson, Cameron; Venayagamoorthy, Ganesh Kumar; Mitra, Pinaki

    2009-01-01

    The application of a spiking neural network (SNN) and a multi-layer perceptron (MLP) for online identification of generator dynamics in a multimachine power system are compared in this paper. An integrate-and-fire model of an SNN which communicates information via the inter-spike interval is applied. The neural network identifiers are used to predict the speed and terminal voltage deviations one time-step ahead of generators in a multimachine power system. The SNN is developed in two steps: (i) neuron centers determined by offline k-means clustering and (ii) output weights obtained by online training. The sensitivity of the SNN to the neuron centers determined in the first step is evaluated on generators of different ratings and parameters. Performances of the SNN and MLP are compared to evaluate robustness on the identification of generator dynamics under small and large disturbances, and to illustrate that SNNs are capable of learning nonlinear dynamics of complex systems.

  8. Development of the Real Time Situation Identification Model for Adaptive Service Support in Vehicular Communication Networks Domain

    Directory of Open Access Journals (Sweden)

    Mindaugas Kurmis

    2013-01-01

    Full Text Available The article discusses analyses and assesses the key proposals how to deal with the situation identification for the heterogeneous service support in vehicular cooperation environment. This is one of the most important topics of the pervasive computing. Without the solution it is impossible to adequately respond to the user's needs and to provide needed services in the right place at the right moment and in the right way. In this work we present our developed real time situation identification model for adaptive service support in vehicular communication networks domain. Our solution is different from the others as it uses additional virtual context information source - information from other vehicles which for our knowledge is not addressed in the past. The simulation results show the promising context exchange rate between vehicles. The other vehicles provided additional context source in our developed model helps to increase situations identification level.

  9. Simultaneous Electroencephalography and Functional Magnetic Resonance Imaging and the Identification of Epileptic Networks in Children.

    Science.gov (United States)

    Maloney, Thomas C; Tenney, Jeffrey R; Szaflarski, Jerzy P; Vannest, Jennifer

    EEG/fMRI takes advantage of the high temporal resolution of EEG in combination with the high spatial resolution of fMRI. These features make it particularly applicable to the study of epilepsy in which the event duration (e.g., interictal epileptiform discharges) is short, typically less than 200 milliseconds. Interictal or ictal discharges can be identified on EEG and be used for source localization in fMRI analyses. The acquisition of simultaneous EEG/fMRI involves the use of specialized EEG hardware that is safe in the MR environment and comfortable to the participant. Advanced data analysis approaches such as independent component analysis conducted alone or sometimes combined with other, e.g., Granger Causality or "sliding window" analyses are currently thought to be most appropriate for EEG/fMRI data. These approaches make it possible to identify networks of brain regions associated with ictal and/or interictal events allowing examination of the mechanisms critical for generation and propagation through these networks. After initial evaluation in adults, EEG/fMRI has been applied to the examination of the pediatric epilepsy syndromes including Childhood Absence Epilepsy, Benign Epilepsy with Centrotemporal Spikes (BECTS), Dravet Syndrome, and Lennox-Gastaut Syndrome. Results of EEG/fMRI studies suggest that the hemodynamic response measured by fMRI may have a different shape in response to epileptic events compared to the response to external stimuli; this may be especially true in the developing brain. Thus, the main goal of this review is to provide an overview of the pediatric applications of EEG/fMRI and its associated findings up until this point.

  10. Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks.

    Science.gov (United States)

    Gao, Zhongke; Jin, Ningde

    2009-06-01

    The identification of flow pattern is a basic and important issue in multiphase systems. Because of the complexity of phase interaction in gas-liquid two-phase flow, it is difficult to discern its flow pattern objectively. In this paper, we make a systematic study on the vertical upward gas-liquid two-phase flow using complex network. Three unique network construction methods are proposed to build three types of networks, i.e., flow pattern complex network (FPCN), fluid dynamic complex network (FDCN), and fluid structure complex network (FSCN). Through detecting the community structure of FPCN by the community-detection algorithm based on K -mean clustering, useful and interesting results are found which can be used for identifying five vertical upward gas-liquid two-phase flow patterns. To investigate the dynamic characteristics of gas-liquid two-phase flow, we construct 50 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of gas-liquid two-phase flow. Furthermore, we construct FSCN and demonstrate how network statistic can be used to reveal the fluid structure of gas-liquid two-phase flow. In this paper, from a different perspective, we not only introduce complex network theory to the study of gas-liquid two-phase flow but also indicate that complex network may be a powerful tool for exploring nonlinear time series in practice.

  11. Identification of a Typical CSTR Using Optimal Focused Time Lagged Recurrent Neural Network Model with Gamma Memory Filter

    Directory of Open Access Journals (Sweden)

    S. N. Naikwad

    2009-01-01

    Full Text Available A focused time lagged recurrent neural network (FTLR NN with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. As CSTR process includes temporal relationship in the input-output mappings, time lagged recurrent neural network is particularly used for identification purpose. The standard back propagation algorithm with momentum term has been proposed in this model. The various parameters like number of processing elements, number of hidden layers, training and testing percentage, learning rule and transfer function in hidden and output layer are investigated on the basis of performance measures like MSE, NMSE, and correlation coefficient on testing data set. Finally effects of different norms are tested along with variation in gamma memory filter. It is demonstrated that dynamic NN model has a remarkable system identification capability for the problems considered in this paper. Thus FTLR NN with gamma memory filter can be used to learn underlying highly nonlinear dynamics of the system, which is a major contribution of this paper.

  12. Identification of bearing supports' force coefficients from rotor responses due to imbalances and impact loads

    Science.gov (United States)

    de Santiago Duran, Oscar Cesar

    Experimental identification of fluid film bearing parameters is vital to validate predictions from often restrictive computational fluid film bearing models and is also promising for condition monitoring and troubleshooting. This dissertation presents the analytical bases of two procedures for bearing supports parameter identification with potential for in-situ implementation. Bearing support coefficients are derived from measurements of rotor responses to impact loads and due to calibrated imbalances in characteristic planes. Subsequent implementation of the procedures to measurements performed in a rigid massive rotor traversing two critical speeds provides force coefficients for a novel bearing support comprising a tilting pad bearing (TPJB ) in series with an integral squeeze film damper (SFD). At a constant rotor speed, the first method requires impacts loads exerted along two lateral planes for identification of frequency-dependent force coefficients. Simulation numerical examples show the method is reliable with a reduced sensitivity to noise as the number of impacts increases (frequency averaging). In the experiments, an ad-hoc fixture delivers impacts to the rotor middle disk at speeds of 2,000 and 4,000 rpm. The experimentally identified force coefficients are in close agreement with predicted coefficients for the series support TPJB-SFD. In particular, damping coefficients are best identified around the system first natural frequency. Bearing stiffness are correctly identified in the low frequency range, but show a marked reduction at higher frequencies apparently due to inertial effects not accounted for in the model. Measurements of rotor response to calibrated imbalances allow identification of speed-dependent force coefficients. The procedure requires a minimum of two different imbalance distributions for identification of force coefficients from the two bearing supports. The rotor responses show minimal cross-coupling effects, as also predicted by

  13. Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae.

    Science.gov (United States)

    Mirzarezaee, Mitra; Araabi, Babak N; Sadeghi, Mehdi

    2010-12-19

    It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of Saccharomyces Cerevisiae's proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes. We extracted different biological characteristics including amino acid sequences, domain contents, repeated domains, functional categories, biological processes, cellular compartments, disordered regions, and position specific scoring matrix from various sources. Several classifiers are examined and the best feature-sets based on average correct classification rate and correlation coefficients of the results are selected. We show that fusion of five feature-sets including domains, Position Specific Scoring Matrix-400, cellular compartments level one, and composition pairs with two and one gaps provide the best discrimination with an average correct classification rate of 77%. We study a variety of known biological feature-sets of the proteins and show that there is a relation between domains, Position Specific Scoring Matrix-400, cellular compartments level one, composition pairs with two and one gaps of Saccharomyces Cerevisiae's proteins, and their roles in the protein interaction network as non-hubs, intermediately connected, party hubs and date hubs. This study also confirms the possibility of predicting non-hubs, party hubs and date hubs

  14. Dose and Time Dependencies in Stress Pathway Responses during Chemical Exposure: Novel Insights from Gene Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Terezinha M. Souza

    2017-10-01

    Full Text Available Perturbation of biological networks is often observed during exposure to xenobiotics, and the identification of disturbed processes, their dynamic traits, and dose–response relationships are some of the current challenges for elucidating the mechanisms determining adverse outcomes. In this scenario, reverse engineering of gene regulatory networks (GRNs from expression data may provide a system-level snapshot embedded within accurate molecular events. Here, we investigate the composition of GRNs inferred from groups of chemicals with two distinct outcomes, namely carcinogenicity [azathioprine (AZA and cyclophosphamide (CYC] and drug-induced liver injury (DILI; diclofenac, nitrofurantoin, and propylthiouracil, and a non-carcinogenic/non-DILI group (aspirin, diazepam, and omeprazole. For this, we analyzed publicly available exposed in vitro human data, taking into account dose and time dependencies. Dose–Time Network Identification (DTNI was applied to gene sets from exposed primary human hepatocytes using four stress pathways, namely endoplasmic reticulum (ER, NF-κB, NRF2, and TP53. Inferred GRNs suggested case specificity, varying in interactions, starting nodes, and target genes across groups. DILI and carcinogenic compounds were shown to directly affect all pathway-based GRNs, while non-DILI/non-carcinogenic chemicals only affected NF-κB. NF-κB-based GRNs clearly illustrated group-specific disturbances, with the cancer-related casein kinase CSNK2A1 being a target gene only in the carcinogenic group, and opposite regulation of NF-κB subunits being observed in DILI and non-DILI/non-carcinogenic groups. Target genes in NRF2-based GRNs shared by DILI and carcinogenic compounds suggested markers of hepatotoxicity. Finally, we indicate several of these group-specific interactions as potentially novel. In summary, our reversed-engineered GRNs are capable of revealing dose dependent, chemical-specific mechanisms of action in stress

  15. Reconstruction of transcription control networks in Mollicutes by high-throughput identification of promoters

    Directory of Open Access Journals (Sweden)

    Gleb Y Fisunov

    2016-12-01

    Full Text Available Bacteria of the class Mollicutes have significantly reduced genomes and gene expression control systems. They are also efficient pathogens that can colonize a broad range of hosts including plants and animals. Despite their simplicity, Mollicutes demonstrate complex transcriptional responses to various conditions, which contradicts their reduction in gene expression regulation mechanisms. We analyzed the conservation and distribution of transcription regulators across the 50 Mollicutes species. The majority of the transcription factors regulate transport and metabolism, and there are four transcription factors that demonstrate significant conservation across the analyzed bacteria. These factors include repressors of chaperone HrcA, cell cycle regulator MraZ and two regulators with unclear function from the WhiA and YebC/PmpR families. We then used three representative species of the major clades of Mollicutes (Acholeplasma laidlawii, Spiroplasma melliferum and Mycoplasma gallisepticum to perform promoters mapping and activity quantitation. We revealed that Mollicutes evolved towards a promoter architecture simplification that correlates with a diminishing role of transcription regulation and an increase in transcriptional noise. Using the identified operons structure and a comparative genomics approach, we reconstructed the transcription control networks for these three species. The organization of the networks reflects the adaptation of bacteria to specific conditions and hosts.

  16. Demand Response in Low Voltage Distribution Networks with High PV Penetration

    DEFF Research Database (Denmark)

    Nainar, Karthikeyan; Pokhrel, Basanta Raj; Pillai, Jayakrishnan Radhakrishna

    2017-01-01

    generation and load forecasts, network topology and market price signals as inputs, limits of network voltages, line power flows, transformer loading and demand response dynamics as constraints to find the required demand response at each time step. The proposed method can be used by the DSOs to purchase...... the required flexibility from the electricity market through an aggregator. The optimum demand response enables consumption of maximum renewable energy within the network constraints. Simulation studies are conducted using Matlab and DigSilent Power factory software on a Danish low-voltage distribution system...

  17. Dissociation of rapid response learning and facilitation in perceptual and conceptual networks of person recognition.

    Science.gov (United States)

    Valt, Christian; Klein, Christoph; Boehm, Stephan G

    2015-08-01

    Repetition priming is a prominent example of non-declarative memory, and it increases the accuracy and speed of responses to repeatedly processed stimuli. Major long-hold memory theories posit that repetition priming results from facilitation within perceptual and conceptual networks for stimulus recognition and categorization. Stimuli can also be bound to particular responses, and it has recently been suggested that this rapid response learning, not network facilitation, provides a sound theory of priming of object recognition. Here, we addressed the relevance of network facilitation and rapid response learning for priming of person recognition with a view to advance general theories of priming. In four experiments, participants performed conceptual decisions like occupation or nationality judgments for famous faces. The magnitude of rapid response learning varied across experiments, and rapid response learning co-occurred and interacted with facilitation in perceptual and conceptual networks. These findings indicate that rapid response learning and facilitation in perceptual and conceptual networks are complementary rather than competing theories of priming. Thus, future memory theories need to incorporate both rapid response learning and network facilitation as individual facets of priming. © 2014 The British Psychological Society.

  18. Blind identification of the number of sub-carriers for orthogonal frequency division multiplexing-based elastic optical networking

    Science.gov (United States)

    Zhao, Lei; Xu, Hengying; Bai, Chenglin

    2018-03-01

    In orthogonal frequency division multiplexing (OFDM)-based elastic optical networking (EON), it is imperative to identify unknown parameters of OFDM-based EON signals quickly, intelligently and robustly. Because the number of sub-carriers determines the size of the sub-carriers spacing and then affects the symbol period of the OFDM and the anti-dispersion capability of the system, the identification of the number of sub-carriers has a profound effect on the identification of other key parameters of the system. In this paper, we proposed a method of number identification for sub-carriers of OFDM-based EON signals with help of high-order cyclic cumulant. The specific fourth-order cyclic cumulant exists only at the location of its sub-carriers frequencies. So the identification of the number of sub-carriers can be implemented by detecting the cyclic-frequencies. The proposed scheme in our study can be divided into three sub-stages, i.e. estimating the spectral range, calculating the high-order cyclic cumulant and identifying the number of sub-carriers. When the optical signal-to-noise ratios (OSNR) varied from 16dB to 22dB, the number of sub-carriers (64-512) was successfully identified in the experiment, and from the statistical point of view, the average identification absolute accuracy (IAAs) exceeded 94%.

  19. Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer.

    Science.gov (United States)

    Chiu, Yu-Chiao; Wang, Li-Ju; Hsiao, Tzu-Hung; Chuang, Eric Y; Chen, Yidong

    2017-10-03

    With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e., dependent on) the expression level of a key modulator gene. Systematic investigations into the modulation by estrogen receptor (ER), the best-known modulator gene, have revealed the functional and prognostic significance in breast cancer. However, a genome-wide identification of key modulator genes that may further unveil the landscape of modulated gene interaction is still lacking. We proposed a systematic workflow to screen for key modulators based on genome-wide gene expression profiles. We designed four modularity parameters to measure the ability of a putative modulator to perturb gene interaction networks. Applying the method to a dataset of 286 breast tumors, we comprehensively characterized the modularity parameters and identified a total of 973 key modulator genes. The modularity of these modulators was verified in three independent breast cancer datasets. ESR1, the encoding gene of ER, appeared in the list, and abundant novel modulators were illuminated. For instance, a prognostic predictor of breast cancer, SFRP1, was found the second modulator. Functional annotation analysis of the 973 modulators revealed involvements in ER-related cellular processes as well as immune- and tumor-associated functions. Here we present, as far as we know, the first comprehensive analysis of key modulator genes on a genome-wide scale. The validity of filtering parameters as well as the conservativity of modulators among cohorts were corroborated. Our data bring new insights into the modulated layer of gene-gene interaction and provide candidates for further biological investigations.

  20. Identification of cell cycle-regulated genes by convolutional neural network.

    Science.gov (United States)

    Liu, Chenglin; Cui, Peng; Huang, Tao

    2017-04-17

    The cell cycle-regulated genes express periodically with the cell cycle stages, and the identification and study of these genes can provide a deep understanding of the cell cycle process. Large false positives and low overlaps are big problems in cell cycle-regulated gene detection. Here, a computational framework called DLGene was proposed for cell cycle-regulated gene detection. It is based on the convolutional neural network, a deep learning algorithm representing raw form of data pattern without assumption of their distribution. First, the expression data was transformed to categorical state data to denote the changing state of gene expression, and four different expression patterns were revealed for the reported cell cycle-regulated genes. Then, DLGene was applied to discriminate the non-cell cycle gene and the four subtypes of cell cycle genes. Its performances were compared with six traditional machine learning methods. At last, the biological functions of representative cell cycle genes for each subtype were analyzed. Our method showed better and more balanced performance of sensitivity and specificity comparing to other machine learning algorithms. The cell cycle genes had very different expression pattern with non-cell cycle genes and among the cell-cycle genes, there were four subtypes. Our method not only detects the cell cycle genes, but also describes its expression pattern, such as when its highest expression level is reached and how it changes with time. For each type, we analyzed the biological functions of the representative genes and such results provided novel insight of the cell cycle mechanisms. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  1. Identification of Gene Networks Associated with Acute Myeloid Leukemia by Comparative Molecular Methylation and Expression Profiling

    Directory of Open Access Journals (Sweden)

    Margaret Dellett

    2010-01-01

    Full Text Available Around 80% of acute myeloid leukemia (AML patients achieve a complete remission, however many will relapse and ultimately die of their disease. The association between karyotype and prognosis has been studied extensively and identified patient cohorts as having favourable [e.g. t(8; 21, inv (16/t(16; 16, t(15; 17], intermediate [e.g. cytogenetically normal (NK-AML] or adverse risk [e.g. complex karyotypes]. Previous studies have shown that gene expression profiling signatures can classify the sub-types of AML, although few reports have shown a similar feature by using methylation markers. The global methylation patterns in 19 diagnostic AML samples were investigated using the Methylated CpG Island Amplification Microarray (MCAM method and CpG island microarrays containing 12,000 CpG sites. The first analysis, comparing favourable and intermediate cytogenetic risk groups, revealed significantly differentially methylated CpG sites (594 CpG islands between the two subgroups. Mutations in the NPM1 gene occur at a high frequency (40% within the NK-AML subgroup and are associated with a more favourable prognosis in these patients. A second analysis comparing the NPM1 mutant and wild-type research study subjects again identified distinct methylation profiles between these two subgroups. Network and pathway analysis revealed possible molecular mechanisms associated with the different risk and/or mutation sub-groups. This may result in a better classification of the risk groups, improved monitoring targets, or the identification of novel molecular therapies.

  2. Identification of direct and indirect social network effects in the pathophysiology of insulin resistance in obese human subjects.

    Science.gov (United States)

    Henning, Christian H C A; Zarnekow, Nana; Hedtrich, Johannes; Stark, Sascha; Türk, Kathrin; Laudes, Matthias

    2014-01-01

    The aim of the present study was to examine to what extent different social network mechanisms are involved in the pathogenesis of obesity and insulin-resistance. We used nonparametric and parametric regression models to analyse whether individual BMI and HOMA-IR are determined by social network characteristics. A total of 677 probands (EGO) and 3033 social network partners (ALTER) were included in the study. Data gathered from the probands include anthropometric measures, HOMA-IR index, health attitudes, behavioural and socio-economic variables and social network data. We found significant treatment effects for ALTERs frequent dieting (pnetwork effect also on EGO's insulin resistance. Most importantly, we also found significant direct social network effects on EGO's insulin resistance, evidenced by an effect of ALTERs frequent dieting (p = 0.033) and ALTERs sport activities (p = 0.041) to decrease EGO's HOMA-IR index independently of EGO's BMI. Social network phenomena appear not only to be relevant for the spread of obesity, but also for the spread of insulin resistance as the basis for type 2 diabetes. Attitudes and behaviour of peer groups influence EGO's health status not only via social mechanisms, but also via socio-biological mechanisms, i.e. higher brain areas might be influenced not only by biological signals from the own organism, but also by behaviour and knowledge from different human individuals. Our approach allows the identification of peer group influence controlling for potential homophily even when using cross-sectional observational data.

  3. Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex: functional and mechanistic implications.

    Directory of Open Access Journals (Sweden)

    Aaron R Wolen

    Full Text Available Individual differences in initial sensitivity to ethanol are strongly related to the heritable risk of alcoholism in humans. To elucidate key molecular networks that modulate ethanol sensitivity we performed the first systems genetics analysis of ethanol-responsive gene expression in brain regions of the mesocorticolimbic reward circuit (prefrontal cortex, nucleus accumbens, and ventral midbrain across a highly diverse family of 27 isogenic mouse strains (BXD panel before and after treatment with ethanol.Acute ethanol altered the expression of ~2,750 genes in one or more regions and 400 transcripts were jointly modulated in all three. Ethanol-responsive gene networks were extracted with a powerful graph theoretical method that efficiently summarized ethanol's effects. These networks correlated with acute behavioral responses to ethanol and other drugs of abuse. As predicted, networks were heavily populated by genes controlling synaptic transmission and neuroplasticity. Several of the most densely interconnected network hubs, including Kcnma1 and Gsk3β, are known to influence behavioral or physiological responses to ethanol, validating our overall approach. Other major hub genes like Grm3, Pten and Nrg3 represent novel targets of ethanol effects. Networks were under strong genetic control by variants that we mapped to a small number of chromosomal loci. Using a novel combination of genetic, bioinformatic and network-based approaches, we identified high priority cis-regulatory candidate genes, including Scn1b, Gria1, Sncb and Nell2.The ethanol-responsive gene networks identified here represent a previously uncharacterized intermediate phenotype between DNA variation and ethanol sensitivity in mice. Networks involved in synaptic transmission were strongly regulated by ethanol and could contribute to behavioral plasticity seen with chronic ethanol. Our novel finding that hub genes and a small number of loci exert major influence over the ethanol

  4. Characterizing individual differences in reward sensitivity from the brain networks involved in response inhibition.

    Science.gov (United States)

    Fuentes-Claramonte, Paola; Ávila, César; Rodríguez-Pujadas, Aina; Costumero, Víctor; Ventura-Campos, Noelia; Bustamante, Juan Carlos; Rosell-Negre, Patricia; Barrós-Loscertales, Alfonso

    2016-01-01

    A "disinhibited" cognitive profile has been proposed for individuals with high reward sensitivity, characterized by increased engagement in goal-directed responses and reduced processing of negative or unexpected cues, which impairs adequate behavioral regulation after feedback in these individuals. This pattern is manifested through deficits in inhibitory control and/or increases in RT variability. In the present work, we aimed to test whether this profile is associated with the activity of functional networks during a stop-signal task using independent component analysis (ICA). Sixty-one participants underwent fMRI while performing a stop-signal task, during which a manual response had to be inhibited. ICA was used to mainly replicate the functional networks involved in the task (Zhang and Li, 2012): two motor networks involved in the go response, the left and right fronto-parietal networks for stopping, a midline error-processing network, and the default-mode network (DMN), which was further subdivided into its anterior and posterior parts. Reward sensitivity was mainly associated with greater activity of motor networks, reduced activity in the midline network during correct stop trials and, behaviorally, increased RT variability. All these variables explained 36% of variance of the SR scores. This pattern of associations suggests that reward sensitivity involves greater motor engagement in the dominant response, more distractibility and reduced processing of salient or unexpected events, which may lead to disinhibited behavior. Copyright © 2015 Elsevier Inc. All rights reserved.

  5. Allele-specific behavior of molecular networks: understanding small-molecule drug response in yeast.

    Directory of Open Access Journals (Sweden)

    Fan Zhang

    Full Text Available The study of systems genetics is changing the way the genetic and molecular basis of phenotypic variation, such as disease susceptibility and drug response, is being analyzed. Moreover, systems genetics aids in the translation of insights from systems biology into genetics. The use of systems genetics enables greater attention to be focused on the potential impact of genetic perturbations on the molecular states of networks that in turn affects complex traits. In this study, we developed models to detect allele-specific perturbations on interactions, in which a genetic locus with alternative alleles exerted a differing influence on an interaction. We utilized the models to investigate the dynamic behavior of an integrated molecular network undergoing genetic perturbations in yeast. Our results revealed the complexity of regulatory relationships between genetic loci and networks, in which different genetic loci perturb specific network modules. In addition, significant within-module functional coherence was found. We then used the network perturbation model to elucidate the underlying molecular mechanisms of individual differences in response to 100 diverse small molecule drugs. As a result, we identified sub-networks in the integrated network that responded to variations in DNA associated with response to diverse compounds and were significantly enriched for known drug targets. Literature mining results provided strong independent evidence for the effectiveness of these genetic perturbing networks in the elucidation of small-molecule responses in yeast.

  6. The construction of corporate social responsibility in network societies: A communication view

    NARCIS (Netherlands)

    Schultz, F.; Castello, I.; Morsing, M.

    2013-01-01

    The paper introduces the communication view on Corporate Social Responsibility (CSR), which regards CSR as communicatively constructed in dynamic interaction processes in today's networked societies. Building on the idea that communication constitutes organizations we discuss the potentially

  7. Fast demand response in support of the active distribution network

    NARCIS (Netherlands)

    MacDougall, P.; Heskes, P.; Crolla, P.; Burt, G.; Warmer, C.

    2013-01-01

    Demand side management has traditionally been investigated for "normal" operation services such as balancing and congestion management. However they potentially could be utilized for Distributed Network Operator (DNO) services. This paper investigates and validates the use of a supply and demand

  8. Emulation of the Active Immune Response in a Computer Network

    Science.gov (United States)

    2009-01-15

    there exist a number of methods connected to processes of optimization intended to solve several problems including immunotherapy and immuno ...researchers and security analysts to respond faster in order to keep up with these attacks. New approaches for network security analysis, reactive and

  9. Structure identification and adaptive synchronization of uncertain general complex dynamical networks

    Energy Technology Data Exchange (ETDEWEB)

    Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teacher' s College, Hubei 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang Jian' an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Lu Hongqian [Shandong Institute of Light Industry, Shandong Jinan 250353 (China)

    2009-12-28

    This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.

  10. Novel Optical Methods for Identification, Imaging, and Preservation of the Cavernous Nerves Responsible for Penile Erections during Prostate Cancer Surgery

    Science.gov (United States)

    2011-03-01

    Novel Optical Methods for Identification, Imaging, and Preservation of the Cavernous Nerves Responsible for Penile Erections during Prostate...5a. CONTRACT NUMBER Preservation of the Cavernous Nerves Responsible for Penile Erections During Prostate Cancer Surgery 5b. GRANT...understanding of the location of the cavernous nerves, which are responsible for erectile function. Advances in id entification and preservation of

  11. Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae

    Directory of Open Access Journals (Sweden)

    Araabi Babak N

    2010-12-01

    Full Text Available Abstract Background It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of Saccharomyces Cerevisiae's proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes. Results We extracted different biological characteristics including amino acid sequences, domain contents, repeated domains, functional categories, biological processes, cellular compartments, disordered regions, and position specific scoring matrix from various sources. Several classifiers are examined and the best feature-sets based on average correct classification rate and correlation coefficients of the results are selected. We show that fusion of five feature-sets including domains, Position Specific Scoring Matrix-400, cellular compartments level one, and composition pairs with two and one gaps provide the best discrimination with an average correct classification rate of 77%. Conclusions We study a variety of known biological feature-sets of the proteins and show that there is a relation between domains, Position Specific Scoring Matrix-400, cellular compartments level one, composition pairs with two and one gaps of Saccharomyces Cerevisiae's proteins, and their roles in the protein interaction network as non-hubs, intermediately connected, party hubs and date hubs. This study also confirms the

  12. Network motif identification and structure detection with exponential random graph models

    Directory of Open Access Journals (Sweden)

    Munni Begum

    2014-12-01

    Full Text Available Local regulatory motifs are identified in the transcription regulatory network of the most studied model organism Escherichia coli (E. coli through graphical models. Network motifs are small structures in a network that appear more frequently than expected by chance alone. We apply social network methodologies such as p* models, also known as Exponential Random Graph Models (ERGMs, to identify statistically significant network motifs. In particular, we generate directed graphical models that can be applied to study interaction networks in a broad range of databases. The Markov Chain Monte Carlo (MCMC computational algorithms are implemented to obtain the estimates of model parameters to the corresponding network statistics. A variety of ERGMs are fitted to identify statistically significant network motifs in transcription regulatory networks of E. coli. A total of nine ERGMs are fitted to study the transcription factor - transcription factor interactions and eleven ERGMs are fitted for the transcription factor-operon interactions. For both of these interaction networks, arc (a directed edge in a directed network and k-istar (or incoming star structures, for values of k between 2 and 10, are found to be statistically significant local structures or network motifs. The goodness of fit statistics are provided to determine the quality of these models.

  13. Response Time Analysis of Messages in Controller Area Network: A Review

    Directory of Open Access Journals (Sweden)

    Gerardine Immaculate Mary

    2013-01-01

    Full Text Available This paper reviews the research work done on the response time analysis of messages in controller area network (CAN from the time CAN specification was submitted for standardization (1990 and became a standard (1993 up to the present (2012. Such research includes the worst-case response time analysis which is deterministic and probabilistic response time analysis which is stochastic. A detailed view on both types of analyses is presented here. In addition to these analyses, there has been research on statistical analysis of controller area network message response times.

  14. Identification of dysfunctional modules and disease genes in congenital heart disease by a network-based approach

    Directory of Open Access Journals (Sweden)

    He Danning

    2011-12-01

    Full Text Available Abstract Background The incidence of congenital heart disease (CHD is continuously increasing among infants born alive nowadays, making it one of the leading causes of infant morbidity worldwide. Various studies suggest that both genetic and environmental factors lead to CHD, and therefore identifying its candidate genes and disease-markers has been one of the central topics in CHD research. By using the high-throughput genomic data of CHD which are available recently, network-based methods provide powerful alternatives of systematic analysis of complex diseases and identification of dysfunctional modules and candidate disease genes. Results In this paper, by modeling the information flow from source disease genes to targets of differentially expressed genes via a context-specific protein-protein interaction network, we extracted dysfunctional modules which were then validated by various types of measurements and independent datasets. Network topology analysis of these modules revealed major and auxiliary pathways and cellular processes in CHD, demonstrating the biological usefulness of the identified modules. We also prioritized a list of candidate CHD genes from these modules using a guilt-by-association approach, which are well supported by various kinds of literature and experimental evidence. Conclusions We provided a network-based analysis to detect dysfunctional modules and disease genes of CHD by modeling the information transmission from source disease genes to targets of differentially expressed genes. Our method resulted in 12 modules from the constructed CHD subnetwork. We further identified and prioritized candidate disease genes of CHD from these dysfunctional modules. In conclusion, module analysis not only revealed several important findings with regard to the underlying molecular mechanisms of CHD, but also suggested the distinct network properties of causal disease genes which lead to identification of candidate CHD genes.

  15. [Rapid Identification of Epicarpium Citri Grandis via Infrared Spectroscopy and Fluorescence Spectrum Imaging Technology Combined with Neural Network].

    Science.gov (United States)

    Pan, Sha-sha; Huang, Fu-rong; Xiao, Chi; Xian, Rui-yi; Ma, Zhi-guo

    2015-10-01

    To explore rapid reliable methods for detection of Epicarpium citri grandis (ECG), the experiment using Fourier Transform Attenuated Total Reflection Infrared Spectroscopy (FTIR/ATR) and Fluorescence Spectrum Imaging Technology combined with Multilayer Perceptron (MLP) Neural Network pattern recognition, for the identification of ECG, and the two methods are compared. Infrared spectra and fluorescence spectral images of 118 samples, 81 ECG and 37 other kinds of ECG, are collected. According to the differences in tspectrum, the spectra data in the 550-1 800 cm(-1) wavenumber range and 400-720 nm wavelength are regarded as the study objects of discriminant analysis. Then principal component analysis (PCA) is applied to reduce the dimension of spectroscopic data of ECG and MLP Neural Network is used in combination to classify them. During the experiment were compared the effects of different methods of data preprocessing on the model: multiplicative scatter correction (MSC), standard normal variable correction (SNV), first-order derivative(FD), second-order derivative(SD) and Savitzky-Golay (SG). The results showed that: after the infrared spectra data via the Savitzky-Golay (SG) pretreatment through the MLP Neural Network with the hidden layer function as sigmoid, we can get the best discrimination of ECG, the correct percent of training set and testing set are both 100%. Using fluorescence spectral imaging technology, corrected by the multiple scattering (MSC) results in the pretreatment is the most ideal. After data preprocessing, the three layers of the MLP Neural Network of the hidden layer function as sigmoid function can get 100% correct percent of training set and 96.7% correct percent of testing set. It was shown that the FTIR/ATR and fluorescent spectral imaging technology combined with MLP Neural Network can be used for the identification study of ECG and has the advantages of rapid, reliable effect.

  16. From dusk till dawn: the Arabidopsis thaliana sugar starving responsive network

    Directory of Open Access Journals (Sweden)

    Maria Cecilia Arias

    2014-09-01

    Full Text Available Plant growth and development are tightly controlled by photosynthetic carbon availability. The understanding of mechanisms governing carbon partitioning in plants will be a valuable tool in order to satisfy the rising global demand for food and biofuel. The goal of this study was to determine if sugar starvation responses were transcriptionally coordinated in Arabidopsis thaliana. A set of sugar-starvation responsive (SSR genes was selected to perform a co-expression network analysis. Posteriorly, a guided-gene approach was used to identify the SSR-network from public data and to discover candidate regulators of this network. In order to validate the SSR network, a global transcriptome analysis was realized on three A. thaliana starch-deficient mutants. The starch-deficient phenotype in leaves induces sugar starvation syndrome at the end of the night due to the absence of photosynthesis. Promoter sequences of genes belonging to the SSR-network were analyzed in silico reveling over-represented motifs implicated in light, abscisic acid and sugar responses. A small cluster of protein encoding genes belonging to different metabolic pathways, including three regulatory proteins, a protein kinase, a transcription factor and a blue light receptor, were identified as the cornerstones of the SSR co-expression network. In summary, a large transcriptionally coordinated SSR network was identified and was validated with transcriptional data from three starch-deficient mutant lines. Candidate master regulators of this network were point out.

  17. Multitask learning of signaling and regulatory networks with application to studying human response to flu.

    Science.gov (United States)

    Jain, Siddhartha; Gitter, Anthony; Bar-Joseph, Ziv

    2014-12-01

    Reconstructing regulatory and signaling response networks is one of the major goals of systems biology. While several successful methods have been suggested for this task, some integrating large and diverse datasets, these methods have so far been applied to reconstruct a single response network at a time, even when studying and modeling related conditions. To improve network reconstruction we developed MT-SDREM, a multi-task learning method which jointly models networks for several related conditions. In MT-SDREM, parameters are jointly constrained across the networks while still allowing for condition-specific pathways and regulation. We formulate the multi-task learning problem and discuss methods for optimizing the joint target function. We applied MT-SDREM to reconstruct dynamic human response networks for three flu strains: H1N1, H5N1 and H3N2. Our multi-task learning method was able to identify known and novel factors and genes, improving upon prior methods that model each condition independently. The MT-SDREM networks were also better at identifying proteins whose removal affects viral load indicating that joint learning can still lead to accurate, condition-specific, networks. Supporting website with MT-SDREM implementation: http://sb.cs.cmu.edu/mtsdrem.

  18. Multitask learning of signaling and regulatory networks with application to studying human response to flu.

    Directory of Open Access Journals (Sweden)

    Siddhartha Jain

    2014-12-01

    Full Text Available Reconstructing regulatory and signaling response networks is one of the major goals of systems biology. While several successful methods have been suggested for this task, some integrating large and diverse datasets, these methods have so far been applied to reconstruct a single response network at a time, even when studying and modeling related conditions. To improve network reconstruction we developed MT-SDREM, a multi-task learning method which jointly models networks for several related conditions. In MT-SDREM, parameters are jointly constrained across the networks while still allowing for condition-specific pathways and regulation. We formulate the multi-task learning problem and discuss methods for optimizing the joint target function. We applied MT-SDREM to reconstruct dynamic human response networks for three flu strains: H1N1, H5N1 and H3N2. Our multi-task learning method was able to identify known and novel factors and genes, improving upon prior methods that model each condition independently. The MT-SDREM networks were also better at identifying proteins whose removal affects viral load indicating that joint learning can still lead to accurate, condition-specific, networks. Supporting website with MT-SDREM implementation: http://sb.cs.cmu.edu/mtsdrem.

  19. A Bayesian network driven approach to model the transcriptional response to nitric oxide in Saccharomyces cerevisiae.

    Directory of Open Access Journals (Sweden)

    Jingchun Zhu

    Full Text Available The transcriptional response to exogenously supplied nitric oxide in Saccharomyces cerevisiae was modeled using an integrated framework of Bayesian network learning and experimental feedback. A Bayesian network learning algorithm was used to generate network models of transcriptional output, followed by model verification and revision through experimentation. Using this framework, we generated a network model of the yeast transcriptional response to nitric oxide and a panel of other environmental signals. We discovered two environmental triggers, the diauxic shift and glucose repression, that affected the observed transcriptional profile. The computational method predicted the transcriptional control of yeast flavohemoglobin YHB1 by glucose repression, which was subsequently experimentally verified. A freely available software application, ExpressionNet, was developed to derive Bayesian network models from a combination of gene expression profile clusters, genetic information and experimental conditions.

  20. Neural network connectivity and response latency modelled by stochastic processes

    DEFF Research Database (Denmark)

    Tamborrino, Massimiliano

    is connected to thousands of other neurons. The rst question is: how to model neural networks through stochastic processes? A multivariate Ornstein-Uhlenbeck process, obtained as a diffusion approximation of a jump process, is the proposed answer. Obviously, dependencies between neurons imply dependencies......Stochastic processes and their rst passage times have been widely used to describe the membrane potential dynamics of single neurons and to reproduce neuronal spikes, respectively.However, cerebral cortex in human brains is estimated to contain 10-20 billions of neurons and each of them...... between their spike times. Therefore, the second question is: how to detect neural network connectivity from simultaneously recorded spike trains? Answering this question corresponds to investigate the joint distribution of sequences of rst passage times. A non-parametric method based on copulas...

  1. Nonprofit Organizations in Disaster Response and Management: A Network Analysis

    OpenAIRE

    NAIM KAPUCU; FARHOD YULDASHEV; MARY ANN FELDHEIM

    2018-01-01

    This paper tracks changes in the national disaster management system with regard to the nonprofit sector by looking at the roles ascribed to nonprofit organizations in the Federal Response Plan (FRP), National Response Plan (NRP), and National Response Framework (NRF). Additionally, the data collected from news reports and organizational after action reports about the inter-organizational interactions of emergency management agencies during the September 11 th attacks ...

  2. Fish community and bioassessment responses to stream network position

    Science.gov (United States)

    Hitt, N.P.; Angermeier, P.L.

    2011-01-01

    If organisms move beyond the boundaries of local sampling units, regional metacommunity dynamics could undermine the ability of bioassessment studies to characterize local environmental quality. We tested the prediction that fish dispersal influences local fish community structure and bioassessment metrics as a function of site position within stream networks. We evaluated fish community data from the US Environmental Protection Agency's Regional Environmental Monitoring and Assessment Program in West Virginia, USA, to compare the influences of stream network position, ecoregion, basin, and stream size on local fish community composition. We assigned sites to 1 of 3 stream network positions: 1) main channels (MC, n  =  12) encompassed streams with upstream catchment areas >200 km2, 2) mainstem tributaries (MT, n  =  43) flowed into MC-sized confluences within 15 fluvial km, 3) headwater tributaries (HT, n  =  31) lacked such riverine confluences within 15 fluvial km. MT and HT sites had similar upstream catchment sizes and landuse gradients, but species richness was greater in MT sites than HT sites, whereas MT and MC sites were not different in this regard. Three bioassessment metrics were greater in MT sites than HT sites (intolerant species richness, cyprinid species richness, benthic species richness), but a multimetric index of biotic integrity did not differ among stream network positions. Ordinations revealed that fish community composition was organized primarily by zoogeographic basin (Monongahela River basin, New River basin, Ohio River basin), ecoregion (Central Appalachian Plateau, Western Appalachian Plateau, Ridge and Valley), and stream size. Riverine specialists were more abundant in MT than HT sites and were more abundant in basins connected to the Ohio River than in basins isolated from the Ohio River by a large waterfall (New River). Our results suggest that contemporary dispersal among streams influences fish community composition

  3. Node Identification Using Inter-Regional Correlation Analysis for Mapping Detailed Connections in Resting State Networks

    Directory of Open Access Journals (Sweden)

    Yong Jeong

    2017-05-01

    Full Text Available Brain function is often characterized by the connections and interactions between highly interconnected brain regions. Pathological disruptions in these networks often result in brain dysfunction, which manifests as brain disease. Typical analysis investigates disruptions in network connectivity based correlations between large brain regions. To obtain a more detailed description of disruptions in network connectivity, we propose a new method where functional nodes are identified in each region based on their maximum connectivity to another brain region in a given network. Since this method provides a unique approach to identifying functionally relevant nodes in a given network, we can provide a more detailed map of brain connectivity and determine new measures of network connectivity. We applied this method to resting state fMRI of Alzheimer's disease patients to validate our method and found decreased connectivity within the default mode network. In addition, new measure of network connectivity revealed a more detailed description of how the network connections deteriorate with disease progression. This suggests that analysis using key relative network hub regions based on regional correlation can be used to detect detailed changes in resting state network connectivity.

  4. Identification of shareholder ethics and responsibilities in online reverse auctions for construction projects.

    Science.gov (United States)

    Hatipkarasulu, Yilmaz; Gill, James H

    2004-04-01

    The increasing number of companies providing internet services and auction tools helped popularize the online reverse auction trend for purchasing commodities and services in the last decade. As a result, a number of owners, both public and private, accepted the online reverse auctions as the bidding technique for their construction projects. Owners, while trying to minimize their costs for construction projects, are also required to address their ethical responsibilities to the shareholders. In the case of online reverse auctions for construction projects, the ethical issues involved in the bidding technique directly reflects on the owner's ethical and social responsibilities to their shareholders. The goal of this paper is to identify the shareholder ethics and responsibilities in online reverse auctions for construction projects by analyzing the ethical issues for the parties involved in the process. The identification of the ethical issues and responsibilities requires clear definition and understanding of professional ethics and the roles of the involved parties. In this paper, first, the concept of professional ethics and social responsibility is described in a general form. To illustrate the ethical issues and responsibilities, a sample case of bidding for a construction project using online reverse auction techniques is presented in which the shareholders were actively involved in questioning the ethical issues. The issues involved in the bidding process and their reflection on the shareholder responsibilities are described and analyzed for each stage of the process. A brief discussion of the overall process is also included to address the general ethical issues involved in online reverse auctions.

  5. Thinking in networks: artistic–architectural responses to ubiquitous information

    Directory of Open Access Journals (Sweden)

    Yvonne Spielmann

    2011-12-01

    Full Text Available The article discusses creative practices that in aesthetical-technical ways intervene into the computer networked communication systems.I am interested in artist practices that use networks in different ways to make us aware about the possibilities to rethink media-cultural environments. I use the example of the Japanese art-architectural group Double Negative Architecture to give an example of creatively thinking in networks.Yvonne Spielmann (Ph.D., Dr. habil. is presently Research Professor and Chair of New Media at The University of the West of Scotland. Her work focuses on inter-relationships between media and culture, technology, art, science and communication, and in particular on Western/European and non-Western/South-East Asian interaction. Milestones of publish research output are four authored monographs and about 90 single authored articles. Her book, “Video, the Reflexive Medium” (published by MIT Press 2008, Japanese edition by Sangen-sha Press 2011 was rewarded the 2009 Lewis Mumford Award for Outstanding Scholarship in the Ecology of Technics. Her most recent book “Hybrid Cultures” was published in German by Suhrkamp Press in 2010, English edition from MIT Press in 2012. Spielmann's work has been published in German and English and has been translated into French, Polish, Croatian, Swedish, Japanese, and Korean. She holds the 2011 Swedish Prize for Swedish–German scientific co-operation.

  6. Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

    Directory of Open Access Journals (Sweden)

    Chen Jiun-Ching

    2007-05-01

    Full Text Available Abstract Background Genome-wide identification of specific oligonucleotides (oligos is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos. Results We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes. Conclusion The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through

  7. Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis.

    Science.gov (United States)

    Liu, Chun-Chi; Lin, Chin-Chung; Li, Ker-Chau; Chen, Wen-Shyen E; Chen, Jiun-Ching; Yang, Ming-Te; Yang, Pan-Chyr; Chang, Pei-Chun; Chen, Jeremy J W

    2007-05-22

    Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos. We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB) algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes. The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through polymerase chain reaction experiments. Specific

  8. Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy Logic

    Science.gov (United States)

    Lu, Thomas; Pham, Timothy; Liao, Jason

    2011-01-01

    This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.

  9. Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data

    DEFF Research Database (Denmark)

    Brorsson, C.; Hansen, Niclas Tue; Hansen, Kasper Lage

    2009-01-01

    region were analysed in 1000 affected offspring trios generated by the Type 1 Diabetes Genetics Consortium (T1DGC). The most associated SNP in each gene was chosen and genes were mapped to ppi networks for identification of interaction partners. The association testing and resulting interacting protein...... are well known in the pathogenesis of T1D, but the modules also contain additional candidates that have been implicated in beta-cell development and diabetic complications. The extensive LD within the MHC region makes it important to develop new methods for analysing genotyping data for identification...... of additional risk genes for T1D. Combining genetic data with knowledge about functional pathways provides new insight into mechanisms underlying T1D....

  10. 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.

  11. Identification of direct and indirect social network effects in the pathophysiology of insulin resistance in obese human subjects.

    Directory of Open Access Journals (Sweden)

    Christian H C A Henning

    Full Text Available OBJECTIVE: The aim of the present study was to examine to what extent different social network mechanisms are involved in the pathogenesis of obesity and insulin-resistance. DESIGN: We used nonparametric and parametric regression models to analyse whether individual BMI and HOMA-IR are determined by social network characteristics. SUBJECTS AND METHODS: A total of 677 probands (EGO and 3033 social network partners (ALTER were included in the study. Data gathered from the probands include anthropometric measures, HOMA-IR index, health attitudes, behavioural and socio-economic variables and social network data. RESULTS: We found significant treatment effects for ALTERs frequent dieting (p<0.001 and ALTERs health oriented nutritional attitudes (p<0.001 on EGO's BMI, establishing a significant indirect network effect also on EGO's insulin resistance. Most importantly, we also found significant direct social network effects on EGO's insulin resistance, evidenced by an effect of ALTERs frequent dieting (p = 0.033 and ALTERs sport activities (p = 0.041 to decrease EGO's HOMA-IR index independently of EGO's BMI. CONCLUSIONS: Social network phenomena appear not only to be relevant for the spread of obesity, but also for the spread of insulin resistance as the basis for type 2 diabetes. Attitudes and behaviour of peer groups influence EGO's health status not only via social mechanisms, but also via socio-biological mechanisms, i.e. higher brain areas might be influenced not only by biological signals from the own organism, but also by behaviour and knowledge from different human individuals. Our approach allows the identification of peer group influence controlling for potential homophily even when using cross-sectional observational data.

  12. Neuronal response impedance mechanism implementing cooperative networks with low firing rates and μs precision.

    Science.gov (United States)

    Vardi, Roni; Goldental, Amir; Marmari, Hagar; Brama, Haya; Stern, Edward A; Sardi, Shira; Sabo, Pinhas; Kanter, Ido

    2015-01-01

    Realizations of low firing rates in neural networks usually require globally balanced distributions among excitatory and inhibitory links, while feasibility of temporal coding is limited by neuronal millisecond precision. We show that cooperation, governing global network features, emerges through nodal properties, as opposed to link distributions. Using in vitro and in vivo experiments we demonstrate microsecond precision of neuronal response timings under low stimulation frequencies, whereas moderate frequencies result in a chaotic neuronal phase characterized by degraded precision. Above a critical stimulation frequency, which varies among neurons, response failures were found to emerge stochastically such that the neuron functions as a low pass filter, saturating the average inter-spike-interval. This intrinsic neuronal response impedance mechanism leads to cooperation on a network level, such that firing rates are suppressed toward the lowest neuronal critical frequency simultaneously with neuronal microsecond precision. Our findings open up opportunities of controlling global features of network dynamics through few nodes with extreme properties.

  13. Development of visible-light responsive and mechanically enhanced "smart" UCST interpenetrating network hydrogels.

    Science.gov (United States)

    Xu, Yifei; Ghag, Onkar; Reimann, Morgan; Sitterle, Philip; Chatterjee, Prithwish; Nofen, Elizabeth; Yu, Hongyu; Jiang, Hanqing; Dai, Lenore L

    2017-12-20

    An interpenetrating polymer network (IPN), chlorophyllin-incorporated environmentally responsive hydrogel was synthesized and exhibited the following features: enhanced mechanical properties, upper critical solution temperature (UCST) swelling behavior, and promising visible-light responsiveness. Poor mechanical properties are known challenges for hydrogel-based materials. By forming an interpenetrating network between polyacrylamide (PAAm) and poly(acrylic acid) (PAAc) polymer networks, the mechanical properties of the synthesized IPN hydrogels were significantly improved compared to hydrogels made of a single network of each polymer. The formation of the interpenetrating network was confirmed by Fourier Transform Infrared Spectroscopy (FTIR), the analysis of glass transition temperature, and a unique UCST responsive swelling behavior, which is in contrast to the more prevalent lower critical solution temperature (LCST) behaviour of environmentally responsive hydrogels. The visible-light responsiveness of the synthesized hydrogel also demonstrated a positive swelling behavior, and the effect of incorporating chlorophyllin as the chromophore unit was observed to reduce the average pore size and further enhance the mechanical properties of the hydrogel. This interpenetrating network system shows potential to serve as a new route in developing "smart" hydrogels using visible-light as a simple, inexpensive, and remotely controllable stimulus.

  14. The Strategic Impact of Corporate Responsibility and Criminal Networks on Value Co-Creation

    Directory of Open Access Journals (Sweden)

    Peter Zettinig

    2011-02-01

    Full Text Available This article is motivated by the increasing concern about the ever-declining security of pharmaceutical products due to the abundance of counterfeit network actors. We argue that if networks are effective mechanisms for criminal organizations to infiltrate into any value chain, then networks should also work for responsible businesses in their quests to counter this phenomenon of value destruction, which is ultimately detrimental to the value co-creation process. Thus, this article demonstrates a nuanced understanding of the strategic impact of corporate responsibility of actors in networks on value co-creation. The current discourse on value co-creation in business networks is structured in such a way that it precludes its inherent corporate responsibility component even though they are not mutually exclusive. Moreover, research on value co-creation aimed at the proactive and responsible defence of a network substance via value co-protection has been mostly scant. We propose a model of value-optimization through value co-protection and ethical responsibility. This way of theorizing has several implications for both policy making and managerial decision making in the pharmaceutical industry and beyond.

  15. Morphology effect on the light scattering and dynamic response of polymer network liquid crystal phase modulator.

    Science.gov (United States)

    Xiangjie, Zhao; Cangli, Liu; Jiazhu, Duan; Jiancheng, Zeng; Dayong, Zhang; Yongquan, Luo

    2014-06-16

    Polymer network liquid crystal (PNLC) was one of the most potential liquid crystal for submillisecond response phase modulation, which was possible to be applied in submillisecond response phase only spatial light modulator. But until now the light scattering when liquid crystal director was reoriented by external electric field limited its phase modulation application. Dynamic response of phase change when high voltage was applied was also not elucidated. The mechanism that determines the light scattering was studied by analyzing the polymer network morphology by SEM method. Samples were prepared by varying the polymerization temperature, UV curing intensity and polymerization time. The morphology effect on the dynamic response of phase change was studied, in which high voltage was usually applied and electro-striction effect was often induced. The experimental results indicate that the polymer network morphology was mainly characterized by cross linked single fibrils, cross linked fibril bundles or even both. Although the formation of fibril bundle usually induced large light scattering, such a polymer network could endure higher voltage. In contrast, although the formation of cross linked single fibrils induced small light scattering, such a polymer network cannot endure higher voltage. There is a tradeoff between the light scattering and high voltage endurance. The electro-optical properties such as threshold voltage and response time were taken to verify our conclusion. For future application, the monomer molecular structure, the liquid crystal solvent and the polymerization conditions should be optimized to generate optimal polymer network morphology.

  16. From network models to network responses: integration of thermodynamic and kinetic properties of yeast genome-scale metabolic networks.

    Science.gov (United States)

    Soh, Keng Cher; Miskovic, Ljubisa; Hatzimanikatis, Vassily

    2012-03-01

    Many important problems in cell biology arise from the dense nonlinear interactions between functional modules. The importance of mathematical modelling and computer simulation in understanding cellular processes is now indisputable and widely appreciated. Genome-scale metabolic models have gained much popularity and utility in helping us to understand and test hypotheses about these complex networks. However, there are some caveats that come with the use and interpretation of different types of metabolic models, which we aim to highlight here. We discuss and illustrate how the integration of thermodynamic and kinetic properties of the yeast metabolic networks in network analyses can help in understanding and utilizing this organism more successfully in the areas of metabolic engineering, synthetic biology and disease treatment. © 2011 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

  17. Brain Networks Responsible for Sense of Agency: An EEG Study.

    Directory of Open Access Journals (Sweden)

    Suk Yun Kang

    Full Text Available Self-agency (SA is a person's feeling that his action was generated by himself. The neural substrates of SA have been investigated in many neuroimaging studies, but the functional connectivity of identified regions has rarely been investigated. The goal of this study is to investigate the neural network related to SA.SA of hand movements was modulated with virtual reality. We examined the cortical network relating to SA modulation with electroencephalography (EEG power spectrum and phase coherence of alpha, beta, and gamma frequency bands in 16 right-handed, healthy volunteers.In the alpha band, significant relative power changes and phase coherence of alpha band were associated with SA modulation. The relative power decrease over the central, bilateral parietal, and right temporal regions (C4, Pz, P3, P4, T6 became larger as participants more effectively controlled the virtual hand movements. The phase coherence of the alpha band within frontal areas (F7-FP2, F7-Fz was directly related to changes in SA. The functional connectivity was lower as the participants felt that they could control their virtual hand. In the other frequency bands, significant phase coherences were observed in the frontal (or central to parietal, temporal, and occipital regions during SA modulation (Fz-O1, F3-O1, Cz-O1, C3-T4L in beta band; FP1-T6, FP1-O2, F7-T4L, F8-Cz in gamma band.Our study suggests that alpha band activity may be the main neural oscillation of SA, which suggests that the neural network within the anterior frontal area may be important in the generation of SA.

  18. Brain Networks Responsible for Sense of Agency: An EEG Study.

    Science.gov (United States)

    Kang, Suk Yun; Im, Chang-Hwan; Shim, Miseon; Nahab, Fatta B; Park, Jihye; Kim, Do-Won; Kakareka, John; Miletta, Nathanial; Hallett, Mark

    2015-01-01

    Self-agency (SA) is a person's feeling that his action was generated by himself. The neural substrates of SA have been investigated in many neuroimaging studies, but the functional connectivity of identified regions has rarely been investigated. The goal of this study is to investigate the neural network related to SA. SA of hand movements was modulated with virtual reality. We examined the cortical network relating to SA modulation with electroencephalography (EEG) power spectrum and phase coherence of alpha, beta, and gamma frequency bands in 16 right-handed, healthy volunteers. In the alpha band, significant relative power changes and phase coherence of alpha band were associated with SA modulation. The relative power decrease over the central, bilateral parietal, and right temporal regions (C4, Pz, P3, P4, T6) became larger as participants more effectively controlled the virtual hand movements. The phase coherence of the alpha band within frontal areas (F7-FP2, F7-Fz) was directly related to changes in SA. The functional connectivity was lower as the participants felt that they could control their virtual hand. In the other frequency bands, significant phase coherences were observed in the frontal (or central) to parietal, temporal, and occipital regions during SA modulation (Fz-O1, F3-O1, Cz-O1, C3-T4L in beta band; FP1-T6, FP1-O2, F7-T4L, F8-Cz in gamma band). Our study suggests that alpha band activity may be the main neural oscillation of SA, which suggests that the neural network within the anterior frontal area may be important in the generation of SA.

  19. Emergence of microbial networks as response to hostile environments.

    Science.gov (United States)

    Madeo, Dario; Comolli, Luis R; Mocenni, Chiara

    2014-01-01

    The majority of microorganisms live in complex communities under varying conditions. One pivotal question in evolutionary biology is the emergence of cooperative traits and their sustainment in altered environments or in the presence of free-riders. Co-occurrence patterns in the spatial distribution of biofilms can help define species' identities, and systems biology tools are revealing networks of interacting microorganisms. However, networks of inter-dependencies involving micro-organisms in the planktonic phase may be just as important, with the added complexity that they are not bounded in space. An integrated approach linking imaging, "Omics" and modeling has the potential to enable new hypothesis and working models. In order to understand how cooperation can emerge and be maintained without abilities like memory or recognition we use evolutionary game theory as the natural framework to model cell-cell interactions arising from evolutive decisions. We consider a finite population distributed in a spatial domain (biofilm), and divided into two interacting classes with different traits. This interaction can be weighted by distance, and produces physical connections between two elements allowing them to exchange finite amounts of energy and matter. Available strategies to each individual of one class in the population are the propensities or "willingness" to connect any individual of the other class. Following evolutionary game theory, we propose a mathematical model which explains the patterns of connections which emerge when individuals are able to find connection strategies that asymptotically optimize their fitness. The process explains the formation of a network for efficiently exchanging energy and matter among individuals and thus ensuring their survival in hostile environments.

  20. Emergence of microbial networks as response to hostile environments

    Directory of Open Access Journals (Sweden)

    Dario eMadeo

    2014-08-01

    Full Text Available The majority of microorganisms live in complex communities under varying conditions. One pivotal question in evolutionary biology is the emergence of cooperative traits and their sustainment in altered environments or in the presence of free-riders. Co-occurrence patterns in the spatial distribution of biofilms can help define species' identities, and systems biology tools are revealing networks of interacting microorganisms. However, networks of inter-dependencies involving micro-organisms in the planktonic phase may be just as important, with the added complexity that they are not bounded in space. An integrated approach linking imaging, ``Omics'' and modeling has the potential to enable new hypothesis and working models. In order to understand how cooperation can emerge and be maintained without abilities like memory or recognition we use evolutionary game theory as the natural framework to model cell-cell interactions arising from evolutive decisions. We consider a finite population distributed in a spatial domain (biofilm, and divided into two interacting classes with different traits. This interaction can be weighted by distance, and produces physical connections between two elements allowing them to exchange finite amounts of energy and matter. Available strategies to each individual of one class in the population are the propensities or ``willingness'' to connect any individual of the other class. Following evolutionary game theory, we propose a mathematical model which explains the patterns of connections which emerge when individuals are able to find connection strategies that asymptotically optimize their fitness. The process explains the formation of a network for efficiently exchanging energy and matter among individuals and thus ensuring their survival in hostile environments.

  1. Networks of High Mutual Information Define the Structural Proximity of Catalytic Sites: Implications for Catalytic Residue Identification

    DEFF Research Database (Denmark)

    Buslje, Cristina Marino; Teppa, Elin; Di Doménico, Tomas

    2010-01-01

    . A structural proximity conservation average score (termed pC) was also calculated and demonstrated to carry distinct information from pMI. A catalytic likeliness score (Cls), combining the KL, pC and pMI measures, was shown to lead to significantly improved prediction accuracy. At a specificity of 0...... throughout a given protein family making identification of CR a challenging task. Here, we put forward the hypothesis that CR carry a particular signature defined by networks of close proximity residues with high mutual information (MI), and that this signature can be applied to distinguish functional from.......90, the Cls method was found to have a sensitivity of 0.816. In summary, we demonstrate that networks of residues with high MI provide a distinct signature on CR and propose that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function...

  2. Demand Response in Low Voltage Distribution Networks with High PV Penetration

    DEFF Research Database (Denmark)

    Nainar, Karthikeyan; Pokhrel, Basanta Raj; Pillai, Jayakrishnan Radhakrishna

    2017-01-01

    . Simulation results show that the proposed method is effective for calculating the optimum demand response. From the test scenarios, it is inferred that absorption of renewable energy from PV increased by 38% applying optimum demand response during the evaluation period in the studied distribution network....

  3. Identification of the actual state and entity availability forecasting in power engineering using neural-network technologies

    Science.gov (United States)

    Protalinsky, O. M.; Shcherbatov, I. A.; Stepanov, P. V.

    2017-11-01

    A growing number of severe accidents in RF call for the need to develop a system that could prevent emergency situations. In a number of cases accident rate is stipulated by careless inspections and neglects in developing repair programs. Across the country rates of accidents are growing because of a so-called “human factor”. In this regard, there has become urgent the problem of identification of the actual state of technological facilities in power engineering using data on engineering processes running and applying artificial intelligence methods. The present work comprises four model states of manufacturing equipment of engineering companies: defect, failure, preliminary situation, accident. Defect evaluation is carried out using both data from SCADA and ASEPCR and qualitative information (verbal assessments of experts in subject matter, photo- and video materials of surveys processed using pattern recognition methods in order to satisfy the requirements). Early identification of defects makes possible to predict the failure of manufacturing equipment using mathematical techniques of artificial neural network. In its turn, this helps to calculate predicted characteristics of reliability of engineering facilities using methods of reliability theory. Calculation of the given parameters provides the real-time estimation of remaining service life of manufacturing equipment for the whole operation period. The neural networks model allows evaluating possibility of failure of a piece of equipment consistent with types of actual defects and their previous reasons. The article presents the grounds for a choice of training and testing samples for the developed neural network, evaluates the adequacy of the neural networks model, and shows how the model can be used to forecast equipment failure. There have been carried out simulating experiments using a computer and retrospective samples of actual values for power engineering companies. The efficiency of the developed

  4. Ubiquitous robust communications for emergency response using multi-operator heterogeneous networks

    Directory of Open Access Journals (Sweden)

    Verikoukis Christos

    2011-01-01

    Full Text Available Abstract A number of disasters in various places of the planet have caused an extensive loss of lives, severe damages to properties and the environment, as well as a tremendous shock to the survivors. For relief and mitigation operations, emergency responders are immediately dispatched to the disaster areas. Ubiquitous and robust communications during the emergency response operations are of paramount importance. Nevertheless, various reports have highlighted that after many devastating events, the current technologies used, failed to support the mission critical communications, resulting in further loss of lives. Inefficiencies of the current communications used for emergency response include lack of technology inter-operability between different jurisdictions, and high vulnerability due to their centralized infrastructure. In this article, we propose a flexible network architecture that provides a common networking platform for heterogeneous multi-operator networks, for interoperation in case of emergencies. A wireless mesh network is the main part of the proposed architecture and this provides a back-up network in case of emergencies. We first describe the shortcomings and limitations of the current technologies, and then we address issues related to the applications and functionalities a future emergency response network should support. Furthermore, we describe the necessary requirements for a flexible, secure, robust, and QoS-aware emergency response multi-operator architecture, and then we suggest several schemes that can be adopted by our proposed architecture to meet those requirements. In addition, we suggest several methods for the re-tasking of communication means owned by independent individuals to provide support during emergencies. In order to investigate the feasibility of multimedia transmission over a wireless mesh network, we measured the performance of a video streaming application in a real wireless metropolitan multi

  5. An Intelligent Traffic Flow Control System Based on Radio Frequency Identification and Wireless Sensor Networks

    National Research Council Canada - National Science Library

    Chao, Kuei-Hsiang; Chen, Pi-Yun

    2014-01-01

    This study primarily focuses on the use of radio frequency identification (RFID) as a form of traffic flow detection, which transmits collected information related to traffic flow directly to a control system through an RS232 interface...

  6. Repeated exposure to media violence is associated with diminished response in an inhibitory frontolimbic network.

    Science.gov (United States)

    Kelly, Christopher R; Grinband, Jack; Hirsch, Joy

    2007-12-05

    Media depictions of violence, although often claimed to induce viewer aggression, have not been shown to affect the cortical networks that regulate behavior. Using functional magnetic resonance imaging (fMRI), we found that repeated exposure to violent media, but not to other equally arousing media, led to both diminished response in right lateral orbitofrontal cortex (right ltOFC) and a decrease in right ltOFC-amygdala interaction. Reduced function in this network has been previously associated with decreased control over a variety of behaviors, including reactive aggression. Indeed, we found reduced right ltOFC responses to be characteristic of those subjects that reported greater tendencies toward reactive aggression. Furthermore, the violence-induced reduction in right ltOFC response coincided with increased throughput to behavior planning regions. These novel findings establish that even short-term exposure to violent media can result in diminished responsiveness of a network associated with behaviors such as reactive aggression.

  7. Systematic reverse engineering of network topologies: a case study of resettable bistable cellular responses.

    Science.gov (United States)

    Mondal, Debasish; Dougherty, Edward; Mukhopadhyay, Abhishek; Carbo, Adria; Yao, Guang; Xing, Jianhua

    2014-01-01

    A focused theme in systems biology is to uncover design principles of biological networks, that is, how specific network structures yield specific systems properties. For this purpose, we have previously developed a reverse engineering procedure to identify network topologies with high likelihood in generating desired systems properties. Our method searches the continuous parameter space of an assembly of network topologies, without enumerating individual network topologies separately as traditionally done in other reverse engineering procedures. Here we tested this CPSS (continuous parameter space search) method on a previously studied problem: the resettable bistability of an Rb-E2F gene network in regulating the quiescence-to-proliferation transition of mammalian cells. From a simplified Rb-E2F gene network, we identified network topologies responsible for generating resettable bistability. The CPSS-identified topologies are consistent with those reported in the previous study based on individual topology search (ITS), demonstrating the effectiveness of the CPSS approach. Since the CPSS and ITS searches are based on different mathematical formulations and different algorithms, the consistency of the results also helps cross-validate both approaches. A unique advantage of the CPSS approach lies in its applicability to biological networks with large numbers of nodes. To aid the application of the CPSS approach to the study of other biological systems, we have developed a computer package that is available in Information S1.

  8. [The Identification of the Origin of Chinese Wolfberry Based on Infrared Spectral Technology and the Artificial Neural Network].

    Science.gov (United States)

    Li, Zhong; Liu, Ming-de; Ji, Shou-xiang

    2016-03-01

    combined with the artificial neural networks is proved to be a reliable and new method for the identification of the original place of Traditional Chinese Medicine.

  9. Identification of Lactobacillus plantarum genes modulating the cytokine response of human peripheral blood mononuclear cells

    Directory of Open Access Journals (Sweden)

    Molenaar Douwe

    2010-11-01

    Full Text Available Abstract Background Modulation of the immune system is one of the most plausible mechanisms underlying the beneficial effects of probiotic bacteria on human health. Presently, the specific probiotic cell products responsible for immunomodulation are largely unknown. In this study, the genetic and phenotypic diversity of strains of the Lactobacillus plantarum species were investigated to identify genes of L. plantarum with the potential to influence the amounts of cytokines interleukin 10 (IL-10 and IL-12 and the ratio of IL-10/IL-12 produced by peripheral blood mononuclear cells (PBMCs. Results A total of 42 Lactobacillus plantarum strains isolated from diverse environmental and human sources were evaluated for their capacity to stimulate cytokine production in PBMCs. The L. plantarum strains induced the secretion of the anti-inflammatory cytokine IL-10 over an average 14-fold range and secretion of the pro-inflammatory cytokine IL-12 over an average 16-fold range. Comparisons of the strain-specific cytokine responses of PBMCs to comparative genome hybridization profiles obtained with L. plantarum WCFS1 DNA microarrays (also termed gene-trait matching resulted in the identification of 6 candidate genetic loci with immunomodulatory capacities. These loci included genes encoding an N-acetyl-glucosamine/galactosamine phosphotransferase system, the LamBDCA quorum sensing system, and components of the plantaricin (bacteriocin biosynthesis and transport pathway. Deletion of these genes in L. plantarum WCFS1 resulted in growth phase-dependent changes in the PBMC IL-10 and IL-12 cytokine profiles compared with wild-type cells. Conclusions The altered PBMC cytokine profiles obtained with the L. plantarum WCFS1 mutants were in good agreement with the predictions made by gene-trait matching for the 42 L. plantarum strains. This study therefore resulted in the identification of genes present in certain strains of L. plantarum which might be responsible for

  10. Proteomic identification of early salicylate- and flg22-responsive redox-sensitive proteins in Arabidopsis

    KAUST Repository

    Liu, Peng

    2015-02-27

    Accumulation of reactive oxygen species (ROS) is one of the early defense responses against pathogen infection in plants. The mechanism about the initial and direct regulation of the defense signaling pathway by ROS remains elusive. Perturbation of cellular redox homeostasis by ROS is believed to alter functions of redox-sensitive proteins through their oxidative modifications. Here we report an OxiTRAQ-based proteomic study in identifying proteins whose cysteines underwent oxidative modifications in Arabidopsis cells during the early response to salicylate or flg22, two defense pathway elicitors that are known to disturb cellular redox homeostasis. Among the salicylate- and/or flg22-responsive redox-sensitive proteins are those involved in transcriptional regulation, chromatin remodeling, RNA processing, post-translational modifications, and nucleocytoplasmic shuttling. The identification of the salicylate-/flg22-responsive redox-sensitive proteins provides a foundation from which further study can be conducted toward understanding biological significance of their oxidative modifications during the plant defense response.

  11. Anomaly based intrusion detection for a biometric identification system using neural networks

    CSIR Research Space (South Africa)

    Mgabile, T

    2012-10-01

    Full Text Available detection technique that analyses the fingerprint biometric network traffic for evidence of intrusion. The neural network algorithm that imitates the way a human brain works is used in this study to classify normal traffic and learn the correct traffic...

  12. Process identification through modular neural networks and rule extraction (extended abstract)

    NARCIS (Netherlands)

    van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, L.; Blockeel, Hendrik; Denecker, Marc

    2002-01-01

    Monolithic neural networks may be trained from measured data to establish knowledge about the process. Unfortunately, this knowledge is not guaranteed to be found and – if at all – hard to extract. Modular neural networks are better suited for this purpose. Domain-ordered by topology, rule

  13. Enriching Professional Learning Networks: A Framework for Identification, Reflection, and Intention

    Science.gov (United States)

    Krutka, Daniel G.; Carpenter, Jeffrey Paul; Trust, Torrey

    2017-01-01

    Many educators in the 21st century utilize social media platforms to enrich professional learning networks (PLNs). PLNs are uniquely personalized networks that can support participatory and continuous learning. Social media services can mediate professional engagements with a wide variety of people, spaces and tools that might not otherwise be…

  14. THE INTERPLANETARY NETWORK RESPONSE TO LIGO GW150914

    Energy Technology Data Exchange (ETDEWEB)

    Hurley, K. [University of California, Berkeley, Space Sciences Laboratory, 7 Gauss Way, Berkeley, CA 94720-7450 (United States); Svinkin, D. S.; Aptekar, R. L.; Golenetskii, S. V.; Frederiks, D. D. [Ioffe Physical Technical Institute, Politekhnicheskaya 26, St. Petersburg 194021 (Russian Federation); Boynton, W. [University of Arizona, Department of Planetary Sciences, Tucson, AZ 85721 (United States); Mitrofanov, I. G.; Golovin, D. V.; Kozyrev, A. S.; Litvak, M. L.; Sanin, A. B. [Space Research Institute, 84/32, Profsoyuznaya, Moscow 117997 (Russian Federation); Rau, A.; Kienlin, A. von; Zhang, X. [Max-Planck-Institut für extraterrestrische Physik, Giessenbachstrasse, Postfach 1312, Garching, D-85748 Germany (Germany); Connaughton, V.; Meegan, C. [University of Alabama in Huntsville, NSSTC, 320 Sparkman Drive, Huntsville, AL 35805 (United States); Cline, T.; Gehrels, N., E-mail: khurley@ssl.berkeley.edu [NASA Goddard Space Flight Center, Code 661, Greenbelt, MD 20771 (United States)

    2016-09-20

    We have performed a blind search for a gamma-ray transient of arbitrary duration and energy spectrum around the time of the LIGO gravitational-wave event GW150914 with the six-spacecraft interplanetary network (IPN). Four gamma-ray bursts were detected between 30 hr prior to the event and 6.1 hr after it, but none could convincingly be associated with GW150914. No other transients were detected down to limiting 15–150 keV fluences of roughly 5 ×10{sup −8}–5 × 10{sup −7} erg cm{sup −2}. We discuss the search strategies and temporal coverage of the IPN on the day of the event and compare the spatial coverage to the region where GW150914 originated. We also report the negative result of a targeted search for the Fermi -GBM event reported in conjunction with GW150914.

  15. Networks of Corporate Social Responsibility in Brazil and Argentina

    Directory of Open Access Journals (Sweden)

    Luciana de Oliveira

    2012-07-01

    Full Text Available In research on CSR in Brazil and Argentina, we saw a huge disparity in the grass-roots of the theme in each country. The hypothesis, stemming from this article, is that the great success of the CSR movement in Brazil and its relatively weak development in Argentina is due to the presence of a hegemonic dispute - in observed dynamic communication - among some segments of business elites regarding the purpose of CSR, present in the Brazilian case and absent in Argentina. This article intends to discuss the concept of hegemonic dispute - highlighting conflict as a fundamental social relationship in the configuration of network topographies of movements to promote CSR in both countries. Such an understanding is essential for professionals and researchers working in the area of ​​organizational communication.

  16. Genome-wide identification of binding sites defines distinct functions for Caenorhabditis elegans PHA-4/FOXA in development and environmental response.

    Directory of Open Access Journals (Sweden)

    Mei Zhong

    2010-02-01

    Full Text Available Transcription factors are key components of regulatory networks that control development, as well as the response to environmental stimuli. We have established an experimental pipeline in Caenorhabditis elegans that permits global identification of the binding sites for transcription factors using chromatin immunoprecipitation and deep sequencing. We describe and validate this strategy, and apply it to the transcription factor PHA-4, which plays critical roles in organ development and other cellular processes. We identified thousands of binding sites for PHA-4 during formation of the embryonic pharynx, and also found a role for this factor during the starvation response. Many binding sites were found to shift dramatically between embryos and starved larvae, from developmentally regulated genes to genes involved in metabolism. These results indicate distinct roles for this regulator in two different biological processes and demonstrate the versatility of transcription factors in mediating diverse biological roles.

  17. Identification of emotion associated brain functional network with phase locking value.

    Science.gov (United States)

    Gonuguntla, V; Mallipeddi, R; Veluvolu, K C

    2016-08-01

    Recognition of discriminative brain functional network pattern and regions corresponding to emotions are important in understanding the neuron functional network underlying the human emotion process. Emotion models mapping onto brain is possible with the help of emotion-specific network patterns and its corresponding brain regions. This paper presents a method to identify emotion related functional connectivity pattern and their distinctive associated regions using EEG phase synchrony (phase locking value (PLV)) connectivity analysis. The emotion-specific channel pairs, reactive band, and synchrony related locations are identified based on the network dissimilarities between emotion and rest tasks. With the most reactive pairs identified, the emotion-specific functional network is formed. The proposed method is validated on `database for emotion analysis using physiological signals (DEAP)' that confirms the distinct nature of identified functional connectivity pattern and the regions corresponding to the emotion.

  18. Constructing an integrated gene similarity network for the identification of disease genes.

    Science.gov (United States)

    Tian, Zhen; Guo, Maozu; Wang, Chunyu; Xing, LinLin; Wang, Lei; Zhang, Yin

    2017-09-20

    Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale. We proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer's disease and predict some novel disease genes that supported by literature. RWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/ .

  19. Response Time Analysis of Messages in Controller Area Network: A Review

    OpenAIRE

    Gerardine Immaculate Mary; Z. C. ALEX; Lawrence Jenkins

    2013-01-01

    This paper reviews the research work done on the response time analysis of messages in controller area network (CAN) from the time CAN specification was submitted for standardization (1990) and became a standard (1993) up to the present (2012). Such research includes the worst-case response time analysis which is deterministic and probabilistic response time analysis which is stochastic. A detailed view on both types of analyses is presented here. In addition to these analyses, there has been...

  20. Parent Social Networks and Parent Responsibility: Implications for School Leadership

    Science.gov (United States)

    Curry, Katherine A.; Adams, Curt M.

    2014-01-01

    Family-school partnerships are difficult to initiate and sustain in ways that actually promote student learning, especially in high-poverty communities. This quantitative study was designed to better understand how social forces shape parent responsibility in education. Based on social cognitive theory as the conceptual framework, the…

  1. Support of Future Disaster Response Using Generalized Access Networks (GANs)

    NARCIS (Netherlands)

    Karagiannis, Georgios; Jones, Valerie M.; Heemstra de Groot, S.M.; Koutsouris, D.; Fotiadis, D.I.

    2006-01-01

    Efficient communication and coordination are major challenges experienced by the emergency services (firebrigade, police, ambulance) during the first response to a major incident. A major incident can happen anywhere and at any time, hence support for emergency communications services should be

  2. Support of Future Disaster Response Using Generalized Access Networks (GANs)

    NARCIS (Netherlands)

    Karagiannis, Georgios; Jones, Valerie M.; Heemstra de Groot, S.M.

    Efficient communication and coordination are major challenges experienced by the emergency services (firebrigade, police, ambulance) during the first response to a major incident. A major incident can happen anywhere and at any time, hence support for emergency communications services should be

  3. Social Networks in Crisis Response: Trust is Vital

    Science.gov (United States)

    2016-06-01

    1  Introduction ...AGILE-17 is the next wargame in the series and the direct successor to LOGWAR-15. 3 Introduction The ad hoc nature of current responses to...women, children, and the elderly. Refugees arrived in poor physical condition. Malaria , gastroenteritis, and various bronchopulmonary infections

  4. Real-Time Transportation Mode Identification Using Artificial Neural Networks Enhanced with Mode Availability Layers: A Case Study in Dubai

    Directory of Open Access Journals (Sweden)

    Young-Ji Byon

    2017-09-01

    Full Text Available Traditionally, departments of transportation (DOTs have dispatched probe vehicles with dedicated vehicles and drivers for monitoring traffic conditions. Emerging assisted GPS (AGPS and accelerometer-equipped smartphones offer new sources of raw data that arise from voluntarily-traveling smartphone users provided that their modes of transportation can correctly be identified. By introducing additional raster map layers that indicate the availability of each mode, it is possible to enhance the accuracy of mode detection results. Even in its simplest form, an artificial neural network (ANN excels at pattern recognition with a relatively short processing timeframe once it is properly trained, which is suitable for real-time mode identification purposes. Dubai is one of the major cities in the Middle East and offers unique environments, such as a high density of extremely high-rise buildings that may introduce multi-path errors with GPS signals. This paper develops real-time mode identification ANNs enhanced with proposed mode availability geographic information system (GIS layers, firstly for a universal mode detection and, secondly for an auto mode detection for the particular intelligent transportation system (ITS application of traffic monitoring, and compares the results with existing approaches. It is found that ANN-based real-time mode identification, enhanced by mode availability GIS layers, significantly outperforms the existing methods.

  5. Location and release time identification of pollution point source in river networks based on the Backward Probability Method.

    Science.gov (United States)

    Ghane, Alireza; Mazaheri, Mehdi; Mohammad Vali Samani, Jamal

    2016-09-15

    The pollution of rivers due to accidental spills is a major threat to environment and human health. To protect river systems from accidental spills, it is essential to introduce a reliable tool for identification process. Backward Probability Method (BPM) is one of the most recommended tools that is able to introduce information related to the prior location and the release time of the pollution. This method was originally developed and employed in groundwater pollution source identification problems. One of the objectives of this study is to apply this method in identifying the pollution source location and release time in surface waters, mainly in rivers. To accomplish this task, a numerical model is developed based on the adjoint analysis. Then the developed model is verified using analytical solution and some real data. The second objective of this study is to extend the method to pollution source identification in river networks. In this regard, a hypothetical test case is considered. In the later simulations, all of the suspected points are identified, using only one backward simulation. The results demonstrated that all suspected points, determined by the BPM could be a possible pollution source. The proposed approach is accurate and computationally efficient and does not need any simplification in river geometry and flow. Due to this simplicity, it is highly recommended for practical purposes. Copyright © 2016. Published by Elsevier Ltd.

  6. Adaptive synchronization of drive-response fractional-order complex dynamical networks with uncertain parameters

    Science.gov (United States)

    Yang, Li-xin; Jiang, Jun

    2014-05-01

    This paper investigates the adaptive synchronization in the drive-response fractional-order dynamical networks with uncertain parameters. By means of both the stability theory of fractional-order differential system and the adaptive control technique, a novel adaptive synchronization controller is developed with a more general and simpler analytical expression, which does not contain the parameters of the complex network, and effective adaptive laws of parameters. Furthermore, the very strong and conservative uniformly Lipschitz condition on the node dynamics of complex network is released. To demonstrate the validity of the proposed method, the examples for the synchronization of systems with the chaotic and hyper-chaotic node dynamics are presented.

  7. A multiple-responsive self-healing supramolecular polymer gel network based on multiple orthogonal interactions.

    Science.gov (United States)

    Zhan, Jiayi; Zhang, Mingming; Zhou, Mi; Liu, Bin; Chen, Dong; Liu, Yuanyuan; Chen, Qianqian; Qiu, Huayu; Yin, Shouchun

    2014-08-01

    Supramolecular polymer networks have attracted considerable attention not only due to their topological importance but also because they can show some fantastic properties such as stimuli-responsiveness and self-healing. Although various supramolecular networks are constructed by supramolecular chemists based on different non-covalent interactions, supramolecular polymer networks based on multiple orthogonal interactions are still rare. Here, a supramolecular polymer network is presented on the basis of the host-guest interactions between dibenzo-24-crown-8 (DB24C8) and dibenzylammonium salts (DBAS), the metal-ligand coordination interactions between terpyridine and Zn(OTf)2 , and between 1,2,3-triazole and PdCl2 (PhCN)2 . The topology of the networks can be easily tuned from monomer to main-chain supramolecular polymer and then to the supramolecular networks. This process is well studied by various characterization methods such as (1) H NMR, UV-vis, DOSY, viscosity, and rheological measurements. More importantly, a supramolecular gel is obtained at high concentrations of the supramolecular networks, which demonstrates both stimuli-responsiveness and self-healing properties. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  8. Prosumer with demand response - Distribution network impact and mitigation

    Energy Technology Data Exchange (ETDEWEB)

    Ackeby, S.; Bollen, M.; Munkhammar, J.

    2013-05-15

    This report is the result from a project funded by ELFORSK done by STRI. The project is studying the effects the introduction of so called 'prosumers' (customers with own production) and electrical vehicles will have on different types of networks. Four different cases are studied covering urban and rural areas with different types of customers. In the urban areas the power through the transformer will be the limiting factor. The major impact in the cases studied is from the introduction of production from photovoltaics at the customer-side of the meter. This will result in an introduction of surplus due to production which in one case led to an increase of the absolute power through the transformer with more than 30 %, which resulted in transformer overloading. In the rural areas the voltage drop or rise will be the limiting factor. The cases studied had already high voltage drops even in the base cases. In the case studies it was seen that the voltage drop could be slightly reduced when introducing more local production, but the production also led to that voltage rise could appear. As a result the interval of the voltage variations was increased, which in turn leads to difficulties with designing the network such that neither overvoltage nor undervoltage occurs. Introducing control algorithms had a very positive effect on reducing the net production from the photovoltaics. Using both hard and soft curtailment made it possible to remove all overcurrents or overvoltages. Using hard curtailment, where all production is turned off during overcurrent or overvoltage, leads however to a large reduction in energy from renewable energy sources. Therefore soft curtailment should as much as possible be used. The control algorithms studied for reducing the net consumption had a more limited effect and even resulted in an increase of the maximum net consumption. When trying to reduce the net consumption during an overload, the reason of the overload could only be

  9. microRNA Expression in Sentinel Nodes from Progressing Melanoma Patients Identifies Networks Associated with Dysfunctional Immune Response

    Directory of Open Access Journals (Sweden)

    Viviana Vallacchi

    2016-12-01

    Full Text Available Sentinel node biopsy (SNB is a main staging biomarker in melanoma and is the first lymph node to drain the tumor, thus representing the immunological site where anti-tumor immune dysfunction is established and where potential prognostic immune markers can be identified. Here we analyzed microRNA (miR profiles in archival tumor-positive SNBs derived from melanoma patients with different outcomes and performed an integrated analysis of transcriptional data to identify deregulated immune signaling networks. Twenty-six miRs were differentially expressed in melanoma-positive SNB samples between patients with disease progression and non-progressing patients, the majority being previously reported in the regulation of immune responses. A significant variation in miR expression levels was confirmed in an independent set of SNB samples. Integrated information from genome-wide transcriptional profiles and in vitro assessment in immune cells led to the identification of miRs associated with the regulation of the TNF receptor superfamily member 8 (TNFRSF8 gene encoding the CD30 receptor, a marker increased in lymphocytes of melanoma patients with progressive disease. These findings indicate that miRs are involved in the regulation of pathways leading to immune dysfunction in the sentinel node and may provide valuable markers for developing prognostic molecular signatures for the identification of stage III melanoma patients at risk of recurrence.

  10. The role of consumer identification with the company on the effects of corporate social responsibility associations on consumer behavior

    OpenAIRE

    Martínez García de Leaniz, Rosa Patricia; Rodríguez del Bosque Rodríguez, Ignacio Alfredo

    2016-01-01

    ABSTRACT: One of the most important aspects in the field of hospitality marketing is to investigate the path to customer loyalty. In addition, another line of research is the construct of corporate social responsibility (CSR) that has recently been incorporated in the customer loyalty model. Therefore, this paper examines the effects of CSR associations on hotel customer loyalty by including the mediation effects of customer-company identification (C-C identification) and customer satisfactio...

  11. Structure Crack Identification Based on Surface-mounted Active Sensor Network with Time-Domain Feature Extraction and Neural Network

    Directory of Open Access Journals (Sweden)

    Chunling DU

    2012-03-01

    Full Text Available In this work the condition of metallic structures are classified based on the acquired sensor data from a surface-mounted piezoelectric sensor/actuator network. The structures are aluminum plates with riveted holes and possible crack damage at these holes. A 400 kHz sine wave burst is used as diagnostic signals. The combination of time-domain S0 waves from received sensor signals is directly used as features and preprocessing is not needed for the dam age detection. Since the time sequence of the extracted S0 has a high dimension, principal component estimation is applied to reduce its dimension before entering NN (neural network training for classification. An LVQ (learning vector quantization NN is used to classify the conditions as healthy or damaged. A number of FEM (finite element modeling results are taken as inputs to the NN for training, since the simulated S0 waves agree well with the experimental results on real plates. The performance of the classification is then validated by using these testing results.

  12. Enzyme Sequestration as a Tuning Point in Controlling Response Dynamics of Signalling Networks.

    Directory of Open Access Journals (Sweden)

    Song Feng

    2016-05-01

    Full Text Available Signalling networks result from combinatorial interactions among many enzymes and scaffolding proteins. These complex systems generate response dynamics that are often essential for correct decision-making in cells. Uncovering biochemical design principles that underpin such response dynamics is a prerequisite to understand evolved signalling networks and to design synthetic ones. Here, we use in silico evolution to explore the possible biochemical design space for signalling networks displaying ultrasensitive and adaptive response dynamics. By running evolutionary simulations mimicking different biochemical scenarios, we find that enzyme sequestration emerges as a key mechanism for enabling such dynamics. Inspired by these findings, and to test the role of sequestration, we design a generic, minimalist model of a signalling cycle, featuring two enzymes and a single scaffolding protein. We show that this simple system is capable of displaying both ultrasensitive and adaptive response dynamics. Furthermore, we find that tuning the concentration or kinetics of the sequestering protein can shift system dynamics between these two response types. These empirical results suggest that enzyme sequestration through scaffolding proteins is exploited by evolution to generate diverse response dynamics in signalling networks and could provide an engineering point in synthetic biology applications.

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

    Science.gov (United States)

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

    2015-10-01

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

  14. The strength of networks: the local NGO response to the tsunami in India.

    Science.gov (United States)

    Kilby, Patrick

    2008-03-01

    This paper examines the role played by a network of 12 local non-governmental organisations (NGOs)--the East Coast Development Forum (ECDF)-in the response to the Indian Ocean tsunami ('Asian tsunami') of 26 December 2004, which devastated the east coast of India. It examines how the ECDF sought to meet the needs of affected people through a direct relief programme, a rehabilitation programme focused on the restoration of livelihoods, and through advocacy to press for changes to government programmes to make them inclusive and to ensure that they satisfy the priority needs of the people most affected. The paper concludes that it was the trust and capacity built up through past network activities of the fisher, dalit, and tribal communities that enabled the ECDF to launch an effective response to the tsunami. A lesson to emerge is that the use of similar existing networks could be employed in other disaster responses around the world.

  15. Application of artificial neural networks for response surface modelling in HPLC method development

    Directory of Open Access Journals (Sweden)

    Mohamed A. Korany

    2012-01-01

    Full Text Available This paper discusses the usefulness of artificial neural networks (ANNs for response surface modelling in HPLC method development. In this study, the combined effect of pH and mobile phase composition on the reversed-phase liquid chromatographic behaviour of a mixture of salbutamol (SAL and guaiphenesin (GUA, combination I, and a mixture of ascorbic acid (ASC, paracetamol (PAR and guaiphenesin (GUA, combination II, was investigated. The results were compared with those produced using multiple regression (REG analysis. To examine the respective predictive power of the regression model and the neural network model, experimental and predicted response factor values, mean of squares error (MSE, average error percentage (Er%, and coefficients of correlation (r were compared. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.

  16. Transcriptional Profiling and Identification of Heat-Responsive Genes in Perennial Ryegrass by RNA-Sequencing

    Directory of Open Access Journals (Sweden)

    Kehua Wang

    2017-06-01

    Full Text Available Perennial ryegrass (Lolium perenne is one of the most widely used forage and turf grasses in the world due to its desirable agronomic qualities. However, as a cool-season perennial grass species, high temperature is a major factor limiting its performance in warmer and transition regions. In this study, a de novo transcriptome was generated using a cDNA library constructed from perennial ryegrass leaves subjected to short-term heat stress treatment. Then the expression profiling and identification of perennial ryegrass heat response genes by digital gene expression analyses was performed. The goal of this work was to produce expression profiles of high temperature stress responsive genes in perennial ryegrass leaves and further identify the potentially important candidate genes with altered levels of transcript, such as those genes involved in transcriptional regulation, antioxidant responses, plant hormones and signal transduction, and cellular metabolism. The de novo assembly of perennial ryegrass transcriptome in this study obtained more total and annotated unigenes compared to previously published ones. Many DEGs identified were genes that are known to respond to heat stress in plants, including HSFs, HSPs, and antioxidant related genes. In the meanwhile, we also identified four gene candidates mainly involved in C4 carbon fixation, and one TOR gene. Their exact roles in plant heat stress response need to dissect further. This study would be important by providing the gene resources for improving heat stress tolerance in both perennial ryegrass and other cool-season perennial grass plants.

  17. An objective approach for feature extraction: distribution analysis and statistical descriptors for scale choice and channel network identification

    Directory of Open Access Journals (Sweden)

    G. Sofia

    2011-05-01

    Full Text Available A statistical approach to LiDAR derived topographic attributes for the automatic extraction of channel network and for the choice of the scale to apply for parameter evaluation is presented in this paper. The basis of this approach is to use distribution analysis and statistical descriptors to identify channels where terrain geometry denotes significant convergences. Two case study areas with different morphology and degree of organization are used with their 1 m LiDAR Digital Terrain Models (DTMs. Topographic attribute maps (curvature and openness for various window sizes are derived from the DTMs in order to detect surface convergences. A statistical analysis on value distributions considering each window size is carried out for the choice of the optimum kernel. We propose a three-step method to extract the network based (a on the normalization and overlapping of openness and minimum curvature to highlight the more likely surface convergences, (b a weighting of the upslope area according to these normalized maps to identify drainage flow paths and flow accumulation consistent with terrain geometry, (c the standard score normalization of the weighted upslope area and the use of standard score values as non subjective threshold for channel network identification. As a final step for optimal definition and representation of the whole network, a noise-filtering and connection procedure is applied. The advantage of the proposed methodology, and the efficiency and accurate localization of extracted features are demonstrated using LiDAR data of two different areas and comparing both extractions with field surveyed networks.

  18. Structured chaos shapes spike-response noise entropy in balanced neural networks

    Directory of Open Access Journals (Sweden)

    Guillaume eLajoie

    2014-10-01

    Full Text Available Large networks of sparsely coupled, excitatory and inhibitory cells occur throughout the brain. For many models of these networks, a striking feature is that their dynamics are chaotic and thus, are sensitive to small perturbations. How does this chaos manifest in the neural code? Specifically, how variable are the spike patterns that such a network produces in response to an input signal? To answer this, we derive a bound for a general measure of variability -- spike-train entropy. This leads to important insights on the variability of multi-cell spike pattern distributions in large recurrent networks of spiking neurons responding to fluctuating inputs. The analysis is based on results from random dynamical systems theory and is complemented by detailed numerical simulations. We find that the spike pattern entropy is an order of magnitude lower than what would be extrapolated from single cells. This holds despite the fact that network coupling becomes vanishingly sparse as network size grows -- a phenomenon that depends on ``extensive chaos, as previously discovered for balanced networks without stimulus drive. Moreover, we show how spike pattern entropy is controlled by temporal features of the inputs. Our findings provide insight into how neural networks may encode stimuli in the presence of inherently chaotic dynamics.

  19. Acute pharmacologically induced shifts in serotonin availability abolish emotion-selective responses to negative face emotions in distinct brain networks

    DEFF Research Database (Denmark)

    Grady, Cheryl Lynn; Siebner, Hartwig R; Hornboll, Bettina

    2013-01-01

    distributed brain responses identified two brain networks with modulations of activity related to face emotion and serotonin level. The first network included the left amygdala, bilateral striatum, and fusiform gyri. During the Control session this network responded only to fearful faces; increasing serotonin...... decreased this response to fear, whereas reducing serotonin enhanced the response of this network to angry faces. The second network involved bilateral amygdala and ventrolateral prefrontal cortex, and these regions also showed increased activity to fear during the Control session. Both drug challenges...

  20. Identification of context-specific gene regulatory networks with GEMULA--Gene Expression Modeling Using LAsso

    NARCIS (Netherlands)

    Geeven, G.; van Kesteren, R.E.; Smit, A.B.; de Gunst, M.C.M.

    2012-01-01

    Motivation: Gene regulatory networks, in which edges between nodes describe interactions between transcriptional regulators and their target genes, determine the coordinated spatiotemporal expression of genes. Especially in higher organisms, context-specific combinatorial regulation by transcription

  1. On the Accurate Identification of Network Paths Having a Common Bottleneck

    Directory of Open Access Journals (Sweden)

    Muhammad Murtaza Yousaf

    2013-01-01

    Full Text Available We present a new mechanism for detecting shared bottlenecks between end-to-end paths in a network. Our mechanism, which only needs one-way delays from endpoints as an input, is based on the well-known linear algebraic approach: singular value decomposition (SVD. Clusters of flows which share a bottleneck are extracted from SVD results by applying an outlier detection method. Simulations with varying topologies and different network conditions show the high accuracy of our technique.

  2. Effective identification of conserved pathways in biological networks using hidden Markov models.

    Directory of Open Access Journals (Sweden)

    Xiaoning Qian

    2009-12-01

    Full Text Available The advent of various high-throughput experimental techniques for measuring molecular interactions has enabled the systematic study of biological interactions on a global scale. Since biological processes are carried out by elaborate collaborations of numerous molecules that give rise to a complex network of molecular interactions, comparative analysis of these biological networks can bring important insights into the functional organization and regulatory mechanisms of biological systems.In this paper, we present an effective framework for identifying common interaction patterns in the biological networks of different organisms based on hidden Markov models (HMMs. Given two or more networks, our method efficiently finds the top matching paths in the respective networks, where the matching paths may contain a flexible number of consecutive insertions and deletions.Based on several protein-protein interaction (PPI networks obtained from the Database of Interacting Proteins (DIP and other public databases, we demonstrate that our method is able to detect biologically significant pathways that are conserved across different organisms. Our algorithm has a polynomial complexity that grows linearly with the size of the aligned paths. This enables the search for very long paths with more than 10 nodes within a few minutes on a desktop computer. The software program that implements this algorithm is available upon request from the authors.

  3. Strategic Clinical Networks: Alberta's Response to Triple Aim.

    Science.gov (United States)

    Noseworthy, Tom; Wasylak, Tracy; O'Neill, Blair J

    2016-01-01

    Verma and Bhatia make a compelling case for the Triple Aim to promote health system innovation and sustainability. We concur. Moreover, the authors offer a useful categorization of policies and actions to advance the Triple Aim under the "classic functions" of financing, stewardship and resource generation (Verma and Bhatia 2016). The argument is tendered that provincial governments should embrace the Triple Aim in the absence of federal government leadership, noting that, by international standards, we are at best mediocre and, more realistically, fighting for the bottom in comparative, annual cross-country surveys. Ignoring federal government participation in Medicare and resorting solely to provincial leadership seems to make sense for the purposes of this discourse; but, it makes no sense at all if we are attempting to achieve high performance in Canada's non-system (Canada Health Action: Building on the Legacy 1997; Commission on the Future of Health Care in Canada 2002; Lewis 2015). As for enlisting provincial governments, we heartily agree. A great deal can be accomplished by the Council of the Federation of Canadian Premiers. But, the entire basis for this philosophy and the reference paper itself assumes a top-down approach to policy and practice. That is what we are trying to change in Alberta and we next discuss. Bottom-up clinically led change, driven by measurement and evidence, has to meet with the top-down approach being presented and widely practiced. While true for each category of financing, stewardship and resource generation, in no place is this truer than what is described and included in "health system stewardship." This commentary draws from Verma and Bhatia (2016) and demonstrates how Alberta, through the use of Strategic Clinical Networks (SCNs), is responding to the Triple Aim. We offer three examples of provincially scaled innovations, each representing one or more arms of the Triple Aim.

  4. DNA-based identification of invasive alien species in relation to Canadian federal policy and law, and the basis of rapid-response management.

    Science.gov (United States)

    Thomas, Vernon G; Hanner, Robert H; Borisenko, Alex V

    2016-11-01

    Managing invasive alien species in Canada requires reliable taxonomic identification as the basis of rapid-response management. This can be challenging, especially when organisms are small and lack morphological diagnostic features. DNA-based techniques, such as DNA barcoding, offer a reliable, rapid, and inexpensive toolkit for taxonomic identification of individual or bulk samples, forensic remains, and even environmental DNA. Well suited for this requirement, they could be more broadly deployed and incorporated into the operating policy and practices of Canadian federal departments and should be authorized under these agencies' articles of law. These include Fisheries and Oceans Canada, Canadian Food Inspection Agency, Transport Canada, Environment Canada, Parks Canada, and Health Canada. These efforts should be harmonized with the appropriate provisions of provincial jurisdictions, for example, the Ontario Invasive Species Act. This approach necessitates that a network of accredited, certified laboratories exists, and that updated DNA reference libraries are readily accessible. Harmonizing this approach is vital among Canadian federal agencies, and between the federal and provincial levels of government. Canadian policy and law must also be harmonized with that of the USA when detecting, and responding to, invasive species in contiguous lands and waters. Creating capacity in legislation for use of DNA-based identifications brings the authority to fund, train, deploy, and certify staff, and to refine further developments in this molecular technology.

  5. Identification of single-nucleotide polymorphism markers associated with cortisol response to crowding in rainbow trout.

    Science.gov (United States)

    Liu, Sixin; Vallejo, Roger L; Gao, Guangtu; Palti, Yniv; Weber, Gregory M; Hernandez, Alvaro; Rexroad, Caird E

    2015-06-01

    Understanding stress responses is essential for improving animal welfare and increasing agriculture production efficiency. Previously, we reported microsatellite markers associated with quantitative trait loci (QTL) affecting plasma cortisol response to crowding in rainbow trout. In this study, our main objectives were to identify single-nucleotide polymorphism (SNP) markers associated with cortisol response to crowding in rainbow trout using both GWAS (genome-wide association studies) and QTL mapping methods and to employ rapidly expanding genomic resources for rainbow trout toward the identification of candidate genes affecting this trait. A three-generation F2 mapping family (2008052) was genotyped using RAD-seq (restriction-site-associated DNA sequencing) to identify 4874 informative SNPs. GWAS identified 26 SNPs associated with cortisol response to crowding whereas QTL mapping revealed two significant QTL on chromosomes Omy8 and Omy12, respectively. Positional candidate genes were identified using marker sequences to search the draft genome assembly of rainbow trout. One of the genes in the QTL interval on Omy12 is a putative serine/threonine protein kinase gene that was differentially expressed in the liver in response to handling and confinement stress in our previous study. A homologue of this gene was differentially expressed in zebrafish embryos exposed to diclofenac, a nonsteroidal anti-inflammatory drug (NSAID) and an environmental toxicant. NSAIDs have been shown to affect the cortisol response in rainbow trout; therefore, this gene is a good candidate based on its physical position and expression. However, the reference genome resources currently available for rainbow trout require continued improvement as demonstrated by the unmapped SNPs and the putative assembly errors detected in this study.

  6. Large-scale identification of potential drug targets based on the topological features of human protein-protein interaction network.

    Science.gov (United States)

    Li, Zhan-Chao; Zhong, Wen-Qian; Liu, Zhi-Qing; Huang, Meng-Hua; Xie, Yun; Dai, Zong; Zou, Xiao-Yong

    2015-04-29

    Identifying potential drug target proteins is a crucial step in the process of drug discovery and plays a key role in the study of the molecular mechanisms of disease. Based on the fact that the majority of proteins exert their functions through interacting with each other, we propose a method to recognize target proteins by using the human protein-protein interaction network and graph theory. In the network, vertexes and edges are weighted by using the confidence scores of interactions and descriptors of protein primary structure, respectively. The novel network topological features are defined and employed to characterize protein using existing databases. A widely used minimum redundancy maximum relevance and random forests algorithm are utilized to select the optimal feature subset and construct model for the identification of potential drug target proteins at the proteome scale. The accuracies of training set and test set are 89.55% and 85.23%. Using the constructed model, 2127 potential drug target proteins have been recognized and 156 drug target proteins have been validated in the database of drug target. In addition, some new drug target proteins can be considered as targets for treating diseases of mucopolysaccharidosis, non-arteritic anterior ischemic optic neuropathy, Bernard-Soulier syndrome and pseudo-von Willebrand, etc. It is anticipated that the proposed method may became a powerful high-throughput virtual screening tool of drug target. Copyright © 2015 Elsevier B.V. All rights reserved.

  7. Identification of candidate genes in Populus cell wall biosynthesis using text-mining, co-expression network and comparative genomics

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Xiaohan [ORNL; Ye, Chuyu [ORNL; Bisaria, Anjali [ORNL; Tuskan, Gerald A [ORNL; Kalluri, Udaya C [ORNL

    2011-01-01

    Populus is an important bioenergy crop for bioethanol production. A greater understanding of cell wall biosynthesis processes is critical in reducing biomass recalcitrance, a major hindrance in efficient generation of ethanol from lignocellulosic biomass. Here, we report the identification of candidate cell wall biosynthesis genes through the development and application of a novel bioinformatics pipeline. As a first step, via text-mining of PubMed publications, we obtained 121 Arabidopsis genes that had the experimental evidences supporting their involvement in cell wall biosynthesis or remodeling. The 121 genes were then used as bait genes to query an Arabidopsis co-expression database and additional genes were identified as neighbors of the bait genes in the network, increasing the number of genes to 548. The 548 Arabidopsis genes were then used to re-query the Arabidopsis co-expression database and re-construct a network that captured additional network neighbors, expanding to a total of 694 genes. The 694 Arabidopsis genes were computationally divided into 22 clusters. Queries of the Populus genome using the Arabidopsis genes revealed 817 Populus orthologs. Functional analysis of gene ontology and tissue-specific gene expression indicated that these Arabidopsis and Populus genes are high likelihood candidates for functional genomics in relation to cell wall biosynthesis.

  8. ECG Identification System Using Neural Network with Global and Local Features

    Science.gov (United States)

    Tseng, Kuo-Kun; Lee, Dachao; Chen, Charles

    2016-01-01

    This paper proposes a human identification system via extracted electrocardiogram (ECG) signals. Two hierarchical classification structures based on global shape feature and local statistical feature is used to extract ECG signals. Global shape feature represents the outline information of ECG signals and local statistical feature extracts the…

  9. Identification of the AQP members involved in abiotic stress responses from Arabidopsis.

    Science.gov (United States)

    Feng, Zhi-Juan; Xu, Sheng-Chun; Liu, Na; Zhang, Gu-Wen; Hu, Qi-Zan; Xu, Zhao-Shi; Gong, Ya-Ming

    2018-03-10

    Aquaporins (AQPs) constitute a highly diverse family of water channel proteins that play crucial biological functions in plant growth and development and stress physiology. In Arabidopsis, 35 AQPs are classified into four subfamilies (PIPs, TIPs, NIPs and SIPs). However, knowledge about the roles of different subfamily AQPs remains limited. Here, we explored the chromosomal location, gene structure and expression patterns of all AQPs in different tissues or under different abiotic stresses based on available microarray data. Tissue expression analysis showed that different AQPs had various expression patterns in tissues (root, leaf, flower and seed). Expression profiles under stress conditions revealed that most AQPs were responsive to osmotic, salt and drought stresses. Phenotypic and physiological identification showed that Tip2;2 loss-of-function mutant exhibited less sensitive to abiotic stresses (mannitol, NaCl and PEG) compared with wild-type, as evident by analysis of germination rate, root growth, survival rate, ion leakage, malondialdehyde (MDA) and proline contents. Mutant of TIP2;2 modulated the transcript levels of SOS1, SOS2, SOS3, DREB1A, DREB2A and P5CS1, under abiotic stress conditions. This study provides a basis for further functional identification of stress-related candidate AQPs in Arabidopsis. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Identification of a Hormone-regulated Dynamic Nuclear Actin Network Associated with Estrogen Receptor α in Human Breast Cancer Cell Nuclei*

    Science.gov (United States)

    Ambrosino, Concetta; Tarallo, Roberta; Bamundo, Angela; Cuomo, Danila; Franci, Gianluigi; Nassa, Giovanni; Paris, Ornella; Ravo, Maria; Giovane, Alfonso; Zambrano, Nicola; Lepikhova, Tatiana; Jänne, Olli A.; Baumann, Marc; Nyman, Tuula A.; Cicatiello, Luigi; Weisz, Alessandro

    2010-01-01

    Estrogen receptor α (ERα) is a modular protein of the steroid/nuclear receptor family of transcriptional regulators that upon binding to the hormone undergoes structural changes, resulting in its nuclear translocation and docking to specific chromatin sites. In the nucleus, ERα assembles in multiprotein complexes that act as final effectors of estrogen signaling to the genome through chromatin remodeling and epigenetic modifications, leading to dynamic and coordinated regulation of hormone-responsive genes. Identification of the molecular partners of ERα and understanding their combinatory interactions within functional complexes is a prerequisite to define the molecular basis of estrogen control of cell functions. To this end, affinity purification was applied to map and characterize the ERα interactome in hormone-responsive human breast cancer cell nuclei. MCF-7 cell clones expressing human ERα fused to a tandem affinity purification tag were generated and used to purify native nuclear ER-containing complexes by IgG-Sepharose affinity chromatography and glycerol gradient centrifugation. Purified complexes were analyzed by two-dimensional DIGE and mass spectrometry, leading to the identification of a ligand-dependent multiprotein complex comprising β-actin, myosins, and several proteins involved in actin filament organization and dynamics and/or known to participate in actin-mediated regulation of gene transcription, chromatin dynamics, and ribosome biogenesis. Time course analyses indicated that complexes containing ERα and actin are assembled in the nucleus early after receptor activation by ligands, and gene knockdown experiments showed that gelsolin and the nuclear isoform of myosin 1c are key determinants for assembly and/or stability of these complexes. Based on these results, we propose that the actin network plays a role in nuclear ERα actions in breast cancer cells, including coordinated regulation of target gene activity, spatial and functional

  11. Identification of a hormone-regulated dynamic nuclear actin network associated with estrogen receptor alpha in human breast cancer cell nuclei.

    Science.gov (United States)

    Ambrosino, Concetta; Tarallo, Roberta; Bamundo, Angela; Cuomo, Danila; Franci, Gianluigi; Nassa, Giovanni; Paris, Ornella; Ravo, Maria; Giovane, Alfonso; Zambrano, Nicola; Lepikhova, Tatiana; Jänne, Olli A; Baumann, Marc; Nyman, Tuula A; Cicatiello, Luigi; Weisz, Alessandro

    2010-06-01

    Estrogen receptor alpha (ERalpha) is a modular protein of the steroid/nuclear receptor family of transcriptional regulators that upon binding to the hormone undergoes structural changes, resulting in its nuclear translocation and docking to specific chromatin sites. In the nucleus, ERalpha assembles in multiprotein complexes that act as final effectors of estrogen signaling to the genome through chromatin remodeling and epigenetic modifications, leading to dynamic and coordinated regulation of hormone-responsive genes. Identification of the molecular partners of ERalpha and understanding their combinatory interactions within functional complexes is a prerequisite to define the molecular basis of estrogen control of cell functions. To this end, affinity purification was applied to map and characterize the ERalpha interactome in hormone-responsive human breast cancer cell nuclei. MCF-7 cell clones expressing human ERalpha fused to a tandem affinity purification tag were generated and used to purify native nuclear ER-containing complexes by IgG-Sepharose affinity chromatography and glycerol gradient centrifugation. Purified complexes were analyzed by two-dimensional DIGE and mass spectrometry, leading to the identification of a ligand-dependent multiprotein complex comprising beta-actin, myosins, and several proteins involved in actin filament organization and dynamics and/or known to participate in actin-mediated regulation of gene transcription, chromatin dynamics, and ribosome biogenesis. Time course analyses indicated that complexes containing ERalpha and actin are assembled in the nucleus early after receptor activation by ligands, and gene knockdown experiments showed that gelsolin and the nuclear isoform of myosin 1c are key determinants for assembly and/or stability of these complexes. Based on these results, we propose that the actin network plays a role in nuclear ERalpha actions in breast cancer cells, including coordinated regulation of target gene

  12. Classification and biomarker identification using gene network modules and support vector machines

    Directory of Open Access Journals (Sweden)

    Showe Louise C

    2009-10-01

    Full Text Available Abstract Background Classification using microarray datasets is usually based on a small number of samples for which tens of thousands of gene expression measurements have been obtained. The selection of the genes most significant to the classification problem is a challenging issue in high dimension data analysis and interpretation. A previous study with SVM-RCE (Recursive Cluster Elimination, suggested that classification based on groups of correlated genes sometimes exhibits better performance than classification using single genes. Large databases of gene interaction networks provide an important resource for the analysis of genetic phenomena and for classification studies using interacting genes. We now demonstrate that an algorithm which integrates network information with recursive feature elimination based on SVM exhibits good performance and improves the biological interpretability of the results. We refer to the method as SVM with Recursive Network Elimination (SVM-RNE Results Initially, one thousand genes selected by t-test from a training set are filtered so that only genes that map to a gene network database remain. The Gene Expression Network Analysis Tool (GXNA is applied to the remaining genes to form n clusters of genes that are highly connected in the network. Linear SVM is used to classify the samples using these clusters, and a weight is assigned to each cluster based on its importance to the classification. The least informative clusters are removed while retaining the remainder for the next classification step. This process is repeated until an optimal classification is obtained. Conclusion More than 90% accuracy can be obtained in classification of selected microarray datasets by integrating the interaction network information with the gene expression information from the microarrays. The Matlab version of SVM-RNE can be downloaded from http://web.macam.ac.il/~myousef

  13. Field Measurement-Based System Identification and Dynamic Response Prediction of a Unique MIT Building

    Science.gov (United States)

    Cha, Young-Jin; Trocha, Peter; Büyüköztürk, Oral

    2016-01-01

    Tall buildings are ubiquitous in major cities and house the homes and workplaces of many individuals. However, relatively few studies have been carried out to study the dynamic characteristics of tall buildings based on field measurements. In this paper, the dynamic behavior of the Green Building, a unique 21-story tall structure located on the campus of the Massachusetts Institute of Technology (MIT, Cambridge, MA, USA), was characterized and modeled as a simplified lumped-mass beam model (SLMM), using data from a network of accelerometers. The accelerometer network was used to record structural responses due to ambient vibrations, blast loading, and the October 16th 2012 earthquake near Hollis Center (ME, USA). Spectral and signal coherence analysis of the collected data was used to identify natural frequencies, modes, foundation rocking behavior, and structural asymmetries. A relation between foundation rocking and structural natural frequencies was also found. Natural frequencies and structural acceleration from the field measurements were compared with those predicted by the SLMM which was updated by inverse solving based on advanced multiobjective optimization methods using the measured structural responses and found to have good agreement. PMID:27376303

  14. Applying Bayesian belief networks in rapid response situations

    Energy Technology Data Exchange (ETDEWEB)

    Gibson, William L [Los Alamos National Laboratory; Deborah, Leishman, A. [Los Alamos National Laboratory; Van Eeckhout, Edward [Los Alamos National Laboratory

    2008-01-01

    The authors have developed an enhanced Bayesian analysis tool called the Integrated Knowledge Engine (IKE) for monitoring and surveillance. The enhancements are suited for Rapid Response Situations where decisions must be made based on uncertain and incomplete evidence from many diverse and heterogeneous sources. The enhancements extend the probabilistic results of the traditional Bayesian analysis by (1) better quantifying uncertainty arising from model parameter uncertainty and uncertain evidence, (2) optimizing the collection of evidence to reach conclusions more quickly, and (3) allowing the analyst to determine the influence of the remaining evidence that cannot be obtained in the time allowed. These extended features give the analyst and decision maker a better comprehension of the adequacy of the acquired evidence and hence the quality of the hurried decisions. They also describe two example systems where the above features are highlighted.

  15. A Game-Theoretic Response Strategy for Coordinator Attack in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jianhua Liu

    2014-01-01

    Full Text Available The coordinator is a specific node that controls the whole network and has a significant impact on the performance in cooperative multihop ZigBee wireless sensor networks (ZWSNs. However, the malicious node attacks coordinator nodes in an effort to waste the resources and disrupt the operation of the network. Attacking leads to a failure of one round of communication between the source nodes and destination nodes. Coordinator selection is a technique that can considerably defend against attack and reduce the data delivery delay, and increase network performance of cooperative communications. In this paper, we propose an adaptive coordinator selection algorithm using game and fuzzy logic aiming at both minimizing the average number of hops and maximizing network lifetime. The proposed game model consists of two interrelated formulations: a stochastic game for dynamic defense and a best response policy using evolutionary game formulation for coordinator selection. The stable equilibrium best policy to response defense is obtained from this game model. It is shown that the proposed scheme can improve reliability and save energy during the network lifetime with respect to security.

  16. Functional brain network modularity predicts response to cognitive training after brain injury.

    Science.gov (United States)

    Arnemann, Katelyn L; Chen, Anthony J-W; Novakovic-Agopian, Tatjana; Gratton, Caterina; Nomura, Emi M; D'Esposito, Mark

    2015-04-14

    We tested the value of measuring modularity, a graph theory metric indexing the relative extent of integration and segregation of distributed functional brain networks, for predicting individual differences in response to cognitive training in patients with brain injury. Patients with acquired brain injury (n = 11) participated in 5 weeks of cognitive training and a comparison condition (brief education) in a crossover intervention study design. We quantified the measure of functional brain network organization, modularity, from functional connectivity networks during a state of tonic attention regulation measured during fMRI scanning before the intervention conditions. We examined the relationship of baseline modularity with pre- to posttraining changes in neuropsychological measures of attention and executive control. The modularity of brain network organization at baseline predicted improvement in attention and executive function after cognitive training, but not after the comparison intervention. Individuals with higher baseline modularity exhibited greater improvements with cognitive training, suggesting that a more modular baseline network state may contribute to greater adaptation in response to cognitive training. Brain network properties such as modularity provide valuable information for understanding mechanisms that influence rehabilitation of cognitive function after brain injury, and may contribute to the discovery of clinically relevant biomarkers that could guide rehabilitation efforts. © 2015 American Academy of Neurology.

  17. Corporate Social Responsibility and Employee Outcomes: A Moderated Mediation Model of Organizational Identification and Moral Identity

    Directory of Open Access Journals (Sweden)

    Wei Wang

    2017-11-01

    Full Text Available Corporate social responsibility (CSR research is not new, but its importance to today’s socially conscious market environment is even more evident in recent years. This study moves beyond CSR as simply the socially responsible actions and policies of organizations and focuses on the complex psychology of CSR as it relates to individuals within the organization. Given CSR can positively affect both the individuals within the organization and the organization itself, better understanding and leveraging the mechanisms and conditions of CSR that facilitate desired employee outcomes is crucial for organizational performance. However, scholars lack consensus in determining a theoretical framework for understanding how and under what conditions CSR will make an impact on employees and ultimately organizational performance. This study adds clarity by exploring the effect of perceived CSR on a more comprehensive set of employees’ attitudinal and behavioral reactions (i.e., turnover intention, in-role job performance, and helping behavior via the mediating mechanism of organizational identification and the moderating condition of moral identity. Hypotheses were derived using social identity theory. Results were based on data obtained from 340 Chinese manufacturing employee-supervisor dyads. This study found that employees’ perceived CSR had an indirect relationship via organizational identification with each of the variables: (1 turnover intention, (2 in-role job performance, and (3 helping behavior. Specifically, the negative relationship between perceived CSR and turnover intention was stronger when employees had higher moral identity and the positive relationship between perceived CSR and in-role job performance and helping behavior was amplified by moral identity. Our findings show how the mediating mechanism of organizational identity and the moderating condition of moral identity work together to improve organizational effectiveness. The

  18. Corporate Social Responsibility and Employee Outcomes: A Moderated Mediation Model of Organizational Identification and Moral Identity.

    Science.gov (United States)

    Wang, Wei; Fu, Ying; Qiu, Huiqing; Moore, James H; Wang, Zhongming

    2017-01-01

    Corporate social responsibility (CSR) research is not new, but its importance to today's socially conscious market environment is even more evident in recent years. This study moves beyond CSR as simply the socially responsible actions and policies of organizations and focuses on the complex psychology of CSR as it relates to individuals within the organization. Given CSR can positively affect both the individuals within the organization and the organization itself, better understanding and leveraging the mechanisms and conditions of CSR that facilitate desired employee outcomes is crucial for organizational performance. However, scholars lack consensus in determining a theoretical framework for understanding how and under what conditions CSR will make an impact on employees and ultimately organizational performance. This study adds clarity by exploring the effect of perceived CSR on a more comprehensive set of employees' attitudinal and behavioral reactions (i.e., turnover intention, in-role job performance, and helping behavior) via the mediating mechanism of organizational identification and the moderating condition of moral identity. Hypotheses were derived using social identity theory. Results were based on data obtained from 340 Chinese manufacturing employee-supervisor dyads. This study found that employees' perceived CSR had an indirect relationship via organizational identification with each of the variables: (1) turnover intention, (2) in-role job performance, and (3) helping behavior. Specifically, the negative relationship between perceived CSR and turnover intention was stronger when employees had higher moral identity and the positive relationship between perceived CSR and in-role job performance and helping behavior was amplified by moral identity. Our findings show how the mediating mechanism of organizational identity and the moderating condition of moral identity work together to improve organizational effectiveness. The findings reveal several

  19. Corporate Social Responsibility and Employee Outcomes: A Moderated Mediation Model of Organizational Identification and Moral Identity

    Science.gov (United States)

    Wang, Wei; Fu, Ying; Qiu, Huiqing; Moore, James H.; Wang, Zhongming

    2017-01-01

    Corporate social responsibility (CSR) research is not new, but its importance to today’s socially conscious market environment is even more evident in recent years. This study moves beyond CSR as simply the socially responsible actions and policies of organizations and focuses on the complex psychology of CSR as it relates to individuals within the organization. Given CSR can positively affect both the individuals within the organization and the organization itself, better understanding and leveraging the mechanisms and conditions of CSR that facilitate desired employee outcomes is crucial for organizational performance. However, scholars lack consensus in determining a theoretical framework for understanding how and under what conditions CSR will make an impact on employees and ultimately organizational performance. This study adds clarity by exploring the effect of perceived CSR on a more comprehensive set of employees’ attitudinal and behavioral reactions (i.e., turnover intention, in-role job performance, and helping behavior) via the mediating mechanism of organizational identification and the moderating condition of moral identity. Hypotheses were derived using social identity theory. Results were based on data obtained from 340 Chinese manufacturing employee-supervisor dyads. This study found that employees’ perceived CSR had an indirect relationship via organizational identification with each of the variables: (1) turnover intention, (2) in-role job performance, and (3) helping behavior. Specifically, the negative relationship between perceived CSR and turnover intention was stronger when employees had higher moral identity and the positive relationship between perceived CSR and in-role job performance and helping behavior was amplified by moral identity. Our findings show how the mediating mechanism of organizational identity and the moderating condition of moral identity work together to improve organizational effectiveness. The findings reveal

  20. Responses to olfactory signals reflect network structure of flower-visitor interactions.

    Science.gov (United States)

    Junker, Robert R; Höcherl, Nicole; Blüthgen, Nico

    2010-07-01

    1. Network analyses provide insights into the diversity and complexity of ecological interactions and have motivated conclusions about community stability and co-evolution. However, biological traits and mechanisms such as chemical signals regulating the interactions between individual species--the microstructure of a network--are poorly understood. 2. We linked the responses of receivers (flower visitors) towards signals (flower scent) to the structure of a highly diverse natural flower-insect network. For each interaction, we define link temperature--a newly developed metric--as the deviation of the observed interaction strength from neutrality, assuming that animals randomly interact with flowers. 3. Link temperature was positively correlated to the specific visitors' responses to floral scents, experimentally examined in a mobile olfactometer. Thus, communication between plants and consumers via phytochemical signals reflects a significant part of the microstructure in a complex network. Negative as well as positive responses towards floral scents contributed to these results, where individual experience was important apart from innate behaviour. 4. Our results indicate that: (1) biological mechanisms have a profound impact on the microstructure of complex networks that underlies the outcome of aggregate statistics, and (2) floral scents act as a filter, promoting the visitation of some flower visitors, but also inhibiting the visitation of others.

  1. Building Service Delivery Networks: Partnership Evolution Among Children's Behavioral Health Agencies in Response to New Funding.

    Science.gov (United States)

    Bunger, Alicia C; Doogan, Nathan J; Cao, Yiwen

    2014-12-01

    Meeting the complex needs of youth with behavioral health problems requires a coordinated network of community-based agencies. Although fiscal scarcity or retrenchment can limit coordinated services, munificence can stimulate service delivery partnerships as agencies expand programs, hire staff, and spend more time coordinating services. This study examines the 2-year evolution of referral and staff expertise sharing networks in response to substantial new funding for services within a regional network of children's mental health organizations. Quantitative network survey data were collected from directors of 22 nonprofit organizations that receive funding from a county government-based behavioral health service fund. Both referral and staff expertise sharing networks changed over time, but results of a stochastic actor-oriented model of network dynamics suggest the nature of this change varies for these networks. Agencies with higher numbers of referral and staff expertise sharing partners tend to maintain these ties and/or develop new relationships over the 2 years. Agencies tend to refer to agencies they trust, but trust was not associated with staff expertise sharing ties. However, agencies maintain or form staff expertise sharing ties with referral partners, or with organizations that provide similar services. In addition, agencies tend to reciprocate staff expertise sharing, but not referrals. Findings suggest that during periods of resource munificence and service expansion, behavioral health organizations build service delivery partnerships in complex ways that build upon prior collaborative history and coordinate services among similar types of providers. Referral partnerships can pave the way for future information sharing relationships.

  2. Gene regulatory network identification from the yeast cell cycle based on a neuro-fuzzy system.

    Science.gov (United States)

    Wang, B H; Lim, J W; Lim, J S

    2016-08-30

    Many studies exist for reconstructing gene regulatory networks (GRNs). In this paper, we propose a method based on an advanced neuro-fuzzy system, for gene regulatory network reconstruction from microarray time-series data. This approach uses a neural network with a weighted fuzzy function to model the relationships between genes. Fuzzy rules, which determine the regulators of genes, are very simplified through this method. Additionally, a regulator selection procedure is proposed, which extracts the exact dynamic relationship between genes, using the information obtained from the weighted fuzzy function. Time-series related features are extracted from the original data to employ the characteristics of temporal data that are useful for accurate GRN reconstruction. The microarray dataset of the yeast cell cycle was used for our study. We measured the mean squared prediction error for the efficiency of the proposed approach and evaluated the accuracy in terms of precision, sensitivity, and F-score. The proposed method outperformed the other existing approaches.

  3. Network science for the identification of novel therapeutic targets in epilepsy [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Rod C. Scott

    2016-05-01

    Full Text Available The quality of life of children with epilepsy is a function of seizures and associated cognitive and behavioral comorbidities. Current treatments are not successful at stopping seizures in approximately 30% of patients despite the introduction of multiple new antiepileptic drugs over the last decade. In addition, modification of seizures has only a modest impact on the comorbidities. Therefore, novel approaches to identify therapeutic targets that improve seizures and comorbidities are urgently required. The potential of network science as applied to genetic, local neural network, and global brain data is reviewed. Several examples of possible new therapeutic approaches defined using novel network tools are highlighted. Further study to translate the findings into clinical practice is now required.

  4. Identification of potential opinion leaders in child health promotion in Sweden using network analysis.

    Science.gov (United States)

    Guldbrandsson, Karin; Nordvik, Monica K; Bremberg, Sven

    2012-08-08

    Opinion leaders are often local individuals with high credibility who can influence other people. Robust effects using opinion leaders in diffusing innovations have been shown in several randomized controlled trials, for example regarding sexually transmitted infections (STI), human immunodeficiency virus (HIV) prevention, mammography rates and caesarean birth delivery rates. In a Cochrane review 2010 it was concluded that the use of opinion leaders can successfully promote evidence-based practice. Thus, using opinion leaders within the public health sector might be one means to speed up the dissemination of health promoting and disease preventing innovations. Social network analysis has been used to trace and map networks, with focus on relationships and positions, in widely spread arenas and topics. The purpose of this study was to use social network analysis in order to identify potential opinion leaders at the arena of child health promotion in Sweden. By using snowball technique a short e-mail question was spread in up to five links, starting from seven initially invited persons. This inquiry resulted in a network consisting of 153 individuals. The most often mentioned actors were researchers, public health officials and paediatricians, or a combination of these professions. Four single individuals were mentioned by five to seven other persons in the network. These individuals obviously possess qualities that make other professionals within the public health sector listen to and trust them. Social network analysis seemed to be a useful method to identify influential persons with high credibility, i.e. potential opinion leaders, at the arena of child health promotion in Sweden. If genuine opinion leaders could be identified directed measures can be carried out in order to spread new and relevant knowledge. This may facilitate for public health actors at the local, regional and national level to more rapidly progress innovations into everyday practice. However

  5. Spatiotemporal properties of sensory responses in vivo are strongly dependent on network context

    Directory of Open Access Journals (Sweden)

    Eugene F. Civillico

    2012-04-01

    Full Text Available Sensory responses in neocortex are strongly modulated by changes in brain state, such as those observed between sleep stages or attentional levels. However, the specific effects of network state changes on the spatiotemporal properties of sensory responses are poorly understood. The slow oscillation, which is observed in neocortex under ketamine-xylazine anesthesia and is characterized by alternating depolarization (up-states and hyperpolarizing (down-states phases, provides an opportunity to study the state-dependence of primary sensory responses in large networks. Here we show using voltage-sensitive dye imaging that multiple properties of whisker-evoked responses are highly dependent on their timing with regard to the ongoing oscillation. In both the up- and down-states, responses spread across large portions of the barrel field, although the up-state responses were reduced in total area due to their sparseness. The depolarizing response in the up-state showed a tendency to propagate along the rows, with an amplitude and slope favoring the higher-numbered arcs. In the up-state, but not in the down-state, the depolarizing response was followed by a hyperpolarizing wave with a consistent spatial structure. We measured the suppression of whisker-evoked responses by a preceding response at 100 ms, and found that suppression showed the same spatial asymmetry as the depolarization. Because the resting level of cells in the up-state is likely to be closer to that in the awake animal, we suggest that the polarities in signal propagation which we observed in the up-state could be used as computational mechanisms in the behaving animal. These results demonstrate the critical importance of ongoing network activity on the dynamics of sensory responses and their integration.

  6. Network analysis of oyster transcriptome revealed a cascade of cellular responses during recovery after heat shock.

    Directory of Open Access Journals (Sweden)

    Lingling Zhang

    Full Text Available Oysters, as a major group of marine bivalves, can tolerate a wide range of natural and anthropogenic stressors including heat stress. Recent studies have shown that oysters pretreated with heat shock can result in induced heat tolerance. A systematic study of cellular recovery from heat shock may provide insights into the mechanism of acquired thermal tolerance. In this study, we performed the first network analysis of oyster transcriptome by reanalyzing microarray data from a previous study. Network analysis revealed a cascade of cellular responses during oyster recovery after heat shock and identified responsive gene modules and key genes. Our study demonstrates the power of network analysis in a non-model organism with poor gene annotations, which can lead to new discoveries that go beyond the focus on individual genes.

  7. Networks and learning: communities, practices and the metaphor of networks–a response

    Directory of Open Access Journals (Sweden)

    Chris Jones

    2004-12-01

    Full Text Available I am pleased to have the opportunity to react to Bruce Ingraham's response to my article ‘Networks and learning: communities, practices and the metaphor of networks' (Jones, 2004. It is rare to have a dialogue with someone who has taken the time and trouble to consider what you have written for a journal. All too often reviewing is a one-way process with the reviewer remaining anonymous. It is all the more pleasant to have a response to what you have written that gets to grips with some of the issues that the author also finds troubling. It is in that spirit that I write this reaction to Ingraham; it is an opportunity for me to develop some of the points he has identified as problematic in the original article. I want to concentrate on two main issues, firstly the network metaphor itself and secondly the usefulness of abstraction and representations of various types.

  8. Definition of Distribution Network Tariffs Considering Distribution Generation and Demand Response

    DEFF Research Database (Denmark)

    Soares, Tiago; Faria, Pedro; Vale, Zita

    2014-01-01

    The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits for the wh......The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits...... the determination of topological distribution factors, and consequent application of the MW-mile method. The application of the proposed tariffs definition methodology is illustrated in a distribution network with 33 buses, 66 DG units, and 32 consumers with DR capacity...

  9. Dynamic swelling behavior of interpenetrating polymer networks in response to temperature and pH

    OpenAIRE

    Slaughter, Brandon V.; Blanchard, Aaron T.; Maass, Katie F.; Peppas, Nicholas A.

    2015-01-01

    Temperature responsive hydrogels based on ionic polymers exhibit swelling transitions in aqueous solutions as a function of shifting pH and ionic strength, in addition to temperature. Applying these hydrogels to useful applications, particularly for biomedical purposes such as drug delivery and regenerative medicine, is critically dependent on understanding the hydrogel solution responses as a function of all three parameters together. In this work, interpenetrating polymer network (IPN) hydr...

  10. Communicative Dynamics and the Polyphony of Corporate Social Responsibility in the Network Society

    DEFF Research Database (Denmark)

    Castello, Itziar; Morsing, Mette; Schultz, Friederike

    2013-01-01

    This paper develops a media theoretical extension of the communicative view on corporate social responsibility by elaborating on the characteristics of network societies, arguing that new media increase the speed and connectivity, and lead to higher plurality and the potential polarization...... of reality constructions. We discuss the implications for corporate social responsibility of becoming more polyphonic and sketch the contours of “communicative legitimacy.” Finally, we present this special issue and develop some questions for future research....

  11. Watchdog Sensor Network with Multi-Stage RF Signal Identification and Cooperative Intrusion Detection

    Science.gov (United States)

    2012-03-01

    Wang University of Western Ontario Approved by Original signed by Rodney Howes Rodney Howes DRDC Centre for Security Science, Project Manager ...utilise les valeurs distinctives du décalage de la porteuse comme une signature dépendante de l’émetteur pour l’identification des utilisateurs et, par...of active users); o Flexibility in frequency (variable bandwidth), in data rate (variable), and in radio resource management (variable power

  12. Rebels with a cause : Group identification as a response to perceived discrimination from the mainstream

    NARCIS (Netherlands)

    Jetten, Jolanda; Branscombe, NR; Schmitt, MT; Spears, R

    Two studies involving people with body piercings tested the hypothesis that perceived discrimination increases group identification. In Study 1, group identification mediated the positive relationship between perceived discrimination and attempts to differentiate the ingroup from the mainstream. In

  13. Global phosphoproteome profiling reveals unanticipated networks responsive to cisplatin treatment of embryonic stem cells

    DEFF Research Database (Denmark)

    Pines, Alex; Kelstrup, Christian D; Vrouwe, Mischa G

    2011-01-01

    (stable isotope labeling by amino acids in cell culture)-labeled murine embryonic stem cells with the anticancer drug cisplatin. Network and pathway analyses indicated that processes related to the DNA damage response and cytoskeleton organization were significantly affected. Although the ATM (ataxia...

  14. Neural networks in high-performance liquid chromatography optimization : Response surface modeling

    NARCIS (Netherlands)

    Metting, H.J; Coenegracht, P.M J

    1996-01-01

    The usefulness of artificial neural networks for response surface modeling in HPLC optimization is compared with (non-)linear regression methods. The number of hidden nodes is optimized by a lateral inhibition method. Overfitting is controlled by cross-validation using the leave one out method

  15. Communicative dynamics and the polyphony of corporate social responsibility in the network society

    NARCIS (Netherlands)

    Castello, I.; Morsing, M.; Schultz, F.

    2013-01-01

    This paper develops a media theoretical extension of the communicative view on corporate social responsibility by elaborating on the characteristics of network societies, arguing that new media increase the speed and connectivity, and lead to higher plurality and the potential polarization of

  16. Collective food purchasing networks in Italy as a case study of responsible innovation

    NARCIS (Netherlands)

    Grasseni, Cristina|info:eu-repo/dai/nl/302281088; Hankins, Jonathan

    2014-01-01

    Based upon fieldwork in Italy and the USA, the authors present work-in-progress insights into solidarity economies, and in particular alternative food networks, as a form of active citizenship that could re-orient the current debate on responsible innovation.

  17. Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model.

    Science.gov (United States)

    Zhao, Kai; Wang, ChengYan; Hu, Juan; Yang, XueDong; Wang, He; Li, FeiYu; Zhang, XiaoDong; Zhang, Jue; Wang, XiaoYing

    2015-07-01

    Computer-aided diagnosis (CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance imaging (MRI), image features from T2-weighted images (T2WI) were extracted and evaluated for the diagnostic performances by using CAD. We extracted 12 quantitative image features from prostate T2-weighted MR images. The importance of each feature in cancer identification was compared in the peripheral zone (PZ) and central gland (CG), respectively. The performance of the computer-aided diagnosis system supported by an artificial neural network was tested. With computer-aided analysis of T2-weighted images, many characteristic features with different diagnostic capabilities can be extracted. We discovered most of the features (10/12) had significant difference (Pimages can reach high accuracy and specificity while maintaining acceptable sensitivity. The outcome is convictive and helpful in medical diagnosis.

  18. Task and task-free fMRI reproducibility comparison for motor network identification

    NARCIS (Netherlands)

    Kristo, G.; Rutten, G.J.; Raemaekers, M.; de Gelder, B.; Rombouts, S.A.R.B.; Ramsey, N.F.

    2014-01-01

    Test-retest reliability of individual functional magnetic resonance imaging (fMRI) results is of importance in clinical practice and longitudinal experiments. While several studies have investigated reliability of task-induced motor network activation, less is known about the reliability of the

  19. Critical Nodes Identification of Power Systems Based on Controllability of Complex Networks

    Directory of Open Access Journals (Sweden)

    Yu-Shuai Li

    2015-09-01

    Full Text Available This paper proposes a new method for assessing the vulnerability of power systems based on the controllability theories of complex networks. A novel controllability index is established, taking into consideration the full controllability of the power systems, for identifying critical nodes. The network controllability model is used to calculate the minimum number of driver nodes (ND, which can solve the computable problems of the controllability of power systems. The proposed approach firstly applies the network controllability theories to research the power systems' vulnerability, which can not only effectively reveal the important nodes but also maintain full control of the power systems. Meanwhile, the method can also overcome the limitation of the hypothesis that the weight of each link or transmission line must be known compared with the existing literature. In addition, the power system is considered as a directed network and the power system model is also redefined. The proposed methodology is then used to identify critical nodes of the IEEE 118 and 300 bus system. The results show that the failure of the critical nodes can clearly increase ND and lead a significant driver node shift. Thus, the rationality and validity are verified.

  20. Systems Level Analysis and Identification of Pathways and Networks Associated with Liver Fibrosis

    Science.gov (United States)

    2014-11-07

    Kamal Kumar1, Danielle L. Ippolito2, John A. Lewis2, Jonathan D. Stallings2, Anders Wallqvist1* 1 Department of Defense Biotechnology High Performance...Coordinated modular functionality and prognostic potential of a heart failure biomarker-driven interaction network. BMC Syst Biol 4: 60. 32. Azuaje FJ, Dewey FE

  1. Analysing larger metropolitan areas : On identification criteria for middle scale networks

    NARCIS (Netherlands)

    Van Nes, A.

    2009-01-01

    The aim of this paper is twofold. Firstly, it discusses how to analyse entire regions with the help of space syntax. Secondly, it demonstrates how a main route network through and between urban areas can be calculated in Depthmap. For this purpose one can combine angular choice with metrical

  2. Large-scale identification of human protein function using topological features of interaction network

    Science.gov (United States)

    Li, Zhanchao; Liu, Zhiqing; Zhong, Wenqian; Huang, Menghua; Wu, Na; Xie, Yun; Dai, Zong; Zou, Xiaoyong

    2016-11-01

    The annotation of protein function is a vital step to elucidate the essence of life at a molecular level, and it is also meritorious in biomedical and pharmaceutical industry. Developments of sequencing technology result in constant expansion of the gap between the number of the known sequences and their functions. Therefore, it is indispensable to develop a computational method for the annotation of protein function. Herein, a novel method is proposed to identify protein function based on the weighted human protein-protein interaction network and graph theory. The network topology features with local and global information are presented to characterise proteins. The minimum redundancy maximum relevance algorithm is used to select 227 optimized feature subsets and support vector machine technique is utilized to build the prediction models. The performance of current method is assessed through 10-fold cross-validation test, and the range of accuracies is from 67.63% to 100%. Comparing with other annotation methods, the proposed way possesses a 50% improvement in the predictive accuracy. Generally, such network topology features provide insights into the relationship between protein functions and network architectures. The source code of Matlab is freely available on request from the authors.

  3. Identification and characterization of starch and inulin modifying network of Aspergillus niger by functional genomics

    NARCIS (Netherlands)

    Yuan, Xiao-Lian

    2008-01-01

    Aspergillus niger produces a wide variety of carbohydrate hydrolytic enzymes which have potential applications in the baking, starch, textile, food and feed industries. The goal of this thesis is to unravel the molecular mechanisms of starch and inulin modifying network of A. niger, in order to

  4. Identification of children's activity type with accelerometer-based neural networks

    NARCIS (Netherlands)

    Vries, S.I. de; Engels, M.; Garre, F.G.

    2011-01-01

    Purpose: The study's purpose was to identify children's physical activity type using artificial neural network (ANN) models based on uniaxial or triaxial accelerometer data from the hip or the ankle. Methods: Fifty-eight children (31 boys and 27 girls, age range = 9-12 yr) performed the following

  5. Neural network approaches to tracer identification as related to PIV research

    Energy Technology Data Exchange (ETDEWEB)

    Seeley, C.H. Jr.

    1992-12-01

    Neural networks have become very powerful tools in many fields of interest. This thesis examines the application of neural networks to another rapidly growing field flow visualization. Flow visualization research is used to experimentally determine how fluids behave and to verify computational results obtained analytically. A form of flow visualization, particle image velocimetry (PIV). determines the flow movement by tracking neutrally buoyant particles suspended in the fluid. PIV research has begun to improve rapidly with the advent of digital imagers, which can quickly digitize an image into arrays of grey levels. These grey level arrays are analyzed to determine the location of the tracer particles. Once the particles positions have been determined across multiple image frames, it is possible to track their movements, and hence, the flow of the fluid. This thesis explores the potential of several different neural networks to identify the positions of the tracer particles. Among these networks are Backpropagation, Kohonen (counter-propagation), and Cellular. Each of these algorithms were employed in their basic form, and training and testing were performed on a synthetic grey level array. Modifications were then made to them in attempts to improve the results.

  6. Survivors' Discursive Construction of Organizational Identification after a Downsizing

    DEFF Research Database (Denmark)

    Aggerholm, Helle Kryger; Andersen, Mona Agerholm

    of transactional contract, 3) contextual dis-identification due to radical, cultural changes, elimination of networks and poor corporate reputation, and 4) procedural dis-identification caused by lack of procedural credibility, disrespect and responsibility avoidance. The results of this study indicate...... identifications. Discourse analysis of the interview data indicate four types of employee identification response categories: 1) non-identification caused by indifference, 2) identification fuelled by job identification, consensus as to the downsizing strategy, sense of procedural justice and acceptance...... that a strong identification with the pre-downsized organization seems to foster a strong sense of dis-identification with the post-downsized organization. The implications of these findings are discussed and recommendations for future research are provided....

  7. Identification of Biomarkers for Defense Response to Plasmopara viticola in a Resistant Grape Variety

    Directory of Open Access Journals (Sweden)

    Giulia Chitarrini

    2017-09-01

    Full Text Available Downy mildew (Plasmopara viticola is one of the most destructive diseases of the cultivated species Vitis vinifera. The use of resistant varieties, originally derived from backcrosses of North American Vitis spp., is a promising solution to reduce disease damage in the vineyards. To shed light on the type and the timing of pathogen-triggered resistance, this work aimed at discovering biomarkers for the defense response in the resistant variety Bianca, using leaf discs after inoculation with a suspension of P. viticola. We investigated primary and secondary metabolism at 12, 24, 48, and 96 h post-inoculation (hpi. We used methods of identification and quantification for lipids (LC-MS/MS, phenols (LC-MS/MS, primary compounds (GC-MS, and semi-quantification for volatile compounds (GC-MS. We were able to identify and quantify or semi-quantify 176 metabolites, among which 53 were modulated in response to pathogen infection. The earliest changes occurred in primary metabolism at 24–48 hpi and involved lipid compounds, specifically unsaturated fatty acid and ceramide; amino acids, in particular proline; and some acids and sugars. At 48 hpi, we also found changes in volatile compounds and accumulation of benzaldehyde, a promoter of salicylic acid-mediated defense. Secondary metabolism was strongly induced only at later stages. The classes of compounds that increased at 96 hpi included phenylpropanoids, flavonols, stilbenes, and stilbenoids. Among stilbenoids we found an accumulation of ampelopsin H + vaticanol C, pallidol, ampelopsin D + quadrangularin A, Z-miyabenol C, and α-viniferin in inoculated samples. Some of these compounds are known as phytoalexins, while others are novel biomarkers for the defense response in Bianca. This work highlighted some important aspects of the host response to P. viticola in a commercial variety under controlled conditions, providing biomarkers for a better understanding of the mechanism of plant defense and a

  8. Identification and Analysis of P53-Mediated Competing Endogenous RNA Network in Human Hepatocellular Carcinoma.

    Science.gov (United States)

    Zhang, Yiming; Kang, Ran; Liu, Wenrong; Yang, Yalan; Ding, Ruofan; Huang, Qingqing; Meng, Junhua; Xiong, Lili; Guo, Zhiyun

    2017-01-01

    Recent studies have indicated that long non-coding RNAs (lncRNAs) and mRNA function as competing endogenous RNAs (ceRNAs) that compete to bind to shared microRNA (miRNA) recognition elements (MREs) to perform specific biological functions during tumorigenesis. The tumor suppressor p53 is a master regulator of cancer-related biological processes by acting as a transcription factor to regulate target genes including miRNA and lncRNA. However, the mechanism in human hepatocellular carcinoma and whether p53-mediated RNA targets could form ceRNA network remain unclear. Here, we identified a series of differential expressed miRNAs, lncRNA and mRNA which were potentially regulated by p53 using RNA sequencing in HepG2. Genomic characteristics comparative analysis showed significant differences between mRNAs and lncRNAs. By integrating experimentally confirmed Ago2 and p53 binding sites, we constructed a highly reliable p53-mediated ceRNA network using hypergeometric test. The KEGG pathway enrichment analysis showed that the ceRNA network highly enriched in the cancer or p53-associated signaling pathways. Finally, using betweenness centrality analysis, we identified five master miRNAs (hsa-miR-3620-5p, hsa-miR-3613-3p, hsa-miR-6881-3p, hsa-miR-6087 and hsa-miR-18a-3p) that regulated most of the target RNAs, suggesting these miRNAs play central roles in the whole p53-mediated ceRNAs network. Taken together, our results provide a new regulatory mechanism of p53 networks for future studies in cancer therapeutics.

  9. Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data.

    Science.gov (United States)

    Fu, Changhe; Deng, Su; Jin, Guangxu; Wang, Xinxin; Yu, Zu-Guo

    2017-09-21

    Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data. We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high. We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously

  10. System-based identification of toxicity pathways associated with multi-walled carbon nanotube-induced pathological responses

    Energy Technology Data Exchange (ETDEWEB)

    Snyder-Talkington, Brandi N. [Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Dymacek, Julian [Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506-6070 (United States); Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506-9300 (United States); Porter, Dale W.; Wolfarth, Michael G.; Mercer, Robert R. [Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Pacurari, Maricica [Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506-9300 (United States); Denvir, James [Department of Biochemistry and Microbiology, Marshall University, Huntington, WV 25755 (United States); Castranova, Vincent [Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Qian, Yong, E-mail: yaq2@cdc.gov [Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Guo, Nancy L., E-mail: lguo@hsc.wvu.edu [Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506-9300 (United States)

    2013-10-15

    The fibrous shape and biopersistence of multi-walled carbon nanotubes (MWCNT) have raised concern over their potential toxicity after pulmonary exposure. As in vivo exposure to MWCNT produced a transient inflammatory and progressive fibrotic response, this study sought to identify significant biological processes associated with lung inflammation and fibrosis pathology data, based upon whole genome mRNA expression, bronchoaveolar lavage scores, and morphometric analysis from C57BL/6J mice exposed by pharyngeal aspiration to 0, 10, 20, 40, or 80 μg MWCNT at 1, 7, 28, or 56 days post-exposure. Using a novel computational model employing non-negative matrix factorization and Monte Carlo Markov Chain simulation, significant biological processes with expression similar to MWCNT-induced lung inflammation and fibrosis pathology data in mice were identified. A subset of genes in these processes was determined to be functionally related to either fibrosis or inflammation by Ingenuity Pathway Analysis and was used to determine potential significant signaling cascades. Two genes determined to be functionally related to inflammation and fibrosis, vascular endothelial growth factor A (vegfa) and C-C motif chemokine 2 (ccl2), were confirmed by in vitro studies of mRNA and protein expression in small airway epithelial cells exposed to MWCNT as concordant with in vivo expression. This study identified that the novel computational model was sufficient to determine biological processes strongly associated with the pathology of lung inflammation and fibrosis and could identify potential toxicity signaling pathways and mechanisms of MWCNT exposure which could be used for future animal studies to support human risk assessment and intervention efforts. - Highlights: • A novel computational model identified toxicity pathways matching in vivo pathology. • Systematic identification of MWCNT-induced biological processes in mouse lungs • MWCNT-induced functional networks of lung

  11. Intelligent detection and identification in fiber-optical perimeter intrusion monitoring system based on the FBG sensor network

    Science.gov (United States)

    Wu, Huijuan; Qian, Ya; Zhang, Wei; Li, Hanyu; Xie, Xin

    2015-12-01

    A real-time intelligent fiber-optic perimeter intrusion detection system (PIDS) based on the fiber Bragg grating (FBG) sensor network is presented in this paper. To distinguish the effects of different intrusion events, a novel real-time behavior impact classification method is proposed based on the essential statistical characteristics of signal's profile in the time domain. The features are extracted by the principal component analysis (PCA), which are then used to identify the event with a K-nearest neighbor classifier. Simulation and field tests are both carried out to validate its effectiveness. The average identification rate (IR) for five sample signals in the simulation test is as high as 96.67%, and the recognition rate for eight typical signals in the field test can also be achieved up to 96.52%, which includes both the fence-mounted and the ground-buried sensing signals. Besides, critically high detection rate (DR) and low false alarm rate (FAR) can be simultaneously obtained based on the autocorrelation characteristics analysis and a hierarchical detection and identification flow.

  12. Genotype 1 hepatitis C virus envelope features that determine antiviral response assessed through optimal covariance networks.

    Directory of Open Access Journals (Sweden)

    John M Murray

    Full Text Available The poor response to the combined antiviral therapy of pegylated alfa-interferon and ribavarin for hepatitis C virus (HCV infection may be linked to mutations in the viral envelope gene E1E2 (env, which can result in escape from the immune response and higher efficacy of viral entry. Mutations that result in failure of therapy most likely require compensatory mutations to achieve sufficient change in envelope structure and function. Compensatory mutations were investigated by determining positions in the E1E2 gene where amino acids (aa covaried across groups of individuals. We assessed networks of covarying positions in E1E2 sequences that differentiated sustained virological response (SVR from non-response (NR in 43 genotype 1a (17 SVR, and 49 genotype 1b (25 SVR chronically HCV-infected individuals. Binary integer programming over covariance networks was used to extract aa combinations that differed between response groups. Genotype 1a E1E2 sequences exhibited higher degrees of covariance and clustered into 3 main groups while 1b sequences exhibited no clustering. Between 5 and 9 aa pairs were required to separate SVR from NR in each genotype. aa in hypervariable region 1 were 6 times more likely than chance to occur in the optimal networks. The pair 531-626 (EI appeared frequently in the optimal networks and was present in 6 of 9 NR in one of the 1a clusters. The most frequent pairs representing SVR were 431-481 (EE, 500-522 (QA in 1a, and 407-434 (AQ in 1b. Optimal networks based on covarying aa pairs in HCV envelope can indicate features that are associated with failure or success to antiviral therapy.

  13. Identification of barley and rye varieties using matrix- assisted laser desorption/ionisation time-of-flight mass spectrometry with neural networks

    DEFF Research Database (Denmark)

    Bloch, H.A.; Petersen, Marianne Kjerstine; Sperotto, Maria Maddalena

    2001-01-01

    developed, which combines analysis of alcohol-soluble wheat proteins (gliadins) using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry with neural networks. Here we have applied the same method for the identification of both barley (Hordeum vulgare L.) and rye (Secale cereale L...

  14. Rapid identification and classification of Listeria spp. and serotype assignment of Listeria monocytogenes using fourier transform-infrared spectroscopy and artificial neural network analysis

    Science.gov (United States)

    The use of Fourier Transform-Infrared Spectroscopy (FT-IR) in conjunction with Artificial Neural Network software, NeuroDeveloper™ was examined for the rapid identification and classification of Listeria species and serotyping of Listeria monocytogenes. A spectral library was created for 245 strains...

  15. Noninvasive presymptomatic detection of Cercospora beticola infection and identification of early metabolic responses in sugar beet

    Directory of Open Access Journals (Sweden)

    Hans-Peter Mock

    2016-09-01

    Full Text Available Cercospora beticola is an economically significant fungal pathogen of sugar beet, and is the causative pathogen of Cercospora leaf spot. Selected host genotypes with contrasting degree of susceptibility to the disease have been exploited to characterize the patterns of metabolite responses to fungal infection, and to devise a pre-symptomatic, non-invasive method of detecting the presence of the pathogen. Sugar beet genotypes were analyzed for metabolite profiles and hyperspectral signatures. Correlation of data matrices from both approaches facilitated identification of candidates for metabolic markers. Hyperspectral imaging was highly predictive with a classification accuracy of 98.5-99.9 % in detecting C. beticola. Metabolite analysis revealed metabolites altered by the host as part of a successful defence response: these were L-DOPA, 12-hydroxyjasmonic acid 12-O-β-D-glucoside, pantothenic acid and 5-O-feruloylquinic acid. The accumulation of glucosylvitexin in the resistant cultivar suggests it acts as a constitutively-produced protectant. The study establishes a proof-of-concept for an unbiased, presymptomatic and non-invasive detection system for the presence of C. beticola. The test needs to be validated with a larger set of genotypes, to be scalable to the level of a crop improvement program, aiming to speed up the selection for resistant cultivars of sugar beet. Untargeted metabolic profiling is a valuable tool to identify metabolites which correlate with hyperspectral data.

  16. Genome-wide identification of soybean WRKY transcription factors in response to salt stress.

    Science.gov (United States)

    Yu, Yanchong; Wang, Nan; Hu, Ruibo; Xiang, Fengning

    2016-01-01

    Members of the large family of WRKY transcription factors are involved in a wide range of developmental and physiological processes, most particularly in the plant response to biotic and abiotic stress. Here, an analysis of the soybean genome sequence allowed the identification of the full complement of 188 soybean WRKY genes. Phylogenetic analysis revealed that soybean WRKY genes were classified into three major groups (I, II, III), with the second group further categorized into five subgroups (IIa-IIe). The soybean WRKYs from each group shared similar gene structures and motif compositions. The location of the GmWRKYs was dispersed over all 20 soybean chromosomes. The whole genome duplication appeared to have contributed significantly to the expansion of the family. Expression analysis by RNA-seq indicated that in soybean root, 66 of the genes responded rapidly and transiently to the imposition of salt stress, all but one being up-regulated. While in aerial part, 49 GmWRKYs responded, all but two being down-regulated. RT-qPCR analysis showed that in the whole soybean plant, 66 GmWRKYs exhibited distinct expression patterns in response to salt stress, of which 12 showed no significant change, 35 were decreased, while 19 were induced. The data present here provide critical clues for further functional studies of WRKY gene in soybean salt tolerance.

  17. Identification of Novel Hypoxia Response Genes in Human Glioma Cell Line A172

    Directory of Open Access Journals (Sweden)

    Fatemeh Baghbani

    2013-05-01

    Full Text Available   Objective(s: Hypoxia is a serious challenge for treatment of solid tumors. This condition has been manifested to exert significant therapeutic effects on glioblastoma multiform or (WHO astrocytoma grade IV. Hypoxia contributes numerous changes in cellular mechanisms such as angiogenesis, metastasis and apoptosis evasion. Furthermore, in molecular level, hypoxia can cause induction of DNA breaks in tumor cells. Identification of mechanisms responsible for these effects can lead to designing more efficient therapeutic strategies against tumor progression which results in improvement of patient prognosis.   Materials and Methods: In order to identify more hypoxia regulated genes which may have a role in glioblastoma progression, cDNA-AFLP was optimized as a Differential display method which is able to identify and isolate transcripts with no prior sequence knowledge. Results: Using this method, the current study identified 120 Transcription Derived Fragments (TDFs which were completely differentially regulated in response to hypoxia. By sequence homology searching, the current study could detect 22 completely differentially regulated known genes and two unknown sequence matching with two chromosome contig and four sequence matches with some Expressed Sequence Tags (ESTs. Conclusion: Further characterizing of these genes may help to achieve better understanding of hypoxia mediated phenotype change in tumor cells.

  18. Identification and functional prediction of stress responsive AP2/ERF transcription factors in Brassica napus by genome-wide analysis.

    Science.gov (United States)

    Owji, Hajar; Hajiebrahimi, Ali; Seradj, Hassan; Hemmati, Shiva

    2017-12-01

    Using homology and domain authentication, 321 putative AP2/ERF transcription factors were identified in Brassica napus, called BnAP2/ERF TFs. BnAP2/ERF TFs were classified into five major subfamilies, including DREB, ERF, AP2, RAV, and BnSoloist. This classification is based on phylogenetic analysis, motif identification, gene structure analysis, and physiochemical characterization. These TFs were annotated based on phylogenetic relationship with Brassica rapa. BnAP2/ERF TFs were located on 19 chromosomes of B. napus. Orthologs and paralogs were identified using synteny-based methods Ks calculation within B. napus genome and between B. napus with other species such as B. rapa, Brassica oleracea, and Arabidopsis thaliana indicated that BnAP2/ERF TFs were formed through duplication events occurred before B. napus formation. Kn/Ks values were between 0 and 1, suggesting the purifying selection among BnAP2/ERF TFs. Gene ontology annotation, cis-regulatory elements and functional interaction networks suggested that BnAP2/ERF TFs participate in response to stressors, including drought, high salinity, heat and cold as well as developmental processes particularly organ specification and embryogenesis. The identified cis-regulatory elements in the upstream of BnAP2/ERF TFs were responsive to abscisic acid. Analysis of the expression data derived from Illumina Hiseq 2000 RNA sequencing revealed that BnAP2/ERF genes were highly expressed in the roots comparing to flower buds, leaves, and stems. Also, the ERF subfamily was over-expressed under salt and fungal treatments. BnERF039 and BnERF245 are candidates for salt-tolerant B. napus. BnERF253-256 and BnERF260-277 are potential cytokinin response factors. BnERF227, BnERF228, BnERF234, BnERF134, BnERF132, BnERF176, and BnERF235 were suggested for resistance against Leptosphaeria maculan and Leptosphaeria biglobosa. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Identification of putative drug targets for human sperm-egg interaction defect using protein network approach.

    Science.gov (United States)

    Sabetian, Soudabeh; Shamsir, Mohd Shahir

    2015-07-18

    Sperm-egg interaction defect is a significant cause of in-vitro fertilization failure for infertile cases. Numerous molecular interactions in the form of protein-protein interactions mediate the sperm-egg membrane interaction process. Recent studies have demonstrated that in addition to experimental techniques, computational methods, namely protein interaction network approach, can address protein-protein interactions between human sperm and egg. Up to now, no drugs have been detected to treat sperm-egg interaction disorder, and the initial step in drug discovery research is finding out essential proteins or drug targets for a biological process. The main purpose of this study is to identify putative drug targets for human sperm-egg interaction deficiency and consider if the detected essential proteins are targets for any known drugs using protein-protein interaction network and ingenuity pathway analysis. We have created human sperm-egg protein interaction networks with high confidence, including 106 nodes and 415 interactions. Through topological analysis of the network with calculation of some metrics, such as connectivity and betweenness centrality, we have identified 13 essential proteins as putative drug targets. The potential drug targets are from integrins, fibronectins, epidermal growth factor receptors, collagens and tetraspanins protein families. We evaluated these targets by ingenuity pathway analysis, and the known drugs for the targets have been detected, and the possible effective role of the drugs on sperm-egg interaction defect has been considered. These results showed that the drugs ocriplasmin (Jetrea©), gefitinib (Iressa©), erlotinib hydrochloride (Tarceva©), clingitide, cetuximab (Erbitux©) and panitumumab (Vectibix©) are possible candidates for efficacy testing for the treatment of sperm-egg interaction deficiency. Further experimental validation can be carried out to confirm these results. We have identified the first potential list of

  20. Identification and characterization of rainfall events responsible for triggering of debris flows and shallow landslides

    Science.gov (United States)

    Iadanza, Carla; Trigila, Alessandro; Napolitano, Francesco

    2016-10-01

    The aim of this study is the development of objective and replicable methodologies for the identification, analysis and characterization of rainfall events responsible for the triggering of shallow landslides and debris flows, in order to define empirical rainfall thresholds. The study area is the province of Trento (6208 km2), located in the north-eastern Alps, and characterized by complex orography, with 70% of the area at an altitude above 1000 m. A rigorous statistical methodology has been defined for the identification of the beginning of the triggering event, based on the critical duration, i.e. the minimum dry period duration separating two stochastically independent rainy periods. The critical duration has been calculated for each rain gauge of the studied area and its variability during the months of the year has been analyzed. An analysis of the rainfall spatial variability in a neighborhood of the landslide detachment zone has been carried out. The adopted methods are: the examination of the Monte Macaion radar maps during some summer convective events, the comparison of rainfall records of rain gauges located in a 10 km buffer around the landslide, and the calculation of the Pearson's correlation coefficient between pairs of neighboring rain gauges. The following rainfall thresholds have been then calibrated with the frequentist approach and compared: average intensity-event duration (I-D), which represents the rainfall event in its entirety, and intensity-duration associated with the event maximum return period (IRP-DRP), which considers the most critical portion of the event. In the absence of information about the landslide time of activation, the end of the triggering event has been identified using two criteria: the rainfall peak intensity and the last registration of the day. The methodology adopted for the objective identification of the beginning of the triggering event has demonstrated good applicability for rainfall induced landslides. During

  1. IL17eScan: A Tool for the Identification of Peptides Inducing IL-17 Response

    Directory of Open Access Journals (Sweden)

    Sudheer Gupta

    2017-10-01

    Full Text Available IL-17 cytokines are pro-inflammatory cytokines and are crucial in host defense against various microbes. Induction of these cytokines by microbial antigens has been investigated in the case of ischemic brain injury, gingivitis, candidiasis, autoimmune myocarditis, etc. In this study, we have investigated the ability of amino acid sequence of antigens to induce IL-17 response using machine-learning approaches. A total of 338 IL-17-inducing and 984 IL-17 non-inducing peptides were retrieved from Immune Epitope Database. 80% of the data were randomly selected as training dataset and rest 20% as validation dataset. To predict the IL-17-inducing ability of peptides/protein antigens, different sequence-based machine-learning models were developed. The performance of support vector machine (SVM and random forest (RF was compared with different parameters to predict IL-17-inducing epitopes (IIEs. The dipeptide composition-based SVM-model displayed an accuracy of 82.4% with Matthews correlation coefficient = 0.62 at polynomial (t = 1 kernel on 10-fold cross-validation and outperformed RF. Amino acid residues Leu, Ser, Arg, Asn, and Phe and dipeptides LL, SL, LK, IL, LI, NL, LR, FK, SF, and LE are abundant in IIEs. The present tool helps in the identification of IIEs using machine-learning approaches. The induction of IL-17 plays an important role in several inflammatory diseases, and identification of such epitopes would be of great help to the immunologists. It is freely available at http://metagenomics.iiserb.ac.in/IL17eScan/ and http://metabiosys.iiserb.ac.in/IL17eScan/.

  2. Transcriptome-wide identification of Camellia sinensis WRKY transcription factors in response to temperature stress.

    Science.gov (United States)

    Wu, Zhi-Jun; Li, Xing-Hui; Liu, Zhi-Wei; Li, Hui; Wang, Yong-Xin; Zhuang, Jing

    2016-02-01

    Tea plant [Camellia sinensis (L.) O. Kuntze] is a leaf-type healthy non-alcoholic beverage crop, which has been widely introduced worldwide. Tea is rich in various secondary metabolites, which are important for human health. However, varied climate and complex geography have posed challenges for tea plant survival. The WRKY gene family in plants is a large transcription factor family that is involved in biological processes related to stress defenses, development, and metabolite synthesis. Therefore, identification and analysis of WRKY family transcription factors in tea plant have a profound significance. In the present study, 50 putative C. sinensis WRKY proteins (CsWRKYs) with complete WRKY domain were identified and divided into three Groups (Group I-III) on the basis of phylogenetic analysis results. The distribution of WRKY family transcription factors among plantae, fungi, and protozoa showed that the number of WRKY genes increased in higher plant, whereas the number of these genes did not correspond to the evolutionary relationships of different species. Structural feature and annotation analysis results showed that CsWRKY proteins contained WRKYGQK/WRKYGKK domains and C2H2/C2HC-type zinc-finger structure: D-X18-R-X1-Y-X2-C-X4-7-C-X23-H motif; CsWRKY proteins may be associated with the biological processes of abiotic and biotic stresses, tissue development, and hormone and secondary metabolite biosynthesis. Temperature stresses suggested that the candidate CsWRKY genes were involved in responses to extreme temperatures. The current study established an extensive overview of the WRKY family transcription factors in tea plant. This study also provided a global survey of CsWRKY transcription factors and a foundation of future functional identification and molecular breeding.

  3. A New Type of Photo-Thermo Staged-Responsive Shape-Memory Polyurethanes Network

    Directory of Open Access Journals (Sweden)

    Jinghao Yang

    2017-07-01

    Full Text Available In this paper, we developed a photo-thermo staged-responsive shape-memory polymer network which has a unique ability of being spontaneously photo-responsive deformable and thermo-responsive shape recovery. This new type of shape-memory polyurethane network (A-SMPUs was successfully synthesized with 4,4-azodibenzoic acid (Azoa, hexamethylenediisocyanate (HDI and polycaprolactone (PCL, followed by chemical cross-linking with glycerol (Gl. The structures, morphology, and shape-memory properties of A-SMPUs have been carefully investigated. The results demonstrate that the A-SMPUs form micro-phase separation structures consisting of a semi-crystallized PCL soft phase and an Azoa amorphous hard phase that could influence the crystallinity of PCL soft phases. The chemical cross-linking provided a stable network and good thermal stability to the A-SMPUs. All A-SMPUs exhibited good triple-shape-memory properties with higher than 97% shape fixity ratio and 95% shape recovery ratio. Additionally, the A-SMPUs with higher Azoa content exhibited interesting photo-thermo two-staged responsiveness. A pre-processed film with orientated Azoa structure exhibited spontaneous curling deformation upon exposing to ultraviolet (UV light, and curling deformation is constant even under Vis light. Finally, the curling deformation can spontaneously recover to the original shape by applying a thermal stimulus. This work demonstrates new synergistically multi-responsive SMPUs that will have many applications in smart science and technology.

  4. Secured Message Transmission in Mobile AD HOC Networks through Identification and Removal of Byzantine Failures

    OpenAIRE

    V Anitha; Akilandeswari, Dr. J.

    2011-01-01

    The emerging need for mobile ad hoc networks and secured data transmission phase is of crucial importance depending upon the environments like military. In this paper, a new way to improve the reliability of message transmission is presented. In the open collaborative MANET environment, any node can maliciously or selfishly disrupt and deny communication of other nodes. Dynamic changing topology makes it hard to determine the adversary nodes that affect the communication in MANET. An SMT prot...

  5. Identification of current attacks and their counter measures in Optical Burst Switched (OBS) network

    OpenAIRE

    Siddharth Singh Chouhan; Prof. Sanjay Sharma

    2012-01-01

    As day by day application grows internet requires large amount of bandwidth. Optical Burst Switching (OBS) is the next generation optical Internet with IP over WDM as the core architecture. It can achieve a balance between Optical Circuit Switching (OCS) and Optical Packet Switching (OPS). Optical network supports huge bandwidth and transmits data at an average rate of 50Tb/s. But we need to exploit the fiber’s huge bandwidth through WDM which is the current favorite multiplexing technology i...

  6. Identification and analysis of signaling networks potentially involved in breast carcinoma metastasis to the brain.

    Directory of Open Access Journals (Sweden)

    Feng Li

    Full Text Available Brain is a common site of breast cancer metastasis associated with significant neurologic morbidity, decreased quality of life, and greatly shortened survival. However, the molecular and cellular mechanisms underpinning brain colonization by breast carcinoma cells are poorly understood. Here, we used 2D-DIGE (Difference in Gel Electrophoresis proteomic analysis followed by LC-tandem mass spectrometry to identify the proteins differentially expressed in brain-targeting breast carcinoma cells (MB231-Br compared with parental MDA-MB-231 cell line. Between the two cell lines, we identified 12 proteins consistently exhibiting greater than 2-fold (p<0.05 difference in expression, which were associated by the Ingenuity Pathway Analysis (IPA with two major signaling networks involving TNFα/TGFβ-, NFκB-, HSP-70-, TP53-, and IFNγ-associated pathways. Remarkably, highly related networks were revealed by the IPA analysis of a list of 19 brain-metastasis-associated proteins identified recently by the group of Dr. A. Sierra using MDA-MB-435-based experimental system (Martin et al., J Proteome Res 2008 7:908-20, or a 17-gene classifier associated with breast cancer brain relapse reported by the group of Dr. J. Massague based on a microarray analysis of clinically annotated breast tumors from 368 patients (Bos et al., Nature 2009 459: 1005-9. These findings, showing that different experimental systems and approaches (2D-DIGE proteomics used on brain targeting cell lines or gene expression analysis of patient samples with documented brain relapse yield highly related signaling networks, suggest strongly that these signaling networks could be essential for a successful colonization of the brain by metastatic breast carcinoma cells.

  7. Automatic identification of terpenoid skeletons by feed-forward neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Emerenciano, Vicente P. [Instituto de Quimica, Universidade de Sao Paulo, Caixa Postal 26077, 05513-970 Sao Paulo, SP (Brazil)]. E-mail: vdpemere@iq.usp.br; Alvarenga, Sandra A.V. [Faculdade de Engenharia de Guaratingueta, UNESP, CEP 12516-410, Guaratingueta, Sao Paulo (Brazil); Scotti, Marcus Tullius [Instituto de Quimica, Universidade de Sao Paulo, Caixa Postal 26077, 05513-970 Sao Paulo, SP (Brazil); Ferreira, Marcelo J.P. [Instituto de Quimica, Universidade de Sao Paulo, Caixa Postal 26077, 05513-970 Sao Paulo, SP (Brazil); Stefani, Ricardo [Departamento de Quimica, FFCLRP, USP, Av. Bandeirantes 3900, CEP 14040-905, Ribeirao Preto, Sao Paulo (Brazil); Nuzillard, Jean-Marc [FRE 2715, University of Reims, Moulin de la Housse, BP 1039, 51687 REIMS Cedex 2 (France)

    2006-10-10

    Feed-forward neural networks (FFNNs) were used to predict the skeletal type of molecules belonging to six classes of terpenoids. A database that contains the {sup 13}C NMR spectra of about 5000 compounds was used to train the FFNNs. An efficient representation of the spectra was designed and the constitution of the best FFNN input vector format resorted from an heuristic approach. The latter was derived from general considerations on terpenoid structures.

  8. Identification of Measures and Indicators for the IT Security of Networked Medical Devices: A Delphi Study.

    Science.gov (United States)

    Leber, Stefan; Ammenwerth, Elske

    2017-01-01

    The networking of medical devices or systems in a hospital network is the foundation for modern medical diagnostics and therapy. This, however, makes possible numerous hazards that could lead to risks for patients, clinical processes or data and information. The aim of the work was to develop a catalogue of measures and indicators for the effective support of the IT risk management process in a health facility. Through a qualitative and quantitative Delphi study among 21 experts, it was possible to identify an initial 51 practice-relevant measures of IT risk management that a hospital should implement. Additionally, 27 indicators were defined which can be used to measure the impact of these measures. Of the 51 measures, 35 were seen as especially important, particularly organizational measures. Of the 27 indicators, six were seen as especially important, particularly indicators to measure networking effectiveness. The study also investigated the impact of the measures on the indicators. A case study is planned to investigate the practicability of the identified measures and indicators.

  9. Identification of unstable network modules reveals disease modules associated with the progression of Alzheimer's disease.

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    Masataka Kikuchi

    Full Text Available Alzheimer's disease (AD, the most common cause of dementia, is associated with aging, and it leads to neuron death. Deposits of amyloid β and aberrantly phosphorylated tau protein are known as pathological hallmarks of AD, but the underlying mechanisms have not yet been revealed. A high-throughput gene expression analysis previously showed that differentially expressed genes accompanying the progression of AD were more down-regulated than up-regulated in the later stages of AD. This suggested that the molecular networks and their constituent modules collapsed along with AD progression. In this study, by using gene expression profiles and protein interaction networks (PINs, we identified the PINs expressed in three brain regions: the entorhinal cortex (EC, hippocampus (HIP and superior frontal gyrus (SFG. Dividing the expressed PINs into modules, we examined the stability of the modules with AD progression and with normal aging. We found that in the AD modules, the constituent proteins, interactions and cellular functions were not maintained between consecutive stages through all brain regions. Interestingly, the modules were collapsed with AD progression, specifically in the EC region. By identifying the modules that were affected by AD pathology, we found the transcriptional regulation-associated modules that interact with the proteasome-associated module via UCHL5 hub protein, which is a deubiquitinating enzyme. Considering PINs as a system made of network modules, we found that the modules relevant to the transcriptional regulation are disrupted in the EC region, which affects the ubiquitin-proteasome system.

  10. Application of neural networks and information theory to the identification of E. coli transcriptional promoters

    Energy Technology Data Exchange (ETDEWEB)

    Abremski, K. (Du Pont Merck Pharmaceutical Co., Wilmington, DE (USA). Experimental Station); Sirotkin, K. (National Center for Biotechnology Information, Bethesda, MD (USA)); Lapedes, A. (Los Alamos National Lab., NM (USA))

    1991-01-01

    The Humane Genome Project has as its eventual goal the determination of the entire DNA sequence of man, which comprises approximately 3 billion base pairs. An important aspect of this project will be the analysis of the sequence to locate regions of biological importance. New computer methods will be needed to automate and facilitate this task. In this paper, we have investigated use of neural networks for the recognition of functional patterns in biological sequences. The prediction of Escherichia coli transcriptional promoters was chosen as a model system for these studies. Two approaches were employed. In the fist method, a mutual information analysis of promoter and nonpromoter sequences was carried out to demonstrate the informative base positions that help to distinguish promoter sequences from non-promoter sequences. These base positions were than used to train a Perceptron to predict new promoter sequences. In the second method, the experimental knowledge of promoters was used to indicate the important base positions in the sequence. These base positions were used to train a back propagation network with hidden units which represented regions of sequence conservation found in promoters. With both types of networks, prediction of new promoter sequences was greater than 96.9%. 12 refs., 1 fig., 4 tabs.

  11. Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.

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    Vipin Narang

    Full Text Available Human gene regulatory networks (GRN can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs. Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data accompanying this manuscript.

  12. Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.

    Science.gov (United States)

    Narang, Vipin; Ramli, Muhamad Azfar; Singhal, Amit; Kumar, Pavanish; de Libero, Gennaro; Poidinger, Michael; Monterola, Christopher

    2015-01-01

    Human gene regulatory networks (GRN) can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs). Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data) accompanying this manuscript.

  13. Hybrid swarm intelligence optimization approach for optimal data storage position identification in wireless sensor networks.

    Science.gov (United States)

    Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam

    2015-01-01

    The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.

  14. Identification of dichloroacetic acid degrading Cupriavidus bacteria in a drinking water distribution network model.

    Science.gov (United States)

    Berthiaume, C; Gilbert, Y; Fournier-Larente, J; Pluchon, C; Filion, G; Jubinville, E; Sérodes, J-B; Rodriguez, M; Duchaine, C; Charette, S J

    2014-01-01

    Bacterial community structure and composition of a drinking water network were assessed to better understand this ecosystem in relation to haloacetic acid (HAA) degradation and to identify new bacterial species having HAA degradation capacities. Biofilm samples were collected from a model system, simulating the end of the drinking water distribution network and supplied with different concentrations of dichloroacetic and trichloroacetic acids at different periods over the course of a year. The samples were analysed by culturing, denaturing gradient gel electrophoresis (DGGE) and sequencing. Pipe diameter and HAA ratios did not impact the bacterial community profiles, but the season had a clear influence. Based on DGGE profiles, it appeared that a particular biomass has developed during the summer compared with the other seasons. Among the bacteria isolated in this study, those from genus Cupriavidus were able to degrade dichloroacetic acid. Moreover, these bacteria degrade dichloroacetic acid at 18°C but not at 10°C. The microbial diversity evolved throughout the experiment, but the bacterial community was distinct during the summer. Results obtained on the capacity of Cupriavidus to degrade DCAA only at 18°C but not at 10°C indicate that water temperature is a major element affecting DCAA degradation and confirming observations made regarding season influence on HAA degradation in the drinking water distribution network. This is the first demonstration of the HAA biodegradation capacity of the genus Cupriavidus. © 2013 The Society for Applied Microbiology.

  15. Explicit Rate Adjustment (ERA: Responsiveness, Network Utilization Efficiency and Fairness for Layered Multicast

    Directory of Open Access Journals (Sweden)

    Somnuk PUANGPRONPITAG

    2005-08-01

    Full Text Available To provide layered multicast with responsiveness, efficiency in network utilization, scalability and fairness (including inter-protocol fairness, intra-protocol fairness, intra-session fairness and TCP-friendliness for layered multicast, we propose in this paper a new multicast congestion control, called Explicit Rate Adjustment (ERA. Our protocol uses an algorithm relying on TCP throughput equation and Packet-bunch Probe techniques to detect optimal bandwidth utilization; then adjusts the reception rate accordingly. We have built ERA into a network simulator (ns2 and demonstrate via simulations that the goals are reached.

  16. Translational Identification of Transcriptional Signatures of Major Depression and Antidepressant Response

    Directory of Open Access Journals (Sweden)

    Mylène Hervé

    2017-08-01

    Full Text Available Major depressive disorder (MDD is a highly prevalent mental illness whose therapy management remains uncertain, with more than 20% of patients who do not achieve response to antidepressants. Therefore, identification of reliable biomarkers to predict response to treatment will greatly improve MDD patient medical care. Due to the inaccessibility and lack of brain tissues from living MDD patients to study depression, researches using animal models have been useful in improving sensitivity and specificity of identifying biomarkers. In the current study, we used the unpredictable chronic mild stress (UCMS model and correlated stress-induced depressive-like behavior (n = 8 unstressed vs. 8 stressed mice as well as the fluoxetine-induced recovery (n = 8 stressed and fluoxetine-treated mice vs. 8 unstressed and fluoxetine-treated mice with transcriptional signatures obtained by genome-wide microarray profiling from whole blood, dentate gyrus (DG, and the anterior cingulate cortex (ACC. Hierarchical clustering and rank-rank hypergeometric overlap (RRHO procedures allowed us to identify gene transcripts with variations that correlate with behavioral profiles. As a translational validation, some of those transcripts were assayed by RT-qPCR with blood samples from 10 severe major depressive episode (MDE patients and 10 healthy controls over the course of 30 weeks and four visits. Repeated-measures ANOVAs revealed candidate trait biomarkers (ARHGEF1, CMAS, IGHMBP2, PABPN1 and TBC1D10C, whereas univariate linear regression analyses uncovered candidates state biomarkers (CENPO, FUS and NUBP1, as well as prediction biomarkers predictive of antidepressant response (CENPO, NUBP1. These data suggest that such a translational approach may offer new leads for clinically valid panels of biomarkers for MDD.

  17. The Community Seismic Network and Quake-Catcher Network: Monitoring building response to earthquakes through community instrumentation

    Science.gov (United States)

    Cheng, M.; Kohler, M. D.; Heaton, T. H.; Clayton, R. W.; Chandy, M.; Cochran, E.; Lawrence, J. F.

    2013-12-01

    The Community Seismic Network (CSN) and Quake-Catcher Network (QCN) are dense networks of low-cost ($50) accelerometers that are deployed by community volunteers in their homes in California. In addition, many accelerometers are installed in public spaces associated with civic services, publicly-operated utilities, university campuses, and high-rise buildings. Both CSN and QCN consist of observation-based structural monitoring which is carried out using records from one to tens of stations in a single building. We have deployed about 150 accelerometers in a number of buildings ranging between five and 23 stories in the Los Angeles region. In addition to a USB-connected device which connects to the host's computer, we have developed a stand-alone sensor-plug-computer device that directly connects to the internet via Ethernet or WiFi. In the case of CSN, the sensors report data to the Google App Engine cloud computing service consisting of data centers geographically distributed across the continent. This robust infrastructure provides parallelism and redundancy during times of disaster that could affect hardware. The QCN sensors, however, are connected to netbooks with continuous data streaming in real-time via the distributed computing Berkeley Open Infrastructure for Network Computing software program to a server at Stanford University. In both networks, continuous and triggered data streams use a STA/LTA scheme to determine the occurrence of significant ground accelerations. Waveform data, as well as derived parameters such as peak ground acceleration, are then sent to the associated archives. Visualization models of the instrumented buildings' dynamic linear response have been constructed using Google SketchUp and MATLAB. When data are available from a limited number of accelerometers installed in high rises, the buildings are represented as simple shear beam or prismatic Timoshenko beam models with soil-structure interaction. Small-magnitude earthquake records

  18. Statistical identification of gene association by CID in application of constructing ER regulatory network

    Directory of Open Access Journals (Sweden)

    Lien Huang-Chun

    2009-03-01

    Full Text Available Abstract Background A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID, is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs (X and their downstream genes (Y based on clinical data. More specifically, we use estrogen receptor α (ERα as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A. Results The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC, Student's t-test (STT, coefficient of determination (CoD, and mutual information (MI. When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y against a discrete variable (X, it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays. Conclusion CID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the

  19. Genome-scale cold stress response regulatory networks in ten Arabidopsis thaliana ecotypes

    Science.gov (United States)

    2013-01-01

    Background Low temperature leads to major crop losses every year. Although several studies have been conducted focusing on diversity of cold tolerance level in multiple phenotypically divergent Arabidopsis thaliana (A. thaliana) ecotypes, genome-scale molecular understanding is still lacking. Results In this study, we report genome-scale transcript response diversity of 10 A. thaliana ecotypes originating from different geographical locations to non-freezing cold stress (10°C). To analyze the transcriptional response diversity, we initially compared transcriptome changes in all 10 ecotypes using Arabidopsis NimbleGen ATH6 microarrays. In total 6061 transcripts were significantly cold regulated (p cold stress regulon genes. Significant numbers of non-synonymous amino acid changes were observed in the coding region of the CBF regulon genes. Considering the limited knowledge about regulatory interactions between transcription factors and their target genes in the model plant A. thaliana, we have adopted a powerful systems genetics approach- Network Component Analysis (NCA) to construct an in-silico transcriptional regulatory network model during response to cold stress. The resulting regulatory network contained 1,275 nodes and 7,720 connections, with 178 transcription factors and 1,331 target genes. Conclusions A. thaliana ecotypes exhibit considerable variation in transcriptome level responses to non-freezing cold stress treatment. Ecotype specific transcripts and related gene ontology (GO) categories were identified to delineate natural variation of cold stress regulated differential gene expression in the model plant A. thaliana. The predicted regulatory network model was able to identify new ecotype specific transcription factors and their regulatory interactions, which might be crucial for their local geographic adaptation to cold temperature. Additionally, since the approach presented here is general, it could be adapted to study networks regulating

  20. Filling the gap between disaster preparedness and response networks of urban emergency management: Following the 2013 Seoul Floods.

    Science.gov (United States)

    Song, Minsun; Jung, Kyujin

    2015-01-01

    To examine the gap between disaster preparedness and response networks following the 2013 Seoul Floods in which the rapid transmission of disaster information and resources was impeded by severe changes of interorganizational collaboration networks. This research uses the 2013 Seoul Emergency Management Survey data that were collected before and after the floods, and total 94 organizations involving in coping with the floods were analyzed in bootstrap independent-sample t-test and social network analysis through UCINET 6 and STATA 12. The findings show that despite the primary network form that is more hierarchical, horizontal collaboration has been relatively invigorated in actual response. Also, interorganizational collaboration networks for response operations seem to be more flexible grounded on improvisation to coping with unexpected victims and damages. Local organizations under urban emergency management are recommended to tightly build a strong commitment for joint response operations through full-size exercises at the metropolitan level before a catastrophic event. Also, interorganizational emergency management networks need to be restructured by reflecting the actual response networks to reduce collaboration risk during a disaster. This research presents a critical insight into inverse thinking of the view designing urban emergency management networks and provides original evidences for filling the gap between previously coordinated networks for disaster preparedness and practical response operations after a disaster.

  1. Rapid Identification and Classification of Listeria spp. and Serotype Assignment of Listeria monocytogenes Using Fourier Transform-Infrared Spectroscopy and Artificial Neural Network Analysis.

    Directory of Open Access Journals (Sweden)

    K F Romanolo

    Full Text Available The use of Fourier Transform-Infrared Spectroscopy (FT-IR in conjunction with Artificial Neural Network software NeuroDeveloper™ was examined for the rapid identification and classification of Listeria species and serotyping of Listeria monocytogenes. A spectral library was created for 245 strains of Listeria spp. to give a biochemical fingerprint from which identification of unknown samples were made. This technology was able to accurately distinguish the Listeria species with 99.03% accuracy. Eleven serotypes of Listeria monocytogenes including 1/2a, 1/2b, and 4b were identified with 96.58% accuracy. In addition, motile and non-motile forms of Listeria were used to create a more robust model for identification. FT-IR coupled with NeuroDeveloper™ appear to be a more accurate and economic choice for rapid identification of pathogenic Listeria spp. than current methods.

  2. Repeated exposure to media violence is associated with diminished response in an inhibitory frontolimbic network.

    Directory of Open Access Journals (Sweden)

    Christopher R Kelly

    Full Text Available BACKGROUND: Media depictions of violence, although often claimed to induce viewer aggression, have not been shown to affect the cortical networks that regulate behavior. METHODOLOGY/PRINCIPAL FINDINGS: Using functional magnetic resonance imaging (fMRI, we found that repeated exposure to violent media, but not to other equally arousing media, led to both diminished response in right lateral orbitofrontal cortex (right ltOFC and a decrease in right ltOFC-amygdala interaction. Reduced function in this network has been previously associated with decreased control over a variety of behaviors, including reactive aggression. Indeed, we found reduced right ltOFC responses to be characteristic of those subjects that reported greater tendencies toward reactive aggression. Furthermore, the violence-induced reduction in right ltOFC response coincided with increased throughput to behavior planning regions. CONCLUSIONS: These novel findings establish that even short-term exposure to violent media can result in diminished responsiveness of a network associated with behaviors such as reactive aggression.

  3. Transcriptional profiling uncovers a network of cholesterol-responsive atherosclerosis target genes.

    Directory of Open Access Journals (Sweden)

    Josefin Skogsberg

    2008-03-01

    Full Text Available Despite the well-documented effects of plasma lipid lowering regimes halting atherosclerosis lesion development and reducing morbidity and mortality of coronary artery disease and stroke, the transcriptional response in the atherosclerotic lesion mediating these beneficial effects has not yet been carefully investigated. We performed transcriptional profiling at 10-week intervals in atherosclerosis-prone mice with human-like hypercholesterolemia and a genetic switch to lower plasma lipoproteins (Ldlr(-/-Apo(100/100Mttp(flox/flox Mx1-Cre. Atherosclerotic lesions progressed slowly at first, then expanded rapidly, and plateaued after advanced lesions formed. Analysis of lesion expression profiles indicated that accumulation of lipid-poor macrophages reached a point that led to the rapid expansion phase with accelerated foam-cell formation and inflammation, an interpretation supported by lesion histology. Genetic lowering of plasma cholesterol (e.g., lipoproteins at this point all together prevented the formation of advanced plaques and parallel transcriptional profiling of the atherosclerotic arterial wall identified 37 cholesterol-responsive genes mediating this effect. Validation by siRNA-inhibition in macrophages incubated with acetylated-LDL revealed a network of eight cholesterol-responsive atherosclerosis genes regulating cholesterol-ester accumulation. Taken together, we have identified a network of atherosclerosis genes that in response to plasma cholesterol-lowering prevents the formation of advanced plaques. This network should be of interest for the development of novel atherosclerosis therapies.

  4. Plasticity of the MAPK signaling network in response to mechanical stress.

    Directory of Open Access Journals (Sweden)

    Andrea M Pereira

    Full Text Available Cells display versatile responses to mechanical inputs and recent studies have identified the mitogen-activated protein kinase (MAPK cascades mediating the biological effects observed upon mechanical stimulation. Although, MAPK pathways can act insulated from each other, several mechanisms facilitate the crosstalk between the components of these cascades. Yet, the combinatorial complexity of potential molecular interactions between these elements have prevented the understanding of their concerted functions. To analyze the plasticity of the MAPK signaling network in response to mechanical stress we performed a non-saturating epistatic screen in resting and stretched conditions employing as readout a JNK responsive dJun-FRET biosensor. By knocking down MAPKs, and JNK pathway regulators, singly or in pairs in Drosophila S2R+ cells, we have uncovered unexpected regulatory links between JNK cascade kinases, Rho GTPases, MAPKs and the JNK phosphatase Puc. These relationships have been integrated in a system network model at equilibrium accounting for all experimentally validated interactions. This model allows predicting the global reaction of the network to its modulation in response to mechanical stress. It also highlights its context-dependent sensitivity.

  5. Remodeling Pearson's Correlation for Functional Brain Network Estimation and Autism Spectrum Disorder Identification

    Directory of Open Access Journals (Sweden)

    Weikai Li

    2017-08-01

    Full Text Available Functional brain network (FBN has been becoming an increasingly important way to model the statistical dependence among neural time courses of brain, and provides effective imaging biomarkers for diagnosis of some neurological or psychological disorders. Currently, Pearson's Correlation (PC is the simplest and most widely-used method in constructing FBNs. Despite its advantages in statistical meaning and calculated performance, the PC tends to result in a FBN with dense connections. Therefore, in practice, the PC-based FBN needs to be sparsified by removing weak (potential noisy connections. However, such a scheme depends on a hard-threshold without enough flexibility. Different from this traditional strategy, in this paper, we propose a new approach for estimating FBNs by remodeling PC as an optimization problem, which provides a way to incorporate biological/physical priors into the FBNs. In particular, we introduce an L1-norm regularizer into the optimization model for obtaining a sparse solution. Compared with the hard-threshold scheme, the proposed framework gives an elegant mathematical formulation for sparsifying PC-based networks. More importantly, it provides a platform to encode other biological/physical priors into the PC-based FBNs. To further illustrate the flexibility of the proposed method, we extend the model to a weighted counterpart for learning both sparse and scale-free networks, and then conduct experiments to identify autism spectrum disorders (ASD from normal controls (NC based on the constructed FBNs. Consequently, we achieved an 81.52% classification accuracy which outperforms the baseline and state-of-the-art methods.

  6. Structural Damage Identification Based on Rough Sets and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Chengyin Liu

    2014-01-01

    Full Text Available This paper investigates potential applications of the rough sets (RS theory and artificial neural network (ANN method on structural damage detection. An information entropy based discretization algorithm in RS is applied for dimension reduction of the original damage database obtained from finite element analysis (FEA. The proposed approach is tested with a 14-bay steel truss model for structural damage detection. The experimental results show that the damage features can be extracted efficiently from the combined utilization of RS and ANN methods even the volume of measurement data is enormous and with uncertainties.

  7. Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent

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    Ibnkahla Mohamed

    2003-01-01

    Full Text Available We use natural gradient (NG learning neural networks (NNs for modeling and identifying nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter followed by a zero-memory nonlinearity . The NN model is composed of a linear adaptive filter followed by a two-layer memoryless nonlinear NN. A Kalman filter-based technique and a search-and-converge method have been employed for the NG algorithm. It is shown that the NG descent learning significantly outperforms the ordinary gradient descent and the Levenberg-Marquardt (LM procedure in terms of convergence speed and mean squared error (MSE performance.

  8. On the increase in network robustness and decrease in network response ability during the aging process: a systems biology approach via microarray data.

    Science.gov (United States)

    Tu, Chien-Ta; Chen, Bor-Sen

    2013-01-01

    Aging, an extremely complex and system-level process, has attracted much attention in medical research, especially since chronic diseases are quite prevalent in the elderly population. These may be the result of both gene mutations that lead to intrinsic perturbations and environmental changes that may stimulate signaling in the body. Therefore, analysis of network robustness to tolerate intrinsic perturbations and network response ability of gene networks to respond to external stimuli during the aging process may provide insight into the systematic changes caused by aging. We first propose novel methods to estimate network robustness and measure network response ability of gene regulatory networks by using their corresponding microarray data in the aging process. Then, we find that an aging-related gene network is more robust to intrinsic perturbations in the elderly than the young, and therefore is less responsive to external stimuli. Finally, we find that the response abilities of individual genes, especially FOXOs, NF-κB, and p53, are significantly different in the young versus the aged subjects. These observations are consistent with experimental findings in the aged population, e.g., elevated incidence of tumorigenesis and diminished resistance to oxidative stress. The proposed method can also be used for exploring and analyzing the dynamic properties of other biological processes via corresponding microarray data to provide useful information on clinical strategy and drug target selection.

  9. Lineup identification accuracy: The effects of alcohol, target presence, confidence ratings, and response time

    OpenAIRE

    Kneller, Wendy; Alistair J. Harvey

    2016-01-01

    Despite the intoxication of many eyewitnesses at crime scenes, only four published studies to date have investigated the effects of alcohol intoxication on eyewitness identification performance. While one found intoxication significantly increased false identification rates from target absent showups, three found no such effect using the more traditional lineup procedure. The present study sought to further explore the effects of alcohol intoxication on identification performance ...

  10. Network analysis of differential expression for the identification of disease-causing genes.

    Directory of Open Access Journals (Sweden)

    Daniela Nitsch

    Full Text Available Genetic studies (in particular linkage and association studies identify chromosomal regions involved in a disease or phenotype of interest, but those regions often contain many candidate genes, only a few of which can be followed-up for biological validation. Recently, computational methods to identify (prioritize the most promising candidates within a region have been proposed, but they are usually not applicable to cases where little is known about the phenotype (no or few confirmed disease genes, fragmentary understanding of the biological cascades involved. We seek to overcome this limitation by replacing knowledge about the biological process by experimental data on differential gene expression between affected and healthy individuals. Considering the problem from the perspective of a gene/protein network, we assess a candidate gene by considering the level of differential expression in its neighborhood under the assumption that strong candidates will tend to be surrounded by differentially expressed neighbors. We define a notion of soft neighborhood where each gene is given a contributing weight, which decreases with the distance from the candidate gene on the protein network. To account for multiple paths between genes, we define the distance using the Laplacian exponential diffusion kernel. We score candidates by aggregating the differential expression of neighbors weighted as a function of distance. Through a randomization procedure, we rank candidates by p-values. We illustrate our approach on four monogenic diseases and successfully prioritize the known disease causing genes.

  11. Identification of phasiRNAs and their drought- responsiveness in Populus trichocarpa.

    Science.gov (United States)

    Shuai, Peng; Su, Yanyan; Liang, Dan; Zhang, Zhoujia; Xia, Xinli; Yin, Weilun

    2016-10-01

    Phased, secondary, small interfering RNA (phasiRNA) perform essential biological functions in plants. However, limited information is available on the role of phasiRNA genes in Populus (poplar), especially during drought stress. In this study, we identified 20 PHAS loci generating 91 phasiRNA in the genome of the model forest tree Populus trichocarpa (P. trichocarpa; western balsam-poplar) using the control and drought libraries. Our analysis indicated that six PHAS (PtPHA14-20) initiated by two Populus-specific miRNAs (miR6445 and miR6427) were specific to Populus. In addition, a total of 47 phasiRNA were found to be drought responsive, and five of them were confirmed by RT-qPCR. The phase cleavage of three PHAS loci by miRNA, and degradation of nine transcript targets by phasiRNA were experimentally confirmed based on degradome data. The identification of these Populus phasiRNA will contribute to a better understanding of their function and regulation during drought stress. © 2016 Federation of European Biochemical Societies.

  12. Rapid identification of transience in streambed conductance by inversion of floodwave responses

    Science.gov (United States)

    Gianni, Guillaume; Richon, Julien; Perrochet, Pierre; Vogel, Alexandre; Brunner, Philip

    2016-04-01

    Streambed conductance controls the interaction between surface and groundwater. However, the streambed conductance is often subject to transience. Directly measuring hydraulic properties in a river yields only point values, is time-consuming and therefore not suited to detect transience of physical properties. Here, we present a method to continuously monitor transience in streambed conductance. Input data are time series of stream stage and near stream hydraulic head. The method is based on the inversion of floodwave responses. The analytical model consists of three parameters: x, the distance between streambank and an observation well, α, the aquifer diffusivity, and a the retardation coefficient that is inversely proportional to the streambed conductance. Estimation of a is carried out over successive time steps in order to identify transience in streambed conductance. The method is tested using synthetic data and is applied to field data from the Rhône River and its alluvial aquifer (Switzerland). The synthetic method demonstrated the robustness of the proposed methodology. Application of the method to the field data allowed identifying transience in streambed properties, following flood events in the Rhône. This method requires transience in the surface water, and the river should not change its width significantly with a rising water level. If these conditions are fulfilled, this method allows for a rapid and effective identification of transience in streambed conductance.

  13. Standardized residual as response function for order identification of multi input intervention analysis

    Science.gov (United States)

    Suhartono, Lee, Muhammad Hisyam; Rezeki, Sri

    2017-05-01

    Intervention analysis is a statistical model in the group of time series analysis which is widely used to describe the effect of an intervention caused by external or internal factors. An example of external factors that often occurs in Indonesia is a disaster, both natural or man-made disaster. The main purpose of this paper is to provide the results of theoretical studies on identification step for determining the order of multi inputs intervention analysis for evaluating the magnitude and duration of the impact of interventions on time series data. The theoretical result showed that the standardized residuals could be used properly as response function for determining the order of multi inputs intervention model. Then, these results are applied for evaluating the impact of a disaster on a real case in Indonesia, i.e. the magnitude and duration of the impact of the Lapindo mud on the volume of vehicles on the highway. Moreover, the empirical results showed that the multi inputs intervention model can describe and explain accurately the magnitude and duration of the impact of disasters on a time series data.

  14. Responses to a self-presented suicide attempt in social media: a social network analysis.

    Science.gov (United States)

    Fu, King-Wa; Cheng, Qijin; Wong, Paul W C; Yip, Paul S F

    2013-01-01

    The self-presentation of suicidal acts in social media has become a public health concern. This article centers on a Chinese microblogger who posted a wrist-cutting picture that was widely circulated in Chinese social media in 2011. This exploratory study examines written reactions of a group of Chinese microbloggers exposed to the post containing a self-harming message and photo. In addition, we investigate the pattern of information diffusion via a social network. We systematically collected and analyzed 5,971 generated microblogs and the network of information diffusion. We found that a significant portion of written responses (36.6%) could help vulnerable netizens by providing peer-support and calls for help. These responses were reposted and diffused via an online social network with markedly more clusters of users--and at a faster pace-- than a set of randomly generated networks. We conclude that social media can be a double-edged sword: While it may contagiously affect others by spreading suicidal thoughts and acts, it may also play a positive role by assisting people at risk for suicide, providing rescue or support. More research is needed to learn how suicidally vulnerable people interact with online suicide information, and how we can effectively intervene.

  15. DReAM: Demand Response Architecture for Multi-level District Heating and Cooling Networks

    Energy Technology Data Exchange (ETDEWEB)

    Bhattacharya, Saptarshi; Chandan, Vikas; Arya, Vijay; Kar, Koushik

    2017-05-19

    In this paper, we exploit the inherent hierarchy of heat exchangers in District Heating and Cooling (DHC) networks and propose DReAM, a novel Demand Response (DR) architecture for Multi-level DHC networks. DReAM serves to economize system operation while still respecting comfort requirements of individual consumers. Contrary to many present day DR schemes that work on a consumer level granularity, DReAM works at a level of hierarchy above buildings, i.e. substations that supply heat to a group of buildings. This improves the overall DR scalability and reduce the computational complexity. In the first step of the proposed approach, mathematical models of individual substations and their downstream networks are abstracted into appropriately constructed low-complexity structural forms. In the second step, this abstracted information is employed by the utility to perform DR optimization that determines the optimal heat inflow to individual substations rather than buildings, in order to achieve the targeted objectives across the network. We validate the proposed DReAM framework through experimental results under different scenarios on a test network.

  16. Exploring the Neural Basis of Avatar Identification in Pathological Internet Gamers and of Self-Reflection in Pathological Social Network Users.

    Science.gov (United States)

    Leménager, Tagrid; Dieter, Julia; Hill, Holger; Hoffmann, Sabine; Reinhard, Iris; Beutel, Martin; Vollstädt-Klein, Sabine; Kiefer, Falk; Mann, Karl

    2016-09-01

    Background and aims Internet gaming addiction appears to be related to self-concept deficits and increased angular gyrus (AG)-related identification with one's avatar. For increased social network use, a few existing studies suggest striatal-related positive social feedback as an underlying factor. However, whether an impaired self-concept and its reward-based compensation through the online presentation of an idealized version of the self are related to pathological social network use has not been investigated yet. We aimed to compare different stages of pathological Internet game and social network use to explore the neural basis of avatar and self-identification in addictive use. Methods About 19 pathological Internet gamers, 19 pathological social network users, and 19 healthy controls underwent functional magnetic resonance imaging while completing a self-retrieval paradigm, asking participants to rate the degree to which various self-concept-related characteristics described their self, ideal, and avatar. Self-concept-related characteristics were also psychometrically assessed. Results Psychometric testing indicated that pathological Internet gamers exhibited higher self-concept deficits generally, whereas pathological social network users exhibit deficits in emotion regulation only. We observed left AG hyperactivations in Internet gamers during avatar reflection and a correlation with symptom severity. Striatal hypoactivations during self-reflection (vs. ideal reflection) were observed in social network users and were correlated with symptom severity. Discussion and conclusion Internet gaming addiction appears to be linked to increased identification with one's avatar, evidenced by high left AG activations in pathological Internet gamers. Addiction to social networks seems to be characterized by emotion regulation deficits, reflected by reduced striatal activation during self-reflection compared to during ideal reflection.

  17. Exploring the Neural Basis of Avatar Identification in Pathological Internet Gamers and of Self-Reflection in Pathological Social Network Users

    Science.gov (United States)

    Leménager, Tagrid; Dieter, Julia; Hill, Holger; Hoffmann, Sabine; Reinhard, Iris; Beutel, Martin; Vollstädt-Klein, Sabine; Kiefer, Falk; Mann, Karl

    2016-01-01

    Background and aims Internet gaming addiction appears to be related to self-concept deficits and increased angular gyrus (AG)-related identification with one’s avatar. For increased social network use, a few existing studies suggest striatal-related positive social feedback as an underlying factor. However, whether an impaired self-concept and its reward-based compensation through the online presentation of an idealized version of the self are related to pathological social network use has not been investigated yet. We aimed to compare different stages of pathological Internet game and social network use to explore the neural basis of avatar and self-identification in addictive use. Methods About 19 pathological Internet gamers, 19 pathological social network users, and 19 healthy controls underwent functional magnetic resonance imaging while completing a self-retrieval paradigm, asking participants to rate the degree to which various self-concept-related characteristics described their self, ideal, and avatar. Self-concept-related characteristics were also psychometrically assessed. Results Psychometric testing indicated that pathological Internet gamers exhibited higher self-concept deficits generally, whereas pathological social network users exhibit deficits in emotion regulation only. We observed left AG hyperactivations in Internet gamers during avatar reflection and a correlation with symptom severity. Striatal hypoactivations during self-reflection (vs. ideal reflection) were observed in social network users and were correlated with symptom severity. Discussion and conclusion Internet gaming addiction appears to be linked to increased identification with one’s avatar, evidenced by high left AG activations in pathological Internet gamers. Addiction to social networks seems to be characterized by emotion regulation deficits, reflected by reduced striatal activation during self-reflection compared to during ideal reflection. PMID:27415603

  18. Repeated Exposure to Media Violence Is Associated with Diminished Response in an Inhibitory Frontolimbic Network

    OpenAIRE

    Kelly, Christopher R.; Jack Grinband; Joy Hirsch

    2007-01-01

    BACKGROUND: Media depictions of violence, although often claimed to induce viewer aggression, have not been shown to affect the cortical networks that regulate behavior. METHODOLOGY/PRINCIPAL FINDINGS: Using functional magnetic resonance imaging (fMRI), we found that repeated exposure to violent media, but not to other equally arousing media, led to both diminished response in right lateral orbitofrontal cortex (right ltOFC) and a decrease in right ltOFC-amygdala interaction. Reduced function...

  19. TCP-ADaLR: TCP with adaptive delay and loss response for broadband GEO satellite networks

    OpenAIRE

    Omueti, Modupe Omogbohun

    2007-01-01

    Transmission Control Protocol (TCP) performance degrades in broadband geostationary satellite networks due to long propagation delays and high bit error rates. In this thesis, we propose TCP with algorithm modifications for adaptive delay and loss response (TCP-ADaLR) to improve TCP performance. TCP-ADaLR incorporates delayed acknowledgement mechanism recommended for Internet hosts. We evaluate and compare the performance of TCP-ADaLR, TCP SACK, and TCP NewReno, with and without delayed ackno...

  20. Metabolic network analysis-based identification of antimicrobial drug targets in category A bioterrorism agents.

    Directory of Open Access Journals (Sweden)

    Yong-Yeol Ahn

    Full Text Available The 2001 anthrax mail attacks in the United States demonstrated the potential threat of bioterrorism, hence driving the need to develop sophisticated treatment and diagnostic protocols to counter biological warfare. Here, by performing flux balance analyses on the fully-annotated metabolic networks of multiple, whole genome-sequenced bacterial strains, we have identified a large number of metabolic enzymes as potential drug targets for each of the three Category A-designated bioterrorism agents including Bacillus anthracis, Francisella tularensis and Yersinia pestis. Nine metabolic enzymes- belonging to the coenzyme A, folate, phosphatidyl-ethanolamine and nuc