WorldWideScience

Sample records for genetic algorithm-adaptive network-based

  1. Complementary Network-Based Approaches for Exploring Genetic Structure and Functional Connectivity in Two Vulnerable, Endemic Ground Squirrels

    Directory of Open Access Journals (Sweden)

    Victoria H. Zero

    2017-06-01

    Full Text Available The persistence of small populations is influenced by genetic structure and functional connectivity. We used two network-based approaches to understand the persistence of the northern Idaho ground squirrel (Urocitellus brunneus and the southern Idaho ground squirrel (U. endemicus, two congeners of conservation concern. These graph theoretic approaches are conventionally applied to social or transportation networks, but here are used to study population persistence and connectivity. Population graph analyses revealed that local extinction rapidly reduced connectivity for the southern species, while connectivity for the northern species could be maintained following local extinction. Results from gravity models complemented those of population graph analyses, and indicated that potential vegetation productivity and topography drove connectivity in the northern species. For the southern species, development (roads and small-scale topography reduced connectivity, while greater potential vegetation productivity increased connectivity. Taken together, the results of the two network-based methods (population graph analyses and gravity models suggest the need for increased conservation action for the southern species, and that management efforts have been effective at maintaining habitat quality throughout the current range of the northern species. To prevent further declines, we encourage the continuation of management efforts for the northern species, whereas conservation of the southern species requires active management and additional measures to curtail habitat fragmentation. Our combination of population graph analyses and gravity models can inform conservation strategies of other species exhibiting patchy distributions.

  2. Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Li, Kangji [Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027 (China); School of Electricity Information Engineering, Jiangsu University, Zhenjiang 212013 (China); Su, Hongye [Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027 (China)

    2010-11-15

    There are several ways to forecast building energy consumption, varying from simple regression to models based on physical principles. In this paper, a new method, namely, the hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system (GA-HANFIS) model is developed. In this model, hierarchical structure decreases the rule base dimension. Both clustering and rule base parameters are optimized by GAs and neural networks (NNs). The model is applied to predict a hotel's daily air conditioning consumption for a period over 3 months. The results obtained by the proposed model are presented and compared with regular method of NNs, which indicates that GA-HANFIS model possesses better performance than NNs in terms of their forecasting accuracy. (author)

  3. Simulating Visual Learning and Optical Illusions via a Network-Based Genetic Algorithm

    Science.gov (United States)

    Siu, Theodore; Vivar, Miguel; Shinbrot, Troy

    We present a neural network model that uses a genetic algorithm to identify spatial patterns. We show that the model both learns and reproduces common visual patterns and optical illusions. Surprisingly, we find that the illusions generated are a direct consequence of the network architecture used. We discuss the implications of our results and the insights that we gain on how humans fall for optical illusions

  4. Distinguishing Byproducts from Non-Adaptive Effects of Algorithmic Adaptations

    Directory of Open Access Journals (Sweden)

    Justin H. Park

    2007-01-01

    Full Text Available I evaluate the use of the byproduct concept in psychology, particularly the adaptation-byproduct distinction that is commonly invoked in discussions of psychological phenomena. This distinction can be problematic when investigating algorithmic mechanisms and their effects, because although all byproducts may be functionless concomitants of adaptations, not all incidental effects of algorithmic adaptations are byproducts (although they have sometimes been labeled as such. I call attention to Sperber's (1994 distinction between proper domains and actual domains of algorithmic mechanisms. Extending Sperber's distinction, I propose the terms adaptive effects and non-adaptive effects, which more accurately capture the phenomena of interest to psychologists and prevent fruitless adaptation-versus-byproduct debates.

  5. Network-Based Effectiveness

    National Research Council Canada - National Science Library

    Friman, Henrik

    2006-01-01

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

  6. Network-Based Effectiveness

    National Research Council Canada - National Science Library

    Friman, Henrik

    2006-01-01

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

  7. Integrative Analysis of Genetic, Genomic, and Phenotypic Data for Ethanol Behaviors: A Network-Based Pipeline for Identifying Mechanisms and Potential Drug Targets.

    Science.gov (United States)

    Bogenpohl, James W; Mignogna, Kristin M; Smith, Maren L; Miles, Michael F

    2017-01-01

    Complex behavioral traits, such as alcohol abuse, are caused by an interplay of genetic and environmental factors, producing deleterious functional adaptations in the central nervous system. The long-term behavioral consequences of such changes are of substantial cost to both the individual and society. Substantial progress has been made in the last two decades in understanding elements of brain mechanisms underlying responses to ethanol in animal models and risk factors for alcohol use disorder (AUD) in humans. However, treatments for AUD remain largely ineffective and few medications for this disease state have been licensed. Genome-wide genetic polymorphism analysis (GWAS) in humans, behavioral genetic studies in animal models and brain gene expression studies produced by microarrays or RNA-seq have the potential to produce nonbiased and novel insight into the underlying neurobiology of AUD. However, the complexity of such information, both statistical and informational, has slowed progress toward identifying new targets for intervention in AUD. This chapter describes one approach for integrating behavioral, genetic, and genomic information across animal model and human studies. The goal of this approach is to identify networks of genes functioning in the brain that are most relevant to the underlying mechanisms of a complex disease such as AUD. We illustrate an example of how genomic studies in animal models can be used to produce robust gene networks that have functional implications, and to integrate such animal model genomic data with human genetic studies such as GWAS for AUD. We describe several useful analysis tools for such studies: ComBAT, WGCNA, and EW_dmGWAS. The end result of this analysis is a ranking of gene networks and identification of their cognate hub genes, which might provide eventual targets for future therapeutic development. Furthermore, this combined approach may also improve our understanding of basic mechanisms underlying gene x

  8. INTEGRATING GENETIC AND STRUCTURAL DATA ON HUMAN PROTEIN KINOME IN NETWORK-BASED MODELING OF KINASE SENSITIVITIES AND RESISTANCE TO TARGETED AND PERSONALIZED ANTICANCER DRUGS.

    Science.gov (United States)

    Verkhivker, Gennady M

    2016-01-01

    The human protein kinome presents one of the largest protein families that orchestrate functional processes in complex cellular networks, and when perturbed, can cause various cancers. The abundance and diversity of genetic, structural, and biochemical data underlies the complexity of mechanisms by which targeted and personalized drugs can combat mutational profiles in protein kinases. Coupled with the evolution of system biology approaches, genomic and proteomic technologies are rapidly identifying and charactering novel resistance mechanisms with the goal to inform rationale design of personalized kinase drugs. Integration of experimental and computational approaches can help to bring these data into a unified conceptual framework and develop robust models for predicting the clinical drug resistance. In the current study, we employ a battery of synergistic computational approaches that integrate genetic, evolutionary, biochemical, and structural data to characterize the effect of cancer mutations in protein kinases. We provide a detailed structural classification and analysis of genetic signatures associated with oncogenic mutations. By integrating genetic and structural data, we employ network modeling to dissect mechanisms of kinase drug sensitivities to oncogenic EGFR mutations. Using biophysical simulations and analysis of protein structure networks, we show that conformational-specific drug binding of Lapatinib may elicit resistant mutations in the EGFR kinase that are linked with the ligand-mediated changes in the residue interaction networks and global network properties of key residues that are responsible for structural stability of specific functional states. A strong network dependency on high centrality residues in the conformation-specific Lapatinib-EGFR complex may explain vulnerability of drug binding to a broad spectrum of mutations and the emergence of drug resistance. Our study offers a systems-based perspective on drug design by unravelling

  9. Elements of Network-Based Assessment

    Science.gov (United States)

    Gibson, David

    2007-01-01

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

  10. Network-based Approaches in Pharmacology.

    Science.gov (United States)

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

    2017-10-01

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

  11. Autonomous power networks based power system

    International Nuclear Information System (INIS)

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

    2006-01-01

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

  12. Network Based High Speed Product Innovation

    DEFF Research Database (Denmark)

    Lindgren, Peter

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

  13. Quantum networks based on cavity QED

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-07-01

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

  14. Network-based recommendation algorithms: A review

    Science.gov (United States)

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

    2016-06-01

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

  15. Designing Network-based Business Model Ontology

    DEFF Research Database (Denmark)

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

    2015-01-01

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

  16. Wireless Sensor Network Based Smart Parking System

    Directory of Open Access Journals (Sweden)

    Jeffrey JOSEPH

    2014-01-01

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

  17. Network-based analysis of proteomic profiles

    KAUST Repository

    Wong, Limsoon

    2016-01-26

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

  18. Neural network based multiscale image restoration approach

    Science.gov (United States)

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

    2007-02-01

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

  19. Network-Based Community Brings forth Sustainable Society

    Science.gov (United States)

    Kikuchi, Toshiko

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

  20. Genetics

    International Nuclear Information System (INIS)

    Hubitschek, H.E.

    1975-01-01

    Progress is reported on the following research projects: genetic effects of high LET radiations; genetic regulation, alteration, and repair; chromosome replication and the division cycle of Escherichia coli; effects of radioisotope decay in the DNA of microorganisms; initiation and termination of DNA replication in Bacillus subtilis; mutagenesis in mouse myeloma cells; lethal and mutagenic effects of near-uv radiation; effect of 8-methoxypsoralen on photodynamic lethality and mutagenicity in Escherichia coli; DNA repair of the lethal effects of far-uv; and near uv irradiation of bacterial cells

  1. Genetics

    DEFF Research Database (Denmark)

    Christensen, Kaare; McGue, Matt

    2016-01-01

    The sequenced genomes of individuals aged ≥80 years, who were highly educated, self-referred volunteers and with no self-reported chronic diseases were compared to young controls. In these data, healthy ageing is a distinct phenotype from exceptional longevity and genetic factors that protect...

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

    African Journals Online (AJOL)

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

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

    Directory of Open Access Journals (Sweden)

    Ling Yongfa

    2016-01-01

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

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

    International Nuclear Information System (INIS)

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

    2008-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2008-02-15

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

  6. Wireless Sensor Network Based Subsurface Contaminant Plume Monitoring

    Science.gov (United States)

    2012-04-16

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

  7. Artificial Neural Network Based State Estimators Integrated into Kalmtool

    DEFF Research Database (Denmark)

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

    2012-01-01

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

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

    African Journals Online (AJOL)

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

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

    NARCIS (Netherlands)

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

    2010-01-01

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

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

    Indian Academy of Sciences (India)

    2016-08-26

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

  11. Automation tools for accelerator control a network based sequencer

    International Nuclear Information System (INIS)

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

    1991-01-01

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

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

    Science.gov (United States)

    Motegi, Shun; Masuda, Naoki

    2012-12-01

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

  13. A Neural Network-Based Interval Pattern Matcher

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2015-07-01

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

  14. Strengthening regional innovation through network-based innovation brokering

    OpenAIRE

    Svare, Helge; Gausdal, Anne Haugen

    2015-01-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

  16. Optical-Correlator Neural Network Based On Neocognitron

    Science.gov (United States)

    Chao, Tien-Hsin; Stoner, William W.

    1994-01-01

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

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

    Science.gov (United States)

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

    2017-02-01

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

  18. Deep Neural Network-Based Chinese Semantic Role Labeling

    Institute of Scientific and Technical Information of China (English)

    ZHENG Xiaoqing; CHEN Jun; SHANG Guoqiang

    2017-01-01

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

  19. A network-based approach to prioritize results from genome-wide association studies.

    Directory of Open Access Journals (Sweden)

    Nirmala Akula

    Full Text Available Genome-wide association studies (GWAS are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI, a network-based method that combines GWAS data with human protein-protein interaction data (PPI. NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call 'trait prioritized sub-networks.' As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn's disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn's disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses.

  20. A Network-Based Approach to Prioritize Results from Genome-Wide Association Studies

    Science.gov (United States)

    Akula, Nirmala; Baranova, Ancha; Seto, Donald; Solka, Jeffrey; Nalls, Michael A.; Singleton, Andrew; Ferrucci, Luigi; Tanaka, Toshiko; Bandinelli, Stefania; Cho, Yoon Shin; Kim, Young Jin; Lee, Jong-Young; Han, Bok-Ghee; McMahon, Francis J.

    2011-01-01

    Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call ‘trait prioritized sub-networks.’ As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn’s disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn’s disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses. PMID:21915301

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

    Directory of Open Access Journals (Sweden)

    Kormeier Benjamin

    2010-06-01

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

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

    Science.gov (United States)

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

    2010-10-27

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

  3. Pathway Relevance Ranking for Tumor Samples through Network-Based Data Integration.

    Directory of Open Access Journals (Sweden)

    Lieven P C Verbeke

    Full Text Available The study of cancer, a highly heterogeneous disease with different causes and clinical outcomes, requires a multi-angle approach and the collection of large multi-omics datasets that, ideally, should be analyzed simultaneously. We present a new pathway relevance ranking method that is able to prioritize pathways according to the information contained in any combination of tumor related omics datasets. Key to the method is the conversion of all available data into a single comprehensive network representation containing not only genes but also individual patient samples. Additionally, all data are linked through a network of previously identified molecular interactions. We demonstrate the performance of the new method by applying it to breast and ovarian cancer datasets from The Cancer Genome Atlas. By integrating gene expression, copy number, mutation and methylation data, the method's potential to identify key pathways involved in breast cancer development shared by different molecular subtypes is illustrated. Interestingly, certain pathways were ranked equally important for different subtypes, even when the underlying (epi-genetic disturbances were diverse. Next to prioritizing universally high-scoring pathways, the pathway ranking method was able to identify subtype-specific pathways. Often the score of a pathway could not be motivated by a single mutation, copy number or methylation alteration, but rather by a combination of genetic and epi-genetic disturbances, stressing the need for a network-based data integration approach. The analysis of ovarian tumors, as a function of survival-based subtypes, demonstrated the method's ability to correctly identify key pathways, irrespective of tumor subtype. A differential analysis of survival-based subtypes revealed several pathways with higher importance for the bad-outcome patient group than for the good-outcome patient group. Many of the pathways exhibiting higher importance for the bad

  4. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

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

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

    Science.gov (United States)

    Patino, H D; Liu, D

    2000-01-01

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

  6. Network-based analysis of software change propagation.

    Science.gov (United States)

    Wang, Rongcun; Huang, Rubing; Qu, Binbin

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Zhang Hongliang

    2018-01-01

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

  8. Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

    Science.gov (United States)

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2015-09-01

    Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

  9. Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

    Directory of Open Access Journals (Sweden)

    Manjunath Patel Gowdru Chandrashekarappa

    2014-01-01

    Full Text Available The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SDAS. Two different neural network based approaches have been proposed to carry out the said task, namely, back propagation neural network (BPNN and genetic algorithm neural network (GA-NN. The batch mode of training is employed for both supervised learning networks and requires huge training data. The requirement of huge training data is generated artificially at random using regression equation derived through real experiments carried out earlier by the same authors. The performances of BPNN and GA-NN models are compared among themselves with those of regression for ten test cases. The results show that both models are capable of making better predictions and the models can be effectively used in shop floor in selection of most influential parameters for the desired outputs.

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

    African Journals Online (AJOL)

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

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

    Science.gov (United States)

    2017-07-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

  13. Network-based analysis of the sphingolipid metabolism in hypertension

    DEFF Research Database (Denmark)

    Fenger, Mogens; Linneberg, Allan; Jeppesen, Jørgen

    2015-01-01

    Common diseases like essential hypertension or diabetes mellitus are complex as they are polygenic in nature, such that each genetic variation only has a small influence on the disease. Genes operates in integrated networks providing the blue-print for all biological processes and conditional...

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

  15. Modeling of the pyruvate production with Escherichia coli: comparison of mechanistic and neural networks-based models.

    Science.gov (United States)

    Zelić, B; Bolf, N; Vasić-Racki, D

    2006-06-01

    Three different models: the unstructured mechanistic black-box model, the input-output neural network-based model and the externally recurrent neural network model were used to describe the pyruvate production process from glucose and acetate using the genetically modified Escherichia coli YYC202 ldhA::Kan strain. The experimental data were used from the recently described batch and fed-batch experiments [ Zelić B, Study of the process development for Escherichia coli-based pyruvate production. PhD Thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, Zagreb, Croatia, July 2003. (In English); Zelić et al. Bioproc Biosyst Eng 26:249-258 (2004); Zelić et al. Eng Life Sci 3:299-305 (2003); Zelić et al Biotechnol Bioeng 85:638-646 (2004)]. The neural networks were built out of the experimental data obtained in the fed-batch pyruvate production experiments with the constant glucose feed rate. The model validation was performed using the experimental results obtained from the batch and fed-batch pyruvate production experiments with the constant acetate feed rate. Dynamics of the substrate and product concentration changes was estimated using two neural network-based models for biomass and pyruvate. It was shown that neural networks could be used for the modeling of complex microbial fermentation processes, even in conditions in which mechanistic unstructured models cannot be applied.

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

    Science.gov (United States)

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

    2016-01-11

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

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

    Directory of Open Access Journals (Sweden)

    Zhang Xuegong

    2011-06-01

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

  18. Network-based identification of biomarkers coexpressed with multiple pathways.

    Science.gov (United States)

    Guo, Nancy Lan; Wan, Ying-Wooi

    2014-01-01

    Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson's correlation networks when evaluated with MSigDB database.

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

    Science.gov (United States)

    2011-03-01

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

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

    Directory of Open Access Journals (Sweden)

    Chen Bor-Sen

    2011-01-01

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

  1. Construction of coffee transcriptome networks based on gene annotation semantics

    Directory of Open Access Journals (Sweden)

    Castillo Luis F.

    2012-12-01

    Full Text Available Gene annotation is a process that encompasses multiple approaches on the analysis of nucleic acids or protein sequences in order to assign structural and functional characteristics to gene models. When thousands of gene models are being described in an organism genome, construction and visualization of gene networks impose novel challenges in the understanding of complex expression patterns and the generation of new knowledge in genomics research. In order to take advantage of accumulated text data after conventional gene sequence analysis, this work applied semantics in combination with visualization tools to build transcriptome networks from a set of coffee gene annotations. A set of selected coffee transcriptome sequences, chosen by the quality of the sequence comparison reported by Basic Local Alignment Search Tool (BLAST and Interproscan, were filtered out by coverage, identity, length of the query, and e-values. Meanwhile, term descriptors for molecular biology and biochemistry were obtained along the Wordnet dictionary in order to construct a Resource Description Framework (RDF using Ruby scripts and Methontology to find associations between concepts. Relationships between sequence annotations and semantic concepts were graphically represented through a total of 6845 oriented vectors, which were reduced to 745 non-redundant associations. A large gene network connecting transcripts by way of relational concepts was created where detailed connections remain to be validated for biological significance based on current biochemical and genetics frameworks. Besides reusing text information in the generation of gene connections and for data mining purposes, this tool development opens the possibility to visualize complex and abundant transcriptome data, and triggers the formulation of new hypotheses in metabolic pathways analysis.

  2. Network-based regularization for high dimensional SNP data in the case-control study of Type 2 diabetes.

    Science.gov (United States)

    Ren, Jie; He, Tao; Li, Ye; Liu, Sai; Du, Yinhao; Jiang, Yu; Wu, Cen

    2017-05-16

    Over the past decades, the prevalence of type 2 diabetes mellitus (T2D) has been steadily increasing around the world. Despite large efforts devoted to better understand the genetic basis of the disease, the identified susceptibility loci can only account for a small portion of the T2D heritability. Some of the existing approaches proposed for the high dimensional genetic data from the T2D case-control study are limited by analyzing a few number of SNPs at a time from a large pool of SNPs, by ignoring the correlations among SNPs and by adopting inefficient selection techniques. We propose a network constrained regularization method to select important SNPs by taking the linkage disequilibrium into account. To accomodate the case control study, an iteratively reweighted least square algorithm has been developed within the coordinate descent framework where optimization of the regularized logistic loss function is performed with respect to one parameter at a time and iteratively cycle through all the parameters until convergence. In this article, a novel approach is developed to identify important SNPs more effectively through incorporating the interconnections among them in the regularized selection. A coordinate descent based iteratively reweighed least squares (IRLS) algorithm has been proposed. Both the simulation study and the analysis of the Nurses's Health Study, a case-control study of type 2 diabetes data with high dimensional SNP measurements, demonstrate the advantage of the network based approach over the competing alternatives.

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

    Science.gov (United States)

    Xia, Youshen; Wang, Jun

    2015-07-01

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

  4. The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations.

    Science.gov (United States)

    Keenan, Alexandra B; Jenkins, Sherry L; Jagodnik, Kathleen M; Koplev, Simon; He, Edward; Torre, Denis; Wang, Zichen; Dohlman, Anders B; Silverstein, Moshe C; Lachmann, Alexander; Kuleshov, Maxim V; Ma'ayan, Avi; Stathias, Vasileios; Terryn, Raymond; Cooper, Daniel; Forlin, Michele; Koleti, Amar; Vidovic, Dusica; Chung, Caty; Schürer, Stephan C; Vasiliauskas, Jouzas; Pilarczyk, Marcin; Shamsaei, Behrouz; Fazel, Mehdi; Ren, Yan; Niu, Wen; Clark, Nicholas A; White, Shana; Mahi, Naim; Zhang, Lixia; Kouril, Michal; Reichard, John F; Sivaganesan, Siva; Medvedovic, Mario; Meller, Jaroslaw; Koch, Rick J; Birtwistle, Marc R; Iyengar, Ravi; Sobie, Eric A; Azeloglu, Evren U; Kaye, Julia; Osterloh, Jeannette; Haston, Kelly; Kalra, Jaslin; Finkbiener, Steve; Li, Jonathan; Milani, Pamela; Adam, Miriam; Escalante-Chong, Renan; Sachs, Karen; Lenail, Alex; Ramamoorthy, Divya; Fraenkel, Ernest; Daigle, Gavin; Hussain, Uzma; Coye, Alyssa; Rothstein, Jeffrey; Sareen, Dhruv; Ornelas, Loren; Banuelos, Maria; Mandefro, Berhan; Ho, Ritchie; Svendsen, Clive N; Lim, Ryan G; Stocksdale, Jennifer; Casale, Malcolm S; Thompson, Terri G; Wu, Jie; Thompson, Leslie M; Dardov, Victoria; Venkatraman, Vidya; Matlock, Andrea; Van Eyk, Jennifer E; Jaffe, Jacob D; Papanastasiou, Malvina; Subramanian, Aravind; Golub, Todd R; Erickson, Sean D; Fallahi-Sichani, Mohammad; Hafner, Marc; Gray, Nathanael S; Lin, Jia-Ren; Mills, Caitlin E; Muhlich, Jeremy L; Niepel, Mario; Shamu, Caroline E; Williams, Elizabeth H; Wrobel, David; Sorger, Peter K; Heiser, Laura M; Gray, Joe W; Korkola, James E; Mills, Gordon B; LaBarge, Mark; Feiler, Heidi S; Dane, Mark A; Bucher, Elmar; Nederlof, Michel; Sudar, Damir; Gross, Sean; Kilburn, David F; Smith, Rebecca; Devlin, Kaylyn; Margolis, Ron; Derr, Leslie; Lee, Albert; Pillai, Ajay

    2018-01-24

    The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability. Copyright © 2017 Elsevier Inc. All rights reserved.

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

    NARCIS (Netherlands)

    Allahyar, A.; De Ridder, J.

    2015-01-01

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

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

    African Journals Online (AJOL)

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

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

    KAUST Repository

    Akram, Jubran; Ovcharenko, Oleg; Peter, Daniel

    2017-01-01

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

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

    NARCIS (Netherlands)

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

    2017-01-01

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

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

    NARCIS (Netherlands)

    Cheak, Alicia; Angehrn, Albert; Sloep, Peter

    2006-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1999-07-10

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

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

    Science.gov (United States)

    Smith, Merritt H.; Pallis, Jani M.

    1993-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Weilong ZHOU

    2014-06-01

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

  13. Design of Artificial Neural Network-Based pH Estimator

    Directory of Open Access Journals (Sweden)

    Shebel A. Alsabbah

    2010-10-01

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

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

    OpenAIRE

    Christian de Peretti; Carole Siani; Mario Cerrato

    2010-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Vuković Najdan L.

    2014-01-01

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

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

    Science.gov (United States)

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

    2010-03-01

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

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

    Science.gov (United States)

    Su, Yaoxin; Gao, Xiufeng; Qiao, Wenxin

    2017-05-01

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

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

    OpenAIRE

    VENKATA REDDY METTU; KRISHAN KUMAR,; SRIKANTH PULLABHATLA

    2011-01-01

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

  19. A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

    Science.gov (United States)

    Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

    2017-12-19

    Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

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

    Science.gov (United States)

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

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

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

    International Nuclear Information System (INIS)

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

    2000-01-01

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

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

    International Nuclear Information System (INIS)

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

    2003-01-01

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

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

    Directory of Open Access Journals (Sweden)

    M. R. S. Birjandi

    2018-03-01

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

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

    Science.gov (United States)

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

    2017-09-01

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

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

    Directory of Open Access Journals (Sweden)

    A. A. Salama

    2015-03-01

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

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

    Directory of Open Access Journals (Sweden)

    Cemal YILMAZ

    2009-02-01

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

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

    Science.gov (United States)

    Liu, Qingshan; Cao, Jinde

    2010-06-01

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

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

    Science.gov (United States)

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

    2005-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Metsma Merilin

    2017-06-01

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

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

    Directory of Open Access Journals (Sweden)

    G. Olivito

    2017-01-01

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

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

    DEFF Research Database (Denmark)

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

    2014-01-01

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

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

    International Nuclear Information System (INIS)

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

    2010-01-01

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

  13. A genetic algorithm for the optimization of fiber angles in composite laminates

    International Nuclear Information System (INIS)

    Hwang, Shun Fa; Hsu, Ya Chu; Chen, Yuder

    2014-01-01

    A genetic algorithm for the optimization of composite laminates is proposed in this work. The well-known roulette selection criterion, one-point crossover operator, and uniform mutation operator are used in this genetic algorithm to create the next population. To improve the hill-climbing capability of the algorithm, adaptive mechanisms designed to adjust the probabilities of the crossover and mutation operators are included, and the elite strategy is enforced to ensure the quality of the optimum solution. The proposed algorithm includes a new operator called the elite comparison, which compares and uses the differences in the design variables of the two best solutions to find possible combinations. This genetic algorithm is tested in four optimization problems of composite laminates. Specifically, the effect of the elite comparison operator is evaluated. Results indicate that the elite comparison operator significantly accelerates the convergence of the algorithm, which thus becomes a good candidate for the optimization of composite laminates.

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

    Science.gov (United States)

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

    2009-01-01

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

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2017-10-02

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

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

    Directory of Open Access Journals (Sweden)

    Jieqiong Wang

    2017-09-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Science.gov (United States)

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

    2009-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  2. Genetic algorithms

    Science.gov (United States)

    Wang, Lui; Bayer, Steven E.

    1991-01-01

    Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.

  3. Overrepresentation of glutamate signaling in Alzheimer's disease: network-based pathway enrichment using meta-analysis of genome-wide association studies.

    Directory of Open Access Journals (Sweden)

    Eduardo Pérez-Palma

    Full Text Available Genome-wide association studies (GWAS have successfully identified several risk loci for Alzheimer's disease (AD. Nonetheless, these loci do not explain the entire susceptibility of the disease, suggesting that other genetic contributions remain to be identified. Here, we performed a meta-analysis combining data of 4,569 individuals (2,540 cases and 2,029 healthy controls derived from three publicly available GWAS in AD and replicated a broad genomic region (>248,000 bp associated with the disease near the APOE/TOMM40 locus in chromosome 19. To detect minor effect size contributions that could help to explain the remaining genetic risk, we conducted network-based pathway analyses either by extracting gene-wise p-values (GW, defined as the single strongest association signal within a gene, or calculated a more stringent gene-based association p-value using the extended Simes (GATES procedure. Comparison of these strategies revealed that ontological sub-networks (SNs involved in glutamate signaling were significantly overrepresented in AD (p<2.7×10(-11, p<1.9×10(-11; GW and GATES, respectively. Notably, glutamate signaling SNs were also found to be significantly overrepresented (p<5.1×10(-8 in the Alzheimer's disease Neuroimaging Initiative (ADNI study, which was used as a targeted replication sample. Interestingly, components of the glutamate signaling SNs are coordinately expressed in disease-related tissues, which are tightly related to known pathological hallmarks of AD. Our findings suggest that genetic variation within glutamate signaling contributes to the remaining genetic risk of AD and support the notion that functional biological networks should be targeted in future therapies aimed to prevent or treat this devastating neurological disorder.

  4. Genetic Mapping

    Science.gov (United States)

    ... greatly advanced genetics research. The improved quality of genetic data has reduced the time required to identify a ... cases, a matter of months or even weeks. Genetic mapping data generated by the HGP's laboratories is freely accessible ...

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

    Science.gov (United States)

    Le, Duc-Hau

    2015-10-01

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

  6. Genetic privacy.

    Science.gov (United States)

    Sankar, Pamela

    2003-01-01

    During the past 10 years, the number of genetic tests performed more than tripled, and public concern about genetic privacy emerged. The majority of states and the U.S. government have passed regulations protecting genetic information. However, research has shown that concerns about genetic privacy are disproportionate to known instances of information misuse. Beliefs in genetic determinacy explain some of the heightened concern about genetic privacy. Discussion of the debate over genetic testing within families illustrates the most recent response to genetic privacy concerns.

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

    Science.gov (United States)

    Wu, Ting-Ting

    2014-06-01

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

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

    Science.gov (United States)

    Park, Dae-Heon; Park, Jang-Woo

    2011-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Jordi Llosa

    2009-09-01

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

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

    CERN Document Server

    Andreazza, A; The ATLAS collaboration

    2013-01-01

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

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

    CERN Document Server

    Pettenati, M C; Pettenati, Corrado

    2000-01-01

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

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

    Science.gov (United States)

    Pucuo, Cairen; Qi, Ai-qin

    2018-03-01

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

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

    Science.gov (United States)

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

    2009-01-01

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

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

    Science.gov (United States)

    Park, Dae-Heon; Park, Jang-Woo

    2011-01-01

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

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

    CERN Document Server

    Mrugalski, Marcin

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Dong Seop Kim

    2018-03-01

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

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

    DEFF Research Database (Denmark)

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

    2014-01-01

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

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

    Science.gov (United States)

    Bai, Yanan; Huang, Ning; Sun, Lina

    2018-06-01

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

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

    Directory of Open Access Journals (Sweden)

    H. MEKKI

    2015-03-01

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

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

    International Nuclear Information System (INIS)

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

    2006-01-01

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

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

    Science.gov (United States)

    Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

    2018-01-01

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

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

    Science.gov (United States)

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

    2007-01-01

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

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

    Science.gov (United States)

    Baluja, S

    1996-01-01

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

  4. Network based control point for UPnP QoS architecture

    DEFF Research Database (Denmark)

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

    2011-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Altaisky Mikhail V.

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Yi-Jen Mon

    2012-10-01

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

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

    CERN Document Server

    Selbach, K E; The ATLAS collaboration

    2012-01-01

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

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

    Science.gov (United States)

    Zhang, Jianhua; Wang, Shuliang; Wang, Xiaoyuan

    2018-04-01

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

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

    Science.gov (United States)

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

    2011-12-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-09-28

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

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

    KAUST Repository

    Akram, Jubran

    2017-08-17

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

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

    Science.gov (United States)

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

    2018-02-01

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

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

    Directory of Open Access Journals (Sweden)

    Yang Li

    2016-01-01

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

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

    Science.gov (United States)

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

    2018-02-01

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

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

    Science.gov (United States)

    Kang, Xiaoyu; Zhao, Lina; Liu, Na; Wang, Xiangping; Zhang, Rongchun; Liu, Zhiguo; Liang, Shuhui; Yao, Shaowei; Tao, Qin; Jia, Hui; Pan, Yanglin; Guo, Xuegang

    2017-10-01

    Online social networking is increasingly being used among medical practitioners. However, few studies have evaluated its use in therapeutic endoscopy. Here, we aimed to analyze the shared topics and activities of a group of endoscopic retrograde cholangiopancreatography (ERCP) doctors in a social networking-based endoscopic retrograde cholangiopancreatography discussion group (EDG). Six ERCP trainers working in Xijing Hospital and 48 graduated endoscopists who had finished ERCP training in the same hospital were invited to join in EDG. All group members were informed not to divulge any private information of patients when using EDG. The activities of group members on EDG were retrospectively extracted. The individual data of the graduated endoscopists were collected by a questionnaire. From June 2014 to May 2015, 6924 messages were posted on EDG, half of which were ERCP related. In total, 214 ERCP-related topics were shared, which could be categorized into three types: sharing experience/cases (52.3%), asking questions (38.3%), and sharing literatures/advances (9.3%). Among the 48 graduated endoscopists, 21 had a low case volume of less than 50 per year and 27 had a high volume case volume of 50 or more. High-volume graduated endoscopists posted more ERCP-related messages (P=0.008) and shared more discussion topics (P=0.003) compared with low-volume graduated endoscopists. A survey showed that EDG was useful for graduated endoscopists in ERCP performance and management of post-ERCP complications, etc. A wide range of ERCP-related topics were shared on the social networking-based EDG. The ERCP-related behaviors on EDG were more active in graduated endoscopists with an ERCP case volume of more than 50 per year.

  16. Network-based landscape of research strengths of universities in Mainland China

    Science.gov (United States)

    Liu, Zihua; Xiao, Qin; Zhan, Qian; Gu, Changgui; Yang, Huijie

    2017-07-01

    A landscape of a complex system presents a quantitative measure of its global state. The profile of research strength in Mainland China is investigated in detail, by which we illustrate a complex network based framework to extract a landscape from detailed records. First, a measure analogous to the Jaccard similarity is proposed to calculate from the presided funds similarities between the top-ranked universities. The neighbor threshold method is employed to reconstruct the similarity network of the universities. Second, the network is divided into communities. In each community the node with the largest degree and the smallest average shortest path length is taken as the representative of the community, called central node. The node bridging each pair of communities is defined to be a boundary. The central nodes and boundaries cooperatively give us a picture of the research strength landscape. Third, the evolutionary behavior is monitored by the fission and fusion probability matrices, elements of which are the percentage of a community at present time that joins into every community at the next time, and the percentage of a community at next time that comes from every present community, respectively. The landscapes in three successive 4-year durations are identified. It was found that some types of universities, such as the medicine&pharmacy and the finance&economy, conserve in single communities in the more than ten years, respectively. The agriculture&forest universities tend to cluster into one community. Meanwhile the engineering type distributes in different communities and tends to mix with the comprehension type. This framework can be used straightforwardly to analyze temporal networks. It provides also a new network-based method for multivariate time series analysis.

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

    Science.gov (United States)

    Guo, T. H.; Musgrave, J.

    1992-11-01

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

  18. Recurrent-neural-network-based Boolean factor analysis and its application to word clustering.

    Science.gov (United States)

    Frolov, Alexander A; Husek, Dusan; Polyakov, Pavel Yu

    2009-07-01

    The objective of this paper is to introduce a neural-network-based algorithm for word clustering as an extension of the neural-network-based Boolean factor analysis algorithm (Frolov , 2007). It is shown that this extended algorithm supports even the more complex model of signals that are supposed to be related to textual documents. It is hypothesized that every topic in textual data is characterized by a set of words which coherently appear in documents dedicated to a given topic. The appearance of each word in a document is coded by the activity of a particular neuron. In accordance with the Hebbian learning rule implemented in the network, sets of coherently appearing words (treated as factors) create tightly connected groups of neurons, hence, revealing them as attractors of the network dynamics. The found factors are eliminated from the network memory by the Hebbian unlearning rule facilitating the search of other factors. Topics related to the found sets of words can be identified based on the words' semantics. To make the method complete, a special technique based on a Bayesian procedure has been developed for the following purposes: first, to provide a complete description of factors in terms of component probability, and second, to enhance the accuracy of classification of signals to determine whether it contains the factor. Since it is assumed that every word may possibly contribute to several topics, the proposed method might be related to the method of fuzzy clustering. In this paper, we show that the results of Boolean factor analysis and fuzzy clustering are not contradictory, but complementary. To demonstrate the capabilities of this attempt, the method is applied to two types of textual data on neural networks in two different languages. The obtained topics and corresponding words are at a good level of agreement despite the fact that identical topics in Russian and English conferences contain different sets of keywords.

  19. Network-based integration of molecular and physiological data elucidates regulatory mechanisms underlying adaptation to high-fat diet

    NARCIS (Netherlands)

    Derous, D.; Kelder, T.; Schothorst, E.M. van; Erk, M. van; Voigt, A.; Klaus, S.; Keijer, J.; Radonjic, M.

    2015-01-01

    Health is influenced by interplay of molecular, physiological and environmental factors. To effectively maintain health and prevent disease, health-relevant relations need to be understood at multiple levels of biological complexity. Network-based methods provide a powerful platform for integration

  20. A Process Perspective on Regulation: A Grounded Theory Study into Regulatory Practice in Newly Liberalized Network-Based Markets

    NARCIS (Netherlands)

    Ubacht, J.

    The transition from a former monopolistic towards a more competitive market in
    newly liberalized network-based markets raises regulatory issues. National Regulatory Authorities (NRA) face the challenge to deal with these issues in order to guide the transition process. Although this transition

  1. Analysing the Correlation between Social Network Analysis Measures and Performance of Students in Social Network-Based Engineering Education

    Science.gov (United States)

    Putnik, Goran; Costa, Eric; Alves, Cátia; Castro, Hélio; Varela, Leonilde; Shah, Vaibhav

    2016-01-01

    Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment,…

  2. Phylogeny and evolutionary histories of Pyrus L. revealed by phylogenetic trees and networks based on data from multiple DNA sequences

    Science.gov (United States)

    Reconstructing the phylogeny of Pyrus has been difficult due to the wide distribution of the genus and lack of informative data. In this study, we collected 110 accessions representing 25 Pyrus species and constructed both phylogenetic trees and phylogenetic networks based on multiple DNA sequence d...

  3. How network-based incubation helps start-up performance : a systematic review against the background of management theories

    NARCIS (Netherlands)

    Eveleens, Chris P.|info:eu-repo/dai/nl/369284704; van Rijnsoever, Frank J.|info:eu-repo/dai/nl/314100334; Niesten, Eva M M I

    2017-01-01

    The literature on how network-based incubation influences the performance of technology-based start-ups has recently grown considerably and provided valuable insights. However, at the same time this literature has become quite fragmented, inconsistently conceptualised, and theoretically

  4. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lund, Ole

    2009-01-01

    this binding event. RESULTS: Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data...

  5. A network of genes, genetic disorders, and brain areas.

    Directory of Open Access Journals (Sweden)

    Satoru Hayasaka

    Full Text Available The network-based approach has been used to describe the relationship among genes and various phenotypes, producing a network describing complex biological relationships. Such networks can be constructed by aggregating previously reported associations in the literature from various databases. In this work, we applied the network-based approach to investigate how different brain areas are associated to genetic disorders and genes. In particular, a tripartite network with genes, genetic diseases, and brain areas was constructed based on the associations among them reported in the literature through text mining. In the resulting network, a disproportionately large number of gene-disease and disease-brain associations were attributed to a small subset of genes, diseases, and brain areas. Furthermore, a small number of brain areas were found to be associated with a large number of the same genes and diseases. These core brain regions encompassed the areas identified by the previous genome-wide association studies, and suggest potential areas of focus in the future imaging genetics research. The approach outlined in this work demonstrates the utility of the network-based approach in studying genetic effects on the brain.

  6. Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

    Science.gov (United States)

    Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

    2018-05-01

    Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

  7. A neural network-based exploratory learning and motor planning system for co-robots

    Directory of Open Access Journals (Sweden)

    Byron V Galbraith

    2015-07-01

    Full Text Available Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or learning by doing, an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.

  8. Reconstruction and Analysis of Human Kidney-Specific Metabolic Network Based on Omics Data

    Directory of Open Access Journals (Sweden)

    Ai-Di Zhang

    2013-01-01

    Full Text Available With the advent of the high-throughput data production, recent studies of tissue-specific metabolic networks have largely advanced our understanding of the metabolic basis of various physiological and pathological processes. However, for kidney, which plays an essential role in the body, the available kidney-specific model remains incomplete. This paper reports the reconstruction and characterization of the human kidney metabolic network based on transcriptome and proteome data. In silico simulations revealed that house-keeping genes were more essential than kidney-specific genes in maintaining kidney metabolism. Importantly, a total of 267 potential metabolic biomarkers for kidney-related diseases were successfully explored using this model. Furthermore, we found that the discrepancies in metabolic processes of different tissues are directly corresponding to tissue's functions. Finally, the phenotypes of the differentially expressed genes in diabetic kidney disease were characterized, suggesting that these genes may affect disease development through altering kidney metabolism. Thus, the human kidney-specific model constructed in this study may provide valuable information for the metabolism of kidney and offer excellent insights into complex kidney diseases.

  9. Neural Networks-Based Forecasting Regarding the Convergence Process of CEE Countries to the Eurozone

    Directory of Open Access Journals (Sweden)

    Magdalena RĂDULESCU

    2014-06-01

    Full Text Available In the crisis frame, many forecasts failed to provide well determined ratios. What we tried to explain in this paper is how some selected Central and Eastern European countries will perform in the near future: Romania, Bulgaria, Hungary, Poland and Czech Republic, using neural networks- based forecasting model which we created for the nominal and real convergence ratios. As a methodology, we propose the forecasting based on artificial neural network (ANN, using the well-known software tool GMDH Shell. For each output variable, we obtain a forecast model, according to previous values and other input related variables, and we applied the model to all countries. Our forecasts are much closer to the partial results of 2013 in the analyzed countries than the European Commission’s or other international organizations’ forecasts. The results of the forecast are important both for governments to design their financial strategies and for the investors in these selected countries. According to our results, the Czech Republic seems to be closer to achieve its nominal convergence in the next two years, but it faces great difficulties in the real convergence area, because it did not overpass the recession.

  10. Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection

    Directory of Open Access Journals (Sweden)

    Erik Marchi

    2017-01-01

    Full Text Available In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these approaches, auditory spectral features of the next short term frame are predicted from the previous frames by means of Long-Short Term Memory recurrent denoising autoencoders. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. There is no evidence of studies focused on comparing previous efforts to automatically recognize novel events from audio signals and giving a broad and in depth evaluation of recurrent neural network-based autoencoders. The present contribution aims to consistently evaluate our recent novel approaches to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases: A3Novelty, PASCAL CHiME, and PROMETHEUS. Besides providing an extensive analysis of novel and state-of-the-art methods, the article shows how RNN-based autoencoders outperform statistical approaches up to an absolute improvement of 16.4% average F-measure over the three databases.

  11. Neural networks based identification and compensation of rate-dependent hysteresis in piezoelectric actuators

    International Nuclear Information System (INIS)

    Zhang, Xinliang; Tan, Yonghong; Su, Miyong; Xie, Yangqiu

    2010-01-01

    This paper presents a method of the identification for the rate-dependent hysteresis in the piezoelectric actuator (PEA) by use of neural networks. In this method, a special hysteretic operator is constructed from the Prandtl-Ishlinskii (PI) model to extract the changing tendency of the static hysteresis. Then, an expanded input space is constructed by introducing the proposed hysteretic operator to transform the multi-valued mapping of the hysteresis into a one-to-one mapping. Thus, a feedforward neural network is applied to the approximation of the rate-independent hysteresis on the constructed expanded input space. Moreover, in order to describe the rate-dependent performance of the hysteresis, a special hybrid model, which is constructed by a linear auto-regressive exogenous input (ARX) sub-model preceded with the previously obtained neural network based rate-independent hysteresis sub-model, is proposed. For the compensation of the effect of the hysteresis in PEA, the PID feedback controller with a feedforward hysteresis compensator is developed for the tracking control of the PEA. Thus, a corresponding inverse model based on the proposed modeling method is developed for the feedforward hysteresis compensator. Finally, both simulations and experimental results on piezoelectric actuator are presented to verify the effectiveness of the proposed approach for the rate-dependent hysteresis.

  12. Analysis of a phase synchronized functional network based on the rhythm of brain activities

    International Nuclear Information System (INIS)

    Li Ling; Jin Zhen-Lan; Li Bin

    2011-01-01

    Rhythm of brain activities represents oscillations of postsynaptic potentials in neocortex, therefore it can serve as an indicator of the brain activity state. In order to check the connectivity of brain rhythm, this paper develops a new method of constructing functional network based on phase synchronization. Electroencephalogram (EEG) data were collected while subjects looking at a green cross in two states, performing an attention task and relaxing with eyes-open. The EEG from these two states was filtered by three band-pass filters to obtain signals of theta (4–7 Hz), alpha (8–13 Hz) and beta (14–30 Hz) bands. Mean resultant length was used to estimate strength of phase synchronization in three bands to construct networks of both states, and mean degree K and cluster coefficient C of networks were calculated as a function of threshold. The result shows higher cluster coefficient in the attention state than in the eyes-open state in all three bands, suggesting that cluster coefficient reflects brain state. In addition, an obvious fronto-parietal network is found in the attention state, which is a well-known attention network. These results indicate that attention modulates the fronto-parietal connectivity in different modes as compared with the eyes-open state. Taken together this method is an objective and important tool to study the properties of neural networks of brain rhythm. (interdisciplinary physics and related areas of science and technology)

  13. Network-based analysis reveals functional connectivity related to internet addiction tendency

    Directory of Open Access Journals (Sweden)

    Tanya eWen

    2016-02-01

    Full Text Available IntroductionPreoccupation and compulsive use of the internet can have negative psychological effects, such that it is increasingly being recognized as a mental disorder. The present study employed network-based statistics to explore how whole-brain functional connections at rest is related to the extent of individual’s level of internet addiction, indexed by a self-rated questionnaire. We identified two topologically significant networks, one with connections that are positively correlated with internet addiction tendency, and one with connections negatively correlated with internet addiction tendency. The two networks are interconnected mostly at frontal regions, which might reflect alterations in the frontal region for different aspects of cognitive control (i.e., for control of internet usage and gaming skills. Next, we categorized the brain into several large regional subgroupings, and found that the majority of proportions of connections in the two networks correspond to the cerebellar model of addiction which encompasses the four-circuit model. Lastly, we observed that the brain regions with the most inter-regional connections associated with internet addiction tendency replicate those often seen in addiction literature, and is corroborated by our meta-analysis of internet addiction studies. This research provides a better understanding of large-scale networks involved in internet addiction tendency and shows that pre-clinical levels of internet addiction are associated with similar regions and connections as clinical cases of addiction.

  14. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    Science.gov (United States)

    Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

    1997-01-01

    One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

  15. A network-based frequency analysis of Inclusive Wealth to track sustainable development in world countries.

    Science.gov (United States)

    Ahmad, Nasir; Derrible, Sybil; Managi, Shunsuke

    2018-07-15

    Using human (HC), natural (NC), and produced (PC) capital from Inclusive Wealth as representatives of the triple bottom line of sustainability and utilizing elements of network science, we introduce a Network-based Frequency Analysis (NFA) method to track sustainable development in world countries from 1990 to 2014. The method compares every country with every other and links them when values are close. The country with the most links becomes the main trend, and the performance of every other country is assessed based on its 'orbital' distance from the main trend. Orbital speeds are then calculated to evaluate country-specific dynamic trends. Overall, we find an optimistic trend for HC only, indicating positive impacts of global initiatives aiming towards socio-economic development in developing countries like the Millennium Development Goals and 'Agenda 21'. However, we also find that the relative performance of most countries has not changed significantly in this period, regardless of their gradual development. Specifically, we measure a decrease in produced and natural capital for most countries, despite an increase in GDP, suggesting unsustainable development. Furthermore, we develop a technique to cluster countries and project the results to 2050, and we find a significant decrease in NC for nearly all countries, suggesting an alarming depletion of natural resources worldwide. Copyright © 2018 Elsevier Ltd. All rights reserved.

  16. Structural design principles of complex bird songs: a network-based approach.

    Directory of Open Access Journals (Sweden)

    Kazutoshi Sasahara

    Full Text Available Bird songs are acoustic communication signals primarily used in male-male aggression and in male-female attraction. These are often monotonous patterns composed of a few phrases, yet some birds have extremely complex songs with a large phrase repertoire, organized in non-random fashion with discernible patterns. Since structure is typically associated with function, the structures of complex bird songs provide important clues to the evolution of animal communication systems. Here we propose an efficient network-based approach to explore structural design principles of complex bird songs, in which the song networks--transition relationships among different phrases and the related structural measures--are employed. We demonstrate how this approach works with an example using California Thrasher songs, which are sequences of highly varied phrases delivered in succession over several minutes. These songs display two distinct features: a large phrase repertoire with a 'small-world' architecture, in which subsets of phrases are highly grouped and linked with a short average path length; and a balanced transition diversity amongst phrases, in which deterministic and non-deterministic transition patterns are moderately mixed. We explore the robustness of this approach with variations in sample size and the amount of noise. Our approach enables a more quantitative study of global and local structural properties of complex bird songs than has been possible to date.

  17. Cross-layer restoration with software defined networking based on IP over optical transport networks

    Science.gov (United States)

    Yang, Hui; Cheng, Lei; Deng, Junni; Zhao, Yongli; Zhang, Jie; Lee, Young

    2015-10-01

    The IP over optical transport network is a very promising networking architecture applied to the interconnection of geographically distributed data centers due to the performance guarantee of low delay, huge bandwidth and high reliability at a low cost. It can enable efficient resource utilization and support heterogeneous bandwidth demands in highly-available, cost-effective and energy-effective manner. In case of cross-layer link failure, to ensure a high-level quality of service (QoS) for user request after the failure becomes a research focus. In this paper, we propose a novel cross-layer restoration scheme for data center services with software defined networking based on IP over optical network. The cross-layer restoration scheme can enable joint optimization of IP network and optical network resources, and enhance the data center service restoration responsiveness to the dynamic end-to-end service demands. We quantitatively evaluate the feasibility and performances through the simulation under heavy traffic load scenario in terms of path blocking probability and path restoration latency. Numeric results show that the cross-layer restoration scheme improves the recovery success rate and minimizes the overall recovery time.

  18. Pedagogy framework design in social networked-based learning: Focus on children with learning difficulties

    Directory of Open Access Journals (Sweden)

    Samira Sadat Sajadi

    2014-09-01

    Full Text Available This paper presents an investigation on the theory of constructivism applicable for learners with learning difficulties, specifically learners with Attention Deficit Hyperactivity Disorder (ADHD. The primary objective of this paper is to determine whether a constructivist technology enhanced learning pedagogy could be used to help ADHD learners cope with their educational needs within a social-media learning environment. Preliminary work is stated here, in which we are seeking evidence to determine the viability of a constructivist approach for learners with ADHD. The novelty of this research lies in the proposals to support ADHD learners to overcome their weaknesses with appropriate pedagogically sound interventions. As a result, a framework has been designed to illuminate areas in which constructivist pedagogies require to address the limitations of ADHD learners. An analytical framework addressing the suitability of a constructivist learning for ADHD is developed from a combination of literature and expert advice from those involved in the education of learners with ADHD. This analytical framework is married to a new model of pedagogy, which the authors have derived from literature analysis. Future work will expand this model to develop a constructivist social network-based learning and eventually test it in specialist schools with ADHD learners.

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

    Science.gov (United States)

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

    2017-08-01

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

  20. Limitations of a metabolic network-based reverse ecology method for inferring host-pathogen interactions.

    Science.gov (United States)

    Takemoto, Kazuhiro; Aie, Kazuki

    2017-05-25

    Host-pathogen interactions are important in a wide range of research fields. Given the importance of metabolic crosstalk between hosts and pathogens, a metabolic network-based reverse ecology method was proposed to infer these interactions. However, the validity of this method remains unclear because of the various explanations presented and the influence of potentially confounding factors that have thus far been neglected. We re-evaluated the importance of the reverse ecology method for evaluating host-pathogen interactions while statistically controlling for confounding effects using oxygen requirement, genome, metabolic network, and phylogeny data. Our data analyses showed that host-pathogen interactions were more strongly influenced by genome size, primary network parameters (e.g., number of edges), oxygen requirement, and phylogeny than the reserve ecology-based measures. These results indicate the limitations of the reverse ecology method; however, they do not discount the importance of adopting reverse ecology approaches altogether. Rather, we highlight the need for developing more suitable methods for inferring host-pathogen interactions and conducting more careful examinations of the relationships between metabolic networks and host-pathogen interactions.

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

    Science.gov (United States)

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

    2010-12-01

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

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

    Science.gov (United States)

    Han, Bing; Taha, Tarek M

    2010-04-01

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

  3. Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection.

    Science.gov (United States)

    Marchi, Erik; Vesperini, Fabio; Squartini, Stefano; Schuller, Björn

    2017-01-01

    In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these approaches, auditory spectral features of the next short term frame are predicted from the previous frames by means of Long-Short Term Memory recurrent denoising autoencoders. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. There is no evidence of studies focused on comparing previous efforts to automatically recognize novel events from audio signals and giving a broad and in depth evaluation of recurrent neural network-based autoencoders. The present contribution aims to consistently evaluate our recent novel approaches to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases: A3Novelty, PASCAL CHiME, and PROMETHEUS. Besides providing an extensive analysis of novel and state-of-the-art methods, the article shows how RNN-based autoencoders outperform statistical approaches up to an absolute improvement of 16.4% average F -measure over the three databases.

  4. Understanding structure of urban traffic network based on spatial-temporal correlation analysis

    Science.gov (United States)

    Yang, Yanfang; Jia, Limin; Qin, Yong; Han, Shixiu; Dong, Honghui

    2017-08-01

    Understanding the structural characteristics of urban traffic network comprehensively can provide references for improving road utilization rate and alleviating traffic congestion. This paper focuses on the spatial-temporal correlations between different pairs of traffic series and proposes a complex network-based method of constructing the urban traffic network. In the network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding spatial-temporal correlation. Further, a modified PageRank algorithm, named the geographical weight-based PageRank algorithm (GWPA), is proposed to analyze the spatial distribution of important segments in the road network. Finally, experiments are conducted by using three kinds of traffic series collected from the urban road network in Beijing. Experimental results show that the urban traffic networks constructed by three traffic variables all indicate both small-world and scale-free characteristics. Compared with the results of PageRank algorithm, GWPA is proved to be valid in evaluating the importance of segments and identifying the important segments with small degree.

  5. Sensor Network-Based and User-Friendly User Location Discovery for Future Smart Homes.

    Science.gov (United States)

    Ahvar, Ehsan; Lee, Gyu Myoung; Han, Son N; Crespi, Noel; Khan, Imran

    2016-06-27

    User location is crucial context information for future smart homes where many location based services will be proposed. This location necessarily means that User Location Discovery (ULD) will play an important role in future smart homes. Concerns about privacy and the need to carry a mobile or a tag device within a smart home currently make conventional ULD systems uncomfortable for users. Future smart homes will need a ULD system to consider these challenges. This paper addresses the design of such a ULD system for context-aware services in future smart homes stressing the following challenges: (i) users' privacy; (ii) device-/tag-free; and (iii) fault tolerance and accuracy. On the other hand, emerging new technologies, such as the Internet of Things, embedded systems, intelligent devices and machine-to-machine communication, are penetrating into our daily life with more and more sensors available for use in our homes. Considering this opportunity, we propose a ULD system that is capitalizing on the prevalence of sensors for the home while satisfying the aforementioned challenges. The proposed sensor network-based and user-friendly ULD system relies on different types of inexpensive sensors, as well as a context broker with a fuzzy-based decision-maker. The context broker receives context information from different types of sensors and evaluates that data using the fuzzy set theory. We demonstrate the performance of the proposed system by illustrating a use case, utilizing both an analytical model and simulation.

  6. Unveiling network-based functional features through integration of gene expression into protein networks.

    Science.gov (United States)

    Jalili, Mahdi; Gebhardt, Tom; Wolkenhauer, Olaf; Salehzadeh-Yazdi, Ali

    2018-06-01

    Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers. Copyright © 2018 Elsevier B.V. All rights reserved.

  7. Resilient Disaster Network Based on Software Defined Cognitive Wireless Network Technology

    Directory of Open Access Journals (Sweden)

    Goshi Sato

    2015-01-01

    Full Text Available In order to temporally recover the information network infrastructure in disaster areas from the Great East Japan Earthquake in 2011, various wireless network technologies such as satellite IP network, 3G, and Wi-Fi were effectively used. However, since those wireless networks are individually introduced and installed but not totally integrated, some of networks were congested due to the sudden network traffic generation and unbalanced traffic distribution, and eventually the total network could not effectively function. In this paper, we propose a disaster resilient network which integrates various wireless networks into a cognitive wireless network that users can use as an access network to the Internet at the serious disaster occurrence. We designed and developed the disaster resilient network based on software defined network (SDN technology to automatically select the best network link and route among the possible access networks to the Internet by periodically monitoring their network states and evaluate those using extended AHP method. In order to verify the usefulness of our proposed system, a prototype system is constructed and its performance is evaluated.

  8. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

    Science.gov (United States)

    Williams-Hayes, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

  9. Alerts Analysis and Visualization in Network-based Intrusion Detection Systems

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Dr. Li [University of Tennessee

    2010-08-01

    The alerts produced by network-based intrusion detection systems, e.g. Snort, can be difficult for network administrators to efficiently review and respond to due to the enormous number of alerts generated in a short time frame. This work describes how the visualization of raw IDS alert data assists network administrators in understanding the current state of a network and quickens the process of reviewing and responding to intrusion attempts. The project presented in this work consists of three primary components. The first component provides a visual mapping of the network topology that allows the end-user to easily browse clustered alerts. The second component is based on the flocking behavior of birds such that birds tend to follow other birds with similar behaviors. This component allows the end-user to see the clustering process and provides an efficient means for reviewing alert data. The third component discovers and visualizes patterns of multistage attacks by profiling the attacker s behaviors.

  10. Authorship attribution of source code by using back propagation neural network based on particle swarm optimization.

    Science.gov (United States)

    Yang, Xinyu; Xu, Guoai; Li, Qi; Guo, Yanhui; Zhang, Miao

    2017-01-01

    Authorship attribution is to identify the most likely author of a given sample among a set of candidate known authors. It can be not only applied to discover the original author of plain text, such as novels, blogs, emails, posts etc., but also used to identify source code programmers. Authorship attribution of source code is required in diverse applications, ranging from malicious code tracking to solving authorship dispute or software plagiarism detection. This paper aims to propose a new method to identify the programmer of Java source code samples with a higher accuracy. To this end, it first introduces back propagation (BP) neural network based on particle swarm optimization (PSO) into authorship attribution of source code. It begins by computing a set of defined feature metrics, including lexical and layout metrics, structure and syntax metrics, totally 19 dimensions. Then these metrics are input to neural network for supervised learning, the weights of which are output by PSO and BP hybrid algorithm. The effectiveness of the proposed method is evaluated on a collected dataset with 3,022 Java files belong to 40 authors. Experiment results show that the proposed method achieves 91.060% accuracy. And a comparison with previous work on authorship attribution of source code for Java language illustrates that this proposed method outperforms others overall, also with an acceptable overhead.

  11. Novel spider-web-like nanoporous networks based on jute cellulose nanowhiskers.

    Science.gov (United States)

    Cao, Xinwang; Wang, Xianfeng; Ding, Bin; Yu, Jianyong; Sun, Gang

    2013-02-15

    Cellulose nanowhiskers as a kind of renewable and biocompatible nanomaterials evoke much interest because of its versatility in various applications. Herein, for the first time, a novel controllable fabrication of spider-web-like nanoporous networks based on jute cellulose nanowhiskers (JCNs) deposited on the electrospun (ES) nanofibrous membrane by simple directly immersion-drying method is reported. Jute cellulose nanowhiskers were extracted from jute fibers with a high yield (over 80%) via a 2,2,6,6-tetramethylpiperidine-1-oxyl radical (TEMPO)/NaBr/NaClO system selective oxidization combined with mechanical homogenization. The morphology of JCNs nanoporous networks/ES nanofibrous membrane architecture, including coverage rate, pore-width and layer-by-layer packing structure of the nanoporous networks, can be finely controlled by regulating the JCNs dispersions properties and drying conditions. The versatile nanoporous network composites based on jute cellulose nanowhiskers with ultrathin diameters (3-10 nm) and nanofibrous membrane supports with diameters of 100-300 nm, would be particularly useful for filter applications. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

  12. A neural network based artificial vision system for licence plate recognition.

    Science.gov (United States)

    Draghici, S

    1997-02-01

    This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%.

  13. A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations

    Directory of Open Access Journals (Sweden)

    Zhenghui Sha

    2018-01-01

    Full Text Available Understanding customer preferences in consideration decisions is critical to choice modeling in engineering design. While existing literature has shown that the exogenous effects (e.g., product and customer attributes are deciding factors in customers’ consideration decisions, it is not clear how the endogenous effects (e.g., the intercompetition among products would influence such decisions. This paper presents a network-based approach based on Exponential Random Graph Models to study customers’ consideration behaviors according to engineering design. Our proposed approach is capable of modeling the endogenous effects among products through various network structures (e.g., stars and triangles besides the exogenous effects and predicting whether two products would be conisdered together. To assess the proposed model, we compare it against the dyadic network model that only considers exogenous effects. Using buyer survey data from the China automarket in 2013 and 2014, we evaluate the goodness of fit and the predictive power of the two models. The results show that our model has a better fit and predictive accuracy than the dyadic network model. This underscores the importance of the endogenous effects on customers’ consideration decisions. The insights gained from this research help explain how endogenous effects interact with exogeous effects in affecting customers’ decision-making.

  14. A Security Assessment Mechanism for Software-Defined Networking-Based Mobile Networks

    Directory of Open Access Journals (Sweden)

    Shibo Luo

    2015-12-01

    Full Text Available Software-Defined Networking-based Mobile Networks (SDN-MNs are considered the future of 5G mobile network architecture. With the evolving cyber-attack threat, security assessments need to be performed in the network management. Due to the distinctive features of SDN-MNs, such as their dynamic nature and complexity, traditional network security assessment methodologies cannot be applied directly to SDN-MNs, and a novel security assessment methodology is needed. In this paper, an effective security assessment mechanism based on attack graphs and an Analytic Hierarchy Process (AHP is proposed for SDN-MNs. Firstly, this paper discusses the security assessment problem of SDN-MNs and proposes a methodology using attack graphs and AHP. Secondly, to address the diversity and complexity of SDN-MNs, a novel attack graph definition and attack graph generation algorithm are proposed. In order to quantify security levels, the Node Minimal Effort (NME is defined to quantify attack cost and derive system security levels based on NME. Thirdly, to calculate the NME of an attack graph that takes the dynamic factors of SDN-MN into consideration, we use AHP integrated with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS as the methodology. Finally, we offer a case study to validate the proposed methodology. The case study and evaluation show the advantages of the proposed security assessment mechanism.

  15. A Security Assessment Mechanism for Software-Defined Networking-Based Mobile Networks.

    Science.gov (United States)

    Luo, Shibo; Dong, Mianxiong; Ota, Kaoru; Wu, Jun; Li, Jianhua

    2015-12-17

    Software-Defined Networking-based Mobile Networks (SDN-MNs) are considered the future of 5G mobile network architecture. With the evolving cyber-attack threat, security assessments need to be performed in the network management. Due to the distinctive features of SDN-MNs, such as their dynamic nature and complexity, traditional network security assessment methodologies cannot be applied directly to SDN-MNs, and a novel security assessment methodology is needed. In this paper, an effective security assessment mechanism based on attack graphs and an Analytic Hierarchy Process (AHP) is proposed for SDN-MNs. Firstly, this paper discusses the security assessment problem of SDN-MNs and proposes a methodology using attack graphs and AHP. Secondly, to address the diversity and complexity of SDN-MNs, a novel attack graph definition and attack graph generation algorithm are proposed. In order to quantify security levels, the Node Minimal Effort (NME) is defined to quantify attack cost and derive system security levels based on NME. Thirdly, to calculate the NME of an attack graph that takes the dynamic factors of SDN-MN into consideration, we use AHP integrated with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) as the methodology. Finally, we offer a case study to validate the proposed methodology. The case study and evaluation show the advantages of the proposed security assessment mechanism.

  16. Network Based Integrated Analysis of Phenotype-Genotype Data for Prioritization of Candidate Symptom Genes

    Directory of Open Access Journals (Sweden)

    Xing Li

    2014-01-01

    Full Text Available Background. Symptoms and signs (symptoms in brief are the essential clinical manifestations for individualized diagnosis and treatment in traditional Chinese medicine (TCM. To gain insights into the molecular mechanism of symptoms, we develop a computational approach to identify the candidate genes of symptoms. Methods. This paper presents a network-based approach for the integrated analysis of multiple phenotype-genotype data sources and the prediction of the prioritizing genes for the associated symptoms. The method first calculates the similarities between symptoms and diseases based on the symptom-disease relationships retrieved from the PubMed bibliographic database. Then the disease-gene associations and protein-protein interactions are utilized to construct a phenotype-genotype network. The PRINCE algorithm is finally used to rank the potential genes for the associated symptoms. Results. The proposed method gets reliable gene rank list with AUC (area under curve 0.616 in classification. Some novel genes like CALCA, ESR1, and MTHFR were predicted to be associated with headache symptoms, which are not recorded in the benchmark data set, but have been reported in recent published literatures. Conclusions. Our study demonstrated that by integrating phenotype-genotype relationships into a complex network framework it provides an effective approach to identify candidate genes of symptoms.

  17. A neural network-based exploratory learning and motor planning system for co-robots.

    Science.gov (United States)

    Galbraith, Byron V; Guenther, Frank H; Versace, Massimiliano

    2015-01-01

    Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or "learning by doing," an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.

  18. Ethernet access network based on free-space optic deployment technology

    Science.gov (United States)

    Gebhart, Michael; Leitgeb, Erich; Birnbacher, Ulla; Schrotter, Peter

    2004-06-01

    The satisfaction of all communication needs from single households and business companies over a single access infrastructure is probably the most challenging topic in communications technology today. But even though the so-called "Last Mile Access Bottleneck" is well known since more than ten years and many distribution technologies have been tried out, the optimal solution has not yet been found and paying commercial access networks offering all service classes are still rare today. Conventional services like telephone, radio and TV, as well as new and emerging services like email, web browsing, online-gaming, video conferences, business data transfer or external data storage can all be transmitted over the well known and cost effective Ethernet networking protocol standard. Key requirements for the deployment technology driven by the different services are high data rates to the single customer, security, moderate deployment costs and good scalability to number and density of users, quick and flexible deployment without legal impediments and high availability, referring to the properties of optical and wireless communication. We demonstrate all elements of an Ethernet Access Network based on Free Space Optic distribution technology. Main physical parts are Central Office, Distribution Network and Customer Equipment. Transmission of different services, as well as configuration, service upgrades and remote control of the network are handled by networking features over one FSO connection. All parts of the network are proven, the latest commercially available technology. The set up is flexible and can be adapted to any more specific need if required.

  19. A network-based system of simulation, control and online assistance for HTR-10

    Energy Technology Data Exchange (ETDEWEB)

    Zhu Shutang [Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084 (China)], E-mail: zhust@tsinghua.edu.cn; Luo Shaojie; Shi Lei [Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084 (China)

    2008-07-15

    A network-based computer system has been developed for HTR-10. This system integrates three subsystems: the simulation subsystem (SIMUSUB), the visualized control designed subsystem (VCDSUB) and the online assistance subsystem (OASUB). The SIMUSUB consists of four functional elements: the simulation calculating server (SCS), the main control client (MCC), the data disposal client (DDC) and the results graphic display client (RGDC), all of which can communicate with each other via network. It is intended to analyze and calculate physical processes of the reactor core, the main loop system and the steam generator, etc., as well as to simulate the normal operational and transient accidents. The result data can be dynamically displayed through the RGDC. The VCDSUB provides a platform for control system modeling where the control flow systems can be automatically generated and graphically simulated. Based on the data from the field bus, the OASUB provides some of the reactor core parameters, which are difficult to measure. This integrated system can be used as an educational tool to understand the design and operational characteristics of the HTR-10, and can also provide online support for operators in the main control room, or as a convenient powerful tool for the control system design.

  20. A network-based system of simulation, control and online assistance for HTR-10

    International Nuclear Information System (INIS)

    Zhu Shutang; Luo Shaojie; Shi Lei

    2008-01-01

    A network-based computer system has been developed for HTR-10. This system integrates three subsystems: the simulation subsystem (SIMUSUB), the visualized control designed subsystem (VCDSUB) and the online assistance subsystem (OASUB). The SIMUSUB consists of four functional elements: the simulation calculating server (SCS), the main control client (MCC), the data disposal client (DDC) and the results graphic display client (RGDC), all of which can communicate with each other via network. It is intended to analyze and calculate physical processes of the reactor core, the main loop system and the steam generator, etc., as well as to simulate the normal operational and transient accidents. The result data can be dynamically displayed through the RGDC. The VCDSUB provides a platform for control system modeling where the control flow systems can be automatically generated and graphically simulated. Based on the data from the field bus, the OASUB provides some of the reactor core parameters, which are difficult to measure. This integrated system can be used as an educational tool to understand the design and operational characteristics of the HTR-10, and can also provide online support for operators in the main control room, or as a convenient powerful tool for the control system design

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

    Directory of Open Access Journals (Sweden)

    Ahcene Farah

    2002-06-01

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

  2. Research on evaluation of degree of complexity of mining fault network based on GIS

    Energy Technology Data Exchange (ETDEWEB)

    Hua Zhang; Yun-jia Wang; Chuan-zhi Liu [China University of Mining and Technology, Jiangsu (China). School of Environment Science and Spatial Informatics

    2007-03-15

    A large number of spatial and attribute data are involved in coal resource evaluation. Databases are a relatively advanced data management technology, but their major defects are the poor graphic and spatial data functions, from which it is difficult to realize scientific management of evaluation data with spatial characteristics and evaluation result maps. On account of these deficiencies, the evaluation of degree of complexity of mining fault network based on a geographic information system (GIS) is proposed which integrates management of spatial and attribute data. A fractal is an index which can reflect the comprehensive information of faults' number, density, size, composition and dynamics mechanism. A fractal dimension is used as the quantitative evaluation index. Evaluation software has been developed based on a component GIS-MapX, with which the degree of complexity of fault network is evaluated quantitatively using the quantitative index of fractal dimensions in Liuqiao No.2 coal mine as an example. Results show that it is effective in acquiring model parameters and enhancing the definition of data and evaluation results with the application of GIS technology. The fault network is a system with fractal structure and its complexity can be described reasonably and accurately by fractal dimension, which provides an effective method for coal resource evaluation. 9 refs., 6 figs., 2 tabs.

  3. A Self-Organizing Incremental Neural Network based on local distribution learning.

    Science.gov (United States)

    Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

    2016-12-01

    In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Sensor Network-Based and User-Friendly User Location Discovery for Future Smart Homes

    Directory of Open Access Journals (Sweden)

    Ehsan Ahvar

    2016-06-01

    Full Text Available User location is crucial context information for future smart homes where many location based services will be proposed. This location necessarily means that User Location Discovery (ULD will play an important role in future smart homes. Concerns about privacy and the need to carry a mobile or a tag device within a smart home currently make conventional ULD systems uncomfortable for users. Future smart homes will need a ULD system to consider these challenges. This paper addresses the design of such a ULD system for context-aware services in future smart homes stressing the following challenges: (i users’ privacy; (ii device-/tag-free; and (iii fault tolerance and accuracy. On the other hand, emerging new technologies, such as the Internet of Things, embedded systems, intelligent devices and machine-to-machine communication, are penetrating into our daily life with more and more sensors available for use in our homes. Considering this opportunity, we propose a ULD system that is capitalizing on the prevalence of sensors for the home while satisfying the aforementioned challenges. The proposed sensor network-based and user-friendly ULD system relies on different types of inexpensive sensors, as well as a context broker with a fuzzy-based decision-maker. The context broker receives context information from different types of sensors and evaluates that data using the fuzzy set theory. We demonstrate the performance of the proposed system by illustrating a use case, utilizing both an analytical model and simulation.

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

    Science.gov (United States)

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

    2017-01-01

    This article describes the development of the social network-based intervention Powerful Together with Diabetes which aims to improve diabetes self-management (DSM) among patients with type 2 diabetes living in socioeconomically deprived neighborhoods by stimulating social support for DSM and diminishing social influences hindering DSM (e.g., peer pressure and social norms). The intervention was specifically developed for patients with Dutch, Turkish, Moroccan, and Surinamese backgrounds. The intervention was developed according to Intervention Mapping. This article describes the first four steps of Intervention Mapping: (1) the needs assessment; (2) development of performance and change objectives; (3) selection of theory-based methods and strategies; and (4) the translation of these into an organized program. These four steps resulted in Powerful Together with Diabetes , a 10-month group-based intervention consisting of 24 meetings, 6 meetings for significant others, and 2 meetings for participants and their spouses. The IM method resulted in a tailored approach with a specific focus on the social networks of its participants. This article concludes that the IM method helped our planning team to tailor the intervention to the needs of our target population and facilitated our evaluation design. However, in hindsight, the intervention could have been improved by investing more in participatory planning and community involvement.

  6. A Network-Based Algorithm for Clustering Multivariate Repeated Measures Data

    Science.gov (United States)

    Koslovsky, Matthew; Arellano, John; Schaefer, Caroline; Feiveson, Alan; Young, Millennia; Lee, Stuart

    2017-01-01

    The National Aeronautics and Space Administration (NASA) Astronaut Corps is a unique occupational cohort for which vast amounts of measures data have been collected repeatedly in research or operational studies pre-, in-, and post-flight, as well as during multiple clinical care visits. In exploratory analyses aimed at generating hypotheses regarding physiological changes associated with spaceflight exposure, such as impaired vision, it is of interest to identify anomalies and trends across these expansive datasets. Multivariate clustering algorithms for repeated measures data may help parse the data to identify homogeneous groups of astronauts that have higher risks for a particular physiological change. However, available clustering methods may not be able to accommodate the complex data structures found in NASA data, since the methods often rely on strict model assumptions, require equally-spaced and balanced assessment times, cannot accommodate missing data or differing time scales across variables, and cannot process continuous and discrete data simultaneously. To fill this gap, we propose a network-based, multivariate clustering algorithm for repeated measures data that can be tailored to fit various research settings. Using simulated data, we demonstrate how our method can be used to identify patterns in complex data structures found in practice.

  7. Genetic modification and genetic determinism

    Science.gov (United States)

    Resnik, David B; Vorhaus, Daniel B

    2006-01-01

    In this article we examine four objections to the genetic modification of human beings: the freedom argument, the giftedness argument, the authenticity argument, and the uniqueness argument. We then demonstrate that each of these arguments against genetic modification assumes a strong version of genetic determinism. Since these strong deterministic assumptions are false, the arguments against genetic modification, which assume and depend upon these assumptions, are therefore unsound. Serious discussion of the morality of genetic modification, and the development of sound science policy, should be driven by arguments that address the actual consequences of genetic modification for individuals and society, not by ones propped up by false or misleading biological assumptions. PMID:16800884

  8. Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

    Directory of Open Access Journals (Sweden)

    Zhaosheng Yang

    2014-01-01

    Full Text Available To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface. The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

  9. From Genetics to Genetic Algorithms

    Indian Academy of Sciences (India)

    Genetic algorithms (GAs) are computational optimisation schemes with an ... The algorithms solve optimisation problems ..... Genetic Algorithms in Search, Optimisation and Machine. Learning, Addison-Wesley Publishing Company, Inc. 1989.

  10. From Genetics to Genetic Algorithms

    Indian Academy of Sciences (India)

    artificial genetic system) string feature or ... called the genotype whereas it is called a structure in artificial genetic ... assigned a fitness value based on the cost function. Better ..... way it has produced complex, intelligent living organisms capable of ...

  11. Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

    Science.gov (United States)

    Kesharaju, Manasa; Nagarajah, Romesh

    2015-09-01

    The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2015-12-01

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

  13. Dynamic neural networks based on-line identification and control of high performance motor drives

    Science.gov (United States)

    Rubaai, Ahmed; Kotaru, Raj

    1995-01-01

    In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

  14. ICS logging solution for network-based attacks using Gumistix technology

    Science.gov (United States)

    Otis, Jeremy R.; Berman, Dustin; Butts, Jonathan; Lopez, Juan

    2013-05-01

    Industrial Control Systems (ICS) monitor and control operations associated with the national critical infrastructure (e.g., electric power grid, oil and gas pipelines and water treatment facilities). These systems rely on technologies and architectures that were designed for system reliability and availability. Security associated with ICS was never an inherent concern, primarily due to the protections afforded by network isolation. However, a trend in ICS operations is to migrate to commercial networks via TCP/IP in order to leverage commodity benefits and cost savings. As a result, system vulnerabilities are now exposed to the online community. Indeed, recent research has demonstrated that many exposed ICS devices are being discovered using readily available applications (e.g., ShodanHQ search engine and Google-esque queries). Due to the lack of security and logging capabilities for ICS, most knowledge about attacks are derived from real world incidents after an attack has already been carried out and the damage has been done. This research provides a method for introducing sensors into the ICS environment that collect information about network-based attacks. The sensors are developed using an inexpensive Gumstix platform that can be deployed and incorporated with production systems. Data obtained from the sensors provide insight into attack tactics (e.g., port scans, Nessus scans, Metasploit modules, and zero-day exploits) and characteristics (e.g., attack origin, frequency, and level of persistence). Findings enable security professionals to draw an accurate, real-time awareness of the threats against ICS devices and help shift the security posture from reactionary to preventative.

  15. Molecular Network-Based Identification of Competing Endogenous RNAs in Thyroid Carcinoma

    Directory of Open Access Journals (Sweden)

    Minjia Lu

    2018-01-01

    Full Text Available RNAs may act as competing endogenous RNAs (ceRNAs, a critical mechanism in determining gene expression regulations in many cancers. However, the roles of ceRNAs in thyroid carcinoma remains elusive. In this study, we have developed a novel pipeline called Molecular Network-based Identification of ceRNA (MNIceRNA to identify ceRNAs in thyroid carcinoma. MNIceRNA first constructs micro RNA (miRNA–messenger RNA (mRNAlong non-coding RNA (lncRNA networks from miRcode database and weighted correlation network analysis (WGCNA, based on which to identify key drivers of differentially expressed RNAs between normal and tumor samples. It then infers ceRNAs of the identified key drivers using the long non-coding competing endogenous database (lnCeDB. We applied the pipeline into The Cancer Genome Atlas (TCGA thyroid carcinoma data. As a result, 598 lncRNAs, 1025 mRNAs, and 90 microRNA (miRNAs were inferred to be differentially expressed between normal and thyroid cancer samples. We then obtained eight key driver miRNAs, among which hsa-mir-221 and hsa-mir-222 were key driver RNAs identified by both miRNA–mRNA–lncRNA and WGCNA network. In addition, hsa-mir-375 was inferred to be significant for patients’ survival with 34 associated ceRNAs, among which RUNX2, DUSP6 and SEMA3D are known oncogenes regulating cellular proliferation and differentiation in thyroid cancer. These ceRNAs are critical in revealing the secrets behind thyroid cancer progression and may serve as future therapeutic biomarkers.

  16. Semi-interpenetrating networks based on POLY(N-isopropyl acrilamide and POLY(N-vinylpyrrolidone

    Directory of Open Access Journals (Sweden)

    Žugić Dragana

    2007-01-01

    Full Text Available Three series of semi-interpenetrating polymer networks based on cross-linked poly(N-isopropylacrylamide, PNIPA, and 1, 2 and 3 wt% of linear poly(N-vinylpyrrolidone, PVP, were synthesized in order to improve the mechanical properties of PNIPA gels. The effect of the incorporation of the linear PVP polymer into the temperature responsive networks on the phase transition temperature, swelling behavior and mechanical properties was studied. Polymer networks with four different crosslinking densities were prepared with various molar ratios (25/1 to 100/1 of the monomer (N-isopropylacrylamide to the cross linker (N,N'-methylene-bisacrylamide. The hydrogels were characterized by determination of the equilibrium degree of swelling at 25 °C, the dynamic shear modulus and the effective crosslinking density, as well as the ultimate hydrogel properties, such as the tensile strength and elongation at break. Furthermore, the deswelling kinetics of the hydrogels was also studied by measuring their water retention capacity. The inclusion of the linear hydrophilic PVP in the PNIPA networks increased the equilibrium degree of swelling, the highest values of which were obtained for samples with 2 and 3 wt% of PVP and the NIPA/MBA molar ratio of 75/1 and 100/1. The highest reinforcement effect, evaluated from the ratio of G'red(semi-IPN to G'red(PNIPA, was obtained by incorporation of 2 wt% PVP. The tensile strength of the semi-IPNs reinforced with linear PVP was higher than that of the PNIPA networks. The elongation at break of these semi-IPNs varied between 22 and 55%, which are 22^11% larger than those for single PNIPA networks. The tensile measurements confirmed that the presence of 2 wt% of the linear polymer significantly reinforced the PNIPA network.

  17. Training and operation of an integrated neuromorphic network based on metal-oxide memristors

    Science.gov (United States)

    Prezioso, M.; Merrikh-Bayat, F.; Hoskins, B. D.; Adam, G. C.; Likharev, K. K.; Strukov, D. B.

    2015-05-01

    Despite much progress in semiconductor integrated circuit technology, the extreme complexity of the human cerebral cortex, with its approximately 1014 synapses, makes the hardware implementation of neuromorphic networks with a comparable number of devices exceptionally challenging. To provide comparable complexity while operating much faster and with manageable power dissipation, networks based on circuits combining complementary metal-oxide-semiconductors (CMOSs) and adjustable two-terminal resistive devices (memristors) have been developed. In such circuits, the usual CMOS stack is augmented with one or several crossbar layers, with memristors at each crosspoint. There have recently been notable improvements in the fabrication of such memristive crossbars and their integration with CMOS circuits, including first demonstrations of their vertical integration. Separately, discrete memristors have been used as artificial synapses in neuromorphic networks. Very recently, such experiments have been extended to crossbar arrays of phase-change memristive devices. The adjustment of such devices, however, requires an additional transistor at each crosspoint, and hence these devices are much harder to scale than metal-oxide memristors, whose nonlinear current-voltage curves enable transistor-free operation. Here we report the experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification). The network can be taught in situ using a coarse-grain variety of the delta rule algorithm to perform the perfect classification of 3 × 3-pixel black/white images into three classes (representing letters). This demonstration is an important step towards much larger and more complex memristive neuromorphic networks.

  18. Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics

    Science.gov (United States)

    2014-01-01

    Background The demand for novel molecularly targeted drugs will continue to rise as we move forward toward the goal of personalizing cancer treatment to the molecular signature of individual tumors. However, the identification of targets and combinations of targets that can be safely and effectively modulated is one of the greatest challenges facing the drug discovery process. A promising approach is to use biological networks to prioritize targets based on their relative positions to one another, a property that affects their ability to maintain network integrity and propagate information-flow. Here, we introduce influence networks and demonstrate how they can be used to generate influence scores as a network-based metric to rank genes as potential drug targets. Results We use this approach to prioritize genes as drug target candidates in a set of ER + breast tumor samples collected during the course of neoadjuvant treatment with the aromatase inhibitor letrozole. We show that influential genes, those with high influence scores, tend to be essential and include a higher proportion of essential genes than those prioritized based on their position (i.e. hubs or bottlenecks) within the same network. Additionally, we show that influential genes represent novel biologically relevant drug targets for the treatment of ER + breast cancers. Moreover, we demonstrate that gene influence differs between untreated tumors and residual tumors that have adapted to drug treatment. In this way, influence scores capture the context-dependent functions of genes and present the opportunity to design combination treatment strategies that take advantage of the tumor adaptation process. Conclusions Influence networks efficiently find essential genes as promising drug targets and combinations of targets to inform the development of molecularly targeted drugs and their use. PMID:24495353

  19. Network-based association of hypoxia-responsive genes with cardiovascular diseases

    International Nuclear Information System (INIS)

    Wang, Rui-Sheng; Oldham, William M; Loscalzo, Joseph

    2014-01-01

    Molecular oxygen is indispensable for cellular viability and function. Hypoxia is a stress condition in which oxygen demand exceeds supply. Low cellular oxygen content induces a number of molecular changes to activate regulatory pathways responsible for increasing the oxygen supply and optimizing cellular metabolism under limited oxygen conditions. Hypoxia plays critical roles in the pathobiology of many diseases, such as cancer, heart failure, myocardial ischemia, stroke, and chronic lung diseases. Although the complicated associations between hypoxia and cardiovascular (and cerebrovascular) diseases (CVD) have been recognized for some time, there are few studies that investigate their biological link from a systems biology perspective. In this study, we integrate hypoxia genes, CVD genes, and the human protein interactome in order to explore the relationship between hypoxia and cardiovascular diseases at a systems level. We show that hypoxia genes are much closer to CVD genes in the human protein interactome than that expected by chance. We also find that hypoxia genes play significant bridging roles in connecting different cardiovascular diseases. We construct a hypoxia-CVD bipartite network and find several interesting hypoxia-CVD modules with significant gene ontology similarity. Finally, we show that hypoxia genes tend to have more CVD interactors in the human interactome than in random networks of matching topology. Based on these observations, we can predict novel genes that may be associated with CVD. This network-based association study gives us a broad view of the relationships between hypoxia and cardiovascular diseases and provides new insights into the role of hypoxia in cardiovascular biology. (paper)

  20. Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer.

    Directory of Open Access Journals (Sweden)

    Matthew Ruffalo

    2015-12-01

    Full Text Available Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these "silent players". For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation.

  1. MSD-MAP: A Network-Based Systems Biology Platform for Predicting Disease-Metabolite Links.

    Science.gov (United States)

    Wathieu, Henri; Issa, Naiem T; Mohandoss, Manisha; Byers, Stephen W; Dakshanamurthy, Sivanesan

    2017-01-01

    Cancer-associated metabolites result from cell-wide mechanisms of dysregulation. The field of metabolomics has sought to identify these aberrant metabolites as disease biomarkers, clues to understanding disease mechanisms, or even as therapeutic agents. This study was undertaken to reliably predict metabolites associated with colorectal, esophageal, and prostate cancers. Metabolite and disease biological action networks were compared in a computational platform called MSD-MAP (Multi Scale Disease-Metabolite Association Platform). Using differential gene expression analysis with patient-based RNAseq data from The Cancer Genome Atlas, genes up- or down-regulated in cancer compared to normal tissue were identified. Relational databases were used to map biological entities including pathways, functions, and interacting proteins, to those differential disease genes. Similar relational maps were built for metabolites, stemming from known and in silico predicted metabolite-protein associations. The hypergeometric test was used to find statistically significant relationships between disease and metabolite biological signatures at each tier, and metabolites were assessed for multi-scale association with each cancer. Metabolite networks were also directly associated with various other diseases using a disease functional perturbation database. Our platform recapitulated metabolite-disease links that have been empirically verified in the scientific literature, with network-based mapping of jointly-associated biological activity also matching known disease mechanisms. This was true for colorectal, esophageal, and prostate cancers, using metabolite action networks stemming from both predicted and known functional protein associations. By employing systems biology concepts, MSD-MAP reliably predicted known cancermetabolite links, and may serve as a predictive tool to streamline conventional metabolomic profiling methodologies. Copyright© Bentham Science Publishers; For any

  2. A network-based gene expression signature informs prognosis and treatment for colorectal cancer patients.

    Directory of Open Access Journals (Sweden)

    Mingguang Shi

    Full Text Available Several studies have reported gene expression signatures that predict recurrence risk in stage II and III colorectal cancer (CRC patients with minimal gene membership overlap and undefined biological relevance. The goal of this study was to investigate biological themes underlying these signatures, to infer genes of potential mechanistic importance to the CRC recurrence phenotype and to test whether accurate prognostic models can be developed using mechanistically important genes.We investigated eight published CRC gene expression signatures and found no functional convergence in Gene Ontology enrichment analysis. Using a random walk-based approach, we integrated these signatures and publicly available somatic mutation data on a protein-protein interaction network and inferred 487 genes that were plausible candidate molecular underpinnings for the CRC recurrence phenotype. We named the list of 487 genes a NEM signature because it integrated information from Network, Expression, and Mutation. The signature showed significant enrichment in four biological processes closely related to cancer pathophysiology and provided good coverage of known oncogenes, tumor suppressors, and CRC-related signaling pathways. A NEM signature-based Survival Support Vector Machine prognostic model was trained using a microarray gene expression dataset and tested on an independent dataset. The model-based scores showed a 75.7% concordance with the real survival data and separated patients into two groups with significantly different relapse-free survival (p = 0.002. Similar results were obtained with reversed training and testing datasets (p = 0.007. Furthermore, adjuvant chemotherapy was significantly associated with prolonged survival of the high-risk patients (p = 0.006, but not beneficial to the low-risk patients (p = 0.491.The NEM signature not only reflects CRC biology but also informs patient prognosis and treatment response. Thus, the network-based

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

    Directory of Open Access Journals (Sweden)

    Qian Li

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

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

    Science.gov (United States)

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

    2011-03-22

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

  5. About Genetic Counselors

    Science.gov (United States)

    ... clinical care in many areas of medicine. Assisted Reproductive Technology/Infertility Genetics Cancer Genetics Cardiovascular Genetics Cystic Fibrosis Genetics Fetal Intervention and Therapy Genetics Hematology Genetics Metabolic Genetics ...

  6. The evolution of network-based business models illustrated through the case study of an entrepreneurship project

    DEFF Research Database (Denmark)

    Lund, Morten; Nielsen, Christian

    2014-01-01

    can gain insight into barriers and enablers relating to different types of loose organisations and how to best manage such relationships and interactions Originality/value: This study adds value to the existing literature by reflecting the dynamics created in the interactions between a business model......-based business model that generates additional value for the core business model and for both the partners and the customers. Research limitations/implications: The results should be taken with caution as they are based on the case study of a single network-based business model. Practical implications: Managers......Purpose: Existing frameworks for understanding and analyzing the value configuration and structuring of partnerships in relation such network-based business models are found to be inferior. The purpose of this paper is therefore to broaden our understanding of how business models may change over...

  7. How network-based incubation helps start-up performance : a systematic review against the background of management theories

    OpenAIRE

    Eveleens, Chris P.; van Rijnsoever, Frank J.; Niesten, Eva M M I

    2017-01-01

    The literature on how network-based incubation influences the performance of technology-based start-ups has recently grown considerably and provided valuable insights. However, at the same time this literature has become quite fragmented, inconsistently conceptualised, and theoretically underdeveloped. Therefore, this article uses three management theories to structure the literature, improve the theoretical underpinning and develop an agenda for further research. The management theories are ...

  8. The Evolution of Network-based Business Models Illustrated Through the Case Study of an Entrepreneurship Project

    Directory of Open Access Journals (Sweden)

    Morten Lund

    2014-08-01

    Full Text Available Purpose: Existing frameworks for understanding and analyzing the value configuration and structuring of partnerships in relation such network-based business models are found to be inferior. The purpose of this paper is therefore to broaden our understanding of how business models may change over time and how the role of strategic partners may differ over time too. Design/methodology/approach: A longitudinal case study spanning over years and mobilising multiple qualitative methods such as interviews, observation and participative observation forms the basis of the data collection. Findings: This paper illustrates how a network-based business model arises and evolves and how the forces of a network structure impact the development of its partner relationships. The contribution of this article is to understanding how partners positioned around a business model can be organized into a network-based business model that generates additional value for the core business model and for both the partners and the customers. Research limitations/implications: The results should be taken with caution as they are based on the case study of a single network-based business model. Practical implications: Managers can gain insight into barriers and enablers relating to different types of loose organisations and how to best manage such relationships and interactions Originality/value: This study adds value to the existing literature by reflecting the dynamics created in the interactions between a business model’s strategic partners and how a how a business model can evolve in a series of distinct phases

  9. Network-based analysis of genotype-phenotype correlations between different inheritance modes.

    Science.gov (United States)

    Hao, Dapeng; Li, Chuanxing; Zhang, Shaojun; Lu, Jianping; Jiang, Yongshuai; Wang, Shiyuan; Zhou, Meng

    2014-11-15

    Recent studies on human disease have revealed that aberrant interaction between proteins probably underlies a substantial number of human genetic diseases. This suggests a need to investigate disease inheritance mode using interaction, and based on which to refresh our conceptual understanding of a series of properties regarding inheritance mode of human disease. We observed a strong correlation between the number of protein interactions and the likelihood of a gene causing any dominant diseases or multiple dominant diseases, whereas no correlation was observed between protein interaction and the likelihood of a gene causing recessive diseases. We found that dominant diseases are more likely to be associated with disruption of important interactions. These suggest inheritance mode should be understood using protein interaction. We therefore reviewed the previous studies and refined an interaction model of inheritance mode, and then confirmed that this model is largely reasonable using new evidences. With these findings, we found that the inheritance mode of human genetic diseases can be predicted using protein interaction. By integrating the systems biology perspectives with the classical disease genetics paradigm, our study provides some new insights into genotype-phenotype correlations. haodapeng@ems.hrbmu.edu.cn or biofomeng@hotmail.com Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  10. Network-Based Detection and Classification of Seismovolcanic Tremors: Example From the Klyuchevskoy Volcanic Group in Kamchatka

    Science.gov (United States)

    Soubestre, Jean; Shapiro, Nikolai M.; Seydoux, Léonard; de Rosny, Julien; Droznin, Dmitry V.; Droznina, Svetlana Ya.; Senyukov, Sergey L.; Gordeev, Evgeniy I.

    2018-01-01

    We develop a network-based method for detecting and classifying seismovolcanic tremors. The proposed approach exploits the coherence of tremor signals across the network that is estimated from the array covariance matrix. The method is applied to four and a half years of continuous seismic data recorded by 19 permanent seismic stations in the vicinity of the Klyuchevskoy volcanic group in Kamchatka (Russia), where five volcanoes were erupting during the considered time period. We compute and analyze daily covariance matrices together with their eigenvalues and eigenvectors. As a first step, most coherent signals corresponding to dominating tremor sources are detected based on the width of the covariance matrix eigenvalues distribution. Thus, volcanic tremors of the two volcanoes known as most active during the considered period, Klyuchevskoy and Tolbachik, are efficiently detected. As a next step, we consider the daily array covariance matrix's first eigenvector. Our main hypothesis is that these eigenvectors represent the principal components of the daily seismic wavefield and, for days with tremor activity, characterize dominant tremor sources. Those daily first eigenvectors, which can be used as network-based fingerprints of tremor sources, are then grouped into clusters using correlation coefficient as a measure of the vector similarity. As a result, we identify seven clusters associated with different periods of activity of four volcanoes: Tolbachik, Klyuchevskoy, Shiveluch, and Kizimen. The developed method does not require a priori knowledge and is fully automatic; and the database of the network-based tremor fingerprints can be continuously enriched with newly available data.

  11. Comparison of home and away-from-home physical activity using accelerometers and cellular network-based tracking devices.

    Science.gov (United States)

    Ramulu, Pradeep Y; Chan, Emilie S; Loyd, Tara L; Ferrucci, Luigi; Friedman, David S

    2012-08-01

    Measuring physical at home and away from home is essential for assessing health and well-being, and could help design interventions to increase physical activity. Here, we describe how physical activity at home and away from home can be quantified by combining information from cellular network-based tracking devices and accelerometers. Thirty-five working adults wore a cellular network-based tracking device and an accelerometer for 6 consecutive days and logged their travel away from home. Performance of the tracking device was determined using the travel log for reference. Tracking device and accelerometer data were merged to compare physical activity at home and away from home. The tracking device detected 98.6% of all away-from-home excursions, accurately measured time away from home and demonstrated few prolonged signal drop-out periods. Most physical activity took place away from home on weekdays, but not on weekends. Subjects were more physically active per unit of time while away from home, particularly on weekends. Cellular network-based tracking devices represent an alternative to global positioning systems for tracking location, and provide information easily integrated with accelerometers to determine where physical activity takes place. Promoting greater time spent away from home may increase physical activity.

  12. A web-based system for neural network based classification in temporomandibular joint osteoarthritis.

    Science.gov (United States)

    de Dumast, Priscille; Mirabel, Clément; Cevidanes, Lucia; Ruellas, Antonio; Yatabe, Marilia; Ioshida, Marcos; Ribera, Nina Tubau; Michoud, Loic; Gomes, Liliane; Huang, Chao; Zhu, Hongtu; Muniz, Luciana; Shoukri, Brandon; Paniagua, Beatriz; Styner, Martin; Pieper, Steve; Budin, Francois; Vimort, Jean-Baptiste; Pascal, Laura; Prieto, Juan Carlos

    2018-07-01

    study demonstrate a comprehensive phenotypic characterization of TMJ health and disease at clinical, imaging and biological levels, using novel flexible and versatile open-source tools for a web-based system that provides advanced shape statistical analysis and a neural network based classification of temporomandibular joint osteoarthritis. Published by Elsevier Ltd.

  13. Designing Dietary Recommendations Using System Level Interactomics Analysis and Network-Based Inference

    Directory of Open Access Journals (Sweden)

    Tingting Zheng

    2017-09-01

    diet in disease development. Due to the complexity of analyzing the food composition and eating patterns of individuals our in silico analysis, using large-scale gene expression datasets and network-based topological features, may serve as a proof-of-concept in nutritional systems biology for identifying diet-disease relationships and subsequently designing dietary recommendations.

  14. Optical Calibration Process Developed for Neural-Network-Based Optical Nondestructive Evaluation Method

    Science.gov (United States)

    Decker, Arthur J.

    2004-01-01

    A completely optical calibration process has been developed at Glenn for calibrating a neural-network-based nondestructive evaluation (NDE) method. The NDE method itself detects very small changes in the characteristic patterns or vibration mode shapes of vibrating structures as discussed in many references. The mode shapes or characteristic patterns are recorded using television or electronic holography and change when a structure experiences, for example, cracking, debonds, or variations in fastener properties. An artificial neural network can be trained to be very sensitive to changes in the mode shapes, but quantifying or calibrating that sensitivity in a consistent, meaningful, and deliverable manner has been challenging. The standard calibration approach has been difficult to implement, where the response to damage of the trained neural network is compared with the responses of vibration-measurement sensors. In particular, the vibration-measurement sensors are intrusive, insufficiently sensitive, and not numerous enough. In response to these difficulties, a completely optical alternative to the standard calibration approach was proposed and tested successfully. Specifically, the vibration mode to be monitored for structural damage was intentionally contaminated with known amounts of another mode, and the response of the trained neural network was measured as a function of the peak-to-peak amplitude of the contaminating mode. The neural network calibration technique essentially uses the vibration mode shapes of the undamaged structure as standards against which the changed mode shapes are compared. The published response of the network can be made nearly independent of the contaminating mode, if enough vibration modes are used to train the net. The sensitivity of the neural network can be adjusted for the environment in which the test is to be conducted. The response of a neural network trained with measured vibration patterns for use on a vibration isolation

  15. Genetic modification and genetic determinism

    Directory of Open Access Journals (Sweden)

    Vorhaus Daniel B

    2006-06-01

    Full Text Available Abstract In this article we examine four objections to the genetic modification of human beings: the freedom argument, the giftedness argument, the authenticity argument, and the uniqueness argument. We then demonstrate that each of these arguments against genetic modification assumes a strong version of genetic determinism. Since these strong deterministic assumptions are false, the arguments against genetic modification, which assume and depend upon these assumptions, are therefore unsound. Serious discussion of the morality of genetic modification, and the development of sound science policy, should be driven by arguments that address the actual consequences of genetic modification for individuals and society, not by ones propped up by false or misleading biological assumptions.

  16. Systems genetics analysis of pharmacogenomics variation during antidepressant treatment

    DEFF Research Database (Denmark)

    Madsen, Majbritt Busk; Kogelman, L J A; Kadarmideen, H N

    2016-01-01

    Selective serotonin reuptake inhibitors (SSRIs) are the most widely used antidepressants, but the efficacy of the treatment varies significantly among individuals. It is believed that complex genetic mechanisms play a part in this variation. We have used a network based approach to unravel the in...... genes involved in calcium homeostasis. In conclusion, we suggest a difference in genetic interaction networks between initial and subsequent SSRI response.The Pharmacogenomics Journal advance online publication, 18 October 2016; doi:10.1038/tpj.2016.68....

  17. Genetic Engineering

    Science.gov (United States)

    Phillips, John

    1973-01-01

    Presents a review of genetic engineering, in which the genotypes of plants and animals (including human genotypes) may be manipulated for the benefit of the human species. Discusses associated problems and solutions and provides an extensive bibliography of literature relating to genetic engineering. (JR)

  18. Genetic Romanticism

    DEFF Research Database (Denmark)

    Tupasela, Aaro

    2016-01-01

    inheritance as a way to unify populations within politically and geographically bounded areas. Thus, new genetics have contributed to the development of genetic romanticisms, whereby populations (human, plant, and animal) can be delineated and mobilized through scientific and medical practices to represent...

  19. Resource Allocation Optimization Model of Collaborative Logistics Network Based on Bilevel Programming

    Directory of Open Access Journals (Sweden)

    Xiao-feng Xu

    2017-01-01

    Full Text Available Collaborative logistics network resource allocation can effectively meet the needs of customers. It can realize the overall benefit maximization of the logistics network and ensure that collaborative logistics network runs orderly at the time of creating value. Therefore, this article is based on the relationship of collaborative logistics network supplier, the transit warehouse, and sellers, and we consider the uncertainty of time to establish a bilevel programming model with random constraints and propose a genetic simulated annealing hybrid intelligent algorithm to solve it. Numerical example shows that the method has stronger robustness and convergence; it can achieve collaborative logistics network resource allocation rationalization and optimization.

  20. Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm.

    Science.gov (United States)

    Mandal, Sudip; Saha, Goutam; Pal, Rajat Kumar

    2017-08-01

    Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.

  1. ChloroP, a neural network-based method for predicting chloroplast transitpeptides and their cleavage sites

    DEFF Research Database (Denmark)

    Emanuelsson, O.; Nielsen, Henrik; von Heijne, Gunnar

    1999-01-01

    the cleavage sites given in SWISS-PROT. An analysis of 715 Arabidopsis thaliana sequences from SWISS-PROT suggests that the ChloroP method should be useful for the identification of putative transit peptides in genome-wide sequence data. The ChloroP predictor is available as a web-server at http......We present a neural network based method (ChloroP) for identifying chloroplast transit peptides and their cleavage sites. Using cross-validation, 88% of the sequences in our homology reduced training set were correctly classified as transit peptides or nontransit peptides. This performance level...

  2. Controlling Chaos and Voltage Collapse using Layered Recurrent Network-based PID-SVC in Power Systems

    Directory of Open Access Journals (Sweden)

    I Made Ginarsa

    2013-11-01

    Full Text Available Chaos and voltage collapse occurred in critical power systems due to disturbing of energy. PID-SVC layered reccurrent neural network-based (LRN-based PID-SVC was proposed to solve this problem. A PID was used to control chaos and voltage collapse. Then, an SVC LRN-based to maintan the load voltage. By using the proposed controller, chaos and voltage collapse were able to suppress and maintain the load voltage around the setting value. Furthemore, the proposed controller gives better response than PI-SVC controller.

  3. Evolutionary genetics

    National Research Council Canada - National Science Library

    Maynard Smith, John

    1989-01-01

    .... It differs from other textbooks of population genetics in applying the basic theory to topics, such as social behaviour, molecular evolution, reiterated DNA, and sex, which are the main subjects...

  4. Genetic Discrimination

    Science.gov (United States)

    ... Care Genomic Medicine Working Group New Horizons and Research Patient Management Policy and Ethics Issues Quick Links for Patient Care Education All About the Human Genome Project Fact Sheets Genetic Education Resources for ...

  5. Arthropod Genetics.

    Science.gov (United States)

    Zumwalde, Sharon

    2000-01-01

    Introduces an activity on arthropod genetics that involves phenotype and genotype identification of the creature and the construction process. Includes a list of required materials and directions to build a model arthropod. (YDS)

  6. Desktop Genetics

    OpenAIRE

    Hough, Soren H; Ajetunmobi, Ayokunmi; Brody, Leigh; Humphryes-Kirilov, Neil; Perello, Edward

    2016-01-01

    Desktop Genetics is a bioinformatics company building a gene-editing platform for personalized medicine. The company works with scientists around the world to design and execute state-of-the-art clustered regularly interspaced short palindromic repeats (CRISPR) experiments. Desktop Genetics feeds the lessons learned about experimental intent, single-guide RNA design and data from international genomics projects into a novel CRISPR artificial intelligence system. We believe that machine learni...

  7. QuakeUp: An advanced tool for a network-based Earthquake Early Warning system

    Science.gov (United States)

    Zollo, Aldo; Colombelli, Simona; Caruso, Alessandro; Elia, Luca; Brondi, Piero; Emolo, Antonio; Festa, Gaetano; Martino, Claudio; Picozzi, Matteo

    2017-04-01

    The currently developed and operational Earthquake Early warning, regional systems ground on the assumption of a point-like earthquake source model and 1-D ground motion prediction equations to estimate the earthquake impact. Here we propose a new network-based method which allows for issuing an alert based upon the real-time mapping of the Potential Damage Zone (PDZ), e.g. the epicentral area where the peak ground velocity is expected to exceed the damaging or strong shaking levels with no assumption about the earthquake rupture extent and spatial variability of ground motion. The platform includes the most advanced techniques for a refined estimation of the main source parameters (earthquake location and magnitude) and for an accurate prediction of the expected ground shaking level. The new software platform (QuakeUp) is under development at the Seismological Laboratory (RISSC-Lab) of the Department of Physics at the University of Naples Federico II, in collaboration with the academic spin-off company RISS s.r.l., recently gemmated by the research group. The system processes the 3-component, real-time ground acceleration and velocity data streams at each station. The signal quality is preliminary assessed by checking the signal-to-noise ratio both in acceleration, velocity and displacement and through dedicated filtering algorithms. For stations providing high quality data, the characteristic P-wave period (τ_c) and the P-wave displacement, velocity and acceleration amplitudes (P_d, Pv and P_a) are jointly measured on a progressively expanded P-wave time window. The evolutionary measurements of the early P-wave amplitude and characteristic period at stations around the source allow to predict the geometry and extent of PDZ, but also of the lower shaking intensity regions at larger epicentral distances. This is done by correlating the measured P-wave amplitude with the Peak Ground Velocity (PGV) and Instrumental Intensity (I_MM) and by mapping the measured and

  8. Systems genetics of obesity in an F2 pig model by genome-wide association, genetic network and pathway analyses

    DEFF Research Database (Denmark)

    Kogelman, Lisette; Pant, Sameer Dinkar; Fredholm, Merete

    2014-01-01

    .g. metabolic processes. WISH networks based on genotypic correlations allowed further identification of various gene ontology terms and pathways related to obesity and related traits, which were not identified by the GWA study. In conclusion, this is the first study to develop a (genetic) obesity index...... investigations focusing on single genetic variants have achieved limited success, and the importance of including genetic interactions is becoming evident. Here, the aim was to perform an integrative genomic analysis in an F2 pig resource population that was constructed with an aim to maximize genetic variation...... of obesity-related phenotypes and genotyped using the 60K SNP chip. Firstly, Genome Wide Association (GWA) analysis was performed on the Obesity Index to locate candidate genomic regions that were further validated using combined Linkage Disequilibrium Linkage Analysis and investigated by evaluation...

  9. J. Genet. classic 101

    Indian Academy of Sciences (India)

    Journal of Genetics, Vol. 85, No. 2, August 2006. 101. Page 2. J. Genet. classic. 102. Journal of Genetics, Vol. 85, No. 2, August 2006. Page 3. J. Genet. classic. Journal of Genetics, Vol. 85, No. 2, August 2006. 103. Page 4. J. Genet. classic. 104. Journal of Genetics, Vol. 85, No. 2, August 2006. Page 5. J. Genet. classic.

  10. J. Genet. classic 37

    Indian Academy of Sciences (India)

    Unknown

    Journal of Genetics, Vol. 84, No. 1, April 2005. 37. Page 2. J. Genet. classic. Journal of Genetics, Vol. 84, No. 1, April 2005. 38. Page 3. J. Genet. classic. Journal of Genetics, Vol. 84, No. 1, April 2005. 39. Page 4. J. Genet. classic. Journal of Genetics, Vol. 84, No. 1, April 2005. 40. Page 5. J. Genet. classic. Journal of ...

  11. A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions

    Directory of Open Access Journals (Sweden)

    Mengqu Ge

    2016-02-01

    Full Text Available As one large class of non-coding RNAs (ncRNAs, long ncRNAs (lncRNAs have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA–protein bipartite network inference (LPBNI. LPBNI aims to identify potential lncRNA–interacting proteins, by making full use of the known lncRNA–protein interactions. Leave-one-out cross validation (LOOCV test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR and protein-based collaborative filtering (ProCF. Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA–interacting proteins.

  12. Synchronization of Hierarchical Time-Varying Neural Networks Based on Asynchronous and Intermittent Sampled-Data Control.

    Science.gov (United States)

    Xiong, Wenjun; Patel, Ragini; Cao, Jinde; Zheng, Wei Xing

    In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.

  13. LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.

    Science.gov (United States)

    Dong, Xinran; Hao, Yun; Wang, Xiao; Tian, Weidong

    2016-01-11

    Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.

  14. An overview of structurally complex network-based modeling of public opinion in the “We the Media” era

    Science.gov (United States)

    Wang, Guanghui; Wang, Yufei; Liu, Yijun; Chi, Yuxue

    2018-05-01

    As the transmission of public opinion on the Internet in the “We the Media” era tends to be supraterritorial, concealed and complex, the traditional “point-to-surface” transmission of information has been transformed into “point-to-point” reciprocal transmission. A foundation for studies of the evolution of public opinion and its transmission on the Internet in the “We the Media” era can be laid by converting the massive amounts of fragmented information on public opinion that exists on “We the Media” platforms into structurally complex networks of information. This paper describes studies of structurally complex network-based modeling of public opinion on the Internet in the “We the Media” era from the perspective of the development and evolution of complex networks. The progress that has been made in research projects relevant to the structural modeling of public opinion on the Internet is comprehensively summarized. The review considers aspects such as regular grid-based modeling of the rules that describe the propagation of public opinion on the Internet in the “We the Media” era, social network modeling, dynamic network modeling, and supernetwork modeling. Moreover, an outlook for future studies that address complex network-based modeling of public opinion on the Internet is put forward as a summary from the perspective of modeling conducted using the techniques mentioned above.

  15. A Neural Network Based Intelligent Predictive Sensor for Cloudiness, Solar Radiation and Air Temperature

    Science.gov (United States)

    Ferreira, Pedro M.; Gomes, João M.; Martins, Igor A. C.; Ruano, António E.

    2012-01-01

    Accurate measurements of global solar radiation and atmospheric temperature, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature. PMID:23202230

  16. Determine the optimal carrier selection for a logistics network based on multi-commodity reliability criterion

    Science.gov (United States)

    Lin, Yi-Kuei; Yeh, Cheng-Ta

    2013-05-01

    From the perspective of supply chain management, the selected carrier plays an important role in freight delivery. This article proposes a new criterion of multi-commodity reliability and optimises the carrier selection based on such a criterion for logistics networks with routes and nodes, over which multiple commodities are delivered. Carrier selection concerns the selection of exactly one carrier to deliver freight on each route. The capacity of each carrier has several available values associated with a probability distribution, since some of a carrier's capacity may be reserved for various orders. Therefore, the logistics network, given any carrier selection, is a multi-commodity multi-state logistics network. Multi-commodity reliability is defined as a probability that the logistics network can satisfy a customer's demand for various commodities, and is a performance indicator for freight delivery. To solve this problem, this study proposes an optimisation algorithm that integrates genetic algorithm, minimal paths and Recursive Sum of Disjoint Products. A practical example in which multi-sized LCD monitors are delivered from China to Germany is considered to illustrate the solution procedure.

  17. Optimal Design of the Feeder-Bus Network Based on the Transfer System

    Directory of Open Access Journals (Sweden)

    Lianbo Deng

    2013-01-01

    Full Text Available This paper studied the classic feeder-bus network design problem (FBNDP, which can be described as follows: for the passenger travel demand between rail stations and bus stops on a given urban transit network, it designs the optimal feeder bus routes and frequencies so as to minimize the passengers’ travel expense and the operator’s cost. We extended the demand pattern of M-to-1 in most existing researches to M-to-M. We comprehensively considered the passenger travel cost, which includes the waiting and riding cost on the bus, riding cost on rail, and transfer cost between these two transportation modes, and presented a new genetic algorithm that determines the optimal feeder-bus operating frequencies under strict constraint conditions. The numerical examples under different demand patterns have been experienced and analysed, which showed the robustness and efficiency of the presented algorithm. We also found that the distribution pattern of the travel demand has a significant influence on the feeder-bus network construction.

  18. Switchgrass genomic diversity, ploidy, and evolution: novel insights from a network-based SNP discovery protocol.

    Directory of Open Access Journals (Sweden)

    Fei Lu

    Full Text Available Switchgrass (Panicum virgatum L. is a perennial grass that has been designated as an herbaceous model biofuel crop for the United States of America. To facilitate accelerated breeding programs of switchgrass, we developed both an association panel and linkage populations for genome-wide association study (GWAS and genomic selection (GS. All of the 840 individuals were then genotyped using genotyping by sequencing (GBS, generating 350 GB of sequence in total. As a highly heterozygous polyploid (tetraploid and octoploid species lacking a reference genome, switchgrass is highly intractable with earlier methodologies of single nucleotide polymorphism (SNP discovery. To access the genetic diversity of species like switchgrass, we developed a SNP discovery pipeline based on a network approach called the Universal Network-Enabled Analysis Kit (UNEAK. Complexities that hinder single nucleotide polymorphism discovery, such as repeats, paralogs, and sequencing errors, are easily resolved with UNEAK. Here, 1.2 million putative SNPs were discovered in a diverse collection of primarily upland, northern-adapted switchgrass populations. Further analysis of this data set revealed the fundamentally diploid nature of tetraploid switchgrass. Taking advantage of the high conservation of genome structure between switchgrass and foxtail millet (Setaria italica (L. P. Beauv., two parent-specific, synteny-based, ultra high-density linkage maps containing a total of 88,217 SNPs were constructed. Also, our results showed clear patterns of isolation-by-distance and isolation-by-ploidy in natural populations of switchgrass. Phylogenetic analysis supported a general south-to-north migration path of switchgrass. In addition, this analysis suggested that upland tetraploid arose from upland octoploid. All together, this study provides unparalleled insights into the diversity, genomic complexity, population structure, phylogeny, phylogeography, ploidy, and evolutionary dynamics

  19. Pollution source localization in an urban water supply network based on dynamic water demand.

    Science.gov (United States)

    Yan, Xuesong; Zhu, Zhixin; Li, Tian

    2017-10-27

    Urban water supply networks are susceptible to intentional, accidental chemical, and biological pollution, which pose a threat to the health of consumers. In recent years, drinking-water pollution incidents have occurred frequently, seriously endangering social stability and security. The real-time monitoring for water quality can be effectively implemented by placing sensors in the water supply network. However, locating the source of pollution through the data detection obtained by water quality sensors is a challenging problem. The difficulty lies in the limited number of sensors, large number of water supply network nodes, and dynamic user demand for water, which leads the pollution source localization problem to an uncertainty, large-scale, and dynamic optimization problem. In this paper, we mainly study the dynamics of the pollution source localization problem. Previous studies of pollution source localization assume that hydraulic inputs (e.g., water demand of consumers) are known. However, because of the inherent variability of urban water demand, the problem is essentially a fluctuating dynamic problem of consumer's water demand. In this paper, the water demand is considered to be stochastic in nature and can be described using Gaussian model or autoregressive model. On this basis, an optimization algorithm is proposed based on these two dynamic water demand change models to locate the pollution source. The objective of the proposed algorithm is to find the locations and concentrations of pollution sources that meet the minimum between the analogue and detection values of the sensor. Simulation experiments were conducted using two different sizes of urban water supply network data, and the experimental results were compared with those of the standard genetic algorithm.

  20. SAMNet: a network-based approach to integrate multi-dimensional high throughput datasets.

    Science.gov (United States)

    Gosline, Sara J C; Spencer, Sarah J; Ursu, Oana; Fraenkel, Ernest

    2012-11-01

    The rapid development of high throughput biotechnologies has led to an onslaught of data describing genetic perturbations and changes in mRNA and protein levels in the cell. Because each assay provides a one-dimensional snapshot of active signaling pathways, it has become desirable to perform multiple assays (e.g. mRNA expression and phospho-proteomics) to measure a single condition. However, as experiments expand to accommodate various cellular conditions, proper analysis and interpretation of these data have become more challenging. Here we introduce a novel approach called SAMNet, for Simultaneous Analysis of Multiple Networks, that is able to interpret diverse assays over multiple perturbations. The algorithm uses a constrained optimization approach to integrate mRNA expression data with upstream genes, selecting edges in the protein-protein interaction network that best explain the changes across all perturbations. The result is a putative set of protein interactions that succinctly summarizes the results from all experiments, highlighting the network elements unique to each perturbation. We evaluated SAMNet in both yeast and human datasets. The yeast dataset measured the cellular response to seven different transition metals, and the human dataset measured cellular changes in four different lung cancer models of Epithelial-Mesenchymal Transition (EMT), a crucial process in tumor metastasis. SAMNet was able to identify canonical yeast metal-processing genes unique to each commodity in the yeast dataset, as well as human genes such as β-catenin and TCF7L2/TCF4 that are required for EMT signaling but escaped detection in the mRNA and phospho-proteomic data. Moreover, SAMNet also highlighted drugs likely to modulate EMT, identifying a series of less canonical genes known to be affected by the BCR-ABL inhibitor imatinib (Gleevec), suggesting a possible influence of this drug on EMT.

  1. Genetic GIScience

    DEFF Research Database (Denmark)

    Jacquez, Geoffrey; Sabel, Clive E; Shi, Chen

    2015-01-01

    The exposome, defined as the totality of an individual's exposures over the life course, is a seminal concept in the environmental health sciences. Although inherently geographic, the exposome as yet is unfamiliar to many geographers. This article proposes a place-based synthesis, genetic...... geographic information science (genetic GIScience), that is founded on the exposome, genome+, and behavome. It provides an improved understanding of human health in relation to biology (the genome+), environmental exposures (the exposome), and their social, societal, and behavioral determinants (the behavome......). Genetic GIScience poses three key needs: first, a mathematical foundation for emergent theory; second, process-based models that bridge biological and geographic scales; third, biologically plausible estimates of space?time disease lags. Compartmental models are a possible solution; this article develops...

  2. Desktop Genetics.

    Science.gov (United States)

    Hough, Soren H; Ajetunmobi, Ayokunmi; Brody, Leigh; Humphryes-Kirilov, Neil; Perello, Edward

    2016-11-01

    Desktop Genetics is a bioinformatics company building a gene-editing platform for personalized medicine. The company works with scientists around the world to design and execute state-of-the-art clustered regularly interspaced short palindromic repeats (CRISPR) experiments. Desktop Genetics feeds the lessons learned about experimental intent, single-guide RNA design and data from international genomics projects into a novel CRISPR artificial intelligence system. We believe that machine learning techniques can transform this information into a cognitive therapeutic development tool that will revolutionize medicine.

  3. New Genetics

    Science.gov (United States)

    ... of the booklet. » more Chapter 1: How Genes Work Covers DNA, RNA, transcription, RNA splicing, translation, ribosomes, antibiotics, genetic diseases, gene chips. » more Chapter 2: RNA and DNA Revealed: New Roles, New Rules Covers microRNAs, RNAi, epigenetics, telomeres, mtDNA, recombinant DNA. » ...

  4. Genetic effects

    International Nuclear Information System (INIS)

    Kato, Hiroo

    1975-01-01

    In 1948-1953 a large scale field survey was conducted to investigate the possible genetic effects of A-bomb radiation on over 70,000 pregnancy terminations in the cities of Hiroshima and Nagasaki. The indices of possible genetic effect including sex ratio, birth weight, frequency of malformation, stillbirth, neonatal death, deaths within 9 months and anthropometric measurements at 9 months of age for these children were investigated in relation to their parent's exposure status to the A-bomb. There were no detectable genetic effects in this sample, except for a slight change in sex ratio which was in the direction to be expected if exposure had induced sex-linked lethal mutations. However, continued study of the sex ratio, based upon birth certificates in Hiroshima and Nagasaki for 1954-1962, did not confirm the earlier trend. Mortality in these children of A-bomb survivors is being followed using a cohort of 54,000 subjects. No clearly significant effect of parental exposure on survival of the children has been demonstrated up to 1972 (age 17 on the average). On the basis of the regression data, the minimal genetic doubling dose of this type of radiation for mutations resulting in death is estimated at 46 rem for the father and 125 rem for the mother. (auth.)

  5. Melanoma genetics

    DEFF Research Database (Denmark)

    Read, Jazlyn; Wadt, Karin A W; Hayward, Nicholas K

    2015-01-01

    Approximately 10% of melanoma cases report a relative affected with melanoma, and a positive family history is associated with an increased risk of developing melanoma. Although the majority of genetic alterations associated with melanoma development are somatic, the underlying presence of herita......Approximately 10% of melanoma cases report a relative affected with melanoma, and a positive family history is associated with an increased risk of developing melanoma. Although the majority of genetic alterations associated with melanoma development are somatic, the underlying presence...... in a combined total of approximately 50% of familial melanoma cases, the underlying genetic basis is unexplained for the remainder of high-density melanoma families. Aside from the possibility of extremely rare mutations in a few additional high penetrance genes yet to be discovered, this suggests a likely...... polygenic component to susceptibility, and a unique level of personal melanoma risk influenced by multiple low-risk alleles and genetic modifiers. In addition to conferring a risk of cutaneous melanoma, some 'melanoma' predisposition genes have been linked to other cancers, with cancer clustering observed...

  6. Genetic Recombination

    Science.gov (United States)

    Whitehouse, H. L. K.

    1973-01-01

    Discusses the mechanisms of genetic recombination with particular emphasis on the study of the fungus Sordaria brevicollis. The study of recombination is facilitated by the use of mutants of this fungus in which the color of the ascospores is affected. (JR)

  7. Genetic analysis

    NARCIS (Netherlands)

    Koornneef, M.; Alonso-Blanco, C.; Stam, P.

    2006-01-01

    The Mendelian analysis of genetic variation, available as induced mutants or as natural variation, requires a number of steps that are described in this chapter. These include the determination of the number of genes involved in the observed trait's variation, the determination of dominance

  8. Molecular genetics

    International Nuclear Information System (INIS)

    Parkinson, D.R.; Krontiris, T.G.

    1986-01-01

    In this chapter the authors review new findings concerning the molecular genetics of malignant melanoma in the context of other information obtained from clinical, epidemiologic, and cytogenetic studies in this malignancy. These new molecular approaches promise to provide a more complete understanding of the mechanisms involved in the development of melanoma, thereby suggesting new methods for its treatment and prevention

  9. J. Genet. classic 235

    Indian Academy of Sciences (India)

    Unknown

    Journal of Genetics, Vol. 83, No. 3, December 2004. 235. Page 2. J. Genet. classic. Journal of Genetics, Vol. 83, No. 3, December 2004. 236. Page 3. J. Genet. classic. Journal of Genetics, Vol. 83, No. 3, December 2004. 237. Page 4. J. Genet. classic. Journal of Genetics, Vol. 83, No. 3, December 2004. 238. Page 5 ...

  10. Genetic effects

    International Nuclear Information System (INIS)

    Bender, M.A.; Abrahamson, S.; Denniston, C.; Schull, W.J.

    1989-01-01

    In this chapter, we present a comprehensive analysis of the major classes of genetic diseases that would be increased as a result of an increased gonadal radiation exposure to a human population. The risk analysis takes on two major forms: the increase in genetic disease that would be observed in the immediate offspring of the exposed population, and the subsequent transmission of the newly induced mutations through future generations. The major classes of genetic disease will be induced at different frequencies, and will also impact differentially in terms of survivability and fertility on the affected individuals and their descendants. Some classes of disease will be expected to persist for only a few generations at most. Other types of genetic disease will persist through a longer period. The classes of genetic diseases studied are: dominant gene mutation, X-linked gene mutation, chromosome disorders and multifactorial disorders which involve the interaction of many mutant genes and environmental factors. For each of these classes we have derived the general equations of mutation induction for the male and female germ cells of critical importance in the mutation process. The frequency of induced mutations will be determined initially by the dose received, the type of radiation and, to some extent at high dose, by the manner in which the dose is received. We have used the modeling analyses to predict the outcomes for two nuclear power plant accident scenarios, the first in which the population receives a chronic dose of 0.1 Gy (10 rad) over a 50-year period, the second in which an equivalent population receives an acute dose of 2 Gy. In both cases the analyses are projected over a period of five generations

  11. Neural Network based Control of SG based Standalone Generating System with Energy Storage for Power Quality Enhancement

    Science.gov (United States)

    Nayar, Priya; Singh, Bhim; Mishra, Sukumar

    2017-08-01

    An artificial intelligence based control algorithm is used in solving power quality problems of a diesel engine driven synchronous generator with automatic voltage regulator and governor based standalone system. A voltage source converter integrated with a battery energy storage system is employed to mitigate the power quality problems. An adaptive neural network based signed regressor control algorithm is used for the estimation of the fundamental component of load currents for control of a standalone system with load leveling as an integral feature. The developed model of the system performs accurately under varying load conditions and provides good dynamic response to the step changes in loads. The real time performance is achieved using MATLAB along with simulink/simpower system toolboxes and results adhere to an IEEE-519 standard for power quality enhancement.

  12. Human Detection System by Fusing Depth Map-Based Method and Convolutional Neural Network-Based Method

    Directory of Open Access Journals (Sweden)

    Anh Vu Le

    2017-01-01

    Full Text Available In this paper, the depth images and the colour images provided by Kinect sensors are used to enhance the accuracy of human detection. The depth-based human detection method is fast but less accurate. On the other hand, the faster region convolutional neural network-based human detection method is accurate but requires a rather complex hardware configuration. To simultaneously leverage the advantages and relieve the drawbacks of each method, one master and one client system is proposed. The final goal is to make a novel Robot Operation System (ROS-based Perception Sensor Network (PSN system, which is more accurate and ready for the real time application. The experimental results demonstrate the outperforming of the proposed method compared with other conventional methods in the challenging scenarios.

  13. 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.

  14. RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

    Science.gov (United States)

    Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour

    2012-09-01

    In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

  15. Development and Flight Testing of a Neural Network Based Flight Control System on the NF-15B Aircraft

    Science.gov (United States)

    Bomben, Craig R.; Smolka, James W.; Bosworth, John T.; Silliams-Hayes, Peggy S.; Burken, John J.; Larson, Richard R.; Buschbacher, Mark J.; Maliska, Heather A.

    2006-01-01

    The Intelligent Flight Control System (IFCS) project at the NASA Dryden Flight Research Center, Edwards AFB, CA, has been investigating the use of neural network based adaptive control on a unique NF-15B test aircraft. The IFCS neural network is a software processor that stores measured aircraft response information to dynamically alter flight control gains. In 2006, the neural network was engaged and allowed to learn in real time to dynamically alter the aircraft handling qualities characteristics in the presence of actual aerodynamic failure conditions injected into the aircraft through the flight control system. The use of neural network and similar adaptive technologies in the design of highly fault and damage tolerant flight control systems shows promise in making future aircraft far more survivable than current technology allows. This paper will present the results of the IFCS flight test program conducted at the NASA Dryden Flight Research Center in 2006, with emphasis on challenges encountered and lessons learned.

  16. Modeling of a 5-cell direct methanol fuel cell using adaptive-network-based fuzzy inference systems

    Science.gov (United States)

    Wang, Rongrong; Qi, Liang; Xie, Xiaofeng; Ding, Qingqing; Li, Chunwen; Ma, ChenChi M.

    The methanol concentrations, temperature and current were considered as inputs, the cell voltage was taken as output, and the performance of a direct methanol fuel cell (DMFC) was modeled by adaptive-network-based fuzzy inference systems (ANFIS). The artificial neural network (ANN) and polynomial-based models were selected to be compared with the ANFIS in respect of quality and accuracy. Based on the ANFIS model obtained, the characteristics of the DMFC were studied. The results show that temperature and methanol concentration greatly affect the performance of the DMFC. Within a restricted current range, the methanol concentration does not greatly affect the stack voltage. In order to obtain higher fuel utilization efficiency, the methanol concentrations and temperatures should be adjusted according to the load on the system.

  17. Development of network based control and data acquisition systems for diagnostics using CCD detectors. Application to LHD experiments

    International Nuclear Information System (INIS)

    Kado, Shinichiro; Nakanishi, Hideya; Ida, Katsumi; Kojima, Mamoru

    2000-01-01

    The needs of CCD detectors as a plasma diagnostic tool have recently been increased. However, many CCD providers have developed their own controlling systems, and it is difficult to customized the usages in order to make them applicable to the network-based data acquisition clusters which consist of various sorts of diagnostics. This paper presents the development of systems in which CCD detectors are controlled and the data are acquired through networks. By making use of the Client/Server (C/S) model in the Windows NT operating system and block transfer method via shared memory relevant to the model, the dependence on the hardware is hidden by the server service, CCD list sequencer. The client program is designed for the LHD (Large Helical Device) discharge operation sequences and the data acquisition system. (author)

  18. Modeling of a 5-cell direct methanol fuel cell using adaptive-network-based fuzzy inference systems

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Rongrong; Li, Chunwen [Department of Automation, Tsinghua University, Beijing 100084 (China); Qi, Liang; Xie, Xiaofeng [Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084 (China); Ding, Qingqing [Department of Electrical Engineering, Tsinghua University, Beijing 100084 (China); Ma, ChenChi M. [National Tsing Hua University, Hsinchu 300 (China)

    2008-12-01

    The methanol concentrations, temperature and current were considered as inputs, the cell voltage was taken as output, and the performance of a direct methanol fuel cell (DMFC) was modeled by adaptive-network-based fuzzy inference systems (ANFIS). The artificial neural network (ANN) and polynomial-based models were selected to be compared with the ANFIS in respect of quality and accuracy. Based on the ANFIS model obtained, the characteristics of the DMFC were studied. The results show that temperature and methanol concentration greatly affect the performance of the DMFC. Within a restricted current range, the methanol concentration does not greatly affect the stack voltage. In order to obtain higher fuel utilization efficiency, the methanol concentrations and temperatures should be adjusted according to the load on the system. (author)

  19. Real-time neural network-based self-tuning control of a nonlinear electro-hydraulic servomotor

    Energy Technology Data Exchange (ETDEWEB)

    Canelon, J.I.; Ortega, A.G. [Univ. del Zulia, Maracaibo, Zulia (Venezuela, Bolivarian Republic of). School of Electrical Engineering; Shieh, L.S. [Houston Univ., Houston, TX (United States). Dept. of Electrical and Computer Engineering; Bastidas, J.I. [Univ. del Zulia, Maracaibo, Zulia (Venezuela, Bolivarian Republic of). School of Mechanical Engineering; Zhang, Y.; Akujuobi, C.M. [Prairie View A and M Univ., Prairie View, TX (United States). Center of Excellence for Communication Systems Technology Research and Dept. of Engineering Technology

    2010-08-13

    For high power applications, hydraulic actuators offer many advantages over electromagnetic actuators, including higher torque/mass ratios; smaller control gains; excellent torque capability; filtered high frequency noise; better heat transfer characteristics; smaller size; higher speed of response of the servomechanism; cheaper hardware; and higher reliability. Therefore, any application that requires a large force applied smoothly by an actuator is a candidate for hydraulic power. Examples of such applications include vehicle steering and braking systems; roll mills; drilling rigs; heavy duty crane and presses; and industrial robots and actuators for aircraft control surfaces such as ailerons and flaps. It is extremely important to create effective control strategies for hydraulic systems. This paper outlined the real-time implementation of a neural network-based approach, for self-tuning control of the angular position of a nonlinear electro-hydraulic servomotor. Using an online training algorithm, a neural network autoregressive moving-average model with exogenous input (ARMAX) model of the system was identified and continuously updated and an optimal linear ARMAX model was determined. The paper briefly depicted the neural network-based self-tuning control approach and a description of the experimental equipment (hardware and software) was presented including the implementation details. The experimental results were discussed and conclusions were summarized. It was found that the approach proved to be very effective in the control of this fast dynamics system, outperforming a fine tuned PI controller. Therefore, although the self-tuning approach was computationally demanding, it was feasible for real-time implementation. 22 refs., 6 figs.

  20. A Digital Architecture for a Network-Based Learning Health System: Integrating Chronic Care Management, Quality Improvement, and Research.

    Science.gov (United States)

    Marsolo, Keith; Margolis, Peter A; Forrest, Christopher B; Colletti, Richard B; Hutton, John J

    2015-01-01

    We collaborated with the ImproveCareNow Network to create a proof-of-concept architecture for a network-based Learning Health System. This collaboration involved transitioning an existing registry to one that is linked to the electronic health record (EHR), enabling a "data in once" strategy. We sought to automate a series of reports that support care improvement while also demonstrating the use of observational registry data for comparative effectiveness research. We worked with three leading EHR vendors to create EHR-based data collection forms. We automated many of ImproveCareNow's analytic reports and developed an application for storing protected health information and tracking patient consent. Finally, we deployed a cohort identification tool to support feasibility studies and hypothesis generation. There is ongoing uptake of the system. To date, 31 centers have adopted the EHR-based forms and 21 centers are uploading data to the registry. Usage of the automated reports remains high and investigators have used the cohort identification tools to respond to several clinical trial requests. The current process for creating EHR-based data collection forms requires groups to work individually with each vendor. A vendor-agnostic model would allow for more rapid uptake. We believe that interfacing network-based registries with the EHR would allow them to serve as a source of decision support. Additional standards are needed in order for this vision to be achieved, however. We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build. We have also highlighted opportunities where sponsors could help accelerate progress.

  1. Genetic effects

    International Nuclear Information System (INIS)

    Abrahamson, S.; Bender, M.; Denniston, C.; Schull, W.

    1985-01-01

    Modeling analyses are used to predict the outcomes for two nuclear power plant accident scenarios, the first in which the population received a chronic dose of 0.1 Gy (10 rad) over a 50 year period, the second in which an equivalent population receives acute dose of 2 Gy. In both cases the analyses are projected over a period of five generations. The risk analysis takes on two major forms: the increase in genetic disease that would be observed in the immediate offspring of the exposed population, and the subsequent transmission of the newly induced mutations through future generations. The classes of genetic diseases studied are: dominant gene mutation, X-linked gene mutation, chromosome disorders and multifactorial disorders which involve the interaction of many mutant genes and environmental factors. 28 references, 3 figures, 5 tables

  2. Cancer Genetics Services Directory

    Science.gov (United States)

    ... Services Directory Cancer Prevention Overview Research NCI Cancer Genetics Services Directory This directory lists professionals who provide services related to cancer genetics (cancer risk assessment, genetic counseling, genetic susceptibility testing, ...

  3. A social network-based intervention stimulating peer influence on children's self-reported water consumption: A randomized control trial.

    Science.gov (United States)

    Smit, Crystal R; de Leeuw, Rebecca N H; Bevelander, Kirsten E; Burk, William J; Buijzen, Moniek

    2016-08-01

    The current pilot study examined the effectiveness of a social network-based intervention using peer influence on self-reported water consumption. A total of 210 children (52% girls; M age = 10.75 ± SD = 0.80) were randomly assigned to either the intervention (n = 106; 52% girls) or control condition (n = 104; 52% girls). In the intervention condition, the most influential children in each classroom were trained to promote water consumption among their peers for eight weeks. The schools in the control condition did not receive any intervention. Water consumption, sugar-sweetened beverage (SSB) consumption, and intentions to drink more water in the near future were assessed by self-report measures before and immediately after the intervention. A repeated measure MANCOVA showed a significant multivariate interaction effect between condition and time (V = 0.07, F(3, 204) = 5.18, p = 0.002, pη(2) = 0.07) on the dependent variables. Further examination revealed significant univariate interaction effects between condition and time on water (p = 0.021) and SSB consumption (p = 0.015) as well as water drinking intentions (p = 0.049). Posthoc analyses showed that children in the intervention condition reported a significant increase in their water consumption (p = 0.018) and a decrease in their SSB consumption (p  0.05). The children who were exposed to the intervention did not report a change in their water drinking intentions over time (p = 0.576) whereas the nonexposed children decreased their intentions (p = 0.026). These findings show promise for a social network-based intervention using peer influence to positively alter consumption behaviors. This RCT was registered in the Australian New Zealand Clinical Trials Registry (ACTRN12614001179628). Study procedures were approved by the Ethics Committee of the Faculty of Social Sciences at Radboud University (ECSW2014-1003-203). Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    Directory of Open Access Journals (Sweden)

    Lund Ole

    2009-09-01

    Full Text Available Abstract Background The major histocompatibility complex (MHC molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event. Results Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods. Conclusion The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCII-2.0.

  5. The performance evaluation of a new neural network based traffic management scheme for a satellite communication network

    Science.gov (United States)

    Ansari, Nirwan; Liu, Dequan

    1991-01-01

    A neural-network-based traffic management scheme for a satellite communication network is described. The scheme consists of two levels of management. The front end of the scheme is a derivation of Kohonen's self-organization model to configure maps for the satellite communication network dynamically. The model consists of three stages. The first stage is the pattern recognition task, in which an exemplar map that best meets the current network requirements is selected. The second stage is the analysis of the discrepancy between the chosen exemplar map and the state of the network, and the adaptive modification of the chosen exemplar map to conform closely to the network requirement (input data pattern) by means of Kohonen's self-organization. On the basis of certain performance criteria, whether a new map is generated to replace the original chosen map is decided in the third stage. A state-dependent routing algorithm, which arranges the incoming call to some proper path, is used to make the network more efficient and to lower the call block rate. Simulation results demonstrate that the scheme, which combines self-organization and the state-dependent routing mechanism, provides better performance in terms of call block rate than schemes that only have either the self-organization mechanism or the routing mechanism.

  6. Immune system and zinc are associated with recurrent aphthous stomatitis. An assessment using a network-based approach.

    Directory of Open Access Journals (Sweden)

    César Rivera

    2017-09-01

    Full Text Available Objective: The aim of this research was to identify genes, proteins and processes from the biomedical information published on recurrent aphthous stomatitis (RAS using network-based foci. Methods: The clinical context was defined using MeSH terms for RAS and biomarkers, combined with words associated with risk. A set of protein coding genes was prioritized using the Génie web server and classified with PANTHER. For defining biologically relevant proteins, protein-protein interaction networks were constructed using Reactome database and Cytoscape. Top 20 proteins were then subjected to functional enrichment using STRING. Results: From 1,075,576 gene-abstract links, 1,491 genes were prioritized. Proteins were related to signaling molecule proteins (n=221, receptor proteins (n=221 and nucleic acid binding proteins (n=169. The network constructed with these proteins included 3,963 nodes and functional analysis showed that main processes involved immune system and zinc ion binding function. Conclusions: For the first time, bioinformatics tools were used for integrating pathways and networks associated with RAS. Molecules and processes associated with immune system recur robustly in all analyzed information. The molecular zinc ion binding function could be an area for exploring more specific and effective therapeutic interventions.

  7. Two-stage neural-network-based technique for Urdu character two-dimensional shape representation, classification, and recognition

    Science.gov (United States)

    Megherbi, Dalila B.; Lodhi, S. M.; Boulenouar, A. J.

    2001-03-01

    This work is in the field of automated document processing. This work addresses the problem of representation and recognition of Urdu characters using Fourier representation and a Neural Network architecture. In particular, we show that a two-stage Neural Network scheme is used here to make classification of 36 Urdu characters into seven sub-classes namely subclasses characterized by seven proposed and defined fuzzy features specifically related to Urdu characters. We show that here Fourier Descriptors and Neural Network provide a remarkably simple way to draw definite conclusions from vague, ambiguous, noisy or imprecise information. In particular, we illustrate the concept of interest regions and describe a framing method that provides a way to make the proposed technique for Urdu characters recognition robust and invariant to scaling and translation. We also show that a given character rotation is dealt with by using the Hotelling transform. This transform is based upon the eigenvalue decomposition of the covariance matrix of an image, providing a method of determining the orientation of the major axis of an object within an image. Finally experimental results are presented to show the power and robustness of the proposed two-stage Neural Network based technique for Urdu character recognition, its fault tolerance, and high recognition accuracy.

  8. A Fuzzy Neural Network Based on Non-Euclidean Distance Clustering for Quality Index Model in Slashing Process

    Directory of Open Access Journals (Sweden)

    Yuxian Zhang

    2015-01-01

    Full Text Available The quality index model in slashing process is difficult to build by reason of the outliers and noise data from original data. To the above problem, a fuzzy neural network based on non-Euclidean distance clustering is proposed in which the input space is partitioned into many local regions by the fuzzy clustering based on non-Euclidean distance so that the computation complexity is decreased, and fuzzy rule number is determined by validity function based on both the separation and the compactness among clusterings. Then, the premise parameters and consequent parameters are trained by hybrid learning algorithm. The parameters identification is realized; meanwhile the convergence condition of consequent parameters is obtained by Lyapunov function. Finally, the proposed method is applied to build the quality index model in slashing process in which the experimental data come from the actual slashing process. The experiment results show that the proposed fuzzy neural network for quality index model has lower computation complexity and faster convergence time, comparing with GP-FNN, BPNN, and RBFNN.

  9. A neural network based implementation of an MPC algorithm applied in the control systems of electromechanical plants

    Science.gov (United States)

    Marusak, Piotr M.; Kuntanapreeda, Suwat

    2018-01-01

    The paper considers application of a neural network based implementation of a model predictive control (MPC) control algorithm to electromechanical plants. Properties of such control plants implicate that a relatively short sampling time should be used. However, in such a case, finding the control value numerically may be too time-consuming. Therefore, the current paper tests the solution based on transforming the MPC optimization problem into a set of differential equations whose solution is the same as that of the original optimization problem. This set of differential equations can be interpreted as a dynamic neural network. In such an approach, the constraints can be introduced into the optimization problem with relative ease. Moreover, the solution of the optimization problem can be obtained faster than when the standard numerical quadratic programming routine is used. However, a very careful tuning of the algorithm is needed to achieve this. A DC motor and an electrohydraulic actuator are taken as illustrative examples. The feasibility and effectiveness of the proposed approach are demonstrated through numerical simulations.

  10. A study on efficient detection of network-based IP spoofing DDoS and malware-infected Systems.

    Science.gov (United States)

    Seo, Jung Woo; Lee, Sang Jin

    2016-01-01

    Large-scale network environments require effective detection and response methods against DDoS attacks. Depending on the advancement of IT infrastructure such as the server or network equipment, DDoS attack traffic arising from a few malware-infected systems capable of crippling the organization's internal network has become a significant threat. This study calculates the frequency of network-based packet attributes and analyzes the anomalies of the attributes in order to detect IP-spoofed DDoS attacks. Also, a method is proposed for the effective detection of malware infection systems triggering IP-spoofed DDoS attacks on an edge network. Detection accuracy and performance of the collected real-time traffic on a core network is analyzed thru the use of the proposed algorithm, and a prototype was developed to evaluate the performance of the algorithm. As a result, DDoS attacks on the internal network were detected in real-time and whether or not IP addresses were spoofed was confirmed. Detecting hosts infected by malware in real-time allowed the execution of intrusion responses before stoppage of the internal network caused by large-scale attack traffic.

  11. Phylogeny and evolutionary histories of Pyrus L. revealed by phylogenetic trees and networks based on data from multiple DNA sequences.

    Science.gov (United States)

    Zheng, Xiaoyan; Cai, Danying; Potter, Daniel; Postman, Joseph; Liu, Jing; Teng, Yuanwen

    2014-11-01

    Reconstructing the phylogeny of Pyrus has been difficult due to the wide distribution of the genus and lack of informative data. In this study, we collected 110 accessions representing 25 Pyrus species and constructed both phylogenetic trees and phylogenetic networks based on multiple DNA sequence datasets. Phylogenetic trees based on both cpDNA and nuclear LFY2int2-N (LN) data resulted in poor resolution, especially, only five primary species were monophyletic in the LN tree. A phylogenetic network of LN suggested that reticulation caused by hybridization is one of the major evolutionary processes for Pyrus species. Polytomies of the gene trees and star-like structure of cpDNA networks suggested rapid radiation is another major evolutionary process, especially for the occidental species. Pyrus calleryana and P. regelii were the earliest diverged Pyrus species. Two North African species, P. cordata, P. spinosa and P. betulaefolia were descendent of primitive stock Pyrus species and still share some common molecular characters. Southwestern China, where a large number of P. pashia populations are found, is probably the most important diversification center of Pyrus. More accessions and nuclear genes are needed for further understanding the evolutionary histories of Pyrus. Copyright © 2014 Elsevier Inc. All rights reserved.

  12. A Network-Based Impact Measure for Propagated Losses in a Supply Chain Network Consisting of Resilient Components

    Directory of Open Access Journals (Sweden)

    Jesus Felix Bayta Valenzuela

    2018-01-01

    Full Text Available The topology of a supply chain network affects the impacts of disruptions in it. We formulate a network-based measure of the impact of a disruption loss in a supply chain propagating downstream from an originating node. The measure takes into account the loss profile of the originating node, the structure of the supply network, and the resilience of the network components. We obtain an analytical expression for the impact measure under a beta-distributed initial loss (generalizable to any continuous distribution supported on the interval 0,1, under a breakthrough scenario (in which a fraction of the initial production loss reaches a focal company downstream as opposed to containment upstream or at the originating point. Furthermore, we obtain a closed-form solution for a supply chain network with a k-ary tree topology; a numerical study is performed for a scale-free network and a random network. Our proposed approach enables the evaluation of potential losses for a focal company considering its supply chain network structure, which may help the company to plan or redesign a robust and resilient network in response to different types of disruptions.

  13. Optimization of Indoor Thermal Comfort Parameters with the Adaptive Network-Based Fuzzy Inference System and Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jing Li

    2017-01-01

    Full Text Available The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS model and improved particle swarm optimization (PSO algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.

  14. Prioritizing disease candidate proteins in cardiomyopathy-specific protein-protein interaction networks based on "guilt by association" analysis.

    Directory of Open Access Journals (Sweden)

    Wan Li

    Full Text Available The cardiomyopathies are a group of heart muscle diseases which can be inherited (familial. Identifying potential disease-related proteins is important to understand mechanisms of cardiomyopathies. Experimental identification of cardiomyophthies is costly and labour-intensive. In contrast, bioinformatics approach has a competitive advantage over experimental method. Based on "guilt by association" analysis, we prioritized candidate proteins involving in human cardiomyopathies. We first built weighted human cardiomyopathy-specific protein-protein interaction networks for three subtypes of cardiomyopathies using the known disease proteins from Online Mendelian Inheritance in Man as seeds. We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on "guilt by association" analysis. It was found that most candidate proteins with high scores shared disease-related pathways with disease seed proteins. These top ranked candidate proteins were related with the corresponding disease subtypes, and were potential disease-related proteins. Cross-validation and comparison with other methods indicated that our approach could be used for the identification of potentially novel disease proteins, which may provide insights into cardiomyopathy-related mechanisms in a more comprehensive and integrated way.

  15. Integration Strategy Is a Key Step in Network-Based Analysis and Dramatically Affects Network Topological Properties and Inferring Outcomes

    Science.gov (United States)

    Jin, Nana; Wu, Deng; Gong, Yonghui; Bi, Xiaoman; Jiang, Hong; Li, Kongning; Wang, Qianghu

    2014-01-01

    An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions), molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches. PMID:25243127

  16. Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M.B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M.S. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Vesovic, B.V. [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)

    1997-12-01

    This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.

  17. An Empirical Study of Neural Network-Based Audience Response Technology in a Human Anatomy Course for Pharmacy Students.

    Science.gov (United States)

    Fernández-Alemán, José Luis; López-González, Laura; González-Sequeros, Ofelia; Jayne, Chrisina; López-Jiménez, Juan José; Carrillo-de-Gea, Juan Manuel; Toval, Ambrosio

    2016-04-01

    This paper presents an empirical study of a formative neural network-based assessment approach by using mobile technology to provide pharmacy students with intelligent diagnostic feedback. An unsupervised learning algorithm was integrated with an audience response system called SIDRA in order to generate states that collect some commonality in responses to questions and add diagnostic feedback for guided learning. A total of 89 pharmacy students enrolled on a Human Anatomy course were taught using two different teaching methods. Forty-four students employed intelligent SIDRA (i-SIDRA), whereas 45 students received the same training but without using i-SIDRA. A statistically significant difference was found between the experimental group (i-SIDRA) and the control group (traditional learning methodology), with T (87) = 6.598, p < 0.001. In four MCQs tests, the difference between the number of correct answers in the first attempt and in the last attempt was also studied. A global effect size of 0.644 was achieved in the meta-analysis carried out. The students expressed satisfaction with the content provided by i-SIDRA and the methodology used during the process of learning anatomy (M = 4.59). The new empirical contribution presented in this paper allows instructors to perform post hoc analyses of each particular student's progress to ensure appropriate training.

  18. Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

    Science.gov (United States)

    Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

    2010-02-01

    Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

  19. A new and accurate fault location algorithm for combined transmission lines using Adaptive Network-Based Fuzzy Inference System

    Energy Technology Data Exchange (ETDEWEB)

    Sadeh, Javad; Afradi, Hamid [Electrical Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, P.O. Box: 91775-1111, Mashhad (Iran)

    2009-11-15

    This paper presents a new and accurate algorithm for locating faults in a combined overhead transmission line with underground power cable using Adaptive Network-Based Fuzzy Inference System (ANFIS). The proposed method uses 10 ANFIS networks and consists of 3 stages, including fault type classification, faulty section detection and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., fundamental component of three phase currents and zero sequence current. Another ANFIS network is used to detect the faulty section, whether the fault is on the overhead line or on the underground cable. Other eight ANFIS networks are utilized to pinpoint the faults (two for each fault type). Four inputs, i.e., the dc component of the current, fundamental frequency of the voltage and current and the angle between them, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on each part of the combined line. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances. Simulation results confirm that the proposed method can be used as an efficient means for accurate fault location on the combined transmission lines. (author)

  20. Development of the Social Network-Based Intervention “Powerful Together with Diabetes” Using Intervention Mapping

    Directory of Open Access Journals (Sweden)

    Charlotte Vissenberg

    2017-12-01

    Full Text Available This article describes the development of the social network-based intervention Powerful Together with Diabetes which aims to improve diabetes self-management (DSM among patients with type 2 diabetes living in socioeconomically deprived neighborhoods by stimulating social support for DSM and diminishing social influences hindering DSM (e.g., peer pressure and social norms. The intervention was specifically developed for patients with Dutch, Turkish, Moroccan, and Surinamese backgrounds. The intervention was developed according to Intervention Mapping. This article describes the first four steps of Intervention Mapping: (1 the needs assessment; (2 development of performance and change objectives; (3 selection of theory-based methods and strategies; and (4 the translation of these into an organized program. These four steps resulted in Powerful Together with Diabetes, a 10-month group-based intervention consisting of 24 meetings, 6 meetings for significant others, and 2 meetings for participants and their spouses. The IM method resulted in a tailored approach with a specific focus on the social networks of its participants. This article concludes that the IM method helped our planning team to tailor the intervention to the needs of our target population and facilitated our evaluation design. However, in hindsight, the intervention could have been improved by investing more in participatory planning and community involvement.

  1. A Space-Time Network-Based Modeling Framework for Dynamic Unmanned Aerial Vehicle Routing in Traffic Incident Monitoring Applications

    Directory of Open Access Journals (Sweden)

    Jisheng Zhang

    2015-06-01

    Full Text Available It is essential for transportation management centers to equip and manage a network of fixed and mobile sensors in order to quickly detect traffic incidents and further monitor the related impact areas, especially for high-impact accidents with dramatic traffic congestion propagation. As emerging small Unmanned Aerial Vehicles (UAVs start to have a more flexible regulation environment, it is critically important to fully explore the potential for of using UAVs for monitoring recurring and non-recurring traffic conditions and special events on transportation networks. This paper presents a space-time network- based modeling framework for integrated fixed and mobile sensor networks, in order to provide a rapid and systematic road traffic monitoring mechanism. By constructing a discretized space-time network to characterize not only the speed for UAVs but also the time-sensitive impact areas of traffic congestion, we formulate the problem as a linear integer programming model to minimize the detection delay cost and operational cost, subject to feasible flying route constraints. A Lagrangian relaxation solution framework is developed to decompose the original complex problem into a series of computationally efficient time-dependent and least cost path finding sub-problems. Several examples are used to demonstrate the results of proposed models in UAVs’ route planning for small and medium-scale networks.

  2. Routing protocol for wireless quantum multi-hop mesh backbone network based on partially entangled GHZ state

    Science.gov (United States)

    Xiong, Pei-Ying; Yu, Xu-Tao; Zhang, Zai-Chen; Zhan, Hai-Tao; Hua, Jing-Yu

    2017-08-01

    Quantum multi-hop teleportation is important in the field of quantum communication. In this study, we propose a quantum multi-hop communication model and a quantum routing protocol with multihop teleportation for wireless mesh backbone networks. Based on an analysis of quantum multi-hop protocols, a partially entangled Greenberger-Horne-Zeilinger (GHZ) state is selected as the quantum channel for the proposed protocol. Both quantum and classical wireless channels exist between two neighboring nodes along the route. With the proposed routing protocol, quantum information can be transmitted hop by hop from the source node to the destination node. Based on multi-hop teleportation based on the partially entangled GHZ state, a quantum route established with the minimum number of hops. The difference between our routing protocol and the classical one is that in the former, the processes used to find a quantum route and establish quantum channel entanglement occur simultaneously. The Bell state measurement results of each hop are piggybacked to quantum route finding information. This method reduces the total number of packets and the magnitude of air interface delay. The deduction of the establishment of a quantum channel between source and destination is also presented here. The final success probability of quantum multi-hop teleportation in wireless mesh backbone networks was simulated and analyzed. Our research shows that quantum multi-hop teleportation in wireless mesh backbone networks through a partially entangled GHZ state is feasible.

  3. Neural network-based voltage regulator for an isolated asynchronous generator supplying three-phase four-wire loads

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Bhim; Kasal, Gaurav Kumar [Department of Electrical Engineering, Indian Institute of Technology, Delhi, Hauz-Khas, New Delhi 110016 (India)

    2008-06-15

    This paper deals with a neural network-based solid state voltage controller for an isolated asynchronous generator (IAG) driven by constant speed prime mover like diesel engine, bio-gas or gasoline engine and supplying three-phase four-wire loads. The proposed control scheme uses an indirect current control and a fast adaptive linear element (adaline) based neural network reference current extractor, which extracts the real positive sequence current component without any phase shift. The neutral current of the source is also compensated by using three single-phase bridge configuration of IGBT (insulated gate bipolar junction transistor) based voltage source converter (VSC) along-with single-phase transformer having self-supported dc bus. The proposed controller provides the functions as a voltage regulator, a harmonic eliminator, a neutral current compensator, and a load balancer. The proposed isolated electrical system with its controller is modeled and simulated in MATLAB along with Simulink and PSB (Power System Block set) toolboxes. The simulated results are presented to demonstrate the capability of an isolated asynchronous generating system driven by a constant speed prime mover for feeding three-phase four-wire loads. (author)

  4. An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments

    International Nuclear Information System (INIS)

    Azadeh, A.; Asadzadeh, S.M.; Ghanbari, A.

    2010-01-01

    Accurate short-term natural gas (NG) demand estimation and forecasting is vital for policy and decision-making process in energy sector. Moreover, conventional methods may not provide accurate results. This paper presents an adaptive network-based fuzzy inference system (ANFIS) for estimation of NG demand. Standard input variables are used which are day of the week, demand of the same day in previous year, demand of a day before and demand of 2 days before. The proposed ANFIS approach is equipped with pre-processing and post-processing concepts. Moreover, input data are pre-processed (scaled) and finally output data are post-processed (returned to its original scale). The superiority and applicability of the ANFIS approach is shown for Iranian NG consumption from 22/12/2007 to 30/6/2008. Results show that ANFIS provides more accurate results than artificial neural network (ANN) and conventional time series approach. The results of this study provide policy makers with an appropriate tool to make more accurate predictions on future short-term NG demand. This is because the proposed approach is capable of handling non-linearity, complexity as well as uncertainty that may exist in actual data sets due to erratic responses and measurement errors.

  5. Development of the Social Network-Based Intervention “Powerful Together with Diabetes” Using Intervention Mapping

    Science.gov (United States)

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

    2017-01-01

    This article describes the development of the social network-based intervention Powerful Together with Diabetes which aims to improve diabetes self-management (DSM) among patients with type 2 diabetes living in socioeconomically deprived neighborhoods by stimulating social support for DSM and diminishing social influences hindering DSM (e.g., peer pressure and social norms). The intervention was specifically developed for patients with Dutch, Turkish, Moroccan, and Surinamese backgrounds. The intervention was developed according to Intervention Mapping. This article describes the first four steps of Intervention Mapping: (1) the needs assessment; (2) development of performance and change objectives; (3) selection of theory-based methods and strategies; and (4) the translation of these into an organized program. These four steps resulted in Powerful Together with Diabetes, a 10-month group-based intervention consisting of 24 meetings, 6 meetings for significant others, and 2 meetings for participants and their spouses. The IM method resulted in a tailored approach with a specific focus on the social networks of its participants. This article concludes that the IM method helped our planning team to tailor the intervention to the needs of our target population and facilitated our evaluation design. However, in hindsight, the intervention could have been improved by investing more in participatory planning and community involvement. PMID:29326916

  6. Molecular genetics

    International Nuclear Information System (INIS)

    Kubitschek, H.E.

    1975-01-01

    Progress is reported on studies on the nature and action of lethal and mutagenic lesions in DNA and the mechanisms by which these are produced in bacteria by ionizing radiation or by decay of radioisotopes incorporated in DNA. Studies of radioisotope decay provide the advantages that the original lesion is localized in the genetic material and the immediate physical and chemical changes that occur at decay are known. Specific types of DNA damage were related to characteristic decay properties of several radioisotopes. Incorporated 125 I, for example, induces a double-stranded break in DNA with almost every decay, but causes remarkably little damage of any other kind to the DNA. (U.S.)

  7. A network-based method to evaluate quality of reproducibility of differential expression in cancer genomics studies.

    Science.gov (United States)

    Li, Robin; Lin, Xiao; Geng, Haijiang; Li, Zhihui; Li, Jiabing; Lu, Tao; Yan, Fangrong

    2015-12-29

    Personalized cancer treatments depend on the determination of a patient's genetic status according to known genetic profiles for which targeted treatments exist. Such genetic profiles must be scientifically validated before they is applied to general patient population. Reproducibility of findings that support such genetic profiles is a fundamental challenge in validation studies. The percentage of overlapping genes (POG) criterion and derivative methods produce unstable and misleading results. Furthermore, in a complex disease, comparisons between different tumor subtypes can produce high POG scores that do not capture the consistencies in the functions. We focused on the quality rather than the quantity of the overlapping genes. We defined the rank value of each gene according to importance or quality by PageRank on basis of a particular topological structure. Then, we used the p-value of the rank-sum of the overlapping genes (PRSOG) to evaluate the quality of reproducibility. Though the POG scores were low in different studies of the same disease, the PRSOG was statistically significant, which suggests that sets of differentially expressed genes might be highly reproducible. Evaluations of eight datasets from breast cancer, lung cancer and four other disorders indicate that quality-based PRSOG method performs better than a quantity-based method. Our analysis of the components of the sets of overlapping genes supports the utility of the PRSOG method.

  8. Network-based multiple sclerosis pathway analysis with GWAS data from 15,000 cases and 30,000 controls

    DEFF Research Database (Denmark)

    Consortium, International Multiple Sclerosis Genetics; Sørensen, Per Soelberg

    2013-01-01

    Multiple sclerosis (MS) is an inflammatory CNS disease with a substantial genetic component, originally mapped to only the human leukocyte antigen (HLA) region. In the last 5 years, a total of seven genome-wide association studies and one meta-analysis successfully identified 57 non-HLA susceptib...

  9. NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis.

    Science.gov (United States)

    Sun, Duanchen; Liu, Yinliang; Zhang, Xiang-Sun; Wu, Ling-Yun

    2017-09-21

    High-throughput experimental techniques have been dramatically improved and widely applied in the past decades. However, biological interpretation of the high-throughput experimental results, such as differential expression gene sets derived from microarray or RNA-seq experiments, is still a challenging task. Gene Ontology (GO) is commonly used in the functional enrichment studies. The GO terms identified via current functional enrichment analysis tools often contain direct parent or descendant terms in the GO hierarchical structure. Highly redundant terms make users difficult to analyze the underlying biological processes. In this paper, a novel network-based probabilistic generative model, NetGen, was proposed to perform the functional enrichment analysis. An additional protein-protein interaction (PPI) network was explicitly used to assist the identification of significantly enriched GO terms. NetGen achieved a superior performance than the existing methods in the simulation studies. The effectiveness of NetGen was explored further on four real datasets. Notably, several GO terms which were not directly linked with the active gene list for each disease were identified. These terms were closely related to the corresponding diseases when accessed to the curated literatures. NetGen has been implemented in the R package CopTea publicly available at GitHub ( http://github.com/wulingyun/CopTea/ ). Our procedure leads to a more reasonable and interpretable result of the functional enrichment analysis. As a novel term combination-based functional enrichment analysis method, NetGen is complementary to current individual term-based methods, and can help to explore the underlying pathogenesis of complex diseases.

  10. 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.

  11. Temporal network based analysis of cell specific vein graft transcriptome defines key pathways and hub genes in implantation injury.

    Directory of Open Access Journals (Sweden)

    Manoj Bhasin

    Full Text Available Vein graft failure occurs between 1 and 6 months after implantation due to obstructive intimal hyperplasia, related in part to implantation injury. The cell-specific and temporal response of the transcriptome to vein graft implantation injury was determined by transcriptional profiling of laser capture microdissected endothelial cells (EC and medial smooth muscle cells (SMC from canine vein grafts, 2 hours (H to 30 days (D following surgery. Our results demonstrate a robust genomic response beginning at 2 H, peaking at 12-24 H, declining by 7 D, and resolving by 30 D. Gene ontology and pathway analyses of differentially expressed genes indicated that implantation injury affects inflammatory and immune responses, apoptosis, mitosis, and extracellular matrix reorganization in both cell types. Through backpropagation an integrated network was built, starting with genes differentially expressed at 30 D, followed by adding upstream interactive genes from each prior time-point. This identified significant enrichment of IL-6, IL-8, NF-κB, dendritic cell maturation, glucocorticoid receptor, and Triggering Receptor Expressed on Myeloid Cells (TREM-1 signaling, as well as PPARα activation pathways in graft EC and SMC. Interactive network-based analyses identified IL-6, IL-8, IL-1α, and Insulin Receptor (INSR as focus hub genes within these pathways. Real-time PCR was used for the validation of two of these genes: IL-6 and IL-8, in addition to Collagen 11A1 (COL11A1, a cornerstone of the backpropagation. In conclusion, these results establish causality relationships clarifying the pathogenesis of vein graft implantation injury, and identifying novel targets for its prevention.

  12. SNRFCB: sub-network based random forest classifier for predicting chemotherapy benefit on survival for cancer treatment.

    Science.gov (United States)

    Shi, Mingguang; He, Jianmin

    2016-04-01

    Adjuvant chemotherapy (CTX) should be individualized to provide potential survival benefit and avoid potential harm to cancer patients. Our goal was to establish a computational approach for making personalized estimates of the survival benefit from adjuvant CTX. We developed Sub-Network based Random Forest classifier for predicting Chemotherapy Benefit (SNRFCB) based gene expression datasets of lung cancer. The SNRFCB approach was then validated in independent test cohorts for identifying chemotherapy responder cohorts and chemotherapy non-responder cohorts. SNRFCB involved the pre-selection of gene sub-network signatures based on the mutations and on protein-protein interaction data as well as the application of the random forest algorithm to gene expression datasets. Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer patients in the chemotherapy responder group (P = 0.008), but it was not beneficial to patients in the chemotherapy non-responder group (P = 0.657). Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer squamous cell carcinoma (SQCC) subtype patients in the chemotherapy responder cohorts (P = 0.024), but it was not beneficial to patients in the chemotherapy non-responder cohorts (P = 0.383). SNRFCB improved prediction performance as compared to the machine learning method, support vector machine (SVM). To test the general applicability of the predictive model, we further applied the SNRFCB approach to human breast cancer datasets and also observed superior performance. SNRFCB could provide recurrent probability for individual patients and identify which patients may benefit from adjuvant CTX in clinical trials.

  13. Sensitive and specific peak detection for SELDI-TOF mass spectrometry using a wavelet/neural-network based approach.

    Directory of Open Access Journals (Sweden)

    Vincent A Emanuele

    Full Text Available SELDI-TOF mass spectrometer's compact size and automated, high throughput design have been attractive to clinical researchers, and the platform has seen steady-use in biomarker studies. Despite new algorithms and preprocessing pipelines that have been developed to address reproducibility issues, visual inspection of the results of SELDI spectra preprocessing by the best algorithms still shows miscalled peaks and systematic sources of error. This suggests that there continues to be problems with SELDI preprocessing. In this work, we study the preprocessing of SELDI in detail and introduce improvements. While many algorithms, including the vendor supplied software, can identify peak clusters of specific mass (or m/z in groups of spectra with high specificity and low false discover rate (FDR, the algorithms tend to underperform estimating the exact prevalence and intensity of peaks in those clusters. Thus group differences that at first appear very strong are shown, after careful and laborious hand inspection of the spectra, to be less than significant. Here we introduce a wavelet/neural network based algorithm which mimics what a team of expert, human users would call for peaks in each of several hundred spectra in a typical SELDI clinical study. The wavelet denoising part of the algorithm optimally smoothes the signal in each spectrum according to an improved suite of signal processing algorithms previously reported (the LibSELDI toolbox under development. The neural network part of the algorithm combines those results with the raw signal and a training dataset of expertly called peaks, to call peaks in a test set of spectra with approximately 95% accuracy. The new method was applied to data collected from a study of cervical mucus for the early detection of cervical cancer in HPV infected women. The method shows promise in addressing the ongoing SELDI reproducibility issues.

  14. Adaptive QoS provision for IEEE 802.16e BWA networks based on cross-layer design

    Directory of Open Access Journals (Sweden)

    Kuo GS

    2011-01-01

    Full Text Available Abstract This article proposes an integrated framework for adaptive QoS provision in IEEE 802.16e broadband wireless access networks based on cross-layer design. On one hand, an efficient admission control (AC algorithm is proposed along with a semi-reservation scheme to guarantee the connection-level QoS. First, to guarantee the service continuity for handoff connections and resource efficiency, our semi-reservation scheme considers both users' handoff probability and average resource consumption together, which effectively avoids resource over-reservation and insufficient reservation. For AC, a new/handoff connection is accepted only when the target cell has enough resource to afford both instantaneous and average resource consumption to meet the average source rate request. On the other hand, a joint resource allocation and packet scheduling scheme is designed to provide packet-level QoS guarantee in term of "QoS rate", which can ensure fairness for the services with identical priority level in case of bandwidth shortage. Particularly, an enhanced bandwidth request scheme is designed to reduce unnecessary BR delay and redundant signaling overhead caused by the existing one in IEEE 802.16e, which further improves the packet-level QoS performance and resource efficiency for uplink transmission. Simulation results show that the proposed approach not only balances the tradeoff among connection blocking rate, connection dropping rate, and connection failure rate, but also achieves low mean packet dropping rate (PDR, small deviation of PDR, and low QoS outage rate. Moreover, high resource efficiency is ensured.

  15. Annotating function to differentially expressed LincRNAs in myelodysplastic syndrome using a network-based method.

    Science.gov (United States)

    Liu, Keqin; Beck, Dominik; Thoms, Julie A I; Liu, Liang; Zhao, Weiling; Pimanda, John E; Zhou, Xiaobo

    2017-09-01

    Long non-coding RNAs (lncRNAs) have been implicated in the regulation of diverse biological functions. The number of newly identified lncRNAs has increased dramatically in recent years but their expression and function have not yet been described from most diseases. To elucidate lncRNA function in human disease, we have developed a novel network based method (NLCFA) integrating correlations between lncRNA, protein coding genes and noncoding miRNAs. We have also integrated target gene associations and protein-protein interactions and designed our model to provide information on the combined influence of mRNAs, lncRNAs and miRNAs on cellular signal transduction networks. We have generated lncRNA expression profiles from the CD34+ haematopoietic stem and progenitor cells (HSPCs) from patients with Myelodysplastic syndromes (MDS) and healthy donors. We report, for the first time, aberrantly expressed lncRNAs in MDS and further prioritize biologically relevant lncRNAs using the NLCFA. Taken together, our data suggests that aberrant levels of specific lncRNAs are intimately involved in network modules that control multiple cancer-associated signalling pathways and cellular processes. Importantly, our method can be applied to prioritize aberrantly expressed lncRNAs for functional validation in other diseases and biological contexts. The method is implemented in R language and Matlab. xizhou@wakehealth.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  16. Leuconostoc mesenteroides growth in food products: prediction and sensitivity analysis by adaptive-network-based fuzzy inference systems.

    Directory of Open Access Journals (Sweden)

    Hue-Yu Wang

    Full Text Available BACKGROUND: An adaptive-network-based fuzzy inference system (ANFIS was compared with an artificial neural network (ANN in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C, pH level (5.5 to 7.5, sodium chloride level (0.25% to 6.25% and sodium nitrite level (0 to 200 ppm on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. METHODS: THE ANFIS AND ANN MODELS WERE COMPARED IN TERMS OF SIX STATISTICAL INDICES CALCULATED BY COMPARING THEIR PREDICTION RESULTS WITH ACTUAL DATA: mean absolute percentage error (MAPE, root mean square error (RMSE, standard error of prediction percentage (SEP, bias factor (Bf, accuracy factor (Af, and absolute fraction of variance (R (2. Graphical plots were also used for model comparison. CONCLUSIONS: The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions.

  17. Genetics and Rheumatic Disease

    Science.gov (United States)

    ... Well with Rheumatic Disease Genetics and Rheumatic Disease Genetics and Rheumatic Disease Fast Facts Studying twins has ... 70%, and for non-identical pairs, even lower. Genetics and ankylosing spondylitis Each rheumatic disease has its ...

  18. Genetic programming in microorganisms

    Energy Technology Data Exchange (ETDEWEB)

    Hopwood, D A

    1981-11-01

    Formerly, when microbiologists had only existing organisms at their disposal whose characteristics could only be changed randomly by genetic experiments, they used to dream of programmed genetic changes. This dream has come true with modern genetic engineering.

  19. Liberalisation in network based industries. An economic analysis by case studies of railway, telecommunication and energy utilities; Liberalisierung von Netzindustrien. Eine oekonomische Analyse am Beispiel der Eisenbahn, der Telekommunikation und der Leitungsgebundenen Energieversorgung

    Energy Technology Data Exchange (ETDEWEB)

    Schulze, A.

    2006-07-01

    The liberalisation of network based industries represents an economic problem, which raises on the one hand a multiplicity of theoretically unresolved questions and for which exist now on the other hand experiences in economic policy in Germany. The causes of the economic problems are not only to be found thereby in certain industry characteristics of the network based industries, but also in a missed special treatment of the network based economic sectors in the past by the economic policy. However, competition pushes in network based industries at borders, because the infrastructure necessary for the production of network based services typically represents an non-open to attack, natural monopoly in the hand of an established, vertically integrated supplier. From it extensive possibilities for the discrimination of competitors result, the competition political action need draw. It applies to analyze these in the available work and to discuss alternative solutions of the discrimination problem. (orig.)

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

    Directory of Open Access Journals (Sweden)

    Emre Guney

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

  1. Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm.

    Science.gov (United States)

    Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K

    2016-01-01

    The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.

  2. Using Network-Based Language Analysis to Bridge Expertise and Cultivate Sensitivity to Differentiated Language Use in Interdisciplinary Geoscience Research

    Science.gov (United States)

    Hannah, M. A.; Simeone, M.

    2017-12-01

    On interdisciplinary teams, expertise is varied, as is evidenced by differences in team members' language use. Developing strategies to combine that expertise and bridge differentiated language practices is especially difficult between geoscience subdisciplines as researchers assume they use a shared language—vocabulary, jargon, codes, linguistic styles. In our paper, we discuss a network-based approach used to identify varied expertise and language practices between geoscientists (n=29) on a NSF team funded to study how deep and surface Earth processes worked together to give rise to the Great Oxygenation Event. We describe how we modeled the team's expertise from a language corpus consisting of 220 oxygen-related terms frequently used by team members and then compared their understanding of the terms to develop interventions to bridge the team's expertise. Corpus terms were identified via team member interviews, observations of members' interactions at research meetings, and discourse analysis of members' publications. Comparisons of members' language use were based on a Likert scale survey that asked members to assess how they understood a term; how frequently they used a term; and whether they conceptualized a term as an object or process. Rather than use our method as a communication audit tool (Zwijze-Koning & de Jong, 2015), teams can proactively use it in a project's early stages to assess the contours of the team's differentiated expertise and show where specialized knowledge resides in the team, where latent or non-obvious expertise exists, where expertise overlaps, and where gaps are in the team's knowledge. With this information, teams can make evidence based recommendations to forward their work such as allocating resources; identifying and empowering members to serve as connectors and lead cross-functional project initiatives; and developing strategies to avoid communication barriers. The method also generates models for teaching language

  3. The Development of a Social Networking-Based Relatedness Intervention Among Young, First-Time Blood Donors: Pilot Study.

    Science.gov (United States)

    Frye, Victoria; Duffy, Louisa; France, Janis L; Kessler, Debra A; Rebosa, Mark; Shaz, Beth H; Carlson, Bruce W; France, Christopher R

    2018-04-26

    Increasing repeat blood donation behavior is a critical public health goal. According to self-determination theory, the process of developing internal motivation to give blood and an associated self-identity as a blood donor may be promoted by feelings of “relatedness” or a connection to other donors, which may be enhanced through social relations and interactions. The purpose of this report it to describe the development and pilot testing of a social networking-based (Facebook) intervention condition designed to increase feelings of relatedness via virtual social interaction and support. To develop the intervention condition content, images, text, polls, and video content were assembled. Ohio University college students (N=127) rated the content (82 images/text) presented by computer in random order using a scale of one to five on various dimensions of relatedness. Mean ratings were calculated and analyses of variance were conducted to assess associations among the dimensions. Based on these results, the relatedness intervention was adapted and evaluated for feasibility, acceptability, and preliminary efficacy among 24 first-time donors, aged 18 to 24 years, in a 30-day pilot trial. Paired t-tests were conducted to examine change over time in relatedness and connectedness. The intervention condition that was developed was acceptable and feasible. Results of the uncontrolled, preintervention, and postintervention evaluation revealed that feelings of individual-level relatedness increased significantly after the intervention. By promoting first-time blood donor relatedness, our goal is to enhance internal motivation for donating and the integration of the blood donor identity, thus increasing the likelihood of future repeat donation. ClinicalTrials.gov NCT02717338; https://clinicaltrials.gov/ct2/show/NCT02717338 (Archived by WebCite at http://www.webcitation.org/6ymHRBCwu) ©Victoria Frye, Louisa Duffy, Janis L France, Debra A Kessler, Mark Rebosa, Beth H Shaz

  4. Model for Building a Distribution Network Based on the Multivariate Analysis of the Industrial and Logistical Potential of Regions

    Directory of Open Access Journals (Sweden)

    Alexander Vladimirovich Kirillov

    2015-12-01

    Full Text Available The international integration of the Russian economy is connected to the need of the realization of the competitive advantages of the geopolitical position of Russia, the industrial potential of regions, the logistic infrastructure of transport corridors. This article discusses the design model of the supply chain (distribution network based on the multivariate analysis and the methodology of the substantiation of its configuration based on the cost factors and the level of the logistics infrastructure development. For solving the problem of placing one or more logistics centers in the service area, a two-stage algorithm is used. At the first stage, the decisions on the reasonability of the choice of one or another version of the development are made with А. В. Кириллов, В. Е. Целин 345 ЭКОНОМИКА РЕГИОНА №4 (2015 the use of the “Make or Buy” standard model. The criterion of decision making is the guaranteed overcoming of the threshold of “indifference” taking into account the statistical characteristics of costs for options of “buy” and “make” depending on the volume of consumption of goods or services. At the second stage, the Ardalan’s heuristic method is used for the evaluation of the choice of placing one or more logistics centers in the service area. The model parameters are based on the assessment of the development prospects of the region and its investment potential (existence and composition of employment, production, natural resources, financial and consumer opportunities, institutional, innovation, infrastructure capacity. Furthermore, such criteria as a regional financial appeal, professionally trained specialists, the competitive advantages of the promoted company and others are analyzed. An additional criterion is the development of the priority matrix, which considers such factors as difficulties of customs registration and certification, a level of regional transport

  5. A new hybrid genetic algorithm for optimizing the single and multivariate objective functions

    Energy Technology Data Exchange (ETDEWEB)

    Tumuluru, Jaya Shankar [Idaho National Laboratory; McCulloch, Richard Chet James [Idaho National Laboratory

    2015-07-01

    In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the most improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.

  6. A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context

    Directory of Open Access Journals (Sweden)

    Valavanis Ioannis K

    2010-09-01

    Full Text Available Abstract Background Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD. The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total, gender, and nutrition (38 in total, e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI as normal (BMI ≤ 25 or overweight (BMI > 25. Two artificial neural network (ANN based methods were designed and used towards the analysis of the available data. These corresponded to i a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN, and ii a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN which combines genetic algorithms and the popular back-propagation training algorithm. Results PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets

  7. A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context.

    Science.gov (United States)

    Valavanis, Ioannis K; Mougiakakou, Stavroula G; Grimaldi, Keith A; Nikita, Konstantina S

    2010-09-08

    Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm. PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets. The ANN based methods revealed factors

  8. Inspirations in medical genetics.

    Science.gov (United States)

    Asadollahi, Reza

    2016-02-01

    There are abundant instances in the history of genetics and medical genetics to illustrate how curiosity, charisma of mentors, nature, art, the saving of lives and many other matters have inspired great discoveries. These achievements from deciphering genetic concepts to characterizing genetic disorders have been crucial for management of the patients. There remains, however, a long pathway ahead. © The Author(s) 2014.

  9. What Is Genetic Ancestry Testing?

    Science.gov (United States)

    ... What is genetic ancestry testing? What is genetic ancestry testing? Genetic ancestry testing, or genetic genealogy, is ... with other groups. For more information about genetic ancestry testing: The University of Utah provides video tutorials ...

  10. The NIH Library of Integrated Network-Based Cellular Signatures (LINCS) Program | Informatics Technology for Cancer Research (ITCR)

    Science.gov (United States)

    By generating and making public data that indicates how cells respond to various genetic and environmental stressors, the LINCS project will help us gain a more detailed understanding of cell pathways and aid efforts to develop therapies that might restore perturbed pathways and networks to their normal states. The LINCS website is a source of information for the research community and general public about the LINCS project. This website along with the LINCS Data Portal contains details about the assays, cell types, and perturbagens that are currently part of the library, as well as links to participating sites, data releases from the sites, and software that can be used for analyzing the data.

  11. Landscape genetics of high mountain frog metapopulations

    Science.gov (United States)

    Murphy, M.A.; Dezzani, R.; Pilliod, D.S.; Storfer, A.

    2010-01-01

    Explaining functional connectivity among occupied habitats is crucial for understanding metapopulation dynamics and species ecology. Landscape genetics has primarily focused on elucidating how ecological features between observations influence gene flow. Functional connectivity, however, may be the result of both these between-site (landscape resistance) landscape characteristics and at-site (patch quality) landscape processes that can be captured using network based models. We test hypotheses of functional connectivity that include both between-site and at-site landscape processes in metapopulations of Columbia spotted frogs (Rana luteiventris) by employing a novel justification of gravity models for landscape genetics (eight microsatellite loci, 37 sites, n = 441). Primarily used in transportation and economic geography, gravity models are a unique approach as flow (e.g. gene flow) is explained as a function of three basic components: distance between sites, production/attraction (e.g. at-site landscape process) and resistance (e.g. between-site landscape process). The study system contains a network of nutrient poor high mountain lakes where we hypothesized a short growing season and complex topography between sites limit R. luteiventris gene flow. In addition, we hypothesized production of offspring is limited by breeding site characteristics such as the introduction of predatory fish and inherent site productivity. We found that R. luteiventris connectivity was negatively correlated with distance between sites, presence of predatory fish (at-site) and topographic complexity (between-site). Conversely, site productivity (as measured by heat load index, at-site) and growing season (as measured by frost-free period between-sites) were positively correlated with gene flow. The negative effect of predation and positive effect of site productivity, in concert with bottleneck tests, support the presence of source-sink dynamics. In conclusion, gravity models provide a

  12. Powerful together with diabetes : The development and evaluation of a social network based intervention for Dutch, Surinamese, Turkish and Moroccan people with type 2 diabetes living in socioeconomically deprived neighbourhoods

    NARCIS (Netherlands)

    Vissenberg, C.

    2017-01-01

    This thesis describes the development and evaluation of the social network based intervention Powerful Together with Diabetes (PTWD) targeted at Dutch, Turkish, Moroccan and Surinamese patients with type 2 diabetes living in socioeconomically deprived neighbourhoods. This intervention aims to

  13. A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions.

    Directory of Open Access Journals (Sweden)

    Min Oh

    Full Text Available The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level. Properly selected features of the network representations of existing drug-disease associations can be used to infer novel indications of existing drugs. To find new drug-disease associations, we generated an integrative genetic network using combinations of interactions, including protein-protein interactions and gene regulatory network datasets. Within this network, network adjacencies of drug-drug and disease-disease were quantified using a scored path between target sets of them. Furthermore, the common topological module of drugs or diseases was extracted, and thereby the distance between topological drug-module and disease (or disease-module and drug was quantified. These quantified scores were used as features for the prediction of novel drug-disease associations. Our classifiers using Random Forest, Multilayer Perceptron and C4.5 showed a high specificity and sensitivity (AUC score of 0.855, 0.828 and 0.797 respectively in predicting novel drug indications, and displayed a better performance than other methods with limited drug and disease properties. Our predictions and current clinical trials overlap significantly across the different phases of drug development. We also identified and visualized the topological modules of predicted drug indications for certain types of cancers, and for Alzheimer's disease. Within the network, those modules show potential pathways that illustrate the mechanisms of new drug indications, including propranolol as a potential anticancer agent and telmisartan as treatment for Alzheimer's disease.

  14. Genetics Home Reference: SADDAN

    Science.gov (United States)

    ... view the expand/collapse boxes. Description SADDAN (severe achondroplasia with developmental delay and acanthosis nigricans) is a ... Genetic Testing (1 link) Genetic Testing Registry: Severe achondroplasia with developmental delay and acanthosis nigricans Other Diagnosis ...

  15. Genetic Brain Disorders

    Science.gov (United States)

    A genetic brain disorder is caused by a variation or a mutation in a gene. A variation is a different form ... mutation is a change in a gene. Genetic brain disorders affect the development and function of the ...

  16. Genetic Testing for ALS

    Science.gov (United States)

    ... genetic counselor can help you work through the pros and cons of genetic testing based on your ... showing symptoms or what their progression will be. Technology is changing rapidly and costs of testing are ...

  17. Genetically engineered foods

    Science.gov (United States)

    Bioengineered foods; GMOs; Genetically modified foods ... helps speed up the process of creating new foods with desired traits. The possible benefits of genetic engineering include: More nutritious food Tastier food Disease- and ...

  18. Prenatal screening and genetics

    DEFF Research Database (Denmark)

    Alderson, P; Aro, A R; Dragonas, T

    2001-01-01

    Although the term 'genetic screening' has been used for decades, this paper discusses how, in its most precise meaning, genetic screening has not yet been widely introduced. 'Prenatal screening' is often confused with 'genetic screening'. As we show, these terms have different meanings, and we...... examine definitions of the relevant concepts in order to illustrate this point. The concepts are i) prenatal, ii) genetic screening, iii) screening, scanning and testing, iv) maternal and foetal tests, v) test techniques and vi) genetic conditions. So far, prenatal screening has little connection...... with precisely defined genetics. There are benefits but also disadvantages in overstating current links between them in the term genetic screening. Policy making and professional and public understandings about screening could be clarified if the distinct meanings of prenatal screening and genetic screening were...

  19. Evaluating human genetic diversity

    National Research Council Canada - National Science Library

    This book assesses the scientific value and merit of research on human genetic differences--including a collection of DNA samples that represents the whole of human genetic diversity--and the ethical...

  20. Genetics Home Reference: osteopetrosis

    Science.gov (United States)

    ... A characteristic of X-linked inheritance is that fathers cannot pass X-linked traits to their sons. ... infantile neuroaxonal dystrophy Genetic Testing Registry: Osteopetrosis autosomal dominant type 1 Genetic Testing Registry: Osteopetrosis autosomal dominant ...

  1. Genetics and Man

    Science.gov (United States)

    Carter, C. O.

    1973-01-01

    Can genetic evolution be controlled by man in a manner which does not violate a civilized, humane, and democratic ethos? The genetics of health and illhealth and of normal variation are discussed with respect to this question. (PEB)

  2. Genetic Science Learning Center

    Science.gov (United States)

    Genetic Science Learning Center Making science and health easy for everyone to understand Home News Our Team What We Do ... Collaboration Conferences Current Projects Publications Contact The Genetic Science Learning Center at The University of Utah is a ...

  3. Genetics Home Reference: homocystinuria

    Science.gov (United States)

    ... an increased risk of abnormal blood clotting, and brittle bones that are prone to fracture ( osteoporosis ) or other ... information about a genetic condition can statistics provide? Why are some genetic conditions more common in particular ...

  4. Protecting genetic privacy.

    Science.gov (United States)

    Roche, P A; Annas, G J

    2001-05-01

    This article outlines the arguments for and against new rules to protect genetic privacy. We explain why genetic information is different to other sensitive medical information, why researchers and biotechnology companies have opposed new rules to protect genetic privacy (and favour anti-discrimination laws instead), and discuss what can be done to protect privacy in relation to genetic-sequence information and to DNA samples themselves.

  5. Genetic Pathways to Insomnia

    OpenAIRE

    Mackenzie J. Lind; Philip R. Gehrman

    2016-01-01

    This review summarizes current research on the genetics of insomnia, as genetic contributions are thought to be important for insomnia etiology. We begin by providing an overview of genetic methods (both quantitative and measured gene), followed by a discussion of the insomnia genetics literature with regard to each of the following common methodologies: twin and family studies, candidate gene studies, and genome-wide association studies (GWAS). Next, we summarize the most recent gene identif...

  6. Application for photovoltaic micro-grid power forecasting usingimproved wavelet neural networks-based on GA%基于遗传算法小波神经网络的光伏微网发电预测

    Institute of Scientific and Technical Information of China (English)

    刘爱国; 黄泽平; 薛云涛; 汪硕承

    2017-01-01

    It is important for the energy conservation and emissions reduction to accurately predicate the power of photovoltaic micro-grid in a certain period of time in the future.In this paper, by comparing the power generation and meteorological history data, analyzes the factors such as weather, solar radiation and temperature in the photovoltaic power generation prediction, meanwhile, based on the global optimization searching performance of the genetic algorithm and the time-frequency localization of the wavelet neural networks, micro-grid photovoltaic power generation forecasting model has been established.Through case analysis, the results show that wavelet neural network based on genetic algorithm has better learning ability and generalization ability.And in the aspect of micro-grid photovoltaic power, the forecasting data as the network input is more valuable in improving the prediction precision of the model.%准确预测光伏微网在未来某确定的时段内的发电功率,对电力系统稳定和经济运行有着重要意义.文中通过对比发电功率和气象等历史数据,分析了在光伏发电中天气、太阳辐射及温度等因素对发电功率预测的影响,同时综合遗传算法全局快速寻优特性与小波分析的时频局部特性,建立基于遗传算法的小波神经网络光伏微网发电预测模型.结果表明,基于遗传算法的小波神经网络模型的学习能力和泛化能力更强,同时把气象预测数据作为网络的输入有利于提高模型的预测精度.

  7. The genetic difference principle.

    Science.gov (United States)

    Farrelly, Colin

    2004-01-01

    In the newly emerging debates about genetics and justice three distinct principles have begun to emerge concerning what the distributive aim of genetic interventions should be. These principles are: genetic equality, a genetic decent minimum, and the genetic difference principle. In this paper, I examine the rationale of each of these principles and argue that genetic equality and a genetic decent minimum are ill-equipped to tackle what I call the currency problem and the problem of weight. The genetic difference principle is the most promising of the three principles and I develop this principle so that it takes seriously the concerns of just health care and distributive justice in general. Given the strains on public funds for other important social programmes, the costs of pursuing genetic interventions and the nature of genetic interventions, I conclude that a more lax interpretation of the genetic difference principle is appropriate. This interpretation stipulates that genetic inequalities should be arranged so that they are to the greatest reasonable benefit of the least advantaged. Such a proposal is consistent with prioritarianism and provides some practical guidance for non-ideal societies--that is, societies that do not have the endless amount of resources needed to satisfy every requirement of justice.

  8. Phenylketonuria Genetic Screening Simulation

    Science.gov (United States)

    Erickson, Patti

    2012-01-01

    After agreeing to host over 200 students on a daylong genetics field trip, the author needed an easy-to-prepare genetics experiment to accompany the DNA-necklace and gel-electrophoresis activities already planned. One of the student's mothers is a pediatric physician at the local hospital, and she suggested exploring genetic-disease screening…

  9. Genetics Home Reference

    Science.gov (United States)

    ... Page Search Home Health Conditions Genes Chromosomes & mtDNA Resources Help Me Understand Genetics Share: Email Facebook Twitter Genetics Home Reference provides consumer-friendly information about the effects of genetic variation on human health. Health Conditions More than 1,200 health ...

  10. Feline genetics: clinical applications and genetic testing.

    Science.gov (United States)

    Lyons, Leslie A

    2010-11-01

    DNA testing for domestic cat diseases and appearance traits is a rapidly growing asset for veterinary medicine. Approximately 33 genes contain 50 mutations that cause feline health problems or alterations in the cat's appearance. A variety of commercial laboratories can now perform cat genetic diagnostics, allowing both the veterinary clinician and the private owner to obtain DNA test results. DNA is easily obtained from a cat via a buccal swab with a standard cotton bud or cytological brush, allowing DNA samples to be easily sent to any laboratory in the world. The DNA test results identify carriers of the traits, predict the incidence of traits from breeding programs, and influence medical prognoses and treatments. An overall goal of identifying these genetic mutations is the correction of the defect via gene therapies and designer drug therapies. Thus, genetic testing is an effective preventative medicine and a potential ultimate cure. However, genetic diagnostic tests may still be novel for many veterinary practitioners and their application in the clinical setting needs to have the same scrutiny as any other diagnostic procedure. This article will review the genetic tests for the domestic cat, potential sources of error for genetic testing, and the pros and cons of DNA results in veterinary medicine. Highlighted are genetic tests specific to the individual cat, which are a part of the cat's internal genome. Copyright © 2010 Elsevier Inc. All rights reserved.

  11. Adaptive logical stochastic resonance in time-delayed synthetic genetic networks

    Science.gov (United States)

    Zhang, Lei; Zheng, Wenbin; Song, Aiguo

    2018-04-01

    In the paper, the concept of logical stochastic resonance is applied to implement logic operation and latch operation in time-delayed synthetic genetic networks derived from a bacteriophage λ. Clear logic operation and latch operation can be obtained when the network is tuned by modulated periodic force and time-delay. In contrast with the previous synthetic genetic networks based on logical stochastic resonance, the proposed system has two advantages. On one hand, adding modulated periodic force to the background noise can increase the length of the optimal noise plateau of obtaining desired logic response and make the system adapt to varying noise intensity. On the other hand, tuning time-delay can extend the optimal noise plateau to larger range. The result provides possible help for designing new genetic regulatory networks paradigm based on logical stochastic resonance.

  12. Preimplantation Genetic Screening and Preimplantation Genetic Diagnosis.

    Science.gov (United States)

    Sullivan-Pyke, Chantae; Dokras, Anuja

    2018-03-01

    Preimplantation genetic testing encompasses preimplantation genetic screening (PGS) and preimplantation genetic diagnosis (PGD). PGS improves success rates of in vitro fertilization by ensuring the transfer of euploid embryos that have a higher chance of implantation and resulting in a live birth. PGD enables the identification of embryos with specific disease-causing mutations and transfer of unaffected embryos. The development of whole genome amplification and genomic tools, including single nucleotide polymorphism microarrays, comparative genomic hybridization microarrays, and next-generation sequencing, has led to faster, more accurate diagnoses that translate to improved pregnancy and live birth rates. Copyright © 2017 Elsevier Inc. All rights reserved.

  13. 100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Hong, Seunghun [Department of Physics and Astronomy, Seoul National University, Shilim-Dong, Kwanak-Gu, Seoul 151-742 (Korea, Republic of); Park, June; Seong, Maeng-Je [Department of Physics, Chung-Ang University, Heukseok-Dong, Dongjak-Gu, Seoul 156-756 (Korea, Republic of); Jhon, Young Min, E-mail: mseong@cau.ac.kr, E-mail: shong@phya.snu.ac.kr [Korea Institute of Science and Technology, Hawolgok-Dong, Seongbuk-Gu, Seoul 136-791 (Korea, Republic of)

    2010-02-05

    Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of {approx}1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

  14. 100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

    International Nuclear Information System (INIS)

    Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Hong, Seunghun; Park, June; Seong, Maeng-Je; Jhon, Young Min

    2010-01-01

    Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of ∼1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

  15. 100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

    Science.gov (United States)

    Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Park, June; Jhon, Young Min; Seong, Maeng-Je; Hong, Seunghun

    2010-02-01

    Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of ~1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

  16. Detection of 5-hydroxytryptamine (5-HT) in vitro using a hippocampal neuronal network-based biosensor with extracellular potential analysis of neurons.

    Science.gov (United States)

    Hu, Liang; Wang, Qin; Qin, Zhen; Su, Kaiqi; Huang, Liquan; Hu, Ning; Wang, Ping

    2015-04-15

    5-hydroxytryptamine (5-HT) is an important neurotransmitter in regulating emotions and related behaviors in mammals. To detect and monitor the 5-HT, effective and convenient methods are demanded in investigation of neuronal network. In this study, hippocampal neuronal networks (HNNs) endogenously expressing 5-HT receptors were employed as sensing elements to build an in vitro neuronal network-based biosensor. The electrophysiological characteristics were analyzed in both neuron and network levels. The firing rates and amplitudes were derived from signal to determine the biosensor response characteristics. The experimental results demonstrate a dose-dependent inhibitory effect of 5-HT on hippocampal neuron activities, indicating the effectiveness of this hybrid biosensor in detecting 5-HT with a response range from 0.01μmol/L to 10μmol/L. In addition, the cross-correlation analysis of HNNs activities suggests 5-HT could weaken HNN connectivity reversibly, providing more specificity of this biosensor in detecting 5-HT. Moreover, 5-HT induced spatiotemporal firing pattern alterations could be monitored in neuron and network levels simultaneously by this hybrid biosensor in a convenient and direct way. With those merits, this neuronal network-based biosensor will be promising to be a valuable and utility platform for the study of neurotransmitter in vitro. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Optimization of the graph model of the water conduit network, based on the approach of search space reducing

    Science.gov (United States)

    Korovin, Iakov S.; Tkachenko, Maxim G.

    2018-03-01

    In this paper we present a heuristic approach, improving the efficiency of methods, used for creation of efficient architecture of water distribution networks. The essence of the approach is a procedure of search space reduction the by limiting the range of available pipe diameters that can be used for each edge of the network graph. In order to proceed the reduction, two opposite boundary scenarios for the distribution of flows are analysed, after which the resulting range is further narrowed by applying a flow rate limitation for each edge of the network. The first boundary scenario provides the most uniform distribution of the flow in the network, the opposite scenario created the net with the highest possible flow level. The parameters of both distributions are calculated by optimizing systems of quadratic functions in a confined space, which can be effectively performed with small time costs. This approach was used to modify the genetic algorithm (GA). The proposed GA provides a variable number of variants of each gene, according to the number of diameters in list, taking into account flow restrictions. The proposed approach was implemented to the evaluation of a well-known test network - the Hanoi water distribution network [1], the results of research were compared with a classical GA with an unlimited search space. On the test data, the proposed trip significantly reduced the search space and provided faster and more obvious convergence in comparison with the classical version of GA.

  18. What Is Genetic Ancestry Testing?

    Science.gov (United States)

    ... consumer genetic testing? What kinds of direct-to-consumer genetic tests are available? What is genetic ancestry testing? What are the benefits and risks of direct-to-consumer genetic testing? ...

  19. Prenatal Genetic Counseling (For Parents)

    Science.gov (United States)

    ... Videos for Educators Search English Español Prenatal Genetic Counseling KidsHealth / For Parents / Prenatal Genetic Counseling What's in ... can they help your family? What Is Genetic Counseling? Genetic counseling is the process of: evaluating family ...

  20. All about Genetics (For Parents)

    Science.gov (United States)

    ... Videos for Educators Search English Español All About Genetics KidsHealth / For Parents / All About Genetics What's in ... the way they pick up special laboratory dyes. Genetic Problems Errors in the genetic code or "gene ...

  1. Molecular genetics made simple

    Directory of Open Access Journals (Sweden)

    Heba Sh. Kassem

    2012-07-01

    Full Text Available Genetics have undoubtedly become an integral part of biomedical science and clinical practice, with important implications in deciphering disease pathogenesis and progression, identifying diagnostic and prognostic markers, as well as designing better targeted treatments. The exponential growth of our understanding of different genetic concepts is paralleled by a growing list of genetic terminology that can easily intimidate the unfamiliar reader. Rendering genetics incomprehensible to the clinician however, defeats the very essence of genetic research: its utilization for combating disease and improving quality of life. Herein we attempt to correct this notion by presenting the basic genetic concepts along with their usefulness in the cardiology clinic. Bringing genetics closer to the clinician will enable its harmonious incorporation into clinical care, thus not only restoring our perception of its simple and elegant nature, but importantly ensuring the maximal benefit for our patients.

  2. Molecular genetics made simple

    Science.gov (United States)

    Kassem, Heba Sh.; Girolami, Francesca; Sanoudou, Despina

    2012-01-01

    Abstract Genetics have undoubtedly become an integral part of biomedical science and clinical practice, with important implications in deciphering disease pathogenesis and progression, identifying diagnostic and prognostic markers, as well as designing better targeted treatments. The exponential growth of our understanding of different genetic concepts is paralleled by a growing list of genetic terminology that can easily intimidate the unfamiliar reader. Rendering genetics incomprehensible to the clinician however, defeats the very essence of genetic research: its utilization for combating disease and improving quality of life. Herein we attempt to correct this notion by presenting the basic genetic concepts along with their usefulness in the cardiology clinic. Bringing genetics closer to the clinician will enable its harmonious incorporation into clinical care, thus not only restoring our perception of its simple and elegant nature, but importantly ensuring the maximal benefit for our patients. PMID:25610837

  3. BPA genetic monitoring - BPA Genetic Monitoring Project

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Initiated in 1989, this study monitors genetic changes associated with hatchery propagation in multiple Snake River sub-basins for Chinook salmon and steelhead. We...

  4. Social network-based recruitment successfully reveals HIV-1 transmission networks among high-risk individuals in El Salvador.

    Science.gov (United States)

    Dennis, Ann M; Murillo, Wendy; de Maria Hernandez, Flor; Guardado, Maria Elena; Nieto, Ana Isabel; Lorenzana de Rivera, Ivette; Eron, Joseph J; Paz-Bailey, Gabriela

    2013-05-01

    HIV in Central America is concentrated among certain groups such as men who have sex with men (MSM) and female sex workers (FSWs). We compared social recruitment chains and HIV transmission clusters from 699 MSM and 787 FSWs to better understand factors contributing to ongoing HIV transmission in El Salvador. Phylogenies were reconstructed using pol sequences from 119 HIV-positive individuals recruited by respondent-driven sampling (RDS) and compared with RDS chains in 3 cities in El Salvador. Transmission clusters with a mean pairwise genetic distance ≤ 0.015 and Bayesian posterior probabilities =1 were identified. Factors associated with cluster membership were evaluated among MSM. Sequences from 34 (43%) MSM and 4 (10%) FSW grouped in 14 transmission clusters. Clusters were defined by risk group (12 MSM clusters) and geographic residence (only 1 spanned separate cities). In 4 MSM clusters (all n = 2), individuals were also members of the same RDS chain, but only 2 had members directly linked through recruitment. All large clusters (n ≥ 3) spanned >1 RDS chain. Among MSM, factors independently associated with cluster membership included recent infection by BED assay (P = 0.02), sex with stable male partners (P = 0.02), and sex with ≥ 3 male partners in the past year (P = 0.04). We found few HIV transmissions corresponding directly with the social recruitment. However, we identified clustering in nearly one-half of MSM suggesting that RDS recruitment was indirectly but successfully uncovering transmission networks, particularly among recent infections. Interrogating RDS chains with phylogenetic analyses may help refine methods for identifying transmission clusters.

  5. Life cycle inventory and risk assessment of genetic modified perennial ryegrass in a technology foresight perspective

    DEFF Research Database (Denmark)

    Borch, K.; Rasmussen, B.; Schleisner, L.

    2000-01-01

    important and uncertain fac-tors for the future direction of GM crops: 1) publicparticipation in regulation, 2) utility value for the consumers, 3) being first to market GM-ryegrass, and 4) an efficient professional network. Based on the identified drivers several scenar-ios were constructed, of which two......, a methodological approach is suggested to analyse the uncertainties that the biotech industry and the authorities face when implementing genetically modified (GM) crops. These uncertainties embracescientific rationality regarding technological development and risk assessments, as well as ethic political and social...

  6. Molecular Population Genetics.

    Science.gov (United States)

    Casillas, Sònia; Barbadilla, Antonio

    2017-03-01

    Molecular population genetics aims to explain genetic variation and molecular evolution from population genetics principles. The field was born 50 years ago with the first measures of genetic variation in allozyme loci, continued with the nucleotide sequencing era, and is currently in the era of population genomics. During this period, molecular population genetics has been revolutionized by progress in data acquisition and theoretical developments. The conceptual elegance of the neutral theory of molecular evolution or the footprint carved by natural selection on the patterns of genetic variation are two examples of the vast number of inspiring findings of population genetics research. Since the inception of the field, Drosophila has been the prominent model species: molecular variation in populations was first described in Drosophila and most of the population genetics hypotheses were tested in Drosophila species. In this review, we describe the main concepts, methods, and landmarks of molecular population genetics, using the Drosophila model as a reference. We describe the different genetic data sets made available by advances in molecular technologies, and the theoretical developments fostered by these data. Finally, we review the results and new insights provided by the population genomics approach, and conclude by enumerating challenges and new lines of inquiry posed by increasingly large population scale sequence data. Copyright © 2017 Casillas and Barbadilla.

  7. Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

    Science.gov (United States)

    Kasiviswanathan, K.; Sudheer, K.

    2013-05-01

    Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived

  8. A neural network-based approach to noise identification of interferometric GW antennas: the case of the 40 m Caltech laser interferometer

    Energy Technology Data Exchange (ETDEWEB)

    Acernese, F [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Barone, F [Istituto Nazionale di Fisica Nucleare, sez. Napoli, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Rosa, M de [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Rosa, R De [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Eleuteri, A [Istituto Nazionale di Fisica Nucleare, sez. Napoli, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Milano, L [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Tagliaferri, R [Dipartimento di Matematica ed Informatica, Universita di Salerno, via S Allende, I-84081 Baronissi (Salerno) (Italy)

    2002-06-21

    In this paper, a neural network-based approach is presented for the real time noise identification of a GW laser interferometric antenna. The 40 m Caltech laser interferometer output data provide a realistic test bed for noise identification algorithms because of the presence of many relevant effects: violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronic noises, glitches and so on. These effects can be assumed to be present in all the first interferometric long baseline GW antennas such as VIRGO, LIGO, GEO and TAMA. For noise identification, we used the Caltech-40 m laser interferometer data. The results we obtained are pretty good notwithstanding the high initial computational cost. The algorithm we propose is general and robust, taking into account that it does not require a priori information on the data, nor a precise model, and it constitutes a powerful tool for time series data analysis.

  9. A neural network-based approach to noise identification of interferometric GW antennas: the case of the 40 m Caltech laser interferometer

    International Nuclear Information System (INIS)

    Acernese, F; Barone, F; Rosa, M de; Rosa, R De; Eleuteri, A; Milano, L; Tagliaferri, R

    2002-01-01

    In this paper, a neural network-based approach is presented for the real time noise identification of a GW laser interferometric antenna. The 40 m Caltech laser interferometer output data provide a realistic test bed for noise identification algorithms because of the presence of many relevant effects: violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronic noises, glitches and so on. These effects can be assumed to be present in all the first interferometric long baseline GW antennas such as VIRGO, LIGO, GEO and TAMA. For noise identification, we used the Caltech-40 m laser interferometer data. The results we obtained are pretty good notwithstanding the high initial computational cost. The algorithm we propose is general and robust, taking into account that it does not require a priori information on the data, nor a precise model, and it constitutes a powerful tool for time series data analysis

  10. Fiber-wireless integrated mobile backhaul network based on a hybrid millimeter-wave and free-space-optics architecture with an adaptive diversity combining technique.

    Science.gov (United States)

    Zhang, Junwen; Wang, Jing; Xu, Yuming; Xu, Mu; Lu, Feng; Cheng, Lin; Yu, Jianjun; Chang, Gee-Kung

    2016-05-01

    We propose and experimentally demonstrate a novel fiber-wireless integrated mobile backhaul network based on a hybrid millimeter-wave (MMW) and free-space-optics (FSO) architecture using an adaptive combining technique. Both 60 GHz MMW and FSO links are demonstrated and fully integrated with optical fibers in a scalable and cost-effective backhaul system setup. Joint signal processing with an adaptive diversity combining technique (ADCT) is utilized at the receiver side based on a maximum ratio combining algorithm. Mobile backhaul transportation of 4-Gb/s 16 quadrature amplitude modulation frequency-division multiplexing (QAM-OFDM) data is experimentally demonstrated and tested under various weather conditions synthesized in the lab. Performance improvement in terms of reduced error vector magnitude (EVM) and enhanced link reliability are validated under fog, rain, and turbulence conditions.

  11. Classification Technique for Ultrasonic Weld Inspection Signals using a Neural Network based on 2-dimensional fourier Transform and Principle Component Analysis

    International Nuclear Information System (INIS)

    Kim, Jae Joon

    2004-01-01

    Neural network-based signal classification systems are increasingly used in the analysis of large volumes of data obtained in NDE applications. Ultrasonic inspection methods on the other hand are commonly used in the nondestructive evaluation of welds to detect flaws. An important characteristic of ultrasonic inspection is the ability to identify the type of discontinuity that gives rise to a peculiar signal. Standard techniques rely on differences in individual A-scans to classify the signals. This paper proposes an ultrasonic signal classification technique based on the information tying in the neighboring signals. The approach is based on a 2-dimensional Fourier transform and the principal component analysis to generate a reduced dimensional feature vector for classification. Results of applying the technique to data obtained from the inspection of actual steel welds are presented

  12. An in-building network based on community access television integration with quadrature phase-shift keying orthogonal frequency-division multiplexing

    International Nuclear Information System (INIS)

    Chen, Chia-Yi; Lin, Ying-Pyng; Lu, Hai-Han; Wu, Po-Yi; Lin, Huang-Chang; Wu, Hsiao-Wen

    2012-01-01

    An in-building network based on cable television (CATV) integration with quadrature phase-shift keying (QPSK) orthogonal frequency-division multiplexing (OFDM) transport over a combination of single-mode fibers (SMF) and perfluorinated graded-index plastic optical fibers (GI-POF) is proposed and experimentally demonstrated. In this system, a 1558.5 nm optical signal is directly transmitted along two fiber spans (20 km SMF + 25 m GI-POF). An optimum guard band is carefully established to ensure that no very substantial signal interference is induced between the CATV and QPSK OFDM bands. Error free transmission with sufficiently low bit error rate values was achieved for 1.25 Gbps/771.5 MHz QPSK OFDM signals; also, acceptable carrier-to-noise ratio, composite second-order, and composite triple-beat performances were obtained for CATV signals. This proposed network is significant because it is economical and convenient to install. (paper)

  13. A neural network-based approach to noise identification of interferometric GW antennas: the case of the 40 m Caltech laser interferometer

    CERN Document Server

    Acernese, F; Rosa, M D; Rosa, R D; Eleuteri, A; Milano, L; Tagliaferri, R

    2002-01-01

    In this paper, a neural network-based approach is presented for the real time noise identification of a GW laser interferometric antenna. The 40 m Caltech laser interferometer output data provide a realistic test bed for noise identification algorithms because of the presence of many relevant effects: violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronic noises, glitches and so on. These effects can be assumed to be present in all the first interferometric long baseline GW antennas such as VIRGO, LIGO, GEO and TAMA. For noise identification, we used the Caltech-40 m laser interferometer data. The results we obtained are pretty good notwithstanding the high initial computational cost. The algorithm we propose is general and robust, taking into account that it does not require a priori information on the data, nor a precise model, and it constitutes a powerful tool for time series data analysis.

  14. Genetics of nonsyndromic obesity.

    Science.gov (United States)

    Lee, Yung Seng

    2013-12-01

    Common obesity is widely regarded as a complex, multifactorial trait influenced by the 'obesogenic' environment, sedentary behavior, and genetic susceptibility contributed by common and rare genetic variants. This review describes the recent advances in understanding the role of genetics in obesity. New susceptibility loci and genetic variants are being uncovered, but the collective effect is relatively small and could not explain most of the BMI heritability. Yet-to-be identified common and rare variants, epistasis, and heritable epigenetic changes may account for part of the 'missing heritability'. Evidence is emerging about the role of epigenetics in determining obesity susceptibility, mediating developmental plasticity, which confers obesity risk from early life experiences. Genetic prediction scores derived from selected genetic variants, and also differential DNA methylation levels and methylation scores, have been shown to correlate with measures of obesity and response to weight loss intervention. Genetic variants, which confer susceptibility to obesity-related morbidities like nonalcoholic fatty liver disease, were also discovered recently. We can expect discovery of more rare genetic variants with the advent of whole exome and genome sequencing, and also greater understanding of epigenetic mechanisms by which environment influences genetic expression and which mediate the gene-environment interaction.

  15. Preimplantation genetic screening.

    Science.gov (United States)

    Harper, Joyce C

    2018-03-01

    Preimplantation genetic diagnosis was first successfully performed in 1989 as an alternative to prenatal diagnosis for couples at risk of transmitting a genetic or chromosomal abnormality, such as cystic fibrosis, to their child. From embryos generated in vitro, biopsied cells are genetically tested. From the mid-1990s, this technology has been employed as an embryo selection tool for patients undergoing in vitro fertilisation, screening as many chromosomes as possible, in the hope that selecting chromosomally normal embryos will lead to higher implantation and decreased miscarriage rates. This procedure, preimplantation genetic screening, was initially performed using fluorescent in situ hybridisation, but 11 randomised controlled trials of screening using this technique showed no improvement in in vitro fertilisation delivery rates. Progress in genetic testing has led to the introduction of array comparative genomic hybridisation, quantitative polymerase chain reaction, and next generation sequencing for preimplantation genetic screening, and three small randomised controlled trials of preimplantation genetic screening using these new techniques indicate a modest benefit. Other trials are still in progress but, regardless of their results, preimplantation genetic screening is now being offered globally. In the near future, it is likely that sequencing will be used to screen the full genetic code of the embryo.

  16. A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly mutated genes in the human cancer genomes.

    Science.gov (United States)

    Cheng, Feixiong; Zhao, Junfei; Fooksa, Michaela; Zhao, Zhongming

    2016-07-01

    Development of computational approaches and tools to effectively integrate multidomain data is urgently needed for the development of newly targeted cancer therapeutics. We proposed an integrative network-based infrastructure to identify new druggable targets and anticancer indications for existing drugs through targeting significantly mutated genes (SMGs) discovered in the human cancer genomes. The underlying assumption is that a drug would have a high potential for anticancer indication if its up-/down-regulated genes from the Connectivity Map tended to be SMGs or their neighbors in the human protein interaction network. We assembled and curated 693 SMGs in 29 cancer types and found 121 proteins currently targeted by known anticancer or noncancer (repurposed) drugs. We found that the approved or experimental cancer drugs could potentially target these SMGs in 33.3% of the mutated cancer samples, and this number increased to 68.0% by drug repositioning through surveying exome-sequencing data in approximately 5000 normal-tumor pairs from The Cancer Genome Atlas. Furthermore, we identified 284 potential new indications connecting 28 cancer types and 48 existing drugs (adjusted P < .05), with a 66.7% success rate validated by literature data. Several existing drugs (e.g., niclosamide, valproic acid, captopril, and resveratrol) were predicted to have potential indications for multiple cancer types. Finally, we used integrative analysis to showcase a potential mechanism-of-action for resveratrol in breast and lung cancer treatment whereby it targets several SMGs (ARNTL, ASPM, CTTN, EIF4G1, FOXP1, and STIP1). In summary, we demonstrated that our integrative network-based infrastructure is a promising strategy to identify potential druggable targets and uncover new indications for existing drugs to speed up molecularly targeted cancer therapeutics. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All

  17. Towards a genetic architecture of cryptic genetic variation

    Indian Academy of Sciences (India)

    Home; Journals; Journal of Genetics; Volume 84; Issue 3. Towards a genetic architecture of cryptic genetic variation and genetic assimilation: the contribution of K. G. Bateman. Ian Dworkin. Commentary on J. Genet. Classic Volume 84 Issue 3 December 2005 pp 223-226 ...

  18. Genetic Testing Registry

    Science.gov (United States)

    ... RefSeqGene UniGene All Genes & Expression Resources... Genetics & Medicine Bookshelf Database of Genotypes and Phenotypes (dbGaP) Genetic Testing ... ProtMap HomoloGene Protein Clusters All Homology Resources... Literature Bookshelf E-Utilities Journals in NCBI Databases MeSH Database ...

  19. Genetics in the courts

    Energy Technology Data Exchange (ETDEWEB)

    Coyle, Heather; Drell, Dan

    2000-12-01

    Various: (1)TriState 2000 Genetics in the Courts (2) Growing impact of the new genetics on the courts (3)Human testing (4) Legal analysis - in re G.C. (5) Legal analysis - GM ''peanots'', and (6) Legal analysis for State vs Miller

  20. Quo Vadis, Medical Genetics?

    Science.gov (United States)

    Czeizel, Andrew E.

    The beginning of human genetics and its medical part: medical genetics was promising in the early decades of this century. Many genetic diseases and defects with Mendelian origin were identified and it helped families with significant genetic burden to limit their child number. Unfortunately this good start was shadowed by two tragic events. On the one hand, in the 1930s and early 1940s the German fascism brought about the dominance of an unscientific eugenics to mask vile political crimes. People with genetic diseases-defects were forced to sterilisation and several of them were killed. On the other hand, in the 1950s lysenkoism inhibitied the evolution of genetics in the Soviet Union and their satelite countries. Lysenko's doctrine declared genetics as a product of imperialism and a guilty science, therefore leading geneticists were ousted form their posts and some of them were executed or put in prison. Past decades genetics has resulted fantastic new results and achieved a leading position within the natural sciences. To my mind, however, the expected wider use of new eugenics indicates a new tragedy and this Cassandra's prediction is the topic of this presentation.

  1. Formal genetic maps

    African Journals Online (AJOL)

    Mohammad Saad Zaghloul Salem

    2014-12-24

    Dec 24, 2014 ... ome/transcriptome/proteome, experimental induced maps that are intentionally designed and con- ... genetic maps imposed their application in nearly all fields of medical genetics including ..... or genes located adjacent to, or near, them. ...... types of markers, e.g., clinical markers (eye color), genomic.

  2. Cryptic Genetic Variation in Evolutionary Developmental Genetics

    Directory of Open Access Journals (Sweden)

    Annalise B. Paaby

    2016-06-01

    Full Text Available Evolutionary developmental genetics has traditionally been conducted by two groups: Molecular evolutionists who emphasize divergence between species or higher taxa, and quantitative geneticists who study variation within species. Neither approach really comes to grips with the complexities of evolutionary transitions, particularly in light of the realization from genome-wide association studies that most complex traits fit an infinitesimal architecture, being influenced by thousands of loci. This paper discusses robustness, plasticity and lability, phenomena that we argue potentiate major evolutionary changes and provide a bridge between the conceptual treatments of macro- and micro-evolution. We offer cryptic genetic variation and conditional neutrality as mechanisms by which standing genetic variation can lead to developmental system drift and, sheltered within canalized processes, may facilitate developmental transitions and the evolution of novelty. Synthesis of the two dominant perspectives will require recognition that adaptation, divergence, drift and stability all depend on similar underlying quantitative genetic processes—processes that cannot be fully observed in continuously varying visible traits.

  3. Genetics of aggression.

    Science.gov (United States)

    Anholt, Robert R H; Mackay, Trudy F C

    2012-01-01

    Aggression mediates competition for food, mating partners, and habitats and, among social animals, establishes stable dominance hierarchies. In humans, abnormal aggression is a hallmark of neuropsychiatric disorders and can be elicited by environmental factors acting on an underlying genetic susceptibility. Identifying the genetic architecture that predisposes to aggressive behavior in people is challenging because of difficulties in quantifying the phenotype, genetic heterogeneity, and uncontrolled environmental conditions. Studies on mice have identified single-gene mutations that result in hyperaggression, contingent on genetic background. These studies can be complemented by systems genetics approaches in Drosophila melanogaster, in which mutational analyses together with genome-wide transcript analyses, artificial selection studies, and genome-wide analysis of epistasis have revealed that a large segment of the genome contributes to the manifestation of aggressive behavior with widespread epistatic interactions. Comparative genomic analyses based on the principle of evolutionary conservation are needed to enable a complete dissection of the neurogenetic underpinnings of this universal fitness trait.

  4. Genetic improvement of vegetables

    International Nuclear Information System (INIS)

    Jaramillo Vasquez, J.G.

    2001-01-01

    Some genetic bases of the improvement of vegetables are given. The objectives of the genetic improvement and the fundamental stages of this process are done. The sources of genetic variation are indicated and they are related the reproduction systems of the main horticultural species. It is analyzed the concept of genetic inheritance like base to determine the procedures more appropriate of improvement. The approaches are discussed, has more than enough phenotypic value, genetic action and genotypic variance; Equally the heredability concepts and value of improvement. The conventional methods of improvement are described, like they are: the introduction of species or varieties, the selection, the pure line, the pedigree method, the selection for families, the recurrent selection, the selection for unique seed, the haploids method, the selection for heterosis and the synthetic varieties

  5. PCR in forensic genetics

    DEFF Research Database (Denmark)

    Morling, Niels

    2009-01-01

    Since the introduction in the mid-1980s of analyses of minisatellites for DNA analyses, a revolution has taken place in forensic genetics. The subsequent invention of the PCR made it possible to develop forensic genetics tools that allow both very informative routine investigations and still more...... and more advanced, special investigations in cases concerning crime, paternity, relationship, disaster victim identification etc. The present review gives an update on the use of DNA investigations in forensic genetics.......Since the introduction in the mid-1980s of analyses of minisatellites for DNA analyses, a revolution has taken place in forensic genetics. The subsequent invention of the PCR made it possible to develop forensic genetics tools that allow both very informative routine investigations and still more...

  6. Genetics Home Reference: isolated growth hormone deficiency

    Science.gov (United States)

    ... can be inherited? More about Inheriting Genetic Conditions Diagnosis & Management Resources Genetic Testing (4 links) Genetic Testing Registry: Ateleiotic dwarfism Genetic Testing Registry: Autosomal dominant isolated somatotropin deficiency ...

  7. Genetic variation and its maintenance

    International Nuclear Information System (INIS)

    Roberts, D.F.; De Stefano, G.F.

    1986-01-01

    This book contains several papers divided among three sections. The section titles are: Genetic Diversity--Its Dimensions; Genetic Diversity--Its Origin and Maintenance; and Genetic Diversity--Applications and Problems of Complex Characters

  8. Genetics Home Reference: Farber lipogranulomatosis

    Science.gov (United States)

    ... features. Type 1 is the most common, or classical, form of this condition and is associated with ... be inherited? More about Inheriting Genetic Conditions Diagnosis & Management Resources Genetic Testing (1 link) Genetic Testing Registry: ...

  9. Network-assisted crop systems genetics: network inference and integrative analysis.

    Science.gov (United States)

    Lee, Tak; Kim, Hyojin; Lee, Insuk

    2015-04-01

    Although next-generation sequencing (NGS) technology has enabled the decoding of many crop species genomes, most of the underlying genetic components for economically important crop traits remain to be determined. Network approaches have proven useful for the study of the reference plant, Arabidopsis thaliana, and the success of network-based crop genetics will also require the availability of a genome-scale functional networks for crop species. In this review, we discuss how to construct functional networks and elucidate the holistic view of a crop system. The crop gene network then can be used for gene prioritization and the analysis of resequencing-based genome-wide association study (GWAS) data, the amount of which will rapidly grow in the field of crop science in the coming years. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Genetic heterogeneity of retinitis pigmentosa

    OpenAIRE

    Hartono, Hartono

    2015-01-01

    Genetic heterogeneity is a phenomenon in which a genetic disease can be transmitted by several modes of inheritance. The understanding of genetic heterogeneity is important in giving genetic counselling.The presence of genetic heterogeneity can be explained by the existence of:1.different mutant alleles at a single locus, and2.mutant alleles at different loci affecting the same enzyme or protein, or affecting different enzymes or proteins.To have an overall understanding of genetic heterogene...

  11. Genetic effects of radiation

    International Nuclear Information System (INIS)

    Selby, P.B.

    1977-01-01

    Many of the most important findings concerning the genetic effects of radiation have been obtained in the Biology Division of Oak Ridge National Laboratory. The paper focuses on some of the major discoveries made in the Biology Division and on a new method of research that assesses damage to the skeletons of mice whose fathers were irradiated. The results discussed have considerable influence upon estimates of genetic risk in humans from radiation, and an attempt is made to put the estimated amount of genetic damage caused by projected nuclear power development into its proper perspective

  12. Genetically Engineered Cyanobacteria

    Science.gov (United States)

    Zhou, Ruanbao (Inventor); Gibbons, William (Inventor)

    2015-01-01

    The disclosed embodiments provide cyanobacteria spp. that have been genetically engineered to have increased production of carbon-based products of interest. These genetically engineered hosts efficiently convert carbon dioxide and light into carbon-based products of interest such as long chained hydrocarbons. Several constructs containing polynucleotides encoding enzymes active in the metabolic pathways of cyanobacteria are disclosed. In many instances, the cyanobacteria strains have been further genetically modified to optimize production of the carbon-based products of interest. The optimization includes both up-regulation and down-regulation of particular genes.

  13. Statistics for Learning Genetics

    Science.gov (United States)

    Charles, Abigail Sheena

    This study investigated the knowledge and skills that biology students may need to help them understand statistics/mathematics as it applies to genetics. The data are based on analyses of current representative genetics texts, practicing genetics professors' perspectives, and more directly, students' perceptions of, and performance in, doing statistically-based genetics problems. This issue is at the emerging edge of modern college-level genetics instruction, and this study attempts to identify key theoretical components for creating a specialized biological statistics curriculum. The goal of this curriculum will be to prepare biology students with the skills for assimilating quantitatively-based genetic processes, increasingly at the forefront of modern genetics. To fulfill this, two college level classes at two universities were surveyed. One university was located in the northeastern US and the other in the West Indies. There was a sample size of 42 students and a supplementary interview was administered to a select 9 students. Interviews were also administered to professors in the field in order to gain insight into the teaching of statistics in genetics. Key findings indicated that students had very little to no background in statistics (55%). Although students did perform well on exams with 60% of the population receiving an A or B grade, 77% of them did not offer good explanations on a probability question associated with the normal distribution provided in the survey. The scope and presentation of the applicable statistics/mathematics in some of the most used textbooks in genetics teaching, as well as genetics syllabi used by instructors do not help the issue. It was found that the text books, often times, either did not give effective explanations for students, or completely left out certain topics. The omission of certain statistical/mathematical oriented topics was seen to be also true with the genetics syllabi reviewed for this study. Nonetheless

  14. Preimplantation genetic diagnosis

    DEFF Research Database (Denmark)

    Bay, Bjorn; Ingerslev, Hans Jakob; Lemmen, Josephine Gabriela

    2016-01-01

    OBJECTIVE: To study whether women conceiving after preimplantation genetic diagnosis (PGD) and their children have greater risks of adverse pregnancy and birth outcomes compared with children conceived spontaneously or after IVF with or without intracytoplasmic sperm injection (ICSI). DESIGN...

  15. Genetics and Neuromuscular Diseases

    Science.gov (United States)

    ... Testing that reveals a young child’s genet- ic destiny may affect relationships within the family or may ... linked inheritance don’t apply at all. An embryo receives its mitochondria from the mother’s egg cell, ...

  16. LSD and Genetic Damage

    Science.gov (United States)

    Dishotsky, Norman I.; And Others

    1971-01-01

    Reviews studies of the effects of lysergic acid diethylamide (LSD) on man and other organisms. Concludes that pure LSD injected in moderate doses does not cause chromosome or detectable genetic damage and is not a teratogen or carcinogen. (JM)

  17. Genetics Home Reference: piebaldism

    Science.gov (United States)

    ... be a feature of other conditions, such as Waardenburg syndrome ; these conditions have other genetic causes and additional ... 140S. Review. Citation on PubMed Spritz RA. Piebaldism, Waardenburg syndrome, and related disorders of melanocyte development. Semin Cutan ...

  18. Genetics Home Reference: sialuria

    Science.gov (United States)

    ... inheritance of sialuria, an inborn error of feedback inhibition. Am J Hum Genet. 2001 Jun;68(6): ... Links Data Files & API Site Map Subscribe Customer Support USA.gov Copyright Privacy Accessibility FOIA Viewers & Players ...

  19. Genetics of complex diseases

    DEFF Research Database (Denmark)

    Mellerup, Erling; Møller, Gert Lykke; Koefoed, Pernille

    2012-01-01

    A complex disease with an inheritable component is polygenic, meaning that several different changes in DNA are the genetic basis for the disease. Such a disease may also be genetically heterogeneous, meaning that independent changes in DNA, i.e. various genotypes, can be the genetic basis...... for the disease. Each of these genotypes may be characterized by specific combinations of key genetic changes. It is suggested that even if all key changes are found in genes related to the biology of a certain disease, the number of combinations may be so large that the number of different genotypes may be close...... to the number of patients suffering from the disease. This hypothesis is based on a study of bipolar disorder....

  20. Genetics of Diabetes

    Science.gov (United States)

    ... A A A Listen En Español Genetics of Diabetes You've probably wondered how you developed diabetes. ... to develop diabetes than others. What Leads to Diabetes? Type 1 and type 2 diabetes have different ...

  1. [The genetics of addictions].

    Science.gov (United States)

    Ibañez Cuadrado, Angela

    2008-01-01

    The addictions are common chronic psychiatric diseases which represent a serious worldwide public-health problem. They have a high prevalence and negative effects at individual, family and societal level, with a high sanitary cost. Epidemiological genetic research has revealed that addictions are moderately to highly heritable. Also the investigation has evidenced that environmental and genetic factors contribute to individual differences in vulnerability to addictions. Advances in the neurobiology of addiction joined to the development of new molecular genetic technologies, have led to the identification of a variety of underlying genes and pathways in addiction process, leading to the description of common molecular mechanisms in substance and behaviour dependencies. Identifying gene-environment interactions is a crucial issue in future research. Other major goal in genetic research is the identification of new therapeutic targets for treatment and prevention.

  2. Genetics for the ophthalmologist

    Directory of Open Access Journals (Sweden)

    Karthikeyan A Sadagopan

    2012-01-01

    Full Text Available The eye has played a major role in human genomics including gene therapy. It is the fourth most common organ system after integument (skin, hair and nails, nervous system, and musculoskeletal system to be involved in genetic disorders. The eye is involved in single gene disorders and those caused by multifactorial etiology. Retinoblastoma was the first human cancer gene to be cloned. Leber hereditary optic neuropathy was the first mitochondrial disorder described. X-Linked red-green color deficiency was the first X-linked disorder described. The eye, unlike any other body organ, allows directly visualization of genetic phenomena such as skewed X-inactivation in the fundus of a female carrier of ocular albinism. Basic concepts of genetics and their application to clinical ophthalmological practice are important not only in making a precise diagnosis and appropriate referral, but also in management and genetic counseling.

  3. Genetics Home Reference: sitosterolemia

    Science.gov (United States)

    ... also helps regulate cholesterol levels in a similar fashion; normally about 50 percent of cholesterol in the ... 10 All Bulletins Features What is direct-to-consumer genetic testing? What are genome editing and CRISPR- ...

  4. Review of genetic concepts

    International Nuclear Information System (INIS)

    Robinson, A.

    1984-01-01

    In recent years, practitioners of medicine have become increasingly aware of the importance of genetics in the understanding of physical and mental health and in the management of disease. The last decades have witnessed unprecedented developments in genetics that have increased our understanding of the basic processes of heredity enormously. New techniques and understanding have provided insights directly applicable to medicine. The fundamental fact of heredity may be considered the ability of living organisms to produce offspring that resemble their parents more than others. One of the basic characteristics of the human condition is the uniqueness and diversity of all individuals. This results from their genetic individuality (with the exception of identical twins) and the interaction of the genetic constitution (the genome) with the environment, which is generally unique to the individual as well. In short, the interaction of genes with the environment is what confers biologic uniqueness to all humans

  5. Genetic Sample Inventory

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This database archives genetic tissue samples from marine mammals collected primarily from the U.S. east coast. The collection includes samples from field programs,...

  6. Specific Genetic Disorders

    Science.gov (United States)

    ... Care Genomic Medicine Working Group New Horizons and Research Patient Management Policy and Ethics Issues Quick Links for Patient Care Education All About the Human Genome Project Fact Sheets Genetic Education Resources for ...

  7. Genetic Mutations in Cancer

    Science.gov (United States)

    Many different types of genetic mutations are found in cancer cells. This infographic outlines certain types of alterations that are present in cancer, such as missense, nonsense, frameshift, and chromosome rearrangements.

  8. Genetic Sample Inventory - NRDA

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This database archives genetic tissue samples from marine mammals collected in the North-Central Gulf of Mexico from 2010-2015. The collection includes samples from...

  9. Regulation of Genetic Tests

    Science.gov (United States)

    ... for Genomics Research Intellectual Property Issues in Genetics Archive Online Bioethics Resources Privacy in Genomics Regulation of ... are not regulated, meaning that they go to market without any independent analysis to verify the claims ...

  10. Genetics of osteoarthritis.

    Science.gov (United States)

    Rodriguez-Fontenla, Cristina; Gonzalez, Antonio

    2015-01-01

    Osteoarthritis (OA) is a complex disease caused by the interaction of multiple genetic and environmental factors. This review focuses on the studies that have contributed to the discovery of genetic susceptibility factors in OA. The most relevant associations discovered until now are discussed in detail: GDF-5, 7q22 locus, MCF2L, DOT1L, NCOA3 and also some important findings from the arcOGEN study. Moreover, the different approaches that can be used to minimize the specific problems of the study of OA genetics are discussed. These include the study of microsatellites, phenotype standardization and other methods such as meta-analysis of GWAS and gene-based analysis. It is expected that these new approaches contribute to finding new susceptibility genetic factors for OA. Copyright © 2014 Elsevier España, S.L.U. All rights reserved.

  11. Evaluating human genetic diversity

    National Research Council Canada - National Science Library

    ... into human evolution and origins and serving as a springboard for important medical research. It also addresses issues of confidentiality and individual privacy for participants in genetic diversity research studies.

  12. Genetics Home Reference: hypercholesterolemia

    Science.gov (United States)

    ... Encyclopedia: Familial hypercholesterolemia Encyclopedia: High blood cholesterol and triglycerides Encyclopedia: Xanthoma Health Topic: Cholesterol Health Topic: High Cholesterol in Children and Teens Health Topic: Lipid Metabolism Disorders Genetic and Rare Diseases Information Center (1 ...

  13. Genetics of bipolar disorder

    Directory of Open Access Journals (Sweden)

    Kerner B

    2014-02-01

    Full Text Available Berit Kerner Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA Abstract: Bipolar disorder is a common, complex genetic disorder, but the mode of transmission remains to be discovered. Many researchers assume that common genomic variants carry some risk for manifesting the disease. The research community has celebrated the first genome-wide significant associations between common single nucleotide polymorphisms (SNPs and bipolar disorder. Currently, attempts are under way to translate these findings into clinical practice, genetic counseling, and predictive testing. However, some experts remain cautious. After all, common variants explain only a very small percentage of the genetic risk, and functional consequences of the discovered SNPs are inconclusive. Furthermore, the associated SNPs are not disease specific, and the majority of individuals with a “risk” allele are healthy. On the other hand, population-based genome-wide studies in psychiatric disorders have rediscovered rare structural variants and mutations in genes, which were previously known to cause genetic syndromes and monogenic Mendelian disorders. In many Mendelian syndromes, psychiatric symptoms are prevalent. Although these conditions do not fit the classic description of any specific psychiatric disorder, they often show nonspecific psychiatric symptoms that cross diagnostic boundaries, including intellectual disability, behavioral abnormalities, mood disorders, anxiety disorders, attention deficit, impulse control deficit, and psychosis. Although testing for chromosomal disorders and monogenic Mendelian disorders is well established, testing for common variants is still controversial. The standard concept of genetic testing includes at least three broad criteria that need to be fulfilled before new genetic tests should be introduced: analytical validity, clinical validity, and clinical utility. These criteria are

  14. Applications of Genetic Programming

    DEFF Research Database (Denmark)

    Gaunholt, Hans; Toma, Laura

    1996-01-01

    In this report a study of genetic programming (GP) has been performed with respect to a number of applications such as Symbolic function regression, Solving Symbolic Differential Equations, Image encoding, the ant problem etc.......In this report a study of genetic programming (GP) has been performed with respect to a number of applications such as Symbolic function regression, Solving Symbolic Differential Equations, Image encoding, the ant problem etc....

  15. Genetics and developmental biology

    International Nuclear Information System (INIS)

    Barnett, W.E.

    1975-01-01

    Progress is reported on research activities in the fields of mutagenesis in Haemophilus influenzae and Escherichia coli; radioinduced chromosomal aberrations in mammalian germ cells; effects of uv radiation on xeroderma pigmentosum skin cells; mutations in Chinese hamster ovary cells; radioinduced hemoglobin variants in the mouse; analysis of mutants in yeast; Drosophila genetics; biochemical genetics of Neurospora; DNA polymerase activity in Xenopus laevis oocytes; uv-induced damage in Bacillus subtilis; and others

  16. Christianity, health, and genetics.

    Science.gov (United States)

    Smith, David H

    2009-02-15

    Health is an intrinsic value that Christians should respect, but it is not the highest value. Christians should be willing to jeopardize their own health for the health of others, and should repudiate any idea that genetic problems are the result of sin. Rather, sin leads us to make genetic problems harder to live with than they should be. (c) 2009 Wiley-Liss, Inc.

  17. Somatic and genetic effects

    International Nuclear Information System (INIS)

    Broerse, J.J.; Barendsen, G.W.; Kal, H.B.; Kogel, A.J. van der

    1983-01-01

    This book contains the extended abstracts of the contributions of the poster workshop sessions on somatic and genetic effects of the 7th international congress of radiation research. They cover the following main topics: haematopoietic and immune systems, mechanisms of late effects in various tissues, endogenous and exogenous factors in radiation carcinogenesis, teratogenic effects, genetic effects, in vitro transformation, tumour induction in different tissues, carcinogenesis in incorporated tissues, cancer epidemology and risk assessment. refs.; figs.; tabs

  18. Contemporary Genetics for Gender Researchers: Not Your Grandma's Genetics Anymore

    Science.gov (United States)

    Salk, Rachel H.; Hyde, Janet S.

    2012-01-01

    Over the past century, much of genetics was deterministic, and feminist researchers framed justified criticisms of genetics research. However, over the past two decades, genetics research has evolved remarkably and has moved far from earlier deterministic approaches. Our article provides a brief primer on modern genetics, emphasizing contemporary…

  19. 50. Brazilian congress on genetics. 50 years developing genetics. Abstracts

    International Nuclear Information System (INIS)

    2004-01-01

    Use of radioisotopes and ionizing radiations in genetics is presented. Several aspects related to men, animals,plants and microorganisms are reported highlighting biological radiation effects, evolution, mutagenesis and genetic engineering. Genetic mapping, gene mutations, genetic diversity, DNA damages, plant cultivation and plant grow are studied as well

  20. Genetics & sport: bioethical concerns.

    Science.gov (United States)

    Miah, Andy

    2012-12-01

    This paper provides an overview of the ethical issues pertaining to the use of genetic insights and techniques in sport. Initially, it considers a range of scientific findings that have stimulated debate about the ethical issues associated with genetics applied to sport. It also outlines some of the early policy responses to these discoveries from world leading sports organizations, along with knowledge about actual use of gene technologies in sport. Subsequently, it considers the challenges with distinguishing between therapeutic use and human enhancement within genetic science, which is a particularly important issue for the world of sport. Next, particular attention is given to the use of genetic information, which raises questions about the legitimacy and reliability of genetic tests, along with the potential public value of having DNA databanks to economize in health care. Finally, the ethics of gene transfer are considered, inviting questions into the values of sport and humanity. It argues that, while gene modification may seem conceptually similar to other forms of doping, the requirements upon athletes are such that new forms of enhancement become increasingly necessary to discover. Insofar as genetic science is able to create safer, more effective techniques of human modification, then it may be an appealing route through which to modify athletes to safeguard the future of elite sports as enterprises of human excellence.

  1. Genetic and environmental interactions

    International Nuclear Information System (INIS)

    Strong, L.C.

    1977-01-01

    Cancer may result from a multistage process occurring over a long period of time. Presumably, initial and progressive stages of carcinogenesis may be modified by both genetic and environmental factors. Theoretically, genetic factors may alter susceptibility to the carcinogenic effects of an environmental agent at the initial exposure due to variation in metabolism of the carcinogen or variation in specific target cell response to the active carcinogen, or during the latent phase due to numerous factors that might increase the probability of tumor expression, including growth-promoting factors or immunodeficiency states. Observed genetic and environmental interactions in carcinogenesis include an association between genetically determined inducibility of aryl hydrocarbon hydroxylase and smoking-related cancers, familial susceptibility to certain environmental carcinogens, an association between hereditary disorders of mutagenesis and carcinogenesis, and enhancement of tissue-specific, dominantly inherited tumor predisposition by radiation. Multiple primary tumors occur frequently in genetically predisposed individuals. Specific markers for susceptibility must be sought in order that high-risk individuals be identified and appropriate measures taken for early cancer detection or prevention. Study of the nature of the genetically determined susceptibility and interactions with environmental agents may be revealing in the understanding of carcinogenesis in general

  2. Dissection of functional lncRNAs in Alzheimer's disease by construction and analysis of lncRNA-mRNA networks based on competitive endogenous RNAs.

    Science.gov (United States)

    Wang, Lian-Kun; Chen, Xiao-Feng; He, Dan-Dan; Li, You; Fu, Jin

    2017-04-08

    Alzheimer's disease (AD) is a neurodegenerative disorder that is the most common cause of dementia in the elderly, and intracellular neurofibrillary tangles (NFTs) are one of the pathological features of AD. Recent studies have suggested long noncoding RNAs (lncRNAs) play important roles in AD. Competing endogenous RNAs (ceRNAs) is a mechanism that has recently been proposed, in which lncRNAs compete for common miRNA-binding sites with mRNAs. However, the roles of lncRNAs and ceRNA in AD NFTs is limited. In this study, we constructed a global triple network based on ceRNA theory, then an AD NFT lncRNA-mRNA network (NFTLMN) was generated. By analyzing the NFTLMN, three lncRNAs (AP000265.1, KB-1460A1.5 and RP11-145M9.4), which are highly related with AD NFTs were identified. To further explore the cross-talk between mRNAs and lncRNAs, a clustering module analysis was performed on the NFTLMN and two AD NFT related modules were identified. Our study provides a better understanding of the molecular basis of AD NFTs and may offer novel treatment strategies for AD. Copyright © 2016. Published by Elsevier Inc.

  3. DCE: A Distributed Energy-Efficient Clustering Protocol for Wireless Sensor Network Based on Double-Phase Cluster-Head Election.

    Science.gov (United States)

    Han, Ruisong; Yang, Wei; Wang, Yipeng; You, Kaiming

    2017-05-01

    Clustering is an effective technique used to reduce energy consumption and extend the lifetime of wireless sensor network (WSN). The characteristic of energy heterogeneity of WSNs should be considered when designing clustering protocols. We propose and evaluate a novel distributed energy-efficient clustering protocol called DCE for heterogeneous wireless sensor networks, based on a Double-phase Cluster-head Election scheme. In DCE, the procedure of cluster head election is divided into two phases. In the first phase, tentative cluster heads are elected with the probabilities which are decided by the relative levels of initial and residual energy. Then, in the second phase, the tentative cluster heads are replaced by their cluster members to form the final set of cluster heads if any member in their cluster has more residual energy. Employing two phases for cluster-head election ensures that the nodes with more energy have a higher chance to be cluster heads. Energy consumption is well-distributed in the proposed protocol, and the simulation results show that DCE achieves longer stability periods than other typical clustering protocols in heterogeneous scenarios.

  4. Artificial neural network-based predictive model for bacterial growth in a simulated medium of modified-atmosphere-packed cooked meat products.

    Science.gov (United States)

    Lou, W; Nakai, S

    2001-04-01

    The data of Devilieghere et al. (Int. J. Food Microbiol. 1999, 46, 57--70) on bacterial growth in a simulated medium of modified-atmosphere-packed cooked meat products was processed for estimating maximum specific growth rate mu(max) and lag phase lambda of Lactobacillus sake using artificial neural networks-based model (ANNM) computation. The comparison between ANNM and response surface methodology (RSM) model showed that the accuracy of ANNM prediction was higher than that of RSM. Two-dimensional and three-dimensional plots of the response surfaces revealed that the relationships of water activity a(w), temperature T, and dissolved CO(2) concentration with mu(max) and lambda were complicated, not just linear or second-order relations. Furthermore, it was possible to compute the sensitivity of the model outputs against each input parameter by using ANNM. The results showed that mu(max) was most sensitive to a(w), T, and dissolved CO(2) in this order; whereas lambda was sensitive to T the most, followed by a(w), and dissolved CO(2) concentrations.

  5. Identification of Gene Biomarkers for Distinguishing Small-Cell Lung Cancer from Non-Small-Cell Lung Cancer Using a Network-Based Approach

    Directory of Open Access Journals (Sweden)

    Fei Long

    2015-01-01

    Full Text Available Lung cancer consists of two main subtypes: small-cell lung cancer (SCLC and non-small-cell lung cancer (NSCLC that are classified according to their physiological phenotypes. In this study, we have developed a network-based approach to identify molecular biomarkers that can distinguish SCLC from NSCLC. By identifying positive and negative coexpression gene pairs in normal lung tissues, SCLC, or NSCLC samples and using functional association information from the STRING network, we first construct a lung cancer-specific gene association network. From the network, we obtain gene modules in which genes are highly functionally associated with each other and are either positively or negatively coexpressed in the three conditions. Then, we identify gene modules that not only are differentially expressed between cancer and normal samples, but also show distinctive expression patterns between SCLC and NSCLC. Finally, we select genes inside those modules with discriminating coexpression patterns between the two lung cancer subtypes and predict them as candidate biomarkers that are of diagnostic use.

  6. Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters

    Science.gov (United States)

    Liu, Zhijian; Liu, Kejun; Li, Hao; Zhang, Xinyu; Jin, Guangya; Cheng, Kewei

    2015-01-01

    Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915measuredsamples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rateand heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08. PMID:26624613

  7. Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data

    Science.gov (United States)

    Koleti, Amar; Terryn, Raymond; Stathias, Vasileios; Chung, Caty; Cooper, Daniel J; Turner, John P; Vidović, Dušica; Forlin, Michele; Kelley, Tanya T; D’Urso, Alessandro; Allen, Bryce K; Torre, Denis; Jagodnik, Kathleen M; Wang, Lily; Jenkins, Sherry L; Mader, Christopher; Niu, Wen; Fazel, Mehdi; Mahi, Naim; Pilarczyk, Marcin; Clark, Nicholas; Shamsaei, Behrouz; Meller, Jarek; Vasiliauskas, Juozas; Reichard, John; Medvedovic, Mario; Ma’ayan, Avi; Pillai, Ajay

    2018-01-01

    Abstract The Library of Integrated Network-based Cellular Signatures (LINCS) program is a national consortium funded by the NIH to generate a diverse and extensive reference library of cell-based perturbation-response signatures, along with novel data analytics tools to improve our understanding of human diseases at the systems level. In contrast to other large-scale data generation efforts, LINCS Data and Signature Generation Centers (DSGCs) employ a wide range of assay technologies cataloging diverse cellular responses. Integration of, and unified access to LINCS data has therefore been particularly challenging. The Big Data to Knowledge (BD2K) LINCS Data Coordination and Integration Center (DCIC) has developed data standards specifications, data processing pipelines, and a suite of end-user software tools to integrate and annotate LINCS-generated data, to make LINCS signatures searchable and usable for different types of users. Here, we describe the LINCS Data Portal (LDP) (http://lincsportal.ccs.miami.edu/), a unified web interface to access datasets generated by the LINCS DSGCs, and its underlying database, LINCS Data Registry (LDR). LINCS data served on the LDP contains extensive metadata and curated annotations. We highlight the features of the LDP user interface that is designed to enable search, browsing, exploration, download and analysis of LINCS data and related curated content. PMID:29140462

  8. High-Speed Rail Train Timetabling Problem: A Time-Space Network Based Method with an Improved Branch-and-Price Algorithm

    Directory of Open Access Journals (Sweden)

    Bisheng He

    2014-01-01

    Full Text Available A time-space network based optimization method is designed for high-speed rail train timetabling problem to improve the service level of the high-speed rail. The general time-space path cost is presented which considers both the train travel time and the high-speed rail operation requirements: (1 service frequency requirement; (2 stopping plan adjustment; and (3 priority of train types. Train timetabling problem based on time-space path aims to minimize the total general time-space path cost of all trains. An improved branch-and-price algorithm is applied to solve the large scale integer programming problem. When dealing with the algorithm, a rapid branching and node selection for branch-and-price tree and a heuristic train time-space path generation for column generation are adopted to speed up the algorithm computation time. The computational results of a set of experiments on China’s high-speed rail system are presented with the discussions about the model validation, the effectiveness of the general time-space path cost, and the improved branch-and-price algorithm.

  9. Network-based characterization of the synaptic proteome reveals that removal of epigenetic regulator Prmt8 restricts proteins associated with synaptic maturation.

    Science.gov (United States)

    Lee, Patrick Kia Ming; Goh, Wilson Wen Bin; Sng, Judy Chia Ghee

    2017-02-01

    The brain adapts to dynamic environmental conditions by altering its epigenetic state, thereby influencing neuronal transcriptional programs. An example of an epigenetic modification is protein methylation, catalyzed by protein arginine methyltransferases (PRMT). One member, Prmt8, is selectively expressed in the central nervous system during a crucial phase of early development, but little else is known regarding its function. We hypothesize Prmt8 plays a role in synaptic maturation during development. To evaluate this, we used a proteome-wide approach to characterize the synaptic proteome of Prmt8 knockout versus wild-type mice. Through comparative network-based analyses, proteins and functional clusters related to neurite development were identified to be differentially regulated between the two genotypes. One interesting protein that was differentially regulated was tenascin-R (TNR). Chromatin immunoprecipitation demonstrated binding of PRMT8 to the tenascin-r (Tnr) promoter. TNR, a component of perineuronal nets, preserves structural integrity of synaptic connections within neuronal networks during the development of visual-somatosensory cortices. On closer inspection, Prmt8 removal increased net formation and decreased inhibitory parvalbumin-positive (PV+) puncta on pyramidal neurons, thereby hindering the maturation of circuits. Consequently, visual acuity of the knockout mice was reduced. Our results demonstrated Prmt8's involvement in synaptic maturation and its prospect as an epigenetic modulator of developmental neuroplasticity by regulating structural elements such as the perineuronal nets. © 2016 International Society for Neurochemistry.

  10. Using Artificial Intelligence to Retrieve the Optimal Parameters and Structures of Adaptive Network-Based Fuzzy Inference System for Typhoon Precipitation Forecast Modeling

    Directory of Open Access Journals (Sweden)

    Chien-Lin Huang

    2015-01-01

    Full Text Available This study aims to construct a typhoon precipitation forecast model providing forecasts one to six hours in advance using optimal model parameters and structures retrieved from a combination of the adaptive network-based fuzzy inference system (ANFIS and artificial intelligence. To enhance the accuracy of the precipitation forecast, two structures were then used to establish the precipitation forecast model for a specific lead-time: a single-model structure and a dual-model hybrid structure where the forecast models of higher and lower precipitation were integrated. In order to rapidly, automatically, and accurately retrieve the optimal parameters and structures of the ANFIS-based precipitation forecast model, a tabu search was applied to identify the adjacent radius in subtractive clustering when constructing the ANFIS structure. The coupled structure was also employed to establish a precipitation forecast model across short and long lead-times in order to improve the accuracy of long-term precipitation forecasts. The study area is the Shimen Reservoir, and the analyzed period is from 2001 to 2009. Results showed that the optimal initial ANFIS parameters selected by the tabu search, combined with the dual-model hybrid method and the coupled structure, provided the favors in computation efficiency and high-reliability predictions in typhoon precipitation forecasts regarding short to long lead-time forecasting horizons.

  11. Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters.

    Science.gov (United States)

    Liu, Zhijian; Liu, Kejun; Li, Hao; Zhang, Xinyu; Jin, Guangya; Cheng, Kewei

    2015-01-01

    Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915 measured samples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rate and heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08.

  12. Development of a network-based information infrastructure for fisheries and hydropower information in the Columbia River Basin : Final project report; TOPICAL

    International Nuclear Information System (INIS)

    Scheibe, Timothy D.; Johnson, Gary E.; Perkins, Bill

    1997-01-01

    The goal of this project was to help develop technology and a unified structure to access and disseminate information related to the Bonneville Power Administration's fish and wildlife responsibility in the Pacific Northwest. BPA desires to increase access to, and exchange of, information produced by the Environment Fish, and Wildlife Group in concert with regional partners. Historically, data and information have been managed through numerous centralized, controlled information systems. Fisheries information has been fragmented and not widely exchanged. Where exchange has occurred, it often is not timely enough to allow resource managers to effectively use the information to guide planning and decision making. This project (and related projects) have successfully developed and piloted a network-based infrastructure that will serve as a vehicle to transparently connect existing information systems in a manner that makes information exchange efficient and inexpensive. This project was designed to provide a mechanism to help BPA address measures in the Northwest Power Planning Council's (NPPC) Fish and Wildlife program: 3.2H Disseminate Research and Monitoring Information and 5.1A.5 manage water supplies in accordance with the Annual Implementation Work Plan. This project also provided resources that can be used to assist monitoring and evaluation of the Program

  13. Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis

    Directory of Open Access Journals (Sweden)

    Zhongrong Zhang

    2016-01-01

    Full Text Available Wind energy has increasingly played a vital role in mitigating conventional resource shortages. Nevertheless, the stochastic nature of wind poses a great challenge when attempting to find an accurate forecasting model for wind power. Therefore, precise wind power forecasts are of primary importance to solve operational, planning and economic problems in the growing wind power scenario. Previous research has focused efforts on the deterministic forecast of wind power values, but less attention has been paid to providing information about wind energy. Based on an optimal Adaptive-Network-Based Fuzzy Inference System (ANFIS and Singular Spectrum Analysis (SSA, this paper develops a hybrid uncertainty forecasting model, IFASF (Interval Forecast-ANFIS-SSA-Firefly Alogorithm, to obtain the upper and lower bounds of daily average wind power, which is beneficial for the practical operation of both the grid company and independent power producers. To strengthen the practical ability of this developed model, this paper presents a comparison between IFASF and other benchmarks, which provides a general reference for this aspect for statistical or artificially intelligent interval forecast methods. The comparison results show that the developed model outperforms eight benchmarks and has a satisfactory forecasting effectiveness in three different wind farms with two time horizons.

  14. Infrared variation reduction by simultaneous background suppression and target contrast enhancement for deep convolutional neural network-based automatic target recognition

    Science.gov (United States)

    Kim, Sungho

    2017-06-01

    Automatic target recognition (ATR) is a traditionally challenging problem in military applications because of the wide range of infrared (IR) image variations and the limited number of training images. IR variations are caused by various three-dimensional target poses, noncooperative weather conditions (fog and rain), and difficult target acquisition environments. Recently, deep convolutional neural network-based approaches for RGB images (RGB-CNN) showed breakthrough performance in computer vision problems, such as object detection and classification. The direct use of RGB-CNN to the IR ATR problem fails to work because of the IR database problems (limited database size and IR image variations). An IR variation-reduced deep CNN (IVR-CNN) to cope with the problems is presented. The problem of limited IR database size is solved by a commercial thermal simulator (OKTAL-SE). The second problem of IR variations is mitigated by the proposed shifted ramp function-based intensity transformation. This can suppress the background and enhance the target contrast simultaneously. The experimental results on the synthesized IR images generated by the thermal simulator (OKTAL-SE) validated the feasibility of IVR-CNN for military ATR applications.

  15. Development of a Network-Based Information Infrastructure for Fisheries and Hydropower Information in the Columbia River Basin : Final Project Report.

    Energy Technology Data Exchange (ETDEWEB)

    Scheibe, Timothy D.; Johnson, Gary E.; Perkins, Bill

    1997-05-01

    The goal of this project was to help develop technology and a unified structure to access and disseminate information related to the Bonneville Power Administration's fish and wildlife responsibility in the Pacific Northwest. BPA desires to increase access to, and exchange of, information produced by the Environment Fish, and Wildlife Group in concert with regional partners. Historically, data and information have been managed through numerous centralized, controlled information systems. Fisheries information has been fragmented and not widely exchanged. Where exchange has occurred, it often is not timely enough to allow resource managers to effectively use the information to guide planning and decision making. This project (and related projects) have successfully developed and piloted a network-based infrastructure that will serve as a vehicle to transparently connect existing information systems in a manner that makes information exchange efficient and inexpensive. This project was designed to provide a mechanism to help BPA address measures in the Northwest Power Planning Council's (NPPC) Fish and Wildlife program: 3.2H Disseminate Research and Monitoring Information and 5.1A.5 manage water supplies in accordance with the Annual Implementation Work Plan. This project also provided resources that can be used to assist monitoring and evaluation of the Program.

  16. Network-based Database Course

    DEFF Research Database (Denmark)

    Nielsen, J.N.; Knudsen, Morten; Nielsen, Jens Frederik Dalsgaard

    A course in database design and implementation has been de- signed, utilizing existing network facilities. The course is an elementary course for students of computer engineering. Its purpose is to give the students a theoretical database knowledge as well as practical experience with design...... and implementation. A tutorial relational database and the students self-designed databases are implemented on the UNIX system of Aalborg University, thus giving the teacher the possibility of live demonstrations in the lecture room, and the students the possibility of interactive learning in their working rooms...

  17. Network-based functional enrichment

    Directory of Open Access Journals (Sweden)

    Poirel Christopher L

    2011-11-01

    Full Text Available Abstract Background Many methods have been developed to infer and reason about molecular interaction networks. These approaches often yield networks with hundreds or thousands of nodes and up to an order of magnitude more edges. It is often desirable to summarize the biological information in such networks. A very common approach is to use gene function enrichment analysis for this task. A major drawback of this method is that it ignores information about the edges in the network being analyzed, i.e., it treats the network simply as a set of genes. In this paper, we introduce a novel method for functional enrichment that explicitly takes network interactions into account. Results Our approach naturally generalizes Fisher’s exact test, a gene set-based technique. Given a function of interest, we compute the subgraph of the network induced by genes annotated to this function. We use the sequence of sizes of the connected components of this sub-network to estimate its connectivity. We estimate the statistical significance of the connectivity empirically by a permutation test. We present three applications of our method: i determine which functions are enriched in a given network, ii given a network and an interesting sub-network of genes within that network, determine which functions are enriched in the sub-network, and iii given two networks, determine the functions for which the connectivity improves when we merge the second network into the first. Through these applications, we show that our approach is a natural alternative to network clustering algorithms. Conclusions We presented a novel approach to functional enrichment that takes into account the pairwise relationships among genes annotated by a particular function. Each of the three applications discovers highly relevant functions. We used our methods to study biological data from three different organisms. Our results demonstrate the wide applicability of our methods. Our algorithms are implemented in C++ and are freely available under the GNU General Public License at our supplementary website. Additionally, all our input data and results are available at http://bioinformatics.cs.vt.edu/~murali/supplements/2011-incob-nbe/.

  18. [Genetic aspects of genealogy].

    Science.gov (United States)

    Tetushkin, E Iu

    2011-11-01

    The supplementary historical discipline genealogy is also a supplementary genetic discipline. In its formation, genetics borrowed from genealogy some methods of pedigree analysis. In the 21th century, it started receiving contribution from computer-aided genealogy and genetic (molecular) genealogy. The former provides novel tools for genetics, while the latter, which employing genetic methods, enriches genetics with new evidence. Genealogists formulated three main laws ofgenealogy: the law of three generations, the law of doubling the ancestry number, and the law of declining ancestry. The significance and meaning of these laws can be fully understood only in light of genetics. For instance, a controversy between the exponential growth of the number of ancestors of an individual, i.e., the law of doubling the ancestry number, and the limited number of the humankind is explained by the presence of weak inbreeding because of sibs' interference; the latter causes the pedigrees' collapse, i.e., explains also the law of diminishing ancestry number. Mathematic modeling of pedigrees' collapse presented in a number of studies showed that the number of ancestors of each individual attains maximum in a particular generation termed ancestry saturated generation. All representatives of this and preceding generation that left progeny are common ancestors of all current members of the population. In subdivided populations, these generations are more ancient than in panmictic ones, whereas in small isolates and social strata with limited numbers of partners, they are younger. The genealogical law of three generations, according to which each hundred years contain on average three generation intervals, holds for generation lengths for Y-chromosomal DNA, typically equal to 31-32 years; for autosomal and mtDNA, this time is somewhat shorter. Moving along ascending lineas, the number of genetically effective ancestors transmitting their DNA fragment to descendants increases far

  19. From observational to dynamic genetics

    Directory of Open Access Journals (Sweden)

    Claire M. A. Haworth

    2014-01-01

    Full Text Available Twin and family studies have shown that most traits are at least moderately heritable. But what are the implications of finding genetic influence for the design of intervention and prevention programs? For complex traits, heritability does not mean immutability, and research has shown that genetic influences can change with age, context and in response to behavioural and drug interventions. The most significant implications for intervention will come when we move from observational genetics to investigating dynamic genetics, including genetically sensitive interventions. Future interventions should be designed to overcome genetic risk and draw upon genetic strengths by changing the environment.

  20. Genetics of human hydrocephalus

    Science.gov (United States)

    Williams, Michael A.; Rigamonti, Daniele

    2006-01-01

    Human hydrocephalus is a common medical condition that is characterized by abnormalities in the flow or resorption of cerebrospinal fluid (CSF), resulting in ventricular dilatation. Human hydrocephalus can be classified into two clinical forms, congenital and acquired. Hydrocephalus is one of the complex and multifactorial neurological disorders. A growing body of evidence indicates that genetic factors play a major role in the pathogenesis of hydrocephalus. An understanding of the genetic components and mechanism of this complex disorder may offer us significant insights into the molecular etiology of impaired brain development and an accumulation of the cerebrospinal fluid in cerebral compartments during the pathogenesis of hydrocephalus. Genetic studies in animal models have started to open the way for understanding the underlying pathology of hydrocephalus. At least 43 mutants/loci linked to hereditary hydrocephalus have been identified in animal models and humans. Up to date, 9 genes associated with hydrocephalus have been identified in animal models. In contrast, only one such gene has been identified in humans. Most of known hydrocephalus gene products are the important cytokines, growth factors or related molecules in the cellular signal pathways during early brain development. The current molecular genetic evidence from animal models indicate that in the early development stage, impaired and abnormal brain development caused by abnormal cellular signaling and functioning, all these cellular and developmental events would eventually lead to the congenital hydrocephalus. Owing to our very primitive knowledge of the genetics and molecular pathogenesis of human hydrocephalus, it is difficult to evaluate whether data gained from animal models can be extrapolated to humans. Initiation of a large population genetics study in humans will certainly provide invaluable information about the molecular and cellular etiology and the developmental mechanisms of human

  1. How Are Genetic Conditions Treated or Managed?

    Science.gov (United States)

    ... mtDNA Resources Help Me Understand Genetics Share: Email Facebook Twitter Home Help Me Understand Genetics Genetic Consultation How are genetic conditions treated or managed? How are genetic conditions treated or managed? Many ...

  2. Integrated genetic analysis microsystems

    International Nuclear Information System (INIS)

    Lagally, Eric T; Mathies, Richard A

    2004-01-01

    With the completion of the Human Genome Project and the ongoing DNA sequencing of the genomes of other animals, bacteria, plants and others, a wealth of new information about the genetic composition of organisms has become available. However, as the demand for sequence information grows, so does the workload required both to generate this sequence and to use it for targeted genetic analysis. Microfabricated genetic analysis systems are well poised to assist in the collection and use of these data through increased analysis speed, lower analysis cost and higher parallelism leading to increased assay throughput. In addition, such integrated microsystems may point the way to targeted genetic experiments on single cells and in other areas that are otherwise very difficult. Concomitant with these advantages, such systems, when fully integrated, should be capable of forming portable systems for high-speed in situ analyses, enabling a new standard in disciplines such as clinical chemistry, forensics, biowarfare detection and epidemiology. This review will discuss the various technologies available for genetic analysis on the microscale, and efforts to integrate them to form fully functional robust analysis devices. (topical review)

  3. Genetics of gallstone disease.

    Directory of Open Access Journals (Sweden)

    Mittal B

    2002-04-01

    Full Text Available Gallstone disease is a complex disorder where both environmental and genetic factors contribute towards susceptibility to the disease. Epidemiological and family studies suggest a strong genetic component in the causation of this disease. Several genetically derived phenotypes in the population are responsible for variations in lipoprotein types, which in turn affect the amount of cholesterol available in the gall bladder. The genetic polymorphisms in various genes for apo E, apo B, apo A1, LDL receptor, cholesteryl ester transfer and LDL receptor-associated protein have been implicated in gallstone formation. However, presently available information on genetic differences is not able to account for a large number of gallstone patients. The molecular studies in the animal models have not only confirmed the present paradigm of gallstone formation but also helped in identification of novel genes in humans, which might play an important role in pathogenesis of the disease. Precise understanding of such genes and their molecular mechanisms may provide the basis of new targets for rational drug designs and dietary interventions.

  4. Genetic classes and genetic categories : Protecting genetic groups through data protection law

    NARCIS (Netherlands)

    Hallinan, Dara; de Hert, Paul; Taylor, L.; Floridi, L.; van der Sloot, B.

    2017-01-01

    Each person shares genetic code with others. Thus, one individual’s genome can reveal information about other individuals. When multiple individuals share aspects of genetic architecture, they form a ‘genetic group’. From a social and legal perspective, two types of genetic group exist: Those which

  5. Genetics of eosinophilic esophagitis.

    Science.gov (United States)

    Kottyan, L C; Rothenberg, M E

    2017-05-01

    Eosinophilic esophagitis (EoE) is a chronic, allergic disease associated with marked mucosal eosinophil accumulation. EoE disease risk is multifactorial and includes environmental and genetic factors. This review will focus on the contribution of genetic variation to EoE risk, as well as the experimental tools and statistical methodology used to identify EoE risk loci. Specific disease-risk loci that are shared between EoE and other allergic diseases (TSLP, LRRC32) or unique to EoE (CAPN14), as well as Mendellian Disorders associated with EoE, will be reviewed in the context of the insight that they provide into the molecular pathoetiology of EoE. We will also discuss the clinical opportunities that genetic analyses provide in the form of decision support tools, molecular diagnostics, and novel therapeutic approaches.

  6. Archaeal extrachromosomal genetic elements

    DEFF Research Database (Denmark)

    Wang, Haina; Peng, Nan; Shah, Shiraz Ali

    2015-01-01

    SUMMARY: Research on archaeal extrachromosomal genetic elements (ECEs) has progressed rapidly in the past decade. To date, over 60 archaeal viruses and 60 plasmids have been isolated. These archaeal viruses exhibit an exceptional diversity in morphology, with a wide array of shapes, such as spind......SUMMARY: Research on archaeal extrachromosomal genetic elements (ECEs) has progressed rapidly in the past decade. To date, over 60 archaeal viruses and 60 plasmids have been isolated. These archaeal viruses exhibit an exceptional diversity in morphology, with a wide array of shapes...... on archaeal ECEs has just started to unravel the molecular biology of these genetic entities and their interactions with archaeal hosts, it is expected to accelerate in the next decade....

  7. Crystal Genetics, Inc.

    Science.gov (United States)

    Kermani, Bahram G

    2016-07-01

    Crystal Genetics, Inc. is an early-stage genetic test company, focused on achieving the highest possible clinical-grade accuracy and comprehensiveness for detecting germline (e.g., in hereditary cancer) and somatic (e.g., in early cancer detection) mutations. Crystal's mission is to significantly improve the health status of the population, by providing high accuracy, comprehensive, flexible and affordable genetic tests, primarily in cancer. Crystal's philosophy is that when it comes to detecting mutations that are strongly correlated with life-threatening diseases, the detection accuracy of every single mutation counts: a single false-positive error could cause severe anxiety for the patient. And, more importantly, a single false-negative error could potentially cost the patient's life. Crystal's objective is to eliminate both of these error types.

  8. Whakapapa, genealogy and genetics.

    Science.gov (United States)

    Evans, Donald

    2012-05-01

    This paper provides part of an analysis of the use of the Maori term whakapapa in a study designed to test the compatibility and commensurability of views of members of the indigenous culture of New Zealand with other views of genetic technologies extant in the country. It is concerned with the narrow sense of whakapapa as denoting biological ancestry, leaving the wider sense of whakapapa as denoting cultural identity for discussion elsewhere. The phenomenon of genetic curiosity is employed to facilitate this comparison. Four levels of curiosity are identified, in the Maori data, which penetrate more or less deeply into the psyche of individuals, affecting their health and wellbeing. These phenomena are compared with non-Maori experiences and considerable commonalities are discovered together with a point of marked difference. The results raise important questions for the ethical application of genetic technologies. © 2010 Blackwell Publishing Ltd.

  9. Genetic autonomic disorders.

    Science.gov (United States)

    Axelrod, Felicia B

    2013-03-01

    Genetic disorders affecting the autonomic nervous system can result in abnormal development of the nervous system or they can be caused by neurotransmitter imbalance, an ion-channel disturbance or by storage of deleterious material. The symptoms indicating autonomic dysfunction, however, will depend upon whether the genetic lesion has disrupted peripheral or central autonomic centers or both. Because the autonomic nervous system is pervasive and affects every organ system in the body, autonomic dysfunction will result in impaired homeostasis and symptoms will vary. The possibility of genetic confirmation by molecular testing for specific diagnosis is increasing but treatments tend to remain only supportive and directed toward particular symptoms. Copyright © 2013 Elsevier Inc. All rights reserved.

  10. Reconstruction and analysis of the lncRNA-miRNA-mRNA network based on competitive endogenous RNA reveal functional lncRNAs in rheumatoid arthritis.

    Science.gov (United States)

    Jiang, Hui; Ma, Rong; Zou, Shubiao; Wang, Yongzhong; Li, Zhuqing; Li, Weiping

    2017-06-01

    Rheumatoid arthritis (RA) is an autoimmune disease with an unknown etiology, occurring in approximately 1.0% of general population. More and more studies have suggested that long non-coding RNAs (lncRNAs) could play important roles in various biological processes and be associated with the pathogenesis of different kinds of diseases including RA. Although a large number of lncRNAs have been found, our knowledge of their function and physiological/pathological significance is still in its infancy. In order to reveal functional lncRNAs and identify the key lncRNAs in RA, we reconstructed a global triple network based on the competitive endogenous RNA (ceRNA) theory using the data from National Center for Biotechnology Information Gene Expression Omnibus and our previous paper. Meanwhile, Gene Ontology (GO) and pathway analysis were performed using Cytoscape plug-in BinGO and Database for Annotation, Visualization, and Integration Discovery (DAVID), respectively. We found that the lncRNA-miRNA-mRNA network was composed of 7 lncRNA nodes, 90 mRNA nodes, 24 miRNA nodes, and 301 edges. The functional assay showed that 147 GO terms and 23 pathways were enriched. In addition, three lncRNAs (S5645.1, XR_006437.1, J01878) were highly related to RA, and therefore, were selected as key lncRNAs. This study suggests that specific lncRNAs are associated with the development of RA, and three lncRNAs (S5645.1, XR_006437.1, J01878) could be used as potential diagnostic biomarkers and therapeutic targets.

  11. A novel neural network based image reconstruction model with scale and rotation invariance for target identification and classification for Active millimetre wave imaging

    Science.gov (United States)

    Agarwal, Smriti; Bisht, Amit Singh; Singh, Dharmendra; Pathak, Nagendra Prasad

    2014-12-01

    Millimetre wave imaging (MMW) is gaining tremendous interest among researchers, which has potential applications for security check, standoff personal screening, automotive collision-avoidance, and lot more. Current state-of-art imaging techniques viz. microwave and X-ray imaging suffers from lower resolution and harmful ionizing radiation, respectively. In contrast, MMW imaging operates at lower power and is non-ionizing, hence, medically safe. Despite these favourable attributes, MMW imaging encounters various challenges as; still it is very less explored area and lacks suitable imaging methodology for extracting complete target information. Keeping in view of these challenges, a MMW active imaging radar system at 60 GHz was designed for standoff imaging application. A C-scan (horizontal and vertical scanning) methodology was developed that provides cross-range resolution of 8.59 mm. The paper further details a suitable target identification and classification methodology. For identification of regular shape targets: mean-standard deviation based segmentation technique was formulated and further validated using a different target shape. For classification: probability density function based target material discrimination methodology was proposed and further validated on different dataset. Lastly, a novel artificial neural network based scale and rotation invariant, image reconstruction methodology has been proposed to counter the distortions in the image caused due to noise, rotation or scale variations. The designed neural network once trained with sample images, automatically takes care of these deformations and successfully reconstructs the corrected image for the test targets. Techniques developed in this paper are tested and validated using four different regular shapes viz. rectangle, square, triangle and circle.

  12. Recruitment and retention in a 10-month social network-based intervention promoting diabetes self-management in socioeconomically deprived patients: a qualitative process evaluation.

    Science.gov (United States)

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

    2017-07-26

    Socioeconomically deprived patients with type 2 diabetes often face challenges with self-management, resulting in more diabetes-related complications. However, these groups are often under-represented in self-management interventions. Evidence on effective recruitment and retention strategies is growing, but lacking for intensive self-management interventions. This study aims to explore recruitment, retention and effective intervention strategies in a 10-month group-based intervention among Dutch, Moroccan, Turkish and Surinamese patients from socioeconomically deprived neighbourhoods. Participants were recruited through general practitioners (GPs) and participated in a 10-month social network-based intervention (10 groups, n=69): Powerful Together with Diabetes . This intervention also targeted the significant others of participants and aimed to increase social support for self-management and to decrease social influences hindering self-management. A qualitative process evaluation was conducted. Retention was measured using log books kept by group leaders. Further, we conducted 17 in-depth interviews with participants (multiethnic sample) and 18 with group leaders. Interviews were transcribed, coded and analysed using framework analyses. The GP's letter and reminder calls, an informational meeting and the intervention's informal nature facilitated recruitment. During the first months, positive group atmosphere, the intervention's perceived usefulness, opportunities to socialise and a reduction in practical barriers facilitated retention. After the first months, conflicting responsibilities and changes in the intervention's nature and planning hindered retention. Calls from group leaders and the prospect of a diploma helped participants overcome these barriers. To promote retention in lengthy self-management interventions, it seems important that patients feel they are going on an outing to a social gathering that is enjoyable, recreational, useful and easy to

  13. The impact of a social network based intervention on self-management behaviours among patients with type 2 diabetes living in socioeconomically deprived neighbourhoods: a mixed methods approach.

    Science.gov (United States)

    Vissenberg, Charlotte; Nierkens, Vera; van Valkengoed, Irene; Nijpels, Giel; Uitewaal, Paul; Middelkoop, Barend; Stronks, Karien

    2017-08-01

    This paper aims to explore the effect of the social network based intervention Powerful Together with Diabetes on diabetes self-management among socioeconomically deprived patients. This 10-month group intervention targeting patients and significant others aimed to improve self-management by stimulating social support and diminishing social influences that hinder self-management. This intervention was evaluated in a quasi-experimental study using a mixed methods approach. Of 131 socioeconomically deprived patients with suboptimal glycaemic control, 69 were assigned to the intervention group and 62 to the control group (standard diabetes education). 27 qualitative in-depth interviews with the participants and 24 with their group leaders were held to study the subjective impact of the intervention. Further, self-management behaviours (medication adherence, diet and physical activity) were assessed at baseline, 10 and 16 months. Data were analysed using framework analyses and a linear mixture model. Qualitative data showed that the intervention group had a better understanding of the way self-management influences diabetes. The intervention group showed more complex self-management behaviours, such as planning ahead, seeking adequate food and physical activity alternatives, and consistently taking their diabetes into consideration when making choices. In participants with complete follow-up data, we found a significant increase in physical activity in the intervention group (3.78 vs. 4.83 days) and no changes in medication adherence and diet. This study indicates that an intensive support group and simultaneously involving significant others might improve diabetes self-management behaviours among socioeconomically deprived patients. More studies are needed to justify further implementation of the intervention. This study is registered in the Dutch Trial Register NTR1886. http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=1886.

  14. Genetics and caries: prospects

    Directory of Open Access Journals (Sweden)

    Alexandre Rezende Vieira

    2012-01-01

    Full Text Available Caries remains the most prevalent non-contagious infectious disease in humans. It is clear that the current approaches to decrease the prevalence of caries in human populations, including water fluoridation and school-based programs, are not enough to protect everyone. The scientific community has suggested the need for innovative work in a number of areas in cariology, encompassing disease etiology, epidemiology, definition, prevention, and treatment. We have pioneered the work on genetic studies to identify genes and genetic markers of diagnostic, prognostic, and therapeutic value. This paper summarizes a presentation that elaborated on these initial findings.

  15. Burkholderia thailandensis: Genetic Manipulation.

    Science.gov (United States)

    Garcia, Erin C

    2017-05-16

    Burkholderia thailandensis is a Gram-negative bacterium endemic to Southeast Asian and northern Australian soils. It is non-pathogenic; therefore, it is commonly used as a model organism for the related human pathogens Burkholderia mallei and Burkholderia pseudomallei. B. thailandensis is relatively easily genetically manipulated and a variety of robust genetic tools can be used in this organism. This unit describes protocols for conjugation, natural transformation, mini-Tn7 insertion, and allelic exchange in B. thailandensis. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

  16. Basic concepts of medical genetics, formal genetics, Part 1

    African Journals Online (AJOL)

    Mohammad Saad Zaghloul Salem

    2013-11-15

    Nov 15, 2013 ... maps of gene loci based on information gathered, formerly, ... represented as figure or text interface data. Relevant ... The Egyptian Journal of Medical Human Genetics ... prophylactic management and genetic counseling. 17.

  17. Genetic Causes of Rickets

    Science.gov (United States)

    Acar, Sezer; Demir, Korcan; Shi, Yufei

    2017-01-01

    Rickets is a metabolic bone disease that develops as a result of inadequate mineralization of growing bone due to disruption of calcium, phosphorus and/or vitamin D metabolism. Nutritional rickets remains a significant child health problem in developing countries. In addition, several rare genetic causes of rickets have also been described, which can be divided into two groups. The first group consists of genetic disorders of vitamin D biosynthesis and action, such as vitamin D-dependent rickets type 1A (VDDR1A), vitamin D-dependent rickets type 1B (VDDR1B), vitamin D-dependent rickets type 2A (VDDR2A), and vitamin D-dependent rickets type 2B (VDDR2B). The second group involves genetic disorders of excessive renal phosphate loss (hereditary hypophosphatemic rickets) due to impairment in renal tubular phosphate reabsorption as a result of FGF23-related or FGF23-independent causes. In this review, we focus on clinical, laboratory and genetic characteristics of various types of hereditary rickets as well as differential diagnosis and treatment approaches. PMID:29280738

  18. Genetics Home Reference: citrullinemia

    Science.gov (United States)

    ... belongs to a class of genetic diseases called urea cycle disorders. Learn more about the genes associated with citrullinemia ... GeneReview: Citrin Deficiency GeneReview: Citrullinemia Type I GeneReview: Urea Cycle Disorders Overview MedlinePlus Encyclopedia: Hereditary Urea Cycle Abnormality National ...

  19. Genetic risks from radiation

    International Nuclear Information System (INIS)

    Selby, P.B.

    Two widely-recognized committees, UNSCEAR and BEIR, have reevaluated their estimates of genetic risks from radiation. Their estimates for gene mutations are based on two different approaches, one being the doubling-dose approach and the other being a new direct approach based on an empirical determination of the amount of dominant induced damage in the skeletons of mice in the first generation following irradiation. The estimates made by these committees are in reasonably good agreement and suggest that the genetic risks from present exposures resultng from nuclear power production are small. There is room for much improvement in the reliability of the risk estimates. The relatively new approach of measuring the amount of induced damage to the mouse skeleton shows great promise of improving knowledge about how changes in the mutation frequency affect the incidence of genetic disorders. Such findings may have considerable influence on genetic risk estimates for radiation and on the development of risk estimates for other less-well-understood environmental mutagens. (author)

  20. Genetics and acronyms

    Directory of Open Access Journals (Sweden)

    Giovanni Corsello

    2014-06-01

    Full Text Available In a global society as the present, the nomenclature and terminology of diseases must be universally accepted among the specialists. This sentence is particularly true in some fields of medicine, as genetics, in which the progress of knowledge has been particularly rapid in last years.Many genetic disorders were termed using the names of the doctor (or the doctors who discovered and described them.The name of doctors and specialist were also frequently used to term sign and symptoms of diseases, including genetic syndromes.More rarely, a new disease received the name of the first patients described.In some cases the authors clearly proposed acronyms, that rapidly diffused as a good method to term genetic diseases and syndromes.Acronyms can be originated from the initial of main signs and symptoms; in some instances the acronym reproduces a word with other kind of semantic suggestions; some acronyms in their list of initials show also numbers, while others show also the initial of the words related to the physiopathology of disease.In more recent years acronyms were proposed to mark multicentric studies. Proceedings of the 10th International Workshop on Neonatology · Cagliari (Italy · October 22nd-25th, 2014 · The last ten years, the next ten years in Neonatology Guest Editors: Vassilios Fanos, Michele Mussap, Gavino Faa, Apostolos Papageorgiou

  1. Genetic pathways to Neurodegeneration

    Indian Academy of Sciences (India)

    Renu

    The extensive resource on ataxia has led to the development of a clinico-genetic ... Keywords: Cerebellar ataxias, SCAs, ARCAs, NGS, Gene network, iPSCs, .... Besides, mutations in different regions of the same gene result in different ..... integration with population data can also allow focussed testing/screening in specific.

  2. Genetics and variation

    Science.gov (United States)

    John R. Jones; Norbert V. DeByle

    1985-01-01

    The broad genotypic variability in quaking aspen (Populus tremuloides Michx.), that results in equally broad phenotypic variability among clones is important to the ecology and management of this species. This chapter considers principles of aspen genetics and variation, variation in aspen over its range, and local variation among clones. For a more...

  3. Genetically Engineering Entomopathogenic Fungi.

    Science.gov (United States)

    Zhao, H; Lovett, B; Fang, W

    2016-01-01

    Entomopathogenic fungi have been developed as environmentally friendly alternatives to chemical insecticides in biocontrol programs for agricultural pests and vectors of disease. However, mycoinsecticides currently have a small market share due to low virulence and inconsistencies in their performance. Genetic engineering has made it possible to significantly improve the virulence of fungi and their tolerance to adverse conditions. Virulence enhancement has been achieved by engineering fungi to express insect proteins and insecticidal proteins/peptides from insect predators and other insect pathogens, or by overexpressing the pathogen's own genes. Importantly, protein engineering can be used to mix and match functional domains from diverse genes sourced from entomopathogenic fungi and other organisms, producing insecticidal proteins with novel characteristics. Fungal tolerance to abiotic stresses, especially UV radiation, has been greatly improved by introducing into entomopathogens a photoreactivation system from an archaean and pigment synthesis pathways from nonentomopathogenic fungi. Conversely, gene knockout strategies have produced strains with reduced ecological fitness as recipients for genetic engineering to improve virulence; the resulting strains are hypervirulent, but will not persist in the environment. Coupled with their natural insect specificity, safety concerns can also be mitigated by using safe effector proteins with selection marker genes removed after transformation. With the increasing public concern over the continued use of synthetic chemical insecticides and growing public acceptance of genetically modified organisms, new types of biological insecticides produced by genetic engineering offer a range of environmentally friendly options for cost-effective control of insect pests. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. Genetics of celiac disease

    NARCIS (Netherlands)

    Ricano-Ponce, Isis; Wijmenga, Cisca; Gutierrez-Achury, Javier

    New insights into the underlying molecular pathophysiology of celiac disease (CeD) over the last few years have been guided by major advances in the fields of genetics and genomics. The development and use of the Immunochip genotyping platform paved the way for the discovery of 39 non-HLA loci

  5. Paper Genetic Engineering.

    Science.gov (United States)

    MacClintic, Scott D.; Nelson, Genevieve M.

    Bacterial transformation is a commonly used technique in genetic engineering that involves transferring a gene of interest into a bacterial host so that the bacteria can be used to produce large quantities of the gene product. Although several kits are available for performing bacterial transformation in the classroom, students do not always…

  6. The genetics of obesity.

    Science.gov (United States)

    All definitions of the metabolic syndrome include some form of obesity as one of the possible features. Body mass index (BMI) has a known genetic component, currently estimated to account for about 70% of the population variance in weight status for non-syndromal obesity. Much research effort has be...

  7. The genetics of Tamarix

    Science.gov (United States)

    Genetic studies have helped us gain basic knowledge of the Tamarix invasion. We now have a better understanding of the species identities involved in the invasion, their evolutionary relationships, and the contribution of hybridization to the invasion. This information can be used to enhance the eff...

  8. Intelligence, Race, and Genetics

    Science.gov (United States)

    Sternberg, Robert J.; Grigorenko, Elena L.; Kidd, Kenneth K.

    2005-01-01

    In this article, the authors argue that the overwhelming portion of the literature on intelligence, race, and genetics is based on folk taxonomies rather than scientific analysis. They suggest that because theorists of intelligence disagree as to what it is, any consideration of its relationships to other constructs must be tentative at best. They…

  9. Preimplantation genetic diagnosis

    Directory of Open Access Journals (Sweden)

    Karin Writzl

    2013-02-01

    Conclusions: Over the last two decades, PGD has been shown to be a reliable and safe genetic test for couples who are at risk of a specific inher - ited disorder. For PGS, the results from several ongoing randomized controlled trials performed at different cell biopsy stage, using array-CGH and SNP array will provide the data needed to evaluate the clinical efficacy.

  10. Punnett and duck genetics

    Indian Academy of Sciences (India)

    25, 191–194; reprinted in this issue as a J. Genet. classic, pages 3–7) ... 1932, starts with Punnett explaining that he started his work on ducks as he was asked ... text of Punnett's later comments on genes and human mental disease. Punnett's ...

  11. Genetic hazards of radiation

    International Nuclear Information System (INIS)

    Searle, A.G.

    1987-01-01

    The difficulties of quantifying genetic radiation effects are discussed, with reference to studies of atomic bomb survivors, and mouse germ-cells. Doubling dose methods of extrapolation and the problems of quantifying risks of diseases of irregular inheritance are also considered. (U.K.)

  12. Genetic technologies and ethics.

    Science.gov (United States)

    Ardekani, Ali M

    2009-01-01

    In the past decade, the human genome has been completely sequenced and the knowledge from it has begun to influence the fields of biological and social sciences in fundamental ways. Identification of about 25000 genes in the human genome is expected to create great benefits in diagnosis and treatment of diseases in the coming years. However, Genetic technologies have also created many interesting and difficult ethical issues which can affect the human societies now and in the future. Application of genetic technologies in the areas of stem cells, cloning, gene therapy, genetic manipulation, gene selection, sex selection and preimplantation diagnosis has created a great potential for the human race to influence and change human life on earth as we know it today. Therefore, it is important for leaders of societies in the modern world to pay attention to the advances in genetic technologies and prepare themselves and those institutions under their command to face the challenges which these new technologies induce in the areas of ethics, law and social policies.

  13. GENETIC ASPECTS OF AUTISM

    Directory of Open Access Journals (Sweden)

    Anastas LAKOSKI

    1997-06-01

    Full Text Available In the first paper on the syndrome of autism, Kanner described it as innate and inborn. He drew attention to the abnormalities in infancy without evidence of prior normal development and the intellectual, non emotional qualities shown by many of the parents and grandparents. Subsequently, the supposed lack of parental warmth led many clinicians to abandon the notions of constitutional deficit in the child and instead to postulate a psychogenic origin etiology was likely, genetic factors probably did not play a major role. Attention was draw to the low rate of autism in siblings, the lack of chromosome anomalies, and the similarities with syndromes associated with known brain trauma. Although the rate of autism in siblings was indeed low, it was much higher than in the general population rate providing a strong pointer to the genetic factors. The recognition that this was so, associated with the parallel finding of apparently high familiar loading for language delay, stimulated the first, systematic, twin study of autism, which suggested a strong genetic component. Subsequent research has produced findings in the same direction, although many questions remain unanswered. In this paper the evidence that has accumulated on genetic influences on autism is summarized and the remained dilemmas on this field are discussed.

  14. Safe genetically engineered plants

    Energy Technology Data Exchange (ETDEWEB)

    Rosellini, D; Veronesi, F [Dipartimento di Biologia Vegetale e Biotecnologie Agroambientali e Zootecniche, Universita degli Studi di Perugia, Borgo XX giugno 74, 06121 Perugia (Italy)

    2007-10-03

    The application of genetic engineering to plants has provided genetically modified plants (GMPs, or transgenic plants) that are cultivated worldwide on increasing areas. The most widespread GMPs are herbicide-resistant soybean and canola and insect-resistant corn and cotton. New GMPs that produce vaccines, pharmaceutical or industrial proteins, and fortified food are approaching the market. The techniques employed to introduce foreign genes into plants allow a quite good degree of predictability of the results, and their genome is minimally modified. However, some aspects of GMPs have raised concern: (a) control of the insertion site of the introduced DNA sequences into the plant genome and of its mutagenic effect; (b) presence of selectable marker genes conferring resistance to an antibiotic or an herbicide, linked to the useful gene; (c) insertion of undesired bacterial plasmid sequences; and (d) gene flow from transgenic plants to non-transgenic crops or wild plants. In response to public concerns, genetic engineering techniques are continuously being improved. Techniques to direct foreign gene integration into chosen genomic sites, to avoid the use of selectable genes or to remove them from the cultivated plants, to reduce the transfer of undesired bacterial sequences, and make use of alternative, safer selectable genes, are all fields of active research. In our laboratory, some of these new techniques are applied to alfalfa, an important forage plant. These emerging methods for plant genetic engineering are briefly reviewed in this work.

  15. Linear genetic programming

    CERN Document Server

    Brameier, Markus

    2007-01-01

    Presents a variant of Genetic Programming that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees. This book serves as a reference for researchers, but also contains sufficient introduction for students and those who are new to the field

  16. Safe genetically engineered plants

    International Nuclear Information System (INIS)

    Rosellini, D; Veronesi, F

    2007-01-01

    The application of genetic engineering to plants has provided genetically modified plants (GMPs, or transgenic plants) that are cultivated worldwide on increasing areas. The most widespread GMPs are herbicide-resistant soybean and canola and insect-resistant corn and cotton. New GMPs that produce vaccines, pharmaceutical or industrial proteins, and fortified food are approaching the market. The techniques employed to introduce foreign genes into plants allow a quite good degree of predictability of the results, and their genome is minimally modified. However, some aspects of GMPs have raised concern: (a) control of the insertion site of the introduced DNA sequences into the plant genome and of its mutagenic effect; (b) presence of selectable marker genes conferring resistance to an antibiotic or an herbicide, linked to the useful gene; (c) insertion of undesired bacterial plasmid sequences; and (d) gene flow from transgenic plants to non-transgenic crops or wild plants. In response to public concerns, genetic engineering techniques are continuously being improved. Techniques to direct foreign gene integration into chosen genomic sites, to avoid the use of selectable genes or to remove them from the cultivated plants, to reduce the transfer of undesired bacterial sequences, and make use of alternative, safer selectable genes, are all fields of active research. In our laboratory, some of these new techniques are applied to alfalfa, an important forage plant. These emerging methods for plant genetic engineering are briefly reviewed in this work

  17. Genetic Resources of Watermelon

    Science.gov (United States)

    As a result of many years of domestication and selection for desirable fruit quality, watermelon cultivars (Citrullus lanatus) share a narrow genetic base. Africa is the center of origin and diversity of watermelon and is considered to be the central continent for collecting and conserving useful ge...

  18. Genetic Dominance & Cellular Processes

    Science.gov (United States)

    Seager, Robert D.

    2014-01-01

    In learning genetics, many students misunderstand and misinterpret what "dominance" means. Understanding is easier if students realize that dominance is not a mechanism, but rather a consequence of underlying cellular processes. For example, metabolic pathways are often little affected by changes in enzyme concentration. This means that…

  19. Genetics Home Reference: retinoblastoma

    Science.gov (United States)

    ... Some studies suggest that additional genetic changes can influence the development of retinoblastoma ; these changes may help explain variations ... usually occurs in childhood, typically leading to the development of ... and there is no family history of the disease. Affected individuals are born ...

  20. Genetic susceptibility of periodontitis

    NARCIS (Netherlands)

    Laine, M.L.; Crielaard, W.; Loos, B.G.

    2012-01-01

    In this systematic review, we explore and summarize the peer-reviewed literature on putative genetic risk factors for susceptibility to aggressive and chronic periodontitis. A comprehensive literature search on the PubMed database was performed using the keywords ‘periodontitis’ or ‘periodontal

  1. Pitfalls in genetic testing

    DEFF Research Database (Denmark)

    Djémié, Tania; Weckhuysen, Sarah; von Spiczak, Sarah

    2016-01-01

    BACKGROUND: Sanger sequencing, still the standard technique for genetic testing in most diagnostic laboratories and until recently widely used in research, is gradually being complemented by next-generation sequencing (NGS). No single mutation detection technique is however perfect in identifying...

  2. Distributed genetic process mining

    NARCIS (Netherlands)

    Bratosin, C.C.; Sidorova, N.; Aalst, van der W.M.P.

    2010-01-01

    Process mining aims at discovering process models from data logs in order to offer insight into the real use of information systems. Most of the existing process mining algorithms fail to discover complex constructs or have problems dealing with noise and infrequent behavior. The genetic process

  3. Journal of Genetics | Indian Academy of Sciences

    Indian Academy of Sciences (India)

    pp 223-226 Commentary on J. Genet. Classic. Towards a genetic architecture of cryptic genetic variation and genetic assimilation: the contribution of K. G. Bateman · Ian Dworkin · More Details Fulltext PDF. pp 227-257 J. Genet. Classic. The Genetic Assimilation of Four Venation Phenocopies (Published on 1959 J. Genet.

  4. [The role of the genetics history in genetics teaching].

    Science.gov (United States)

    Li, Ming-Hui

    2006-08-01

    The research of the scientific history and development status reflect the science and technology level of a nation. The genetic history is one of the branches of the life science and the 21st century is life science century. The genetics history in the teaching of genetics not only can help students get familiar with the birth and development of genetics, but also enhance their thinking ability and scientific qualities. The roles and approaches of teaching are discussed in this paper.

  5. The genetics of diabetes

    Directory of Open Access Journals (Sweden)

    Barjaktarović Nada

    2007-01-01

    Full Text Available Pathogenesis of diabetes is still a mystery for medicine, the real challenge currently being the identification of genetic factors and specific mutations that cause the disease. Heterogeneity of diabetes hampers research, only a few loci inside the human genome being correlated with predisposition for disease till now. Insulin-dependent diabetes - IDDM (T1DM develops through autoimmune destruction of pancreatic beta cells. HLA complex on the short arm of chromosome 6 (6p21, where very important genes responsible for immunological condition of the person are located, plays a very important role in genetic predisposition for T1DM. Beside this region, there are also other loci in the human genome (on chromosomes 1, 2 and 11 where a correlation with T1DM has been shown. Correlation between HLA systems and T1DM was first described for class I alleles, but recently attention has been drawn to class II loci which seem to be the cause of primary predisposition for T1DM. In the case of non-insulin-dependent diabetes - NIDDM (T2DM, the situation proved to be even more complex. Only a few genetic loci on chromosomes 11, 13 and 20 and MODY variant on chromosomes 7 and 12 have been identified by now. There are two theories about genetic basis of T2DM: the first stipulates that the genetic predisposition is determined through numerous loci, each individually responsible for a small part of predisposition; the second claims that there are a limited number of "major" genes probably functioning on a polygenic basis. Further research in this area is definitely needed to enable an accurate calculation of the risks of the disease and possible consequences during a lifetime of a person.

  6. Genetic conservation and paddlefish propagation

    Science.gov (United States)

    Sloss, Brian L.; Klumb, Robert A.; Heist, Edward J.

    2009-01-01

    The conservation of genetic diversity of our natural resources is overwhelmingly one of the central foci of 21st century management practices. Three recommendations related to the conservation of paddlefish Polyodon spathula genetic diversity are to (1) identify genetic diversity at both nuclear and mitochondrial DNA loci using a suggested list of 20 sampling locations, (2) use genetic diversity estimates to develop genetic management units, and (3) identify broodstock sources to minimize effects of supplemental stocking on the genetic integrity of native paddlefish populations. We review previous genetic work on paddlefish and described key principles and concepts associated with maintaining genetic diversity within and among paddlefish populations and also present a genetic case study of current paddlefish propagation at the U.S. Fish and Wildlife Service Gavins Point National Fish Hatchery. This study confirmed that three potential sources of broodfish were genetically indistinguishable at the loci examined, allowing the management agencies cooperating on this program flexibility in sampling gametes. This study also showed significant bias in the hatchery occurred in terms of male reproductive contribution, which resulted in a shift in the genetic diversity of progeny compared to the broodfish. This shift was shown to result from differential male contributions, partially attributed to the mode of egg fertilization. Genetic insights enable implementation of a paddlefish propagation program within an adaptive management strategy that conserves inherent genetic diversity while achieving demographic goals.

  7. Genetic Engineering and the Amelioration of Genetic Defect

    Science.gov (United States)

    Lederberg, Joshua

    1970-01-01

    Discusses the claims for a brave new world of genetic manipulation" and concludes that if we could agree upon applying genetic (or any other effective) remedies to global problems we probably would need no rescourse to them. Suggests that effective methods of preventing genetic disease are prevention of mutations and detection and…

  8. NCI Dictionary of Genetics Terms

    Science.gov (United States)

    A dictionary of more than 150 genetics-related terms written for healthcare professionals. This resource was developed to support the comprehensive, evidence-based, peer-reviewed PDQ cancer genetics information summaries.

  9. Genetics Home Reference: Kleefstra syndrome

    Science.gov (United States)

    ... 5 links) Diagnostic Tests Drug Therapy Genetic Counseling Palliative Care Surgery and Rehabilitation Related Information How are genetic ... G, Tzioumi D, Sillence DO, Mowat D. Three patients with terminal deletions within the subtelomeric region of chromosome 9q. ...

  10. Genetics Home Reference: diastrophic dysplasia

    Science.gov (United States)

    ... my area? Other Names for This Condition Diastrophic dwarfism DTD Related Information How are genetic conditions and ... 2 links) Health Topic: Bone Diseases Health Topic: Dwarfism Genetic and Rare Diseases Information Center (1 link) ...

  11. Clinical Cancer Genetics and Prevention

    Science.gov (United States)

    Olufunmilayo F. Olopade MD, FACP, Professor of Medicine and Human Genetics and Director of the Cancer Risk Clinic Department of Medicine, BSD Section of Hematology/Oncology University of Chicago, presented "Clinical Cancer Genetics and Prevention".

  12. Genetics Home Reference: lactose intolerance

    Science.gov (United States)

    ... Thomas MG, Swallow DM. Lactose digestion and the evolutionary genetics of lactase persistence. Hum Genet. 2009 Jan; ... genome editing and CRISPR-Cas9? What is precision medicine? What is newborn screening? New Pages Alopecia areata ...

  13. The Genetics of Pork Quality

    NARCIS (Netherlands)

    Wijk, van H.J.

    2006-01-01

    This thesis describes the genetics of carcass composition and pork quality traits. A large population of commercial finishers was extensively phenotyped for growth, carcass composition and meat quality traits. Genetic parameters were estimated based on those measurements. The population was

  14. [Public health, genetics and ethics].

    Science.gov (United States)

    Kottow, Miguel H

    2002-10-01

    Genetics research has shown enormous developments in recent decades, although as yet with only limited clinical application. Bioethical analysis has been unable to deal with the vast problems of genetics because emphasis has been put on the principlism applied to both clinical and research bioethics. Genetics nevertheless poses its most complex moral dilemmas at the public level, where a social brand of ethics ought to supersede the essentially interpersonal perspective of principlism. A more social understanding of ethics in genetics is required to unravel issues such as research and clinical explorations, ownership and patents, genetic manipulation, and allocation of resources. All these issues require reflection based on the requirements of citizenry, consideration of common assets, and definition of public policies in regulating genetic endeavors and protecting the society as a whole Bioethics has privileged the approach to individual ethical issues derived from genetic intervention, thereby neglecting the more salient aspects of genetics and social ethics.

  15. Selected Readings in Genetic Engineering

    Science.gov (United States)

    Mertens, Thomas R.; Robinson, Sandra K.

    1973-01-01

    Describes different sources of readings for understanding issues and concepts of genetic engineering. Broad categories of reading materials are: concerns about genetic engineering; its background; procedures; and social, ethical and legal issues. References are listed. (PS)

  16. Genetics Home Reference: CLPB deficiency

    Science.gov (United States)

    ... of CLPB is associated with congenital microcephaly, severe encephalopathy and 3-methylglutaconic aciduria. J Med Genet. 2015 ... genetic testing? What is precision medicine? What is newborn screening? New Pages LMNA-related congenital muscular dystrophy ...

  17. Genetics Home Reference: PURA syndrome

    Science.gov (United States)

    ... TJ, Vreeburg M, Rouhl RPW, Stevens SJC, Stegmann APA, Schieving J, Pfundt R, van Dijk K, Smeets ... article on PubMed Central More from Genetics Home Reference Bulletins Genetics Home Reference Celebrates Its 15th Anniversary ...

  18. Genetics Home Reference: Kniest dysplasia

    Science.gov (United States)

    ... may include a rounded upper back that also curves to the side ( kyphoscoliosis ), severely flattened bones of ... Information What information about a genetic condition can statistics provide? Why are some genetic conditions more common ...

  19. Genetics Home Reference: Carpenter syndrome

    Science.gov (United States)

    ... deformed hips, a rounded upper back that also curves to the side ( kyphoscoliosis ), and knees that are ... Information What information about a genetic condition can statistics provide? Why are some genetic conditions more common ...

  20. Genetics Home Reference: Czech dysplasia

    Science.gov (United States)

    ... such as a rounded upper back that also curves to the side ( kyphoscoliosis ). Some people with Czech ... Information What information about a genetic condition can statistics provide? Why are some genetic conditions more common ...

  1. Genetics Home Reference: Winchester syndrome

    Science.gov (United States)

    ... bones ( osteoporosis ) throughout the skeleton. These abnormalities make bones brittle and more prone to fracture. The bone abnormalities ... information about a genetic condition can statistics provide? Why are some genetic conditions more common in particular ...

  2. [Genetic information and future medicine].

    Science.gov (United States)

    Sakurai, Akihiro

    2012-11-01

    Rapid technological advances in genetic analysis have revealed the genetic background of various diseases. Elucidation of the genes responsible for a disease enables better clinical management of the disease and helps to develop targeted drugs. Also, early diagnosis and management of at-risk family members can be made by identification of a genetic disease in the proband. On the other hand, genetic issues often cause psychological distress to the family. To perform genetic testing appropriately and to protect patients and family members from any harm, guidelines for genetic testing were released from the alliance of Japanese genetics-related academic societies in 2003. As genetic testing is becoming incorporated into clinical practice more broadly, the guideline was revised and released by the Japanese Society of Medical Sciences in 2011. All medical professionals in Japan are expected to follow this guideline.

  3. Foundations of genetic algorithms 1991

    CERN Document Server

    1991-01-01

    Foundations of Genetic Algorithms 1991 (FOGA 1) discusses the theoretical foundations of genetic algorithms (GA) and classifier systems.This book compiles research papers on selection and convergence, coding and representation, problem hardness, deception, classifier system design, variation and recombination, parallelization, and population divergence. Other topics include the non-uniform Walsh-schema transform; spurious correlations and premature convergence in genetic algorithms; and variable default hierarchy separation in a classifier system. The grammar-based genetic algorithm; condition

  4. Genetics and epigenetics of obesity

    OpenAIRE

    Herrera, Blanca M.; Keildson, Sarah; Lindgren, Cecilia M.

    2011-01-01

    Obesity results from interactions between environmental and genetic factors. Despite a relatively high heritability of common, non-syndromic obesity (40?70%), the search for genetic variants contributing to susceptibility has been a challenging task. Genome wide association (GWA) studies have dramatically changed the pace of detection of common genetic susceptibility variants. To date, more than 40 genetic variants have been associated with obesity and fat distribution. However, since these v...

  5. Genetics Home Reference: ulcerative colitis

    Science.gov (United States)

    ... are some genetic conditions more common in particular ethnic groups? Genetic Changes A variety of genetic and environmental factors are likely involved in the development of ulcerative colitis . Recent studies have identified variations in dozens of genes that may be linked ...

  6. Genetic variation in California oaks

    Science.gov (United States)

    Constance I. Millar; Diane L. Delany; Lawrence A. Riggs

    1990-01-01

    In forestry the importance of genetic variation for successful reproduction, survival and growth has been widely documented for commercial conifers; until recently, little genetic work has been done on the California oaks. Even before the nature of genetic variation was scientifically investigated, its importance was suspected in operational forestry. Many failures of...

  7. Genetics in Relation to Biology.

    Science.gov (United States)

    Stewart, J. Bird

    1987-01-01

    Claims that most instruction dealing with genetics is limited to sex education and personal hygiene. Suggests that the biology curriculum should begin to deal with other issues related to genetics, including genetic normality, prenatal diagnoses, race, and intelligence. Predicts these topics will begin to appear in British examination programs.…

  8. Moral Fantasy in Genetic Engineering.

    Science.gov (United States)

    Boone, C. Keith

    1984-01-01

    Discusses the main ethical issues generated by the new genetics and suggests ways to think about them. Concerns include "playing God," violation of the natural order of the universe, and abuse of genetic technology. Critical distinctions for making difficult decisions about genetic engineering issues are noted. (DH)

  9. Genetic Counseling in Mental Retardation.

    Science.gov (United States)

    Bowen, Peter

    The task of the genetic counselor who identifies genetic causes of mental retardation and assists families to understand risk of recurrence is described. Considered are chromosomal genetic disorders such as Down's syndrome, inherited disorders such as Tay-Sachs disease, identification by testing the amniotic fluid cells (amniocentresis) in time…

  10. Genetical Genomics for Evolutionary Studies

    NARCIS (Netherlands)

    Prins, J.C.P.; Smant, G.; Jansen, R.C.

    2012-01-01

    Genetical genomics combines acquired high-throughput genomic data with genetic analysis. In this chapter, we discuss the application of genetical genomics for evolutionary studies, where new high-throughput molecular technologies are combined with mapping quantitative trait loci (QTL) on the genome

  11. Genetic effects of ionising radiation

    International Nuclear Information System (INIS)

    Saunders, P.

    1981-01-01

    The mutagenic effects of ionising radiation on germ cells with resulting genetic abnormalities in subsequent generations, are considered. Having examined a simple model to explain the interaction of ionising radiation with genetic material and discussed its limitations, the methods whereby mutations are transmitted are discussed. Methods of estimating genetic risks and the results of such studies are examined. (U.K.)

  12. Genetic transformation of forest trees

    African Journals Online (AJOL)

    Admin

    In this review, the recent progress on genetic transformation of forest trees were discussed. Its described also, different applications of genetic engineering for improving forest trees or understanding the mechanisms governing genes expression in woody plants. Key words: Genetic transformation, transgenic forest trees, ...

  13. Adults' perceptions of genetic counseling and genetic testing.

    Science.gov (United States)

    Houfek, Julia Fisco; Soltis-Vaughan, Brigette S; Atwood, Jan R; Reiser, Gwendolyn M; Schaefer, G Bradley

    2015-02-01

    This study described the perceptions of genetic counseling and testing of adults (N = 116) attending a genetic education program. Understanding perceptions of genetic counseling, including the importance of counseling topics, will contribute to patient-focused care as clinical genetic applications for common, complex disorders evolve. Participants completed a survey addressing: the importance of genetic counseling topics, benefits and negative effects of genetic testing, and sharing test results. Topics addressing practical information about genetic conditions were rated most important; topics involving conceptual genetic/genomic principles were rated least important. The most frequently identified benefit and negative effect of testing were prevention/early detection/treatment and psychological distress. Participants perceived that they were more likely to share test results with first-degree than other relatives. Findings suggest providing patients with practical information about genetic testing and genetic contributions to disease, while also determining whether their self-care abilities would be enhanced by teaching genetic/genomic principles. Copyright © 2014 Elsevier Inc. All rights reserved.

  14. Genetic secrets: Protecting privacy and confidentiality in the genetic era

    Energy Technology Data Exchange (ETDEWEB)

    Rothstein, M.A. [ed.

    1998-07-01

    Few developments are likely to affect human beings more profoundly in the long run than the discoveries resulting from advances in modern genetics. Although the developments in genetic technology promise to provide many additional benefits, their application to genetic screening poses ethical, social, and legal questions, many of which are rooted in issues of privacy and confidentiality. The ethical, practical, and legal ramifications of these and related questions are explored in depth. The broad range of topics includes: the privacy and confidentiality of genetic information; the challenges to privacy and confidentiality that may be projected to result from the emerging genetic technologies; the role of informed consent in protecting the confidentiality of genetic information in the clinical setting; the potential uses of genetic information by third parties; the implications of changes in the health care delivery system for privacy and confidentiality; relevant national and international developments in public policies, professional standards, and laws; recommendations; and the identification of research needs.

  15. Eugenics and genetic testing.

    Science.gov (United States)

    Holtzman, N A

    1998-01-01

    Pressures to lower health-care costs remain an important stimulus to eugenic approaches. Prenatal diagnosis followed by abortion of affected fetuses has replaced sterilization as the major eugenic technique. Voluntary acceptance has replaced coercion, but subtle pressures undermine personal autonomy. The failure of the old eugenics to accurately predict who will have affected offspring virtually disappears when prenatal diagnosis is used to predict Mendelian disorders. However, when prenatal diagnosis is used to detect inherited susceptibilities to adult-onset, common, complex disorders, considerable uncertainty is inherent in the prediction. Intolerance and the resurgence of genetic determinism are current pressures for a eugenic approach. The increasing use of carrier screening (to identify those at risk of having affected offspring) and of prenatal diagnosis could itself generate intolerance for those who refuse the procedures. Genetic determinism deflects society from social action that would reduce the burden of disease far more than even the maximum use of eugenics.

  16. Advances in human genetics

    Energy Technology Data Exchange (ETDEWEB)

    Harris, H.; Hirschhorn, K. (eds.)

    1993-01-01

    This book has five chapters covering peroxisomal diseases, X-linked immunodeficiencies, genetic mutations affecting human lipoproteins and their receptors and enzymes, genetic aspects of cancer, and Gaucher disease. The chapter on peroxisomes covers their discovery, structure, functions, disorders, etc. The chapter on X-linked immunodeficiencies discusses such diseases as agammaglobulinemia, severe combined immunodeficiency, Wiskott-Aldrich syndrome, animal models, linkage analysis, etc. Apolipoprotein formation, synthesis, gene regulation, proteins, etc. are the main focus of chapter 3. The chapter on cancer covers such topics as oncogene mapping and the molecular characterization of some recessive oncogenes. Gaucher disease is covered from its diagnosis, classification, and prevention, to its organ system involvement and molecular biology.

  17. Genetics of Alcoholism.

    Science.gov (United States)

    Zhu, Ena C; Soundy, Timothy J; Hu, Yueshan

    2017-05-01

    Consuming excessive amounts of alcohol has the potential to modify an individual's brain and lead to alcohol dependence. Alcohol use leads to 88,000 deaths every year in the U.S. alone and can lead to other health issues including cancers, such as colorectal cancer, and mental health problems. While drinking behavior varies due to environmental factors, genetic factors also contribute to the risk of alcoholism. Certain genes affecting alcohol metabolism and neurotransmitters have been found to contribute to or inhibit the risk. Geneenvironment interactions may also play a role in the susceptibility of alcoholism. With a better understanding of the different components that can contribute to alcoholism, more personalized treatment could cater to the individual. This review discusses the major genetic factors and some small variants in other genes that contribute to alcoholism, as well as considers the gene-environmental interactions. Copyright© South Dakota State Medical Association.

  18. MEDICAL GENETICS AND ETHICS

    Directory of Open Access Journals (Sweden)

    Vladimir TRAJKOVSKI

    1999-05-01

    Full Text Available Fast development of medical genetics and it’s subdisciplines is noticed in last thirty years. Modern diagnostic methods made possible to establish human genome and its impairment. In human genetics, ethic is main principle in working. Ethic is science about biggest goodness for human or society, and its aim pro­tecting human health.Today's conditions for leaving and science development open a wide way for ethical approaches, but also for non-ethical manipulations with human even before his conception. We must keep to attitude that without law, with our behavior will must conduct our conscience. It is best to have neutral eugenetic attitude, which allows free ethical choice of each individual, in any case, for the well being of man.

  19. Genetic engineering in biotechnology

    Energy Technology Data Exchange (ETDEWEB)

    Bedate, C.A.; Morales, J.C.; Lopez, E.H.

    1981-09-01

    The objective of this book is to encourage the use of genetic engineering for economic development. The report covers: (1) Precedents of genetic engineering; (2) a brief description of the technology, including the transfer of DNA in bacteria (vectors, E. coli and B. subtilis hosts, stages, and technical problems), practical examples of techniques used and their products (interferon; growth hormone; insulin; treatment of blood cells, Talasemia, and Lesch-Nyhan syndrome; and more nutritious soya), transfer to higher organisms, and cellular fusion; (3) biological risks and precautions; (4) possible applications (production of hydrogen, hydrocarbons, alcohol, chemicals, enzymes, peptides, viral antigens, monoclonal antibodies, genes, proteins, and insecticides; metal extraction; nitrogen fixation; biodegradation; and new varieties of plants and animals; and (5) international activities.

  20. Genetic discrimination: international perspectives.

    Science.gov (United States)

    Otlowski, M; Taylor, S; Bombard, Y

    2012-01-01

    Genetic discrimination (GD) is a complex, multifaceted ethical, psychosocial, and legal phenomenon. It is defined as the differential treatment of asymptomatic individuals or their relatives on the basis of their real or assumed genetic characteristics. This article presents an overview of GD within the contemporary international context. It describes the concept of GD and its contextual features, reviews research evidence regarding people's experiences of GD and the impact of GD within a range of domains, and provides an overview of legal and policy responses to GD that have emerged globally. We argue that GD is a significant and internationally established phenomenon that requires multilevel responses to ensure social justice and equitable outcomes for all citizens. Future research should monitor GD and its impacts within the community as well as institutions and should evaluate the effectiveness of legislative, policy, community education, and systemic responses.