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Sample records for genetic algorithm-adaptive network-based

  1. New intensity-hue-saturation pan-sharpening method based on texture analysis and genetic algorithm-adaption

    NASA Astrophysics Data System (ADS)

    Masoudi, Rasoul; Kabiri, Peyman

    2014-01-01

    Pansharpening aims to fuse a low-resolution multispectral image with a high-resolution panchromatic image to create a multispectral image with high spatial and spectral resolution. The intensity-hue-saturation (IHS) fusion method transforms an image from RGB space to IHS space. This paper reports a method to improve the spectral resolution of a final multispectral image. The proposed method implies two modifications on the basic IHS method to improve the sharpness of the final image. First, the paper proposes a method based on a genetic algorithm to find the weight of each band of multispectral image in the fusion process. Later on, a texture-based technique is proposed to save the spectral information of the final image with respect to the texture boundaries. Spectral quality metrics in terms of SAM, SID, Q-average, RASE, RMSE, CC, ERGAS and UIQI are used in our experiments. Experimental results on IKONOS and QuickBird data show that the proposed method is more efficient than the original IHS-based fusion approach and some of its extensions, such as IKONOS IHS, edge-adaptive IHS and explicit band coefficient IHS, in preserving spectral information of multispectral images.

  2. Network-based gene prediction for Plasmodium falciparum malaria towards genetics-based drug discovery

    PubMed Central

    2015-01-01

    Background Malaria is the most deadly parasitic infectious disease. Existing drug treatments have limited efficacy in malaria elimination, and the complex pathogenesis of the disease is not fully understood. Detecting novel malaria-associated genes not only contributes in revealing the disease pathogenesis, but also facilitates discovering new targets for anti-malaria drugs. Methods In this study, we developed a network-based approach to predict malaria-associated genes. We constructed a cross-species network to integrate human-human, parasite-parasite and human-parasite protein interactions. Then we extended the random walk algorithm on this network, and used known malaria genes as the seeds to find novel candidate genes for malaria. Results We validated our algorithms using 77 known malaria genes: 14 human genes and 63 parasite genes were ranked averagely within top 2% and top 4%, respectively among human and parasite genomes. We also evaluated our method for predicting novel malaria genes using a set of 27 genes with literature supporting evidence. Our approach ranked 12 genes within top 1% and 24 genes within top 5%. In addition, we demonstrated that top-ranked candied genes were enriched for drug targets, and identified commonalities underlying top-ranked malaria genes through pathway analysis. In summary, the candidate malaria-associated genes predicted by our data-driven approach have the potential to guide genetics-based anti-malaria drug discovery. PMID:26099491

  3. PGTandMe: social networking-based genetic testing and the evolving research model.

    PubMed

    Koch, Valerie Gutmann

    2012-01-01

    The opportunity to use extensive genetic data, personal information, and family medical history for research purposes may be naturally appealing to the personal genetic testing (PGT) industry, which is already coupling direct-to-consumer (DTC) products with social networking technologies, as well as to potential industry or institutional partners. This article evaluates the transformation in research that the hybrid of PGT and social networking will bring about, and--highlighting the challenges associated with a new paradigm of "patient-driven" genomic research--focuses on the consequences of shifting the structure, locus, timing, and scope of research through genetic crowd-sourcing. This article also explores potential ethical, legal, and regulatory issues that arise from the hybrid between personal genomic research and online social networking, particularly regarding informed consent, institutional review board (IRB) oversight, and ownership/intellectual property (IP) considerations.

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

    NASA Astrophysics Data System (ADS)

    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

  5. Application of BP Neural Network Based on Genetic Algorithm in Quantitative Analysis of Mixed GAS

    NASA Astrophysics Data System (ADS)

    Chen, Hongyan; Liu, Wenzhen; Qu, Jian; Zhang, Bing; Li, Zhibin

    Aiming at the problem of mixed gas detection in neural network and analysis on the principle of gas detection. Combining BP algorithm of genetic algorithm with hybrid gas sensors, a kind of quantitative analysis system of mixed gas is designed. The local minimum of network learning is the main reason which affects the precision of gas analysis. On the basis of the network study to improve the learning algorithms, the analyses and tests for CO, CO2 and HC compounds were tested. The results showed that the above measures effectively improve and enhance the accuracy of the neural network for gas analysis.

  6. State estimation for delayed genetic regulatory networks based on passivity theory.

    PubMed

    Vembarasan, V; Nagamani, G; Balasubramaniam, P; Park, Ju H

    2013-08-01

    This paper is concerned with the state estimation problem for delayed genetic regulatory networks (GRNs) based on passivity analysis approach. The main purpose of the problem is to design the estimator to approximate the true concentrations of the mRNA and protein through available measurement outputs. Time-varying delays are explicitly assumed to be non-differentiable and constraint on the derivative of the time-varying delay is less than one can be removed. Based on the Lyapunov-Krasovskii functionals involving triple integral terms, using some integral inequalities and convex combination technique, a delay-dependent passivity criterion is established for GRNs in terms of linear matrix inequalities (LMIs) that can efficiently be solved by any available LMI solvers. Finally, numerical examples and simulation are presented to demonstrate the efficiency of the proposed estimation schemes.

  7. A neural-network-based exponential H∞ synchronisation for chaotic secure communication via improved genetic algorithm

    NASA Astrophysics Data System (ADS)

    Hsiao, Feng-Hsiag

    2016-10-01

    In this study, a novel approach via improved genetic algorithm (IGA)-based fuzzy observer is proposed to realise exponential optimal H∞ synchronisation and secure communication in multiple time-delay chaotic (MTDC) systems. First, an original message is inserted into the MTDC system. Then, a neural-network (NN) model is employed to approximate the MTDC system. Next, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. Based on this LDI state-space representation, this study proposes a delay-dependent exponential stability criterion derived in terms of Lyapunov's direct method, thus ensuring that the trajectories of the slave system approach those of the master system. Subsequently, the stability condition of this criterion is reformulated into a linear matrix inequality (LMI). Due to GA's random global optimisation search capabilities, the lower and upper bounds of the search space can be set so that the GA will seek better fuzzy observer feedback gains, accelerating feedback gain-based synchronisation via the LMI-based approach. IGA, which exhibits better performance than traditional GA, is used to synthesise a fuzzy observer to not only realise the exponential synchronisation, but also achieve optimal H∞ performance by minimizing the disturbance attenuation level and recovering the transmitted message. Finally, a numerical example with simulations is given in order to demonstrate the effectiveness of our approach.

  8. Integrating clinical and laboratory data in genetic studies of complex phenotypes: a network-based data management system.

    PubMed

    McMahon, F J; Thomas, C J; Koskela, R J; Breschel, T S; Hightower, T C; Rohrer, N; Savino, C; McInnis, M G; Simpson, S G; DePaulo, J R

    1998-05-01

    The identification of genes underlying a complex phenotype can be a massive undertaking, and may require a much larger sample size than thought previously. The integration of such large volumes of clinical and laboratory data has become a major challenge. In this paper we describe a network-based data management system designed to address this challenge. Our system offers several advantages. Since the system uses commercial software, it obviates the acquisition, installation, and debugging of privately-available software, and is fully compatible with Windows and other commercial software. The system uses relational database architecture, which offers exceptional flexibility, facilitates complex data queries, and expedites extensive data quality control. The system is particularly designed to integrate clinical and laboratory data efficiently, producing summary reports, pedigrees, and exported files containing both phenotype and genotype data in a virtually unlimited range of formats. We describe a comprehensive system that manages clinical, DNA, cell line, and genotype data, but since the system is modular, researchers can set up only those elements which they need immediately, expanding later as needed. PMID:9603614

  9. Integrating clinical and laboratory data in genetic studies of complex phenotypes: a network-based data management system.

    PubMed

    McMahon, F J; Thomas, C J; Koskela, R J; Breschel, T S; Hightower, T C; Rohrer, N; Savino, C; McInnis, M G; Simpson, S G; DePaulo, J R

    1998-05-01

    The identification of genes underlying a complex phenotype can be a massive undertaking, and may require a much larger sample size than thought previously. The integration of such large volumes of clinical and laboratory data has become a major challenge. In this paper we describe a network-based data management system designed to address this challenge. Our system offers several advantages. Since the system uses commercial software, it obviates the acquisition, installation, and debugging of privately-available software, and is fully compatible with Windows and other commercial software. The system uses relational database architecture, which offers exceptional flexibility, facilitates complex data queries, and expedites extensive data quality control. The system is particularly designed to integrate clinical and laboratory data efficiently, producing summary reports, pedigrees, and exported files containing both phenotype and genotype data in a virtually unlimited range of formats. We describe a comprehensive system that manages clinical, DNA, cell line, and genotype data, but since the system is modular, researchers can set up only those elements which they need immediately, expanding later as needed.

  10. Network-based SNP meta-analysis identifies joint and disjoint genetic features across common human diseases

    PubMed Central

    2012-01-01

    Background Genome-wide association studies (GWAS) have provided a large set of genetic loci influencing the risk for many common diseases. Association studies typically analyze one specific trait in single populations in an isolated fashion without taking into account the potential phenotypic and genetic correlation between traits. However, GWA data can be efficiently used to identify overlapping loci with analogous or contrasting effects on different diseases. Results Here, we describe a new approach to systematically prioritize and interpret available GWA data. We focus on the analysis of joint and disjoint genetic determinants across diseases. Using network analysis, we show that variant-based approaches are superior to locus-based analyses. In addition, we provide a prioritization of disease loci based on network properties and discuss the roles of hub loci across several diseases. We demonstrate that, in general, agonistic associations appear to reflect current disease classifications, and present the potential use of effect sizes in refining and revising these agonistic signals. We further identify potential branching points in disease etiologies based on antagonistic variants and describe plausible small-scale models of the underlying molecular switches. Conclusions The observation that a surprisingly high fraction (>15%) of the SNPs considered in our study are associated both agonistically and antagonistically with related as well as unrelated disorders indicates that the molecular mechanisms influencing causes and progress of human diseases are in part interrelated. Genetic overlaps between two diseases also suggest the importance of the affected entities in the specific pathogenic pathways and should be investigated further. PMID:22988944

  11. [Application of artificial neural network based on the genetic algorithm in predicting the root distribution of winter wheat].

    PubMed

    Luo, Changshou; Zuo, Qiang; Li, Baoguo

    2004-02-01

    In this study, a controlled experiment of winter wheat under water stress at the seedling stage was conducted in soil columns in greenhouse. Based on the data gotten from the experiment, a model to estimate root length density distribution was developed through optimizing the weights of neural network by genetic algorithm. The neural network model was constructed by using forward neural network framework, by applying the strategy of the roulette wheel selection and reserving the most optimizing series of weights, which were composed by real codes. This model was applied to predict the root length density distribution of winter wheat, and the predicted root length density had good agreement with experiment data. The way could save a lot of manpower and material resources for determining the root length density distribution of winter wheat.

  12. MOEPGA: A novel method to detect protein complexes in yeast protein-protein interaction networks based on MultiObjective Evolutionary Programming Genetic Algorithm.

    PubMed

    Cao, Buwen; Luo, Jiawei; Liang, Cheng; Wang, Shulin; Song, Dan

    2015-10-01

    The identification of protein complexes in protein-protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions.

  13. Network-Based Management Procedures.

    ERIC Educational Resources Information Center

    Buckner, Allen L.

    Network-based management procedures serve as valuable aids in organizational management, achievement of objectives, problem solving, and decisionmaking. Network techniques especially applicable to educational management systems are the program evaluation and review technique (PERT) and the critical path method (CPM). Other network charting…

  14. Neural network based architectures for aerospace applications

    NASA Technical Reports Server (NTRS)

    Ricart, Richard

    1987-01-01

    A brief history of the field of neural networks research is given and some simple concepts are described. In addition, some neural network based avionics research and development programs are reviewed. The need for the United States Air Force and NASA to assume a leadership role in supporting this technology is stressed.

  15. Elements of Network-Based Assessment

    ERIC Educational Resources Information Center

    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…

  16. Network-Based Classrooms: Promises and Realities.

    ERIC Educational Resources Information Center

    Bruce, Bertram C., Ed.; And Others

    Exploring how new technologies and new pedagogies transform and are transformed by existing institutions, this book presents 14 essays that discuss network-based classrooms in which students use communications software on computer networks to converse in writing. The first part of the book discusses general themes and issues of the ENFI…

  17. Genetics

    MedlinePlus

    ... Inheritance; Heterozygous; Inheritance patterns; Heredity and disease; Heritable; Genetic markers ... The chromosomes are made up of strands of genetic information called DNA. Each chromosome contains sections of ...

  18. Optimal halftoning for network-based imaging

    NASA Astrophysics Data System (ADS)

    Ostromoukhov, Victor

    2000-12-01

    In this contribution, we introduce a multiple depth progressive representation for network-based still and moving images. A simple quantization algorithm associated with this representation provides optimal image quality. By optimum, we mean the best possible visual quality for a given value of information under real life constraints such as physical, psychological , or legal constraints. A special variant of the algorithm, multi-depth coherent error diffusion, addresses a specific problem of temporal coherence between frames in moving images. The output produced with our algorithm is visually pleasant because its Fourier spectrum is close to the 'blue noise'.

  19. Neural network based temporal video segmentation.

    PubMed

    Cao, X; Suganthan, P N

    2002-01-01

    The organization of video information in video databases requires automatic temporal segmentation with minimal user interaction. As neural networks are capable of learning the characteristics of various video segments and clustering them accordingly, in this paper, a neural network based technique is developed to segment the video sequence into shots automatically and with a minimum number of user-defined parameters. We propose to employ growing neural gas (GNG) networks and integrate multiple frame difference features to efficiently detect shot boundaries in the video. Experimental results are presented to illustrate the good performance of the proposed scheme on real video sequences. PMID:12370954

  20. Network-Based Protein Biomarker Discovery Platforms

    PubMed Central

    Kim, Minhyung

    2016-01-01

    The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms. PMID:27103885

  1. Social network based microblog user behavior analysis

    NASA Astrophysics Data System (ADS)

    Yan, Qiang; Wu, Lianren; Zheng, Lan

    2013-04-01

    The influence of microblog on information transmission is becoming more and more obvious. By characterizing the behavior of following and being followed as out-degree and in-degree respectively, a microblog social network was built in this paper. It was found to have short diameter of connected graph, short average path length and high average clustering coefficient. The distributions of out-degree, in-degree and total number of microblogs posted present power-law characters. The exponent of total number distribution of microblogs is negatively correlated with the degree of each user. With the increase of degree, the exponent decreases much slower. Based on empirical analysis, we proposed a social network based human dynamics model in this paper, and pointed out that inducing drive and spontaneous drive lead to the behavior of posting microblogs. The simulation results of our model match well with practical situation.

  2. Network-based recommendation algorithms: A review

    NASA Astrophysics Data System (ADS)

    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.

  3. Network-based stochastic semisupervised learning.

    PubMed

    Silva, Thiago Christiano; Zhao, Liang

    2012-03-01

    Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

  4. Automation of Network-Based Scientific Workflows

    SciTech Connect

    Altintas, I.; Barreto, R.; Blondin, J. M.; Cheng, Z.; Critchlow, T.; Khan, A.; Klasky, Scott A; Ligon, J.; Ludaescher, B.; Mouallem, P. A.; Parker, S.; Podhorszki, Norbert; Shoshani, A.; Silva, C.; Vouk, M. A.

    2007-01-01

    Comprehensive, end-to-end, data and workflow management solutions are needed to handle the increasing complexity of processes and data volumes associated with modern distributed scientific problem solving, such as ultra-scale simulations and high-throughput experiments. The key to the solution is an integrated network-based framework that is functional, dependable, fault-tolerant, and supports data and process provenance. Such a framework needs to make development and use of application workflows dramatically easier so that scientists' efforts can shift away from data management and utility software development to scientific research and discovery An integrated view of these activities is provided by the notion of scientific workflows - a series of structured activities and computations that arise in scientific problem-solving. An information technology framework that supports scientific workflows is the Ptolemy II based environment called Kepler. This paper discusses the issues associated with practical automation of scientific processes and workflows and illustrates this with workflows developed using the Kepler framework and tools.

  5. Convolutional Neural Network Based dem Super Resolution

    NASA Astrophysics Data System (ADS)

    Chen, Zixuan; Wang, Xuewen; Xu, Zekai; Hou, Wenguang

    2016-06-01

    DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.

  6. Network-Based Approaches in Drug Discovery and Early Development

    PubMed Central

    Harrold, JM; Ramanathan, M; Mager, DE

    2015-01-01

    Identification of novel targets is a critical first step in the drug discovery and development process. Most diseases such as cancer, metabolic disorders, and neurological disorders are complex, and their pathogenesis involves multiple genetic and environmental factors. Finding a viable drug target–drug combination with high potential for yielding clinical success within the efficacy–toxicity spectrum is extremely challenging. Many examples are now available in which network-based approaches show potential for the identification of novel targets and for the repositioning of established targets. The objective of this article is to highlight network approaches for identifying novel targets with greater chances of gaining approved drugs with maximal efficacy and minimal side effects. Further enhancement of these approaches may emerge from effectively integrating computational systems biology with pharmacodynamic systems analysis. Coupling genomics, proteomics, and metabolomics databases with systems pharmacology modeling may aid in the development of disease-specific networks that can be further used to build confidence in target identification. PMID:24025802

  7. Genetics

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The genus Capsicum represents one of several well characterized Solanaceous genera. A wealth of classical and molecular genetics research is available for the genus. Information gleaned from its cultivated relatives, tomato and potato, provide further insight for basic and applied studies. Early ...

  8. Genetics

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Maintaining genetic variation in wild populations of Arctic organisms is fundamental to the long-term persistence of high latitude biodiversity. Variability is important because it provides options for species to respond to changing environmental conditions and novel challenges such as emerging path...

  9. Genetic-Algorithm Tool For Search And Optimization

    NASA Technical Reports Server (NTRS)

    Wang, Lui; Bayer, Steven

    1995-01-01

    SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.

  10. Evaluation of Network-Based Minimally Invasive VR Surgery Simulator.

    PubMed

    Tagawa, Kazuyoshi; Tanaka, Hiromi T; Kurumi, Yoshimasa; Komori, Masaru; Morikawa, Shigehiro

    2016-01-01

    In this paper, we report a result of an experiment of a field trial of our network-based minimally invasive surgery simulator. In our previous paper, we proposed a network-based visuohaptic surgery training system for laparoscopic surgery. In addition, we proposed a volume-based haptic communication approach, which allows participants at remote sites on the network to simultaneously interact with the same target object in virtual environments presented by multi-level computer performance systems, by only exchanging a small set of manipulation parameters for the target object and additional packet for synchronization of status of binary tree and deformation of shared volume model. We implemented the approach into our network-based surgery simulator, and field trial of the simulator at three locations was performed. PMID:27046613

  11. CFD Optimization on Network-Based Parallel Computer System

    NASA Technical Reports Server (NTRS)

    Cheung, Samson H.; VanDalsem, William (Technical Monitor)

    1994-01-01

    Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advance computational fluid dynamics codes, which is computationally expensive in mainframe supercomputer. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computer on a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package has been applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.

  12. Parallel CFD design on network-based computer

    NASA Technical Reports Server (NTRS)

    Cheung, Samson

    1995-01-01

    Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advanced computational fluid dynamics codes, which can be computationally expensive on mainframe supercomputers. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computing environment utilizing a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package is applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.

  13. Neural Network Based System for Equipment Startup Surveillance

    1996-12-18

    NEBSESS is a system for equipment surveillance and fault detection which relies on a neural-network based means for diagnosing disturbances during startup and for automatically actuating the Sequential Probability Ratio Test (SPRT) as a signal validation means during steady-state operation.

  14. A Cultural Approach to Networked-Based Mobile Education

    ERIC Educational Resources Information Center

    Koskimaa, Raine; Lehtonen, Miika; Heinonen, Ulla; Ruokamo, Heli; Tissari, Varpu; Vahtivuori-Hanninen, Sanna; Tella, Seppo

    2007-01-01

    This paper discusses cultural conditions for networked-based mobile education. In our paper, we demonstrate how an Integrated Meta-Model that we have been developing in our MOMENTS project, i.e. Models and Methods for Future Knowledge Construction: Interdisciplinary Implementations with Mobile Technologies, can be used as a heuristic tool for…

  15. Network-based in silico drug efficacy screening.

    PubMed

    Guney, Emre; Menche, Jörg; Vidal, Marc; Barábasi, Albert-László

    2016-01-01

    The increasing cost of drug development together with a significant drop in the number of new drug approvals raises the need for innovative approaches for target identification and efficacy prediction. Here, we take advantage of our increasing understanding of the network-based origins of diseases to introduce a drug-disease proximity measure that quantifies the interplay between drugs targets and diseases. By correcting for the known biases of the interactome, proximity helps us uncover the therapeutic effect of drugs, as well as to distinguish palliative from effective treatments. Our analysis of 238 drugs used in 78 diseases indicates that the therapeutic effect of drugs is localized in a small network neighborhood of the disease genes and highlights efficacy issues for drugs used in Parkinson and several inflammatory disorders. Finally, network-based proximity allows us to predict novel drug-disease associations that offer unprecedented opportunities for drug repurposing and the detection of adverse effects.

  16. Network-based in silico drug efficacy screening

    PubMed Central

    Guney, Emre; Menche, Jörg; Vidal, Marc; Barábasi, Albert-László

    2016-01-01

    The increasing cost of drug development together with a significant drop in the number of new drug approvals raises the need for innovative approaches for target identification and efficacy prediction. Here, we take advantage of our increasing understanding of the network-based origins of diseases to introduce a drug-disease proximity measure that quantifies the interplay between drugs targets and diseases. By correcting for the known biases of the interactome, proximity helps us uncover the therapeutic effect of drugs, as well as to distinguish palliative from effective treatments. Our analysis of 238 drugs used in 78 diseases indicates that the therapeutic effect of drugs is localized in a small network neighborhood of the disease genes and highlights efficacy issues for drugs used in Parkinson and several inflammatory disorders. Finally, network-based proximity allows us to predict novel drug-disease associations that offer unprecedented opportunities for drug repurposing and the detection of adverse effects. PMID:26831545

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

    PubMed

    Motegi, Shun; Masuda, Naoki

    2012-01-01

    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.

  18. Design principles for clinical network-based proteomics.

    PubMed

    Goh, Wilson Wen Bin; Wong, Limsoon

    2016-07-01

    Integrating biological networks with proteomics is a tantalizing option for system-level analysis; for example it can help remove false-positives from proteomics data and improve coverage by detecting false-negatives, as well as resolving inconsistent inter-sample protein expression due to biological heterogeneity. Yet, designing a robust network-based analysis strategy on proteomics data is nontrivial. The issues include dealing with test set bias caused by, for example, inappropriate normalization procedure, devising appropriate benchmarking criteria and formulating statistically robust feature-selection techniques. Given the increasing importance of proteomics in contemporary clinical studies, more powerful network-based approaches are needed. We provide some design principles and considerations that can help achieve this, while taking into account the idiosyncrasies of proteomics data. PMID:27240775

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

    NASA Astrophysics Data System (ADS)

    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.

  20. Improved community model for social networks based on social mobility

    NASA Astrophysics Data System (ADS)

    Lu, Zhe-Ming; Wu, Zhen; Luo, Hao; Wang, Hao-Xian

    2015-07-01

    This paper proposes an improved community model for social networks based on social mobility. The relationship between the group distribution and the community size is investigated in terms of communication rate and turnover rate. The degree distributions, clustering coefficients, average distances and diameters of networks are analyzed. Experimental results demonstrate that the proposed model possesses the small-world property and can reproduce social networks effectively and efficiently.

  1. Design development of a neural network-based telemetry monitor

    NASA Technical Reports Server (NTRS)

    Lembeck, Michael F.

    1992-01-01

    This paper identifies the requirements and describes an architectural framework for an artificial neural network-based system that is capable of fulfilling monitoring and control requirements of future aerospace missions. Incorporated into this framework are a newly developed training algorithm and the concept of cooperative network architectures. The feasibility of such an approach is demonstrated for its ability to identify faults in low frequency waveforms.

  2. Gene identification and analysis: an application of neural network-based information fusion

    SciTech Connect

    Matis, S.; Xu, Y.; Shah, M.B.; Mural, R.J.; Einstein, J.R.; Uberbacher, E.C.

    1996-10-01

    Identifying genes within large regions of uncharacterized DNA is a difficult undertaking and is currently the focus of many research efforts. We describe a gene localization and modeling system called GRAIL. GRAIL is a multiple sensor-neural network based system. It localizes genes in anonymous DNA sequence by recognizing gene features related to protein-coding slice sites, and then combines the recognized features using a neural network system. Localized coding regions are then optimally parsed into a gene mode. RNA polymerase II promoters can also be predicted. Through years of extensive testing, GRAIL consistently localizes about 90 percent of coding portions of test genes with a false positive rate of about 10 percent. A number of genes for major genetic diseases have been located through the use of GRAIL, and over 1000 research laboratories worldwide use GRAIL on regular bases for localization of genes on their newly sequenced DNA.

  3. Neural Network Based Montioring and Control of Fluidized Bed.

    SciTech Connect

    Bodruzzaman, M.; Essawy, M.A.

    1996-04-01

    The goal of this project was to develop chaos analysis and neural network-based modeling techniques and apply them to the pressure-drop data obtained from the Fluid Bed Combustion (FBC) system (a small scale prototype model) located at the Federal Energy Technology Center (FETC)-Morgantown. The second goal was to develop neural network-based chaos control techniques and provide a suggestive prototype for possible real-time application to the FBC system. The experimental pressure data were collected from a cold FBC experimental set-up at the Morgantown Center. We have performed several analysis on these data in order to unveil their dynamical and chaotic characteristics. The phase-space attractors were constructed from the one dimensional time series data, using the time-delay embedding method, for both normal and abnormal conditions. Several identifying parameters were also computed from these attractors such as the correlation dimension, the Kolmogorov entropy, and the Lyapunov exponents. These chaotic attractor parameters can be used to discriminate between the normal and abnormal operating conditions of the FBC system. It was found that, the abnormal data has higher correlation dimension, larger Kolmogorov entropy and larger positive Lyapunov exponents as compared to the normal data. Chaotic system control using neural network based techniques were also investigated and compared to conventional chaotic system control techniques. Both types of chaotic system control techniques were applied to some typical chaotic systems such as the logistic, the Henon, and the Lorenz systems. A prototype model for real-time implementation of these techniques has been suggested to control the FBC system. These models can be implemented for real-time control in a next phase of the project after obtaining further measurements from the experimental model. After testing the control algorithms developed for the FBC model, the next step is to implement them on hardware and link them to

  4. Network Medicine: A Network-based Approach to Human Diseases

    NASA Astrophysics Data System (ADS)

    Ghiassian, Susan Dina

    With the availability of large-scale data, it is now possible to systematically study the underlying interaction maps of many complex systems in multiple disciplines. Statistical physics has a long and successful history in modeling and characterizing systems with a large number of interacting individuals. Indeed, numerous approaches that were first developed in the context of statistical physics, such as the notion of random walks and diffusion processes, have been applied successfully to study and characterize complex systems in the context of network science. Based on these tools, network science has made important contributions to our understanding of many real-world, self-organizing systems, for example in computer science, sociology and economics. Biological systems are no exception. Indeed, recent studies reflect the necessity of applying statistical and network-based approaches in order to understand complex biological systems, such as cells. In these approaches, a cell is viewed as a complex network consisting of interactions among cellular components, such as genes and proteins. Given the cellular network as a platform, machinery, functionality and failure of a cell can be studied with network-based approaches, a field known as systems biology. Here, we apply network-based approaches to explore human diseases and their associated genes within the cellular network. This dissertation is divided in three parts: (i) A systematic analysis of the connectivity patterns among disease proteins within the cellular network. The quantification of these patterns inspires the design of an algorithm which predicts a disease-specific subnetwork containing yet unknown disease associated proteins. (ii) We apply the introduced algorithm to explore the common underlying mechanism of many complex diseases. We detect a subnetwork from which inflammatory processes initiate and result in many autoimmune diseases. (iii) The last chapter of this dissertation describes the

  5. Network-based Modeling of the Human Gut Microbiome

    PubMed Central

    Naqvi, Ammar; Rangwala, Huzefa; Keshavarzian, Ali

    2013-01-01

    In this paper we used a network-based approach to characterize the microflora abundance in colonic mucosal samples and correlate potential interactions between the identified species with respect to the healthy and diseased states. We analyzed the modelled network by computing several local and global network statistics, identified recurring patterns or motifs, fit the network models to a family of well-studied graph models. This study has demonstrated, for the first time, an approach that differentiated the gut microbiota in Alcoholic subjects and Healthy subjects using topological network analysis of the gut microbiome. PMID:20491063

  6. Network-based modeling of the human gut microbiome.

    PubMed

    Naqvi, Ammar; Rangwala, Huzefa; Keshavarzian, Ali; Gillevet, Patrick

    2010-05-01

    In this article, we used a network-based approach to characterize the microflora abundance in colonic mucosal samples and correlate potential interactions between the identified species with respect to the healthy and diseased states. We analyzed the modelled network by computing several local and global network statistics, identified recurring patterns or motifs, fit the network models to a family of well-studied graph models. This study has demonstrated, for the first time, an approach that differentiated the gut microbiota in alcoholic subjects and healthy subjects using topological network analysis of the gut microbiome. PMID:20491063

  7. Different types of synchronization in coupled network based chaotic circuits

    NASA Astrophysics Data System (ADS)

    Srinivasan, K.; Chandrasekar, V. K.; Gladwin Pradeep, R.; Murali, K.; Lakshmanan, M.

    2016-10-01

    We propose a simple and new unified method to achieve lag, complete and anticipatory synchronizations in coupled nonlinear systems. It can be considered as an alternative to the subsystem and intentional parameter mismatch methods. This novel method is illustrated in a unidirectionally coupled RC phase shift network based Chua's circuit. Employing feedback coupling, different types of chaos synchronization are observed experimentally and numerically in coupled identical chaotic oscillators without using time delay. With a simple switch in the experimental set up we observe different kinds of synchronization. We also analyze the coupled system with numerical simulations.

  8. Non-linear system control using a recurrent fuzzy neural network based on improved particle swarm optimisation

    NASA Astrophysics Data System (ADS)

    Lin, Cheng-Jian; Lee, Chi-Yung

    2010-04-01

    This article introduces a recurrent fuzzy neural network based on improved particle swarm optimisation (IPSO) for non-linear system control. An IPSO method which consists of the modified evolutionary direction operator (MEDO) and the Particle Swarm Optimisation (PSO) is proposed in this article. A MEDO combining the evolutionary direction operator and the migration operation is also proposed. The MEDO will improve the global search solution. Experimental results have shown that the proposed IPSO method controls the magnetic levitation system and the planetary train type inverted pendulum system better than the traditional PSO and the genetic algorithm methods.

  9. Analog neural network-based helicopter gearbox health monitoring system.

    PubMed

    Monsen, P T; Dzwonczyk, M; Manolakos, E S

    1995-12-01

    The development of a reliable helicopter gearbox health monitoring system (HMS) has been the subject of considerable research over the past 15 years. The deployment of such a system could lead to a significant saving in lives and vehicles as well as dramatically reduce the cost of helicopter maintenance. Recent research results indicate that a neural network-based system could provide a viable solution to the problem. This paper presents two neural network-based realizations of an HMS system. A hybrid (digital/analog) neural system is proposed as an extremely accurate off-line monitoring tool used to reduce helicopter gearbox maintenance costs. In addition, an all analog neural network is proposed as a real-time helicopter gearbox fault monitor that can exploit the ability of an analog neural network to directly compute the discrete Fourier transform (DFT) as a sum of weighted samples. Hardware performance results are obtained using the Integrated Neural Computing Architecture (INCA/1) analog neural network platform that was designed and developed at The Charles Stark Draper Laboratory. The results indicate that it is possible to achieve a 100% fault detection rate with 0% false alarm rate by performing a DFT directly on the first layer of INCA/1 followed by a small-size two-layer feed-forward neural network and a simple post-processing majority voting stage.

  10. Performance Evaluation in Network-Based Parallel Computing

    NASA Technical Reports Server (NTRS)

    Dezhgosha, Kamyar

    1996-01-01

    Network-based parallel computing is emerging as a cost-effective alternative for solving many problems which require use of supercomputers or massively parallel computers. The primary objective of this project has been to conduct experimental research on performance evaluation for clustered parallel computing. First, a testbed was established by augmenting our existing SUNSPARCs' network with PVM (Parallel Virtual Machine) which is a software system for linking clusters of machines. Second, a set of three basic applications were selected. The applications consist of a parallel search, a parallel sort, a parallel matrix multiplication. These application programs were implemented in C programming language under PVM. Third, we conducted performance evaluation under various configurations and problem sizes. Alternative parallel computing models and workload allocations for application programs were explored. The performance metric was limited to elapsed time or response time which in the context of parallel computing can be expressed in terms of speedup. The results reveal that the overhead of communication latency between processes in many cases is the restricting factor to performance. That is, coarse-grain parallelism which requires less frequent communication between processes will result in higher performance in network-based computing. Finally, we are in the final stages of installing an Asynchronous Transfer Mode (ATM) switch and four ATM interfaces (each 155 Mbps) which will allow us to extend our study to newer applications, performance metrics, and configurations.

  11. Home medical monitoring network based on embedded technology

    NASA Astrophysics Data System (ADS)

    Liu, Guozhong; Deng, Wenyi; Yan, Bixi; Lv, Naiguang

    2006-11-01

    Remote medical monitoring network for long-term monitoring of physiological variables would be helpful for recovery of patients as people are monitored at more comfortable conditions. Furthermore, long-term monitoring would be beneficial to investigate slowly developing deterioration in wellness status of a subject and provide medical treatment as soon as possible. The home monitor runs on an embedded microcomputer Rabbit3000 and interfaces with different medical monitoring module through serial ports. The network based on asymmetric digital subscriber line (ADSL) or local area network (LAN) is established and a client - server model, each embedded home medical monitor is client and the monitoring center is the server, is applied to the system design. The client is able to provide its information to the server when client's request of connection to the server is permitted. The monitoring center focuses on the management of the communications, the acquisition of medical data, and the visualization and analysis of the data, etc. Diagnosing model of sleep apnea syndrome is built basing on ECG, heart rate, respiration wave, blood pressure, oxygen saturation, air temperature of mouth cavity or nasal cavity, so sleep status can be analyzed by physiological data acquired as people in sleep. Remote medical monitoring network based on embedded micro Internetworking technology have advantages of lower price, convenience and feasibility, which have been tested by the prototype.

  12. Neural network based analysis for chemical sensor arrays

    SciTech Connect

    Hashem, S.; Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

    1995-04-01

    Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. In this paper, we examine the effectiveness of using artificial neural networks for real-time data analysis of a sensor array. Analyzing the sensor data in parallel may allow for rapid identification of contaminants in the field without requiring highly selective individual sensors. We use a prototype sensor array which consists of nine tin-oxide Taguchi-type sensors, a temperature sensor, and a humidity sensor. We illustrate that by using neural network based analysis of the sensor data, the selectivity of the sensor array may be significantly improved, especially when some (or all) the sensors are not highly selective.

  13. Design of an adaptive neural network based power system stabilizer.

    PubMed

    Liu, Wenxin; Venayagamoorthy, Ganesh K; Wunsch, Donald C

    2003-01-01

    Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, this paper presents an indirect adaptive neural network based power system stabilizer (IDNC) design. The proposed IDNC consists of a neuro-controller, which is used to generate a supplementary control signal to the excitation system, and a neuro-identifier, which is used to model the dynamics of the power system and to adapt the neuro-controller parameters. The proposed method has the features of a simple structure, adaptivity and fast response. The proposed IDNC is evaluated on a single machine infinite bus power system under different operating conditions and disturbances to demonstrate its effectiveness and robustness. PMID:12850048

  14. Neural network based feed-forward high density associative memory

    NASA Technical Reports Server (NTRS)

    Daud, T.; Moopenn, A.; Lamb, J. L.; Ramesham, R.; Thakoor, A. P.

    1987-01-01

    A novel thin film approach to neural-network-based high-density associative memory is described. The information is stored locally in a memory matrix of passive, nonvolatile, binary connection elements with a potential to achieve a storage density of 10 to the 9th bits/sq cm. Microswitches based on memory switching in thin film hydrogenated amorphous silicon, and alternatively in manganese oxide, have been used as programmable read-only memory elements. Low-energy switching has been ascertained in both these materials. Fabrication and testing of memory matrix is described. High-speed associative recall approaching 10 to the 7th bits/sec and high storage capacity in such a connection matrix memory system is also described.

  15. Network-Based Analysis of Schizophrenia Genome-Wide Association Data to Detect the Joint Functional Association Signals.

    PubMed

    Chang, Suhua; Fang, Kechi; Zhang, Kunlin; Wang, Jing

    2015-01-01

    Schizophrenia is a common psychiatric disorder with high heritability and complex genetic architecture. Genome-wide association studies (GWAS) have identified several significant loci associated with schizophrenia. However, the explained heritability is still low. Growing evidence has shown schizophrenia is attributable to multiple genes with moderate effects. In-depth mining and integration of GWAS data is urgently expected to uncover disease-related gene combination patterns. Network-based analysis is a promising strategy to better interpret GWAS to identify disease-related network modules. We performed a network-based analysis on three independent schizophrenia GWASs by using a refined analysis framework, which included a more accurate gene P-value calculation, dynamic network module searching algorithm and detailed functional analysis for the obtained modules genes. The result generated 79 modules including 238 genes, which form a highly connected subnetwork with more statistical significance than expected by chance. The result validated several reported disease genes, such as MAD1L1, MCC, SDCCAG8, VAT1L, MAPK14, MYH9 and FXYD6, and also obtained several novel candidate genes and gene-gene interactions. Pathway enrichment analysis of the module genes suggested they were enriched in several neural and immune system related pathways/GO terms, such as neurotrophin signaling pathway, synaptosome, regulation of protein ubiquitination, and antigen processing and presentation. Further crosstalk analysis revealed these pathways/GO terms were cooperated with each other, and identified several important genes, which might play vital roles to connect these functions. Our network-based analysis of schizophrenia GWASs will facilitate the understanding of genetic mechanisms of schizophrenia. PMID:26193471

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

    PubMed Central

    Verbeke, Lieven P. C.; Van den Eynden, Jimmy; Fierro, Ana Carolina; Demeester, Piet; Fostier, Jan; Marchal, Kathleen

    2015-01-01

    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-outcome patient group

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

    PubMed Central

    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 of the complex genotype determines the state and dynamics of any trait, which may be modified to various extent by non-genetic factors. Thus, diseases are heterogenous ensembles of conditions with a common endpoint. Numerous studies have been performed to define genes of importance for a trait or disease, but only a few genes with small effect have been identified. The major reasons for this modest progress is the unresolved heterogeneity of the regulation of blood pressure and the shortcomings of the prevailing monogenic approach to capture genetic effects in a polygenic condition. Here, a two-step procedure is presented in which physiological heterogeneity is disentangled and genetic effects are analyzed by variance decomposition of genetic interactions and by an information theoretical approach including 162 single nucleotide polymorphisms (SNP) in 84 genes in the sphingolipid metabolism and related networks in blood pressure regulation. As expected, almost no genetic main effects were detected. In contrast, two-gene interactions established the entire sphingolipid metabolic and related genetic network to be highly involved in the regulation of blood pressure. The pattern of interaction clearly revealed that epistasis does not necessarily reflects the topology of the metabolic pathways i.e., the flow of metabolites. Rather, the enzymes and proteins are integrated in complex cellular substructures where communication flows between the components of the networks, which may be composite in structure. The heritabilities for diastolic and systolic blood pressure were estimated to be 0.63 and 0.01, which may in fact be the maximum heritabilities of these traits. This procedure

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

    PubMed

    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 of the complex genotype determines the state and dynamics of any trait, which may be modified to various extent by non-genetic factors. Thus, diseases are heterogenous ensembles of conditions with a common endpoint. Numerous studies have been performed to define genes of importance for a trait or disease, but only a few genes with small effect have been identified. The major reasons for this modest progress is the unresolved heterogeneity of the regulation of blood pressure and the shortcomings of the prevailing monogenic approach to capture genetic effects in a polygenic condition. Here, a two-step procedure is presented in which physiological heterogeneity is disentangled and genetic effects are analyzed by variance decomposition of genetic interactions and by an information theoretical approach including 162 single nucleotide polymorphisms (SNP) in 84 genes in the sphingolipid metabolism and related networks in blood pressure regulation. As expected, almost no genetic main effects were detected. In contrast, two-gene interactions established the entire sphingolipid metabolic and related genetic network to be highly involved in the regulation of blood pressure. The pattern of interaction clearly revealed that epistasis does not necessarily reflects the topology of the metabolic pathways i.e., the flow of metabolites. Rather, the enzymes and proteins are integrated in complex cellular substructures where communication flows between the components of the networks, which may be composite in structure. The heritabilities for diastolic and systolic blood pressure were estimated to be 0.63 and 0.01, which may in fact be the maximum heritabilities of these traits. This procedure

  19. Identifying node role in social network based on multiple indicators.

    PubMed

    Huang, Shaobin; Lv, Tianyang; Zhang, Xizhe; Yang, Yange; Zheng, Weimin; Wen, Chao

    2014-01-01

    It is a classic topic of social network analysis to evaluate the importance of nodes and identify the node that takes on the role of core or bridge in a network. Because a single indicator is not sufficient to analyze multiple characteristics of a node, it is a natural solution to apply multiple indicators that should be selected carefully. An intuitive idea is to select some indicators with weak correlations to efficiently assess different characteristics of a node. However, this paper shows that it is much better to select the indicators with strong correlations. Because indicator correlation is based on the statistical analysis of a large number of nodes, the particularity of an important node will be outlined if its indicator relationship doesn't comply with the statistical correlation. Therefore, the paper selects the multiple indicators including degree, ego-betweenness centrality and eigenvector centrality to evaluate the importance and the role of a node. The importance of a node is equal to the normalized sum of its three indicators. A candidate for core or bridge is selected from the great degree nodes or the nodes with great ego-betweenness centrality respectively. Then, the role of a candidate is determined according to the difference between its indicators' relationship with the statistical correlation of the overall network. Based on 18 real networks and 3 kinds of model networks, the experimental results show that the proposed methods perform quite well in evaluating the importance of nodes and in identifying the node role.

  20. Neural Network Based Intrusion Detection System for Critical Infrastructures

    SciTech Connect

    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.

  1. Network-based reading system for lung cancer screening CT

    NASA Astrophysics Data System (ADS)

    Fujino, Yuichi; Fujimura, Kaori; Nomura, Shin-ichiro; Kawashima, Harumi; Tsuchikawa, Megumu; Matsumoto, Toru; Nagao, Kei-ichi; Uruma, Takahiro; Yamamoto, Shinji; Takizawa, Hotaka; Kuroda, Chikazumi; Nakayama, Tomio

    2006-03-01

    This research aims to support chest computed tomography (CT) medical checkups to decrease the death rate by lung cancer. We have developed a remote cooperative reading system for lung cancer screening over the Internet, a secure transmission function, and a cooperative reading environment. It is called the Network-based Reading System. A telemedicine system involves many issues, such as network costs and data security if we use it over the Internet, which is an open network. In Japan, broadband access is widespread and its cost is the lowest in the world. We developed our system considering human machine interface and security. It consists of data entry terminals, a database server, a computer aided diagnosis (CAD) system, and some reading terminals. It uses a secure Digital Imaging and Communication in Medicine (DICOM) encrypting method and Public Key Infrastructure (PKI) based secure DICOM image data distribution. We carried out an experimental trial over the Japan Gigabit Network (JGN), which is the testbed for the Japanese next-generation network, and conducted verification experiments of secure screening image distribution, some kinds of data addition, and remote cooperative reading. We found that network bandwidth of about 1.5 Mbps enabled distribution of screening images and cooperative reading and that the encryption and image distribution methods we proposed were applicable to the encryption and distribution of general DICOM images via the Internet.

  2. Earthquake networks based on space-time influence domain

    NASA Astrophysics Data System (ADS)

    He, Xuan; Zhao, Hai; Cai, Wei; Liu, Zheng; Si, Shuai-Zong

    2014-08-01

    A new construction method of earthquake networks based on the theory of complex networks is presented in this paper. We propose a space-time influence domain for each earthquake to quantify the subsequence of earthquakes which are directly influenced by the former earthquake. The size of the domain is according to the magnitude of earthquake. In this way, the seismic data in the region of California are mapped to a topology of earthquake network. It is discovered that the earthquake networks in different time spans behave as scale-free networks. This result can be interpreted in terms of the Gutenberg-Richter law. Discovery of small-world characteristic is also reported on the earthquake network constructed by our method. The Omori law emerges as a feature of seismicity for the out-going links of the network. These characteristics highlight a novel aspect of seismicity as a complex phenomenon and will help us to reveal the internal mechanism of seismic system.

  3. Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

    PubMed

    Zhang, Yanjun; Tao, Gang; Chen, Mou

    2016-09-01

    This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.

  4. Network-based analysis of software change propagation.

    PubMed

    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.

  5. PROMISCUOUS: a database for network-based drug-repositioning

    PubMed Central

    von Eichborn, Joachim; Murgueitio, Manuela S.; Dunkel, Mathias; Koerner, Soeren; Bourne, Philip E.; Preissner, Robert

    2011-01-01

    The procedure of drug approval is time-consuming, costly and risky. Accidental findings regarding multi-specificity of approved drugs led to block-busters in new indication areas. Therefore, the interest in systematically elucidating new areas of application for known drugs is rising. Furthermore, the knowledge, understanding and prediction of so-called off-target effects allow a rational approach to the understanding of side-effects. With PROMISCUOUS we provide an exhaustive set of drugs (25 000), including withdrawn or experimental drugs, annotated with drug–protein and protein–protein relationships (21 500/104 000) compiled from public resources via text and data mining including manual curation. Measures of structural similarity for drugs as well as known side-effects can be easily connected to protein–protein interactions to establish and analyse networks responsible for multi-pharmacology. This network-based approach can provide a starting point for drug-repositioning. PROMISCUOUS is publicly available at http://bioinformatics.charite.de/promiscuous. PMID:21071407

  6. Pattern recognition tool based on complex network-based approach

    NASA Astrophysics Data System (ADS)

    Casanova, Dalcimar; Backes, André Ricardo; Martinez Bruno, Odemir

    2013-02-01

    This work proposed a generalization of the method proposed by the authors: 'A complex network-based approach for boundary shape analysis'. Instead of modelling a contour into a graph and use complex networks rules to characterize it, here, we generalize the technique. This way, the work proposes a mathematical tool for characterization signals, curves and set of points. To evaluate the pattern description power of the proposal, an experiment of plat identification based on leaf veins image are conducted. Leaf vein is a taxon characteristic used to plant identification proposes, and one of its characteristics is that these structures are complex, and difficult to be represented as a signal or curves and this way to be analyzed in a classical pattern recognition approach. Here, we model the veins as a set of points and model as graphs. As features, we use the degree and joint degree measurements in a dynamic evolution. The results demonstrates that the technique has a good power of discrimination and can be used for plant identification, as well as other complex pattern recognition tasks.

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

    PubMed

    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. PMID:25532191

  8. Neural networks-based damage detection for bridges considering errors in baseline finite element models

    NASA Astrophysics Data System (ADS)

    Lee, Jong Jae; Lee, Jong Won; Yi, Jin Hak; Yun, Chung Bang; Jung, Hie Young

    2005-02-01

    Structural health monitoring has become an important research topic in conjunction with damage assessment and safety evaluation of structures. The use of system identification approaches for damage detection has been expanded in recent years owing to the advancements in signal analysis and information processing techniques. Soft computing techniques such as neural networks and genetic algorithm have been utilized increasingly for this end due to their excellent pattern recognition capability. In this study, a neural networks-based damage detection method using the modal properties is presented, which can effectively consider the modelling errors in the baseline finite element model from which the training patterns are to be generated. The differences or the ratios of the mode shape components between before and after damage are used as the input to the neural networks in this method, since they are found to be less sensitive to the modelling errors than the mode shapes themselves. Two numerical example analyses on a simple beam and a multi-girder bridge are presented to demonstrate the effectiveness of the proposed method. Results of laboratory test on a simply supported bridge model and field test on a bridge with multiple girders confirm the applicability of the present method.

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

    PubMed

    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.

  10. Variables of Interest in Exploring the Reflective Outcomes of Network-based Communication.

    ERIC Educational Resources Information Center

    Hawkes, Mark

    2001-01-01

    Explored the opportunities presented by network-based communication to facilitate collaborative critical reflection between elementary and middle school teachers who were working on a curriculum development project. Considers self-efficacy and discusses results that showed that collaboratively produced network-based communication was significantly…

  11. Genomic connectivity networks based on the BrainSpan atlas of the developing human brain

    NASA Astrophysics Data System (ADS)

    Mahfouz, Ahmed; Ziats, Mark N.; Rennert, Owen M.; Lelieveldt, Boudewijn P. F.; Reinders, Marcel J. T.

    2014-03-01

    The human brain comprises systems of networks that span the molecular, cellular, anatomic and functional levels. Molecular studies of the developing brain have focused on elucidating networks among gene products that may drive cellular brain development by functioning together in biological pathways. On the other hand, studies of the brain connectome attempt to determine how anatomically distinct brain regions are connected to each other, either anatomically (diffusion tensor imaging) or functionally (functional MRI and EEG), and how they change over development. A global examination of the relationship between gene expression and connectivity in the developing human brain is necessary to understand how the genetic signature of different brain regions instructs connections to other regions. Furthermore, analyzing the development of connectivity networks based on the spatio-temporal dynamics of gene expression provides a new insight into the effect of neurodevelopmental disease genes on brain networks. In this work, we construct connectivity networks between brain regions based on the similarity of their gene expression signature, termed "Genomic Connectivity Networks" (GCNs). Genomic connectivity networks were constructed using data from the BrainSpan Transcriptional Atlas of the Developing Human Brain. Our goal was to understand how the genetic signatures of anatomically distinct brain regions relate to each other across development. We assessed the neurodevelopmental changes in connectivity patterns of brain regions when networks were constructed with genes implicated in the neurodevelopmental disorder autism (autism spectrum disorder; ASD). Using graph theory metrics to characterize the GCNs, we show that ASD-GCNs are relatively less connected later in development with the cerebellum showing a very distinct expression of ASD-associated genes compared to other brain regions.

  12. Dynamic Network-Based Epistasis Analysis: Boolean Examples

    PubMed Central

    Azpeitia, Eugenio; Benítez, Mariana; Padilla-Longoria, Pablo; Espinosa-Soto, Carlos; Alvarez-Buylla, Elena R.

    2011-01-01

    In this article we focus on how the hierarchical and single-path assumptions of epistasis analysis can bias the inference of gene regulatory networks. Here we emphasize the critical importance of dynamic analyses, and specifically illustrate the use of Boolean network models. Epistasis in a broad sense refers to gene interactions, however, as originally proposed by Bateson, epistasis is defined as the blocking of a particular allelic effect due to the effect of another allele at a different locus (herein, classical epistasis). Classical epistasis analysis has proven powerful and useful, allowing researchers to infer and assign directionality to gene interactions. As larger data sets are becoming available, the analysis of classical epistasis is being complemented with computer science tools and system biology approaches. We show that when the hierarchical and single-path assumptions are not met in classical epistasis analysis, the access to relevant information and the correct inference of gene interaction topologies is hindered, and it becomes necessary to consider the temporal dynamics of gene interactions. The use of dynamical networks can overcome these limitations. We particularly focus on the use of Boolean networks that, like classical epistasis analysis, relies on logical formalisms, and hence can complement classical epistasis analysis and relax its assumptions. We develop a couple of theoretical examples and analyze them from a dynamic Boolean network model perspective. Boolean networks could help to guide additional experiments and discern among alternative regulatory schemes that would be impossible or difficult to infer without the elimination of these assumption from the classical epistasis analysis. We also use examples from the literature to show how a Boolean network-based approach has resolved ambiguities and guided epistasis analysis. Our article complements previous accounts, not only by focusing on the implications of the hierarchical and

  13. A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies

    PubMed Central

    Freytag, Saskia; Manitz, Juliane; Schlather, Martin; Kneib, Thomas; Amos, Christopher I.; Risch, Angela; Chang-Claude, Jenny; Heinrich, Joachim; Bickeböller, Heike

    2014-01-01

    Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). Here, the kernel converts genomic information of two individuals to a quantitative value reflecting their genetic similarity. With the selection of the kernel one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms. PMID:24434848

  14. Network-based survival-associated module biomarker and its crosstalk with cell death genes in ovarian cancer

    PubMed Central

    Jin, Nana; Wu, Hao; Miao, Zhengqiang; Huang, Yan; Hu, Yongfei; Bi, Xiaoman; Wu, Deng; Qian, Kun; Wang, Liqiang; Wang, Changliang; Wang, Hongwei; Li, Kongning; Li, Xia; Wang, Dong

    2015-01-01

    Ovarian cancer remains a dismal disease with diagnosing in the late, metastatic stages, therefore, there is a growing realization of the critical need to develop effective biomarkers for understanding underlying mechanisms. Although existing evidences demonstrate the important role of the single genetic abnormality in pathogenesis, the perturbations of interactors in the complex network are often ignored. Moreover, ovarian cancer diagnosis and treatment still exist a large gap that need to be bridged. In this work, we adopted a network-based survival-associated approach to capture a 12-gene network module based on differential co-expression PPI network in the advanced-stage, high-grade ovarian serous cystadenocarcinoma. Then, regulatory genes (protein-coding genes and non-coding genes) direct interacting with the module were found to be significantly overlapped with cell death genes. More importantly, these overlapping genes tightly clustered together pointing to the module, deciphering the crosstalk between network-based survival-associated module and cell death in ovarian cancer. PMID:26099452

  15. Network-Based Multiple Sclerosis Pathway Analysis with GWAS Data from 15,000 Cases and 30,000 Controls

    PubMed Central

    Baranzini, Sergio E.; Khankhanian, Pouya; Patsopoulos, Nikolaos A.; Li, Michael; Stankovich, Jim; Cotsapas, Chris; Søndergaard, Helle Bach; Ban, Maria; Barizzone, Nadia; Bergamaschi, Laura; Booth, David; Buck, Dorothea; Cavalla, Paola; Celius, Elisabeth G.; Comabella, Manuel; Comi, Giancarlo; Compston, Alastair; Cournu-Rebeix, Isabelle; D’alfonso, Sandra; Damotte, Vincent; Din, Lennox; Dubois, Bénédicte; Elovaara, Irina; Esposito, Federica; Fontaine, Bertrand; Franke, Andre; Goris, An; Gourraud, Pierre-Antoine; Graetz, Christiane; Guerini, Franca R.; Guillot-Noel, Léna; Hafler, David; Hakonarson, Hakon; Hall, Per; Hamsten, Anders; Harbo, Hanne F.; Hemmer, Bernhard; Hillert, Jan; Kemppinen, Anu; Kockum, Ingrid; Koivisto, Keijo; Larsson, Malin; Lathrop, Mark; Leone, Maurizio; Lill, Christina M.; Macciardi, Fabio; Martin, Roland; Martinelli, Vittorio; Martinelli-Boneschi, Filippo; McCauley, Jacob L.; Myhr, Kjell-Morten; Naldi, Paola; Olsson, Tomas; Oturai, Annette; Pericak-Vance, Margaret A.; Perla, Franco; Reunanen, Mauri; Saarela, Janna; Saker-Delye, Safa; Salvetti, Marco; Sellebjerg, Finn; Sørensen, Per Soelberg; Spurkland, Anne; Stewart, Graeme; Taylor, Bruce; Tienari, Pentti; Winkelmann, Juliane; Zipp, Frauke; Ivinson, Adrian J.; Haines, Jonathan L.; Sawcer, Stephen; DeJager, Philip; Hauser, Stephen L.; Oksenberg, Jorge R.

    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 susceptibility loci. Here, we merged nominal statistical evidence of association and physical evidence of interaction to conduct a protein-interaction-network-based pathway analysis (PINBPA) on two large genetic MS studies comprising a total of 15,317 cases and 29,529 controls. The distribution of nominally significant loci at the gene level matched the patterns of extended linkage disequilibrium in regions of interest. We found that products of genome-wide significantly associated genes are more likely to interact physically and belong to the same or related pathways. We next searched for subnetworks (modules) of genes (and their encoded proteins) enriched with nominally associated loci within each study and identified those modules in common between the two studies. We demonstrate that these modules are more likely to contain genes with bona fide susceptibility variants and, in addition, identify several high-confidence candidates (including BCL10, CD48, REL, TRAF3, and TEC). PINBPA is a powerful approach to gaining further insights into the biology of associated genes and to prioritizing candidates for subsequent genetic studies of complex traits. PMID:23731539

  16. Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways

    PubMed Central

    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. PMID:25392692

  17. FCDECOMP: decomposition of metabolic networks based on flux coupling relations.

    PubMed

    Rezvan, Abolfazl; Marashi, Sayed-Amir; Eslahchi, Changiz

    2014-10-01

    A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.

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

    PubMed

    Castillo, Luis F; Galeano, Narmer; Isaza, Gustavo A; Gaitán, Alvaro

    2012-07-24

    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.

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

    PubMed

    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.

  20. Network-based drug discovery by integrating systems biology and computational technologies.

    PubMed

    Leung, Elaine L; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua; Liu, Liang

    2013-07-01

    Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple '-omics' databases. The newly developed algorithm- or network-based computational models can tightly integrate '-omics' databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various '-omics' platforms and computational tools would accelerate development of network-based drug discovery and network medicine.

  1. Network-based drug discovery by integrating systems biology and computational technologies

    PubMed Central

    Leung, Elaine L.; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua

    2013-01-01

    Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple ‘-omics’ databases. The newly developed algorithm- or network-based computational models can tightly integrate ‘-omics’ databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various ‘-omics’ platforms and computational tools would accelerate development of network-based drug discovery and network medicine. PMID:22877768

  2. MLEM algorithm adaptation for improved SPECT scintimammography

    NASA Astrophysics Data System (ADS)

    Krol, Andrzej; Feiglin, David H.; Lee, Wei; Kunniyur, Vikram R.; Gangal, Kedar R.; Coman, Ioana L.; Lipson, Edward D.; Karczewski, Deborah A.; Thomas, F. Deaver

    2005-04-01

    Standard MLEM and OSEM algorithms used in SPECT Tc-99m sestamibi scintimammography produce hot-spot artifacts (HSA) at the image support peripheries. We investigated a suitable adaptation of MLEM and OSEM algorithms needed to reduce HSA. Patients with suspicious breast lesions were administered 10 mCi of Tc-99m sestamibi and SPECT scans were acquired for patients in prone position with uncompressed breasts. In addition, to simulate breast lesions, some patients were imaged with a number of breast skin markers each containing 1 mCi of Tc-99m. In order to reduce HSA in reconstruction, we removed from the backprojection step the rays that traverse the periphery of the support region on the way to a detector bin, when their path length through this region was shorter than some critical length. Such very short paths result in a very low projection counts contributed to the detector bin, and consequently to overestimation of the activity in the peripheral voxels in the backprojection step-thus creating HSA. We analyzed the breast-lesion contrast and suppression of HSA in the images reconstructed using standard and modified MLEM and OSEM algorithms vs. critical path length (CPL). For CPL >= 0.01 pixel size, we observed improved breast-lesion contrast and lower noise in the reconstructed images, and a very significant reduction of HSA in the maximum intensity projection (MIP) images.

  3. A Network-Based Curriculum: The Basis for the Design of a New Learning System.

    ERIC Educational Resources Information Center

    Hathaway, Warren E.

    1989-01-01

    Describes the steps needed to develop a computer-managed learning system that is derived from a network-based curriculum such as the Program Evaluation and Review Technique (PERT). System components that are described include curriculum mapping, including objectives; lesson outlines; and a framework for evaluating students and instructional…

  4. Exchanging Ideas with Peers in Network-based Classrooms: An Aid or a Pain?

    ERIC Educational Resources Information Center

    Sengupta, Sima

    2001-01-01

    Examines the nature of peer exchanges in two partial network-based classes and the conflicts learners face in this situation where all information is text-based and archived. Provides a picture of how learners used the available technology to interact with peers and their comments on how this mode of delivery extended their traditional notions of…

  5. Learning Styles and Student Attitudes toward Various Aspects of Network-based Instruction.

    ERIC Educational Resources Information Center

    Federico, Pat-Anthony

    2000-01-01

    Describes a study conducted at the Naval Postgraduate School to determine student attitudes toward various aspects of network-based instruction. Discusses Internet technology; Web-based education; online learning; learning styles; and results from Kolb's Learning Style Inventory, the Hidden Figures Test, and a number of multivariate procedures.…

  6. Network-Based Meta-Analyses of Associations of Multiple Gene Expression Profiles with Bone Mineral Density Variations in Women

    PubMed Central

    Niu, Tianhua; Zhou, Yu; Zhang, Lan; Zeng, Yong; Zhu, Wei; Wang, Yu-ping; Deng, Hong-wen

    2016-01-01

    Background Existing microarray studies of bone mineral density (BMD) have been critical for understanding the pathophysiology of osteoporosis, and have identified a number of candidate genes. However, these studies were limited by their relatively small sample sizes and were usually analyzed individually. Here, we propose a novel network-based meta-analysis approach that combines data across six microarray studies to identify functional modules from human protein-protein interaction (PPI) data, and highlight several differentially expressed genes (DEGs) and a functional module that may play an important role in BMD regulation in women. Methods Expression profiling studies were identified by searching PubMed, Gene Expression Omnibus (GEO) and ArrayExpress. Two meta-analysis methods were applied across different gene expression profiling studies. The first, a nonparametric Fisher’s method, combined p-values from individual experiments to identify genes with large effect sizes. The second method combined effect sizes from individual datasets into a meta-effect size to gain a higher precision of effect size estimation across all datasets. Genes with Q test’s p-values < 0.05 or I2 values > 50% were assessed by a random effects model and the remainder by a fixed effects model. Using Fisher’s combined p-values, functional modules were identified through an integrated analysis of microarray data in the context of large protein–protein interaction (PPI) networks. Two previously published meta-analysis studies of genome-wide association (GWA) datasets were used to determine whether these module genes were genetically associated with BMD. Pathway enrichment analysis was performed with a hypergeometric test. Results Six gene expression datasets were identified, which included a total of 249 (129 high BMD and 120 low BMD) female subjects. Using a network-based meta-analysis, a consensus module containing 58 genes (nodes) and 83 edges was detected. Pathway enrichment

  7. Network-Based Analysis of eQTL Data to Prioritize Driver Mutations

    PubMed Central

    De Maeyer, Dries; Weytjens, Bram; De Raedt, Luc; Marchal, Kathleen

    2016-01-01

    In clonal systems, interpreting driver genes in terms of molecular networks helps understanding how these drivers elicit an adaptive phenotype. Obtaining such a network-based understanding depends on the correct identification of driver genes. In clonal systems, independent evolved lines can acquire a similar adaptive phenotype by affecting the same molecular pathways, a phenomenon referred to as parallelism at the molecular pathway level. This implies that successful driver identification depends on interpreting mutated genes in terms of molecular networks. Driver identification and obtaining a network-based understanding of the adaptive phenotype are thus confounded problems that ideally should be solved simultaneously. In this study, a network-based eQTL method is presented that solves both the driver identification and the network-based interpretation problem. As input the method uses coupled genotype-expression phenotype data (eQTL data) of independently evolved lines with similar adaptive phenotypes and an organism-specific genome-wide interaction network. The search for mutational consistency at pathway level is defined as a subnetwork inference problem, which consists of inferring a subnetwork from the genome-wide interaction network that best connects the genes containing mutations to differentially expressed genes. Based on their connectivity with the differentially expressed genes, mutated genes are prioritized as driver genes. Based on semisynthetic data and two publicly available data sets, we illustrate the potential of the network-based eQTL method to prioritize driver genes and to gain insights in the molecular mechanisms underlying an adaptive phenotype. The method is available at http://bioinformatics.intec.ugent.be/phenetic_eqtl/index.html PMID:26802430

  8. An antibiotic target ranking and prioritization pipeline combining sequence, structure and network-based approaches exemplified for Serratia marcescens.

    PubMed

    Gupta, Shishir K; Gross, Roy; Dandekar, Thomas

    2016-10-10

    We investigate a drug target screening pipeline comparing sequence, structure and network-based criteria for prioritization. Serratia marcescens, an opportunistic pathogen, serves as test case. We rank according to (i) availability of three dimensional structures and lead compounds, (ii) not occurring in man and general sequence conservation information, and (iii) network information on the importance of the protein (conserved protein-protein interactions; metabolism; reported to be an essential gene in other organisms). We identify 45 potential anti-microbial drug targets in S. marcescens with KdsA involved in LPS biosynthesis as top candidate drug target. LpxC and FlgB are further top-ranked targets identified by interactome analysis not suggested before for S. marcescens. Pipeline, targets and complementarity of the three approaches are evaluated by available experimental data and genetic evidence and against other antibiotic screening pipelines. This supports reliable drug target identification and prioritization for infectious agents (bacteria, parasites, fungi) by these bundled complementary criteria.

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

    NASA Technical Reports Server (NTRS)

    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.

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

    SciTech Connect

    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. Evaluating the performances of statistical and neural network based control charts

    NASA Astrophysics Data System (ADS)

    Teoh, Kok Ban; Ong, Hong Choon

    2015-10-01

    Control chart is used widely in many fields and traditional control chart is no longer adequate in detecting a sudden change in a particular process. So, run rules which are built in into Shewhart X ¯ control chart while Exponential Weighted Moving Average control chart (EWMA), Cumulative Sum control chart (CUSUM) and neural network based control chart are introduced to overcome the limitation regarding to the sensitivity of traditional control chart. In this study, the average run length (ARL) and median run length (MRL) in the shifts in the process mean of control charts mentioned will be computed. We will show that interpretations based only on the ARL can be misleading. Thus, MRL is also used to evaluate the performances of the control charts. From this study, neural network based control chart is found to possess a better performance than run rules of Shewhart X ¯ control chart, EWMA and CUSUM control chart.

  12. Functional-network-based gene set analysis using gene-ontology.

    PubMed

    Chang, Billy; Kustra, Rafal; Tian, Weidong

    2013-01-01

    To account for the functional non-equivalence among a set of genes within a biological pathway when performing gene set analysis, we introduce GOGANPA, a network-based gene set analysis method, which up-weights genes with functions relevant to the gene set of interest. The genes are weighted according to its degree within a genome-scale functional network constructed using the functional annotations available from the gene ontology database. By benchmarking GOGANPA using a well-studied P53 data set and three breast cancer data sets, we will demonstrate the power and reproducibility of our proposed method over traditional unweighted approaches and a competing network-based approach that involves a complex integrated network. GOGANPA's sole reliance on gene ontology further allows GOGANPA to be widely applicable to the analysis of any gene-ontology-annotated genome. PMID:23418449

  13. Improved Crack Type Classification Neural Network based on Square Sub-images of Pavement Surface

    NASA Astrophysics Data System (ADS)

    Lee, Byoung Jik; Lee, Hosin “David”

    The previous neural network based on the proximity values was developed using rectangular pavement images. However, the proximity value derived from the rectangular image was biased towards transverse cracking. By sectioning the rectangular image into a set of square sub-images, the neural network based on the proximity value became more robust and consistent in determining a crack type. This paper presents an improved neural network to determine a crack type from a pavement surface image based on square sub-images over the neural network trained using rectangular pavement images. The advantage of using square sub-image is demonstrated by using sample images of transverse cracking, longitudinal cracking and alligator cracking.

  14. Cancer Markers Selection Using Network-Based Cox Regression: A Methodological and Computational Practice

    PubMed Central

    Iuliano, Antonella; Occhipinti, Annalisa; Angelini, Claudia; De Feis, Italia; Lió, Pietro

    2016-01-01

    International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Cox regression models. Although these models provide a good statistical framework to analyze omic data, there is still a lack of studies that illustrate advantages and drawbacks in integrating biological information and selecting groups of biomarkers. In fact, classical Cox regression algorithms focus on the selection of a single biomarker, without taking into account the strong correlation between genes. Even though network-based Cox regression algorithms overcome such drawbacks, such network-based approaches are less widely used within the life science community. In this article, we aim to provide a clear methodological framework on the use of such approaches in order to turn cancer research results into clinical applications. Therefore, we first discuss the rationale and the practical usage of three recently proposed network-based Cox regression algorithms (i.e., Net-Cox, AdaLnet, and fastcox). Then, we show how to combine existing biological knowledge and available data with such algorithms to identify networks of cancer biomarkers and to estimate survival of patients. Finally, we describe in detail a new permutation-based approach to better validate the significance of the selection in terms of cancer gene signatures and pathway/networks identification. We illustrate the proposed methodology by means of both simulations and real case studies. Overall, the aim of our work is two-fold. Firstly, to show how network-based Cox regression models can be used to integrate biological knowledge (e.g., multi-omics data) for the analysis of survival data. Secondly, to provide a clear methodological and computational approach for

  15. Further Investigation of Receding Horizion-Based Controllers and Neural Network-Based Systems

    NASA Technical Reports Server (NTRS)

    Kelkar, Atul G.; Haley, Pamela J. (Technical Monitor)

    2000-01-01

    This report provides a comprehensive summary of the research work performed over the entire duration of the co-operative research agreement between NASA Langley Research Center and Kansas State University. This summary briefly lists the findings and also suggests possible future directions for the continuation of the subject research in the area of Generalized Predictive Control (GPC) and Network Based Generalized Predictive Control (NGPC).

  16. Network-based function prediction and interactomics: the case for metabolic enzymes.

    PubMed

    Janga, S C; Díaz-Mejía, J Javier; Moreno-Hagelsieb, G

    2011-01-01

    As sequencing technologies increase in power, determining the functions of unknown proteins encoded by the DNA sequences so produced becomes a major challenge. Functional annotation is commonly done on the basis of amino-acid sequence similarity alone. Long after sequence similarity becomes undetectable by pair-wise comparison, profile-based identification of homologs can often succeed due to the conservation of position-specific patterns, important for a protein's three dimensional folding and function. Nevertheless, prediction of protein function from homology-driven approaches is not without problems. Homologous proteins might evolve different functions and the power of homology detection has already started to reach its maximum. Computational methods for inferring protein function, which exploit the context of a protein in cellular networks, have come to be built on top of homology-based approaches. These network-based functional inference techniques provide both a first hand hint into a proteins' functional role and offer complementary insights to traditional methods for understanding the function of uncharacterized proteins. Most recent network-based approaches aim to integrate diverse kinds of functional interactions to boost both coverage and confidence level. These techniques not only promise to solve the moonlighting aspect of proteins by annotating proteins with multiple functions, but also increase our understanding on the interplay between different functional classes in a cell. In this article we review the state of the art in network-based function prediction and describe some of the underlying difficulties and successes. Given the volume of high-throughput data that is being reported the time is ripe to employ these network-based approaches, which can be used to unravel the functions of the uncharacterized proteins accumulating in the genomic databases.

  17. [Genetics and genetic counseling].

    PubMed

    Izzi, Claudia; Liut, Francesca; Dallera, Nadia; Mazza, Cinzia; Magistroni, Riccardo; Savoldi, Gianfranco; Scolari, Francesco

    2016-01-01

    Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most frequent genetic disease, characterized by progressive development of bilateral renal cysts. Two causative genes have been identified: PKD1 and PKD2. ADPKD phenotype is highly variable. Typically, ADPKD is an adult onset disease. However, occasionally, ADPKD manifests as very early onset disease. The phenotypic variability of ADPKD can be explained at three genetic levels: genic, allelic and gene modifier effects. Recent advances in molecular screening for PKD gene mutations and the introduction of the new next generation sequencing (NGS)- based genotyping approach have generated considerable improvement regarding the knowledge of genetic basis of ADPKD. The purpose of this article is to provide a comprehensive review of the genetics of ADPKD, focusing on new insights in genotype-phenotype correlation and exploring novel clinical approach to genetic testing. Evaluation of these new genetic information requires a multidisciplinary approach involving a nephrologist and a clinical geneticist. PMID:27067213

  18. Candidate gene association study in pediatric acute lymphoblastic leukemia evaluated by Bayesian network based Bayesian multilevel analysis of relevance

    PubMed Central

    2012-01-01

    Background We carried out a candidate gene association study in pediatric acute lymphoblastic leukemia (ALL) to identify possible genetic risk factors in a Hungarian population. Methods The results were evaluated with traditional statistical methods and with our newly developed Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA) method. We collected genomic DNA and clinical data from 543 children, who underwent chemotherapy due to ALL, and 529 healthy controls. Altogether 66 single nucleotide polymorphisms (SNPs) in 19 candidate genes were genotyped. Results With logistic regression, we identified 6 SNPs in the ARID5B and IKZF1 genes associated with increased risk to B-cell ALL, and two SNPs in the STAT3 gene, which decreased the risk to hyperdiploid ALL. Because the associated SNPs were in linkage in each gene, these associations corresponded to one signal per gene. The odds ratio (OR) associated with the tag SNPs were: OR = 1.69, P = 2.22x10-7 for rs4132601 (IKZF1), OR = 1.53, P = 1.95x10-5 for rs10821936 (ARID5B) and OR = 0.64, P = 2.32x10-4 for rs12949918 (STAT3). With the BN-BMLA we confirmed the findings of the frequentist-based method and received additional information about the nature of the relations between the SNPs and the disease. E.g. the rs10821936 in ARID5B and rs17405722 in STAT3 showed a weak interaction, and in case of T-cell lineage sample group, the gender showed a weak interaction with three SNPs in three genes. In the hyperdiploid patient group the BN-BMLA detected a strong interaction among SNPs in the NOTCH1, STAT1, STAT3 and BCL2 genes. Evaluating the survival rate of the patients with ALL, the BN-BMLA showed that besides risk groups and subtypes, genetic variations in the BAX and CEBPA genes might also influence the probability of survival of the patients. Conclusions In the present study we confirmed the roles of genetic variations in ARID5B and IKZF1 in the susceptibility to B-cell ALL

  19. Medical genetics

    SciTech Connect

    Nora, J.J.; Fraser, F.C.

    1989-01-01

    This book presents a discussion of medical genetics for the practitioner treating or counseling patients with genetic disease. It includes a discussion of the relationship of heredity and diseases, the chromosomal basis for heredity, gene frequencies, and genetics of development and maldevelopment. The authors also focus on teratology, somatic cell genetics, genetics and cancer, genetics of behavior.

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

    NASA Astrophysics Data System (ADS)

    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. A network-based method for identifying prognostic gene modules in lung squamous carcinoma

    PubMed Central

    Zhang, Kaitai; Wang, Guiqi; Zhang, Lei; An, Ning; Cheng, Shujun

    2016-01-01

    Similarities in gene expression between both developing embryonic and precancerous tissues and cancer tissues may help identify much-needed biomarkers and therapeutic targets in lung squamous carcinoma. In this study, human lung samples representing ten successive time points, from embryonic development to carcinogenesis, were used to construct global gene expression profiles. Differentially expressed genes with similar expression in precancerous and cancer samples were identified. Using a network-based greedy searching algorithm to analyze the training cohort (n = 69) and three independent testing cohorts, we successfully identified a significant 22-gene module in which expression levels were correlated with overall survival in lung squamous carcinoma patients. PMID:26919109

  2. Adaptive Critic Neural Network-Based Terminal Area Energy Management and Approach and Landing Guidance

    NASA Technical Reports Server (NTRS)

    Grantham, Katie

    2003-01-01

    Reusable Launch Vehicles (RLVs) have different mission requirements than the Space Shuttle, which is used for benchmark guidance design. Therefore, alternative Terminal Area Energy Management (TAEM) and Approach and Landing (A/L) Guidance schemes can be examined in the interest of cost reduction. A neural network based solution for a finite horizon trajectory optimization problem is presented in this paper. In this approach the optimal trajectory of the vehicle is produced by adaptive critic based neural networks, which were trained off-line to maintain a gradual glideslope.

  3. Neural-network-based speed controller for induction motors using inverse dynamics model

    NASA Astrophysics Data System (ADS)

    Ahmed, Hassanein S.; Mohamed, Kamel

    2016-08-01

    Artificial Neural Networks (ANNs) are excellent tools for controller design. ANNs have many advantages compared to traditional control methods. These advantages include simple architecture, training and generalization and distortion insensitivity to nonlinear approximations and nonexact input data. Induction motors have many excellent features, such as simple and rugged construction, high reliability, high robustness, low cost, minimum maintenance, high efficiency, and good self-starting capabilities. In this paper, we propose a neural-network-based inverse model for speed controllers for induction motors. Simulation results show that the ANNs have a high tracing capability.

  4. Concept of distributed corporative wireless vehicle voice networks based on radio-over-fiber technique

    NASA Astrophysics Data System (ADS)

    Bourdine, Anton V.; Bukashkin, Sergey A.; Buzov, Alexander V.; Kubanov, Victor P.; Praporshchikov, Denis E.; Tyazhev, Anatoly I.

    2016-03-01

    This work is concerned on description of the concept of corporative wireless vehicle voice networks based on Radioover- Fiber (RoF) technology, which is integration of wireless and fiber optic networks. The concept of RoF means to transport data over optical fibers by modulating lightwave with radio frequency signal or at the intermediate frequency/baseband that provides to take advantage of the low loss and large bandwidth of an optical fiber together with immunity to electromagnetic influence, flexibility and transparence. A brief overview of key RoF techniques as well as comparative analysis and ability of its application for wireless vehicle voice network realization is presented.

  5. Analysis and research on the balanced distribution of the network-based data in parallel database

    NASA Astrophysics Data System (ADS)

    He, JunHua

    2011-12-01

    The rapid development of parallel computer systems, making parallel operating environment gradually mature and widely used in scientific computing and research in many fields, thus parallel database of research becomes more and more attention and research has become an important database field of study. This network-based parallel cluster of characteristics and the current parallel computer system new trends, analyzes the network parallel clusters of workstations, parallel database data skew problem in data distribution characteristics of the environment is proposed with the ability to adapt to the dynamic data balanced distribution programs.

  6. Computing network-based features from physiological time series: application to sepsis detection.

    PubMed

    Santaniello, Sabato; Granite, Stephen J; Sarma, Sridevi V; Winslow, Raimond L

    2014-01-01

    Sepsis is a systemic deleterious host response to infection. It is a major healthcare problem that affects millions of patients every year in the intensive care units (ICUs) worldwide. Despite the fact that ICU patients are heavily instrumented with physiological sensors, early sepsis detection remains challenging, perhaps because clinicians identify sepsis by using static scores derived from bed-side measurements individually, i.e., without systematically accounting for potential interactions between these signals and their dynamics. In this study, we apply network-based data analysis to take into account interactions between bed-side physiological time series (PTS) data collected in ICU patients, and we investigate features to distinguish between sepsis and non-sepsis conditions. We treated each PTS source as a node on a graph and we retrieved the graph connectivity matrix over time by tracking the correlation between each pair of sources' signals over consecutive time windows. Then, for each connectivity matrix, we computed the eigenvalue decomposition. We found that, even though raw PTS measurements may have indistinguishable distributions in non-sepsis and early sepsis states, the median /I of the eigenvalues computed from the same data is statistically different (p <; 0.001) in the two states and the evolution of /I may reflect the disease progression. Although preliminary, these findings suggest that network-based features computed from continuous PTS data may be useful for early sepsis detection. PMID:25570825

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

    PubMed

    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.

  8. A network-based training environment: a medical image processing paradigm.

    PubMed

    Costaridou, L; Panayiotakis, G; Sakellaropoulos, P; Cavouras, D; Dimopoulos, J

    1998-01-01

    The capability of interactive multimedia and Internet technologies is investigated with respect to the implementation of a distance learning environment. The system is built according to a client-server architecture, based on the Internet infrastructure, composed of server nodes conceptually modelled as WWW sites. Sites are implemented by customization of available components. The environment integrates network-delivered interactive multimedia courses, network-based tutoring, SIG support, information databases of professional interest, as well as course and tutoring management. This capability has been demonstrated by means of an implemented system, validated with digital image processing content, specifically image enhancement. Image enhancement methods are theoretically described and applied to mammograms. Emphasis is given to the interactive presentation of the effects of algorithm parameters on images. The system end-user access depends on available bandwidth, so high-speed access can be achieved via LAN or local ISDN connections. Network based training offers new means of improved access and sharing of learning resources and expertise, as promising supplements in training. PMID:9922949

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

    PubMed

    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. PMID:27149687

  10. Robust Analysis of Network-Based Real-Time Kinematic for GNSS-Derived Heights.

    PubMed

    Bae, Tae-Suk; Grejner-Brzezinska, Dorota; Mader, Gerald; Dennis, Michael

    2015-10-26

    New guidelines and procedures for real-time (RT) network-based solutions are required in order to support Global Navigation Satellite System (GNSS) derived heights. Two kinds of experiments were carried out to analyze the performance of the network-based real-time kinematic (RTK) solutions. New test marks were installed in different surrounding environments, and the existing GPS benchmarks were used for analyzing the effect of different factors, such as baseline lengths, antenna types, on the final accuracy and reliability of the height estimation. The RT solutions are categorized into three groups: single-base RTK, multiple-epoch network RTK (mRTN), and single-epoch network RTK (sRTN). The RTK solution can be biased up to 9 mm depending on the surrounding environment, but there was no notable bias for a longer reference base station (about 30 km) In addition, the occupation time for the network RTK was investigated in various cases. There is no explicit bias in the solution for different durations, but smoother results were obtained for longer durations. Further investigation is needed into the effect of changing the occupation time between solutions and into the possibility of using single-epoch solutions in precise determination of heights by GNSS.

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

    PubMed

    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

  12. Computing network-based features from physiological time series: application to sepsis detection.

    PubMed

    Santaniello, Sabato; Granite, Stephen J; Sarma, Sridevi V; Winslow, Raimond L

    2014-01-01

    Sepsis is a systemic deleterious host response to infection. It is a major healthcare problem that affects millions of patients every year in the intensive care units (ICUs) worldwide. Despite the fact that ICU patients are heavily instrumented with physiological sensors, early sepsis detection remains challenging, perhaps because clinicians identify sepsis by using static scores derived from bed-side measurements individually, i.e., without systematically accounting for potential interactions between these signals and their dynamics. In this study, we apply network-based data analysis to take into account interactions between bed-side physiological time series (PTS) data collected in ICU patients, and we investigate features to distinguish between sepsis and non-sepsis conditions. We treated each PTS source as a node on a graph and we retrieved the graph connectivity matrix over time by tracking the correlation between each pair of sources' signals over consecutive time windows. Then, for each connectivity matrix, we computed the eigenvalue decomposition. We found that, even though raw PTS measurements may have indistinguishable distributions in non-sepsis and early sepsis states, the median /I of the eigenvalues computed from the same data is statistically different (p <; 0.001) in the two states and the evolution of /I may reflect the disease progression. Although preliminary, these findings suggest that network-based features computed from continuous PTS data may be useful for early sepsis detection.

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

    PubMed

    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.

  14. Identifying influential nodes in dynamic social networks based on degree-corrected stochastic block model

    NASA Astrophysics Data System (ADS)

    Wang, Tingting; Dai, Weidi; Jiao, Pengfei; Wang, Wenjun

    2016-05-01

    Many real-world data can be represented as dynamic networks which are the evolutionary networks with timestamps. Analyzing dynamic attributes is important to understanding the structures and functions of these complex networks. Especially, studying the influential nodes is significant to exploring and analyzing networks. In this paper, we propose a method to identify influential nodes in dynamic social networks based on identifying such nodes in the temporal communities which make up the dynamic networks. Firstly, we detect the community structures of all the snapshot networks based on the degree-corrected stochastic block model (DCBM). After getting the community structures, we capture the evolution of every community in the dynamic network by the extended Jaccard’s coefficient which is defined to map communities among all the snapshot networks. Then we obtain the initial influential nodes of the dynamic network and aggregate them based on three widely used centrality metrics. Experiments on real-world and synthetic datasets demonstrate that our method can identify influential nodes in dynamic networks accurately, at the same time, we also find some interesting phenomena and conclusions for those that have been validated in complex network or social science.

  15. Medical genetics

    SciTech Connect

    Jorde, L.B.; Carey, J.C.; White, R.L.

    1995-10-01

    This book on the subject of medical genetics is a textbook aimed at a very broad audience: principally, medical students, nursing students, graduate, and undergraduate students. The book is actually a primer of general genetics as applied to humans and provides a well-balanced introduction to the scientific and clinical basis of human genetics. The twelve chapters include: Introduction, Basic Cell Biology, Genetic Variation, Autosomal Dominant and Recessive Inheritance, Sex-linked and Mitochondrial Inheritance, Clinical Cytogenetics, Gene Mapping, Immunogenetics, Cancer Genetics, Multifactorial Inheritance and Common Disease, Genetic Screening, Genetic Diagnosis and Gene Therapy, and Clinical Genetics and Genetic Counseling.

  16. Genetic algorithms

    NASA Technical Reports Server (NTRS)

    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.

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

    PubMed

    Pérez-Palma, Eduardo; Bustos, Bernabé I; Villamán, Camilo F; Alarcón, Marcelo A; Avila, Miguel E; Ugarte, Giorgia D; Reyes, Ariel E; Opazo, Carlos; De Ferrari, Giancarlo V

    2014-01-01

    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.

  18. Overrepresentation of Glutamate Signaling in Alzheimer's Disease: Network-Based Pathway Enrichment Using Meta-Analysis of Genome-Wide Association Studies

    PubMed Central

    Villamán, Camilo F.; Alarcón, Marcelo A.; Avila, Miguel E.; Ugarte, Giorgia D.; Reyes, Ariel E.; Opazo, Carlos; De Ferrari, Giancarlo V.

    2014-01-01

    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. PMID:24755620

  19. New Genetics

    MedlinePlus

    ... human genome, behavioral genetics, pharmacogenetics, drug resistance, biofilms, computer modeling. » more Chapter 5: 21st-Century Genetics Covers systems biology, GFP, genetic testing, privacy concerns, DNA forensics, ...

  20. Genetic Counseling

    MedlinePlus

    Genetic counseling provides information and support to people who have, or may be at risk for, genetic disorders. A ... meets with you to discuss genetic risks. The counseling may be for yourself or a family member. ...

  1. Genetic Counseling

    MedlinePlus

    ... Articles Genetic Counseling Information For... Media Policy Makers Genetic Counseling Language: English Español (Spanish) Recommend on Facebook ... informed decisions about testing and treatment. Reasons for Genetic Counseling There are many reasons that people go ...

  2. Network-based H∞ synchronization control of time-delay neural networks with communication constraints

    NASA Astrophysics Data System (ADS)

    Dong, Hui; Ling, Rongyao; Zhang, Dan

    2016-03-01

    This paper is concerned with the network-based H∞ synchronization control for a class of discrete time-delay neural networks, and attention is focused on how to reduce the communication rate since the communication resource is limited. Techniques such as the measurement size reduction, signal quantization and stochastic signal transmission are introduced to achieve the above goal. An uncertain switched system model is first proposed to capture the above-networked uncertainties. Based on the switched system theory and Lyapunov stability approach, a sufficient condition is obtained such that the closed-loop synchronization system is exponentially stable in the mean-square sense with a prescribed H∞ performance level. The controller gains are determined by solving a set of linear matrix inequalities (LMIs). A numerical example is finally presented to show the effectiveness of the proposed design method.

  3. Dynamics of a network-based SIS epidemic model with nonmonotone incidence rate

    NASA Astrophysics Data System (ADS)

    Li, Chun-Hsien

    2015-06-01

    This paper studies the dynamics of a network-based SIS epidemic model with nonmonotone incidence rate. This type of nonlinear incidence can be used to describe the psychological effect of certain diseases spread in a contact network at high infective levels. We first find a threshold value for the transmission rate. This value completely determines the dynamics of the model and interestingly, the threshold is not dependent on the functional form of the nonlinear incidence rate. Furthermore, if the transmission rate is less than or equal to the threshold value, the disease will die out. Otherwise, it will be permanent. Numerical experiments are given to illustrate the theoretical results. We also consider the effect of the nonlinear incidence on the epidemic dynamics.

  4. Combined imaging and neural-network-based approach in solid waste sorting

    NASA Astrophysics Data System (ADS)

    Alunni, Stefano; Bonifazi, Giuseppe; de Carli, Alessandro; Massacci, Paolo

    2002-04-01

    Solid waste recycling is more and more increasing according to the need to realize dismantled material recovery and to reduce overall environmental pollution. When a recycling strategy is applied sorting strategies have to be developed and implemented. Such an approach ca be considered as the second logical step of the process that is, after that the attributes (physical, chemical, morphological, morphometrical, textural, etc.) of the materials resulting from classical processing (comminution, classification, separation, etc.) are detected and numerically modeled. The resulting feature vector need to be handled by a software architecture performing the required recognition/classification procedure and defining the quality of the investigated products. From the results further feed-back or feed-forward control strategies can be applied in order to improve equipment or processing architectures performances. In this paper are analyzed and described neural network based sorting strategies applied with reference to fluff (light fraction of the materials resulting from car dismantling) recognition.

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

    PubMed

    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. PMID:24568697

  6. Optimization on a Network-based Parallel Computer System for Supersonic Laminar Wing Design

    NASA Technical Reports Server (NTRS)

    Garcia, Joseph A.; Cheung, Samson; Holst, Terry L. (Technical Monitor)

    1995-01-01

    A set of Computational Fluid Dynamics (CFD) routines and flow transition prediction tools are integrated into a network based parallel numerical optimization routine. Through this optimization routine, the design of a 2-D airfoil and an infinitely swept wing will be studied in order to advance the design cycle capability of supersonic laminar flow wings. The goal of advancing supersonic laminar flow wing design is achieved by wisely choosing the design variables used in the optimization routine. The design variables are represented by the theory of Fourier series and potential theory. These theories, combined with the parallel CFD flow routines and flow transition prediction tools, provide a design space for a global optimal point to be searched. Finally, the parallel optimization routine enables gradient evaluations to be performed in a fast and parallel fashion.

  7. Research on target recognition techniques of radar networking based on fuzzy mathematics

    NASA Astrophysics Data System (ADS)

    Guan, Chengbin; Wang, Guohong; Guan, Chengzhun; Pan, Jinshan

    2007-11-01

    Nowadays there are more and more targets, so it is more difficult for radar networking to track the important targets. To reduce the pressure on radar networking and the waste of ammunition, it is very necessary for radar networking to recognize the targets. Two target recognition approaches of radar networking based on fuzzy mathematics are proposed in this paper, which are multi-level fuzzy synthetical evaluation technique and lattice approaching degree technique. By analyzing the principles, the application techniques are given, the merits and shortcomings are also analyzed, and applying environments are advised. Another emphasis is the compare between the multiple mono-level fuzzy synthetical evaluation and the multi-level fuzzy synthetical evaluation, an instance is carried out to illuminate the problem, then the results are analyzed in theory, the conclusions are gotten which can be instructions for application in engineering.

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

    NASA Astrophysics Data System (ADS)

    Altaisky, Mikhail V.; Zolnikova, Nadezhda N.; Kaputkina, Natalia E.; Krylov, Victor A.; Lozovik, Yurii E.; Dattani, Nikesh S.

    2016-02-01

    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.

  9. Skyline Query Processing in Sensor Network Based on Data Centric Storage

    PubMed Central

    Song, Seokil; Kwak, Yunsik; Lee, Seokhee

    2011-01-01

    Data centric storages for sensor networks have been proposed to efficiently process multi-dimensional range queries as well as exact matches. Usually, a sensor network does not process only one type of the query, but processes various types of queries such as range queries, exact matches and skyline queries. Therefore, a sensor network based on a data centric storage for range queries and exact matches should process skyline queries efficiently. However, existing algorithms for skyline queries have not considered the features of data centric storages. Some of the data centric storages store similar data in sensor nodes that are placed on geographically similar locations. Consequently, all data are ordered in a sensor network. In this paper, we propose a new skyline query processing algorithm that exploits the above features of data centric storages. PMID:22346642

  10. A neural network-based power system stabilizer using power flow characteristics

    SciTech Connect

    Park, Y.M.; Choi, M.S.; Lee, K.Y.

    1996-06-01

    A neural network-based Power System Stabilizer (Neuro-PSS) is designed for a generator connected to a multi-machine power system utilizing the nonlinear power flow dynamics. The uses of power flow dynamics provide a PSS for a wide range operation with reduced size neutral networks. The Neuro-PSS consists of two neutral networks: Neuro-Identifier and Neuro-Controller. The low-frequency oscillation is modeled by the Neuro-Identifier using the power flow dynamics, then a Generalized Backpropagation-Thorough-Time (GBTT) algorithm is developed to train the Neuro-Controller. The simulation results show that the Neuro-PSS designed in this paper performs well with good damping in a wide operation range compared with the conventional PSS.

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

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

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

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

    PubMed

    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. PMID:18263046

  13. IA and PA network-based computation of coordinating combat behaviors in the military MAS

    NASA Astrophysics Data System (ADS)

    Xia, Zuxun; Fang, Huijia

    2004-09-01

    In the military multi-agent system every agent needs to analyze the dependent and temporal relations among the tasks or combat behaviors for working-out its plans and getting the correct behavior sequences, it could guarantee good coordination, avoid unexpected damnification and guard against bungling the change of winning a battle due to the possible incorrect scheduling and conflicts. In this paper IA and PA network based computation of coordinating combat behaviors is put forward, and emphasize particularly on using 5x5 matrix to represent and compute the temporal binary relation (between two interval-events, two point-events or between one interval-event and one point-event), this matrix method makes the coordination computing convenience than before.

  14. Back propagation neural network based control for the heating system of a polysilicon reduction furnace.

    PubMed

    Cheng, Yuhua; Chen, Kai; Bai, Libing; Dai, Meizhi

    2013-12-01

    In this paper, the Back Propagation (BP) neural network based control strategy is proposed for the heating system of a polysilicon reduction furnace. It is applied to obtain the control signal I(d), which is used to adjust the heating power through operations of the silicon core temperature, furnace temperature, silicon core voltage, and resistance of the current control cycle. With the control signal I(d) the polycrystalline silicon can be heated from room temperature to the required temperature smoothly and steadily. The proposed BP network applied in this paper can obtain the accurate control signal I(d) and achieve the precise control purpose. This paper presents the principle of the BP network and demonstrates the effectiveness of the BP network in the heating system of a polysilicon reduction furnace by combining the simulation analysis with experimental results.

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

    SciTech Connect

    Núñez, Rubén Antón, Ignacio; Askins, Steve; Sala, Gabriel; Domínguez, César; Voarino, Philippe; Steiner, Marc; Siefer, Gerald; Fucci, Rafaelle; Roca, Franco; Minuto, Alessandro; Morabito, Paolo

    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.

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

    PubMed

    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.

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

    PubMed Central

    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

  18. Magnetic response of aperiodic wire networks based on Fibonacci distortions of square antidot lattices

    NASA Astrophysics Data System (ADS)

    Farmer, B.; Bhat, V. S.; Sklenar, J.; Teipel, E.; Woods, J.; Ketterson, J. B.; Hastings, J. T.; De Long, L. E.

    2015-05-01

    The static and dynamic magnetic responses of patterned ferromagnetic thin films are uniquely altered in the case of aperiodic patterns that retain long-range order (e.g., quasicrystals). We have fabricated permalloy wire networks based on periodic square antidot lattices (ADLs) distorted according to an aperiodic Fibonacci sequence applied to two lattice translations, d1 = 1618 nm and d2 = 1000 nm. The wire segment thickness is fixed at t = 25 nm, and the width W varies from 80 to 510 nm. We measured the DC magnetization between room temperature and 5 K. Room-temperature, narrow-band (9.7 GHz) ferromagnetic resonance (FMR) spectra were acquired for various directions of applied magnetic field. The DC magnetization curves exhibited pronounced step anomalies and plateaus that signal flux closure states. Although the Fibonacci distortion breaks the fourfold symmetry of a finite periodic square ADL, the FMR data exhibit fourfold rotational symmetry with respect to the applied DC magnetic field direction.

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

    PubMed Central

    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

  20. Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach

    PubMed Central

    Emig, Dorothea; Ivliev, Alexander; Pustovalova, Olga; Lancashire, Lee; Bureeva, Svetlana; Nikolsky, Yuri; Bessarabova, Marina

    2013-01-01

    The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example. PMID:23593264

  1. Network-based characterization of drug-regulated genes, drug targets, and toxicity.

    PubMed

    Kotlyar, Max; Fortney, Kristen; Jurisica, Igor

    2012-08-01

    Proteins do not exert their effects in isolation of one another, but interact together in complex networks. In recent years, sophisticated methods have been developed to leverage protein-protein interaction (PPI) network structure to improve several stages of the drug discovery process. Network-based methods have been applied to predict drug targets, drug side effects, and new therapeutic indications. In this paper we have two aims. First, we review the past contributions of network approaches and methods to drug discovery, and discuss their limitations and possible future directions. Second, we show how past work can be generalized to gain a more complete understanding of how drugs perturb networks. Previous network-based characterizations of drug effects focused on the small number of known drug targets, i.e., direct binding partners of drugs. However, drugs affect many more genes than their targets - they can profoundly affect the cell's transcriptome. For the first time, we use networks to characterize genes that are differentially regulated by drugs. We found that drug-regulated genes differed from drug targets in terms of functional annotations, cellular localizations, and topological properties. Drug targets mainly included receptors on the plasma membrane, down-regulated genes were largely in the nucleus and were enriched for DNA binding, and genes lacking drug relationships were enriched in the extracellular region. Network topology analysis indicated several significant graph properties, including high degree and betweenness for the drug targets and drug-regulated genes, though possibly due to network biases. Topological analysis also showed that proteins of down-regulated genes appear to be frequently involved in complexes. Analyzing network distances between regulated genes, we found that genes regulated by structurally similar drugs were significantly closer than genes regulated by dissimilar drugs. Finally, network centrality of a drug

  2. Network-based gene expression analysis of intracranial aneurysm tissue reveals role of antigen presenting cells.

    PubMed

    Krischek, B; Kasuya, H; Tajima, A; Akagawa, H; Sasaki, T; Yoneyama, T; Ujiie, H; Kubo, O; Bonin, M; Takakura, K; Hori, T; Inoue, I

    2008-07-17

    Little is known about the pathology and pathogenesis of the rupture of intracranial aneurysms. For a better understanding of the molecular processes involved in intracranial aneurysm (IA) formation we performed a gene expression analysis comparing ruptured and unruptured aneurysm tissue to a control artery. Tissue samples of six ruptured and four unruptured aneurysms, and four cerebral arteries serving as controls, were profiled using oligonucleotide microarrays. Gene ontology classification of the differentially expressed genes was analyzed and regulatory functional networks and canonical pathways were identified with a network-based computational pathway analysis tool. Real time reverse transcription polymerase chain reaction (RT-PCR) and immunohistochemical staining were performed as confirmation. Analysis of aneurysmal and control tissue revealed 521 differentially expressed genes. The most significantly associated gene ontology term was antigen processing (P=1.64E-16). Further network-based analysis showed the top scoring regulatory functional network to be built around overexpressed major histocompatibility class (MHC) I and II complex related genes and confirmed the canonical pathway "Antigen Presentation" to have the highest upregulation in IA tissue (P=7.3E-10). Real time RT-PCR showed significant overexpression of MHC class II genes. Immunohistochemical staining showed strong positivity for MHC II molecule specific antibody (HLA II), for CD68 (macrophages, monocytes), for CD45RO (T-cells) and HLA I antibody. Our results offer strong evidence for MHC class II gene overexpression in human IA tissue and that antigen presenting cells (macrophages, monocytes) play a key role in IA formation. PMID:18538937

  3. Neural network based load and price forecasting and confidence interval estimation in deregulated power markets

    NASA Astrophysics Data System (ADS)

    Zhang, Li

    With the deregulation of the electric power market in New England, an independent system operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process. The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies. In this research, the complexity of load and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for load forecasting, a neural network based load forecaster was developed, and has been in daily industry use since summer 1998 with good performance. MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

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

    ERIC Educational Resources Information Center

    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,…

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

    NASA Astrophysics Data System (ADS)

    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.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    SciTech Connect

    Yang, Dr. Li

    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.

  9. Network-Based Analysis Reveals Functional Connectivity Related to Internet Addiction Tendency

    PubMed Central

    Wen, Tanya; Hsieh, Shulan

    2016-01-01

    Preoccupation 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. PMID:26869896

  10. Spreading Effect in Industrial Complex Network Based on Revised Structural Holes Theory

    PubMed Central

    Ye, Qing; Guan, Jun

    2016-01-01

    This paper analyzed the spreading effect of industrial sectors with complex network model under perspective of econophysics. Input-output analysis, as an important research tool, focuses more on static analysis. However, the fundamental aim of industry analysis is to figure out how interaction between different industries makes impacts on economic development, which turns out to be a dynamic process. Thus, industrial complex network based on input-output tables from WIOD is proposed to be a bridge connecting accurate static quantitative analysis and comparable dynamic one. With application of revised structural holes theory, flow betweenness and random walk centrality were respectively chosen to evaluate industrial sectors’ long-term and short-term spreading effect process in this paper. It shows that industries with higher flow betweenness or random walk centrality would bring about more intensive industrial spreading effect to the industrial chains they stands in, because value stream transmission of industrial sectors depends on how many products or services it can get from the other ones, and they are regarded as brokers with bigger information superiority and more intermediate interests. PMID:27218468

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

    PubMed Central

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

    2015-01-01

    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. PMID:26694409

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

    PubMed

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

    2015-01-01

    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. PMID:26694409

  13. DDA: A Novel Network-Based Scoring Method to Identify Disease–Disease Associations

    PubMed Central

    Suratanee, Apichat; Plaimas, Kitiporn

    2015-01-01

    Categorizing human diseases provides higher efficiency and accuracy for disease diagnosis, prognosis, and treatment. Disease–disease association (DDA) is a precious information that indicates the large-scale structure of complex relationships of diseases. However, the number of known and reliable associations is very small. Therefore, identification of DDAs is a challenging task in systems biology and medicine. Here, we developed a novel network-based scoring algorithm called DDA to identify the relationships between diseases in a large-scale study. Our method is developed based on a random walk prioritization in a protein–protein interaction network. This approach considers not only whether two diseases directly share associated genes but also the statistical relationships between two different diseases using known disease-related genes. Predicted associations were validated by known DDAs from a database and literature supports. The method yielded a good performance with an area under the curve of 71% and outperformed other standard association indices. Furthermore, novel DDAs and relationships among diseases from the clusters analysis were reported. This method is efficient to identify disease–disease relationships on an interaction network and can also be generalized to other association studies to further enhance knowledge in medical studies. PMID:26673408

  14. Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network.

    PubMed

    Al-Harazi, Olfat; Al Insaif, Sadiq; Al-Ajlan, Monirah A; Kaya, Namik; Dzimiri, Nduna; Colak, Dilek

    2016-06-20

    A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field. PMID:27318646

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

    PubMed

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

    2016-01-01

    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.

  16. Adaptive network based on fuzzy inference system for equilibrated urea concentration prediction.

    PubMed

    Azar, Ahmad Taher

    2013-09-01

    Post-dialysis urea rebound (PDUR) has been attributed mostly to redistribution of urea from different compartments, which is determined by variations in regional blood flows and transcellular urea mass transfer coefficients. PDUR occurs after 30-90min of short or standard hemodialysis (HD) sessions and after 60min in long 8-h HD sessions, which is inconvenient. This paper presents adaptive network based on fuzzy inference system (ANFIS) for predicting intradialytic (Cint) and post-dialysis urea concentrations (Cpost) in order to predict the equilibrated (Ceq) urea concentrations without any blood sampling from dialysis patients. The accuracy of the developed system was prospectively compared with other traditional methods for predicting equilibrated urea (Ceq), post dialysis urea rebound (PDUR) and equilibrated dialysis dose (eKt/V). This comparison is done based on root mean squares error (RMSE), normalized mean square error (NRMSE), and mean absolute percentage error (MAPE). The ANFIS predictor for Ceq achieved mean RMSE values of 0.3654 and 0.4920 for training and testing, respectively. The statistical analysis demonstrated that there is no statistically significant difference found between the predicted and the measured values. The percentage of MAE and RMSE for testing phase is 0.63% and 0.96%, respectively. PMID:23806679

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

    PubMed

    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.

  18. NaviCell Web Service for network-based data visualization.

    PubMed

    Bonnet, Eric; Viara, Eric; Kuperstein, Inna; Calzone, Laurence; Cohen, David P A; Barillot, Emmanuel; Zinovyev, Andrei

    2015-07-01

    Data visualization is an essential element of biological research, required for obtaining insights and formulating new hypotheses on mechanisms of health and disease. NaviCell Web Service is a tool for network-based visualization of 'omics' data which implements several data visual representation methods and utilities for combining them together. NaviCell Web Service uses Google Maps and semantic zooming to browse large biological network maps, represented in various formats, together with different types of the molecular data mapped on top of them. For achieving this, the tool provides standard heatmaps, barplots and glyphs as well as the novel map staining technique for grasping large-scale trends in numerical values (such as whole transcriptome) projected onto a pathway map. The web service provides a server mode, which allows automating visualization tasks and retrieving data from maps via RESTful (standard HTTP) calls. Bindings to different programming languages are provided (Python and R). We illustrate the purpose of the tool with several case studies using pathway maps created by different research groups, in which data visualization provides new insights into molecular mechanisms involved in systemic diseases such as cancer and neurodegenerative diseases.

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

    PubMed Central

    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. PMID:26257640

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

    PubMed

    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. PMID:26257640

  1. NaviCell Web Service for network-based data visualization

    PubMed Central

    Bonnet, Eric; Viara, Eric; Kuperstein, Inna; Calzone, Laurence; Cohen, David P. A.; Barillot, Emmanuel; Zinovyev, Andrei

    2015-01-01

    Data visualization is an essential element of biological research, required for obtaining insights and formulating new hypotheses on mechanisms of health and disease. NaviCell Web Service is a tool for network-based visualization of ‘omics’ data which implements several data visual representation methods and utilities for combining them together. NaviCell Web Service uses Google Maps and semantic zooming to browse large biological network maps, represented in various formats, together with different types of the molecular data mapped on top of them. For achieving this, the tool provides standard heatmaps, barplots and glyphs as well as the novel map staining technique for grasping large-scale trends in numerical values (such as whole transcriptome) projected onto a pathway map. The web service provides a server mode, which allows automating visualization tasks and retrieving data from maps via RESTful (standard HTTP) calls. Bindings to different programming languages are provided (Python and R). We illustrate the purpose of the tool with several case studies using pathway maps created by different research groups, in which data visualization provides new insights into molecular mechanisms involved in systemic diseases such as cancer and neurodegenerative diseases. PMID:25958393

  2. Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network.

    PubMed

    Al-Harazi, Olfat; Al Insaif, Sadiq; Al-Ajlan, Monirah A; Kaya, Namik; Dzimiri, Nduna; Colak, Dilek

    2016-06-20

    A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.

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

    PubMed Central

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

    2016-01-01

    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. PMID:27355951

  4. Network-based identification of reliable bio-markers for cancers.

    PubMed

    Deng, Shiguo; Qi, Jingchao; Stephen, Mutua; Qiu, Lu; Yang, Huijie

    2015-10-21

    Finding bio-markers for complex disease from gene expression profiles attracts extensive attentions for its potential use in diagnosis, therapy, and drug design. In this paper we propose a network-based method to seek high-confident bio-markers from candidate genes collected in the literature. The algorithm includes three consequent steps. First, one can collect the proposed bio-markers in literature as being the preliminary candidate; Second, a spanning-tree based threshold can be used to reconstruct gene networks for normal and cancer samples; Third, by jointly using of degree changes and distribution of the candidates in communities, one can filter out the low-confident genes. The survival candidates are high-confident genes. Specially, we consider expression profiles for carcinoma of colon. A total of 34 preliminary bio-markers collected from literature are evaluated and a set of 16 genes are proposed as high confident bio-markers, which behave high performance in distinguishing normal and cancer samples.

  5. AB-polymer networks based on oligo(epsilon-caprolactone) segments showing shape-memory properties.

    PubMed

    Lendlein, A; Schmidt, A M; Langer, R

    2001-01-30

    Although shape-memory metal alloys have wide use in medicine and other areas, improved properties, particularly easy shaping, high shape stability, and adjustable transition temperature, are realizable only by polymer systems. In this paper, a polymer system of shape-memory polymer networks based on oligo(epsilon-caprolactone) dimethacrylate as crosslinker and n-butyl acrylate as comonomer was introduced. The influence of two structural parameters, the molecular weight of oligo(epsilon-caprolactone) dimethacrylate and the weight content of n-butyl acrylate, on macroscopic properties of polymer networks such as thermal and mechanical properties has been investigated. Tensile tests above and below melting temperature showed a decrease in the elastic modulus with increasing comonomer weight content. The crystallization behavior of the new materials has been investigated, and key parameters for the programming procedure of the temporary shape have been evaluated. Shape-memory properties have been quantified by thermocyclic experiments. All samples reached uniform deformation properties with recovery rates above 99% after 3 cycles. Whereas strain recovery increased with increasing n-butyl acrylate content, strain fixity decreased, reflecting the decreasing degree of crystallinity of the material. PMID:11158558

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

    USGS Publications Warehouse

    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.

  7. Development and comparison of neural network based soft sensors for online estimation of cement clinker quality.

    PubMed

    Pani, Ajaya Kumar; Vadlamudi, Vamsi Krishna; Mohanta, Hare Krishna

    2013-01-01

    The online estimation of process outputs mostly related to quality, as opposed to their belated measurement by means of hardware measuring devices and laboratory analysis, represents the most valuable feature of soft sensors. As of now there have been very few attempts for soft sensing of cement clinker quality which is mostly done by offline laboratory analysis resulting at times in low quality clinker. In the present work three different neural network based soft sensors have been developed for online estimation of cement clinker properties. Different input and output data for a rotary cement kiln were collected from a cement plant producing 10,000 tons of clinker per day. The raw data were pre-processed to remove the outliers and the resulting missing data were imputed. The processed data were then used to develop a back propagation neural network model, a radial basis network model and a regression network model to estimate the clinker quality online. A comparison of the estimation capabilities of the three models has been done by simulation of the developed models. It was observed that radial basis network model produced better estimation capabilities than the back propagation and regression network models. PMID:22940135

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

    PubMed

    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. PMID:23399256

  9. A multiple-responsive self-healing supramolecular polymer gel network based on multiple orthogonal interactions.

    PubMed

    Zhan, Jiayi; Zhang, Mingming; Zhou, Mi; Liu, Bin; Chen, Dong; Liu, Yuanyuan; Chen, Qianqian; Qiu, Huayu; Yin, Shouchun

    2014-08-01

    Supramolecular polymer networks have attracted considerable attention not only due to their topological importance but also because they can show some fantastic properties such as stimuli-responsiveness and self-healing. Although various supramolecular networks are constructed by supramolecular chemists based on different non-covalent interactions, supramolecular polymer networks based on multiple orthogonal interactions are still rare. Here, a supramolecular polymer network is presented on the basis of the host-guest interactions between dibenzo-24-crown-8 (DB24C8) and dibenzylammonium salts (DBAS), the metal-ligand coordination interactions between terpyridine and Zn(OTf)2 , and between 1,2,3-triazole and PdCl2 (PhCN)2 . The topology of the networks can be easily tuned from monomer to main-chain supramolecular polymer and then to the supramolecular networks. This process is well studied by various characterization methods such as (1) H NMR, UV-vis, DOSY, viscosity, and rheological measurements. More importantly, a supramolecular gel is obtained at high concentrations of the supramolecular networks, which demonstrates both stimuli-responsiveness and self-healing properties. PMID:24943122

  10. AB-polymer networks based on oligo(epsilon-caprolactone) segments showing shape-memory properties.

    PubMed

    Lendlein, A; Schmidt, A M; Langer, R

    2001-01-30

    Although shape-memory metal alloys have wide use in medicine and other areas, improved properties, particularly easy shaping, high shape stability, and adjustable transition temperature, are realizable only by polymer systems. In this paper, a polymer system of shape-memory polymer networks based on oligo(epsilon-caprolactone) dimethacrylate as crosslinker and n-butyl acrylate as comonomer was introduced. The influence of two structural parameters, the molecular weight of oligo(epsilon-caprolactone) dimethacrylate and the weight content of n-butyl acrylate, on macroscopic properties of polymer networks such as thermal and mechanical properties has been investigated. Tensile tests above and below melting temperature showed a decrease in the elastic modulus with increasing comonomer weight content. The crystallization behavior of the new materials has been investigated, and key parameters for the programming procedure of the temporary shape have been evaluated. Shape-memory properties have been quantified by thermocyclic experiments. All samples reached uniform deformation properties with recovery rates above 99% after 3 cycles. Whereas strain recovery increased with increasing n-butyl acrylate content, strain fixity decreased, reflecting the decreasing degree of crystallinity of the material.

  11. Belief Network based Disambiguation of Object Reference in Spoken Dialogue System

    NASA Astrophysics Data System (ADS)

    Yamakata, Yoko; Kawahara, Tatsuya; Okuno, Hiroshi G.; Minoh, Michihiko

    This paper discusses a problem of human-machine interaction when spoken word to object reference ambiguity occurs. We study joint activity of several agents in which a remote robot finds an object while communicating with the user over a voice-only channel. We focus on the problem in which the robot disambiguates the reference of the uttered word or phrase to the target object. For example, the utterance of the word ``cup'' may refer to a ``teacup'', a ``coffee cup'', or even a ``glass'' for different users in some situations. This reference (hereafter, ``object reference'') is user and situation dependent. We conducted two experiments. The first experiment including 12 subjects confirmed that the user model of object references is significant. In the second experiment conducted on 20 subjects, we show the model reference sensitivity to the situation. In addition to the ambiguity of the object reference, the actual system must cope with two sources of uncertainty: speech and image recognition. We present the belief network based probabilistic reasoning system to determine the object reference. The resulting system demonstrates that the number of interactions needed to find a common reference is reduced as the user model is refined.

  12. A web of possibilities: network-based discovery of protein interaction codes.

    PubMed

    Winter, Daniel L; Erce, Melissa A; Wilkins, Marc R

    2014-12-01

    Many proteins, including p53, the FoxO transcription factors, RNA polymerase II, pRb, and the chaperones, have extensive post-translational modifications (PTMs). Many of these modifications modulate protein-protein interactions, controlling interaction presence/absence and specificity. Here we propose the notion of the interaction code, a widespread means by which modifications are used to control interactions in the proteome. Minimal interaction codes are likely to exist on proteins that have two modifications and two or more interaction partners. By contrast, complex interaction codes are likely to be found on "date hub" proteins that have many interactions, many PTMs, or are targeted by many modifying and demodifying enzymes. Proteins with new interaction codes should be discoverable by examining protein interaction networks, annotated with PTMs and protein-modifying enzyme-substrate links. Multiple instances or combinations of phosphorylation, acetylation, methylation, O-GlcNAc, or ubiquitination will likely form interaction codes, especially when colocated on a protein's single interaction interface. A network-based example of code discovery is given, predicting the yeast protein Npl3p to have a methylation/phosphorylation-dependent interaction code.

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

    PubMed

    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.

  14. Gigabit network-based three-dimensional trial service on media delivery platform

    NASA Astrophysics Data System (ADS)

    Kim, Nac-Woo; Son, Seung-Chul; Lee, Byung-Tak

    2011-09-01

    Recently, as effective demand for high-quality, large-capacity content such as three-dimensional (3D), multiangle, and gigabit-web has increased, a network infrastructure capable of satisfying future broadcast and communication service requirements is required. In this paper, we introduce a convergence service based on a gigabit network and then propose a technique for delivering gigabit 3D content. Here, the term 3D content delivery technique refers to an overlay-multicast-based distributed service platform that is comprised of a media relay agent and a management server. The service platform is designed to back up both live video and file-based video streaming. Using this platform, we can provide 3D remote education and 3D multiangle services via 3D-based video streaming between a service provider and service consumers dispersed at different locations. To evaluate our 3D content delivery technique, we run a series of trials of gigabit network-based 3D trial services to service subscribers. Then, we conduct a survey to measure user satisfaction with the 3D delivery service and simulated network and service quality. From experimental results, we confirm that this type of distributed service platform can be used as the delivery framework for applications such as realistic 3D-based seminars or 3D video conferences.

  15. Global reconstruction of the human metabolic network based on genomic and bibliomic data

    PubMed Central

    Duarte, Natalie C.; Becker, Scott A.; Jamshidi, Neema; Thiele, Ines; Mo, Monica L.; Vo, Thuy D.; Srivas, Rohith; Palsson, Bernhard Ø.

    2007-01-01

    Metabolism is a vital cellular process, and its malfunction is a major contributor to human disease. Metabolic networks are complex and highly interconnected, and thus systems-level computational approaches are required to elucidate and understand metabolic genotype–phenotype relationships. We have manually reconstructed the global human metabolic network based on Build 35 of the genome annotation and a comprehensive evaluation of >50 years of legacy data (i.e., bibliomic data). Herein we describe the reconstruction process and demonstrate how the resulting genome-scale (or global) network can be used (i) for the discovery of missing information, (ii) for the formulation of an in silico model, and (iii) as a structured context for analyzing high-throughput biological data sets. Our comprehensive evaluation of the literature revealed many gaps in the current understanding of human metabolism that require future experimental investigation. Mathematical analysis of network structure elucidated the implications of intracellular compartmentalization and the potential use of correlated reaction sets for alternative drug target identification. Integrated analysis of high-throughput data sets within the context of the reconstruction enabled a global assessment of functional metabolic states. These results highlight some of the applications enabled by the reconstructed human metabolic network. The establishment of this network represents an important step toward genome-scale human systems biology. PMID:17267599

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

    PubMed Central

    Zhang, Ai-Di; Dai, Shao-Xing; Huang, Jing-Fei

    2013-01-01

    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. PMID:24222897

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

    PubMed

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

    2016-01-01

    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. PMID:27355951

  18. Structural Design Principles of Complex Bird Songs: A Network-Based Approach

    PubMed Central

    Sasahara, Kazutoshi; Cody, Martin L.; Cohen, David; Taylor, Charles E.

    2012-01-01

    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. PMID:23028539

  19. Cartographic generalization of urban street networks based on gravitational field theory

    NASA Astrophysics Data System (ADS)

    Liu, Gang; Li, Yongshu; Li, Zheng; Guo, Jiawei

    2014-05-01

    The automatic generalization of urban street networks is a constant and important aspect of geographical information science. Previous studies show that the dual graph for street-street relationships more accurately reflects the overall morphological properties and importance of streets than do other methods. In this study, we construct a dual graph to represent street-street relationship and propose an approach to generalize street networks based on gravitational field theory. We retain the global structural properties and topological connectivity of an original street network and borrow from gravitational field theory to define the gravitational force between nodes. The concept of multi-order neighbors is introduced and the gravitational force is taken as the measure of the importance contribution between nodes. The importance of a node is defined as the result of the interaction between a given node and its multi-order neighbors. Degree distribution is used to evaluate the level of maintaining the global structure and topological characteristics of a street network and to illustrate the efficiency of the suggested method. Experimental results indicate that the proposed approach can be used in generalizing street networks and retaining their density characteristics, connectivity and global structure.

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

    NASA Technical Reports Server (NTRS)

    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.

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

    PubMed

    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.

  2. Fuzzy neural-network-based segmentation of multispectral magnetic-resonance brain images

    NASA Astrophysics Data System (ADS)

    Blonda, Palma N.; Bennardo, A.; Satalino, Giuseppe; Pasquariello, Guido; De Blasi, Roberto A.; Milella, D.

    1996-06-01

    This study investigates the applicability of a multimodular neuro-fuzzy system in the multispectral analysis of magnetic resonance (MR) images of the human brain. The system consists of two components: an unsupervised neural module for image segmentation in tissue regions and a supervised module for tissue labeling. The former is the fuzzy Kohonen clustering network (FKCN). The latter is a feed-forward network based on the back-propagation learning rule. The results obtained with the FKCN have been compared with those extracted by a self organizing map (SOM). The system has been used to analyze the multispectral MR brain images of a healthy volunteer. The data set included the proton density (PD), T2, T1 weighted spin-echo (SE) bands and a new T1- weighted three dimensional sequence, i.e. the magnetization- prepared rapid gradient echo (MP-RAGE). One of the main objectives of this study has been to evaluate the usefulness of brain imaging with the MP-RAGE sequence in view of automatic tissue classification. To this purpose, a quantitative evaluation has been provided on the base of some labeled areas selected interactively by a neuro- radiologist from the input raw images. Quantitative results seem to indicate that the MP-RAGE sequence may provide higher tissue separability than the T1-weighted SE sequence.

  3. Neural Network Based Representation of UH-60A Pilot and Hub Accelerations

    NASA Technical Reports Server (NTRS)

    Kottapalli, Sesi

    2000-01-01

    Neural network relationships between the full-scale, experimental hub accelerations and the corresponding pilot floor vertical vibration are studied. The present physics-based, quantitative effort represents an initial systematic study on the UH-60A Black Hawk hub accelerations. The NASA/Army UH-60A Airloads Program flight test database was used. A 'maneuver-effect-factor (MEF)', derived using the roll-angle and the pitch-rate, was used. Three neural network based representation-cases were considered. The pilot floor vertical vibration was considered in the first case and the hub accelerations were separately considered in the second case. The third case considered both the hub accelerations and the pilot floor vertical vibration. Neither the advance ratio nor the gross weight alone could be used to predict the pilot floor vertical vibration. However, the advance ratio and the gross weight together could be used to predict the pilot floor vertical vibration over the entire flight envelope. The hub accelerations data were modeled and found to be of very acceptable quality. The hub accelerations alone could not be used to predict the pilot floor vertical vibration. Thus, the hub accelerations alone do not drive the pilot floor vertical vibration. However, the hub accelerations, along with either the advance ratio or the gross weight or both, could be used to satisfactorily predict the pilot floor vertical vibration. The hub accelerations are clearly a factor in determining the pilot floor vertical vibration.

  4. Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

    PubMed Central

    Jiang, Jing; Lu, Weiqiang; Li, Weihua; Liu, Guixia; Zhou, Weixing; Huang, Jin; Tang, Yun

    2012-01-01

    Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning. PMID:22589709

  5. Genetic Mapping

    MedlinePlus

    ... Genetic Education Resources for Teachers Genomic Careers National DNA Day Online Education Kit Online Genetics Education Resources ... prevalent. Using various laboratory techniques, the scientists isolate DNA from these samples and examine it for unique ...

  6. Genetic Disorders

    MedlinePlus

    ... This can cause a medical condition called a genetic disorder. You can inherit a gene mutation from ... during your lifetime. There are three types of genetic disorders: Single-gene disorders, where a mutation affects ...

  7. Genetic modification and genetic determinism

    PubMed Central

    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. Genetic principles.

    PubMed

    Abuelo, D

    1987-01-01

    The author discusses the basic principles of genetics, including the classification of genetic disorders and a consideration of the rules and mechanisms of inheritance. The most common pitfalls in clinical genetic diagnosis are described, with emphasis on the problem of the negative or misleading family history.

  9. Imaging Genetics

    ERIC Educational Resources Information Center

    Munoz, Karen E.; Hyde, Luke W.; Hariri, Ahmad R.

    2009-01-01

    Imaging genetics is an experimental strategy that integrates molecular genetics and neuroimaging technology to examine biological mechanisms that mediate differences in behavior and the risks for psychiatric disorder. The basic principles in imaging genetics and the development of the field are discussed.

  10. Genetic Testing

    MedlinePlus

    ... genetic tests for several reasons. These include Finding genetic diseases in unborn babies Finding out if people carry a gene for a disease and might pass it on to their children Screening embryos for disease Testing for genetic diseases in adults before they cause ...

  11. Potential Parasite Transmission in Multi-Host Networks Based on Parasite Sharing

    PubMed Central

    Pilosof, Shai; Morand, Serge; Krasnov, Boris R.; Nunn, Charles L.

    2015-01-01

    Epidemiological networks are commonly used to explore dynamics of parasite transmission among individuals in a population of a given host species. However, many parasites infect multiple host species, and thus multi-host networks may offer a better framework for investigating parasite dynamics. We investigated the factors that influence parasite sharing – and thus potential transmission pathways – among rodent hosts in Southeast Asia. We focused on differences between networks of a single host species and networks that involve multiple host species. In host-parasite networks, modularity (the extent to which the network is divided into subgroups of rodents that interact with similar parasites) was higher in the multi-species than in the single-species networks. This suggests that phylogeny affects patterns of parasite sharing, which was confirmed in analyses showing that it predicted affiliation of individuals to modules. We then constructed “potential transmission networks” based on the host-parasite networks, in which edges depict the similarity between a pair of individuals in the parasites they share. The centrality of individuals in these networks differed between multi- and single-species networks, with species identity and individual characteristics influencing their position in the networks. Simulations further revealed that parasite dynamics differed between multi- and single-species networks. We conclude that multi-host networks based on parasite sharing can provide new insights into the potential for transmission among hosts in an ecological community. In addition, the factors that determine the nature of parasite sharing (i.e. structure of the host-parasite network) may impact transmission patterns. PMID:25748947

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

    NASA Astrophysics Data System (ADS)

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

    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.

  14. Improved methods in neural network-based adaptive output feedback control, with applications to flight control

    NASA Astrophysics Data System (ADS)

    Kim, Nakwan

    Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

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

    NASA Technical Reports Server (NTRS)

    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.

  16. A Network-Based Method to Assess the Statistical Significance of Mild Co-Regulation Effects

    PubMed Central

    Horvát, Emőke-Ágnes; Zhang, Jitao David; Uhlmann, Stefan; Sahin, Özgür; Zweig, Katharina Anna

    2013-01-01

    Recent development of high-throughput, multiplexing technology has initiated projects that systematically investigate interactions between two types of components in biological networks, for instance transcription factors and promoter sequences, or microRNAs (miRNAs) and mRNAs. In terms of network biology, such screening approaches primarily attempt to elucidate relations between biological components of two distinct types, which can be represented as edges between nodes in a bipartite graph. However, it is often desirable not only to determine regulatory relationships between nodes of different types, but also to understand the connection patterns of nodes of the same type. Especially interesting is the co-occurrence of two nodes of the same type, i.e., the number of their common neighbours, which current high-throughput screening analysis fails to address. The co-occurrence gives the number of circumstances under which both of the biological components are influenced in the same way. Here we present SICORE, a novel network-based method to detect pairs of nodes with a statistically significant co-occurrence. We first show the stability of the proposed method on artificial data sets: when randomly adding and deleting observations we obtain reliable results even with noise exceeding the expected level in large-scale experiments. Subsequently, we illustrate the viability of the method based on the analysis of a proteomic screening data set to reveal regulatory patterns of human microRNAs targeting proteins in the EGFR-driven cell cycle signalling system. Since statistically significant co-occurrence may indicate functional synergy and the mechanisms underlying canalization, and thus hold promise in drug target identification and therapeutic development, we provide a platform-independent implementation of SICORE with a graphical user interface as a novel tool in the arsenal of high-throughput screening analysis. PMID:24039936

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

    PubMed

    Zhang, Wei; Chang, Jae-Woong; Lin, Lilong; Minn, Kay; Wu, Baolin; Chien, Jeremy; Yong, Jeongsik; Zheng, Hui; Kuang, Rui

    2015-12-01

    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/.

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

    PubMed Central

    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

    PubMed Central

    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 an hypoxia-CVD bipartite network and find several interesting hypoxia-CVD modules with significant Gene Ontology (GO) 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. PMID:25530704

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

    NASA Astrophysics Data System (ADS)

    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.

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

    NASA Technical Reports Server (NTRS)

    Williams, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team is to develop and flight-test control systems that use neural network technology to optimize the performance of the aircraft under nominal conditions as well as stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. 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 the baseline aerodynamic derivatives in flight. This set of open-loop flight tests was performed in preparation for a future phase of flights 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 a pitch frequency sweep and an 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. An examination of flight data shows that addition of the flight-identified aerodynamic derivative increments into the simulation improved the pitch handling qualities of the aircraft.

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

    PubMed

    Zhang, Wei; Chang, Jae-Woong; Lin, Lilong; Minn, Kay; Wu, Baolin; Chien, Jeremy; Yong, Jeongsik; Zheng, Hui; Kuang, Rui

    2015-12-01

    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/. PMID:26699225

  3. A Network-Based Multi-Target Computational Estimation Scheme for Anticoagulant Activities of Compounds

    PubMed Central

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

    2011-01-01

    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 method for

  4. Topological Properties of Large-Scale Cortical Networks Based on Multiple Morphological Features in Amnestic Mild Cognitive Impairment

    PubMed Central

    Li, Qiongling; Li, Xinwei; Wang, Xuetong; Li, Yuxia; Li, Kuncheng; Yu, Yang; Yin, Changhao; Li, Shuyu; Han, Ying

    2016-01-01

    Previous studies have demonstrated that amnestic mild cognitive impairment (aMCI) has disrupted properties of large-scale cortical networks based on cortical thickness and gray matter volume. However, it is largely unknown whether the topological properties of cortical networks based on geometric measures (i.e., sulcal depth, curvature, and metric distortion) change in aMCI patients compared with normal controls because these geometric features of cerebral cortex may be related to its intrinsic connectivity. Here, we compare properties in cortical networks constructed by six different morphological features in 36 aMCI participants and 36 normal controls. Six cortical features (3 volumetric and 3 geometric features) were extracted for each participant, and brain abnormities in aMCI were identified by cortical network based on graph theory method. All the cortical networks showed small-world properties. Regions showing significant differences mainly located in the medial temporal lobe and supramarginal and right inferior parietal lobe. In addition, we also found that the cortical networks constructed by cortical thickness and sulcal depth showed significant differences between the two groups. Our results indicated that geometric measure (i.e., sulcal depth) can be used to construct network to discriminate individuals with aMCI from controls besides volumetric measures. PMID:27057360

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

    NASA Technical Reports Server (NTRS)

    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

  6. Network-based dynamic modeling and control strategies in complex diseases

    NASA Astrophysics Data System (ADS)

    Gomez Tejeda Zanudo, Jorge

    In order to understand how the interactions of molecular components inside cells give rise to cellular function, creating models that incorporate the current biological knowledge while also making testable predictions that guide experimental work is of utmost importance. Creating such models is a challenging task in complex diseases such as cancer, in which numerous components are known to play an important role. To model the dynamics of the networks underlying complex diseases I use network-based models with discrete dynamics, which have been shown to reproduce the qualitative dynamics of a multitude of cellular systems while requiring only the combinatorial nature of the interactions and qualitative information on the desired/undesired states. I developed analytical and computational tools based on a type of function-dependent subnetwork that stabilizes in a steady state regardless of the state of the rest of the network, and which I termed stable motif. Based on the concept of stable motif, I proposed a method to identify a model's dynamical attractors, which have been found to be identifiable with the cell fates and cell behaviors of modeled organisms. I also proposed a stable-motif-based control method that identifies targets whose manipulation ensures the convergence of the model towards an attract or of interest. The identified control targets can be single or multiple nodes, are proven to always drive any initial condition to the desired attractor, and need to be applied only transiently to be effective. I illustrated the potential of these methods by collaborating with wet-lab cancer biologists to construct and analyze a model for a process involved in the spread of cancer cells (epithelial-mesenchymal transition), and also applied them to several published models for complex diseases, such as a type of white blood cell cancer (T-LGL leukemia). These methods allowed me to find attractors of larger models than what was previously possible, identify the

  7. Genetic barcodes

    DOEpatents

    Weier, Heinz -Ulrich G

    2015-08-04

    Herein are described multicolor FISH probe sets termed "genetic barcodes" targeting several cancer or disease-related loci to assess gene rearrangements and copy number changes in tumor cells. Two, three or more different fluorophores are used to detect the genetic barcode sections thus permitting unique labeling and multilocus analysis in individual cell nuclei. Gene specific barcodes can be generated and combined to provide both numerical and structural genetic information for these and other pertinent disease associated genes.

  8. Paleopopulation genetics.

    PubMed

    Wall, Jeffrey D; Slatkin, Montgomery

    2012-01-01

    Paleopopulation genetics is a new field that focuses on the population genetics of extinct groups and ancestral populations (i.e., populations ancestral to extant groups). With recent advances in DNA sequencing technologies, we now have unprecedented ability to directly assay genetic variation from fossils. This allows us to address issues, such as past population structure, changes in population size, and evolutionary relationships between taxa, at a much greater resolution than can traditional population genetics studies. In this review, we discuss recent developments in this emerging field as well as prospects for the future. PMID:22994357

  9. Genetic Engineering

    ERIC Educational Resources Information Center

    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)

  10. Integrative Network-based Analysis of Magnetic Resonance Spectroscopy and Genome Wide Expression in Glioblastoma multiforme

    PubMed Central

    Heiland, Dieter Henrik; Mader, Irina; Schlosser, Pascal; Pfeifer, Dietmar; Carro, Maria Stella; Lange, Thomas; Schwarzwald, Ralf; Vasilikos, Ioannis; Urbach, Horst; Weyerbrock, Astrid

    2016-01-01

    The goal of this study was to identify correlations between metabolites from proton MR spectroscopy and genetic pathway activity in glioblastoma multiforme (GBM). Twenty patients with primary GBM were analysed by short echo-time chemical shift imaging and genome-wide expression analyses. Weighed Gene Co-Expression Analysis was used for an integrative analysis of imaging and genetic data. N-acetylaspartate, normalised to the contralateral healthy side (nNAA), was significantly correlated to oligodendrocytic and neural development. For normalised creatine (nCr), a group with low nCr was linked to the mesenchymal subtype, while high nCr could be assigned to the proneural subtype. Moreover, clustering of normalised glutamine and glutamate (nGlx) revealed two groups, one with high nGlx being attributed to the neural subtype, and one with low nGlx associated with the classical subtype. Hence, the metabolites nNAA, nCr, and nGlx correlate with a specific gene expression pattern reflecting the previously described subtypes of GBM. Moreover high nNAA was associated with better clinical prognosis, whereas patients with lower nNAA revealed a shorter progression-free survival (PFS). PMID:27350391

  11. Reverse engineering of gene regulatory networks based on S-systems and Bat algorithm.

    PubMed

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

    2016-06-01

    The correct inference of gene regulatory networks for the understanding of the intricacies of the complex biological regulations remains an intriguing task for researchers. With the availability of large dimensional microarray data, relationships among thousands of genes can be simultaneously extracted. Among the prevalent models of reverse engineering genetic networks, S-system is considered to be an efficient mathematical tool. In this paper, Bat algorithm, based on the echolocation of bats, has been used to optimize the S-system model parameters. A decoupled S-system has been implemented to reduce the complexity of the algorithm. Initially, the proposed method has been successfully tested on an artificial network with and without the presence of noise. Based on the fact that a real-life genetic network is sparsely connected, a novel Accumulative Cardinality based decoupled S-system has been proposed. The cardinality has been varied from zero up to a maximum value, and this model has been implemented for the reconstruction of the DNA SOS repair network of Escherichia coli. The obtained results have shown significant improvements in the detection of a greater number of true regulations, and in the minimization of false detections compared to other existing methods.

  12. Reverse engineering of gene regulatory networks based on S-systems and Bat algorithm.

    PubMed

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

    2016-06-01

    The correct inference of gene regulatory networks for the understanding of the intricacies of the complex biological regulations remains an intriguing task for researchers. With the availability of large dimensional microarray data, relationships among thousands of genes can be simultaneously extracted. Among the prevalent models of reverse engineering genetic networks, S-system is considered to be an efficient mathematical tool. In this paper, Bat algorithm, based on the echolocation of bats, has been used to optimize the S-system model parameters. A decoupled S-system has been implemented to reduce the complexity of the algorithm. Initially, the proposed method has been successfully tested on an artificial network with and without the presence of noise. Based on the fact that a real-life genetic network is sparsely connected, a novel Accumulative Cardinality based decoupled S-system has been proposed. The cardinality has been varied from zero up to a maximum value, and this model has been implemented for the reconstruction of the DNA SOS repair network of Escherichia coli. The obtained results have shown significant improvements in the detection of a greater number of true regulations, and in the minimization of false detections compared to other existing methods. PMID:26932274

  13. Network-based rehabilitation increases formal support of frail elderly home-dwelling persons in Finland: randomised controlled trial.

    PubMed

    Ollonqvist, Kirsi; Aaltonen, Tuula; Karppi, Sirkka-Liisa; Hinkka, Katariina; Pöntinen, Seppo

    2008-03-01

    The AGE study is a national randomised, long-term, multicentre research project aimed at comparing a new network-based rehabilitation programme with the use of standard health and social services. The use of home help services is associated with increasing age, living alone and having difficulties with activities of daily living. During a rehabilitation intervention the elderly participants' need for care can be assessed. The focus of this paper is to investigate the possible effects of the network-based rehabilitation programme on the use of informal and formal support among home-dwelling elderly at a high risk of long-term institutionalisation. The randomised controlled trial with a 12-month follow-up was implemented in 7 rehabilitation centres and 41 municipalities in Finland. The participants were recruited between January and October 2002. A total of 708 home-dwelling persons aged 65 years or older with progressively decreasing functional capacity and at the risk of being institutionalised within 2 years participated. Persons with acute or progressive diseases or poor cognitive capacity (Mini Mental State Examination<18 points), and those who had participated in any inpatient rehabilitation during the preceding 5 years, were excluded. Participants were randomly allocated to the intervention group (n=343) or to the control group (n=365). The intervention consisted of a network-based rehabilitation programme specifically designed for frail elderly people. Main outcome measures included the help received from relatives and municipal or private services. The use of municipal services increased more in the intervention group (P<0.05) than in the control group. Support from relatives decreased in the control group. The rehabilitees' ability to manage with daily activities decreased and they received additional help; hence, in this respect the rehabilitation model seems successful. A longer follow-up within the still ongoing AGE study is needed to verify whether the

  14. Fast computing global structural balance in signed networks based on memetic algorithm

    NASA Astrophysics Data System (ADS)

    Sun, Yixiang; Du, Haifeng; Gong, Maoguo; Ma, Lijia; Wang, Shanfeng

    2014-12-01

    Structural balance is a large area of study in signed networks, and it is intrinsically a global property of the whole network. Computing global structural balance in signed networks, which has attracted some attention in recent years, is to measure how unbalanced a signed network is and it is a nondeterministic polynomial-time hard problem. Many approaches are developed to compute global balance. However, the results obtained by them are partial and unsatisfactory. In this study, the computation of global structural balance is solved as an optimization problem by using the Memetic Algorithm. The optimization algorithm, named Meme-SB, is proposed to optimize an evaluation function, energy function, which is used to compute a distance to exact balance. Our proposed algorithm combines Genetic Algorithm and a greedy strategy as the local search procedure. Experiments on social and biological networks show the excellent effectiveness and efficiency of the proposed method.

  15. A neural network-based classification of environment dynamics models for compliant control of manipulation robots.

    PubMed

    Katic, D; Vukobratovic, M

    1998-01-01

    In this paper, a new method for selecting the appropriate compliance control parameters for robot machining tasks based on connectionist classification of unknown dynamic environments, is proposed. The method classifies the type of environment by using multilayer perceptron, and then, determines the control parameters for compliance control using the estimated characteristics. An important feature is that the process of pattern association can work in an on-line mode as a part of selected compliance control algorithm. Convergence process is improved by using evolutionary approach (genetic algorithms) in order to choose the optimal topology of the proposed multilayer perceptron. Compliant motion simulation experiments with robotic arm placed in contact with dynamic environment, described by the stiffness model and by the general impedance model, have been performed in order to verify the proposed approach.

  16. Arthropod Genetics.

    ERIC Educational Resources Information Center

    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)

  17. Genetic Discrimination

    MedlinePlus

    ... Medicine Working Group New Horizons and Research Patient Management Policy and Ethics Issues Quick Links for Patient Care ... genetic discrimination. April 25, 2007, Statement of Administration Policy, Office of Management and Budget Official Statement from the Office of ...

  18. Ciona Genetics

    PubMed Central

    Veeman, Michael T.; Chiba, Shota; Smith, William C.

    2010-01-01

    Ascidians, such as Ciona, are invertebrate chordates with simple embryonic body plans and small, relatively non-redundant genomes. Ciona genetics is in its infancy compared to many other model systems, but it provides a powerful method for studying this important vertebrate outgroup. Here we give basic methods for genetic analysis of Ciona, including protocols for controlled crosses both by natural spawning and by the surgical isolation of gametes; the identification and propagation of mutant lines; and strategies for positional cloning. PMID:21805273

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

    PubMed

    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.

  20. A wearable sensor module with a neural-network-based activity classification algorithm for daily energy expenditure estimation.

    PubMed

    Lin, Che-Wei; Yang, Ya-Ting C; Wang, Jeen-Shing; Yang, Yi-Ching

    2012-09-01

    This paper presents a wearable module and neural-network-based activity classification algorithm for energy expenditure estimation. The purpose of our design is first to categorize physical activities with similar intensity levels, and then to construct energy expenditure regression (EER) models using neural networks in order to optimize the estimation performance. The classification of physical activities for EER model construction is based on the acceleration and ECG signal data collected by wearable sensor modules developed by our research lab. The proposed algorithm consists of procedures for data collection, data preprocessing, activity classification, feature selection, and construction of EER models using neural networks. In order to reduce the computational load and achieve satisfactory estimation performance, we employed sequential forward and backward search strategies for feature selection. Two representative neural networks, a radial basis function network (RBFN) and a generalized regression neural network (GRNN), were employed as EER models for performance comparisons. Our experimental results have successfully validated the effectiveness of our wearable sensor module and its neural-network-based activity classification algorithm for energy expenditure estimation. In addition, our results demonstrate the superior performance of GRNN as compared to RBFN.

  1. SurvNet: a web server for identifying network-based biomarkers that most correlate with patient survival data.

    PubMed

    Li, Jun; Roebuck, Paul; Grünewald, Stefan; Liang, Han

    2012-07-01

    An important task in biomedical research is identifying biomarkers that correlate with patient clinical data, and these biomarkers then provide a critical foundation for the diagnosis and treatment of disease. Conventionally, such an analysis is based on individual genes, but the results are often noisy and difficult to interpret. Using a biological network as the searching platform, network-based biomarkers are expected to be more robust and provide deep insights into the molecular mechanisms of disease. We have developed a novel bioinformatics web server for identifying network-based biomarkers that most correlate with patient survival data, SurvNet. The web server takes three input files: one biological network file, representing a gene regulatory or protein interaction network; one molecular profiling file, containing any type of gene- or protein-centred high-throughput biological data (e.g. microarray expression data or DNA methylation data); and one patient survival data file (e.g. patients' progression-free survival data). Given user-defined parameters, SurvNet will automatically search for subnetworks that most correlate with the observed patient survival data. As the output, SurvNet will generate a list of network biomarkers and display them through a user-friendly interface. SurvNet can be accessed at http://bioinformatics.mdanderson.org/main/SurvNet.

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

    NASA Astrophysics Data System (ADS)

    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.

  3. Enhancing Functional Robustness of Gene Regulatory Networks Based on Fitness Landscape Design

    NASA Astrophysics Data System (ADS)

    Kim, Kyung

    We aim to develop design principles for enhancing functional robustness of engineered cells using gene-network topology. We observed the effect of genetic regulation types (inhibition and activation) on robustness. Inhibition was much more stable than activation in E. coli. In the case of activation, if the upstream activator expression is shutdown by mutation, then its downstream expression is shut down as well. Without activation, the activator shutdown due to mutation will make its downstream expression ``remains`` turned off. Thus, the change in the metabolic load is higher in the activation case. Therefore, the stronger activation, the less robust the circuits are. In the inhibition case, we found that the story becomes opposite. When an inhibitor expression is shut down by mutation, the downstream expression turns on because the inhibitor is not expressed. This compensates changes in the metabolic load that might have been decreased without the inhibition. This result presents potential significant roles of network topology on the robustness of engineered cellular networks. This also emphasizes that the concept of fitness landscape, where the local slope corresponds to the fitness difference between different genotypes, can be useful to design robust gene circuits. We acknowledge the support of the NSF (MCB Award # 1515280).

  4. A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature.

    PubMed

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

    2012-11-12

    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.

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

    PubMed Central

    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

  6. Specific Genetic Disorders

    MedlinePlus

    ... of Genetic Terms Definitions for genetic terms Specific Genetic Disorders Many human diseases have a genetic component. ... Condition in an Adult The Undiagnosed Diseases Program Genetic Disorders Achondroplasia Alpha-1 Antitrypsin Deficiency Antiphospholipid Syndrome ...

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

    PubMed Central

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

    2012-01-01

    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. PMID:23060147

  8. Genetic haemochromatosis.

    PubMed

    Rosalki, S B

    2002-10-01

    Genetic haemochromatosis is an autosomal recessive inherited disorder of iron metabolism due to mutation of the HFE gene. In homozygotes (1 in 300 of the UK population), this results in excessive iron absorption from the gut and its deposition in major body organs. This may give rise to fatigue, arthritis, cardiac failure, diabetes mellitus, hepatic cirrhosis or skin pigmentation, occurring predominantly in males over 50 years of age. Identification uses measurement of serum iron, iron-binding capacity (or transferrin) and ferritin, together with initial or confirmatory genetic DNA studies.

  9. [The study on the characters of membrane protein interaction and its network based on integrated intelligence method].

    PubMed

    Shen, Yizhen; Ding, Yongsheng; Hao, Kuangrong

    2011-08-01

    Membrane protein and its interaction network have become a novel research direction in bioinformatics. In this paper, a novel membrane protein interaction network simulator is proposed for system biology studies by integrated intelligence method including spectrum analysis, fuzzy K-Nearest Neighbor(KNN) algorithm and so on. We consider biological system as a set of active computational components interacting with each other and with the external environment. Then we can use the network simulator to construct membrane protein interaction networks. Based on the proposed approach, we found that the membrane protein interaction network almost has some dynamic and collective characteristics, such as small-world network, scale free distributing, and hierarchical module structure. These properties are similar to those of other extensively studied protein interaction networks. The present studies on the characteristics of the membrane protein interaction network will be valuable for its relatively biological and medical studies. PMID:21936357

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

    PubMed

    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. PMID:22738782

  11. Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors.

    PubMed

    Godino-Llorente, J I; Gómez-Vilda, P

    2004-02-01

    It is well known that vocal and voice diseases do not necessarily cause perceptible changes in the acoustic voice signal. Acoustic analysis is a useful tool to diagnose voice diseases being a complementary technique to other methods based on direct observation of the vocal folds by laryngoscopy. Through the present paper two neural-network based classification approaches applied to the automatic detection of voice disorders will be studied. Structures studied are multilayer perceptron and learning vector quantization fed using short-term vectors calculated accordingly to the well-known Mel Frequency Coefficient cepstral parameterization. The paper shows that these architectures allow the detection of voice disorders--including glottic cancer--under highly reliable conditions. Within this context, the Learning Vector quantization methodology demonstrated to be more reliable than the multilayer perceptron architecture yielding 96% frame accuracy under similar working conditions.

  12. Integration of FTTH and GI-POF in-house networks based on injection locking and direct-detection techniques.

    PubMed

    Peng, Hsiao-Chun; Lu, Hai-Han; Li, Chung-Yi; Su, Heng-Sheng; Hsu, Chin-Tai

    2011-03-28

    An integration of fiber-to-the-home (FTTH) and graded-index plastic optical fiber (GI-POF) in-house networks based on injection-locked vertical cavity surface emitting lasers (VCSELs) and direct-detection technique is proposed and experimentally demonstrated. Sufficient low bit error rate (BER) values were obtained over a combination of 20-km single-mode fiber (SMF) and 50-m GI-POF links. Signal qualities satisfy the worldwide interoperability for microwave access (WiMAX) requirement with data signals of 20 Mbps/5.8 GHz and 70 Mbps/10 GHz, respectively. Since our proposed network does not use sophisticated and expensive RF devices in premises, it reveals a prominent one with simpler and more economic advantages. Our proposed architecture is suitable for the SMF-based primary and GI-POF-based in-house networks. PMID:21451701

  13. A statistical approach in human brain connectome of Parkinson Disease in elderly people using Network Based Statistics.

    PubMed

    Aarabi, Mohammad Hadi; Kamalian, Aida; Mohajer, Bahram; Shandiz, Mahdi Shirin; Eqlimi, Ehsan; Shojaei, Ahmad; Safabakhsh, Hamidreza

    2015-08-01

    Parkinson's Disease (PD) is a progressive neurodegenerative disorder assumed to involve different areas of CNS and PNS. Thus, Diffusion Tensor Imaging (DTI) is used to examine the areas engaged in PD neurodegeneration. In the present study, we computed average tract length and fiber volume as a measure of white matter integrity and adopted Network Based Statistics (NBS) to conduct group analyses between age- and gender-matched PD patients and healthy control connectivity matrices. NBS is a powerful statistical tool that utilizes the presence of every link in connectivity matrices and controls family wise error rates (in weak sense). The major regions with significantly reduced interconnecting fiber volume or average tract length were cingulum, temporal lobe, frontal lobe, parahippocampus, hippocampus, olfactory lobe, and occipital lobe. PMID:26737248

  14. Brain Functional Effects of Psychopharmacological Treatments in Schizophrenia: A Network-based Functional Perspective Beyond Neurotransmitter Systems.

    PubMed

    De Rossi, Pietro; Chiapponi, Chiara; Spalletta, Gianfranco

    2015-01-01

    Psychopharmacological treatments for schizophrenia have always been a matter of debate and a very important issue in public health given the chronic, relapsing and disabling nature of the disorder. A thorough understanding of the pros and cons of currently available pharmacological treatments for schizophrenia is critical to better capture the features of treatment-refractory clinical pictures and plan the developing of new treatment strategies. This review focuses on brain functional changes induced by antipsychotic drugs as assessed by modern functional neuroimaging techniques (i.e. fMRI, PET, SPECT, MRI spectroscopy). The most important papers on this topic are reviewed in order to draw an ideal map of the main functional changes occurring in the brain during antipsychotic treatment. This supports the hypothesis that a network-based perspective and a functional connectivity approach are needed to fill the currently existing gap of knowledge in the field of psychotropic drugs and their mechanisms of action beyond neurotransmitter systems. PMID:26412063

  15. Dynamic Neural Network-Based Pulsed Plasma Thruster (PPT) Fault Detection and Isolation for Formation Flying of Satellites

    NASA Astrophysics Data System (ADS)

    Valdes, A.; Khorasani, K.

    The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for the Pulsed Plasma Thrusters (PPTs) that are used in the Attitude Control Subsystem (ACS) of satellites that are tasked to perform a formation flying mission. By using data collected from the relative attitudes of the formation flying satellites our proposed "High Level" FDI scheme can detect the pair of thrusters which is faulty, however fault isolation cannot be accomplished. Based on the "High Level" FDI scheme and the DNN-based "Low Level" FDI scheme developed earlier by the authors, an "Integrated" DNN-based FDI scheme is then proposed. To demonstrate the FDI capabilities of the proposed schemes various fault scenarios are simulated.

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

    NASA Technical Reports Server (NTRS)

    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.

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

    NASA Astrophysics Data System (ADS)

    Nayar, Priya; Singh, Bhim; Mishra, Sukumar

    2016-09-01

    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.

  18. Integration of FTTH and GI-POF in-house networks based on injection locking and direct-detection techniques.

    PubMed

    Peng, Hsiao-Chun; Lu, Hai-Han; Li, Chung-Yi; Su, Heng-Sheng; Hsu, Chin-Tai

    2011-03-28

    An integration of fiber-to-the-home (FTTH) and graded-index plastic optical fiber (GI-POF) in-house networks based on injection-locked vertical cavity surface emitting lasers (VCSELs) and direct-detection technique is proposed and experimentally demonstrated. Sufficient low bit error rate (BER) values were obtained over a combination of 20-km single-mode fiber (SMF) and 50-m GI-POF links. Signal qualities satisfy the worldwide interoperability for microwave access (WiMAX) requirement with data signals of 20 Mbps/5.8 GHz and 70 Mbps/10 GHz, respectively. Since our proposed network does not use sophisticated and expensive RF devices in premises, it reveals a prominent one with simpler and more economic advantages. Our proposed architecture is suitable for the SMF-based primary and GI-POF-based in-house networks.

  19. Influence of Acoustic Feedback on the Learning Strategies of Neural Network-Based Sound Classifiers in Digital Hearing Aids

    NASA Astrophysics Data System (ADS)

    Cuadra, Lucas; Alexandre, Enrique; Gil-Pita, Roberto; Vicen-Bueno, Raúl; Álvarez, Lorena

    2009-12-01

    Sound classifiers embedded in digital hearing aids are usually designed by using sound databases that do not include the distortions associated to the feedback that often occurs when these devices have to work at high gain and low gain margin to oscillation. The consequence is that the classifier learns inappropriate sound patterns. In this paper we explore the feasibility of using different sound databases (generated according to 18 configurations of real patients), and a variety of learning strategies for neural networks in the effort of reducing the probability of erroneous classification. The experimental work basically points out that the proposed methods assist the neural network-based classifier in reducing its error probability in more than 18%. This helps enhance the elderly user's comfort: the hearing aid automatically selects, with higher success probability, the program that is best adapted to the changing acoustic environment the user is facing.

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

    PubMed

    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.

  1. Genetic Disorders

    MedlinePlus

    ... of pregnancy loss. How do I know which tests to have? Your health care provider or a genetic counselor can discuss all of the testing options with you and help you decide based on your individual risk factors. Do I have to have these tests? Whether you want to be tested is a ...

  2. Genetic Recombination

    ERIC Educational Resources Information Center

    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)

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

    PubMed Central

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

    2015-01-01

    Introduction: 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. Description of Architecture: 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. Suggestions for Future Use: 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. Conclusions: 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. PMID:26357665

  4. Cancer Genetics Services Directory

    MedlinePlus

    ... Overview–for health professionals Research NCI Cancer Genetics Services Directory This directory lists professionals who provide services related to cancer genetics (cancer risk assessment, genetic ...

  5. Genetic testing, genetic medicine, and managed care.

    PubMed

    Rothstein, M A; Hoffman, S

    1999-01-01

    As modern human genetics moves from the research setting to the clinical setting, it will encounter the managed care system. Issues of cost, access, and quality of care will affect the availability and nature of genetic testing, genetic counseling, and genetic therapies. This Article will explore such issues as professional education, coverage of genetic services, privacy and confidentiality, and liability. It will conclude with a series of recommendations for the practice of genetic medicine in the age of managed care.

  6. Human genetics

    SciTech Connect

    Carlson, E.A.

    1984-01-01

    This text provides full and balanced coverage of the concepts requisite for a thorough understanding of human genetics. Applications to both the individual and society are integrated throughout the lively and personal narrative, and the essential principles of heredity are clearly presented to prepare students for informed participation in public controversies. High-interest, controversial topics, including recombinant DNA technology, oncogenes, embryo transfer, environmental mutagens and carcinogens, IQ testing, and eugenics encourage understanding of important social issues.

  7. Mitochondrial genetics

    PubMed Central

    Chinnery, Patrick Francis; Hudson, Gavin

    2013-01-01

    Introduction In the last 10 years the field of mitochondrial genetics has widened, shifting the focus from rare sporadic, metabolic disease to the effects of mitochondrial DNA (mtDNA) variation in a growing spectrum of human disease. The aim of this review is to guide the reader through some key concepts regarding mitochondria before introducing both classic and emerging mitochondrial disorders. Sources of data In this article, a review of the current mitochondrial genetics literature was conducted using PubMed (http://www.ncbi.nlm.nih.gov/pubmed/). In addition, this review makes use of a growing number of publically available databases including MITOMAP, a human mitochondrial genome database (www.mitomap.org), the Human DNA polymerase Gamma Mutation Database (http://tools.niehs.nih.gov/polg/) and PhyloTree.org (www.phylotree.org), a repository of global mtDNA variation. Areas of agreement The disruption in cellular energy, resulting from defects in mtDNA or defects in the nuclear-encoded genes responsible for mitochondrial maintenance, manifests in a growing number of human diseases. Areas of controversy The exact mechanisms which govern the inheritance of mtDNA are hotly debated. Growing points Although still in the early stages, the development of in vitro genetic manipulation could see an end to the inheritance of the most severe mtDNA disease. PMID:23704099

  8. Genetic Testing (For Parents)

    MedlinePlus

    ... Story" 5 Things to Know About Zika & Pregnancy Genetic Testing KidsHealth > For Parents > Genetic Testing Print A ... blood, skin, bone, or other tissue is needed. Genetic Testing During Pregnancy For genetic testing before birth, ...

  9. Cancer Genetics Services Directory

    MedlinePlus

    ... 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, ...

  10. Genetic toxicology.

    PubMed

    Kramer, P J

    1998-04-01

    Systems for testing genetic toxicology are components of carcinogenic and genetic risk assessment. Present routine genotoxicity-testing is based on at least 20 years of development during which many different test systems have been introduced and used. Today, it is clear that no single test is capable of detecting all genotoxic agents. Therefore, the usual approach is to perform a standard battery of in-vitro and in-vivo tests for genotoxicity. Work-groups of the European Union (EU), the Organization for Economic Co-operation and Development (OECD), and, very recently, the work-group of the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) have defined such standard battery tests. These and some currently used supplementary or confirmatory tests are briefly discussed here. Additional test systems for the assessment of genotoxic and carcinogenic hazard and risk are seriously needed. These tests must be more relevant to man than are current assays and less demanding in respect of cost, time and number of animals. Another aspect for reassessment derives from the actual situation in the pharmaceutical industry. Companies have to prepare for the world economy of the 21st century. Therefore, pharmaceutical research is speeding up tremendously by use of tools such as genomics, combinatorial chemistry, high throughput screening and proteomics. Toxicology and genotoxicology need to re-evaluate their changing environment and must find ways to respond to these needs. In conclusion, genetic toxicology needs to answer questions coming from two major directions: hazard and risk identification and high throughput testing. PMID:9625484

  11. Strong electroactive biodegradable shape memory polymer networks based on star-shaped polylactide and aniline trimer for bone tissue engineering.

    PubMed

    Xie, Meihua; Wang, Ling; Ge, Juan; Guo, Baolin; Ma, Peter X

    2015-04-01

    Preparation of functional shape memory polymer (SMP) for tissue engineering remains a challenge. Here the synthesis of strong electroactive shape memory polymer (ESMP) networks based on star-shaped polylactide (PLA) and aniline trimer (AT) is reported. Six-armed PLAs with various chain lengths were chemically cross-linked to synthesize SMP. After addition of an electroactive AT segment into the SMP, ESMP was obtained. The polymers were characterized by (1)H NMR, GPC, FT-IR, CV, DSC, DMA, tensile test, and degradation test. The SMP and ESMP exhibited strong mechanical properties (modulus higher than GPa) and excellent shape memory performance: short recovery time (several seconds), high recovery ratio (over 94%), and high fixity ratio (almost 100%). Moreover, cyclic voltammetry test confirmed the electroactivity of the ESMP. The ESMP significantly enhanced the proliferation of C2C12 cells compared to SMP and linear PLA (control). In addition, the ESMP greatly improved the osteogenic differentiation of C2C12 myoblast cells compared to PH10 and PLA in terms of ALP enzyme activity, immunofluorescence staining, and relative gene expression by quantitative real-time polymerase chain reaction (qRT-PCR). These intelligent SMPs and electroactive SMP with strong mechanical properties, tunable degradability, good electroactivity, biocompatibility, and enhanced osteogenic differentiation of C2C12 cells show great potential for bone regeneration.

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

    NASA Technical Reports Server (NTRS)

    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.

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

    PubMed Central

    Zhang, Jisheng; Jia, Limin; Niu, Shuyun; Zhang, Fan; Tong, Lu; Zhou, Xuesong

    2015-01-01

    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. PMID:26076404

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

    PubMed

    Li, Wan; Chen, Lina; He, Weiming; Li, Weiguo; Qu, Xiaoli; Liang, Binhua; Gao, Qianping; Feng, Chenchen; Jia, Xu; Lv, Yana; Zhang, Siya; Li, Xia

    2013-01-01

    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. Design and analysis of a conceptual wavelength-division multiplexing optical network based on self-similarity

    NASA Astrophysics Data System (ADS)

    Kuo, Shu-Tsung; Kao, Ming-Seng

    2010-02-01

    We propose a novel multilevel wavelength-division multiplexing optical network based on the concept of self-similarity to simplify network structure and operation. Our approach lies on a simple network topology as well as an efficient wavelength management scheme being uniformly applied to all network levels. The proposed network is a compound packet-switched and wavelength-routed network with packet switching performed on the bottom level and wavelength routing on all upper levels. This network retains the efficiency of packet-switched networks and the simplicity of wavelength-routed networks. Switching operations are concentrated at some special nodes in the multilevel network, significantly simplifying the node configuration and wavelength routing. Moreover, the idea of λ bands is applied to unify wavelength management on all network levels. Network analysis is performed to assess the feasibility of our approach. A queueing model using the quality of service enhanced optical burst switching protocol is employed to analyze the blocking performance of the proposed network. Also, the numerical results based on the queueing model are provided.

  16. Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity

    PubMed Central

    Chen, Xing; Clarence Yan, Chenggang; Luo, Cai; Ji, Wen; Zhang, Yongdong; Dai, Qionghai

    2015-01-01

    Increasing evidence has indicated that plenty of lncRNAs play important roles in many critical biological processes. Developing powerful computational models to construct lncRNA functional similarity network based on heterogeneous biological datasets is one of the most important and popular topics in the fields of both lncRNAs and complex diseases. Functional similarity network consturction could benefit the model development for both lncRNA function inference and lncRNA-disease association identification. However, little effort has been attempted to analysis and calculate lncRNA functional similarity on a large scale. In this study, based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases, we developed two novel lncRNA functional similarity calculation models (LNCSIM). LNCSIM was evaluated by introducing similarity scores into the model of Laplacian Regularized Least Squares for LncRNA–Disease Association (LRLSLDA) for lncRNA-disease association prediction. As a result, new predictive models improved the performance of LRLSLDA in the leave-one-out cross validation of various known lncRNA-disease associations datasets. Furthermore, some of the predictive results for colorectal cancer and lung cancer were verified by independent biological experimental studies. It is anticipated that LNCSIM could be a useful and important biological tool for human disease diagnosis, treatment, and prevention. PMID:26061969

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

    PubMed

    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.

  18. Identification of a multi-cancer gene expression biomarker for cancer clinical outcomes using a network-based algorithm.

    PubMed

    Martinez-Ledesma, Emmanuel; Verhaak, Roeland G W; Treviño, Victor

    2015-01-01

    Cancer types are commonly classified by histopathology and more recently through molecular characteristics such as gene expression, mutations, copy number variations, and epigenetic alterations. These molecular characterizations have led to the proposal of prognostic biomarkers for many cancer types. Nevertheless, most of these biomarkers have been proposed for a specific cancer type or even specific subtypes. Although more challenging, it is useful to identify biomarkers that can be applied for multiple types of cancer. Here, we have used a network-based exploration approach to identify a multi-cancer gene expression biomarker highly connected by ESR1, PRKACA, LRP1, JUN and SMAD2 that can be predictive of clinical outcome in 12 types of cancer from The Cancer Genome Atlas (TCGA) repository. The gene signature of this biomarker is highly supported by cancer literature, biological terms, and prognostic power in other cancer types. Additionally, the signature does not seem to be highly associated with specific mutations or copy number alterations. Comparisons with cancer-type specific and other multi-cancer biomarkers in TCGA and other datasets showed that the performance of the proposed multi-cancer biomarker is superior, making the proposed approach and multi-cancer biomarker potentially useful in research and clinical settings.

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

    PubMed

    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. PMID:26815339

  20. Is a Complex Neural Network Based Air Quality Prediction Model Better Than a Simple One? A Bayesian Point of View

    NASA Astrophysics Data System (ADS)

    Hoi, K. I.; Yuen, K. V.; Mok, K. M.

    2010-05-01

    In this study the neural network based air quality prediction model was tested in a typical coastal city, Macau, with Latitude 22° 10'N and Longitude 113° 34'E. By using five years of air quality and meteorological data recorded at an ambient air quality monitoring station between 2001 and 2005, it was found that the performance of the ANN model was generally improved by increasing the number of hidden neurons in the training phase. However, the performance of the ANN model was not sensitive to the change in the number of hidden neurons during the prediction phase. Therefore, the improvement in the error statistics for a complex ANN model in the training phase may be only caused by the overfitting of the data. In addition, the posterior PDF of the parameter vector conditional on the training dataset was investigated for different number of hidden neurons. It was found that the parametric space for a simple ANN model was globally identifiable and the Levenberg-Marquardt backpropagation algorithm was able to locate the optimal parameter vector. However, the parameter vector might contain redundant parameters and the parametric space was not globally identifiable when the model class became complex. In addition, the Levenberg-Marquardt backpropagation algorithm was unable to locate the most optimal parameter vector in this situation. Finally, it was concluded that the a more complex MLP model, that fits the data better, is not necessarily better than a simple one.

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

    PubMed

    Zhang, Jisheng; Jia, Limin; Niu, Shuyun; Zhang, Fan; Tong, Lu; Zhou, Xuesong

    2015-06-12

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

    PubMed

    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.

  3. Neural-network-based online HJB solution for optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems.

    PubMed

    Liu, Derong; Wang, Ding; Wang, Fei-Yue; Li, Hongliang; Yang, Xiong

    2014-12-01

    In this paper, the infinite horizon optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems is investigated using neural-network-based online solution of Hamilton-Jacobi-Bellman (HJB) equation. By establishing an appropriate bounded function and defining a modified cost function, the optimal robust guaranteed cost control problem is transformed into an optimal control problem. It can be observed that the optimal cost function of the nominal system is nothing but the optimal guaranteed cost of the original uncertain system. A critic neural network is constructed to facilitate the solution of the modified HJB equation corresponding to the nominal system. More importantly, an additional stabilizing term is introduced for helping to verify the stability, which reinforces the updating process of the weight vector and reduces the requirement of an initial stabilizing control. The uniform ultimate boundedness of the closed-loop system is analyzed by using the Lyapunov approach as well. Two simulation examples are provided to verify the effectiveness of the present control approach. PMID:25415951

  4. A network-based approach for predicting Hsp27 knock-out targets in mouse skeletal muscles

    PubMed Central

    Kammoun, Malek; Picard, Brigitte; Henry-Berger, Joëlle; Cassar-Malek, Isabelle

    2013-01-01

    Thanks to genomics, we have previously identified markers of beef tenderness, and computed a bioinformatic analysis that enabled us to build an interactome in which we found Hsp27 at a crucial node. Here, we have used a network-based approach for understanding the contribution of Hsp27 to tenderness through the prediction of its interactors related to tenderness. We have revealed the direct interactors of Hsp27. The predicted partners of Hsp27 included proteins involved in different functions, e.g. members of Hsp families (Hsp20, Cryab, Hsp70a1a, and Hsp90aa1), regulators of apoptosis (Fas, Chuk, and caspase-3), translation factors (Eif4E, and Eif4G1), cytoskeletal proteins (Desmin) and antioxidants (Sod1). The abundances of 15 proteins were quantified by Western blotting in two muscles of HspB1-null mice and their controls. We observed changes in the amount of most of the Hsp27 predicted targets in mice devoid of Hsp27 mainly in the most oxidative muscle. Our study demonstrates the functional links between Hsp27 and its predicted targets. It suggests that Hsp status, apoptotic processes and protection against oxidative stress are crucial for post-mortem muscle metabolism, subsequent proteolysis, and therefore for beef tenderness. PMID:24688716

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

    PubMed

    Zhang, Jisheng; Jia, Limin; Niu, Shuyun; Zhang, Fan; Tong, Lu; Zhou, Xuesong

    2015-01-01

    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. PMID:26076404

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

    SciTech Connect

    Djukanovic, M.B.; Calovic, M.S.; Vesovic, B.V.; Sobajic, D.J.

    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.

  7. miRNet - dissecting miRNA-target interactions and functional associations through network-based visual analysis

    PubMed Central

    Fan, Yannan; Siklenka, Keith; Arora, Simran K.; Ribeiro, Paula; Kimmins, Sarah; Xia, Jianguo

    2016-01-01

    MicroRNAs (miRNAs) can regulate nearly all biological processes and their dysregulation is implicated in various complex diseases and pathological conditions. Recent years have seen a growing number of functional studies of miRNAs using high-throughput experimental technologies, which have produced a large amount of high-quality data regarding miRNA target genes and their interactions with small molecules, long non-coding RNAs, epigenetic modifiers, disease associations, etc. These rich sets of information have enabled the creation of comprehensive networks linking miRNAs with various biologically important entities to shed light on their collective functions and regulatory mechanisms. Here, we introduce miRNet, an easy-to-use web-based tool that offers statistical, visual and network-based approaches to help researchers understand miRNAs functions and regulatory mechanisms. The key features of miRNet include: (i) a comprehensive knowledge base integrating high-quality miRNA-target interaction data from 11 databases; (ii) support for differential expression analysis of data from microarray, RNA-seq and quantitative PCR; (iii) implementation of a flexible interface for data filtering, refinement and customization during network creation; (iv) a powerful fully featured network visualization system coupled with enrichment analysis. miRNet offers a comprehensive tool suite to enable statistical analysis and functional interpretation of various data generated from current miRNA studies. miRNet is freely available at http://www.mirnet.ca. PMID:27105848

  8. sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

    PubMed Central

    Luo, Heng; Ye, Hao; Ng, Hui Wen; Sakkiah, Sugunadevi; Mendrick, Donna L.; Hong, Huixiao

    2016-01-01

    Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system. PMID:27558848

  9. sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides.

    PubMed

    Luo, Heng; Ye, Hao; Ng, Hui Wen; Sakkiah, Sugunadevi; Mendrick, Donna L; Hong, Huixiao

    2016-01-01

    Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system. PMID:27558848

  10. Metadata Standard and Data Exchange Specifications to Describe, Model, and Integrate Complex and Diverse High-Throughput Screening Data from the Library of Integrated Network-based Cellular Signatures (LINCS).

    PubMed

    Vempati, Uma D; Chung, Caty; Mader, Chris; Koleti, Amar; Datar, Nakul; Vidović, Dušica; Wrobel, David; Erickson, Sean; Muhlich, Jeremy L; Berriz, Gabriel; Benes, Cyril H; Subramanian, Aravind; Pillai, Ajay; Shamu, Caroline E; Schürer, Stephan C

    2014-06-01

    The National Institutes of Health Library of Integrated Network-based Cellular Signatures (LINCS) program is generating extensive multidimensional data sets, including biochemical, genome-wide transcriptional, and phenotypic cellular response signatures to a variety of small-molecule and genetic perturbations with the goal of creating a sustainable, widely applicable, and readily accessible systems biology knowledge resource. Integration and analysis of diverse LINCS data sets depend on the availability of sufficient metadata to describe the assays and screening results and on their syntactic, structural, and semantic consistency. Here we report metadata specifications for the most important molecular and cellular components and recommend them for adoption beyond the LINCS project. We focus on the minimum required information to model LINCS assays and results based on a number of use cases, and we recommend controlled terminologies and ontologies to annotate assays with syntactic consistency and semantic integrity. We also report specifications for a simple annotation format (SAF) to describe assays and screening results based on our metadata specifications with explicit controlled vocabularies. SAF specifically serves to programmatically access and exchange LINCS data as a prerequisite for a distributed information management infrastructure. We applied the metadata specifications to annotate large numbers of LINCS cell lines, proteins, and small molecules. The resources generated and presented here are freely available.

  11. Reconstruction and analysis of correlation networks based on GC-MS metabolomics data for young hypertensive men.

    PubMed

    Wang, Le; Hou, Entai; Wang, Lijun; Wang, Yanjun; Yang, Lingjian; Zheng, Xiaohui; Xie, Guangqi; Sun, Qiong; Liang, Mingyu; Tian, Zhongmin

    2015-01-01

    The awareness, treatment, and control rates of hypertension for young adults are much lower than average. It is urgently needed to explore the variances of metabolic profiles for early diagnosis and treatment of hypertension. In current study, we applied a GC-MS based metabolomics platform coupled with a network approach to analyze plasma samples from young hypertensive men and age-matched healthy controls. Our findings confirmed distinct metabolic footprints of young hypertensive men. The significantly altered metabolites between two groups were enriched for the biological module of amino acids biosynthesis. The correlations of GC-MS metabolomics data were then visualized as networks based on Pearson correlation coefficient (threshold=0.6). The plasma metabolites identified by GC-MS and the significantly altered metabolites (P<0.05) between patients and controls were respectively included as nodes of a network. Statistical and topological characteristics of the networks were studied in detail. A few amino acids, glycine, lysine, and cystine, were screened as hub metabolites with higher values of degree (k), and also obtained highest scores of three centrality indices. The short average path lengths and high clustering coefficients of the networks revealed a small-world property, indicating that variances of these amino acids have a major impact on the metabolic change in young hypertensive men. These results suggested that disorders of amino acid metabolism might play an important role in predisposing young men to developing hypertension. The combination of metabolomics and network methods would provide another perspective on expounding the molecular mechanism underlying complex diseases.

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

    PubMed

    Bonthala, Venkata Suresh; Mayes, Katie; Moreton, Joanna; Blythe, Martin; Wright, Victoria; May, Sean Tobias; Massawe, Festo; Mayes, Sean; Twycross, Jamie

    2016-01-01

    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. PMID:26859686

  13. Entropy and gravity concepts as new methodological indexes to investigate technological convergence: patent network-based approach.

    PubMed

    Cho, Yongrae; Kim, Minsung

    2014-01-01

    The volatility and uncertainty in the process of technological developments are growing faster than ever due to rapid technological innovations. Such phenomena result in integration among disparate technology fields. At this point, it is a critical research issue to understand the different roles and the propensity of each element technology for technological convergence. In particular, the network-based approach provides a holistic view in terms of technological linkage structures. Furthermore, the development of new indicators based on network visualization can reveal the dynamic patterns among disparate technologies in the process of technological convergence and provide insights for future technological developments. This research attempts to analyze and discover the patterns of the international patent classification codes of the United States Patent and Trademark Office's patent data in printed electronics, which is a representative technology in the technological convergence process. To this end, we apply the physical idea as a new methodological approach to interpret technological convergence. More specifically, the concepts of entropy and gravity are applied to measure the activities among patent citations and the binding forces among heterogeneous technologies during technological convergence. By applying the entropy and gravity indexes, we could distinguish the characteristic role of each technology in printed electronics. At the technological convergence stage, each technology exhibits idiosyncratic dynamics which tend to decrease technological differences and heterogeneity. Furthermore, through nonlinear regression analysis, we have found the decreasing patterns of disparity over a given total period in the evolution of technological convergence. This research has discovered the specific role of each element technology field and has consequently identified the co-evolutionary patterns of technological convergence. These new findings on the evolutionary

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

    PubMed Central

    Bonthala, Venkata Suresh; Mayes, Katie; Moreton, Joanna; Blythe, Martin; Wright, Victoria; May, Sean Tobias; Massawe, Festo; Mayes, Sean; Twycross, Jamie

    2016-01-01

    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. PMID:26859686

  15. From Neuromuscular Activation to End-point Locomotion: An Artificial Neural Network-based Technique for Neural Prostheses

    PubMed Central

    Chang, Chia-Lin; Jin, Zhanpeng; Chang, Hou-Cheng; Cheng, Allen C.

    2009-01-01

    Neuroprostheses, implantable or non-invasive ones, are promising techniques to enable paralyzed individuals with conditions, such as spinal cord injury or spina bifida (SB), to control their limbs voluntarily. Direct cortical control of invasive neuroprosthetic devices and robotic arms have recently become feasible for primates. However, little is known about designing non-invasive, closed-loop neuromuscular control strategies for neural prostheses. Our goal was to investigate if an Artificial Neural Network-based (ANN-based) model for closed-loop controlled neural prostheses could use neuromuscular activation recorded from individuals with impaired spinal cord to predict their end-point gait parameters (such as stride length and step width). We recruited 12 persons with SB (5 females and 7 males) and collected their neuromuscular activation and end-point gait parameters during overground walking. Our results show that the proposed ANN-based technique can achieve a highly accurate prediction (e.g., R-values of 0.92 – 0.97, ANN (tansig+tansig) for single composition of data sets) for altered end-point locomotion. Compared to traditional robust regression, this technique can provide up to 80% more accurate prediction. Our results suggest that more precise control of complex neural prostheses during locomotion can be achieved by engaging neuromuscular activity as intrinsic feedback to generate end-point leg movement. This ANN-based model allows a seamless incorporation of neuromuscular activity, detected from paralyzed individuals, to adaptively predict their altered gait patterns, which can be employed to provide closed-loop feedback information for neural prostheses. PMID:19389678

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

    PubMed Central

    Geng, Haijiang; Li, Zhihui; Li, Jiabing; Lu, Tao; Yan, Fangrong

    2015-01-01

    BACKGROUND 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. RESULTS 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. CONCLUSIONS 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. PMID:26556852

  17. Functional characterization of somatic mutations in cancer using network-based inference of protein activity | Office of Cancer Genomics

    Cancer.gov

    Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible.

  18. Construction of co-expression network based on natural expression variation of xylogenesis-related transcripts in Eucalyptus tereticornis.

    PubMed

    Dharanishanthi, Veeramuthu; Dasgupta, Modhumita Ghosh

    2016-10-01

    Natural genetic variation is randomly distributed and gene expression patterns vary widely in natural populations. These variations are an effect of multifactorial genetic perturbations resulting in different phenotypes. Genome-wide analysis can be used to comprehend the genetic basis governing this naturally occurring developmental variation. Secondary growth is a highly complex trait and systems genetics models are presently being applied to understand the molecular architecture of wood formation. In the present study, the natural variation in expression patterns of 18,987 transcripts expressed in the developing xylem tissues were documented across four phenotypes of Eucalyptus tereticornis with distinct holocellulose/klason lignin content. The differentially expressed genes across all the phenotypes were used to construct co-expression networks and sub-network 2 with 380 nodes and 17,711 edges was determined as the network of relevance, including 30 major cell wall biogenesis related transcripts with 2394 interactions and 10 families of transcription factors with 3360 interactions. EYE [EMBRYO YELLOW] was identified as major hub transcript with 173 degrees which interacted with known cell wall biogenesis genes. K-mean clustering was also performed for differentially expressed transcripts and two clusters discriminated the phenotypes based on their holocellulose/klason lignin content. The cluster based networks were enriched with GOs related to cell wall biogenesis and sugar metabolism. The networks developed in the present study enabled identification of critical regulators and novel transcripts whose expression variation could presumably govern the phenotypic variation in wood properties across E. tereticornis. PMID:27465117

  19. Genetic Testing for ALS

    MedlinePlus

    ... Involved Donate Familial Amyotrophic Lateral Sclerosis (FALS) and Genetic Testing By Deborah Hartzfeld, MS, CGC, Certified Genetic ... guarantee a person will develop symptoms of ALS. Genetic Counseling If there is more than one person ...

  20. Jurisprudence and genetics.

    PubMed

    Kaveny, M C

    1999-03-01

    This section of the Notes in Moral Theology attempts to grapple with the proper relation of law and morality in three emerging issues connected with genetics: cloning, discrimination on the basis of genetic information, and patenting of genetic material.

  1. Network-Based Output Tracking Control for a Class of T-S Fuzzy Systems That Can Not Be Stabilized by Nondelayed Output Feedback Controllers.

    PubMed

    Zhang, Dawei; Han, Qing-Long; Jia, Xinchun

    2015-08-01

    This paper investigates network-based output tracking control for a T-S fuzzy system that can not be stabilized by a nondelayed fuzzy static output feedback controller, but can be stabilized by a delayed fuzzy static output feedback controller. By intentionally introducing a communication network that produces proper network-induced delays in the feedback control loop, a stable and satisfactory tracking control can be ensured for the T-S fuzzy system. Due to the presence of network-induced delays, the fuzzy system and the fuzzy tracking controller operate in an asynchronous way. Taking the asynchronous operation and network-induced delays into consideration, the network-based tracking control system is modeled as an asynchronous T-S fuzzy system with an interval time-varying delay. A new delay-dependent criterion for L2 -gain tracking performance is derived by using the deviation bounds of asynchronous normalized membership functions and a complete Lyapunov-Krasovskii functional. Applying a particle swarm optimization technique with the feasibility of the derived criterion, a novel design algorithm is presented to determine the minimum L2 -gain tracking performance and control gains simultaneously. The effectiveness of the proposed method is illustrated by performing network-based output tracking control of a Duffing-Van der Pol's oscillator. PMID:25222965

  2. Mars rover navigation using pseudolite transceiver arrays: Network-based ranging and extended self-calibration algorithm

    NASA Astrophysics Data System (ADS)

    Matsuoka, Masayoshi

    2005-07-01

    A Self-Calibrating Pseudolite Array (SCPA) is a self-deployable GPS pseudolite-based local-area navigation system that can be used on future robotic and manned planetary explorations. By utilizing bidirectional pseudolite transceivers, the SCPA can provide common global positioning to multiple agents working in a local designated area of a remote planet, including all the benefits of satellite-based carrier-phase differential GPS, such as drift-free, centimeter-level, and three-dimensional positioning, without requiring a satellite constellation above the remote planet. Previous work has demonstrated that changing the relative array geometry by moving a roving transceiver unit enables the SCPA to self-calibrate both the array locations and the rover trajectory to centimeter-level accuracy. This self-calibration capability has overcome the difficulty of autonomous robotic deployment of the pseudolite-based navigation system on remote planets, eliminating the need for accurate a priori position information or precise placement of the array. However, early field trials raised the issue of robustness due to pseudolite signal dropouts caused by multi-path fading, cycle slips, or losing line-of-sight. In order to complete the self-calibration process successfully, the SCPA was required to maintain all the signal locks over the entire calibration maneuver; the lack of necessary ranging measurements due to any signal dropout requires the process to start over. This dissertation solves this robustness issue by two new methods: network-based ranging and an extended self-calibration algorithm. The combination of the two algorithms yields a dual-fault-tolerant system, tolerating at least any two simultaneous dropouts intermittently during the calibration process while still operating in the minimum one-mobile three-stationary transceiver configuration with single-frequency pseudolite signals. The resulting improved robustness has been demonstrated in field trials using the K9

  3. Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals

    NASA Astrophysics Data System (ADS)

    Mekanik, F.; Imteaz, M. A.; Talei, A.

    2016-05-01

    Accurate seasonal rainfall forecasting is an important step in the development of reliable runoff forecast models. The large scale climate modes affecting rainfall in Australia have recently been proven useful in rainfall prediction problems. In this study, adaptive network-based fuzzy inference systems (ANFIS) models are developed for the first time for southeast Australia in order to forecast spring rainfall. The models are applied in east, center and west Victoria as case studies. Large scale climate signals comprising El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Inter-decadal Pacific Ocean (IPO) are selected as rainfall predictors. Eight models are developed based on single climate modes (ENSO, IOD, and IPO) and combined climate modes (ENSO-IPO and ENSO-IOD). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson correlation coefficient (r) and root mean square error in probability (RMSEP) skill score are used to evaluate the performance of the proposed models. The predictions demonstrate that ANFIS models based on individual IOD index perform superior in terms of RMSE, MAE and r to the models based on individual ENSO indices. It is further discovered that IPO is not an effective predictor for the region and the combined ENSO-IOD and ENSO-IPO predictors did not improve the predictions. In order to evaluate the effectiveness of the proposed models a comparison is conducted between ANFIS models and the conventional Artificial Neural Network (ANN), the Predictive Ocean Atmosphere Model for Australia (POAMA) and climatology forecasts. POAMA is the official dynamic model used by the Australian Bureau of Meteorology. The ANFIS predictions certify a superior performance for most of the region compared to ANN and climatology forecasts. POAMA performs better in regards to RMSE and MAE in east and part of central Victoria, however, compared to ANFIS it shows weaker results in west Victoria in terms of prediction errors and RMSEP skill

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

    PubMed

    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. PMID:26989410

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

    PubMed

    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.

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

    PubMed Central

    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. PMID:26989410

  7. The genetics of immunity.

    PubMed

    Lazzaro, Brian P; Schneider, David S

    2014-06-17

    In this commentary, Brian P. Lazzaro and David S. Schneider examine the topic of the Genetics of Immunity as explored in this month's issues of GENETICS and G3: Genes|Genomes|Genetics. These inaugural articles are part of a joint Genetics of Immunity collection (ongoing) in the GSA journals.

  8. Interactive Genetics Tutorial Project.

    ERIC Educational Resources Information Center

    Wisconsin Univ., Madison. Dept. of Curriculum and Instruction.

    The Interactive Genetics Tutorial (IGT) project and the Intelligent Tutoring System for the IGT project named MENDEL supplement genetics instruction in biology courses by providing students with experience in designing, conducting, and evaluating genetics experiments. The MENDEL software is designed to: (1) simulate genetics experiments that…

  9. The Genetics of Immunity

    PubMed Central

    Lazzaro, Brian P.; Schneider, David S.

    2014-01-01

    In this commentary, Brian P. Lazzaro and David S. Schneider examine the topic of the Genetics of Immunity as explored in this month's issues of GENETICS and G3: Genes|Genomes|Genetics. These inaugural articles are part of a joint Genetics of Immunity collection (ongoing) in the GSA journals. PMID:24939182

  10. How Are Genetic Conditions Diagnosed?

    MedlinePlus

    ... Consultation How are genetic conditions diagnosed? How are genetic conditions diagnosed? A doctor may suspect a diagnosis ... and advocacy resources. For more information about diagnosing genetic conditions: Genetics Home Reference provides information about genetic ...

  11. Update: Biochemistry of Genetic Manipulation.

    ERIC Educational Resources Information Center

    Barker, G. R.

    1983-01-01

    Various topics on the biochemistry of genetic manipulation are discussed. These include genetic transformation and DNA; genetic expression; DNA replication, repair, and mutation; technology of genetic manipulation; and applications of genetic manipulation. Other techniques employed are also considered. (JN)

  12. Landscape genetics of high mountain frog metapopulations

    USGS Publications Warehouse

    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

  13. Genetic engineering, medicine and medical genetics.

    PubMed

    Motulsky, A G

    1984-01-01

    The impact of DNA technology in the near future will be on the manufacture of biologic agents and reagents that will lead to improved therapy and diagnosis. The use of DNA technology for prenatal and preclinical diagnosis in genetic diseases is likely to affect management of genetic diseases considerably. New and old questions regarding selective abortion and the psychosocial impact of early diagnosis of late appearing diseases and of genetic susceptibilities are being raised. Somatic therapy with isolated genes to treat disease has not been achieved. True germinal genetic engineering is far off for humans but may find applications in animal agriculture.

  14. The Sensitivity of Genetic Connectivity Measures to Unsampled and Under-Sampled Sites

    PubMed Central

    Koen, Erin L.; Bowman, Jeff; Garroway, Colin J.; Wilson, Paul J.

    2013-01-01

    Landscape genetic analyses assess the influence of landscape structure on genetic differentiation. It is rarely possible to collect genetic samples from all individuals on the landscape and thus it is important to assess the sensitivity of landscape genetic analyses to the effects of unsampled and under-sampled sites. Network-based measures of genetic distance, such as conditional genetic distance (cGD), might be particularly sensitive to sampling intensity because pairwise estimates are relative to the entire network. We addressed this question by subsampling microsatellite data from two empirical datasets. We found that pairwise estimates of cGD were sensitive to both unsampled and under-sampled sites, and FST, Dest, and deucl were more sensitive to under-sampled than unsampled sites. We found that the rank order of cGD was also sensitive to unsampled and under-sampled sites, but not enough to affect the outcome of Mantel tests for isolation by distance. We simulated isolation by resistance and found that although cGD estimates were sensitive to unsampled sites, by increasing the number of sites sampled the accuracy of conclusions drawn from landscape genetic analyses increased, a feature that is not possible with pairwise estimates of genetic differentiation such as FST, Dest, and deucl. We suggest that users of cGD assess the sensitivity of this measure by subsampling within their own network and use caution when making extrapolations beyond their sampled network. PMID:23409155

  15. Basic genetics for dermatologists.

    PubMed

    Kumaran, Muthu Sendhil; De, Dipankar

    2013-01-01

    During the past few decades, advances in the field of molecular genetics have enriched us in understanding the pathogenesis of diseases, their identification, and appropriate therapeutic interventions. In the last 20 years, genetic basis of more than 350 monogenic skin diseases have been elucidated and is counting. The widespread use of molecular genetics as a tool in diagnosis is not practiced routinely due to genetic heterogenicity, limited access and low sensitivity. In this review, we have presented the very basics of genetics so as to enable dermatologists to have working understanding of medical genetics.

  16. Physicians' Knowledge of Genetics and Genetic Tests.

    ERIC Educational Resources Information Center

    Hofman, Karen J.; And Others

    1993-01-01

    A survey of 1,140 primary care physicians and psychiatrists who graduated from medical school from 1950 through 1985 indicated that knowledge of genetics is increasing, especially among more recent graduates, but deficiencies remain. Need is seen for greater emphasis on genetics to reduce chance of physician error as more tests become available.…

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

    PubMed

    Oh, Min; Ahn, Jaegyoon; Yoon, Youngmi

    2014-01-01

    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.

  18. Genetics Home Reference: porphyria

    MedlinePlus

    ... of iron in the liver, alcohol consumption, smoking, hepatitis C or HIV infection, or certain hormones. Mutations in ... Diagnostic Tests Drug Therapy Surgery and Rehabilitation Genetic Counseling Palliative Care Related Information How are genetic conditions ...

  19. Genetic Brain Disorders

    MedlinePlus

    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 ...

  20. Frontotemporal Dementia: Genetics

    MedlinePlus

    ... Calendar of Events Fundraising Events Conferences Press Releases Genetics of FTD After receiving a diagnosis of FTD ... that recent advances in science have brought the genetics of FTD into much better focus. In 2012, ...

  1. Genetics of Hearing Loss

    MedlinePlus

    ... in Latin America Information For... Media Policy Makers Genetics of Hearing Loss Language: English Español (Spanish) Recommend ... of hearing loss in babies is due to genetic causes. There are also a number of things ...

  2. Genetic Disease Foundation

    MedlinePlus

    ... Newly Diagnosed Patients There are over 6,000 genetic disorders that can be passed down through the ... mission to help prevent, manage and treat inherited genetic diseases. View our latest News Brief here . You ...

  3. Genetics Home Reference

    MedlinePlus

    Skip Navigation Bar Home Current Issue Past Issues Genetics Home Reference Past Issues / Spring 2007 Table of ... of this page please turn Javascript on. The Genetics Home Reference (GHR) Web site — ghr.nlm.nih. ...

  4. Genetics Home Reference: adermatoglyphia

    MedlinePlus

    Skip to main content Your Guide to Understanding Genetic Conditions Enable Javascript for addthis links to activate. ... Conditions Genes Chromosomes & mtDNA Resources Help Me Understand Genetics Home Health Conditions adermatoglyphia adermatoglyphia Enable Javascript to ...

  5. Genetics Home Reference: retinoblastoma

    MedlinePlus

    ... Arias VE. Trilateral retinoblastoma. Pediatr Blood Cancer. 2007 Mar;48(3):306-10. Review. Citation on PubMed ... for genetic counseling. Am J Hum Genet. 1998 Mar;62(3):610-9. Citation on PubMed or ...

  6. Behavioral genetics and taste

    PubMed Central

    Boughter, John D; Bachmanov, Alexander A

    2007-01-01

    This review focuses on behavioral genetic studies of sweet, umami, bitter and salt taste responses in mammals. Studies involving mouse inbred strain comparisons and genetic analyses, and their impact on elucidation of taste receptors and transduction mechanisms are discussed. Finally, the effect of genetic variation in taste responsiveness on complex traits such as drug intake is considered. Recent advances in development of genomic resources make behavioral genetics a powerful approach for understanding mechanisms of taste. PMID:17903279

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

    PubMed

    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.

  8. 100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors.

    PubMed

    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 approximately 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.

  9. Genetics by the Numbers

    MedlinePlus

    ... Life Science > Genetics by the Numbers Inside Life Science View All Articles | Inside Life Science Home Page Genetics by the Numbers By Chelsea ... Genetics NIH's National DNA Day This Inside Life Science article also appears on LiveScience . Learn about related ...

  10. Statistics for Learning Genetics

    ERIC Educational Resources Information Center

    Charles, Abigail Sheena

    2012-01-01

    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,…

  11. The Genetics of Personality.

    ERIC Educational Resources Information Center

    Holden, Constance

    1987-01-01

    Reports on the findings of several studies into the genetic similarities of twins. Focuses on the relationships between personality and behavioral genetics and argues that genetic similarity seems to be a better predictor than environmental factors. Discusses psychopathology, cognitive abilities, and personality. (TW)

  12. Phenylketonuria Genetic Screening Simulation

    ERIC Educational Resources Information Center

    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…

  13. Feline Genetics: Clinical Applications and Genetic Testing

    PubMed Central

    Lyons, Leslie A.

    2010-01-01

    DNA testing for domestic cat diseases and appearance traits is a rapidly growing asset for veterinary medicine. Approximately thirty-three genes contain fifty 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 using 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. PMID:21147473

  14. Feline genetics: clinical applications and genetic testing.

    PubMed

    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.

  15. How Is Genetic Testing Done?

    MedlinePlus

    ... Testing How is genetic testing done? How is genetic testing done? Once a person decides to proceed ... is called informed consent . For more information about genetic testing procedures: The Genetic Science Learning Center at ...

  16. Prenatal Genetic Counseling (For Parents)

    MedlinePlus

    ... 5 Things to Know About Zika & Pregnancy Prenatal Genetic Counseling KidsHealth > For Parents > Prenatal Genetic Counseling Print ... how can they help your family? What Is Genetic Counseling? Genetic counseling is the process of: evaluating ...

  17. Genetic technology: Promises and problems

    NASA Technical Reports Server (NTRS)

    Frankel, M. S.

    1975-01-01

    Issues concerning the use of genetic technology are discussed. Some areas discussed include treating genetic disease, prenatal diagnosis and selective abortion, screening for genetic disease, and genetic counseling. Policy issues stemming from these capabilities are considered.

  18. Genetic selection and conservation of genetic diversity*.

    PubMed

    Blackburn, H D

    2012-08-01

    For 100s of years, livestock producers have employed various types of selection to alter livestock populations. Current selection strategies are little different, except our technologies for selection have become more powerful. Genetic resources at the breed level have been in and out of favour over time. These resources are the raw materials used to manipulate populations, and therefore, they are critical to the past and future success of the livestock sector. With increasing ability to rapidly change genetic composition of livestock populations, the conservation of these genetic resources becomes more critical. Globally, awareness of the need to steward genetic resources has increased. A growing number of countries have embarked on large scale conservation efforts by using in situ, ex situ (gene banking), or both approaches. Gene banking efforts have substantially increased and data suggest that gene banks are successfully capturing genetic diversity for research or industry use. It is also noteworthy that both industry and the research community are utilizing gene bank holdings. As pressures grow to meet consumer demands and potential changes in production systems, the linkage between selection goals and genetic conservation will increase as a mechanism to facilitate continued livestock sector development. PMID:22827378

  19. Genetic selection and conservation of genetic diversity*.

    PubMed

    Blackburn, H D

    2012-08-01

    For 100s of years, livestock producers have employed various types of selection to alter livestock populations. Current selection strategies are little different, except our technologies for selection have become more powerful. Genetic resources at the breed level have been in and out of favour over time. These resources are the raw materials used to manipulate populations, and therefore, they are critical to the past and future success of the livestock sector. With increasing ability to rapidly change genetic composition of livestock populations, the conservation of these genetic resources becomes more critical. Globally, awareness of the need to steward genetic resources has increased. A growing number of countries have embarked on large scale conservation efforts by using in situ, ex situ (gene banking), or both approaches. Gene banking efforts have substantially increased and data suggest that gene banks are successfully capturing genetic diversity for research or industry use. It is also noteworthy that both industry and the research community are utilizing gene bank holdings. As pressures grow to meet consumer demands and potential changes in production systems, the linkage between selection goals and genetic conservation will increase as a mechanism to facilitate continued livestock sector development.

  20. Genetics of the Connectome

    PubMed Central

    Thompson, Paul M.; Ge, Tian; Glahn, David C.; Jahanshad, Neda; Nichols, Thomas E.

    2014-01-01

    Connectome genetics attempts to discover how genetic factors affect brain connectivity. Here we review a variety of genetic analysis methods – such as genome-wide association studies (GWAS), linkage and candidate gene studies – that have been fruitfully adapted to imaging data to implicate specific variants in the genome for brain-related traits. We then review studies of that emphasized the genetic influences on brain connectivity. Some of these perform genetic analysis of brain integrity and connectivity using diffusion MRI, and others have mapped genetic effects on functional networks using resting state functional MRI. Connectome-wide genome-wide scans have also been conducted, and we review the multivariate methods required to handle the extremely high dimension of genomic and the network data. We also review some consortium efforts, such as ENIGMA, that offer the power to detect robust common genetic associations using phenotypic harmonization procedures and meta-analysis. Current work on connectome genetics is advancing on many fronts and promises to shed light on how disease risk genes affect the brain. It is already discovering new genetic loci and even entire genetic networks that affect brain organization and connectivity. PMID:23707675

  1. Wavelets meet genetic imaging

    NASA Astrophysics Data System (ADS)

    Wang, Yu-Ping

    2005-08-01

    Genetic image analysis is an interdisciplinary area, which combines microscope image processing techniques with the use of biochemical probes for the detection of genetic aberrations responsible for cancers and genetic diseases. Recent years have witnessed parallel and significant progress in both image processing and genetics. On one hand, revolutionary multiscale wavelet techniques have been developed in signal processing and applied mathematics in the last decade, providing sophisticated tools for genetic image analysis. On the other hand, reaping the fruit of genome sequencing, high resolution genetic probes have been developed to facilitate accurate detection of subtle and cryptic genetic aberrations. In the meantime, however, they bring about computational challenges for image analysis. In this paper, we review the fruitful interaction between wavelets and genetic imaging. We show how wavelets offer a perfect tool to address a variety of chromosome image analysis problems. In fact, the same word "subband" has been used in the nomenclature of cytogenetics to describe the multiresolution banding structure of the chromosome, even before its appearance in the wavelet literature. The application of wavelets to chromosome analysis holds great promise in addressing several computational challenges in genetics. A variety of real world examples such as the chromosome image enhancement, compression, registration and classification will be demonstrated. These examples are drawn from fluorescence in situ hybridization (FISH) and microarray (gene chip) imaging experiments, which indicate the impact of wavelets on the diagnosis, treatments and prognosis of cancers and genetic diseases.

  2. Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis).

    PubMed

    VanderWaal, Kimberly L; Atwill, Edward R; Isbell, Lynne A; McCowan, Brenda

    2014-03-01

    Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home-range overlap among individuals. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied 'bottleneck' positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super-spreaders or transmission bottlenecks in the population. These results emphasize the importance of association patterns in

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

    NASA Astrophysics Data System (ADS)

    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

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

    PubMed

    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. PMID:27128036

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

    PubMed

    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.

  6. Performance of the MM/GBSA scoring using a binding site hydrogen bond network-based frame selection: the protein kinase case.

    PubMed

    Adasme-Carreño, Francisco; Muñoz-Gutierrez, Camila; Caballero, Julio; Alzate-Morales, Jans H

    2014-07-21

    A conformational selection method, based on hydrogen bond (Hbond) network analysis, has been designed in order to rationalize the configurations sampled using molecular dynamics (MD), which are commonly used in the estimation of the relative binding free energy of ligands to macromolecules through the MM/GBSA or MM/PBSA method. This approach makes use of protein-ligand complexes obtained from X-ray crystallographic data, as well as from molecular docking calculations. The combination of several computational approaches, like long MD simulations on protein-ligand complexes, Hbond network-based selection by scripting techniques and finally MM/GBSA, provides better statistical correlations against experimental binding data than previous similar reported studies. This approach has been successfully applied in the ranking of several protein kinase inhibitors (CDK2, Aurora A and p38), which present both diverse and related chemical structures. PMID:24901037

  7. Integrative omics reveals MYCN as a global suppressor of cellular signalling and enables network-based therapeutic target discovery in neuroblastoma

    PubMed Central

    Fey, Dirk; Iljin, Kristiina; Mehta, Jai Prakash; Killick, Kate; Whilde, Jenny; Turriziani, Benedetta; Haapa-Paananen, Saija; Fey, Vidal; Fischer, Matthias; Westermann, Frank; Henrich, Kai-Oliver; Bannert, Steffen; Higgins, Desmond G.; Kolch, Walter

    2015-01-01

    Despite intensive study, many mysteries remain about the MYCN oncogene's functions. Here we focus on MYCN's role in neuroblastoma, the most common extracranial childhood cancer. MYCN gene amplification occurs in 20% of cases, but other recurrent somatic mutations are rare. This scarcity of tractable targets has hampered efforts to develop new therapeutic options. We employed a multi-level omics approach to examine MYCN functioning and identify novel therapeutic targets for this largely un-druggable oncogene. We used systems medicine based computational network reconstruction and analysis to integrate a range of omic techniques: sequencing-based transcriptomics, genome-wide chromatin immunoprecipitation, siRNA screening and interaction proteomics, revealing that MYCN controls highly connected networks, with MYCN primarily supressing the activity of network components. MYCN's oncogenic functions are likely independent of its classical heterodimerisation partner, MAX. In particular, MYCN controls its own protein interaction network by transcriptionally regulating its binding partners. Our network-based approach identified vulnerable therapeutically targetable nodes that function as critical regulators or effectors of MYCN in neuroblastoma. These were validated by siRNA knockdown screens, functional studies and patient data. We identified β-estradiol and MAPK/ERK as having functional cross-talk with MYCN and being novel targetable vulnerabilities of MYCN-amplified neuroblastoma. These results reveal surprising differences between the functioning of endogenous, overexpressed and amplified MYCN, and rationalise how different MYCN dosages can orchestrate cell fate decisions and cancerous outcomes. Importantly, this work describes a systems-level approach to systematically uncovering network based vulnerabilities and therapeutic targets for multifactorial diseases by integrating disparate omic data types. PMID:26673823

  8. From integrative genomics to systems genetics in the rat to link genotypes to phenotypes

    PubMed Central

    Moreno-Moral, Aida

    2016-01-01

    ABSTRACT Complementary to traditional gene mapping approaches used to identify the hereditary components of complex diseases, integrative genomics and systems genetics have emerged as powerful strategies to decipher the key genetic drivers of molecular pathways that underlie disease. Broadly speaking, integrative genomics aims to link cellular-level traits (such as mRNA expression) to the genome to identify their genetic determinants. With the characterization of several cellular-level traits within the same system, the integrative genomics approach evolved into a more comprehensive study design, called systems genetics, which aims to unravel the complex biological networks and pathways involved in disease, and in turn map their genetic control points. The first fully integrated systems genetics study was carried out in rats, and the results, which revealed conserved trans-acting genetic regulation of a pro-inflammatory network relevant to type 1 diabetes, were translated to humans. Many studies using different organisms subsequently stemmed from this example. The aim of this Review is to describe the most recent advances in the fields of integrative genomics and systems genetics applied in the rat, with a focus on studies of complex diseases ranging from inflammatory to cardiometabolic disorders. We aim to provide the genetics community with a comprehensive insight into how the systems genetics approach came to life, starting from the first integrative genomics strategies [such as expression quantitative trait loci (eQTLs) mapping] and concluding with the most sophisticated gene network-based analyses in multiple systems and disease states. Although not limited to studies that have been directly translated to humans, we will focus particularly on the successful investigations in the rat that have led to primary discoveries of genes and pathways relevant to human disease. PMID:27736746

  9. Genetic Variation and Atherosclerosis

    PubMed Central

    Biros, Erik; Karan, Mirko; Golledge, Jonathan

    2008-01-01

    A family history of atherosclerosis is independently associated with an increased incidence of cardiovascular events. The genetic factors underlying the importance of inheritance in atherosclerosis are starting to be understood. Genetic variation, such as mutations or common polymorphisms has been shown to be involved in modulation of a range of risk factors, such as plasma lipoprotein levels, inflammation and vascular calcification. This review presents examples of present studies of the role of genetic polymorphism in atherosclerosis. PMID:19424482

  10. Caging and Uncaging Genetics

    PubMed Central

    Little, Tom J.; Colegrave, Nick

    2016-01-01

    It is important for biology to understand if observations made in highly reductionist laboratory settings generalise to harsh and noisy natural environments in which genetic variation is sorted to produce adaptation. But what do we learn by studying, in the laboratory, a genetically diverse population that mirrors the wild? What is the best design for studying genetic variation? When should we consider it at all? The right experimental approach depends on what you want to know. PMID:27458971

  11. Genetic toxicology: web resources.

    PubMed

    Young, Robert R

    2002-04-25

    Genetic toxicology is the scientific discipline dealing with the effects of chemical, physical and biological agents on the heredity of living organisms. The Internet offers a wide range of online digital resources for the field of Genetic Toxicology. The history of genetic toxicology and electronic data collections are reviewed. Web-based resources at US National Library of Medicine (NLM), including MEDLINE, PUBMED, Gateway, Entrez, and TOXNET, are discussed. Search strategies and Medical Subject Headings (MeSH) are reviewed in the context of genetic toxicology. The TOXNET group of databases are discussed with emphasis on those databases with genetic toxicology content including GENE-TOX, TOXLINE, Hazardous Substances Data Bank, Integrated Risk Information System, and Chemical Carcinogenesis Research Information System. Location of chemical information including chemical structure and linkage to health and regulatory information using CHEMIDPLUS at NLM and other databases is reviewed. Various government agencies have active genetic toxicology research programs or use genetic toxicology data to assist fulfilling the agency's mission. Online resources at the US Food and Drug Administration (FDA), the US Environmental Protection Agency (EPA), the National Institutes of Environmental Health Sciences, and the National Toxicology Program (NTP) are outlined. Much of the genetic toxicology for pharmaceuticals, industrial chemicals and pesticides that is performed in the world is regulatory-driven. Regulatory web resources are presented for the laws mandating testing, guidelines on study design, Good Laboratory Practice (GLP) regulations, and requirements for electronic data collection and reporting. The Internet provides a range of other supporting resources to the field of genetic toxicology. The web links for key professional societies and journals in genetic toxicology are listed. Distance education, educational media resources, and job placement services are also

  12. Genetic regulatory network motifs constrain adaptation through curvature in the landscape of mutational (co)variance.

    PubMed

    Hether, Tyler D; Hohenlohe, Paul A

    2014-04-01

    Systems biology is accumulating a wealth of understanding about the structure of genetic regulatory networks, leading to a more complete picture of the complex genotype-phenotype relationship. However, models of multivariate phenotypic evolution based on quantitative genetics have largely not incorporated a network-based view of genetic variation. Here we model a set of two-node, two-phenotype genetic network motifs, covering a full range of regulatory interactions. We find that network interactions result in different patterns of mutational (co)variance at the phenotypic level (the M-matrix), not only across network motifs but also across phenotypic space within single motifs. This effect is due almost entirely to mutational input of additive genetic (co)variance. Variation in M has the effect of stretching and bending phenotypic space with respect to evolvability, analogous to the curvature of space-time under general relativity, and similar mathematical tools may apply in each case. We explored the consequences of curvature in mutational variation by simulating adaptation under divergent selection with gene flow. Both standing genetic variation (the G-matrix) and rate of adaptation are constrained by M, so that G and adaptive trajectories are curved across phenotypic space. Under weak selection the phenotypic mean at migration-selection balance also depends on M. PMID:24219635

  13. Genetics of Sarcoidosis.

    PubMed

    Fingerlin, Tasha E; Hamzeh, Nabeel; Maier, Lisa A

    2015-12-01

    Sarcoidosis is a disease with highly variable presentation and progression; although it is hypothesized that disease phenotype is related to genetic variation, how much of this variability is driven by genetic factors is not known. The HLA region is the most strongly and consistently associated genetic risk factor for sarcoidosis, supporting the notion that sarcoidosis is an exposure-mediated immunologic disease. Most of the genetic etiology of sarcoidosis remains unknown in terms of the specific variants that increase risk in various populations, their biologic functions, and how they interact with environmental exposures.

  14. Engineering genetic injustice.

    PubMed

    Wenz, Peter

    2005-02-01

    In their jointly written book, From Chance to Choice: Genetics and Justice, Allen Buchanan, Dan Brock, Norman Daniels and Daniel Wikler defend 'the development and deployment of genetic intervention technologies..', including genetic enhancements, against charges that they exacerbate injustice. The present paper examines some of their arguments. The first section shows that the authors confuse real societies with just societies. The second shows that without this confusion, their arguments reveal the enormous justice-impairing potential of deploying genetic enhancements in such societies as the United States.

  15. Genetics, society, and decisions

    SciTech Connect

    Kowles, R.V.

    1985-01-01

    This book provides a conceptual understanding of the biology of genes and also gives current events and controversies in the field. Basic transmission genetics, molecular genetics, and population genetics are covered, with additional discussions relating to such topics as agriculture, aging, forensic science, genetic counseling, gene splicing, and recombinant DNA. Low level radiation and its effects, drugs and heredity, IQ, heredity and racial variation, and creationism versus evolution are also described. ''Billboard'' style diagrams visually explain important concepts. Boldfaced key terms are defined within the text and in a comprehensive glossary. Selected readings, discussion questions and problems, and excellent chapter summaries further aid study.

  16. Judaism, genetic screening and genetic therapy.

    PubMed

    Rosner, F

    1998-01-01

    Genetic screening, gene therapy and other applications of genetic engineering are permissible in Judaism when used for the treatment, cure, or prevention of disease. Such genetic manipulation is not considered to be a violation of God's natural law, but a legitimate implementation of the biblical mandate to heal. If Tay-Sachs disease, diabetes, hemophilia, cystic fibrosis, Huntington's disease or other genetic diseases can be cured or prevented by "gene surgery," then it is certainly permitted in Jewish law. Genetic premarital screening is encouraged in Judaism for the purpose of discouraging at-risk marriages for a fatal illness such as Tay-Sachs disease. Neonatal screening for treatable conditions such as phenylketonuria is certainly desirable and perhaps required in Jewish law. Preimplantation screening and the implantation of only "healthy" zygotes into the mother's womb to prevent the birth of an affected child are probably sanctioned in Jewish law. Whether or not these assisted reproduction techniques may be used to choose the sex of one's offspring, to prevent the birth of a child with a sex-linked disease such as hemophilia, has not yet been ruled on by modern rabbinic decisions. Prenatal screening with the specific intent of aborting an affected fetus is not allowed according to most rabbinic authorities, although a minority view permits it "for great need." Not to have children if both parents are carriers of genetic diseases such as Tay-Sachs is not a Jewish option. Preimplantation screening is preferable. All screening test results must remain confidential. Judaism does not permit the alteration or manipulation of physical traits and characteristics such as height, eye and hair color, facial features and the like, when such change provides no useful benefit to mankind. On the other hand, it is permissible to clone organisms and microorganisms to facilitate the production of insulin, growth hormone, and other agents intended to benefit mankind and to

  17. Ethical issues in genetics.

    PubMed

    Shannon, T A

    1999-03-01

    The first section of the Notes on Moral Theology reviews ethical issues in genetics through the lenses of privacy-confidentiality; risk-benefit analysis in relation to prenatal diagnosis and gene therapy; and freedom-determinism/human dignity in the context of cloning. The author provides an overview of developments in genetics and highlights thematic issues common to these developments.

  18. Genetics of aging bone.

    PubMed

    Adams, Douglas J; Rowe, David W; Ackert-Bicknell, Cheryl L

    2016-08-01

    With aging, the skeleton experiences a number of changes, which include reductions in mass and changes in matrix composition, leading to fragility and ultimately an increase of fracture risk. A number of aspects of bone physiology are controlled by genetic factors, including peak bone mass, bone shape, and composition; however, forward genetic studies in humans have largely concentrated on clinically available measures such as bone mineral density (BMD). Forward genetic studies in rodents have also heavily focused on BMD; however, investigations of direct measures of bone strength, size, and shape have also been conducted. Overwhelmingly, these studies of the genetics of bone strength have identified loci that modulate strength via influencing bone size, and may not impact the matrix material properties of bone. Many of the rodent forward genetic studies lacked sufficient mapping resolution for candidate gene identification; however, newer studies using genetic mapping populations such as Advanced Intercrosses and the Collaborative Cross appear to have overcome this issue and show promise for future studies. The majority of the genetic mapping studies conducted to date have focused on younger animals and thus an understanding of the genetic control of age-related bone loss represents a key gap in knowledge.

  19. PopGenetics.

    ERIC Educational Resources Information Center

    Johnson, David A.

    1995-01-01

    Describes new software called "PopGenetics" that is useful for conducting hands-on laboratory activities that illustrate population genetics. Users investigate the effects of selection and random drift on changes in allelic frequencies, the effect of mutation, and interactions among these three parameters. Designed for use on Macintosh computers.…

  20. Genetics and Developmental Psychology

    ERIC Educational Resources Information Center

    Plomin, Robert

    2004-01-01

    One of the major changes in developmental psychology during the past 50 years has been the acceptance of the important role of nature (genetics) as well as nurture (environment). Past research consisting of twin and adoption studies has shown that genetic influence is substantial for most domains of developmental psychology. Present research…

  1. Quo Vadis, Medical Genetics?

    NASA Astrophysics Data System (ADS)

    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.

  2. Genetics in the courts

    SciTech Connect

    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

  3. Cryptic Genetic Variation in Evolutionary Developmental Genetics

    PubMed Central

    Paaby, Annalise B.; Gibson, Greg

    2016-01-01

    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. PMID:27304973

  4. Genetic Influences on Learning Disabilties I: Clinical Genetics.

    ERIC Educational Resources Information Center

    Smith, Shelley D.; Pennington, Bruce F.

    1983-01-01

    A discussion of basic genetic principles is followed by a review of selected genetic syndromes involving learning disabilites (such as Noonan Syndrome, Neurofibromatosis, Pheuylketonuria, and cleft lip and palate). Guidelines for securing a genetic evaluation are given. (CL)

  5. Evolutionary behavioral genetics

    PubMed Central

    Zietsch, Brendan P.; de Candia, Teresa R; Keller, Matthew C.

    2014-01-01

    We describe the scientific enterprise at the intersection of evolutionary psychology and behavioral genetics—a field that could be termed Evolutionary Behavioral Genetics—and how modern genetic data is revolutionizing our ability to test questions in this field. We first explain how genetically informative data and designs can be used to investigate questions about the evolution of human behavior, and describe some of the findings arising from these approaches. Second, we explain how evolutionary theory can be applied to the investigation of behavioral genetic variation. We give examples of how new data and methods provide insight into the genetic architecture of behavioral variation and what this tells us about the evolutionary processes that acted on the underlying causal genetic variants. PMID:25587556

  6. [Genetics of endogenous psychoses].

    PubMed

    Zerbin-Rüdin, E

    1979-01-01

    As the endogeneous psychoses do not show a Mendelian mode of inheritance, empirical risk figures have to be calculated. They are heterogeneous and nurture as well as nature have a share in the manifestation of illness. The genetic basis of the schizophrenias is demonstrated by twin and adoption studies. The concordance rate in monozygotic twins is four times the rate in dizygotic twins. Schizophrenia is to be found in the biological families of schizophrenic adoptees but not in the adoptive families. However, despite their genetic identity, monozygotic twins do not show 100% concordance but 60% only. Nongenetic factors must be considered. Obviously they are nonspecific and vary between individuals. The same principles apply to the affective psychoses. At present research is most interested in the problem of heterogeneity. Do pure depressive and manic-depressive disease form one genetic entity, or two different ones, or have they in common part of their genetic basis? Some remarks on genetic counselling are made.

  7. Genetic variation and its maintenance

    SciTech Connect

    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, Disease Prevention and Treatment

    MedlinePlus

    ... for the genetic terms used on this page Genetics, Disease Prevention and Treatment Overview How can learning ... gov] Top of page How can knowing about genetics help treat disease? Every year, more than two ...

  9. National Society of Genetic Counselors

    MedlinePlus

    ... us: About NSGC About NSGC Join NSGC About Genetic Counselors NSGC in the News NSGC Leadership In ... Opportunities AEC Sponsors Healthcare Providers How can a genetic counselor help my practice? Genetic counselors can help ...

  10. Genetics of population isolates.

    PubMed

    Arcos-Burgos, M; Muenke, M

    2002-04-01

    Genetic isolates, as shown empirically by the Finnish, Old Order Amish, Hutterites, Sardinian and Jewish communities among others, represent a most important and powerful tool in genetically mapping inherited disorders. The main features associated with that genetic power are the existence of multigenerational pedigrees which are mostly descended from a small number of founders a short number of generations ago, environmental and phenotypic homogeneity, restricted geographical distribution, the presence of exhaustive and detailed records correlating individuals in very well ascertained pedigrees, and inbreeding as a norm. On the other hand, the presence of a multifounder effect or admixture among divergent populations in the founder time (e.g. the Finnish and the Paisa community from Colombia) will theoretically result in increased linkage disequilibrium among adjacent loci. The present review evaluates the historical context and features of some genetic isolates with emphasis on the basic population genetic concepts of inbreeding and genetic drift, and also the state-of-the-art in mapping traits, both Mendelian and complex, on genetic isolates. PMID:12030885

  11. Genetic autonomic disorders.

    PubMed

    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. PMID:23465768

  12. Genetics of Obesity.

    PubMed

    Srivastava, Apurva; Srivastava, Neena; Mittal, Balraj

    2016-10-01

    Numerous classical genetic studies have proved that genes are contributory factors for obesity. Genes are directly responsible for obesity associated disorders such as Bardet-Biedl and Prader-Willi syndromes. However, both genes as well as environment are associated with obesity in the general population. Genetic epidemiological approaches, particularly genome-wide association studies, have unraveled many genes which play important roles in human obesity. Elucidation of their biological functions can be very useful for understanding pathobiology of obesity. In the near future, further exploration of obesity genetics may help to develop useful diagnostic and predictive tests for obesity treatment. PMID:27605733

  13. Welcome to Neurology: Genetics.

    PubMed

    Pulst, Stefan M

    2015-06-01

    The powers of human genetics and genetic technologies have transformed the complexities of neurology and neuroscience at the basic, translational, and now also the clinical level. We have left an era of black and white views of causative genetic variation and are entering a period of more than 50 shades of grey, fascinated with DNA variants that increase or decrease risk, epigenetic modification, and an unexpectedly large number of variants of unknown or potentially pathogenic significance. Loss-of-function alleles and even complete human gene knockouts for certain genes appear to be compatible with a normal phenotype. PMID:27066541

  14. Genetic Stroke Syndromes

    PubMed Central

    Barrett, Kevin M.; Meschia, James F.

    2014-01-01

    Purpose of Review: This review describes the clinical and radiographic features, genetic determinants, and treatment options for the most well-characterized monogenic disorders associated with stroke. Recent Findings: Stroke is a phenotype of many clinically important inherited disorders. Recognition of the clinical manifestations of genetic disorders associated with stroke is important for accurate diagnosis and prognosis. Genetic studies have led to the discovery of specific mutations associated with the clinical phenotypes of many inherited stroke syndromes. Summary: Several inherited causes of stroke have established and effective therapies, further underscoring the importance of timely diagnosis. PMID:24699489

  15. Genetics of Male Infertility.

    PubMed

    Neto, Filipe Tenorio Lira; Bach, Phil Vu; Najari, Bobby Baback; Li, Philip Shihua; Goldstein, Marc

    2016-10-01

    While 7 % of the men are infertile, currently, a genetic etiology is identified in less than 25 % of those men, and 30 % of the infertile men lack a definitive diagnosis, falling in the "idiopathic infertility" category. Advances in genetics and epigenetics have led to several proposed mechanisms for male infertility. These advances may result in new diagnostic tools, treatment approaches, and better counseling with regard to treatment options and prognosis. In this review, we focus on clinical aspects of male infertility and the role of genetics in elucidating etiologies and the potential of treatments. PMID:27502429

  16. Ethics and genetic testing.

    PubMed

    Grady, C

    1999-01-01

    The tremendous growth in knowledge about genes and genetic technologies will eventually enable us to know individual genotypes and susceptibilities to disease, perhaps even before birth. Each new genetic test developed raises serious issues for individuals and society on the circumstances under which genetic information should be sought and the uses that should be made of such information. Ethical reflection and analysis will help us to prepare for the responsible use of information about genotypes so that individuals, both now and in the future, are benefited and not harmed, so that justice is served, and so that confidentiality and privacy, and respect for the autonomy, dignity, and differences of each individual, are preserved.

  17. Genetically Engineered Cyanobacteria

    NASA Technical Reports Server (NTRS)

    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.

  18. Genetics Home Reference: Turner syndrome

    MedlinePlus

    ... pregnancies that do not survive to term (miscarriages and stillbirths). Related Information What information about a genetic condition can statistics provide? Why are some genetic conditions more common ...

  19. Genetic variants and their interactions in disease risk prediction – machine learning and network perspectives

    PubMed Central

    2013-01-01

    A central challenge in systems biology and medical genetics is to understand how interactions among genetic loci contribute to complex phenotypic traits and human diseases. While most studies have so far relied on statistical modeling and association testing procedures, machine learning and predictive modeling approaches are increasingly being applied to mining genotype-phenotype relationships, also among those associations that do not necessarily meet statistical significance at the level of individual variants, yet still contributing to the combined predictive power at the level of variant panels. Network-based analysis of genetic variants and their interaction partners is another emerging trend by which to explore how sub-network level features contribute to complex disease processes and related phenotypes. In this review, we describe the basic concepts and algorithms behind machine learning-based genetic feature selection approaches, their potential benefits and limitations in genome-wide setting, and how physical or genetic interaction networks could be used as a priori information for providing improved predictive power and mechanistic insights into the disease networks. These developments are geared toward explaining a part of the missing heritability, and when combined with individual genomic profiling, such systems medicine approaches may also provide a principled means for tailoring personalized treatment strategies in the future. PMID:23448398

  20. Genetics and Genetic Testing in Pancreatic Cancer.

    PubMed

    Whitcomb, David C; Shelton, Celeste A; Brand, Randall E

    2015-10-01

    Genetic testing of germline DNA is used in patients suspected of being at risk of pancreatic ductal adenocarcinoma (PDAC) to better define the individual's risk and to determine the mechanism of risk. A high genetic risk increases the pretest probability that a biomarker of early cancer is a true positive and warrants further investigation. The highest PDAC risk is generally associated with a hereditary predisposition. However, the majority of PDAC results from complex, progressive gene-environment interactions that currently fall outside the traditional risk models. Over many years, the combination of inflammation, exposure to DNA-damaging toxins, and failed DNA repair promote the accumulation of somatic mutations in pancreatic cells; PDAC risk is further increased by already present oncogenic germline mutations. Predictive models and new technologies are needed to classify patients into more accurate and mechanistic PDAC risk categories that can be linked to improved surveillance and preventative strategies.

  1. Genetic engineering of rotaviruses by reverse genetics.

    PubMed

    Komoto, Satoshi; Taniguchi, Koki

    2013-07-01

    The rotavirus genome is composed of 11 gene segments of dsRNA. A recent breakthrough in the field of rotaviruses is the development of a reverse genetics system for generating recombinant rotaviruses possessing a gene segment derived from cloned cDNA. Although this approach is a helper virus-driven system that is technically limited and gives low levels of recombinant viruses, it allows alteration of the rotavirus genome, thus contributing to our understanding of these medically important viruses. So far, this approach has successfully been applied to three of the 11 viral segments in our laboratory and others, and the efficiency of recovery of recombinant viruses has been improved. However, we are still waiting for the development of a helper virus-free reverse genetics system for generating an infectious rotavirus entirely from cDNAs, as has been achieved for other members of the Reoviridae family.

  2. Genetics of osteoarthritis.

    PubMed

    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.

  3. Genetics Home Reference: galactosialidosis

    MedlinePlus

    ... down sugar molecules (oligosaccharides) attached to certain proteins (glycoproteins) or fats (glycolipids). Cathepsin A is also found ... Inherited Metabolic Diseases ISMRD: The International Advocate for Glycoprotein Storage Diseases Genetic Testing Registry (1 link) Combined ...

  4. Transgenerational genetic effects.

    PubMed

    Nelson, Vicki R; Nadeau, Joseph H

    2010-12-01

    Since Mendel, studies of phenotypic variation and disease risk have emphasized associations between genotype and phenotype among affected individuals in families and populations. Although this paradigm has led to important insights into the molecular basis for many traits and diseases, most of the genetic variants that control the inheritance of these conditions continue to elude detection. Recent studies suggest an alternative mode of inheritance where genetic variants that are present in one generation affect phenotypes in subsequent generations, thereby decoupling the conventional relations between genotype and phenotype, and perhaps, contributing to 'missing heritability'. Under some conditions, these transgenerational genetic effects can be as frequent and strong as conventional inheritance, and can persist for multiple generations. Growing evidence suggests that RNA mediates these heritable epigenetic changes. The primary challenge now is to identify the molecular basis for these effects, characterize mechanisms and determine whether transgenerational genetic effects occur in humans.

  5. Genetics Home Reference: osteopetrosis

    MedlinePlus

    ... Open All Close All Description Osteopetrosis is a bone disease that makes bones abnormally dense and prone to ... Other Names for This Condition congenital osteopetrosis marble bone disease osteopetroses Related Information How are genetic conditions and ...

  6. The Genetics of Dementia

    PubMed Central

    Farlow, Janice L.; Foroud, Tatiana

    2014-01-01

    Over the past decade, there has been a dramatic evolution of genetic methodologies that can be used to identify genes contributing to disease. Initially, the focus was primarily on classical linkage analysis; more recently, genomewide association studies, and high-throughput whole genome and whole exome sequencing have provided efficient approaches to detect common and rare variation contributing to disease risk. Application of these methodologies to dementias has led to the nomination of dozens of causative and susceptibility genes, solidifying the recognition that genetic factors are important contributors to the disease processes. In this review, the authors focus on current knowledge of the genetics of Alzheimer's disease and frontotemporal lobar degeneration. A working understanding of the genes relevant to common dementias will become increasingly critical, as options for genetic testing and eventually gene-specific therapeutics are developed. PMID:24234360

  7. [Genetics of neuropathies].

    PubMed

    Gess, B; Schirmacher, A; Young, P

    2013-02-01

    Hereditary neuropathies belong to the most common neurogenetic disorders. They appear mostly as sensory and motor neuropathies but there are also pure sensory, pure motor as well as sensory and autonomic hereditary neuropathies. In clinical practice, knowledge of hereditary neuropathies is important in order to recognize them among polyneuropathies and achieve a successful genetic diagnosis. The molecular genetics of hereditary neuropathies are very heterogeneous with currently more than 40 known disease-causing genes. The 4 most common genes account for almost 90% of the genetically diagnosed hereditary neuropathies. In this review article we provide an overview of the currently known genes and propose a rational genetic work-up protocol of the most common genes.

  8. Genetics Home Reference: alkaptonuria

    MedlinePlus

    ... homogentisate oxidase. This enzyme helps break down the amino acids phenylalanine and tyrosine, which are important building blocks ... Resources MedlinePlus (2 links) Encyclopedia: Alkaptonuria Health Topic: Amino Acid Metabolism Disorders Genetic and Rare Diseases Information Center ( ...

  9. Genetics Home Reference: histidinemia

    MedlinePlus

    ... condition characterized by elevated blood levels of the amino acid histidine, a building block of most proteins. Histidinemia ... Additional Information & Resources MedlinePlus (2 links) Health Topic: Amino Acid Metabolism Disorders Health Topic: Newborn Screening Genetic and ...

  10. Genetics Home Reference: hyperlysinemia

    MedlinePlus

    ... condition characterized by elevated blood levels of the amino acid lysine, a building block of most proteins. Hyperlysinemia ... Additional Information & Resources MedlinePlus (2 links) Health Topic: Amino Acid Metabolism Disorders Health Topic: Newborn Screening Genetic and ...

  11. Genetics Home Reference: neuroblastoma

    MedlinePlus

    ... Help Me Understand Genetics Home Health Conditions neuroblastoma neuroblastoma Enable Javascript to view the expand/collapse boxes. Download PDF Open All Close All Description Neuroblastoma is a type of cancer that most often ...

  12. Genetics Home Reference: acatalasemia

    MedlinePlus

    ... particular ethnic groups? Genetic Changes Mutations in the CAT gene can cause acatalasemia . This gene provides instructions ... DNA, proteins, and cell membranes. Mutations in the CAT gene greatly reduce the activity of catalase. A ...

  13. Annual review of genetics

    SciTech Connect

    Campbelll, A. . Aerosol Lab.)

    1988-01-01

    This book discusses the papers on genome organization in mammals. Various species mentioned are: cats; dogs; rodents; primates; chinese hamster, cows, horses, pigs, etc. Genetic mapping, biological evolution and DNA sequencing are briefly discussed.

  14. Genetics of Bone Density

    MedlinePlus

    ... study linked 32 novel genetic regions to bone mineral density. The findings may help researchers understand why ... or treating osteoporosis. Bones are made of a mineral and protein scaffold filled with bone cells. Bone ...

  15. Genetics Home Reference: macrozoospermia

    MedlinePlus

    ... leads to an inability to father biological children (infertility). In affected males, almost all sperm cells have ... Sperm Analysis Centers for Disease Control and Prevention: Infertility FAQs Genetic Testing Registry: Infertility associated with multi- ...

  16. Genetics Home Reference: hypochondroplasia

    MedlinePlus

    ... Description Hypochondroplasia is a form of short-limbed dwarfism. This condition affects the conversion of cartilage into ... Resources MedlinePlus (2 links) Encyclopedia: Lordosis Health Topic: Dwarfism Genetic and Rare Diseases Information Center (1 link) ...

  17. Genetic research in space

    NASA Technical Reports Server (NTRS)

    Delone, N. L.; Antipov, V. V.; Ilyin, Ye. A.

    1988-01-01

    The role of the genetic apparatus in the adaptation of the organism to conditions of weightlessness is studied. The investigation includes studies at the gene, chromosome, cell, tissue, and organism levels, as well as studies at the population level.

  18. Genetics Home Reference: anencephaly

    MedlinePlus

    ... Help Me Understand Genetics Home Health Conditions anencephaly anencephaly Enable Javascript to view the expand/collapse boxes. Download PDF Open All Close All Description Anencephaly is a condition that prevents the normal development ...

  19. Difficult Decisions: Genetic Screening.

    ERIC Educational Resources Information Center

    Slesnick, Irwin L.; Parakh, Jal S.

    1988-01-01

    Discusses the issue of mandatory genetic screening. Poses the question of the benefits, drawbacks, and motives involved. Provides a discussion activity to be used in high school biology classes consisting of a hypothetical situation and several questions. (CW)

  20. Genetic Testing and PXE

    MedlinePlus

    ... with PXE International's board certified genetic counselor, please call 202.362.9599. Leave your name, address, email and phone ... Connecticut Avenue NW - Suite 404 • Washington DC 20008-2304 • Telephone: 202.362.9599

  1. Genetics Home Reference: sialidosis

    MedlinePlus

    ... syndrome Related Information How are genetic conditions and genes named? ... Morrone A. Type II sialidosis: review of the clinical spectrum and identification of a new splicing defect with chitotriosidase assessment in two patients. J ...

  2. Genetics of Diabetes

    MedlinePlus

    ... 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 ...

  3. Evolving genetic code

    PubMed Central

    OHAMA, Takeshi; INAGAKI, Yuji; BESSHO, Yoshitaka; OSAWA, Syozo

    2008-01-01

    In 1985, we reported that a bacterium, Mycoplasma capricolum, used a deviant genetic code, namely UGA, a “universal” stop codon, was read as tryptophan. This finding, together with the deviant nuclear genetic codes in not a few organisms and a number of mitochondria, shows that the genetic code is not universal, and is in a state of evolution. To account for the changes in codon meanings, we proposed the codon capture theory stating that all the code changes are non-disruptive without accompanied changes of amino acid sequences of proteins. Supporting evidence for the theory is presented in this review. A possible evolutionary process from the ancient to the present-day genetic code is also discussed. PMID:18941287

  4. LSD and Genetic Damage

    ERIC Educational Resources Information Center

    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)

  5. Genetics for the ophthalmologist.

    PubMed

    Sadagopan, Karthikeyan A; Capasso, Jenina; Levin, Alex V

    2012-09-01

    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. PMID:23439654

  6. Genetics of adrenal tumors.

    PubMed

    Opocher, G; Schiavi, F; Cicala, M V; Patalano, A; Mariniello, B; Boaretto, F; Zovato, S; Pignataro, V; Macino, B; Negro, I; Mantero, F

    2009-06-01

    The impact of genetics and genomics on clinical medicine is becoming more and more important. Endocrinology pioneered the development of molecular medicine, but also the study of adrenal tumors had a great impact in this field. Particularly important was the detection of genetics of tumors derived from the adrenal medulla, as well as that of those derived from the sympathetic and parasympathetic paraganglia. The identification of mutations in one of the several pheochromocytoma/paraganglioma susceptibility genes may indicate a specific clinical management drive. Less well understood is the genetics of adrenal cortex tumors, in particular adrenocortical carcinoma, a rare and particularly aggressive disease. There are only a few examples of hereditary transmission of adrenocortical carcinoma, but the analysis of low penetrance genes by genome wide association study may enable us to discover new genetic mechanisms responsible for adrenocortical-derived tumors. PMID:19471236

  7. Transgenerational genetic effects

    PubMed Central

    Nelson, Vicki R; Nadeau, Joseph H

    2012-01-01

    Since Mendel, studies of phenotypic variation and disease risk have emphasized associations between genotype and phenotype among affected individuals in families and populations. Although this paradigm has led to important insights into the molecular basis for many traits and diseases, most of the genetic variants that control the inheritance of these conditions continue to elude detection. Recent studies suggest an alternative mode of inheritance where genetic variants that are present in one generation affect phenotypes in subsequent generations, thereby decoupling the conventional relations between genotype and phenotype, and perhaps, contributing to ‘missing heritability’. Under some conditions, these transgenerational genetic effects can be as frequent and strong as conventional inheritance, and can persist for multiple generations. Growing evidence suggests that RNA mediates these heritable epigenetic changes. The primary challenge now is to identify the molecular basis for these effects, characterize mechanisms and determine whether transgenerational genetic effects occur in humans. PMID:22122083

  8. Genetics Home Reference: hypochondrogenesis

    MedlinePlus

    ... and Rare Diseases Information Center Frequency Hypochondrogenesis and achondrogenesis , type 2 (a similar skeletal disorder) together affect ... of hypochondrogenesis: Genetic Testing Registry: ... Achondrogenesis These resources from MedlinePlus offer information about the ...

  9. [Genetic engineering in plants].

    PubMed

    Demarly, Y

    1992-11-01

    Until recent years, plant genetic was involved in heredity studies through the analysis of segregations in progenies after crossing. New potentiality arose as genetic tools with the use of dissociated plant elements, transforming and cultivating them in vitro. When plants are regenerated from manipulated tissues, new structures of varieties (clones) new genotypes (transgenic plants) and new regulations of genes expression (vitrovariants) open new ways for plant genetic engineering. Progressively these technological tools are integrated in the methods of plant breeding. Yet all possible consequences of these new types of heredity and of these new genetic structures must be evaluated. As first priority the analysis of possible incidences in the field of food, nutrition and health gives the basis for diagnostics and organisations aiming to avoid the release of genotypes which could have unwanted effects.

  10. [Genetic effects of radiation].

    PubMed

    Nakamura, Nori

    2012-03-01

    This paper is a short review of genetic effect of radiation. This includes methods and results of a large-scale genetic study on specific loci in mice and of various studies in the offspring of atomic-bomb survivors. As for the latter, there is no results obtained which suggest the effect of parental exposure to radiation. Further, in recent years, studies are conducted to the offspring born to parents who were survivors of childhood cancers. In several reports, the mean gonad dose is quite large whereas in most instances, the results do not indicate genetic effect following parental exposure to radiation. Possible reasons for the difficulties in detecting genetic effect of radiation are discussed. PMID:22514926

  11. Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters

    PubMed Central

    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

  12. A Network-Based Data Integration Approach to Support Drug Repurposing and Multi-Target Therapies in Triple Negative Breast Cancer.

    PubMed

    Vitali, Francesca; Cohen, Laurie D; Demartini, Andrea; Amato, Angela; Eterno, Vincenzo; Zambelli, Alberto; Bellazzi, Riccardo

    2016-01-01

    The integration of data and knowledge from heterogeneous sources can be a key success factor in drug design, drug repurposing and multi-target therapies. In this context, biological networks provide a useful instrument to highlight the relationships and to model the phenomena underlying therapeutic action in cancer. In our work, we applied network-based modeling within a novel bioinformatics pipeline to identify promising multi-target drugs. Given a certain tumor type/subtype, we derive a disease-specific Protein-Protein Interaction (PPI) network by combining different data-bases and knowledge repositories. Next, the application of suitable graph-based algorithms allows selecting a set of potentially interesting combinations of drug targets. A list of drug candidates is then extracted by applying a recent data fusion approach based on matrix tri-factorization. Available knowledge about selected drugs mechanisms of action is finally exploited to identify the most promising candidates for planning in vitro studies. We applied this approach to the case of Triple Negative Breast Cancer (TNBC), a subtype of breast cancer whose biology is poorly understood and that lacks of specific molecular targets. Our "in-silico" findings have been confirmed by a number of in vitro experiments, whose results demonstrated the ability of the method to select candidates for drug repurposing. PMID:27632168

  13. A Comparison of Network-based Strategies for Screening At-Risk Hispanic/Latino Adolescents and Young Adults for Undiagnosed Asymptomatic HIV Infection

    PubMed Central

    Boyer, Cherrie B.; Robles-Schrader, Grisel M.; Li, Su X.; Miller, Robin L.; Korelitz, James; Price, Georgine N.; Rivera Torres, Carmen M.; Chutuape, Kate S.; Stines, Stephanie J.; Straub, Diane M.; Peralta, Ligia; Febo, Irma; Hightow-Weidman, Lisa; Gonin, René; Kapogiannis, Bill G.; Ellen, Jonathan M.

    2014-01-01

    Purpose Hispanic/Latino adolescents and young adults are disproportionately impacted by the HIV/AIDS epidemic; yet, little is known about the best strategies to increase HIV testing in this group. Network-based approaches are feasible and acceptable means for screening at-risk adults for HIV infection, but it is unknown whether these approaches are appropriate for at-risk young Hispanics/Latinos. Thus, we compared an alternative venue-based testing (AVT) strategy with a social and sexual network referral (SSNIT) strategy. Methods All participants were Hispanics/Latinos, aged 13–24 years with self-reported HIV risk; they were recruited from 11 cities in the U.S. and Puerto Rico, and completed an audio computer-assisted self-interview and underwent HIV screening. Results 1,596 participants (94.5% of those approached) were enrolled: 784 (49.1%) through AVT and 812 (50.9%) through SSNIT. HIV infection was identified in three SSNIT (0.37%) and four AVT (0.51%) participants (p=0.7213). Conclusions Despite high levels of HIV risk, a low prevalence of HIV infection was identified with no differences by recruitment strategy. We found overwhelming support for the acceptability and feasibility of AVT and SSNIT for engaging and screening at-risk young Hispanics/Latinos. Further research is needed to better understand how to strategically implement such strategies to improve identification of undiagnosed HIV infection. PMID:25223476

  14. Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying NetGenerator V2.0

    PubMed Central

    2013-01-01

    Background Inference of gene-regulatory networks (GRNs) is important for understanding behaviour and potential treatment of biological systems. Knowledge about GRNs gained from transcriptome analysis can be increased by multiple experiments and/or multiple stimuli. Since GRNs are complex and dynamical, appropriate methods and algorithms are needed for constructing models describing these dynamics. Algorithms based on heuristic approaches reduce the effort in parameter identification and computation time. Results The NetGenerator V2.0 algorithm, a heuristic for network inference, is proposed and described. It automatically generates a system of differential equations modelling structure and dynamics of the network based on time-resolved gene expression data. In contrast to a previous version, the inference considers multi-stimuli multi-experiment data and contains different methods for integrating prior knowledge. The resulting significant changes in the algorithmic procedures are explained in detail. NetGenerator is applied to relevant benchmark examples evaluating the inference for data from experiments with different stimuli. Also, the underlying GRN of chondrogenic differentiation, a real-world multi-stimulus problem, is inferred and analysed. Conclusions NetGenerator is able to determine the structure and parameters of GRNs and their dynamics. The new features of the algorithm extend the range of possible experimental set-ups, results and biological interpretations. Based upon benchmarks, the algorithm provides good results in terms of specificity, sensitivity, efficiency and model fit. PMID:23280066

  15. Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters.

    PubMed

    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.

  16. Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters.

    PubMed

    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. PMID:26624613

  17. A Network-Based Data Integration Approach to Support Drug Repurposing and Multi-Target Therapies in Triple Negative Breast Cancer

    PubMed Central

    Cohen, Laurie D.; Demartini, Andrea; Amato, Angela; Eterno, Vincenzo; Zambelli, Alberto; Bellazzi, Riccardo

    2016-01-01

    The integration of data and knowledge from heterogeneous sources can be a key success factor in drug design, drug repurposing and multi-target therapies. In this context, biological networks provide a useful instrument to highlight the relationships and to model the phenomena underlying therapeutic action in cancer. In our work, we applied network-based modeling within a novel bioinformatics pipeline to identify promising multi-target drugs. Given a certain tumor type/subtype, we derive a disease-specific Protein-Protein Interaction (PPI) network by combining different data-bases and knowledge repositories. Next, the application of suitable graph-based algorithms allows selecting a set of potentially interesting combinations of drug targets. A list of drug candidates is then extracted by applying a recent data fusion approach based on matrix tri-factorization. Available knowledge about selected drugs mechanisms of action is finally exploited to identify the most promising candidates for planning in vitro studies. We applied this approach to the case of Triple Negative Breast Cancer (TNBC), a subtype of breast cancer whose biology is poorly understood and that lacks of specific molecular targets. Our “in-silico” findings have been confirmed by a number of in vitro experiments, whose results demonstrated the ability of the method to select candidates for drug repurposing. PMID:27632168

  18. A network based method for analysis of lncRNA-disease associations and prediction of lncRNAs implicated in diseases.

    PubMed

    Yang, Xiaofei; Gao, Lin; Guo, Xingli; Shi, Xinghua; Wu, Hao; Song, Fei; Wang, Bingbo

    2014-01-01

    Increasing evidence has indicated that long non-coding RNAs (lncRNAs) are implicated in and associated with many complex human diseases. Despite of the accumulation of lncRNA-disease associations, only a few studies had studied the roles of these associations in pathogenesis. In this paper, we investigated lncRNA-disease associations from a network view to understand the contribution of these lncRNAs to complex diseases. Specifically, we studied both the properties of the diseases in which the lncRNAs were implicated, and that of the lncRNAs associated with complex diseases. Regarding the fact that protein coding genes and lncRNAs are involved in human diseases, we constructed a coding-non-coding gene-disease bipartite network based on known associations between diseases and disease-causing genes. We then applied a propagation algorithm to uncover the hidden lncRNA-disease associations in this network. The algorithm was evaluated by leave-one-out cross validation on 103 diseases in which at least two genes were known to be involved, and achieved an AUC of 0.7881. Our algorithm successfully predicted 768 potential lncRNA-disease associations between 66 lncRNAs and 193 diseases. Furthermore, our results for Alzheimer's disease, pancreatic cancer, and gastric cancer were verified by other independent studies.

  19. Optimal Communication Network-Based H∞ Quantized Control With Packet Dropouts for a Class of Discrete-Time Neural Networks With Distributed Time Delay.

    PubMed

    Han, Qing-Long; Liu, Yurong; Yang, Fuwen

    2016-02-01

    This paper is concerned with optimal communication network-based H∞ quantized control for a discrete-time neural network with distributed time delay. Control of the neural network (plant) is implemented via a communication network. Both quantization and communication network-induced data packet dropouts are considered simultaneously. It is assumed that the plant state signal is quantized by a logarithmic quantizer before transmission, and communication network-induced packet dropouts can be described by a Bernoulli distributed white sequence. A new approach is developed such that controller design can be reduced to the feasibility of linear matrix inequalities, and a desired optimal control gain can be derived in an explicit expression. It is worth pointing out that some new techniques based on a new sector-like expression of quantization errors, and the singular value decomposition of a matrix are developed and employed in the derivation of main results. An illustrative example is presented to show the effectiveness of the obtained results.

  20. Development of a Network-Based Information Infrastructure for Fisheries and Hydropower Information in the Columbia River Basin : Final Project Report.

    SciTech Connect

    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.

  1. Osteonecrosis in genetic disorders

    PubMed Central

    Masi, Laura; Falchetti, Alberto; Brandi, Maria Luisa

    2007-01-01

    The avascular necrosis of bone is characterized by an abnormality of tissue that can occur whenever a disease process causes major cell stress. Some evidence supports a role for genetic factors in some avascular necrosis suggesting that gene mutations could play a role in the pathogenesis of osteonecrosis. These genetic studies provide hope that tools for identifying high risk patients will be available in the future. PMID:22460749

  2. Genetics of epilepsy

    PubMed Central

    Vadlamudi, Lata; Milne, Roger L.; Lawrence, Kate; Heron, Sarah E.; Eckhaus, Jazmin; Keay, Deborah; Connellan, Mary; Torn-Broers, Yvonne; Howell, R. Anne; Mulley, John C.; Scheffer, Ingrid E.; Dibbens, Leanne M.; Hopper, John L.

    2014-01-01

    Objective: Analysis of twins with epilepsy to explore the genetic architecture of specific epilepsies, to evaluate the applicability of the 2010 International League Against Epilepsy (ILAE) organization of epilepsy syndromes, and to integrate molecular genetics with phenotypic analyses. Methods: A total of 558 twin pairs suspected to have epilepsy were ascertained from twin registries (69%) or referral (31%). Casewise concordance estimates were calculated for epilepsy syndromes. Epilepsies were then grouped according to the 2010 ILAE organizational scheme. Molecular genetic information was utilized where applicable. Results: Of 558 twin pairs, 418 had confirmed seizures. A total of 534 twin individuals were affected. There were higher twin concordance estimates for monozygotic (MZ) than for dizygotic (DZ) twins for idiopathic generalized epilepsies (MZ = 0.77; DZ = 0.35), genetic epilepsy with febrile seizures plus (MZ = 0.85; DZ = 0.25), and focal epilepsies (MZ = 0.40; DZ = 0.03). Utilizing the 2010 ILAE scheme, the twin data clearly demonstrated genetic influences in the syndromes designated as genetic. Of the 384 tested twin individuals, 10.9% had mutations of large effect in known epilepsy genes or carried validated susceptibility alleles. Conclusions: Twin studies confirm clear genetic influences for specific epilepsies. Analysis of the twin sample using the 2010 ILAE scheme strongly supported the validity of grouping the “genetic” syndromes together and shows this organizational scheme to be a more flexible and biologically meaningful system than previous classifications. Successful selected molecular testing applied to this cohort is the prelude to future large-scale next-generation sequencing of epilepsy research cohorts. Insights into genetic architecture provided by twin studies provide essential data for optimizing such approaches. PMID:25107880

  3. Genetic testing in hyperlipidemia.

    PubMed

    Bilen, Ozlem; Pokharel, Yashashwi; Ballantyne, Christie M

    2015-05-01

    Hereditary dyslipidemias are often underdiagnosed and undertreated, yet with significant health implications, most importantly causing preventable premature cardiovascular diseases. The commonly used clinical criteria to diagnose hereditary lipid disorders are specific but are not very sensitive. Genetic testing may be of value in making accurate diagnosis and improving cascade screening of family members, and potentially, in risk assessment and choice of therapy. This review focuses on using genetic testing in the clinical setting for lipid disorders, particularly familial hypercholesterolemia.

  4. Genetic Testing in Hyperlipidemia.

    PubMed

    Bilen, Ozlem; Pokharel, Yashashwi; Ballantyne, Christie M

    2016-03-01

    Hereditary dyslipidemias are often underdiagnosed and undertreated, yet with significant health implications, most importantly causing preventable premature cardiovascular diseases. The commonly used clinical criteria to diagnose hereditary lipid disorders are specific but are not very sensitive. Genetic testing may be of value in making accurate diagnosis and improving cascade screening of family members, and potentially, in risk assessment and choice of therapy. This review focuses on using genetic testing in the clinical setting for lipid disorders, particularly familial hypercholesterolemia.

  5. Genetics of Takotsubo Syndrome.

    PubMed

    Limongelli, Giuseppe; Masarone, Daniele; Maddaloni, Valeria; Rubino, Marta; Fratta, Fiorella; Cirillo, Annapaola; Ludovica, Spinelli Barrile; Pacileo, Roberta; Fusco, Adelaide; Coppola, Guido Ronald; Pisacane, Francesca; Bossone, Eduardo; Calabrò, Paolo; Calabrò, Raffaele; Russo, Maria Giovanna; Pacileo, Giuseppe

    2016-10-01

    Takotsubo syndrome (TTS) is an enigmatic disease with a multifactorial and still unresolved pathogenesis. A genetic predisposition has been suggested based on the few familial TTS cases. Conflicting results have been published regarding the role of functional polymorphisms in relevant candidate genes, such as α1-, β1-, and β2-adrenergic receptors; G protein-coupled receptor kinase 5; and estrogen receptors. Further research is required to help clarify the role of genetic susceptibility in TTS. PMID:27638020

  6. Primer on molecular genetics

    SciTech Connect

    Not Available

    1992-04-01

    This report is taken from the April 1992 draft of the DOE Human Genome 1991--1992 Program Report, which is expected to be published in May 1992. The primer is intended to be an introduction to basic principles of molecular genetics pertaining to the genome project. The material contained herein is not final and may be incomplete. Techniques of genetic mapping and DNA sequencing are described.

  7. Contemporary Genetics for Gender Researchers: Not Your Grandma's Genetics Anymore

    ERIC Educational Resources Information Center

    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…

  8. Genetics & sport: bioethical concerns.

    PubMed

    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. PMID:22830450

  9. [Molecular and genetic epidemiology].

    PubMed

    Kang, D H

    2001-04-21

    Molecular epidemiology is defined as "the use of biological markers in epidemiologic research" and genetic epidemiology is defined as "the study of the interaction between genetic and environmental factors in epidemiologic research". Traditional epidemiologic approaches defined as "the study of the distribution and determinants of disease frequency in human population" could not address the importance of genetic susceptibility of humans in disease occurrence. However, the use of biological or genetic markers identified and characterized by the help of advance in molecular biology and human genetics now can provide us better understanding of multi-factorial or multistep disease occurrence in humans. Biological markers used in molecular epidemiology are classified into three groups: biomarkers of exposure (i.e., carcinogen metabolites in human urine, DNA-adducts, etc.), biomarkers of effects (i.e., oncoproteins, tumor markers, etc.), and biomarkers of susceptibility (i.e., genetic polymorphisms of carcinogen metabolism enzymes, DNA repair, etc.). Susceptibility genes involved in disease pathogenesis are categorized into two groups: high penetrance genes (i.e., BRAC1, RB, etc.) and low penetrance genes (i.e., GSTs, XRCC1, etc.). This paper will address the usefulnesses of bomarkers in edpidemiologic research and will show the examples of the use of selected low penetrance genes involved in human carcinogenesis. The importance of multidisciplinary approaches among epidemiologists, molecular biologists, and human geneticists will also be discussed.

  10. Genetics & sport: bioethical concerns.

    PubMed

    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.

  11. Ecogeographic Genetic Epidemiology

    PubMed Central

    Sloan, Chantel D.; Duell, Eric J.; Shi, Xun; Irwin, Rebecca; Andrew, Angeline S.; Williams, Scott M.; Moore, Jason H.

    2009-01-01

    Complex diseases such as cancer and heart disease result from interactions between an individual's genetics and environment, i.e. their human ecology. Rates of complex diseases have consistently demonstrated geographic patterns of incidence, or spatial “clusters” of increased incidence relative to the general population. Likewise, genetic subpopulations and environmental influences are not evenly distributed across space. Merging appropriate methods from genetic epidemiology, ecology and geography will provide a more complete understanding of the spatial interactions between genetics and environment that result in spatial patterning of disease rates. Geographic Information Systems (GIS), which are tools designed specifically for dealing with geographic data and performing spatial analyses to determine their relationship, are key to this kind of data integration. Here the authors introduce a new interdisciplinary paradigm, ecogeographic genetic epidemiology, which uses GIS and spatial statistical analyses to layer genetic subpopulation and environmental data with disease rates and thereby discern the complex gene-environment interactions which result in spatial patterns of incidence. PMID:19025788

  12. Genetic Manipulation in Pigs

    PubMed Central

    Sachs, David H.; Galli, Cesare

    2009-01-01

    Purpose of Review Recent developments in the field of genetic engineering have made it possible to add, delete or exchange genes from one species to another. This technology has special relevance to the field of xenotransplantation, in which the elimination of a species-specific disparity could make the difference between success or failure of an organ transplant. This review focuses on developments in both the techniques and applications of genetically modified animals. Recent Findings Advances have been made using existing techniques for genetic modifications of swine and in the development of new, emerging technologies, including enzymatic engineering and the use of siRNA. Applications of the modified animals have provided evidence that genetically modified swine have the potential to overcome both physiologic and immunologic barriers that have previously impeded this field. Use of GalT-KO animals as donors have shown marked improvements in xenograft survivals. Summary Techniques for genetic engineering of swine have been directed toward avoiding naturally existing cellular and antibody responses to species-specific antigens. Organs from genetically engineered animals have enjoyed markedly improved survivals in non-human primates, especially in protocols directed toward the induction of tolerance, presumably by avoiding immunization to new antigens. PMID:19469029

  13. Genetic aspects of arteriosclerosis.

    PubMed

    Goldbourt, U; Neufeld, H N

    1986-01-01

    This review discusses the genetic factors in the development of arteriosclerosis and coronary heart disease (CHD). In several studies, multivariate analysis of prospective mortality/morbidity data and angiographic findings have indicated that a family history of CHD contributed to CHD risk independently of the established risk factors. In addition, ethnic groups that differ in the prevalence and incidence of CHD also markedly differ in blood groups and protein-enzymatic markers. These or other genetic differences may affect CHD rates. Data from fraternal and identical twins, the source of some early genetic CHD findings, are reviewed. Genetic disorders of lipoprotein metabolism and transport, such as familial hypercholesterolemia, as well as other monogenic disorders are discussed. The role of apoprotein E polymorphism i other monogenic disorders are discussed. The role of apoprotein E polymorphism in determining plasma LDL variability among individuals is considered. Recombinant DNA technology, molecular cloning, and the identification of restriction fragment length polymorphisms are new tools for investigators who assess DNA polymorphism. Recent advances in that domain include: DNA polymorphisms affecting blood levels of apo A-I and A-II, association of a DNA insertion on chromosome 19 with severe premature atherosclerosis, and information concerning linkage of the genes for various apolipoproteins. In addition, the evidence for a major genetic component in diabetes mellitus and research into the genetic aspects of hypertension are reviewed. The male/female ratio in pathologically and epidemiologically assessed atherosclerosis may provide clues to the role of genetics. Early structural changes in the coronary artery intima are compatible with the ethnic and gender predilection. A key question in understanding underlying mechanisms in atherosclerosis is why coronary arteries are occluded in individuals whose other arterial systems are largely unaffected. The

  14. High Points of Human Genetics

    ERIC Educational Resources Information Center

    Stern, Curt

    1975-01-01

    Discusses such high points of human genetics as the study of chromosomes, somatic cell hybrids, the population formula: the Hardy-Weinberg Law, biochemical genetics, the single-active X Theory, behavioral genetics and finally how genetics can serve humanity. (BR)

  15. [Genetic aspects of genealogy].

    PubMed

    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

  16. From observational to dynamic genetics

    PubMed Central

    Haworth, Claire M. A.; Davis, Oliver S. P.

    2014-01-01

    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 behavioral 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. PMID:24478793

  17. Genetic epidemiology, genetic maps and positional cloning.

    PubMed Central

    Morton, Newton E

    2003-01-01

    Genetic epidemiology developed in the middle of the last century, focused on inherited causes of disease but with methods and results applicable to other traits and even forensics. Early success with linkage led to the localization of genes contributing to disease, and ultimately to the Human Genome Project. The discovery of millions of DNA markers has encouraged more efficient positional cloning by linkage disequilibrium (LD), using LD maps and haplotypes in ways that are rapidly evolving. This has led to large international programmes, some promising and others alarming, with laws about DNA patenting and ethical guidelines for responsible research still struggling to be born. PMID:14561327

  18. Frequently Asked Questions about Genetic Testing

    MedlinePlus

    ... sobre las pruebas genéticas Frequently Asked Questions About Genetic Testing What is genetic testing? What can I ... find more information about genetic testing? What is genetic testing? Genetic testing uses laboratory methods to look ...

  19. [Genetics of pediatric obesity].

    PubMed

    Peralta-Romero, José de Jesús; Gómez-Zamudio, Jaime Héctor; Estrada-Velasco, Bárbara; Karam-Araujo, Roberto; Cruz-López, Miguel

    2014-01-01

    Obesity is a major health problem around the globe. The statistics of overweight and obesity at early ages have reached alarming levels and placed our country in the first place in regard to childhood obesity. In the development of obesity two major factors take part, one genetic and the other one environmental. From the perspective of environmental changes both overweight and obesity result from the imbalance in the energy balance: people ingest more energy than they expend. Despite people live in the same obesogenic environment not all of them develop obesity; it requires genetic factors for this to happen. This review focuses on the description of the main methodologies to find genetic markers, as well as the main loci in candidate genes, whose single nucleotide polymorphisms (SNPs) are associated with obesity and its comorbidities in children, highlighting the association of these genes in the Mexican population. Knowledge of the genetic markers associated with obesity will help to understand the molecular and physiological mechanisms, the genetic background and changes in body mass index in the Mexican population. This information is useful for the planning of new hypotheses in the search for new biomarkers that can be used in a predictive and preventive way, as well as for the development of new therapeutic strategies.

  20. Genetics of Vesicoureteral Reflux

    PubMed Central

    Ninoa, F.; Ilaria, M.; Noviello, C.; Santoro, L.; Rätsch, I.M.; Martino, A.; Cobellis, G.

    2016-01-01

    Vesicoureteral reflux (VUR) is the retrograde passage of urine from the bladder to the upper urinary tract. It is the most common congenital urological anomaly affecting 1-2% of children and 30-40% of patients with urinary tract infections. VUR is a major risk factor for pyelonephritic scarring and chronic renal failure in children. It is the result of a shortened intravesical ureter with an enlarged or malpositioned ureteric orifice. An ectopic embryonal ureteric budding development is implicated in the pathogenesis of VUR, which is a complex genetic developmental disorder. Many genes are involved in the ureteric budding formation and subsequently in the urinary tract and kidney development. Previous studies demonstrate an heterogeneous genetic pattern of VUR. In fact no single major locus or gene for primary VUR has been identified. It is likely that different forms of VUR with different genetic determinantes are present. Moreover genetic studies of syndromes with associated VUR have revealed several possible candidate genes involved in the pathogenesis of VUR and related urinary tract malformations. Mutations in genes essential for urinary tract morphogenesis are linked to numerous congenital syndromes, and in most of those VUR is a feature. The Authors provide an overview of the developmental processes leading to the VUR. The different genes and signaling pathways controlling the embryonal urinary tract development are analyzed. A better understanding of VUR genetic bases could improve the management of this condition in children. PMID:27013925

  1. Behavioural genetics and temperament.

    PubMed

    Plomin, R

    1982-01-01

    Three recent developments in behavioural genetics are relevant to temperament research. First, the search for genetic influences on temperament has been frustrated by the finding that twin studies using self-report and parental rating instruments detect a genetic influence for all personality traits whereas twin studies using objective assessments rarely find a genetic influence. Secondly, environmental influences salient to temperament appear to operate in such a way as to make members of a family (siblings, for example) as different from one another as are individuals in different families. The importance of such 'E1', or non-shared, environmental influences suggests the need for studies of more than one child per family. Thirdly, adoption studies are needed to complement the extensive research on temperament in twins. In addition to its usefulness in isolating genetic influences, the adoption design can study environmental influence devoid of the confounding effects of hereditary influences; it can also isolate interactions between genotypes and environments. Preliminary results from the Colorado Adoption Project show little relationship between the temperaments of adopted and non-adopted one-year-olds and the personality characteristics of thier parents. Measures of the home and family environment did not relate to infant temperament, and no genotype-environment interaction was detected.

  2. Genetical background of intelligence.

    PubMed

    Junkiert-Czarnecka, Anna; Haus, Olga

    2016-01-01

    Intelligence as an ability to reason, think abstractly and adapt effectively to the environment is a subject of research in the field of psychology, neurobiology, and in the last twenty years genetics as well. Genetical testing of twins carried out from XX century indicated heritebility of intelligence, therefore confirmed an influence of genetic factor on cognitive processes. Studies on genetic background of intelligence focus on dopaminergic (DRD2, DRD4, COMT, SLC6A3, DAT1, CCKAR) and adrenergic system (ADRB2, CHRM2) genes as well as, neutrofins (BDNF) and oxidative stress genes (LTF, PRNP). Positive effect of investigated gene polymorphism was indicated by variation c.957C>T DRD2 gene (if in polymorphic site is thymine), polymorphism c.472G>A COMT gene (presence of adenine) and also gene ADRB2 c.46A->G (guanine), CHRM2 (thymine in place c.1890A>T) and BDNF (guanine in place c.472G>A) Obtained results indicate that intelligence is a feature dependent not only on genetic but also an environmental factor. PMID:27333929

  3. Genetic modulation of homocysteinemia.

    PubMed

    Rozen, R

    2000-01-01

    With the identification of hyperhomocysteinemia as a risk factor for cardiovascular disease, an understanding of the genetic determinants of plasma homocysteine is important for prevention and treatment. It has been known for some time that homocystinuria, a rare inborn error of metabolism, can be due to genetic mutations that severely disrupt homocysteine metabolism. A more recent development is the finding that milder, but more common, genetic mutations in the same enzymes might also contribute to an elevation in plasma homocysteine. The best example of this concept is a missense mutation (alanine to valine) at base pair (bp) 677 of methylenetetrahydrofolate reductase (MTHFR), the enzyme that provides the folate derivative for conversion of homocysteine to methionine. This mutation results in mild hyperhomocysteinemia, primarily when folate levels are low, providing a rationale (folate supplementation) for overcoming the genetic deficiency. Additional genetic variants in MTHFR and in other enzymes of homocysteine metabolism are being identified as the cDNAs/genes become isolated. These variants include a glutamate to alanine mutation (bp 1298) in MTHFR, an aspartate to glycine mutation (bp 2756) in methionine synthase, and an isoleucine to methionine mutation (bp 66) in methionine synthase reductase. These variants have been identified relatively recently; therefore additional investigations are required to determine their clinical significance with respect to mild hyperhomocysteinemia and vascular disease.

  4. Constraints in Genetic Programming

    NASA Technical Reports Server (NTRS)

    Janikow, Cezary Z.

    1996-01-01

    Genetic programming refers to a class of genetic algorithms utilizing generic representation in the form of program trees. For a particular application, one needs to provide the set of functions, whose compositions determine the space of program structures being evolved, and the set of terminals, which determine the space of specific instances of those programs. The algorithm searches the space for the best program for a given problem, applying evolutionary mechanisms borrowed from nature. Genetic algorithms have shown great capabilities in approximately solving optimization problems which could not be approximated or solved with other methods. Genetic programming extends their capabilities to deal with a broader variety of problems. However, it also extends the size of the search space, which often becomes too large to be effectively searched even by evolutionary methods. Therefore, our objective is to utilize problem constraints, if such can be identified, to restrict this space. In this publication, we propose a generic constraint specification language, powerful enough for a broad class of problem constraints. This language has two elements -- one reduces only the number of program instances, the other reduces both the space of program structures as well as their instances. With this language, we define the minimal set of complete constraints, and a set of operators guaranteeing offspring validity from valid parents. We also show that these operators are not less efficient than the standard genetic programming operators if one preprocesses the constraints - the necessary mechanisms are identified.

  5. Extensive genetics of ALS

    PubMed Central

    Calvo, Andrea; Mazzini, Letizia; Cantello, Roberto; Mora, Gabriele; Moglia, Cristina; Corrado, Lucia; D'Alfonso, Sandra; Majounie, Elisa; Renton, Alan; Pisano, Fabrizio; Ossola, Irene; Brunetti, Maura; Traynor, Bryan J.; Restagno, Gabriella

    2012-01-01

    Objective: To assess the frequency and clinical characteristics of patients with mutations of major amyotrophic lateral sclerosis (ALS) genes in a prospectively ascertained, population-based epidemiologic series of cases. Methods: The study population includes all ALS cases diagnosed in Piemonte, Italy, from January 2007 to June 2011. Mutations of SOD1, TARDBP, ANG, FUS, OPTN, and C9ORF72 have been assessed. Results: Out of the 475 patients included in the study, 51 (10.7%) carried a mutation of an ALS-related gene (C9ORF72, 32; SOD1, 10; TARDBP, 7; FUS, 1; OPTN, 1; ANG, none). A positive family history for ALS or frontotemporal dementia (FTD) was found in 46 (9.7%) patients. Thirty-one (67.4%) of the 46 familial cases and 20 (4.7%) of the 429 sporadic cases had a genetic mutation. According to logistic regression modeling, besides a positive family history for ALS or FTD, the chance to carry a genetic mutation was related to the presence of comorbid FTD (odds ratio 3.5; p = 0.001), and age at onset ≤54 years (odds ratio 1.79; p = 0.012). Conclusions: We have found that ∼11% of patients with ALS carry a genetic mutation, with C9ORF72 being the commonest genetic alteration. Comorbid FTD or a young age at onset are strong indicators of a possible genetic origin of the disease. PMID:23100398

  6. Genetics of Vesicoureteral Reflux.

    PubMed

    Nino, F; Ilari, M; Noviello, C; Santoro, L; Rätsch, I M; Martino, A; Cobellis, G

    2016-02-01

    Vesicoureteral reflux (VUR) is the retrograde passage of urine from the bladder to the upper urinary tract. It is the most common congenital urological anomaly affecting 1-2% of children and 30-40% of patients with urinary tract infections. VUR is a major risk factor for pyelonephritic scarring and chronic renal failure in children. It is the result of a shortened intravesical ureter with an enlarged or malpositioned ureteric orifice. An ectopic embryonal ureteric budding development is implicated in the pathogenesis of VUR, which is a complex genetic developmental disorder. Many genes are involved in the ureteric budding formation and subsequently in the urinary tract and kidney development. Previous studies demonstrate an heterogeneous genetic pattern of VUR. In fact no single major locus or gene for primary VUR has been identified. It is likely that different forms of VUR with different genetic determinantes are present. Moreover genetic studies of syndromes with associated VUR have revealed several possible candidate genes involved in the pathogenesis of VUR and related urinary tract malformations. Mutations in genes essential for urinary tract morphogenesis are linked to numerous congenital syndromes, and in most of those VUR is a feature. The Authors provide an overview of the developmental processes leading to the VUR. The different genes and signaling pathways controlling the embryonal urinary tract development are analyzed. A better understanding of VUR genetic bases could improve the management of this condition in children. PMID:27013925

  7. Assessment of structural connectivity in the preterm brain at term equivalent age using diffusion MRI and T2 relaxometry: a network-based analysis.

    PubMed

    Pannek, Kerstin; Hatzigeorgiou, Xanthy; Colditz, Paul B; Rose, Stephen

    2013-01-01

    Preterm birth is associated with a high prevalence of adverse neurodevelopmental outcome. Non-invasive techniques which can probe the neural correlates underpinning these deficits are required. This can be achieved by measuring the structural network of connections within the preterm infant's brain using diffusion MRI and tractography. We used diffusion MRI and T2 relaxometry to identify connections with altered white matter properties in preterm infants compared to term infants. Diffusion and T2 data were obtained from 9 term neonates and 18 preterm-born infants (born <32 weeks gestational age) at term equivalent age. Probabilistic tractography incorporating multiple fibre orientations was used in combination with the Johns Hopkins neonatal brain atlas to calculate the structural network of connections. Connections of altered diffusivity or T2, as well as their relationship with gestational age at birth and postmenstrual age at the time of MRI, were identified using the network based statistic framework. A total of 433 connections were assessed. FA was significantly reduced in 17, and T2 significantly increased in 18 connections in preterm infants, following correction for multiple comparisons. Cortical networks associated with affected connections mainly involved left frontal and temporal cortical areas: regions which are associated with working memory, verbal comprehension and higher cognitive function--deficits which are often observed later in children and adults born preterm. Gestational age at birth correlated with T2, but not diffusion in several connections. We found no association between diffusion or T2 and postmenstrual age at the time of MRI in preterm infants. This study demonstrates that alterations in the structural network of connections can be identified in preterm infants at term equivalent age, and that incorporation of non-diffusion measures such as T2 in the connectome framework provides complementary information for the assessment of brain

  8. Assessment of Structural Connectivity in the Preterm Brain at Term Equivalent Age Using Diffusion MRI and T2 Relaxometry: A Network-Based Analysis

    PubMed Central

    Pannek, Kerstin; Hatzigeorgiou, Xanthy; Colditz, Paul B.; Rose, Stephen

    2013-01-01

    Preterm birth is associated with a high prevalence of adverse neurodevelopmental outcome. Non-invasive techniques which can probe the neural correlates underpinning these deficits are required. This can be achieved by measuring the structural network of connections within the preterm infant's brain using diffusion MRI and tractography. We used diffusion MRI and T2 relaxometry to identify connections with altered white matter properties in preterm infants compared to term infants. Diffusion and T2 data were obtained from 9 term neonates and 18 preterm-born infants (born <32 weeks gestational age) at term equivalent age. Probabilistic tractography incorporating multiple fibre orientations was used in combination with the Johns Hopkins neonatal brain atlas to calculate the structural network of connections. Connections of altered diffusivity or T2, as well as their relationship with gestational age at birth and postmenstrual age at the time of MRI, were identified using the network based statistic framework. A total of 433 connections were assessed. FA was significantly reduced in 17, and T2 significantly increased in 18 connections in preterm infants, following correction for multiple comparisons. Cortical networks associated with affected connections mainly involved left frontal and temporal cortical areas: regions which are associated with working memory, verbal comprehension and higher cognitive function – deficits which are often observed later in children and adults born preterm. Gestational age at birth correlated with T2, but not diffusion in several connections. We found no association between diffusion or T2 and postmenstrual age at the time of MRI in preterm infants. This study demonstrates that alterations in the structural network of connections can be identified in preterm infants at term equivalent age, and that incorporation of non-diffusion measures such as T2 in the connectome framework provides complementary information for the assessment of brain

  9. Identification of human protein complexes from local sub-graphs of protein-protein interaction network based on random forest with topological structure features.

    PubMed

    Li, Zhan-Chao; Lai, Yan-Hua; Chen, Li-Li; Zhou, Xuan; Dai, Zong; Zou, Xiao-Yong

    2012-03-01

    In the post-genomic era, one of the most important and challenging tasks is to identify protein complexes and further elucidate its molecular mechanisms in specific biological processes. Previous computational approaches usually identify protein complexes from protein interaction network based on dense sub-graphs and incomplete priori information. Additionally, the computational approaches have little concern about the biological properties of proteins and there is no a common evaluation metric to evaluate the performance. So, it is necessary to construct novel method for identifying protein complexes and elucidating the function of protein complexes. In this study, a novel approach is proposed to identify protein complexes using random forest and topological structure. Each protein complex is represented by a graph of interactions, where descriptor of the protein primary structure is used to characterize biological properties of protein and vertex is weighted by the descriptor. The topological structure features are developed and used to characterize protein complexes. Random forest algorithm is utilized to build prediction model and identify protein complexes from local sub-graphs instead of dense sub-graphs. As a demonstration, the proposed approach is applied to protein interaction data in human, and the satisfied results are obtained with accuracy of 80.24%, sensitivity of 81.94%, specificity of 80.07%, and Matthew's correlation coefficient of 0.4087 in 10-fold cross-validation test. Some new protein complexes are identified, and analysis based on Gene Ontology shows that the complexes are likely to be true complexes and play important roles in the pathogenesis of some diseases. PCI-RFTS, a corresponding executable program for protein complexes identification, can be acquired freely on request from the authors.

  10. A neural network-based method for merging ocean color and Argo data to extend surface bio-optical properties to depth: Retrieval of the particulate backscattering coefficient

    NASA Astrophysics Data System (ADS)

    Sauzède, R.; Claustre, H.; Uitz, J.; Jamet, C.; Dall'Olmo, G.; D'Ortenzio, F.; Gentili, B.; Poteau, A.; Schmechtig, C.

    2016-04-01

    The present study proposes a novel method that merges satellite ocean color bio-optical products with Argo temperature-salinity profiles to infer the vertical distribution of the particulate backscattering coefficient (bbp). This neural network-based method (SOCA-BBP for Satellite Ocean-Color merged with Argo data to infer the vertical distribution of the Particulate Backscattering coefficient) uses three main input components: (1) satellite-based surface estimates of bbp and chlorophyll a concentration matched up in space and time with (2) depth-resolved physical properties derived from temperature-salinity profiles measured by Argo profiling floats and (3) the day of the year of the considered satellite-Argo matchup. The neural network is trained and validated using a database including 4725 simultaneous profiles of temperature-salinity and bio-optical properties collected by Bio-Argo floats, with concomitant satellite-derived products. The Bio-Argo profiles are representative of the global open-ocean in terms of oceanographic conditions, making the proposed method applicable to most open-ocean environments. SOCA-BBP is validated using 20% of the entire database (global error of 21%). We present additional validation results based on two other independent data sets acquired (1) by four Bio-Argo floats deployed in major oceanic basins, not represented in the database used to train the method; and (2) during an AMT (Atlantic Meridional Transect) field cruise in 2009. These validation tests based on two fully independent data sets indicate the robustness of the predicted vertical distribution of bbp. To illustrate the potential of the method, we merged monthly climatological Argo profiles with ocean color products to produce a depth-resolved climatology of bbp for the global ocean.

  11. A novel neural network based image reconstruction model with scale and rotation invariance for target identification and classification for Active millimetre wave imaging

    NASA Astrophysics Data System (ADS)

    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. A Physics-driven Neural Networks-based Simulation System (PhyNNeSS) for multimodal interactive virtual environments involving nonlinear deformable objects

    PubMed Central

    De, Suvranu; Deo, Dhannanjay; Sankaranarayanan, Ganesh; Arikatla, Venkata S.

    2012-01-01

    Background While an update rate of 30 Hz is considered adequate for real time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. Methods In this work we present PhyNNeSS - a Physics-driven Neural Networks-based Simulation System - to address this long-standing technical challenge. The first step is an off-line pre-computation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. Results We present realistic simulation examples from interactive surgical simulation with real time force feedback. As an example, we have developed a deformable human stomach model and a Penrose-drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. Conclusions A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based pre-computational step allows training of neural networks which may be used in real time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal

  13. Development of a network based model to simulate the between-farm transmission of the porcine reproductive and respiratory syndrome virus.

    PubMed

    Thakur, Krishna K; Sanchez, Javier; Hurnik, Daniel; Poljak, Zvonimir; Opps, Sheldon; Revie, Crawford W

    2015-11-18

    Contact structure within a population can significantly affect the outcomes of infectious disease spread models. The objective of this study was to develop a network based simulation model for the between-farm spread of porcine reproductive and respiratory syndrome virus to assess the impact of contact structure on between-farm transmission of PRRS virus. For these farm level models, a hypothetical population of 500 swine farms following a multistage production system was used. The contact rates between farms were based on a study analyzing movement of pigs in Canada, while disease spread parameters were extracted from published literature. Eighteen distinct scenarios were designed and simulated by varying the mode of transmission (direct versus direct and indirect contact), type of index herd (farrowing, nursery and finishing), and the presumed network structures among swine farms (random, scale-free and small-world). PRRS virus was seeded in a randomly selected farm and 500 iterations of each scenario were simulated for 52 weeks. The median epidemic size by the end of the simulated period and percentage die-out for each scenario, were the key outcomes captured. Scenarios with scale-free network models resulted in the largest epidemic sizes, while scenarios with random and small-world network models resulted in smaller and similar epidemic sizes. Similarly, stochastic die-out percentage was least for scenarios with scale-free networks followed by random and small-world networks. Findings of the study indicated that incorporating network structures among the swine farms had a considerable impact on the spread of PRRS virus, highlighting the importance of understanding and incorporating realistic contact structures when developing infectious disease spread models for similar populations.

  14. A Physics-driven Neural Networks-based Simulation System (PhyNNeSS) for multimodal interactive virtual environments involving nonlinear deformable objects.

    PubMed

    De, Suvranu; Deo, Dhannanjay; Sankaranarayanan, Ganesh; Arikatla, Venkata S

    2011-08-01

    BACKGROUND: While an update rate of 30 Hz is considered adequate for real time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. METHODS: In this work we present PhyNNeSS - a Physics-driven Neural Networks-based Simulation System - to address this long-standing technical challenge. The first step is an off-line pre-computation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. RESULTS: We present realistic simulation examples from interactive surgical simulation with real time force feedback. As an example, we have developed a deformable human stomach model and a Penrose-drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. CONCLUSIONS: A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based pre-computational step allows training of neural networks which may be used in real time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal

  15. Genetics in Osteoarthritis

    PubMed Central

    Fernández-Moreno, Mercedes; Rego, Ignacio; Carreira-Garcia, Vanessa; Blanco, Francisco J

    2008-01-01

    Osteoarthritis is a degenerative articular disease with complex pathogeny because diverse factors interact causing a process of deterioration of the cartilage. Despite the multifactorial nature of this pathology, from the 50’s it´s known that certain forms of osteoarthritis are related to a strong genetic component. The genetic bases of this disease do not follow the typical patterns of mendelian inheritance and probably they are related to alterations in multiple genes. The identification of a high number of candidate genes to confer susceptibility to the development of the osteoarthritis shows the complex nature of this disease. At the moment, the genetic mechanisms of this disease are not known, however, which seems clear is that expression levels of several genes are altered, and that the inheritance will become a substantial factor in future considerations of diagnosis and treatment of the osteoarthritis. PMID:19516961

  16. Population genetics of Lithuanians.

    PubMed

    Ku inskas, V

    2001-01-01

    The primary objective of this article was to overview the present-day knowledge on genetic features of the Lithuanian population. Genetic differentiation within the Lithuanian population and the relationship between Lithuanians and other European populations was analysed by means of blood groups, serum protein polymorphisms and DNA markers including mtDNA. The results of the research have shown small differences between present-day Lithuanian ethnolinguistic groups, which probably go back to the prehistoric Baltic tribal structure. The Baltic peoples show a mixture of eastern and western genetic traits, e.g. a high frequency of the blood group B combined with a very high frequency of the Rh-negative blood group. Studies of the Baltic 'tribal gene' LWb indicate the presence of a considerable Baltic admixture in the neighbouring Finno-Ugric and Slavic populations.

  17. Intelligence, race, and genetics.

    PubMed

    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 further argue that race is a social construction with no scientific definition. Thus, studies of the relationship between race and other constructs may serve social ends but cannot serve scientific ends. No gene has yet been conclusively linked to intelligence, so attempts to provide a compelling genetic link of race to intelligence are not feasible at this time. The authors also show that heritability, a behavior-genetic concept, is inadequate in regard to providing such a link.

  18. Imposing genetic diversity.

    PubMed

    Sparrow, Robert

    2015-01-01

    The idea that a world in which everyone was born "perfect" would be a world in which something valuable was missing often comes up in debates about the ethics of technologies of prenatal testing and preimplantation genetic diagnosis (PGD). This thought plays an important role in the "disability critique" of prenatal testing. However, the idea that human genetic variation is an important good with significant benefits for society at large is also embraced by a wide range of figures writing in the bioethics literature, including some who are notoriously hostile to the idea that we should not select against disability. By developing a number of thought experiments wherein we are to contemplate increasing genetic diversity from a lower baseline in order to secure this value, I argue that this powerful intuition is more problematic than is generally recognized, especially where the price of diversity is the well-being of particular individuals. PMID:26030484

  19. Enhancing genetic virtue.

    PubMed

    Walker, Mark

    2009-09-01

    The Genetic Virtue Project (GVP) is a proposed interdisciplinary effort between philosophers, psychologists and geneticists to discover and enhance human ethics using biotechnology genetic correlates of virtuous behavior. The empirical plausibility that virtues have biological correlates is based on the claims that (a) virtues are a subset of personality, specifically, personality traits conceived of as "enduring behaviors," and (b) that there is ample evidence that personality traits have a genetic basis. The moral necessity to use the GVP for moral enhancement is based on the claims that we should eliminate evil (as understood generically, not religiously), as some evil is a function of human nature. The GVP is defended against several ethical and political criticisms.

  20. Genetics of opiate addiction.

    PubMed

    Reed, Brian; Butelman, Eduardo R; Yuferov, Vadim; Randesi, Matthew; Kreek, Mary Jeanne

    2014-11-01

    Addiction to MOP-r agonists such as heroin (and also addiction to prescription opioids) has reemerged as an epidemic in the twenty first century, causing massive morbidity. Understanding the genetics contributing to susceptibility to this disease is crucial for the identification of novel therapeutic targets, and also for discovery of genetic markers which would indicate relative protection or vulnerability from addiction, and relative responsiveness to pharmacotherapy. This information could thus eventually inform clinical practice. In this review, we focus primarily on association studies of heroin and opiate addiction, and further describe the studies which have been replicated in this field, and are thus more likely to be useful for translational efforts.

  1. [Genetics of schizophrenia].

    PubMed

    Jitoku, Daisuke; Yoshikawa, Takeo

    2013-04-01

    Many studies have reported that genetic variants play a major role in the pathogenesis of schizophrenia. In the past five years, the genetics knowledge base of schizophrenia has advanced considerably. These advances have mostly been through genome-wide association studies (GWAS) and copy number variations (CNVs) studies. Moreover, exome sequencing by using the next-generation DNA sequencers is launched in recent years. In the next few years, the high-throughput sequencing is likely to play an important role. These findings have produced new hypotheses about etiology of schizophrenia and they can indicate future research strategies. Therefore, it is expected that further studies will yield deeper insights. PMID:23678585

  2. Dynamic regulation of genetic pathways and targets during aging in Caenorhabditis elegans

    PubMed Central

    Zhou, Tao; Shao, Jiaofang; Ren, Xiaoliang; Zhao, Zhongying

    2014-01-01

    Numerous genetic targets and some individual pathways associated with aging have been identified using the worm model. However, less is known about the genetic mechanisms of aging in genome wide, particularly at the level of multiple pathways as well as the regulatory networks during aging. Here, we employed the gene expression datasets of three time points during aging in Caenorhabditis elegans (C. elegans) and performed the approach of gene set enrichment analysis (GSEA) on each dataset between adjacent stages. As a result, multiple genetic pathways and targets were identified as significantly down- or up-regulated. Among them, 5 truly aging-dependent signaling pathways including MAPK signaling pathway, mTOR signaling pathway, Wnt signaling pathway, TGF-beta signaling pathway and ErbB signaling pathway as well as 12 significantly associated genes were identified with dynamic expression pattern during aging. On the other hand, the continued declines in the regulation of several metabolic pathways have been demonstrated to display age-related changes. Furthermore, the reconstructed regulatory networks based on three of aging related Chromatin immunoprecipitation experiments followed by sequencing (ChIP–seq) datasets and the expression matrices of 154 involved genes in above signaling pathways provide new insights into aging at the multiple pathways level. The combination of multiple genetic pathways and targets needs to be taken into consideration in future studies of aging, in which the dynamic regulation would be uncovered. PMID:24739375

  3. Dynamic regulation of genetic pathways and targets during aging in Caenorhabditis elegans.

    PubMed

    He, Kan; Zhou, Tao; Shao, Jiaofang; Ren, Xiaoliang; Zhao, Zhongying; Liu, Dahai

    2014-03-01

    Numerous genetic targets and some individual pathways associated with aging have been identified using the worm model. However, less is known about the genetic mechanisms of aging in genome wide, particularly at the level of multiple pathways as well as the regulatory networks during aging. Here, we employed the gene expression datasets of three time points during aging in Caenorhabditis elegans (C. elegans) and performed the approach of gene set enrichment analysis (GSEA) on each dataset between adjacent stages. As a result, multiple genetic pathways and targets were identified as significantly down- or up-regulated. Among them, 5 truly aging-dependent signaling pathways including MAPK signaling pathway, mTOR signaling pathway, Wnt signaling pathway, TGF-beta signaling pathway and ErbB signaling pathway as well as 12 significantly associated genes were identified with dynamic expression pattern during aging. On the other hand, the continued declines in the regulation of several metabolic pathways have been demonstrated to display age-related changes. Furthermore, the reconstructed regulatory networks based on three of aging related Chromatin immunoprecipitation experiments followed by sequencing (ChIP-seq) datasets and the expression matrices of 154 involved genes in above signaling pathways provide new insights into aging at the multiple pathways level. The combination of multiple genetic pathways and targets needs to be taken into consideration in future studies of aging, in which the dynamic regulation would be uncovered.

  4. A Genetic Comparison of Two Alleged Subspecies of Philippine Cynomolgus Macaques

    PubMed Central

    Smith, David Glenn; Ng, Jillian; George, Debra; Trask, Jessica Satkoski; Houghton, Paul; Singh, Balbir; Villano, Jason; Kanthaswamy, Sreetharan

    2014-01-01

    Two subspecies of cynomolgus macaques (Macaca fascicularis) are alleged to co-exist in the Philippines, M. f. philippensis in the north and M. f. fascicularis in the south. However, genetic differences between the cynomolgus macaques in the two regions have never been studied to document the propriety of their subspecies status. We genotyped samples of cynomolgus macaques from Batangas in southwestern Luzon and Zamboanga in southwestern Mindanao for 15 short tandem repeat (STR) loci and sequenced an 835 bp fragment of the mtDNA of these animals. The STR genotypes were compared with those of cynomolgus macaques from southern Sumatra, Singapore, Mauritius and Cambodia, and the mtDNA sequences of both Philippine populations were compared with those of cynomolgus macaques from southern Sumatra, Indonesia and Sarawak, Malaysia. We conducted STRUCTURE and PCA analyses based on the STRs and constructed a median joining network based on the mtDNA sequences. The Philippine population from Batangas exhibited much less genetic diversity and greater genetic divergence from all other populations, including the Philippine population from Zamboanga. Sequences from both Batangas and Zamboanga were most closely related to two different mtDNA haplotypes from Sarawak from which they are apparently derived. Those from Zamboanga were more recently derived than those from Batangas, consistent with their later arrival in the Philippines. However, clustering analyses do not support a sufficient genetic distinction of cynomolgus macaques from Batangas from other regional populations assigned to subspecies M. f. fascicularis to warrant the subspecies distinction M. f. philippensis. PMID:24979664

  5. The genetics of the epilepsies.

    PubMed

    El Achkar, Christelle M; Olson, Heather E; Poduri, Annapurna; Pearl, Phillip L

    2015-07-01

    While genetic causes of epilepsy have been hypothesized from the time of Hippocrates, the advent of new genetic technologies has played a tremendous role in elucidating a growing number of specific genetic causes for the epilepsies. This progress has contributed vastly to our recognition of the epilepsies as a diverse group of disorders, the genetic mechanisms of which are heterogeneous. Genotype-phenotype correlation, however, is not always clear. Nonetheless, the developments in genetic diagnosis raise the promise of a future of personalized medicine. Multiple genetic tests are now available, but there is no one test for all possible genetic mutations, and the balance between cost and benefit must be weighed. A genetic diagnosis, however, can provide valuable information regarding comorbidities, prognosis, and even treatment, as well as allow for genetic counseling. In this review, we will discuss the genetic mechanisms of the epilepsies as well as the specifics of particular genetic epilepsy syndromes. We will include an overview of the available genetic testing methods, the application of clinical knowledge into the selection of genetic testing, genotype-phenotype correlations of epileptic disorders, and therapeutic advances as well as a discussion of the importance of genetic counseling. PMID:26008807

  6. Genetic algorithms based on genetic grammar

    NASA Astrophysics Data System (ADS)

    Hofestaedt, Ralf; Mueller, Hermann

    1992-08-01

    The cell represents the basic unit of life. It can be interpreted as a chemical machine which can solve special problems. The present knowledge we have of molecular biology allows the characterization of the metabolism as a processing method. This method is an evolutionary product which has been developed over millions of years. First we will present the analyzed features of the metabolism. Then we will go on to compare this processing method with methods which are discussed in computer science. The comparison shows that there is no method in the field of computer science which uses all the metabolic features. This is the reason why we formalize the metabolic processing method. In this paper we choose to use a grammatical formalism. A genetic grammar is the basis of the metabolic system which represents the metabolic processing method. The basic unit of this system (logic unit) will be shown. This allows the discussion of the complexity of realizing the metabolic system in hardware.

  7. Pediatric genetic disorders of lens.

    PubMed

    Nihalani, Bharti R

    2014-12-01

    Pediatric genetic disorders of lens include various cataractous and non-cataractous anomalies. The purpose of this review is to help determine the genetic cause based on the lens appearance, ocular and systemic associations. Children with bilateral cataracts require a comprehensive history, ophthalmic and systemic examination to guide further genetic evaluation. With advancements in genetics, it is possible to determine the genetic mutations and assess phenotype genotype correlation in different lens disorders. The genetic diagnosis helps the families to better understand the disorder and develop realistic expectations as to the course of their child's disorder. PMID:27625879

  8. Pediatric genetic disorders of lens

    PubMed Central

    Nihalani, Bharti R.

    2014-01-01

    Pediatric genetic disorders of lens include various cataractous and non-cataractous anomalies. The purpose of this review is to help determine the genetic cause based on the lens appearance, ocular and systemic associations. Children with bilateral cataracts require a comprehensive history, ophthalmic and systemic examination to guide further genetic evaluation. With advancements in genetics, it is possible to determine the genetic mutations and assess phenotype genotype correlation in different lens disorders. The genetic diagnosis helps the families to better understand the disorder and develop realistic expectations as to the course of their child's disorder.

  9. Paper Genetic Engineering.

    ERIC Educational Resources Information Center

    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…

  10. Genetic Building Blocks

    ERIC Educational Resources Information Center

    Roberg, Ezra

    2004-01-01

    The "Central Dogma" of genetics states that one gene, located in a DNA molecule, is ultimately translated into one protein. As important as this idea is, many teachers shy away from teaching the actual mechanism of gene translation, and many students find the concepts abstract and inaccessible. This article describes a unit, called Genetics…

  11. Solving Problems in Genetics

    ERIC Educational Resources Information Center

    Aznar, Mercedes Martinez; Orcajo, Teresa Ibanez

    2005-01-01

    A teaching unit on genetics and human inheritance using problem-solving methodology was undertaken with fourth-level Spanish Secondary Education students (15 year olds). The goal was to study certain aspects of the students' learning process (concepts, procedures and attitude) when using this methodology in the school environment. The change…

  12. Molecular genetics of ependymoma

    PubMed Central

    Yao, Yuan; Mack, Stephen C.; Taylor, Michael D.

    2011-01-01

    Brain tumors are the leading cause of cancer death in children, with ependymoma being the third most common and posing a significant clinical burden. Its mechanism of pathogenesis, reliable prognostic indicators, and effective treatments other than surgical resection have all remained elusive. Until recently, ependymoma research was hindered by the small number of tumors available for study, low resolution of cytogenetic techniques, and lack of cell lines and animal models. Ependymoma heterogeneity, which manifests as variations in tumor location, patient age, histological grade, and clinical behavior, together with the observation of a balanced genomic profile in up to 50% of cases, presents additional challenges in understanding the development and progression of this disease. Despite these difficulties, we have made significant headway in the past decade in identifying the genetic alterations and pathways involved in ependymoma tumorigenesis through collaborative efforts and the application of microarray-based genetic (copy number) and transcriptome profiling platforms. Genetic characterization of ependymoma unraveled distinct mRNA-defined subclasses and led to the identification of radial glial cells as its cell type of origin. This review summarizes our current knowledge in the molecular genetics of ependymoma and proposes future research directions necessary to further advance this field. PMID:21959044

  13. Genetically Engineering Entomopathogenic Fungi.

    PubMed

    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. PMID:27131325

  14. Genetics of atopic dermatitis.

    PubMed

    Bussmann, Caroline; Weidinger, Stephan; Novak, Natalija

    2011-09-01

    Atopic dermatitis (AD) is a multifactorial disease, with a strong genetic predisposition. Genome-wide studies as well as candidate gene studies revealed several susceptibility loci. Since the observation of a strong association of "loss of function" mutations in the filaggrin gene with AD, the epidermal barrier was rediscovered as important pathophysiological co-factor of this disease. PMID:21518425

  15. [Genetics of contact allergy].

    PubMed

    Schnuch, A

    2011-10-01

    The genetics of contact allergy (CA) is still only partly understood, despite decades of research. This might be due to inadequately defined phenotypes used in the past. Therefore we suggested studying an extreme phenotype, namely, polysensitization (sensitization to 3 or more unrelated allergens). Another approach to unravel the genetics of CA has been the study of candidate genes. In this review, we summarize studies on the associations between genetic variation (e.g. SNPs) in certain candidate genes and CA. The following polymorphisms and mutations were studied: (1) filaggrin, (2) N-acetyltransferase (NAT1 and 2), (3) glutathione-S-transferase (GST M and T), (4) manganese superoxide dismutase, (5) angiotensin-converting enzyme (ACE), (6) tumor necrosis factor (TNF), and (7) interleukin-16 (IL16). The polymorphisms of NAT1/2, GST M/T, ACE, TNF, and IL16 were shown to be associated with an increased risk of CA. In one of our studies, the increased risk conferred by the TNF and IL16 polymorphisms was confined to polysensitized individuals. Other relevant candidate genes may be identified by studying diseases related to CA in terms of clinical symptoms, a more general pathology (inflammation) and possibly an overlapping genetic background, such as irritant contact dermatitis. PMID:21904893

  16. The revised genetic code

    NASA Astrophysics Data System (ADS)

    Ninio, Jacques

    1990-03-01

    Recent findings on the genetic code are reviewed, including selenocysteine usage, deviations in the assignments of sense and nonsense codons, RNA editing, natural ribosomal frameshifts and non-orthodox codon-anticodon pairings. A multi-stage codon reading process is presented.

  17. Genetics and Genomics

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Good progress is being made on genetics and genomics of sugar beet, however it is in process and the tools are now being generated and some results are being analyzed. The GABI BeetSeq project released a first draft of the sugar beet genome of KWS2320, a dihaploid (see http://bvseq.molgen.mpg.de/Gen...

  18. Safe genetically engineered plants

    NASA Astrophysics Data System (ADS)

    Rosellini, D.; Veronesi, F.

    2007-10-01

    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.

  19. Genetic disorders of collagen.

    PubMed Central

    Tsipouras, P; Ramirez, F

    1987-01-01

    Osteogenesis imperfecta, Ehlers-Danlos syndrome, and Marfan syndrome form a group of genetic disorders of connective tissue. These disorders exhibit remarkable clinical heterogeneity which reflects their underlying biochemical and molecular differences. Defects in collagen types I and III have been found in all three syndromes. PMID:3543367

  20. Behavioural genetics: An introduction.

    PubMed

    Sluyter, F; Ellenbroek, B A

    1999-06-01

    Behavioural genetics is the study of the hereditary influence on behaviour, and can therefore be regarded as the intersection between behavioural sciences and genetics. As with most other fields of research it is difficult to exactly pinpoint when behavioural genetics started. In fact, one might say that the notion behavioural traits can be inherited may have appeared in human thought as early at 8000 BC, when the domestication of the dog began. The scientific era of behavioural genetics is generally considered to start with Charles Darwin. In his famous book On the Origin of Species by Means of natural Selection, or the Preservation of favoured Races in the Struggle for Life, published in 1859 (and sold out the first day), he devoted an entire chapter on instinctive behavioural patterns. Some years later, in his book The Descent of Man and Selection in Relation to Sex, he clearly stated that the difference between the mind of a human being and the mind of an animal 'is certainly one of degree and not of kind'. Moreover he gave considerable thought that mental powers (and insanity) are heritable aspects.

  1. Intelligence, Race, and Genetics

    ERIC Educational Resources Information Center

    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…

  2. Genetic Dominance & Cellular Processes

    ERIC Educational Resources Information Center

    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…

  3. Maize Genetic Resources

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This chapter describes the resources held at the Maize Genetics Cooperation • Stock Center in detail and also provides some information about the North Central Regional Plant Introduction Station (NCRPIS) in Ames, IA, Centro Internacional de Mejoramiento de Maiz y Trigo (CIMMYT) in Mexico, and the N...

  4. Genetics of Retinoblastoma.

    PubMed

    Mallipatna, Ashwin; Marino, Meghan; Singh, Arun D

    2016-01-01

    Retinoblastoma is a malignant retinal tumor that affects young children. Mutations in the RB1 gene cause retinoblastoma. Mutations in both RB1 alleles within the precursor retinal cell are essential, with one mutation that may be germline or somatic and the second one that is always somatic. Identification of the RB1 germline status of a patient allows differentiation between sporadic and heritable retinoblastoma variants. Application of this knowledge is crucial for assessing short-term (risk of additional tumors in the same eye and other eye) and long-term (risk of nonocular malignant tumors) prognosis and offering cost-effective surveillance strategies. Genetic testing and genetic counseling are therefore essential components of care for all children diagnosed with retinoblastoma. The American Joint Committee on Cancer has acknowledged the importance of detecting this heritable trait and has introduced the letter "H" to denote a heritable trait of all cancers, starting with retinoblastoma (in publication). In this article, we discuss the clinically relevant aspects of genetic testing and genetic counseling for a child with retinoblastoma. PMID:27488068

  5. Genetics Home Reference: sialuria

    MedlinePlus

    ... conditions more common in particular ethnic groups? Genetic Changes Mutations in the GNE gene cause sialuria . The GNE gene provides instructions for making an enzyme found in cells and tissues throughout the body. This enzyme is involved in a chemical pathway that produces sialic acid, which is a ...

  6. Genetically Engineering Entomopathogenic Fungi.

    PubMed

    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.

  7. Genetics and the nephron.

    PubMed

    Marlais, M; Coward, R J

    2014-04-01

    There have been phenomenal advances in our understanding of renal biology over the last 20 years through our ability to define the genetic mutations causing kidney disease in children. This review will take you through a trip down the nephron and highlight how these conditions may present to the paediatrician and the molecular basis for their biological effects.

  8. Genetic recombination. [Escherichia coli

    SciTech Connect

    Stahl, F.W.

    1987-02-01

    The molecular pathways of gene recombination are explored and compared in studies of the model organisms, Escherichia coli and phase lambda. In the discussion of data from these studies it seems that recombination varies with the genetic idiosyncrasies of the organism and may also vary within a single organism.

  9. Genetic Technologies and Ethics

    PubMed Central

    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. PMID:23908725

  10. [Genetic predisposition for alcoholism].

    PubMed

    Agarwal-Kozlowski, K; Agarwal, D P

    2000-04-01

    A number of socio-economic, cultural, biobehavioral factors and ethnic/gender differences are among the strongest determinants of drinking patterns in a society. Both epidemiological and clinical studies have implicated the excessive use of alcohol in the risk of developing a variety of organ, neuronal and metabolic disorders. Alcohol abuse related metabolic derangements affect almost all body organs and their functions. Race and gender differences in drinking patterns may play an important role in the development of medical conditions associated with alcohol abuse. The incidence of alcoholism in a community is influenced by per capita alcohol consumption and covariates with the relative price and availability of alcoholic drinks. The majority of the family, twin and adoption studies suggest that alcoholism is familial, a significant proportion of which can be attributed to genetic factors. The question is how much of the variance is explained by genetic factors and to what degree is this genetically mediated disorder moderated by personal characteristics. Among the most salient personal characteristics moderating, the genetic vulnerability may be factors such as age, ethnicity, and presence of psychiatric co morbidity. Cultural factors and familial environmental factors are most likely predictors as well.

  11. Genetics of alcoholism.

    PubMed

    Edenberg, Howard J; Foroud, Tatiana

    2014-01-01

    Multiple lines of evidence strongly indicate that genetic factors contribute to the risk for alcohol use disorders (AUD). There is substantial heterogeneity in AUD, which complicates studies seeking to identify specific genetic factors. To identify these genetic effects, several different alcohol-related phenotypes have been analyzed, including diagnosis and quantitative measures related to AUDs. Study designs have used candidate gene analyses, genetic linkage studies, genomewide association studies (GWAS), and analyses of rare variants. Two genes that encode enzymes of alcohol metabolism have the strongest effect on AUD: aldehyde dehydrogenase 2 and alcohol dehydrogenase 1B each has strongly protective variants that reduce risk, with odds ratios approximately 0.2-0.4. A number of other genes important in AUD have been identified and replicated, including GABRA2 and alcohol dehydrogenases 1B and 4. GWAS have identified additional candidates. Rare variants are likely also to play a role; studies of these are just beginning. A multifaceted approach to gene identification, targeting both rare and common variations and assembling much larger datasets for meta-analyses, is critical for identifying the key genes and pathways important in AUD.

  12. Demonstration: Genetic Jewelry

    ERIC Educational Resources Information Center

    Atkins, Thomas; Roderick, Joyce

    2006-01-01

    In order for students to understand genetics and evolution, they must first understand the structure of the DNA molecule. The function of DNA proceeds from its unique structure, a structure beautifully adapted for information storage, transcription, translation into amino acid sequences, replication, and time travel. The activity described in this…

  13. The new genetics

    SciTech Connect

    Jaroff, L.

    1991-01-01

    Knowing the location and make-up of each of the 50,000 to 100,000 human genes will revolutionize the practice of medicine. This knowledge will lead to tailor-made therapies not only for treating disease but also for preventing it - in short, to a new concept of patient care. The Human Genome Project, a 15-year, $3 billion quest to determine the nucleotide sequence of the entire human genome, will make this possible. In The New Genetics, Leon Jaroff recounts the long path of discovery thatt has led to this huge new scientific venture - from the theory of heredity put forth by Aristotle more than 2,000 years ago to the current attempts to treat adenosine deaminase (ADA) deficiency and malignant melanoma via gene therapy. Against this background, the geneticists, molecular biologists, clinicians, and ethicists involved in the Human Genome Project describe their work and how it will provide physicians with ever more precise and effective tools to treat human disease. Jaroff also reveals the other, more problematic side of the story. Patients with an undesirable genetic profile may be subject to discrimination by private insurers. Physicians who fail to recommend genetic screening may find themselves victims of malpractice or wrongful-life suits. Indeed, these issues and others have already begun to affect physicians. The New Genetics makes it abundantly clear tha a revolution has arrived, and that physicians must be prepared to cope with the new order.

  14. Genetic mechanisms of parenting.

    PubMed

    Mileva-Seitz, Viara R; Bakermans-Kranenburg, Marian J; van IJzendoorn, Marinus H

    2016-01-01

    This article is part of a Special Issue "Parental Care". The complexities of parenting behavior in humans have been studied for decades. Only recently did we begin to probe the genetic and epigenetic mechanisms underlying these complexities. Much of the research in this field continues to be informed by animal studies, where genetic manipulations and invasive tools allow to peek into and directly observe the brain during the expression of maternal behavior. In humans, studies of adult twins who are parents can suggest dimensions of parenting that might be more amenable to a genetic influence. Candidate gene studies can test specific genes in association with parental behavior based on prior knowledge of those genes' function. Gene-by-environment interactions of a specific kind indicating differential susceptibility to the environment might explain why some parents are more resilient and others are more vulnerable to stressful life events. Epigenetic studies can provide the bridge often necessary to explain why some individuals behave differently from others despite common genetic influences. There is a much-needed expansion in parenting research to include not only mothers as the focus-as has been the case almost exclusively to date-but also fathers, grandparents, and other caregivers.

  15. Oprelvekin. Genetics Institute.

    PubMed

    Sitaraman, S V; Gewirtz, A T

    2001-10-01

    Genetics Institute has developed and launched oprelvekin (rhIL-11; Neumega), a recombinant form of human IL-11. In November 1997, the FDA cleared oprelvekin for the prevention of severe thrombocytopenia and the reduction of the need for platelet transfusions following myelosuppressive chemotherapy in susceptible patients with non-myeloid malignancies 12703021. The product was launched at the end of 1997 [312556]. By December 1999, phase III trials for Crohn's disease (CD) were underway [363007]. Genetics Institute had commenced a 150-patient phase II trial for mild-to-moderate CD and mucositis and the company planned to file regulatory procedures for the indication of CD in 1999 [271210]. An oral formulation for this indication has been developed. Oprelvekin is also undergoing phase I clinical trials for colitis [396157], phase II clinical trials for rheumatoid arthritis [413835] and clinical trials for psoriasis [299644]. In March 1997, Wyeth-Ayerst became the licensee for Europe, Africa, Latin America and Asia (with the exception of Japan). Genetics Institute holds marketing rights for North America [239273]. In Japan, oprelvekin is being developed by Genetics Institute and Yamanouchi; phase III trials have commenced [295049] and were ongoing in May 2001 [411763]. In April 1996, analysts at Yamaichi estimated launch in 2001 and maximum annual sales of over yen 10 billion [215896]. In January 1998, Morgan Stanley Dean Witter predicted Yamanouchi's share of sales to be yen 1 billion in 2001, rising to yen 2 billion in 2002 [315458]. Sales of oprelvekin were US $34 million for Genetics institute in fiscal 2000 while, in July 2001, Credit Suisse First Boston estimated that this figure will be US $30 million and US $34 million in 2001 and 2002, respectively [416883]. PMID:11890354

  16. [Genetics and public health].

    PubMed

    Penchaszadeh, V B

    1993-07-01

    In order to draw attention to the need for public health action in genetics in Latin America, the author begins by giving a brief review of congenital anomalies, including hereditary diseases and chromosomal anomalies. He notes that these defects affect at least 5% of live births in the different regions of the world, regardless of the development status or ethnic make-up of their populations. In the Region of the Americas, birth defects rank somewhere between second and fifth place among causes of death in children under 1 year of age, and account for 2% to 27% of infant mortality. It is logical to expect that these disorders will take on more relative importance as the general indicators of child health improve, as has been the case in industrialized countries. The fact that pathologies of genetic origin affect a wide range of organs and systems, are chronic, and require expensive therapy and rehabilitation means that they demand services that countries must be prepared to provide. The author proposes three general objectives for health activities regarding genetics: to minimize clinical manifestations in individuals who are born with congenital anomalies by means of adequate care at all service levels; to improve the quality of life for those individuals and their families by helping them to become involved in the normal life of their communities; and to ensure that people at high risk of conceiving children with genetic diseases receive counseling and support services so that they can exercise their right to informed reproduction. Finally, he recommends eight strategies for setting up genetic health programs with the resources available in each country. PMID:8373531

  17. Genetic Engineering and the Amelioration of Genetic Defect

    ERIC Educational Resources Information Center

    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 containment of…

  18. Genetics Home Reference: lactose intolerance

    MedlinePlus

    ... DM. Lactose digestion and the evolutionary genetics of lactase persistence. Hum Genet. 2009 Jan;124(6):579-91. ... Swallow DM, Thomas MG. A worldwide correlation of lactase persistence phenotype and genotypes. BMC Evol Biol. 2010 Feb ...

  19. Genetics Home Reference: Pompe disease

    MedlinePlus

    ... of Pompe disease: Baby's First Test GeneReview: Glycogen Storage Disease Type II (Pompe Disease) Genetic Testing Registry: Glycogen storage disease type II, infantile Genetic Testing Registry: Glycogen ...

  20. Genetics Home Reference: Northern epilepsy

    MedlinePlus

    ... Understand Genetics Home Health Conditions Northern epilepsy Northern epilepsy Enable Javascript to view the expand/collapse boxes. Download PDF Open All Close All Description Northern epilepsy is a genetic condition that causes recurrent seizures ( ...

  1. Genetic discrimination in the workplace.

    PubMed

    Miller, P S

    1998-01-01

    Author argues that the Americans with Disabilities Act prohibits discrimination against workers based on their genetic makeup. He also examines state legislation and recently proposed federal legislation prohibiting genetic discrimination.

  2. Genetics Home Reference: frontometaphyseal dysplasia

    MedlinePlus

    ... bowed limbs, an abnormal curvature of the spine ( scoliosis ), and abnormalities of the fingers and hands. Characteristic ... and genetic heterogeneity in frontometaphyseal dysplasia: severe progressive scoliosis in two families. Am J Med Genet A. ...

  3. NCI Dictionary of Genetics Terms

    Cancer.gov

    A dictionary of more than 150 genetics-related terms written for healthcare professionals, developed to support the comprehensive, evidence-based, peer-reviewed PDQ cancer genetics information summaries.

  4. [Hereditary hearing loss: genetic counselling].

    PubMed

    Cabanillas Farpón, Rubén; Cadiñanos Bañales, Juan

    2012-01-01

    The aim of this review is to provide an updated overview of hereditary hearing loss, with special attention to the etiological diagnosis of sensorineural hearing loss, the genes most frequently mutated in our environment, the techniques available for their analysis and the clinical implications of genetic diagnosis. More than 60% of childhood sensorineural hearing loss is genetic. In adults, the percentage of hereditary hearing loss is unknown. Genetic testing is the highest yielding test for evaluating patients with sensorineural hearing loss. The process of genetic counselling is intended to inform patients and their families of the medical, psychological and familial implications of genetic diseases, as well as the risks, benefits and limitations of genetic testing. The implementation of any genetic analysis must be always preceded by an appropriate genetic counselling process.

  5. Genetic Testing and Eye Disease

    MedlinePlus

    ... a History of Eye Disease, Do You Need Genetic Testing? Mar. 23, 2012 Thanks to news coverage, ... of breast or ovarian cancer. Physicians now use genetic tests to decide on treatment for some types ...

  6. Genetic Features of Turner Syndrome

    MedlinePlus

    ... Current Studies Publications Lab Staff Contact Info Links Genetic Features Quick Navigation Introduction X-monosomy X-mosaicism ... Figure 3. X Chromosome Abnormalities Figure 4. Mosaicism Genetic Features of Turner Syndrome Turner syndrome is a ...

  7. Genetics Home Reference: diastrophic dysplasia

    MedlinePlus

    ... 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) ...

  8. Evolutionary Genetics: Reuse, Recycle, Converge.

    PubMed

    Miller, Charles J J; Matute, Daniel R

    2016-09-26

    Our understanding of how genetic changes underlie the evolution of traits is growing fast. Two new studies now show that changes in the same genetic loci can drive the evolution of the same trait in multiple Drosophila species. PMID:27676299

  9. Genetic Screening for Employment Purposes.

    ERIC Educational Resources Information Center

    Olian, Judy D.

    1984-01-01

    Discusses genetic screening in the employment context, which involves identification of individuals hypersusceptible to toxins in the work environment. Examines the status of genetic screening devices against standard testing and legal criteria. (LLL)

  10. Selected Readings in Genetic Engineering

    ERIC Educational Resources Information Center

    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)

  11. Thoughts on Human Genetics Education.

    ERIC Educational Resources Information Center

    Epstein, Charles J.

    1980-01-01

    The director of the Birth Defects Center at the University of California at San Francisco addresses the reasons for developing good ways of teaching human genetics. Genetic counseling is discussed within the context of several case histories. (SA)

  12. Genetics Home Reference: Silver syndrome

    MedlinePlus

    ... Me Understand Genetics Home Health Conditions Silver syndrome Silver syndrome Enable Javascript to view the expand/collapse boxes. Download PDF Open All Close All Description Silver syndrome belongs to a group of genetic disorders ...

  13. Genetics Home Reference: adiposis dolorosa

    MedlinePlus

    Skip to main content Your Guide to Understanding Genetic Conditions Enable Javascript for addthis links to activate. ... Conditions Genes Chromosomes & mtDNA Resources Help Me Understand Genetics Home Health Conditions adiposis dolorosa adiposis dolorosa Enable ...

  14. Genetics Home Reference: Canavan disease

    MedlinePlus

    Skip to main content Your Guide to Understanding Genetic Conditions Enable Javascript for addthis links to activate. ... Conditions Genes Chromosomes & mtDNA Resources Help Me Understand Genetics Home Health Conditions Canavan disease Canavan disease Enable ...

  15. Genetics Home Reference: Carney complex

    MedlinePlus

    Skip to main content Your Guide to Understanding Genetic Conditions Enable Javascript for addthis links to activate. ... Conditions Genes Chromosomes & mtDNA Resources Help Me Understand Genetics Home Health Conditions Carney complex Carney complex Enable ...

  16. Genetics Home Reference: cardiofaciocutaneous syndrome

    MedlinePlus

    Skip to main content Your Guide to Understanding Genetic Conditions Enable Javascript for addthis links to activate. ... Conditions Genes Chromosomes & mtDNA Resources Help Me Understand Genetics Home Health Conditions cardiofaciocutaneous syndrome cardiofaciocutaneous syndrome Enable ...

  17. Genetics Home Reference: Caffey disease

    MedlinePlus

    Skip to main content Your Guide to Understanding Genetic Conditions Enable Javascript for addthis links to activate. ... Conditions Genes Chromosomes & mtDNA Resources Help Me Understand Genetics Home Health Conditions Caffey disease Caffey disease Enable ...

  18. Genetics Home Reference: Blau syndrome

    MedlinePlus

    ... inherited version of the disorder called early-onset sarcoidosis. Related Information What does it mean if a ... Genetic Testing Registry: Blau syndrome Genetic Testing Registry: Sarcoidosis, early-onset Merck Manual Consumer Version: Overview of ...

  19. Genetics Home Reference: Feingold syndrome

    MedlinePlus

    ... Brunner HG. Feingold syndrome: clinical review and genetic mapping. Am J Med Genet A. 2003 Nov 1; ... Brunner HG. MYCN haploinsufficiency is associated with reduced brain size and intestinal atresias in Feingold syndrome. Nat ...

  20. [Genetic information and future medicine].

    PubMed

    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.