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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 Prediction of Cancer under Genetic Storm

    PubMed Central

    Ay, Ahmet; Gong, Dihong; Kahveci, Tamer

    2014-01-01

    Classification of cancer patients using traditional methods is a challenging task in the medical practice. Owing to rapid advances in microarray technologies, currently expression levels of thousands of genes from individual cancer patients can be measured. The classification of cancer patients by supervised statistical learning algorithms using the gene expression datasets provides an alternative to the traditional methods. Here we present a new network-based supervised classification technique, namely the NBC method. We compare NBC to five traditional classification techniques (support vector machines (SVM), k-nearest neighbor (kNN), naïve Bayes (NB), C4.5, and random forest (RF)) using 50–300 genes selected by five feature selection methods. Our results on five large cancer datasets demonstrate that NBC method outperforms traditional classification techniques. Our analysis suggests that using symmetrical uncertainty (SU) feature selection method with NBC method provides the most accurate classification strategy. Finally, in-depth analysis of the correlation-based co-expression networks chosen by our network-based classifier in different cancer classes shows that there are drastic changes in the network models of different cancer types. PMID:25368507

  3. Integrative network-based approach identifies key genetic elements in breast invasive carcinoma

    PubMed Central

    2015-01-01

    Background Breast cancer is a genetically heterogeneous type of cancer that belongs to the most prevalent types with a high mortality rate. Treatment and prognosis of breast cancer would profit largely from a correct classification and identification of genetic key drivers and major determinants driving the tumorigenesis process. In the light of the availability of tumor genomic and epigenomic data from different sources and experiments, new integrative approaches are needed to boost the probability of identifying such genetic key drivers. We present here an integrative network-based approach that is able to associate regulatory network interactions with the development of breast carcinoma by integrating information from gene expression, DNA methylation, miRNA expression, and somatic mutation datasets. Results Our results showed strong association between regulatory elements from different data sources in terms of the mutual regulatory influence and genomic proximity. By analyzing different types of regulatory interactions, TF-gene, miRNA-mRNA, and proximity analysis of somatic variants, we identified 106 genes, 68 miRNAs, and 9 mutations that are candidate drivers of oncogenic processes in breast cancer. Moreover, we unraveled regulatory interactions among these key drivers and the other elements in the breast cancer network. Intriguingly, about one third of the identified driver genes are targeted by known anti-cancer drugs and the majority of the identified key miRNAs are implicated in cancerogenesis of multiple organs. Also, the identified driver mutations likely cause damaging effects on protein functions. The constructed gene network and the identified key drivers were compared to well-established network-based methods. Conclusion The integrated molecular analysis enabled by the presented network-based approach substantially expands our knowledge base of prospective genomic drivers of genes, miRNAs, and mutations. For a good part of the identified key drivers

  4. 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. PMID:22616542

  5. 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

  6. Stability of genetic regulatory networks based on switched systems and mixed time-delays.

    PubMed

    Wang, Lan; Luo, Zong-Ping; Yang, Hui-Lin; Cao, Jinde

    2016-08-01

    In this paper, the switched genetic regulatory networks (GRNs) are modeled from a real biological system, based on switched systems, noise and mixed time-delays. Global asymptotical stability for the proposed switched GRNs are studied by the Lyapunov method and the matrix inequality techniques. Some new sufficient conditions are obtained to ensure the global asymptotical stability of the proposed switched GRNs. Furthermore, the proposed LMI results are computationally efficient as it can be solved numerically with standard commercial software. Finally, an example is provided to illustrate the usefulness of the results. PMID:27326659

  7. 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.

  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. 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. PMID:26298638

  10. A Gene-Phenotype Network Based on Genetic Variability for Drought Responses Reveals Key Physiological Processes in Controlled and Natural Environments

    PubMed Central

    Rengel, David; Arribat, Sandrine; Maury, Pierre; Martin-Magniette, Marie-Laure; Hourlier, Thibaut; Laporte, Marion; Varès, Didier; Carrère, Sébastien; Grieu, Philippe; Balzergue, Sandrine; Gouzy, Jérôme

    2012-01-01

    Identifying the connections between molecular and physiological processes underlying the diversity of drought stress responses in plants is key for basic and applied science. Drought stress response involves a large number of molecular pathways and subsequent physiological processes. Therefore, it constitutes an archetypical systems biology model. We first inferred a gene-phenotype network exploiting differences in drought responses of eight sunflower (Helianthus annuus) genotypes to two drought stress scenarios. Large transcriptomic data were obtained with the sunflower Affymetrix microarray, comprising 32423 probesets, and were associated to nine morpho-physiological traits (integrated transpired water, leaf transpiration rate, osmotic potential, relative water content, leaf mass per area, carbon isotope discrimination, plant height, number of leaves and collar diameter) using sPLS regression. Overall, we could associate the expression patterns of 1263 probesets to six phenotypic traits and identify if correlations were due to treatment, genotype and/or their interaction. We also identified genes whose expression is affected at moderate and/or intense drought stress together with genes whose expression variation could explain phenotypic and drought tolerance variability among our genetic material. We then used the network model to study phenotypic changes in less tractable agronomical conditions, i.e. sunflower hybrids subjected to different watering regimes in field trials. Mapping this new dataset in the gene-phenotype network allowed us to identify genes whose expression was robustly affected by water deprivation in both controlled and field conditions. The enrichment in genes correlated to relative water content and osmotic potential provides evidence of the importance of these traits in agronomical conditions. PMID:23056196

  11. 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…

  12. Network based high performance concurrent computing

    SciTech Connect

    Sunderam, V.S.

    1991-01-01

    The overall objectives of this project are to investigate research issues pertaining to programming tools and efficiency issues in network based concurrent computing systems. The basis for these efforts is the PVM project that evolved during my visits to Oak Ridge Laboratories under the DOE Faculty Research Participation program; I continue to collaborate with researchers at Oak Ridge on some portions of the project.

  13. Dissecting a Network-Based Education System

    ERIC Educational Resources Information Center

    Davis, Tiffany; Yoo, Seong-Moo; Pan, Wendi

    2005-01-01

    The Alabama Learning Exchange (ALEX; www.alex.state.al.us) is a network-based education system designed and implemented to help improve education in Alabama. It accomplishes this goal by providing a single location for the state's K-12 educators to find information that will help improve their classroom effectiveness. The ALEX system includes…

  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

    Homozygous; 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. 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 ...

  19. 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'.

  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. 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.

  2. Network-based modular latent structure analysis

    PubMed Central

    2014-01-01

    Background High-throughput expression data, such as gene expression and metabolomics data, exhibit modular structures. Groups of features in each module follow a latent factor model, while between modules, the latent factors are quasi-independent. Recovering the latent factors can shed light on the hidden regulation patterns of the expression. The difficulty in detecting such modules and recovering the latent factors lies in the high dimensionality of the data, and the lack of knowledge in module membership. Methods Here we describe a method based on community detection in the co-expression network. It consists of inference-based network construction, module detection, and interacting latent factor detection from modules. Results In simulations, the method outperformed projection-based modular latent factor discovery when the input signals were not Gaussian. We also demonstrate the method's value in real data analysis. Conclusions The new method nMLSA (network-based modular latent structure analysis) is effective in detecting latent structures, and is easy to extend to non-linear cases. The method is available as R code at http://web1.sph.emory.edu/users/tyu8/nMLSA/. PMID:25435002

  3. 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.

  4. 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.

  5. 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 ...

  6. 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...

  7. Network-Based Methods to Identify Highly Discriminating Subsets of Biomarkers.

    PubMed

    Sajjadi, Seyed Javad; Qian, Xiaoning; Zeng, Bo; Adl, Amin Ahmadi

    2014-01-01

    Complex diseases such as various types of cancer and diabetes are conjectured to be triggered and influenced by a combination of genetic and environmental factors. To integrate potential effects from interplay among underlying candidate factors, we propose a new network-based framework to identify effective biomarkers by searching for groups of synergistic risk factors with high predictive power to disease outcome. An interaction network is constructed with node weights representing individual predictive power of candidate factors and edge weights capturing pairwise synergistic interactions among factors. We then formulate this network-based biomarker identification problem as a novel graph optimization model to search for multiple cliques with maximum overall weight, which we denote as the Maximum Weighted Multiple Clique Problem (MWMCP). To achieve optimal or near optimal solutions, both an analytical algorithm based on column generation method and a fast heuristic for large-scale networks have been derived. Our algorithms for MWMCP have been implemented to analyze two biomedical data sets: a Type 1 Diabetes (T1D) data set from the Diabetes Prevention Trial-Type 1 (DPT-1) study, and a breast cancer genomics data set for metastasis prognosis. The results demonstrate that our network-based methods can identify important biomarkers with better prediction accuracy compared to the conventional feature selection that only considers individual effects. PMID:26357040

  8. 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.

  9. 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

  10. 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.

  11. CFD Optimization on Network-Based Parallel Computer System

    NASA Technical Reports Server (NTRS)

    Cheung, Samson H.; Holst, Terry L. (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. Network based high performance concurrent computing. Progress report, [FY 1991

    SciTech Connect

    Sunderam, V.S.

    1991-12-31

    The overall objectives of this project are to investigate research issues pertaining to programming tools and efficiency issues in network based concurrent computing systems. The basis for these efforts is the PVM project that evolved during my visits to Oak Ridge Laboratories under the DOE Faculty Research Participation program; I continue to collaborate with researchers at Oak Ridge on some portions of the project.

  13. Neural Network Based System for Equipment Startup Surveillance

    Energy Science and Technology Software Center (ESTSC)

    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. 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

  15. 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

  16. Performance evaluation of cellular phone network based portable ECG device.

    PubMed

    Hong, Joo-Hyun; Cha, Eun-Jong; Lee, Tae-Soo

    2008-01-01

    In this study, cellular phone network based portable ECG device was developed and three experiments were performed to evaluate the accuracy, reliability and operability, applicability during daily life of the developed device. First, ECG signals were measured using the developed device and Biopac device (reference device) during sitting and marking time and compared to verify the accuracy of R-R intervals. Second, the reliable data transmission to remote server was verified on two types of simulated emergency event using patient simulator. Third, during daily life with five types of motion, accuracy of data transmission to remote server was verified on two types of event occurring. By acquiring and comparing subject's biomedical signal and motion signal, the accuracy, reliability and operability, applicability during daily life of the developed device were verified. Therefore, cellular phone network based portable ECG device can monitor patient with inobtrusive manner. PMID:19162767

  17. 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.

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

    PubMed Central

    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. PMID:23226590

  19. 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.

  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. 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

  2. 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

  3. Feature Selection for Neural Network Based Stock Prediction

    NASA Astrophysics Data System (ADS)

    Sugunnasil, Prompong; Somhom, Samerkae

    We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search through the generated set with hill climbing approach. The self-organizing map based stock prediction model is utilized as the prediction method. We conduct the experiment on data sets of the Microsoft Corporation, General Electric Co. and Ford Motor Co. The results show that our feature selection method can improve the efficiency of the neural network based stock prediction.

  4. Optical-Correlator Neural Network Based On Neocognitron

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin; Stoner, William W.

    1994-01-01

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

  5. A unified neural-network-based speaker localization technique.

    PubMed

    Arslan, G; Sakarya, F A

    2000-01-01

    Locating and tracking a speaker in real time using microphone arrays is important in many applications such as hands-free video conferencing, speech processing in large rooms, and acoustic echo cancellation. A speaker can be moving from the far field to the near field of the array, or vice versa. Many neural-network-based localization techniques exist, but they are applicable to either far-field or near-field sources, and are computationally intensive for real-time speaker localization applications because of the wide-band nature of the speech. We propose a unified neural-network-based source localization technique, which is simultaneously applicable to wide-band and narrow-band signal sources that are in the far field or near field of a microphone array. The technique exploits a multilayer perceptron feedforward neural network structure and forms the feature vectors by computing the normalized instantaneous cross-power spectrum samples between adjacent pairs of sensors. Simulation results indicate that our technique is able to locate a source with an absolute error of less than 3.5 degrees at a signal-to-noise ratio of 20 dB and a sampling rate of 8000 Hz at each sensor. PMID:18249826

  6. 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.

  7. 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. PMID:8550948

  8. 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.

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

    PubMed Central

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

    2011-01-01

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

  10. 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

  11. 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.

  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. Blur identification by multilayer neural network based on multivalued neurons.

    PubMed

    Aizenberg, Igor; Paliy, Dmitriy V; Zurada, Jacek M; Astola, Jaakko T

    2008-05-01

    A multilayer neural network based on multivalued neurons (MLMVN) is a neural network with a traditional feedforward architecture. At the same time, this network has a number of specific different features. Its backpropagation learning algorithm is derivative-free. The functionality of MLMVN is superior to that of the traditional feedforward neural networks and of a variety kernel-based networks. Its higher flexibility and faster adaptation to the target mapping enables to model complex problems using simpler networks. In this paper, the MLMVN is used to identify both type and parameters of the point spread function, whose precise identification is of crucial importance for the image deblurring. The simulation results show the high efficiency of the proposed approach. It is confirmed that the MLMVN is a powerful tool for solving classification problems, especially multiclass ones. PMID:18467216

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

    PubMed Central

    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

  15. 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

  16. 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

  17. Bayesian Model Selection with Network Based Diffusion Analysis.

    PubMed

    Whalen, Andrew; Hoppitt, William J E

    2016-01-01

    A number of recent studies have used Network Based Diffusion Analysis (NBDA) to detect the role of social transmission in the spread of a novel behavior through a population. In this paper we present a unified framework for performing NBDA in a Bayesian setting, and demonstrate how the Watanabe Akaike Information Criteria (WAIC) can be used for model selection. We present a specific example of applying this method to Time to Acquisition Diffusion Analysis (TADA). To examine the robustness of this technique, we performed a large scale simulation study and found that NBDA using WAIC could recover the correct model of social transmission under a wide range of cases, including under the presence of random effects, individual level variables, and alternative models of social transmission. This work suggests that NBDA is an effective and widely applicable tool for uncovering whether social transmission underpins the spread of a novel behavior, and may still provide accurate results even when key model assumptions are relaxed. PMID:27092089

  18. 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. PMID:25089823

  19. An artificial neural network based matching metric for iris identification

    NASA Astrophysics Data System (ADS)

    Broussard, Randy P.; Kennell, Lauren R.; Ives, Robert W.; Rakvic, Ryan N.

    2008-02-01

    The iris is currently believed to be the most accurate biometric for human identification. The majority of fielded iris identification systems are based on the highly accurate wavelet-based Daugman algorithm. Another promising recognition algorithm by Ives et al uses Directional Energy features to create the iris template. Both algorithms use Hamming distance to compare a new template to a stored database. Hamming distance is an extremely fast computation, but weights all regions of the iris equally. Work from multiple authors has shown that different regions of the iris contain varying levels of discriminatory information. This research evaluates four post-processing similarity metrics for accuracy impacts on the Directional Energy and wavelets based algorithms. Each metric builds on the Hamming distance method in an attempt to use the template information in a more salient manner. A similarity metric extracted from the output stage of a feed-forward multi-layer perceptron artificial neural network demonstrated the most promise. Accuracy tables and ROC curves of tests performed on the publicly available Chinese Academy of Sciences Institute of Automation database show that the neural network based distance achieves greater accuracy than Hamming distance at every operating point, while adding less than one percent computational overhead.

  20. Xconf: a network-based image conferencing system.

    PubMed

    Lemkin, P F

    1993-02-01

    People often need to get together to share and discuss small amounts of image and textual data, but this is difficult when they are not located in the same place. One solution to this problem is Xconf, a multimedia computer conferencing groupware tool using existing national and international networks (the Internet). Simultaneous conferencing supports real-time interaction between multiple remote computer displays. Xconf multimedia may include conversational text, images, pointers to objects in images, and group execution of programs. Conferencing may take place with or without images. Interaction is tightly coupled with all users aware of global changes to the shared session and alternatively, individuals may monitor a specific subgroup of other users to concentrate on what they are discussing. Collaborative groups who have access to both computer networks and networked based X-Window System graphics displays can participate in a conference. Xconf is an X-Window "client" program which provides multimedia conferencing support for a group of X-Window displays. Because it is centralized, no software other than the standard X-Window System is required on any of the participants display systems. Key data structures and algorithms for image conferencing are present. PMID:8444025

  1. Ordinal regression neural networks based on concentric hyperspheres.

    PubMed

    Gutiérrez, Pedro Antonio; Tiňo, Peter; Hervás-Martínez, César

    2014-11-01

    Threshold models are one of the most common approaches for ordinal regression, based on projecting patterns to the real line and dividing this real line in consecutive intervals, one interval for each class. However, finding such one-dimensional projection can be too harsh an imposition for some datasets. This paper proposes a multidimensional latent space representation with the purpose of relaxing this projection, where the different classes are arranged based on concentric hyperspheres, each class containing the previous classes in the ordinal scale. The proposal is implemented through a neural network model, each dimension being a linear combination of a common set of basis functions. The model is compared to a nominal neural network, a neural network based on the proportional odds model and to other state-of-the-art ordinal regression methods for a total of 12 datasets. The proposed latent space shows an improvement on the two performance metrics considered, and the model based on the three-dimensional latent space obtains competitive performance when compared to the other methods. PMID:25078110

  2. 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.

  3. Identifying Node Role in Social Network Based on Multiple Indicators

    PubMed Central

    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. PMID:25089823

  4. 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. PMID:26285223

  5. 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.

  6. Network-Based Analysis of Software Change Propagation

    PubMed Central

    Wang, Rongcun; 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. PMID:24790557

  7. 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.

  8. 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.

  9. Network-based target ranking for polypharmacological therapies.

    PubMed

    Vitali, Francesca; Mulas, Francesca; Marini, Pietro; Bellazzi, Riccardo

    2013-10-01

    With the growing understanding of complex diseases, the focus of drug discovery has shifted from the well-accepted "one target, one drug" model, to a new "multi-target, multi-drug" model, aimed at systemically modulating multiple targets. In this context, polypharmacology has emerged as a new paradigm to overcome the recent decline in productivity of pharmaceutical research. However, finding methods to evaluate multicomponent therapeutics and ranking synergistic agent combinations is still a demanding task. At the same time, the data gathered on complex diseases has been progressively collected in public data and knowledge repositories, such as protein-protein interaction (PPI) databases. The PPI networks are increasingly used as universal platforms for data integration and analysis. A novel computational network-based approach for feasible and efficient identification of multicomponent synergistic agents is proposed in this paper. Given a complex disease, the method exploits the topological features of the related PPI network to identify possible combinations of hit targets. The best ranked combinations are subsequently computed on the basis of a synergistic score. We illustrate the potential of the method through a study on Type 2 Diabetes Mellitus. The results highlight its ability to retrieve novel target candidates, which role is also confirmed by the analysis of the related literature. PMID:23850841

  10. Bayesian Model Selection with Network Based Diffusion Analysis

    PubMed Central

    Whalen, Andrew; Hoppitt, William J. E.

    2016-01-01

    A number of recent studies have used Network Based Diffusion Analysis (NBDA) to detect the role of social transmission in the spread of a novel behavior through a population. In this paper we present a unified framework for performing NBDA in a Bayesian setting, and demonstrate how the Watanabe Akaike Information Criteria (WAIC) can be used for model selection. We present a specific example of applying this method to Time to Acquisition Diffusion Analysis (TADA). To examine the robustness of this technique, we performed a large scale simulation study and found that NBDA using WAIC could recover the correct model of social transmission under a wide range of cases, including under the presence of random effects, individual level variables, and alternative models of social transmission. This work suggests that NBDA is an effective and widely applicable tool for uncovering whether social transmission underpins the spread of a novel behavior, and may still provide accurate results even when key model assumptions are relaxed. PMID:27092089

  11. Network-Based Study Reveals Potential Infection Pathways of Hepatitis-C Leading to Various Diseases

    PubMed Central

    Mukhopadhyay, Anirban; Maulik, Ujjwal

    2014-01-01

    Protein-protein interaction network-based study of viral pathogenesis has been gaining popularity among computational biologists in recent days. In the present study we attempt to investigate the possible pathways of hepatitis-C virus (HCV) infection by integrating the HCV-human interaction network, human protein interactome and human genetic disease association network. We have proposed quasi-biclique and quasi-clique mining algorithms to integrate these three networks to identify infection gateway host proteins and possible pathways of HCV pathogenesis leading to various diseases. Integrated study of three networks, namely HCV-human interaction network, human protein interaction network, and human proteins-disease association network reveals potential pathways of infection by the HCV that lead to various diseases including cancers. The gateway proteins have been found to be biologically coherent and have high degrees in human interactome compared to the other virus-targeted proteins. The analyses done in this study provide possible targets for more effective anti-hepatitis-C therapeutic involvement. PMID:24743187

  12. 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

  13. 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…

  14. 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.

  15. 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

  16. 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

  17. Key requirements of packet transport network based on MPLS-TP

    NASA Astrophysics Data System (ADS)

    Huang, Feng; Yi, Xiaobo; Zhang, Hanzheng; Gong, Ping

    2009-11-01

    Requirement of packet transport network based on MPLS-TP are analyzed including in transport plane, OAM, survivability, QoS, control plane and management plane. MPLS-TP standard status is also introduced.

  18. 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

  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. PMID:25913233

  20. Huge capacity fiber-optic sensing network based on ultra-weak draw tower gratings

    NASA Astrophysics Data System (ADS)

    Yang, Minghong; Bai, Wei; Guo, Huiyong; Wen, Hongqiao; Yu, Haihu; Jiang, Desheng

    2016-03-01

    This paper reviews the work on huge capacity fiber-optic sensing network based on ultra-weak draw tower gratings developed at the National Engineering Laboratory for Fiber Optic Sensing Technology (NEL-FOST), Wuhan University of Technology, China. A versatile drawing tower grating sensor network based on ultra-weak fiber Bragg gratings (FBGs) is firstly proposed and demonstrated. The sensing network is interrogated with time- and wavelength-division multiplexing method, which is very promising for the large-scale sensing network.

  1. Biodegradable Photo-Crosslinked Thin Polymer Networks Based on Vegetable Oil Hydroxyfatty Acids

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Novel crosslinked thin polymer networks based on vegetable oil hydroxyfatty acids (HFAs) were prepared by UV photopolymerization and their mechanical properties were evaluated. Two raw materials, castor oil and 7,10-dihydroxy-8(E)-octadecenoic acid (DOD) were used as sources of mono- and di-HFAs, r...

  2. 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.…

  3. From Formal Training to Communities of Practice via Network-based Learning.

    ERIC Educational Resources Information Center

    Trentin, Guglielmo

    2001-01-01

    Discussion of the need for training and lifelong learning in light of new information and communication technologies focuses on small businesses with few employees who need rapid and continuous training. Topics include communities of practice; network-based learning; distance education; enterprise training; mutual training; knowledge creation;…

  4. Analysis and research on technology system of space broadband network based on laser link

    NASA Astrophysics Data System (ADS)

    Chang, Chengwu; Fei, Ligang; Chen, Erhu; Kou, Baohua; Cheng, Liyu

    2015-10-01

    Space broadband network based on laser link represents the future development direction, Europe, the United States, Japan and other space powers have been researching the theory of space laser communication and the key technology constantly, and have carried out the key technology test of inter-satellite laser communication and satellite-ground laser communication on orbit. However, what is the technology system of space broadband network based on laser link? up to now, it is still controversial, such as wavelength, coding, and modulation mode, exchange mode and so on. Here, by analyzing all kinds of space laser communication test and its technology parameters, combined with the application requirement of space broadband network, a set of technology system for space broadband network based on laser link is put forward, although just a preliminary research result. At first, this paper introduces the basic conception of space broadband network based on laser link, defines the space laser broadband network technology system and its research scope. Then analyze the main contents of space laser broadband network technology system, especially the technical route choice involved, and by studying, the related suggestions are given. Finally, with the development of space broadband network, the issue of standardization in space laser communication technology system is put forward, in order to cause attaches great importance to scientific research institutes and relevant experts.

  5. 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

  6. 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

  7. 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. PMID:27425866

  8. [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

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. Radial Basis Function Neural Network-based PID model for functional electrical stimulation system control.

    PubMed

    Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong

    2009-01-01

    Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model. PMID:19964991

  14. Functional-Network-Based Gene Set Analysis Using Gene-Ontology

    PubMed Central

    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

  15. Self-Organized Criticality in Small-World Networks Based on the Social Balance Dynamics

    NASA Astrophysics Data System (ADS)

    Meng, Qing-Kuan

    2011-11-01

    A node model is proposed to study the self-organized criticality in the small-world networks which represent the social networks. Based on the node model and the social balance dynamics, the social networks are mapped to the thermodynamic systems and the phenomena are studied with physical methods. It is found that the avalanche in the small-world networks at the critical state satisfies the power-law distribution spatially and temporally.

  16. 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).

  17. 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

  18. Artificial neural network based on SQUIDs: demonstration of network training and operation

    NASA Astrophysics Data System (ADS)

    Chiarello, F.; Carelli, P.; Castellano, M. G.; Torrioli, G.

    2013-12-01

    We propose a scheme for the realization of artificial neural networks based on superconducting quantum interference devices (SQUIDs). In order to demonstrate the operation of this scheme we designed and successfully tested a small network that implements an XOR gate and is trained by means of examples. The proposed scheme can be particularly convenient as support for superconducting applications such as detectors for astrophysics, high energy experiments, medicine imaging and so on.

  19. Neural network-based combustion optimization reduces NOx emissions while improving performance

    SciTech Connect

    Booth, R.C.; Roland, W.B.

    1998-07-01

    This paper presents the benefits of applying an on-line, real-time neural network to several bituminous coal fired utility boilers. The system helps reduce NOx emissions up to 60%, meeting compliance while it improves heat rate up to 2% overall (5% at low load) and reduces LOI as much as 30% through combustion optimization alone. The system can avoid or postpone large capital expenditures for low NOx burners, overfire air boiler modifications, SCRs, and SNCRs. The neural network-based system has been applied on 11 electric utility boilers that represent a wide range of furnace and burner types including units with tangential-, cell-, single wall-, and opposed wall-burner arrangements that have ranged in capacity from 146 to 800 MW in an advisory mode. Several sites have employed the neural network-based system for closed-loop supervisory combustion control. Boiler combustion profiles change continuously due to coal quality, boiler loading, changes in slag/soot deposits, ambient conditions, and the condition of plant equipment. Through on-line retraining, the neural network-based system optimizes the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to reduce NO{sub x} emissions and improve heat rate simultaneously.

  20. Neural network-based combustion optimization reduces NOx emissions while improving performance

    SciTech Connect

    Booth, R.C.; Roland, W.B. Jr.

    1998-12-31

    The NeuSIGHT neural network based system has been applied to units with tangential-, cell-, single wall-, and opposed wall-burner arrangements that have ranged in capacity from 146 to 800 MW in an advisory mode. Several sites have employed the neural network-based system for closed-loop supervisory combustion control. Boiler combustion profiles change continuously due to coal quality, boiler loading, changes in slag/soot deposits, ambient conditions, and the condition of plant equipment. Through on-line retraining, the neural network-based system optimizes the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to reduce NO{sub x} emissions and improve heat rate simultaneously. This paper presents the benefits of applying an on-line, real-time neural network to several commercially operating bituminous coal fired utility boilers. The system helps reduce NO{sub x} emissions up to 60%, meeting compliance while it improves heat rate up to 2% overall (5% at low load) and reduces LOI as much as 30% through combustion optimization alone. The system can avoid or postpone large capital expenditures for low NO{sub x} burners, overfire air boiler modifications, SCRs, and SNCRs.

  1. 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

  2. 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.

  3. Network-based metaanalysis identifies HNF4A and PTBP1 as longitudinally dynamic biomarkers for Parkinson's disease.

    PubMed

    Santiago, Jose A; Potashkin, Judith A

    2015-02-17

    Environmental and genetic factors are likely to be involved in the pathogenesis of Parkinson's disease (PD), the second most prevalent neurodegenerative disease among the elderly. Network-based metaanalysis of four independent microarray studies identified the hepatocyte nuclear factor 4 alpha (HNF4A), a transcription factor associated with gluconeogenesis and diabetes, as a central regulatory hub gene up-regulated in blood of PD patients. In parallel, the polypyrimidine tract binding protein 1 (PTBP1), involved in the stabilization and mRNA translation of insulin, was identified as the most down-regulated gene. Quantitative PCR assays revealed that HNF4A and PTBP1 mRNAs were up- and down-regulated, respectively, in blood of 51 PD patients and 45 controls nested in the Diagnostic and Prognostic Biomarkers for Parkinson's Disease. These results were confirmed in blood of 50 PD patients compared with 46 healthy controls nested in the Harvard Biomarker Study. Relative abundance of HNF4A mRNA correlated with the Hoehn and Yahr stage at baseline, suggesting its clinical utility to monitor disease severity. Using both markers, PD patients were classified with 90% sensitivity and 80% specificity. Longitudinal performance analysis demonstrated that relative abundance of HNF4A and PTBP1 mRNAs significantly decreased and increased, respectively, in PD patients during the 3-y follow-up period. The inverse regulation of HNF4A and PTBP1 provides a molecular rationale for the altered insulin signaling observed in PD patients. The longitudinally dynamic biomarkers identified in this study may be useful for monitoring disease-modifying therapies for PD. PMID:25646437

  4. Evaluation of a Partial Genome Screening of Two Asthma Susceptibility Regions Using Bayesian Network Based Bayesian Multilevel Analysis of Relevance

    PubMed Central

    Antal, Péter; Kiszel, Petra Sz.; Gézsi, András; Hadadi, Éva; Virág, Viktor; Hajós, Gergely; Millinghoffer, András; Nagy, Adrienne; Kiss, András; Semsei, Ágnes F.; Temesi, Gergely; Melegh, Béla; Kisfali, Péter; Széll, Márta; Bikov, András; Gálffy, Gabriella; Tamási, Lilla; Falus, András; Szalai, Csaba

    2012-01-01

    Genetic studies indicate high number of potential factors related to asthma. Based on earlier linkage analyses we selected the 11q13 and 14q22 asthma susceptibility regions, for which we designed a partial genome screening study using 145 SNPs in 1201 individuals (436 asthmatic children and 765 controls). The results were evaluated with traditional frequentist methods and we applied a new statistical method, called Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA). This method uses Bayesian network representation to provide detailed characterization of the relevance of factors, such as joint significance, the type of dependency, and multi-target aspects. We estimated posteriors for these relations within the Bayesian statistical framework, in order to estimate the posteriors whether a variable is directly relevant or its association is only mediated. With frequentist methods one SNP (rs3751464 in the FRMD6 gene) provided evidence for an association with asthma (OR = 1.43(1.2–1.8); p = 3×10−4). The possible role of the FRMD6 gene in asthma was also confirmed in an animal model and human asthmatics. In the BN-BMLA analysis altogether 5 SNPs in 4 genes were found relevant in connection with asthma phenotype: PRPF19 on chromosome 11, and FRMD6, PTGER2 and PTGDR on chromosome 14. In a subsequent step a partial dataset containing rhinitis and further clinical parameters was used, which allowed the analysis of relevance of SNPs for asthma and multiple targets. These analyses suggested that SNPs in the AHNAK and MS4A2 genes were indirectly associated with asthma. This paper indicates that BN-BMLA explores the relevant factors more comprehensively than traditional statistical methods and extends the scope of strong relevance based methods to include partial relevance, global characterization of relevance and multi-target relevance. PMID:22432035

  5. Network-based biomarkers enhance classical approaches to prognostic gene expression signatures

    PubMed Central

    2014-01-01

    Background Classical approaches to predicting patient clinical outcome via gene expression information are primarily based on differential expression of unrelated genes (single-gene approaches) or genes related by, for example, biologic pathway or function (gene-sets). Recently, network-based approaches utilising interaction information between genes have emerged. An open problem is whether such approaches add value to the more traditional methods of signature modelling. We explored this question via comparison of the most widely employed single-gene, gene-set, and network-based methods, using gene expression microarray data from two different cancers: melanoma and ovarian. We considered two kinds of network approaches. The first of these identifies informative genes using gene expression and network connectivity information combined, the latter drawn from prior knowledge of protein-protein interactions. The second approach focuses on identification of informative sub-networks (small networks of interacting proteins, again from prior knowledge networks). For all methods we performed 100 rounds of 5-fold cross-validation under 3 different classifiers. For network-based approaches, we considered two different protein-protein interaction networks. We quantified resulting patterns of misclassification and discussed the relative value of each relative to ongoing development of prognostic biomarkers. Results We found that single-gene, gene-set and network methods yielded similar error rates in melanoma and ovarian cancer data. Crucially, however, our novel and detailed patient-level analyses revealed that the different methods were correctly classifying alternate subsets of patients in each cohort. We also found that the network-based NetRank feature selection method was the most stable. Conclusions Next-generation methods of gene expression signature modelling harness data from external networks and are foreshadowed as a standard mode of analysis. But what do they add

  6. 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.

  7. Exploring poly-beta-hydroxy-butyrate metabolism through network-based extreme pathway analysis.

    PubMed

    Ding, Dewu; Ding, Yanrui; Cai, Yujie; Chen, Shouwen; Xu, Wenbo

    2008-01-01

    The objective of this article is to obtain a more detailed insight into poly-beta-hydroxybutyrate (PHB) metabolism through network-based metabolic pathway analysis. We employ extreme pathways to perform this study, because calculating and interpreting extreme pathways is a promising way for pathway analysis and metabolic engineering. After giving an in silico model of butanoate metabolism of Bacillus thuringiensis 97-27 (btk), extreme pathways were calculated and classified. Furthermore, the type I and II extreme pathways were further classified and analyzed in detail based on their structure and functional capabilities. Besides "historical" biochemical pathways, the results also suggest that there are some novel pathways. PMID:18600631

  8. 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.

  9. 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.

  10. 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.

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

    PubMed

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

    2015-01-01

    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. PMID:26516856

  12. Network-Based Biomarkers in Alzheimer’s Disease: Review and Future Directions

    PubMed Central

    Gomez-Ramirez, Jaime; Wu, Jinglong

    2014-01-01

    By 2050 it is estimated that the number of worldwide Alzheimer’s disease (AD) patients will quadruple from the current number of 36 million people. To date, no single test, prior to postmortem examination, can confirm that a person suffers from AD. Therefore, there is a strong need for accurate and sensitive tools for the early diagnoses of AD. The complex etiology and multiple pathogenesis of AD call for a system-level understanding of the currently available biomarkers and the study of new biomarkers via network-based modeling of heterogeneous data types. In this review, we summarize recent research on the study of AD as a connectivity syndrome. We argue that a network-based approach in biomarker discovery will provide key insights to fully understand the network degeneration hypothesis (disease starts in specific network areas and progressively spreads to connected areas of the initial loci-networks) with a potential impact for early diagnosis and disease-modifying treatments. We introduce a new framework for the quantitative study of biomarkers that can help shorten the transition between academic research and clinical diagnosis in AD. PMID:24550828

  13. 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

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

    PubMed Central

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

    2015-01-01

    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. PMID:26516856

  15. 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

  16. 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

  17. 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

  18. 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.

  19. Forecasting of Air Quality Index in Delhi Using Neural Network Based on Principal Component Analysis

    NASA Astrophysics Data System (ADS)

    Kumar, Anikender; Goyal, P.

    2013-04-01

    Forecasting of the air quality index (AQI) is one of the topics of air quality research today as it is useful to assess the effects of air pollutants on human health in urban areas. It has been learned in the last decade that airborne pollution has been a serious and will be a major problem in Delhi in the next few years. The air quality index is a number, based on the comprehensive effect of concentrations of major air pollutants, used by Government agencies to characterize the quality of the air at different locations, which is also used for local and regional air quality management in many metro cities of the world. Thus, the main objective of the present study is to forecast the daily AQI through a neural network based on principal component analysis (PCA). The AQI of criteria air pollutants has been forecasted using the previous day's AQI and meteorological variables, which have been found to be nearly same for weekends and weekdays. The principal components of a neural network based on PCA (PCA-neural network) have been computed using a correlation matrix of input data. The evaluation of the PCA-neural network model has been made by comparing its results with the results of the neural network and observed values during 2000-2006 in four different seasons through statistical parameters, which reveal that the PCA-neural network is performing better than the neural network in all of the four seasons.

  20. 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. PMID:25029520

  1. Nhs: Network-based Hierarchical Segmentation for Cryo-EM Density Maps

    PubMed Central

    Burger, Virginia; Chennubhotla, Chakra

    2012-01-01

    Electron cryo-microscopy (cryo-EM) experiments yield low-resolution (3–30Å) 3D-density maps of macromolecules. These density maps are segmented to identify structurally distinct proteins, protein domains, and sub-units. Such partitioning aids the inference of protein motions and guides fitting of high-resolution atomistic structures. Cryo-EM density map segmentation has traditionally required tedious and subjective manual partitioning or semi-supervised computational methods, while validation of resulting segmentations has remained an open problem in this field. Our network-based bias-free segmentation method for cryo-EM density map segmentation, Nhs (Network-based hierarchical segmentation), provides the user with a multi-scale partitioning, reflecting local and global clustering, while requiring no user input. This approach models each map as a graph, where map voxels constitute nodes and edges connect neighboring voxels. Nhs initiates Markov diffusion (or random walk) on the weighted graph. As Markov probabilities homogenize through diffusion, an intrinsic segmentation emerges. We validate the segmentations with ground-truth maps based on atomistic models. When implemented on density maps in the 2010 Cryo-EM Modeling Challenge, Nhs efficiently and objectively partitions macromolecules into structurally and functionally relevant sub-regions at multiple scales. PMID:22696408

  2. 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, ...

  3. 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 ...

  4. 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. PMID:24755620

  5. 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.

  6. 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.

  7. 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

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

    NASA Astrophysics Data System (ADS)

    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 Id, 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 Id 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 Id 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.

  9. 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.

  10. 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.

  11. 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.

  12. Evaluation of neural network based real time maximum power tracking controller for PV system

    SciTech Connect

    Hiyama, Takashi; Kouzuma, Shinichi; Imakubo, Tomofumi; Ortmeyer, T.H.

    1995-09-01

    This paper presents a neural network based maximum power tracking controller for interconnected PV systems to commercial power sources. The neural network is utilized to identify the optimal operating voltage of the PV system. The controller generates the control signal in real time, and the control signal is fed back to the voltage control loop of the inverter to shift the terminal voltage of the PV system to the identified optimal one, which yields the maximum power generation. The controller is a PI type one. The proportion an the integral gains are set to their optimal values to achieve the fast response and also to prevent the overshoot and also the undershoot. The continuous measurement is required for the open circuit voltage on the monitoring cell, and also for the terminal voltage of the PV system. Because of the accurate identification of the optimal operating voltage of the PV system, more than 99% power is drawn for the actual maximum power.

  13. INFERRING FUNCTIONAL NETWORK-BASED SIGNATURES VIA STRUCTURALLY-WEIGHTED LASSO MODEL

    PubMed Central

    Zhu, Dajiang; Shen, Dinggang; Liu, Tianming

    2014-01-01

    Most current research approaches for functional/effective connectivity analysis focus on pair-wise connectivity and cannot deal with network-scale functional interactions. In this paper, we propose a structurally-weighted LASSO (SW-LASSO) regression model to represent the functional interaction among multiple regions of interests (ROIs) based on resting state fMRI (R-fMRI) data. The structural connectivity constraints derived from diffusion tenor imaging (DTI) data will guide the selection of the weights which adjust the penalty levels of different coefficients corresponding to different ROIs. Using the Default Mode Network (DMN) as a test-bed, our results indicate that the learned SW-LASSO has good capability of differentiating Mild Cognitive Impairment (MCI) subjects from their normal controls and has promising potential to characterize the brain functions among different condition, thus serving as the functional network-based signature. PMID:25002915

  14. 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

  15. The estimation of neurotransmitter release probability in feedforward neuronal network based on adaptive synchronization

    NASA Astrophysics Data System (ADS)

    Xue, Ming; Wang, Jiang; Jia, Chenhui; Yu, Haitao; Deng, Bin; Wei, Xile; Che, Yanqiu

    2013-03-01

    In this paper, we proposed a new approach to estimate unknown parameters and topology of a neuronal network based on the adaptive synchronization control scheme. A virtual neuronal network is constructed as an observer to track the membrane potential of the corresponding neurons in the original network. When they achieve synchronization, the unknown parameters and topology of the original network are obtained. The method is applied to estimate the real-time status of the connection in the feedforward network and the neurotransmitter release probability of unreliable synapses is obtained by statistic computation. Numerical simulations are also performed to demonstrate the effectiveness of the proposed adaptive controller. The obtained results may have important implications in system identification in neural science.

  16. Neural network-based control for the fiber placement composite manufacturing process

    NASA Astrophysics Data System (ADS)

    Lichtenwalner, P. F.

    1993-10-01

    At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional-integral (PI) controller. However, after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A cerebellar model articulation controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM-compatible 386 PC with an A/D board interface to the machine.

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

    NASA Astrophysics Data System (ADS)

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

    2007-01-01

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

  18. 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.

  19. 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.

  20. 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

  1. Real-time neural network based camera localization and its extension to mobile robot control.

    PubMed

    Choi, D H; Oh, S Y

    1997-06-01

    The feasibility of using neural networks for camera localization and mobile robot control is investigated here. This approach has the advantages of eliminating the laborious and error-prone process of imaging system modeling and calibration procedures. Basically, two different approaches of using neural networks are introduced of which one is a hybrid approach combining neural networks and the pinhole-based analytic solution while the other is purely neural network based. These techniques have been tested and compared through both simulation and real-time experiments and are shown to yield more precise localization than analytic approaches. Furthermore, this neural localization method is also shown to be directly applicable to the navigation control of an experimental mobile robot along the hallway purely guided by a dark wall strip. It also facilitates multi-sensor fusion through the use of multiple sensors of different types for control due to the network's capability of learning without models. PMID:9427102

  2. 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.

  3. Generic mesoscopic neural networks based on statistical mechanics of neocortical interactions

    NASA Astrophysics Data System (ADS)

    Ingber, Lester

    1992-02-01

    A series of papers has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electrical-chemical properties of synaptic interactions, demonstrating its capability in describing large-scale properties of short-term memory and electroencephalographic systematics. This methodology also defines an algorithm to construct a mesoscopic neural network, based on realistic neocortical processes and parameters, to record patterns of brain activity and to compute the evolution of this system. Furthermore, this algorithm is quite generic and can be used to similarly process information in other systems, especially, but not limited to, those amenable to modeling by mathematical physics techniques alternatively described by path-integral Lagrangians, Fokker-Planck equations, or Langevin rate equations. This methodology is made possible and practical by a confluence of techniques drawn from SMNI itself, modern methods of functional stochastic calculus defining nonlinear Lagrangians, very fast simulated reannealing, and parallel-processing computation.

  4. A Generic Neural Network-Based Tutorial Supervisor for Computer Aided Instruction

    PubMed Central

    Bergeron, B.P.; Morse, A.N.; Greenes, R.A.

    1990-01-01

    When working with review materials in a self-test mode, student involvement is maximized when the problems presented variably fall within, and occasionally slightly beyond, a student's current level of ability or training. Because of the many difficulties associated with developing generic, fully adaptive systems with rule-based expert system technology, we have focused on using neural network technology as a practical, domain-independent means of optimizing the presentation of multimedia educational programs. The pattern classification capabilities of a neural network-based tutorial supervisor, developed as a series of external commands, have been used to successfully mediate the presentation of image-intensive courseware in cardiac pathophysiology. Research issues include identifying how to extend this approach to dynamically-generated courseware content, e.g., graphic simulations, and determining the educational effectiveness of various control algorithms used to assign students to problem sets of different levels of difficulty.

  5. 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

  6. 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.

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

    NASA Technical Reports Server (NTRS)

    Guo, T. H.; Musgrave, J.

    1992-01-01

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

  8. 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

  9. FERAL: network-based classifier with application to breast cancer outcome prediction

    PubMed Central

    Allahyar, Amin; de Ridder, Jeroen

    2015-01-01

    Motivation: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed. In spite of the initial claims, recent studies revealed that neither performance nor consistency can be improved using these methods. NOPs typically rely on the construction of meta-genes by averaging the expression of several genes connected in a network that encodes protein interactions or pathway information. In this article, we expose several fundamental issues in NOPs that impede on the prediction power, consistency of discovered markers and obscures biological interpretation. Results: To overcome these issues, we propose FERAL, a network-based classifier that hinges upon the Sparse Group Lasso which performs simultaneous selection of marker genes and training of the prediction model. An important feature of FERAL, and a significant departure from existing NOPs, is that it uses multiple operators to summarize genes into meta-genes. This gives the classifier the opportunity to select the most relevant meta-gene for each gene set. Extensive evaluation revealed that the discovered markers are markedly more stable across independent datasets. Moreover, interpretation of the marker genes detected by FERAL reveals valuable mechanistic insight into the etiology of breast cancer. Availability and implementation: All code is available for download at: http://homepage.tudelft.nl/53a60/resources/FERAL/FERAL.zip. Contact: j.deridder@tudelft.nl Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26072498

  10. 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

  11. Reserve networks based on richness hotspots and representation vary with scale.

    PubMed

    Shriner, Susan A; Wilson, Kenneth R; Flather, Curtis H

    2006-10-01

    While the importance of spatial scale in ecology is well established, few studies have investigated the impact of data grain on conservation planning outcomes. In this study, we compared species richness hotspot and representation networks developed at five grain sizes. We used species distribution maps for mammals and birds developed by the Arizona and New Mexico Gap Analysis Programs (GAP) to produce 1-km2, 100-kmn2, 625-km2, 2500-km2, and 10,000-km2 grid cell resolution distribution maps. We used these distribution maps to generate species richness and hotspot (95th quantile) maps for each taxon in each state. Species composition information at each grain size was used to develop two types of representation networks using the reserve selection software MARXAN. Reserve selection analyses were restricted to Arizona birds due to considerable computation requirements. We used MARXAN to create best reserve networks based on the minimum area required to represent each species at least once and equal area networks based on irreplaceability values. We also measured the median area of each species' distribution included in hotspot (mammals and birds of Arizona and New Mexico) and irreplaceability (Arizona birds) networks across all species. Mean area overlap between richness hotspot reserves identified at the five grain sizes was 29% (grand mean for four within-taxon/state comparisons), mean overlap for irreplaceability reserve networks was 32%, and mean overlap for best reserve networks was 53%. Hotspots for mammals and birds showed low overlap with a mean of 30%. Comparison of hotspots and irreplaceability networks showed very low overlap with a mean of 13%. For hotspots, median species distribution area protected within reserves declined monotonically from a high of 11% for 1-km2 networks down to 6% for 10,000-km2 networks. Irreplaceability networks showed a similar, but more variable, pattern of decline. This work clearly shows that map resolution has a profound

  12. 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 ...

  13. Genetic counseling

    MedlinePlus

    ... this page: //medlineplus.gov/ency/patientinstructions/000510.htm Genetic counseling To use the sharing features on this ... cystic fibrosis or Down syndrome. Who May Want Genetic Counseling? It is up to you whether or ...

  14. Genetic counseling

    MedlinePlus

    Genetics is the study of heredity, the process of a parent passing certain genes on to their ... certain diseases are also often determined by genes. Genetic counseling is the process where parents can learn ...

  15. 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 ...

  16. 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...

  17. 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,…

  18. Phylogeny and evoluntionary 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...

  19. 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

  20. 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.

  1. Epidemic Spreading in an Animal Trade Network - Comparison of Distance-Based and Network-Based Control Measures.

    PubMed

    Büttner, K; Krieter, J; Traulsen, A; Traulsen, I

    2016-02-01

    This study considered a simple SIR model for the spread of epidemics amongst holdings of a producer community in Northern Germany, based on the directed network of animal movements. The aim of this study was to evaluate the efficiency of different control measures to reduce the epidemic size substantially. The currently applied control measures based on the distance to an infected holding were compared with the control measures based on the specific network-based centrality parameters. We found that network-based measures led to a more efficient control of epidemics with a much smaller number of removed holdings. To assess the impact of different holding types, the analysed control measures were implemented by both including and excluding these holding types. The comparison revealed a crucial role of multipliers in the spread of an epidemic. The network-based control measures depending on the removal by out-degree, outgoing infection chain, betweenness centrality and outgoing closeness centrality showed the best results: In the three-year network, on average, 2.75, 4.15, 3.73 and 3.43 times more holdings had to be removed by the control measures based on the 1, 3, 5 and 10 km radius to reduce the epidemic to the same size compared with the network-based control measures. In an area with a higher holding density, the improvement of the network-based control measures may become even more obvious. The removal of holdings based on the above-mentioned centrality parameters did thus not only rapidly decompose the network into fragments, but also reduced the epidemic size most efficiently. PMID:25056832

  2. 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

  3. Prediction of drug-target interactions and drug repositioning via network-based inference.

    PubMed

    Cheng, Feixiong; Liu, Chuang; 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

  4. 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.

  5. 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

  6. 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

  7. 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

  8. 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

  9. Network-Based Inference Framework for Identifying Cancer Genes from Gene Expression Data

    PubMed Central

    Yang, Bo; Zhang, Junying; Yin, Yaling; Zhang, Yuanyuan

    2013-01-01

    Great efforts have been devoted to alleviate uncertainty of detected cancer genes as accurate identification of oncogenes is of tremendous significance and helps unravel the biological behavior of tumors. In this paper, we present a differential network-based framework to detect biologically meaningful cancer-related genes. Firstly, a gene regulatory network construction algorithm is proposed, in which a boosting regression based on likelihood score and informative prior is employed for improving accuracy of identification. Secondly, with the algorithm, two gene regulatory networks are constructed from case and control samples independently. Thirdly, by subtracting the two networks, a differential-network model is obtained and then used to rank differentially expressed hub genes for identification of cancer biomarkers. Compared with two existing gene-based methods (t-test and lasso), the method has a significant improvement in accuracy both on synthetic datasets and two real breast cancer datasets. Furthermore, identified six genes (TSPYL5, CD55, CCNE2, DCK, BBC3, and MUC1) susceptible to breast cancer were verified through the literature mining, GO analysis, and pathway functional enrichment analysis. Among these oncogenes, TSPYL5 and CCNE2 have been already known as prognostic biomarkers in breast cancer, CD55 has been suspected of playing an important role in breast cancer prognosis from literature evidence, and other three genes are newly discovered breast cancer biomarkers. More generally, the differential-network schema can be extended to other complex diseases for detection of disease associated-genes. PMID:24073403

  10. A network-based geriatric rehabilitation programme: study design and baseline characteristics of the patients.

    PubMed

    Hinkka, Katariina; Karppi, Sirkka-Liisa; Aaltonen, Tuula; Ollonqvist, Kirsi; Grönlund, Rainer; Salmelainen, Ulla; Puukka, Pauli; Tilvis, Reijo

    2006-06-01

    The objective of this paper is to present the design and participants of an ongoing randomized controlled trial on a network-based geriatric rehabilitation programme, targeted at frail elderly persons with progressively declining health and a high risk of institutionalization. Forty-one municipalities, seven rehabilitation centres and a total of 741 frail elderly (65+years) community-living persons participated in the study. Assessments included measurements of physical capacity (balance, handgrip strength, walking speed), Functional Independence Measure, Geriatric Depression Scale, 15 Dimension quality of life questionnaire and Mini Mental State Examination. Questionnaires covered physical, social and psychological factors. The participants were old (mean age 78 years, range 65-96) and mainly female (86%). They were physically frail and most of them (66%) had experienced deterioration of health within 1 year. The majority lived alone (72%) and received regular help from other people (99%). The mean Mini Mental State Examination and Geriatric Depression Scale scores were 25.2 and 4.1 points, respectively. Depressive mood (Geriatric Depression Scale>6 points) was found in 17% and declined cognitive function (Mini Mental State Examination<24 points) in 28% of the participants. Differences between the randomized intervention and control groups were insignificant. Since the number of participants is sufficient statistically, the prospects for evaluating the effectiveness of the programme, and identifying potential benefactors, are good. PMID:16609319

  11. Using AGNESS (A Generalized Network-based Expert System Shell) for matching images

    NASA Technical Reports Server (NTRS)

    Pong, Ting-Chuen; Lee, Chung-Mong; Slagle, James

    1988-01-01

    The image correspondence problem is considered the most difficult step in both stereo and motion analysis. Stereo vision is useful in determining the 3-D positions of points on visible surface in a scene. Motion analysis is useful in determining the spatial and temporal relationships of objects in an environment. Besides stereo and motion analysis, there is the image correspondence problem. Most of this work is based on point or local area properties of the observed gray level values in 2-D images. A global and general approach to this problem is described by using a knowledge-based system. The knowledge it uses consists of both physical properties and spatial relationships of the edges and regions extracted from the given images. The physical component depends on features of the edge or region) in isolation. The spatial component involves the set of edges and regions adjacent to a given edge (or region) of the first image and the set of edges and regions adjacent to each potentially matching edge (or region) of the second image; thus the spatial context of each edge or region is considered. A computational network is used to represent this knowledge, it allows the computation of the likelihood of matching two edges or regions with logical and heuristic operators. An expert system shell called AGNESS (A Generalized Network-based Expert System Shell) is used to build a prototype system.

  12. 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

  13. Bayesian prediction of earthquake network based on space-time influence domain

    NASA Astrophysics Data System (ADS)

    Zhang, Ya; Zhao, Hai; He, Xuan; Pei, Fan-Dong; Li, Guang-Guang

    2016-03-01

    Bayesian networks (BNs) are used to analyze the conditional dependencies among different events, which are expressed by conditional probability. Scientists have already investigated the seismic activities by using BNs. Recently, earthquake network is used as a novel methodology to analyze the relationships among the earthquake events. In this paper, we propose a way to predict earthquake from a new perspective. The BN is constructed after processing, which is derived from the earthquake network based on space-time influence domain. And then, the BN parameters are learnt by using the cases which are designed from the seismic data in the period between 00:00:00 on January 1, 1992 and 00:00:00 on January 1, 2012. At last, predictions are done for the data in the period between 00:00:00 on January 1, 2012 and 00:00:00 on January 1, 2015 combining the BN with the parameters. The results show that the success rate of the prediction including delayed prediction is about 65%. It is also discovered that the predictions for some nodes have high rate of accuracy under investigation.

  14. 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

  15. A new method of identifying influential nodes in complex networks based on TOPSIS

    NASA Astrophysics Data System (ADS)

    Du, Yuxian; Gao, Cai; Hu, Yong; Mahadevan, Sankaran; Deng, Yong

    2014-04-01

    In complex networks, identifying influential nodes is the very important part of reliability analysis, which has been a key issue in analyzing the structural organization of a network. In this paper, a new evaluation method of node importance in complex networks based on technique for order performance by similarity to ideal solution (TOPSIS) approach is proposed. TOPSIS as a multiple attribute decision making (MADM) technique has been an important branch of decision making since then. In addition, TOPSIS is first applied to identify influential nodes in a complex network in this open issue. In different types of networks in which the information goes by different ways, we consider several different centrality measures as the multi-attribute of complex network in TOPSIS application. TOPSIS is utilized to aggregate the multi-attribute to obtain the evaluation of node importance of each node. It is not limited to only one centrality measure, but considers different centrality measures, because every centrality measure has its own disadvantage and limitation. Then, we use the Susceptible-Infected (SI) model to evaluate the performance. Numerical examples are given to show the efficiency and practicability of the proposed method.

  16. 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

  17. Sparse network-based models for patient classification using fMRI

    PubMed Central

    Rosa, Maria J.; Portugal, Liana; Hahn, Tim; Fallgatter, Andreas J.; Garrido, Marta I.; Shawe-Taylor, John; Mourao-Miranda, Janaina

    2015-01-01

    Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from healthy participants. However, predictive patterns obtained from whole-brain voxel-based features are difficult to interpret in terms of the underlying neurobiology. Many psychiatric disorders, such as depression and schizophrenia, are thought to be brain connectivity disorders. Therefore, pattern recognition based on network models might provide deeper insights and potentially more powerful predictions than whole-brain voxel-based approaches. Here, we build a novel sparse network-based discriminative modeling framework, based on Gaussian graphical models and L1-norm regularized linear Support Vector Machines (SVM). In addition, the proposed framework is optimized in terms of both predictive power and reproducibility/stability of the patterns. Our approach aims to provide better pattern interpretation than voxel-based whole-brain approaches by yielding stable brain connectivity patterns that underlie discriminative changes in brain function between the groups. We illustrate our technique by classifying patients with major depressive disorder (MDD) and healthy participants, in two (event- and block-related) fMRI datasets acquired while participants performed a gender discrimination and emotional task, respectively, during the visualization of emotional valent faces. PMID:25463459

  18. 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

  19. 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

  20. 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

  1. Network-based representation of energy transfer in unsteady separated flow

    NASA Astrophysics Data System (ADS)

    Nair, Aditya; Taira, Kunihiko

    2015-11-01

    We construct a network-based representation of energy pathways in unsteady separated flows using a POD-Galerkin projection model. In this formulation, we regard the POD modes as the network nodes and the energy transfer between the modes as the network edges. Based on the energy transfer analysis performed by Noack et al. (2008), edge weights are characterized on the interaction graph. As an example, we examine the energy transfer within the two-dimensional incompressible flow over a circular cylinder. In particular, we analyze the energy pathways involved in flow transition from the unstable symmetric steady state to periodic shedding cycle. The growth of perturbation energy over the network is examined to highlight key features of flow physics and to determine how the energy transfer can be influenced. Furthermore, we implement closed-loop flow control on the POD-Galerkin model to alter the energy interaction path and modify the global behavior of the wake dynamics. The insights gained will be used to perform further network analysis on fluid flows with added complexity. Work supported by US Army Research Office (W911NF-14-1-0386) and US Air Force Office of Scientific Research (YIP: FA9550-13-1-0183).

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

    PubMed

    Draghici, S

    1997-02-01

    This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%. PMID:9228583

  3. Network-Based Analysis on Orthogonal Separation of Human Plasma Uncovers Distinct High Density Lipoprotein Complexes.

    PubMed

    Li, Hailong; Gordon, Scott M; Zhu, Xiaoting; Deng, Jingyuan; Swertfeger, Debi K; Davidson, W Sean; Lu, L Jason

    2015-08-01

    High density lipoprotein (HDL) particles are blood-borne complexes whose plasma levels have been associated with protection from cardiovascular disease (CVD). Recent studies have demonstrated the existence of distinct HDL subspecies; however, these have been difficult to isolate and characterize biochemically. Here, we present the first report that employs a network-based approach to systematically infer HDL subspecies. Healthy human plasma was separated into 58 fractions using our previously published three orthogonal chromatography techniques. Similar local migration patterns among HDL proteins were captured with a novel similarity score, and individual comigration networks were constructed for each fraction. By employing a graph mining algorithm, we identified 183 overlapped cliques, among which 38 were further selected as candidate HDL subparticles. Each of these 38 subparticles had at least two literature supports. In addition, GO function enrichment analysis showed that they were enriched with fundamental biological and CVD protective functions. Furthermore, gene knockout experiments in mouse model supported the validity of these subparticles related to three apolipoproteins. Finally, analysis of an apoA-I deficient human patient's plasma provided additional support for apoA-I related complexes. Further biochemical characterization of these putative subspecies may facilitate the mechanistic research of CVD and guide targeted therapeutics aimed at its mitigation. PMID:26057100

  4. Discovering potential cancer driver genes by an integrated network-based approach.

    PubMed

    Shi, Kai; Gao, Lin; Wang, Bingbo

    2016-08-16

    Although a lot of methods have been proposed to identify driver genes, how to separate the driver mutations from the passenger mutations is still a challenging problem in cancer genomics. The detection of driver genes with rare mutation and low accuracy is unsolved better. In this study, we present an integrated network-based approach to locate potential driver genes in a cohort of patients. The approach is composed of two steps including a network diffusion step and an aggregated ranking step, which fuses the correlation between the gene mutations and gene expression, the relationship between the mutated genes and the heterogeneous characteristic of the patient mutation. We analyze three cancer datasets including Glioblastoma multiforme, Ovarian cancer and Breast cancer. Our method has not only identified the known driver genes with high-frequency mutations, but also discovered the potential driver genes with a rare mutation. At the same time, validation by literature search and functional enrichment analysis reveal that the predicted genes are obviously related to these three kinds of cancers. PMID:27426053

  5. 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. PMID:25958393

  6. 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

  7. 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. PMID:26247140

  8. A neural network-based optimization algorithm for the weapon-target assignment problem

    SciTech Connect

    Wacholder, E.

    1989-02-01

    A neural network-based algorithm was developed for the Weapon-Target Assignment Problem (WTAP) in Ballistic Missile Defense (BMD). An optimal assignment policy is one which allocates targets to weapon platforms such that the total expected leakage value of targets surviving the defense is minimized. This involves the minimization of a non-linear objective function subject to inequality constraints specifying the maximum number of interceptors available to each platform and the maximum number of interceptors allowed to be fired at each target as imposed by the Battle Management/Command Control and Communications (BM/C/sup 3/) system. The algorithm consists of solving a system of ODEs trajectories and variables. Simulations of the algorithm on PC and VAX computers were carried out using a simple numerical scheme. In all the battle instances tested, the algorithm has proven to be stable and to converge to solutions very close to global optima. The time to achieve convergence was consistently less than the time constant of the network's processing elements (neurons). This implies that fast solutions can be realized if the algorithm is implemented in hardware circuits. Three series of battle scenarios are analyzed and discussed in this report. Input data and results are presented in detail. The main advantage of this algorithm is that it can be adapted to either a special-purpose hardware circuit or a general-purpose concurrent machine to yield fast and accurate solutions to difficult decision problems. 40 refs., 8 figs., 8 tabs.

  9. 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.

  10. 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.

  11. 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.

  12. Network-based evaluation of infrasound source location at Sakurajima Volcano, Japan

    NASA Astrophysics Data System (ADS)

    McKee, K. F.; Fee, D.; Rowell, C. R.; Johnson, J. B.; Yokoo, A.; Matoza, R. S.

    2013-12-01

    An important step in advancing the science and application of volcano infrasound is improved source location and characterization. Here we evaluate different network-based infrasonic source location methods, primarily srcLoc and semblance, using data collected at Sakurajima Volcano, Japan in July 2013. We investigate these methods in 2- and 3-dimensions to assess the necessity of considering 3-D sensor and vent locations. In addition, we compare source locations found using array back azimuth projection from dual arrays. The effect of significant local topography on source location will also be evaluated. Preliminary analysis indicates periods of high- and low-level activity, suggesting different processes occurring in the upper conduit and vent. Network processing will be applied to determine signal versus noise, a technique which illuminates when the volcano is producing infrasound, to further investigate these processes. We combine this with other methods to identify the number and style of eruptions. By bringing together source location, timing of activity level, type of activity (such as tremor, explosions, etc.), and number of events, we aim to improve understanding of the activity and associated infrasound signals at Sakurajima Volcano.

  13. 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

  14. Network-based auto-probit modeling for protein function prediction.

    PubMed

    Jiang, Xiaoyu; Gold, David; Kolaczyk, Eric D

    2011-09-01

    Predicting the functional roles of proteins based on various genome-wide data, such as protein-protein association networks, has become a canonical problem in computational biology. Approaching this task as a binary classification problem, we develop a network-based extension of the spatial auto-probit model. In particular, we develop a hierarchical Bayesian probit-based framework for modeling binary network-indexed processes, with a latent multivariate conditional autoregressive Gaussian process. The latter allows for the easy incorporation of protein-protein association network topologies-either binary or weighted-in modeling protein functional similarity. We use this framework to predict protein functions, for functions defined as terms in the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functionality. Furthermore, we show how a natural extension of this framework can be used to model and correct for the high percentage of false negative labels in training data derived from GO, a serious shortcoming endemic to biological databases of this type. Our method performance is evaluated and compared with standard algorithms on weighted yeast protein-protein association networks, extracted from a recently developed integrative database called Search Tool for the Retrieval of INteracting Genes/proteins (STRING). Results show that our basic method is competitive with these other methods, and that the extended method-incorporating the uncertainty in negative labels among the training data-can yield nontrivial improvements in predictive accuracy. PMID:21133881

  15. 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.

  16. 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.

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

    PubMed

    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

  18. 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

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

    PubMed

    Xing, Lizhi; 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

  20. 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.

  1. 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

  2. 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

  3. 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.

  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. 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.

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

    PubMed Central

    Ruffalo, Matthew

    2015-01-01

    Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA) generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these “silent players”. For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation. PMID:26683094

  7. 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.

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

    PubMed

    Ruffalo, Matthew; Koyutürk, Mehmet; Sharan, Roded

    2015-12-01

    Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA) generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these "silent players". For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation. PMID:26683094

  9. Network-based sub-network signatures unveil the potential for acute myeloid leukemia therapy.

    PubMed

    Shi, Mingguang; Wu, Min; Pan, Ping; Zhao, Rui

    2014-12-01

    Although gene expression profiling studies of acute myeloid leukemia (AML) patients have provided key insights into potential diagnostic and prognostic markers and therapeutic targets, it is not clear that the patterns of molecular heterogeneity affect the tumor biology and respond to the treatment. We hypothesized that network-based gene expression signatures of AML represent the mechanistically important genes and may improve the predicted performance of prognosis and clinical outcome. We provided the random walk with restart (RWR) analysis to discover the sub-network of genomic alterations. The RWR approach integrates the signature genes derived from the random forest (RF) analysis as "seeds" to identify genes critical to the AML recurrence phenotype. To test whether the 81-gene biomarkers could predict AML recurrence, we developed Survival Support Vector Machine (SSVM) models using a gene expression dataset and test on an independent dataset. The random forest classifier was built based on 81-gene biomarkers to separate the AML patients into "recurrence" and "non-recurrence" groups. The 81-gene biomarkers showed significant enrichment related to cancer pathophysiology and provided good coverage of sub-network biomarkers and AML-related signaling pathways. The SSVM-based score was significantly associated with overall survival (hazard ratio [HR], 2.16; 95% confidence interval [CI], 1.18-3.97; p = 0.01). Similar results were obtained with reversed training and testing datasets (hazard ratio [HR], 1.6; 95% confidence interval [CI], 1.08-2.37; p = 0.02). The 81-gene biomarker based RF classifier improved classification performance. Overall, 81-gene biomarkers might be useful prognostic and predictive molecular markers to predict the clinical outcome of AML patients. PMID:25313005

  10. Functional Module Search in Protein Networks based on Semantic Similarity Improves the Analysis of Proteomics Data*

    PubMed Central

    Boyanova, Desislava; Nilla, Santosh; Klau, Gunnar W.; Dandekar, Thomas; Müller, Tobias; Dittrich, Marcus

    2014-01-01

    The continuously evolving field of proteomics produces increasing amounts of data while improving the quality of protein identifications. Albeit quantitative measurements are becoming more popular, many proteomic studies are still based on non-quantitative methods for protein identification. These studies result in potentially large sets of identified proteins, where the biological interpretation of proteins can be challenging. Systems biology develops innovative network-based methods, which allow an integrated analysis of these data. Here we present a novel approach, which combines prior knowledge of protein-protein interactions (PPI) with proteomics data using functional similarity measurements of interacting proteins. This integrated network analysis exactly identifies network modules with a maximal consistent functional similarity reflecting biological processes of the investigated cells. We validated our approach on small (H9N2 virus-infected gastric cells) and large (blood constituents) proteomic data sets. Using this novel algorithm, we identified characteristic functional modules in virus-infected cells, comprising key signaling proteins (e.g. the stress-related kinase RAF1) and demonstrate that this method allows a module-based functional characterization of cell types. Analysis of a large proteome data set of blood constituents resulted in clear separation of blood cells according to their developmental origin. A detailed investigation of the T-cell proteome further illustrates how the algorithm partitions large networks into functional subnetworks each representing specific cellular functions. These results demonstrate that the integrated network approach not only allows a detailed analysis of proteome networks but also yields a functional decomposition of complex proteomic data sets and thereby provides deeper insights into the underlying cellular processes of the investigated system. PMID:24807868

  11. Network-based analysis for identification of candidate genes for colorectal cancer progression.

    PubMed

    Tsuji, Shingo; Midorikawa, Yutaka; Seki, Motoaki; Takayama, Tadatoshi; Sugiyama, Yasuyuki; Aburatani, Hiroyuki

    2016-08-01

    Although high-throughput biological technologies have been producing a vast amount of multi-omics data regarding cancer genomics and several disease susceptible genes have been reported, many of these genes are likely to be irrelevant for the cancer process because only one feature of the tumor pathway could be focused on. By identifying 'CpG core', which was extracted from CpG sites in genomic DNA by our newly developed method, we performed integrated analysis using gene expression and DNA methylation profiles of 116 colorectal cancer samples. First, based on gene expression values, colorectal cancer samples were divided into three clusters (Cluster-1, -2, and -3) by k-means clustering. The 5-year overall survival rates of colorectal cancer patients were 74.8%, 29.2%, and 29.4% in Cluster-1, -2, and -3, respectively, and the prognosis of Cluster-2 was significantly poorer than that of the other two clusters owing to liver metastasis (P < 0.001). Second, each cluster was divided into two subgroups based on methylation status, and the 5-year overall survival rate of Cluster-1H (36.8%) was significantly shorter than that of Cluster-1L (96.1%) due to the accumulation of aberrant DNA methylation (P = 0.014). Third, network-based analysis using expression and methylation profiles demonstrated that nucleoporin family genes were downregulated in Cluster-2 and that the PTX3 gene was highly methylated in Cluster-1H. These combined data indicate that integrated analysis can identify disease characteristics that would be missed using single comprehensive analysis, and that multiple pathways would play pivotal roles in the liver metastasis of colorectal cancer. PMID:27255996

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

    PubMed

    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 10(14) 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. PMID:25951284

  14. A hierarchical network-based algorithm for multi-scale watershed delineation

    NASA Astrophysics Data System (ADS)

    Castronova, Anthony M.; Goodall, Jonathan L.

    2014-11-01

    Watershed delineation is a process for defining a land area that contributes surface water flow to a single outlet point. It is a commonly used in water resources analysis to define the domain in which hydrologic process calculations are applied. There has been a growing effort over the past decade to improve surface elevation measurements in the U.S., which has had a significant impact on the accuracy of hydrologic calculations. Traditional watershed processing on these elevation rasters, however, becomes more burdensome as data resolution increases. As a result, processing of these datasets can be troublesome on standard desktop computers. This challenge has resulted in numerous works that aim to provide high performance computing solutions to large data, high resolution data, or both. This work proposes an efficient watershed delineation algorithm for use in desktop computing environments that leverages existing data, U.S. Geological Survey (USGS) National Hydrography Dataset Plus (NHD+), and open source software tools to construct watershed boundaries. This approach makes use of U.S. national-level hydrography data that has been precomputed using raster processing algorithms coupled with quality control routines. Our approach uses carefully arranged data and mathematical graph theory to traverse river networks and identify catchment boundaries. We demonstrate this new watershed delineation technique, compare its accuracy with traditional algorithms that derive watershed solely from digital elevation models, and then extend our approach to address subwatershed delineation. Our findings suggest that the open-source hierarchical network-based delineation procedure presented in the work is a promising approach to watershed delineation that can be used summarize publicly available datasets for hydrologic model input pre-processing. Through our analysis, we explore the benefits of reusing the NHD+ datasets for watershed delineation, and find that the our technique

  15. 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

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

    PubMed Central

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

    2015-01-01

    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

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. Unconventional optical imaging using a high-speed neural network based smart sensor

    NASA Astrophysics Data System (ADS)

    Arrasmith, William W.

    2006-05-01

    The advancement of neural network methods and technologies is finding applications in many fields and disciplines of interest to the defense, intelligence, and homeland security communities. Rapidly reconfigurable sensors for real or near-real time signal or image processing can be used for multi-functional purposes such as image compression, target tracking, image fusion, edge detection, thresholding, pattern recognition, and atmospheric turbulence compensation to name a few. A neural network based smart sensor is described that can accomplish these tasks individually or in combination, in real-time or near real-time. As a computationally intensive example, the case of optical imaging through volume turbulence is addressed. For imaging systems in the visible and near infrared part of the electromagnetic spectrum, the atmosphere is often the dominant factor in reducing the imaging system's resolution and image quality. The neural network approach described in this paper is shown to present a viable means for implementing turbulence compensation techniques for near-field and distributed turbulence scenarios. Representative high-speed neural network hardware is presented. Existing 2-D cellular neural network (CNN) hardware is capable of 3 trillion operations per second with peta-operations per second possible using current 3-D manufacturing processes. This hardware can be used for high-speed applications that require fast convolutions and de-convolutions. Existing 3-D artificial neural network technology is capable of peta-operations per second and can be used for fast array processing operations. Methods for optical imaging through distributed turbulence are discussed, simulation results are presented and computational and performance assessments are provided.

  2. Neural network based supervisory & closed loop controls for NOx emission reductions and heat rate improvement

    SciTech Connect

    Radl, B.J.; Corfman, D.; Kish, B.

    1995-12-31

    This paper discusses the operational experience gained from installing a neural network based supervisor setpoint control system for selected combustion parameters at Penn Power`s New Castle station. The primary goal of the program is to reduce NOx emissions while maintaining or improving heat rate. The program was jointly funded by Ohio Edison, U.S. Department of Energy (DOE) and Pegasus Technologies Corp. The target power station, Penn Power`s New Castle Unit 5, is a 1950`s vintage Babcock & Wilcox wall fired furnace with gross generation capacity of 150 MW. Before installation of the neural network system (NeuSIGHT), NOx averaged 0.75 to 0.80 lbs/mbtu at full load conditions. Previous testing reduced this from 1.0 lbs/mbtu under normal operating conditions. To meet the new Pennsylvania DER limits, which set an absolute tonnage limit on NOx, and operate for a full year, a further NOx reduction of 20% was required. The control system setup interfaced a Unix workstation to a Bailey Controls N90 DCS. The neural network and data collection/processing system resided on the workstation. New setpoints were determined by the neural network periodically. These setpoints were constrained within existing control system limits. The objective was to model the multi-dimensional and non-linear problem of NOx formation in the furnace with a neural network. Once modeled the neural network performed many {open_quote}what if{close_quote} simulations to optimize setpoints for the current operating conditions. To keep up with changes in operating conditions the neural network was set to continually learn from the most recent set of measurements. Conditioning algorithms for the input data and output setpoints were developed to handle the inherently {open_quote}noisy{close_quote} input data and to provide stable output recommendations. Test results and parameters used for combustion optimization are summarized in this paper.

  3. 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

  4. 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

  5. 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

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

    PubMed

    Kesharaju, Manasa; Nagarajah, Romesh

    2015-09-01

    The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. PMID:26081920

  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. 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

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

    PubMed

    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

  10. Quality assessment of a network-based RTK GPS service in the UK

    NASA Astrophysics Data System (ADS)

    Aponte, Jose; Meng, Xiaolin; Hill, Chris; Moore, Terry; Burbidge, Mark; Dodson, Alan

    2009-03-01

    Network-based Real Time Kinematic (NRTK) GPS positioning is considered to be a superior solution compared to the conventional single reference station based Real Time Kinematic (RTK) GPS positioning technique whose accuracy is highly affected by the distance dependent errors such as satellite orbital and atmospheric biases. NRTK GPS positioning uses raw measurements gathered from a network of Continuously Operating Reference Stations (CORS) in order to generate more reliable error models that can mitigate the distance dependent errors within the area covered by the CORS. This technique has been developed and tested considerably during recent years and the overall performance in terms of achievable accuracies, reliability and mobility is as good as or even better than can be achieved using the conventional RTK GPS positioning technique. Currently, there are several commercial NRTK services around the world. In the United Kingdom (UK), for instance, Leica Geosystems in partnership with Ordnance Survey has been offering a NRTK GPS service since 2006. This service is called SmartNet and it can provide continuous centimetric level of accuracy to its subscribers. However, NRTK GPS positioning is particularly constrained by wireless data link coverage, correction transmission delay and completeness, GPS signal availability, etc., which could downgrade the positioning quality of the NRTK results. The paper presents some preliminary testing results of an investigation of the SmartNet service from the end users' point of view. A snapshot of the service's performance was carried out as part of a recent PhD studentship jointly awarded by the UK's Engineering and Physical Sciences Research Council (EPSRC) and Leica Geosystems (UK) to conduct comprehensive research into NRTK GPS quality control measures at the Institute of Engineering Surveying and Space Geodesy (IESSG), the University of Nottingham. In order to evaluate the service's quality several static and

  11. 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

  12. 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)

  13. NET-GE: a novel NETwork-based Gene Enrichment for detecting biological processes associated to Mendelian diseases

    PubMed Central

    2015-01-01

    Background Enrichment analysis is a widely applied procedure for shedding light on the molecular mechanisms and functions at the basis of phenotypes, for enlarging the dataset of possibly related genes/proteins and for helping interpretation and prioritization of newly determined variations. Several standard and Network-based enrichment methods are available. Both approaches rely on the annotations that characterize the genes/proteins included in the input set; network based ones also include in different ways physical and functional relationships among different genes or proteins that can be extracted from the available biological networks of interactions. Results Here we describe a novel procedure based on the extraction from the STRING interactome of sub-networks connecting proteins that share the same Gene Ontology(GO) terms for Biological Process (BP). Enrichment analysis is performed by mapping the protein set to be analyzed on the sub-networks, and then by collecting the corresponding annotations. We test the ability of our enrichment method in finding annotation terms disregarded by other enrichment methods available. We benchmarked 244 sets of proteins associated to different Mendelian diseases, according to the OMIM web resource. In 143 cases (58%), the network-based procedure extracts GO terms neglected by the standard method, and in 86 cases (35%), some of the newly enriched GO terms are not included in the set of annotations characterizing the input proteins. We present in detail six cases where our network-based enrichment provides an insight into the biological basis of the diseases, outperforming other freely available network-based methods. Conclusions Considering a set of proteins in the context of their interaction network can help in better defining their functions. Our novel method exploits the information contained in the STRING database for building the minimal connecting network containing all the proteins annotated with the same GO term

  14. Genetic counseling.

    PubMed

    Fraser, F C

    1974-09-01

    A workshop was sponsored by the National Genetics Foundation to evaluate and make recommendations about the status of genetic counseling, its goals, nature, achievements, and needs. The process of genetic workup and counseling is divided into 5 stages: validation of the diagnosis; obtaining family history; estimation of the risk of recurrence; helping the family make a decision and take appropriate action; and extending counseling to other members of the family. Counseling can be directed at individuals or at special groups with the potential of carrying such diseases as sickle cell amenia or Tay-Sachs. No consensus exists on an optimal counseling approach. Genetic counseling is regarded as a team effort, requiring, in addition to the counselor, laboratory facilities and a variety of specialists. The source of payment for genetic counseling services is regarded as a problem of increasing concern. Generally, the fee paid rarely covers the cost of the many procedures and it is suggested that the cost, like that of other public health services, should be subsidized by the state. Considerable argument exists over whether a genetic counselor must have a M.D. degree or whether a Ph. D. in medical genetics is suitable enough. The quality of much genetic counseling, which is often done in the office of doctors unskilled in the field, would be increased if better training in genetics were offered to medical students and if physicians were informed of the existence of counseling centers. Further, there is a growing feeling that some sort of accreditation of genetic counselors is desirable. PMID:4609197

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

    PubMed

    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

  16. 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

  17. 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

  18. 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 ...

  19. RNA genetics

    SciTech Connect

    Domingo, E. ); Holland, J.J. . Dept. of Biology); Ahlquist, P. . Dept. of Plant Pathology)

    1988-01-01

    This book contains the proceedings on RNA genetics: Retroviruses, Viroids, and RNA recombination, Volume 2. Topics covered include: Replication of retrovirus genomes, Hepatitis B virus replication, and Evolution of RNA viruses.

  20. 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)

  1. Genetic Screening

    PubMed Central

    Burke, Wylie; Tarini, Beth; Press, Nancy A.; Evans, James P.

    2011-01-01

    Current approaches to genetic screening include newborn screening to identify infants who would benefit from early treatment, reproductive genetic screening to assist reproductive decision making, and family history assessment to identify individuals who would benefit from additional prevention measures. Although the traditional goal of screening is to identify early disease or risk in order to implement preventive therapy, genetic screening has always included an atypical element—information relevant to reproductive decisions. New technologies offer increasingly comprehensive identification of genetic conditions and susceptibilities. Tests based on these technologies are generating a different approach to screening that seeks to inform individuals about all of their genetic traits and susceptibilities for purposes that incorporate rapid diagnosis, family planning, and expediting of research, as well as the traditional screening goal of improving prevention. Use of these tests in population screening will increase the challenges already encountered in genetic screening programs, including false-positive and ambiguous test results, overdiagnosis, and incidental findings. Whether this approach is desirable requires further empiric research, but it also requires careful deliberation on the part of all concerned, including genomic researchers, clinicians, public health officials, health care payers, and especially those who will be the recipients of this novel screening approach. PMID:21709145

  2. 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.

  3. Inverse dynamical photon scattering (IDPS): an artificial neural network based algorithm for three-dimensional quantitative imaging in optical microscopy.

    PubMed

    Jiang, Xiaoming; Van den Broek, Wouter; Koch, Christoph T

    2016-04-01

    Inverse dynamical photon scattering (IDPS), an artificial neural network based algorithm for three-dimensional quantitative imaging in optical microscopy, is introduced. Because the inverse problem entails numerical minimization of an explicit error metric, it becomes possible to freely choose a more robust metric, to introduce regularization of the solution, and to retrieve unknown experimental settings or microscope values, while the starting guess is simply set to zero. The regularization is accomplished through an alternate directions augmented Lagrangian approach, implemented on a graphics processing unit. These improvements are demonstrated on open source experimental data, retrieving three-dimensional amplitude and phase for a thick specimen. PMID:27136994

  4. Genetic screening

    PubMed Central

    Andermann, Anne; Blancquaert, Ingeborg

    2010-01-01

    Abstract OBJECTIVE To provide a primer for primary care professionals who are increasingly called upon to discuss the growing number of genetic screening services available and to help patients make informed decisions about whether to participate in genetic screening, how to interpret results, and which interventions are most appropriate. QUALITY OF EVIDENCE As part of a larger research program, a wide literature relating to genetic screening was reviewed. PubMed and Internet searches were conducted using broad search terms. Effort was also made to identify the gray literature. MAIN MESSAGE Genetic screening is a type of public health program that is systematically offered to a specified population of asymptomatic individuals with the aim of providing those identified as high risk with prevention, early treatment, or reproductive options. Ensuring an added benefit from screening, as compared with standard clinical care, and preventing unintended harms, such as undue anxiety or stigmatization, depends on the design and implementation of screening programs, including the recruitment methods, education and counseling provided, timing of screening, predictive value of tests, interventions available, and presence of oversight mechanisms and safeguards. There is therefore growing apprehension that economic interests might lead to a market-driven approach to introducing and expanding screening before program effectiveness, acceptability, and feasibility have been demonstrated. As with any medical intervention, there is a moral imperative for genetic screening to do more good than harm, not only from the perspective of individuals and families, but also for the target population and society as a whole. CONCLUSION Primary care professionals have an important role to play in helping their patients navigate the rapidly changing terrain of genetic screening services by informing them about the benefits and risks of new genetic and genomic technologies and empowering them to

  5. 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 ...

  6. A neural network based ensemble approach for improving the accuracy of meteorological fields used for regional air quality modeling.

    PubMed

    Cheng, Shuiyuan; Li, Li; Chen, Dongsheng; Li, Jianbing

    2012-12-15

    A neural network based ensemble methodology was presented in this study to improve the accuracy of meteorological input fields for regional air quality modeling. Through nonlinear integration of simulation results from two meteorological models (MM5 and WRF), the ensemble approach focused on the optimization of meteorological variable values (temperature, surface air pressure, and wind field) in the vertical layer near ground. To illustrate the proposed approach, a case study in northern China during two selected air pollution events, in 2006, was conducted. The performances of the MM5, the WRF, and the ensemble approach were assessed using different statistical measures. The results indicated that the ensemble approach had a higher simulation accuracy than the MM5 and the WRF model. Performance was improved by more than 12.9% for temperature, 18.7% for surface air pressure field, and 17.7% for wind field. The atmospheric PM(10) concentrations in the study region were also simulated by coupling the air quality model CMAQ with the MM5 model, the WRF model, and the ensemble model. It was found that the modeling accuracy of the ensemble-CMAQ model was improved by more than 7.0% and 17.8% when compared to the MM5-CMAQ and the WRF-CMAQ models, respectively. The proposed neural network based meteorological modeling approach holds great potential for improving the performance of regional air quality modeling. PMID:23000477

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

    PubMed Central

    Ge, Mengqu; Li, Ao; Wang, Minghui

    2016-01-01

    As one large class of non-coding RNAs (ncRNAs), long ncRNAs (lncRNAs) have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA–protein bipartite network inference (LPBNI). LPBNI aims to identify potential lncRNA–interacting proteins, by making full use of the known lncRNA–protein interactions. Leave-one-out cross validation (LOOCV) test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR) and protein-based collaborative filtering (ProCF). Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA–interacting proteins. PMID:26917505

  8. 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. PMID:22570412

  9. Parallel implementation of high-speed, phase diverse atmospheric turbulence compensation method on a neural network-based architecture

    NASA Astrophysics Data System (ADS)

    Arrasmith, William W.; Sullivan, Sean F.

    2008-04-01

    Phase diversity imaging methods work well in removing atmospheric turbulence and some system effects from predominantly near-field imaging systems. However, phase diversity approaches can be computationally intensive and slow. We present a recently adapted, high-speed phase diversity method using a conventional, software-based neural network paradigm. This phase-diversity method has the advantage of eliminating many time consuming, computationally heavy calculations and directly estimates the optical transfer function from the entrance pupil phases or phase differences. Additionally, this method is more accurate than conventional Zernike-based, phase diversity approaches and lends itself to implementation on parallel software or hardware architectures. We use computer simulation to demonstrate how this high-speed, phase diverse imaging method can be implemented on a parallel, highspeed, neural network-based architecture-specifically the Cellular Neural Network (CNN). The CNN architecture was chosen as a representative, neural network-based processing environment because 1) the CNN can be implemented in 2-D or 3-D processing schemes, 2) it can be implemented in hardware or software, 3) recent 2-D implementations of CNN technology have shown a 3 orders of magnitude superiority in speed, area, or power over equivalent digital representations, and 4) a complete development environment exists. We also provide a short discussion on processing speed.

  10. 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

    2016-01-01

    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. PMID:26750448

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

    PubMed Central

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

    2016-01-01

    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. PMID:26750448

  12. 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

    2012-01-01

    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

  13. 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

  14. 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.

  15. A network-based approach for resistance transmission in bacterial populations.

    PubMed

    Gehring, Ronette; Schumm, Phillip; Youssef, Mina; Scoglio, Caterina

    2010-01-01

    Horizontal transfer of mobile genetic elements (conjugation) is an important mechanism whereby resistance is spread through bacterial populations. The aim of our work is to develop a mathematical model that quantitatively describes this process, and to use this model to optimize antimicrobial dosage regimens to minimize resistance development. The bacterial population is conceptualized as a compartmental mathematical model to describe changes in susceptible, resistant, and transconjugant bacteria over time. This model is combined with a compartmental pharmacokinetic model to explore the effect of different plasma drug concentration profiles. An agent-based simulation tool is used to account for resistance transfer occurring when two bacteria are adjacent or in close proximity. In addition, a non-linear programming optimal control problem is introduced to minimize bacterial populations as well as the drug dose. Simulation and optimization results suggest that the rapid death of susceptible individuals in the population is pivotal in minimizing the number of transconjugants in a population. This supports the use of potent antimicrobials that rapidly kill susceptible individuals and development of dosage regimens that maintain effective antimicrobial drug concentrations for as long as needed to kill off the susceptible population. Suggestions are made for experiments to test the hypotheses generated by these simulations. PMID:19747924

  16. Neural-network-based adaptive UPFC for improving transient stability performance of power system.

    PubMed

    Mishra, Sukumar

    2006-03-01

    This paper uses the recently proposed H(infinity)-learning method, for updating the parameter of the radial basis function neural network (RBFNN) used as a control scheme for the unified power flow controller (UPFC) to improve the transient stability performance of a multimachine power system. The RBFNN uses a single neuron architecture whose input is proportional to the difference in error and the updating of its parameters is carried via a proportional value of the error. Also, the coefficients of the difference of error, error, and auxiliary signal used for improving damping performance are depicted by a genetic algorithm. The performance of the newly designed controller is evaluated in a four-machine power system subjected to different types of disturbances. The newly designed single-neuron RBFNN-based UPFC exhibits better damping performance compared to the conventional PID as well as the extended Kalman filter (EKF) updating-based RBFNN scheme, making the unstable cases stable. Its simple architecture reduces the computational burden, thereby making it attractive for real-time implementation. Also, all the machines are being equipped with the conventional power system stabilizer (PSS) to study the coordinated effect of UPFC and PSS in the system. PMID:16566472

  17. 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).

  18. 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 ...

  19. 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)

  20. The multiscale backbone of the human phenotype network based on biological pathways

    PubMed Central

    2014-01-01

    Background Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways. Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects, and expanding previous studies from a gene-centric approach to that of shared cell-processes. Results The resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. We extract the multi-scale information backbone of the PHPN based on the local densities of the network and discarding weak connection. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present examples of expected clinical connections identified by PHPN as proof of principle. Conclusions We unveil a previously uncharacterized connection between phenotype modules and discuss potential mechanistic connections that are obvious only in retrospect. The PHPN shows tremendous potential to become a useful tool both in the unveiling of the diseases’ common biology, and in the elaboration of diagnosis and treatments. PMID:24460644

  1. 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 ...

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

    PubMed Central

    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

  3. Routing and wavelength assignment algorithms for all-optical WDM networks based on virtual multiple self-healing ring architecture

    NASA Astrophysics Data System (ADS)

    Ishikawa, Akio; Kishi, Yoji

    2000-09-01

    This paper newly proposes a self-healing architecture in all- optical WDM networks based on virtual embedded multiple rings (Virtual Multiple Self Healing Rings: VM-SHR). Focusing upon the network design aspect of the proposed architecture, this paper describes design methodologies for VM-SHR networks. For two major problems in all-optical WDM network design, that is, the connection routing and wavelength assignment problems, we first established solution models based on mathematical programming formulation, each of which can be solved by common integer programming algorithms, respectively. In addition, we also developed an efficient heuristic algorithm for the wavelength assignment problem. Their usefulness and performance are demonstrated through the extensive simulation results.

  4. 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

  5. [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

  6. 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.

  7. 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

  8. 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.

  9. 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.

  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. 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

  12. 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.

  13. 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

  14. Cancer Genetics Services Directory

    MedlinePlus

    ... Prevention 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 counseling, genetic susceptibility testing, ...

  15. 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, ...

  16. Genetically engineered foods

    MedlinePlus

    ... plants or animals) inserted into their genetic codes. Genetic engineering can be done with plants, animals, or bacteria ... have been genetically engineering plants since the 1990s. Genetic engineering allows scientists to speed this process up by ...

  17. 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

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

    PubMed

    Chen, Xing; Yan, Chenggang Clarence; 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 construction 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

  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. 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

  2. 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. PMID:25742188

  3. Network-based approach to identify prognostic biomarkers for estrogen receptor-positive breast cancer treatment with tamoxifen.

    PubMed

    Liu, Rong; Guo, Cheng-Xian; Zhou, Hong-Hao

    2015-01-01

    This study aims to identify effective gene networks and prognostic biomarkers associated with estrogen receptor positive (ER+) breast cancer using human mRNA studies. Weighted gene coexpression network analysis was performed with a complex ER+ breast cancer transcriptome to investigate the function of networks and key genes in the prognosis of breast cancer. We found a significant correlation of an expression module with distant metastasis-free survival (HR = 2.25; 95% CI .21.03-4.88 in discovery set; HR = 1.78; 95% CI = 1.07-2.93 in validation set). This module contained genes enriched in the biological process of the M phase. From this module, we further identified and validated 5 hub genes (CDK1, DLGAP5, MELK, NUSAP1, and RRM2), the expression levels of which were strongly associated with poor survival. Highly expressed MELK indicated poor survival in luminal A and luminal B breast cancer molecular subtypes. This gene was also found to be associated with tamoxifen resistance. Results indicated that a network-based approach may facilitate the discovery of biomarkers for the prognosis of ER+ breast cancer and may also be used as a basis for establishing personalized therapies. Nevertheless, before the application of this approach in clinical settings, in vivo and in vitro experiments and multi-center randomized controlled clinical trials are still needed. PMID:25756514

  4. 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

  5. Reliability prediction for evolutionary product in the conceptual design phase using neural network-based fuzzy synthetic assessment

    NASA Astrophysics Data System (ADS)

    Liu, Yu; Huang, Hong-Zhong; Ling, Dan

    2013-03-01

    Reliability prediction plays an important role in product lifecycle management. It has been used to assess various reliability indices (such as reliability, availability and mean time to failure) before a new product is physically built and/or put into use. In this article, a novel approach is proposed to facilitate reliability prediction for evolutionary products during their early design stages. Due to the lack of sufficient data in the conceptual design phase, reliability prediction is not a straightforward task. Taking account of the information from existing similar products and knowledge from domain experts, a neural network-based fuzzy synthetic assessment (FSA) approach is proposed to predict the reliability indices that a new evolutionary product could achieve. The proposed approach takes advantage of the capability of the back-propagation neural network in terms of constructing highly non-linear functional relationship and combines both the data sets from existing similar products and subjective knowledge from domain experts. It is able to reach a more accurate prediction than the conventional FSA method reported in the literature. The effectiveness and advantages of the proposed method are demonstrated via a case study of the fuel injection pump and a comparative study.

  6. 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

  7. 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.

  8. 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

  9. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2010-02-01

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

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

    PubMed Central

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

    2014-01-01

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

  12. 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

  13. Network-based approach to identify prognostic biomarkers for estrogen receptor–positive breast cancer treatment with tamoxifen

    PubMed Central

    Liu, Rong; Guo, Cheng-Xian; Zhou, Hong-Hao

    2015-01-01

    This study aims to identify effective gene networks and prognostic biomarkers associated with estrogen receptor positive (ER+) breast cancer using human mRNA studies. Weighted gene coexpression network analysis was performed with a complex ER+ breast cancer transcriptome to investigate the function of networks and key genes in the prognosis of breast cancer. We found a significant correlation of an expression module with distant metastasis-free survival (HR = 2.25; 95% CI .21.03–4.88 in discovery set; HR = 1.78; 95% CI = 1.07–2.93 in validation set). This module contained genes enriched in the biological process of the M phase. From this module, we further identified and validated 5 hub genes (CDK1, DLGAP5, MELK, NUSAP1, and RRM2), the expression levels of which were strongly associated with poor survival. Highly expressed MELK indicated poor survival in luminal A and luminal B breast cancer molecular subtypes. This gene was also found to be associated with tamoxifen resistance. Results indicated that a network-based approach may facilitate the discovery of biomarkers for the prognosis of ER+ breast cancer and may also be used as a basis for establishing personalized therapies. Nevertheless, before the application of this approach in clinical settings, in vivo and in vitro experiments and multi-center randomized controlled clinical trials are still needed. PMID:25756514

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

    PubMed

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

    2016-07-01

    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

  15. Experimental demonstration of elastic optical networks based on enhanced software defined networking (eSDN) for data center application.

    PubMed

    Zhang, Jie; Yang, Hui; Zhao, Yongli; Ji, Yuefeng; Li, Hui; Lin, Yi; Li, Gang; Han, Jianrui; Lee, Young; Ma, Teng

    2013-11-01

    Due to the high burstiness and high-bandwidth characteristics of the applications, data center interconnection by elastic optical networks have attracted much attention of network operators and service providers. Many data center applications require lower delay and higher availability with the end-to-end guaranteed quality of service. In this paper, we propose and implement a novel elastic optical network based on enhanced software defined networking (eSDN) architecture for data center application, by introducing a transport-aware cross stratum optimization (TA-CSO) strategy. eSDN can enable cross stratum optimization of application and elastic optical network stratum resources and provide the elastic physical layer parameter adjustment, e.g., modulation format and bandwidth. We have designed and verified experimentally software defined path provisioning on our testbed with four real OpenFlow-enabled elastic optical nodes for data center application. The overall feasibility and efficiency of the proposed architecture is also experimentally demonstrated and compared with individual CSO and physical layer adjustment strategies in terms of path setup/release/adjustment latency, blocking probability and resource occupation rate. PMID:24216922

  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. Genetic risks and genetic model specification.

    PubMed

    Zheng, Gang; Zhang, Wei; Xu, Jinfeng; Yuan, Ao; Li, Qizhai; Gastwirth, Joseph L

    2016-08-21

    Genetic risks and genetic models are often used in design and analysis of genetic epidemiology studies. A genetic model is defined in terms of two genetic risk measures: genotype relative risk and odds ratio. The impacts of choosing a risk measure on the resulting genetic models are studied in the power to detect association and deviation from Hardy-Weinberg equilibrium in cases using genetic relative risk. Extensive simulations demonstrate that the power of a study to detect associations using odds ratio is lower than that using relative risk with the same value when other parameters are fixed. When the Hardy-Weinberg equilibrium holds in the general population, the genetic model can be inferred by the deviation from Hardy-Weinberg equilibrium in only cases. Furthermore, it is more efficient than that based on the deviation from Hardy-Weinberg equilibrium in all cases and controls. PMID:27181372

  18. Medical genetics and genetic counseling in Chile.

    PubMed

    Margarit, Sonia B; Alvarado, Mónica; Alvarez, Karin; Lay-Son, Guillermo

    2013-12-01

    In the South American Republic of Chile genetic counseling is not currently recognized as an independent clinical discipline, and in general is provided by physicians with training in clinical genetics. At present only one genetic counselor and 28 clinical geneticists practice in this country of over 16 million inhabitants. Pediatric dysmorphology constitutes the primary area of practice in clinical genetics. Although the country has a universal health care system and an adequate level of health care, genetic conditions are not considered a health care priority and there is a lack of clinical and laboratory resources designated for clinical genetics services. Multiple educational, cultural and financial barriers exist to the growth and development of genetic counseling services in Chile. However, during the last 10 years increased awareness of the importance of identifying individuals at risk for inherited cancer syndromes led to growing interest in the practice of cancer genetics. PMID:23744184

  19. Applying the New Genetics

    ERIC Educational Resources Information Center

    Sorenson, James

    1976-01-01

    New developments in the prediction and treatment of genetic diseases are presented. Genetic counseling and the role of the counselor, and rights of individuals to reproduce versus societal impact of genetic disorders, are discussed. (RW)

  20. Genetic Differences in Intelligence

    ERIC Educational Resources Information Center

    Intellect, 1977

    1977-01-01

    The Genetics Society of America has released a statement saying that the possibility of a "genetic difference in intelligence between races" is still an open question and warning against "the misuse of genetics for political purposes". (Editor)

  1. 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 ...

  2. 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

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

    PubMed

    Shi, Mingguang; He, Jianmin

    2016-04-22

    Adjuvant chemotherapy (CTX) should be individualized to provide potential survival benefit and avoid potential harm to cancer patients. Our goal was to establish a computational approach for making personalized estimates of the survival benefit from adjuvant CTX. We developed Sub-Network based Random Forest classifier for predicting Chemotherapy Benefit (SNRFCB) based gene expression datasets of lung cancer. The SNRFCB approach was then validated in independent test cohorts for identifying chemotherapy responder cohorts and chemotherapy non-responder cohorts. SNRFCB involved the pre-selection of gene sub-network signatures based on the mutations and on protein-protein interaction data as well as the application of the random forest algorithm to gene expression datasets. Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer patients in the chemotherapy responder group (P = 0.008), but it was not beneficial to patients in the chemotherapy non-responder group (P = 0.657). Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer squamous cell carcinoma (SQCC) subtype patients in the chemotherapy responder cohorts (P = 0.024), but it was not beneficial to patients in the chemotherapy non-responder cohorts (P = 0.383). SNRFCB improved prediction performance as compared to the machine learning method, support vector machine (SVM). To test the general applicability of the predictive model, we further applied the SNRFCB approach to human breast cancer datasets and also observed superior performance. SNRFCB could provide recurrent probability for individual patients and identify which patients may benefit from adjuvant CTX in clinical trials. PMID:26864276

  4. 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

  5. Co-expression network-based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm

    PubMed Central

    YUE, HONG; YANG, BO; YANG, FANG; HU, XIAO-LI; KONG, FAN-BIN

    2016-01-01

    Recent progress in bioinformatics has facilitated the clarification of biological processes associated with complex diseases. Numerous methods of co-expression analysis have been proposed for use in the study of pairwise relationships among genes. In the present study, a combined network based on gene pairs was constructed following the conversion and combination of gene pair score values using a novel algorithm across multiple approaches. Three hippocampal expression profiles of patients with Alzheimer's disease (AD) and normal controls were extracted from the ArrayExpress database, and a total of 144 differentially expressed (DE) genes across multiple studies were identified by a rank product (RP) method. Five groups of co-expression gene pairs and five networks were identified and constructed using four existing methods [weighted gene co-expression network analysis (WGCNA), empirical Bayesian (EB), differentially co-expressed genes and links (DCGL), search tool for the retrieval of interacting genes/proteins database (STRING)] and a novel rank-based algorithm with combined score, respectively. Topological analysis indicated that the co-expression network constructed by the WGCNA method had the tendency to exhibit small-world characteristics, and the combined co-expression network was confirmed to be a scale-free network. Functional analysis of the co-expression gene pairs was conducted by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The co-expression gene pairs were mostly enriched in five pathways, namely proteasome, oxidative phosphorylation, Parkinson's disease, Huntington's disease and AD. This study provides a new perspective to co-expression analysis. Since different methods of analysis often present varying abilities, the novel combination algorithm may provide a more credible and robust outcome, and could be used to complement to traditional co-expression analysis. PMID:27168792

  6. Network-based approach identified cell cycle genes as predictor of overall survival in lung adenocarcinoma patients.

    PubMed

    Li, Yafei; Tang, Hui; Sun, Zhifu; Bungum, Aaron O; Edell, Eric S; Lingle, Wilma L; Stoddard, Shawn M; Zhang, Mingrui; Jen, Jin; Yang, Ping; Wang, Liang

    2013-04-01

    Lung adenocarcinoma is the most common type of primary lung cancer. The purpose of this study was to delineate gene expression patterns for survival prediction in lung adenocarcinoma. Gene expression profiles of 82 (discovery set) and 442 (validation set 1) lung adenocarcinoma tumor tissues were analyzed using a systems biology-based network approach. We also examined the expression profiles of 78 adjacent normal lung tissues from 82 patients. We found a significant correlation of an expression module with overall survival (adjusted hazard ratio or HR=1.71; 95% CI=1.06-2.74 in discovery set; adjusted HR=1.26; 95% CI=1.08-1.49 in validation set 1). This expression module contained genes enriched in the biological process of the cell cycle. Interestingly, the cell cycle gene module and overall survival association were also significant in normal lung tissues (adjusted HR=1.91; 95% CI, 1.32-2.75). From these survival-related modules, we further defined three hub genes (UBE2C, TPX2, and MELK) whose expression-based risk indices were more strongly associated with poor 5-year survival (HR=3.85, 95% CI=1.34-11.05 in discovery set; HR=1.72, 95% CI=1.21-2.46 in validation set 1; and HR=3.35, 95% CI=1.08-10.04 in normal lung set). The 3-gene prognostic result was further validated using 92 adenocarcinoma tumor samples (validation set 2); patients with a high-risk gene signature have a 1.52-fold increased risk (95% CI, 1.02-2.24) of death than patients with a low-risk gene signature. These results suggest that a network-based approach may facilitate discovery of key genes that are closely linked to survival in patients with lung adenocarcinoma. PMID:23357462

  7. 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

  8. Entropy and Gravity Concepts as New Methodological Indexes to Investigate Technological Convergence: Patent Network-Based Approach

    PubMed Central

    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

  9. 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

  10. 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.

  11. 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…

  12. 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

  13. 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

  14. 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)

  15. 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 ...

  16. 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

  17. 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

  18. A network-based integrative approach to prioritize reliable hits from multiple genome-wide RNAi screens in Drosophila

    PubMed Central

    Wang, Li; Tu, Zhidong; Sun, Fengzhu

    2009-01-01

    Background The recently developed RNA interference (RNAi) technology has created an unprecedented opportunity which allows the function of individual genes in whole organisms or cell lines to be interrogated at genome-wide scale. However, multiple issues, such as off-target effects or low efficacies in knocking down certain genes, have produced RNAi screening results that are often noisy and that potentially yield both high rates of false positives and false negatives. Therefore, integrating RNAi screening results with other information, such as protein-protein interaction (PPI), may help to address these issues. Results By analyzing 24 genome-wide RNAi screens interrogating various biological processes in Drosophila, we found that RNAi positive hits were significantly more connected to each other when analyzed within a protein-protein interaction network, as opposed to random cases, for nearly all screens. Based on this finding, we developed a network-based approach to identify false positives (FPs) and false negatives (FNs) in these screening results. This approach relied on a scoring function, which we termed NePhe, to integrate information obtained from both PPI network and RNAi screening results. Using a novel rank-based test, we compared the performance of different NePhe scoring functions and found that diffusion kernel-based methods generally outperformed others, such as direct neighbor-based methods. Using two genome-wide RNAi screens as examples, we validated our approach extensively from multiple aspects. We prioritized hits in the original screens that were more likely to be reproduced by the validation screen and recovered potential FNs whose involvements in the biological process were suggested by previous knowledge and mutant phenotypes. Finally, we demonstrated that the NePhe scoring system helped to biologically interpret RNAi results at the module level. Conclusion By comprehensively analyzing multiple genome-wide RNAi screens, we conclude that

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

    PubMed

    Derous, Davina; Kelder, Thomas; van Schothorst, Evert M; van Erk, Marjan; Voigt, Anja; Klaus, Susanne; Keijer, Jaap; Radonjic, Marijana

    2015-07-01

    Health is influenced by interplay of molecular, physiological and environmental factors. To effectively maintain health and prevent disease, health-relevant relations need to be understood at multiple levels of biological complexity. Network-based methods provide a powerful platform for integration and mining of data and knowledge characterizing different aspects of health. Previously, we have reported physiological and gene expression changes associated with adaptation of murine epididymal white adipose tissue (eWAT) to 5 days and 12 weeks of high-fat diet (HFD) and low-fat diet feeding (Voigt et al. in Mol Nutr Food Res 57:1423-1434, 2013. doi: 10.1002/mnfr.201200671 ). In the current study, we apply network analysis on this dataset to comprehensively characterize mechanisms driving the short- and long-term adaptation of eWAT to HFD across multiple levels of complexity. We built a three-layered interaction network comprising enriched biological processes, their transcriptional regulators and associated changes in physiological parameters. The multi-layered network model reveals that early eWAT adaptation to HFD feeding involves major changes at a molecular level, including activation of TGF-β signalling pathway, immune and stress response and downregulation of mitochondrial functioning. Upon prolonged HFD intake, initial transcriptional response tails off, mitochondrial functioning is even further diminished, and in turn the relation between eWAT gene expression and physiological changes becomes more prominent. In particular, eWAT weight and total energy intake negatively correlate with cellular respiration process, revealing mitochondrial dysfunction as a hallmark of late eWAT adaptation to HFD. Apart from global understanding of the time-resolved adaptation to HFD, the multi-layered network model allows several novel mechanistic hypotheses to emerge: (1) early activation of TGF-β signalling as a trigger for structural and morphological changes in

  20. 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

  1. Large Scale Application of Neural Network Based Semantic Role Labeling for Automated Relation Extraction from Biomedical Texts

    PubMed Central

    Barnickel, Thorsten; Weston, Jason; Collobert, Ronan; Mewes, Hans-Werner; Stümpflen, Volker

    2009-01-01

    To reduce the increasing amount of time spent on literature search in the life sciences, several methods for automated knowledge extraction have been developed. Co-occurrence based approaches can deal with large text corpora like MEDLINE in an acceptable time but are not able to extract any specific type of semantic relation. Semantic relation extraction methods based on syntax trees, on the other hand, are computationally expensive and the interpretation of the generated trees is difficult. Several natural language processing (NLP) approaches for the biomedical domain exist focusing specifically on the detection of a limited set of relation types. For systems biology, generic approaches for the detection of a multitude of relation types which in addition are able to process large text corpora are needed but the number of systems meeting both requirements is very limited. We introduce the use of SENNA (“Semantic Extraction using a Neural Network Architecture”), a fast and accurate neural network based Semantic Role Labeling (SRL) program, for the large scale extraction of semantic relations from the biomedical literature. A comparison of processing times of SENNA and other SRL systems or syntactical parsers used in the biomedical domain revealed that SENNA is the fastest Proposition Bank (PropBank) conforming SRL program currently available. 89 million biomedical sentences were tagged with SENNA on a 100 node cluster within three days. The accuracy of the presented relation extraction approach was evaluated on two test sets of annotated sentences resulting in precision/recall values of 0.71/0.43. We show that the accuracy as well as processing speed of the proposed semantic relation extraction approach is sufficient for its large scale application on biomedical text. The proposed approach is highly generalizable regarding the supported relation types and appears to be especially suited for general-purpose, broad-scale text mining systems. The presented approach

  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. 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

  4. Landscape genetics of high mountain frog metapopulations.

    PubMed

    Murphy, Melanie A; Dezzani, R; Pilliod, D S; Storfer, A

    2010-09-01

    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

  5. Global genetic analysis.

    PubMed

    Elahi, Elahe; Kumm, Jochen; Ronaghi, Mostafa

    2004-01-31

    The introduction of molecular markers in genetic analysis has revolutionized medicine. These molecular markers are genetic variations associated with a predisposition to common diseases and individual variations in drug responses. Identification and genotyping a vast number of genetic polymorphisms in large populations are increasingly important for disease gene identification, pharmacogenetics and population-based studies. Among variations being analyzed, single nucleotide polymorphisms seem to be most useful in large-scale genetic analysis. This review discusses approaches for genetic analysis, use of different markers, and emerging technologies for large-scale genetic analysis where millions of genotyping need to be performed. PMID:14761299

  6. 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

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

    PubMed Central

    Kogelman, Lisette J. A.; Pant, Sameer D.; Fredholm, Merete; Kadarmideen, Haja N.

    2014-01-01

    Obesity is a complex condition with world-wide exponentially rising prevalence rates, linked with severe diseases like Type 2 Diabetes. Economic and welfare consequences have led to a raised interest in a better understanding of the biological and genetic background. To date, whole genome investigations focusing on single genetic variants have achieved limited success, and the importance of including genetic interactions is becoming evident. Here, the aim was to perform an integrative genomic analysis in an F2 pig resource population that was constructed with an aim to maximize genetic variation of obesity-related phenotypes and genotyped using the 60K SNP chip. Firstly, Genome Wide Association (GWA) analysis was performed on the Obesity Index to locate candidate genomic regions that were further validated using combined Linkage Disequilibrium Linkage Analysis and investigated by evaluation of haplotype blocks. We built Weighted Interaction SNP Hub (WISH) and differentially wired (DW) networks using genotypic correlations amongst obesity-associated SNPs resulting from GWA analysis. GWA results and SNP modules detected by WISH and DW analyses were further investigated by functional enrichment analyses. The functional annotation of SNPs revealed several genes associated with obesity, e.g., NPC2 and OR4D10. Moreover, gene enrichment analyses identified several significantly associated pathways, over and above the GWA study results, that may influence obesity and obesity related diseases, e.g., metabolic processes. WISH networks based on genotypic correlations allowed further identification of various gene ontology terms and pathways related to obesity and related traits, which were not identified by the GWA study. In conclusion, this is the first study to develop a (genetic) obesity index and employ systems genetics in a porcine model to provide important insights into the complex genetic architecture associated with obesity and many biological pathways that underlie

  8. Identification of genetic networks.

    PubMed Central

    Xiong, Momiao; Li, Jun; Fang, Xiangzhong

    2004-01-01

    In this report, we propose the use of structural equations as a tool for identifying and modeling genetic networks and genetic algorithms for searching the most likely genetic networks that best fit the data. After genetic networks are identified, it is fundamental to identify those networks influencing cell phenotypes. To accomplish this task we extend the concept of differential expression of the genes, widely used in gene expression data analysis, to genetic networks. We propose a definition for the differential expression of a genetic network and use the generalized T2 statistic to measure the ability of genetic networks to distinguish different phenotypes. However, describing the differential expression of genetic networks is not enough for understanding biological systems because differences in the expression of genetic networks do not directly reflect regulatory strength between gene activities. Therefore, in this report we also introduce the concept of differentially regulated genetic networks, which has the potential to assess changes of gene regulation in response to perturbation in the environment and may provide new insights into the mechanism of diseases and biological processes. We propose five novel statistics to measure the differences in regulation of genetic networks. To illustrate the concepts and methods for reconstruction of genetic networks and identification of association of genetic networks with function, we applied the proposed models and algorithms to three data sets. PMID:15020486

  9. 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 ...

  10. 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 ...

  11. 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. ...

  12. 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 ...

  13. 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, ...

  14. 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 ...

  15. 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 ...

  16. 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 ...

  17. Genetics Home Reference: microphthalmia

    MedlinePlus

    ... including clouding of the lens of the eye ( cataract ) and a narrowed opening of the eye (narrowed ... GeneReview: Microphthalmia/Anophthalmia/Coloboma Spectrum Genetic Testing Registry: Cataract, congenital, with microphthalmia Genetic Testing Registry: Cataract, microphthalmia ...

  18. Latest Research: Genetic Links

    MedlinePlus

    ... Current Issue Past Issues Feature: Vision Latest Research: Genetic Links Past Issues / Summer 2008 Table of Contents ... laboratories is one way the NEI is expanding genetic testing of eye diseases. Photo courtesy of National ...

  19. 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

  20. Genetics in psychiatry

    PubMed Central

    Umesh, Shreekantiah; Nizamie, Shamshul Haque

    2014-01-01

    Today, psychiatrists are focusing on genetics aspects of various psychiatric disorders not only for a future classification of psychiatric disorders but also a notion that genetics would aid in the development of new medications to treat these disabling illnesses. This review therefore emphasizes on the basics of genetics in psychiatry as well as focuses on the emerging picture of genetics in psychiatry and their future implications. PMID:25400339

  1. Introductory molecular genetics

    SciTech Connect

    Edwards-Moulds, J.

    1986-01-01

    This book begins with an overview of the current principles of genetics and molecular genetics. Over this foundation, it adds detailed and specialized information: a description of the translation, transcription, expression and regulation of DNA and RNA; a description of the manipulation of genetic material via promoters, enhancers, and gene splicing; and a description of cloning techniques, especially those for blood group genes. The last chapter looks to the impact of molecular genetics on transfusion medicine.

  2. 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…

  3. 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 ...

  4. The genetic difference principle.

    PubMed

    Farrelly, Colin

    2004-01-01

    In the newly emerging debates about genetics and justice three distinct principles have begun to emerge concerning what the distributive aim of genetic interventions should be. These principles are: genetic equality, a genetic decent minimum, and the genetic difference principle. In this paper, I examine the rationale of each of these principles and argue that genetic equality and a genetic decent minimum are ill-equipped to tackle what I call the currency problem and the problem of weight. The genetic difference principle is the most promising of the three principles and I develop this principle so that it takes seriously the concerns of just health care and distributive justice in general. Given the strains on public funds for other important social programmes, the costs of pursuing genetic interventions and the nature of genetic interventions, I conclude that a more lax interpretation of the genetic difference principle is appropriate. This interpretation stipulates that genetic inequalities should be arranged so that they are to the greatest reasonable benefit of the least advantaged. Such a proposal is consistent with prioritarianism and provides some practical guidance for non-ideal societies--that is, societies that do not have the endless amount of resources needed to satisfy every requirement of justice. PMID:15186680

  5. 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)

  6. 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,…

  7. 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

  8. 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. PMID:21147473

  9. 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 ...

  10. 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 ...

  11. 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.

  12. Genetics of familial hypercholesterolemia.

    PubMed

    Brautbar, Ariel; Leary, Emili; Rasmussen, Kristen; Wilson, Don P; Steiner, Robert D; Virani, Salim

    2015-04-01

    Familial hypercholesterolemia (FH) is a genetic disorder characterized by elevated low-density lipoprotein (LDL) cholesterol and premature cardiovascular disease, with a prevalence of approximately 1 in 200-500 for heterozygotes in North America and Europe. Monogenic FH is largely attributed to mutations in the LDLR, APOB, and PCSK9 genes. Differential diagnosis is critical to distinguish FH from conditions with phenotypically similar presentations to ensure appropriate therapeutic management and genetic counseling. Accurate diagnosis requires careful phenotyping based on clinical and biochemical presentation, validated by genetic testing. Recent investigations to discover additional genetic loci associated with extreme hypercholesterolemia using known FH families and population studies have met with limited success. Here, we provide a brief overview of the genetic determinants, differential diagnosis, genetic testing, and counseling of FH genetics. PMID:25712136

  13. Synthetic Genetic Arrays: Automation of Yeast Genetics.

    PubMed

    Kuzmin, Elena; Costanzo, Michael; Andrews, Brenda; Boone, Charles

    2016-01-01

    Genome-sequencing efforts have led to great strides in the annotation of protein-coding genes and other genomic elements. The current challenge is to understand the functional role of each gene and how genes work together to modulate cellular processes. Genetic interactions define phenotypic relationships between genes and reveal the functional organization of a cell. Synthetic genetic array (SGA) methodology automates yeast genetics and enables large-scale and systematic mapping of genetic interaction networks in the budding yeast,Saccharomyces cerevisiae SGA facilitates construction of an output array of double mutants from an input array of single mutants through a series of replica pinning steps. Subsequent analysis of genetic interactions from SGA-derived mutants relies on accurate quantification of colony size, which serves as a proxy for fitness. Since its development, SGA has given rise to a variety of other experimental approaches for functional profiling of the yeast genome and has been applied in a multitude of other contexts, such as genome-wide screens for synthetic dosage lethality and integration with high-content screening for systematic assessment of morphology defects. SGA-like strategies can also be implemented similarly in a number of other cell types and organisms, includingSchizosaccharomyces pombe,Escherichia coli, Caenorhabditis elegans, and human cancer cell lines. The genetic networks emerging from these studies not only generate functional wiring diagrams but may also play a key role in our understanding of the complex relationship between genotype and phenotype. PMID:27037078

  14. 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

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

    PubMed Central

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

    2013-01-01

    Objective HIV in Central America is concentrated among certain groups such as men who have sex with men (MSM) and female sex workers (FSW). We compared social recruitment chains and HIV transmission clusters from 699 MSM and 757 FSW to better understand factors contributing to ongoing HIV transmission in El Salvador. Methods Phylogenies were reconstructed using pol sequences from 119 HIV-positive individuals recruited by respondent driven sampling (RDS) and compared to RDS chains in three cities in El Salvador. Transmission clusters with a mean pairwise genetic distance ≤0.015 and Bayesian posterior probabilities=1 were identified. Factors associated with cluster membership were evaluated among MSM. Results Sequences from 34 (43%) MSM and 4 (10%) FSW grouped in 14 transmission clusters. Clusters were defined by risk group (12 MSM clusters) and geographic residence (only one spanned separate cities). In 4 MSM clusters (all n=2), individuals were also members of the same RDS chain but only 2 had members directly linked through recruitment. All large clusters (n≥3) spanned more than one RDS chain. Among MSM, factors independently associated with cluster membership included recent infection by BED assay (P=0.02), sex with stable male partners (P=0.02), and sex with ≥3 male partners in past year (P=0.04). Conclusions We found few HIV transmissions corresponding directly with the social recruitment. However, we identified clustering in nearly one half of MSM suggesting RDS recruitment was indirectly but successfully uncovering transmission networks, particularly among recent infections. Interrogating RDS chains with phylogenetic analyses may help refine methods for identifying transmission clusters. PMID:23364512

  16. 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

  17. 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

  18. 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.

  19. Is genetic evolution predictable?

    PubMed

    Stern, David L; Orgogozo, Virginie

    2009-02-01

    Ever since the integration of Mendelian genetics into evolutionary biology in the early 20th century, evolutionary geneticists have for the most part treated genes and mutations as generic entities. However, recent observations indicate that all genes are not equal in the eyes of evolution. Evolutionarily relevant mutations tend to accumulate in hotspot genes and at specific positions within genes. Genetic evolution is constrained by gene function, the structure of genetic networks, and population biology. The genetic basis of evolution may be predictable to some extent, and further understanding of this predictability requires incorporation of the specific functions and characteristics of genes into evolutionary theory. PMID:19197055

  20. Genetics in Non-Genetic Model Systems

    PubMed Central

    Lois, Carlos; Groves, James O

    2011-01-01

    The past few decades have seen the field of genetic engineering evolve at a rapid pace, with neuroscientists now equipped with a wide range of tools for the manipulation of an animal's genome in order to study brain function. However, the number of species to which these technologies have been applied, namely the fruit fly, C. elegans, zebrafish and mouse, remains relatively few. This review will discuss the variety of approaches to genetic modification that have been developed in such traditional ‘genetic systems’, and highlight the progress that has been made to translate these technologies to alternative species such as rats, monkeys and birds, where certain neurobiological questions may be better studied. PMID:22119141

  1. 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

  2. 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

  3. 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

  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. 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

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

    PubMed

    Duffy, David J; Krstic, Aleksandar; Halasz, Melinda; Schwarzl, Thomas; 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

    2015-12-22

    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

  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. Blackberry Breeding and Genetics

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Plant Breeding Reviews has been published since the early 1980s and each edition presents a thorough review of the state of the are on breeding and genetics of specific crop plant. The extensive chapter on blackberry breeding and genetics is organized as follows: INTRODUCTION (Origin and Speciation...

  9. Genetic differential calculus.

    PubMed

    Mott, Richard

    2015-09-01

    High-throughput analysis of the phenotypes of mouse genetic knockouts presents several challenges, such as systematic measurement biases that can vary with time. A report from the EUMODIC consortium presents data from 320 genetic knockouts generated using standardized phenotyping pipelines and new statistical analyses aimed at increasing reproducibility across centers. PMID:26313224

  10. 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

  11. 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…

  12. 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. PMID:27272104

  13. [Human genetics and ethics].

    PubMed

    Zergollern, L

    1990-01-01

    Many new problems and dilemmas have occurred in the practice of medical geneticists with the development of human genetics and its subdisciplines--molecular genetics, ethic genetics and juridical genetics. Devoid of the possibility to get adequate education, genetic informer or better to say, counsellor, although a scientist and a professional who has already formed his ethic attitudes, often finds himself in a dilemma when he has to decide whether a procedure made possible by progress of science is ethical or not. Thus, due to different attitudes, same decision is ethical for some, while for the others it is not. Ethic committees are groups of moral and good people trying to find an objective approach to certain genetic and ethic problems. There are more and more ethically unanswered questions in modern human genetics, and particularly in medical genetics. Medical geneticist-ethicist still encounters numerous problems in his work. These are, for example, experiments with human gametes and embryos, possibilities of hybridization of human gametes with animal gametes, in vitro fertilization, detection of heterozygotes and homozygotes for monogene diseases. early detection of chromosomopathies, substitute mothers, homo and hetero insemination, transplantation of fetal and cadeveric organs, uncontrolled consumption of alcohol and drugs, environmental pollution, etc. It is almost impossible to create a single attitude which shall be shared by all those engaged in human health protection. Therefore, it is best to have a neutral eugenetic attitude which allows free ethical choice of each individual, in any case, for the well-being of man. PMID:2366624

  14. Soybean Molecular Genetic Diversity

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A history of the various DNA marker types used in the assessment of molecular genetic diversity in soybean [Glycine max (L.) Merr.] is followed by a description of a number of studies on the assessment of genetic diversity. These studies include a review of reports on 1) the quantification and comp...

  15. 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)

  16. Cryptic Genetic Variation in Evolutionary Developmental Genetics.

    PubMed

    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

  17. 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

  18. Genetics of stroke

    PubMed Central

    Guo, Jin-min; Liu, Ai-jun; Su, Ding-feng

    2010-01-01

    Stroke is the second most common cause of death and the most common cause of disability in developed countries. Stroke is a multi-factorial disease caused by a combination of environmental and genetic factors. Numerous epidemiologic studies have documented a significant genetic component in the occurrence of strokes. Genes encoding products involved in lipid metabolism, thrombosis, and inflammation are believed to be potential genetic factors for stroke. Although a large group of candidate genes have been studied, most of the epidemiological results are conflicting. Studies of stroke as a monogenic disease have made huge progress, and animal models serve as an indispensable tool to dissect the complex genetics of stroke. In the present review, we provide insight into the role of in vivo stroke models for the study of stroke genetics. PMID:20729874

  19. 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

  20. Genetics of gastrointestinal atresias.

    PubMed

    Celli, Jacopo

    2014-08-01

    Gastrointestinal atresias are a common and serious feature within the spectrum of gastrointestinal malformations. Atresias tend to be lethal, although, now-days surgery and appropriate care can restore function to the affected organs. In spite of their frequency, their life threatening condition and report history gastrointestinal atresias' etiology remains mostly unclarified. Gastrointestinal atresias can occur as sporadic but they are more commonly seen in association with other anomalies. For the syndromic cases there is mounting evidence of a strong genetic component. Sporadic cases are generally thought to originate from mechanical or vascular incidents in utero, especially for the atresias of the lower intestinal tract. However, recent data show that a genetic component may be present also in these cases. Embryological and genetic studies are starting to uncover the mechanism of gastrointestinal development and their genetic components. Here we present an overview of the current knowledge of gastrointestinal atresias, their syndromic forms and the genetic pathways involved in gastrointestinal malformation. PMID:25019371

  1. 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.

  2. Genetics & the Quality of Life.

    ERIC Educational Resources Information Center

    McInerney, Joseph D.

    1989-01-01

    Describes the contribution made to the quality of human life by the study of genetics. Presents a description of the current status of genetics education. Suggests changes in genetics education necessary to keep up with new developments. (39 references) (CW)

  3. MedlinePlus: Genetic Counseling

    MedlinePlus

    ... Here Frequently Asked Questions about Genetic Counseling (National Human Genome Research Institute) Genetic Counseling (Centers for Disease Control and Prevention) Genetic Counseling (March of Dimes Birth Defects Foundation) Also in Spanish Making Sense of ...

  4. 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 ...

  5. 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 ...

  6. Dairy Cattle: Breeding and Genetics

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Five primary factors affect breeding genetically improved dairy cattle: 1) identification, 2) pedigree, 3) performance recording, 4) artificial insemination, and 5) genetic evaluation systems (traditional and genomic). Genetic progress can be measured as increased efficiency (higher performance with...

  7. 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

  8. [Genetics of idiopathic epilepsies].

    PubMed

    Weber, Y G; Lerche, H

    2013-02-01

    Idiopathic epilepsies are genetically determined. They are characterized by the observed seizure types, an age-dependent onset, electroencephalographic criteria and concomitant symptoms, such as movement disorders or developmental delay. The main subtypes are the idiopathic (i) generalized, (ii) the focal epilepsies including the benign syndromes of early childhood and (iii) the epileptic encephalopathies as well as the fever-associated syndromes. In recent years, an increasing number of mutations have been identified in genes encoding ion channels, proteins associated to the vesical synaptic cycle or proteins involved in energy metabolism. These mechanisms are pathophysiologically plausible as they influence neuronal excitability. The large number of genetic defects in epilepsy complicates the genetic diagnostic analysis but novel genetic methods are available covering all known genes at a reasonable price. The proof of a genetic defect leads to a definitive diagnosis, is important for the prognostic and genetic counselling and may influence therapeutic decisions in some cases, so that genetic diagnostic testing is becoming increasingly more important and meaningful in many cases in daily clinical practice. PMID:23392265

  9. 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

  10. 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

  11. 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

  12. Statistics for Learning Genetics

    NASA Astrophysics Data System (ADS)

    Charles, Abigail Sheena

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

  13. 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

  14. Genetics and antisocial behavior.

    PubMed

    Joseph, Jay

    2003-01-01

    This commentary article reviews a recent meta-analysis of genetic influences on antisocial behavior by Rhee and Waldman (2002). The authors combined the results of 51 twin and adoption studies and concluded that antisocial behavior has an important genetic component. However, twin and adoption studies contain several methodological flaws and are subject to the confounding influence of environmental factors. Therefore, Rhee and Waldman's conclusions in favor of genetic influences are not supported by the evidence. Two additional topics are Rhee and Waldman's incorrect description of the heritability concept and their failure to discuss several German criminal twin studies published during the Nazi era. PMID:15279006

  15. 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.

  16. Molecular Genetics of Mycobacteriophages

    PubMed Central

    HATFULL, GRAHAM F.

    2014-01-01

    Mycobacteriophages have provided numerous essential tools for mycobacterial genetics, including delivery systems for transposons, reporter genes, and allelic exchange substrates, and components for plasmid vectors and mutagenesis. Their genetically diverse genomes also reveal insights into the broader nature of the phage population and the evolutionary mechanisms that give rise to it. The substantial advances in our understanding of the biology of mycobacteriophages including a large collection of completely sequenced genomes indicates a rich potential for further contributions in tuberculosis genetics and beyond. PMID:25328854

  17. Genetic Time Travel.

    PubMed

    Krause, Johannes; Pääbo, Svante

    2016-05-01

    At its core, genetics is a historical discipline. Mutations are passed on from generation to generation and accumulate as a result of chance as well as of selection within and between populations and species. However, until recently, geneticists were confined to the study of present-day genetic variation and could only indirectly make inferences about the historical processes that resulted in the variation in present-day gene pools. This "time trap" has now been overcome thanks to the ability to analyze DNA extracted from ancient remains, and this is about to revolutionize several aspects of genetics. PMID:27183562

  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. 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. PMID:26255042

  20. 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. PMID:24992825

  1. 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 ...

  2. 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 ( ...

  3. 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 ...

  4. 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 ...

  5. 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 ...

  6. 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

  7. 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 ...

  8. 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 ...

  9. 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 ...

  10. 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 ...

  11. 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- ...

  12. 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) ...

  13. 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)

  14. Determinants of genetic diversity.

    PubMed

    Ellegren, Hans; Galtier, Nicolas

    2016-07-01

    Genetic polymorphism varies among species and within genomes, and has important implications for the evolution and conservation of species. The determinants of this variation have been poorly understood, but population genomic data from a wide range of organisms now make it possible to delineate the underlying evolutionary processes, notably how variation in the effective population size (Ne) governs genetic diversity. Comparative population genomics is on its way to providing a solution to 'Lewontin's paradox' - the discrepancy between the many orders of magnitude of variation in population size and the much narrower distribution of diversity levels. It seems that linked selection plays an important part both in the overall genetic diversity of a species and in the variation in diversity within the genome. Genetic diversity also seems to be predictable from the life history of a species. PMID:27265362

  15. Genetics Home Reference: hemophilia

    MedlinePlus

    ... Help Me Understand Genetics Home Health Conditions hemophilia hemophilia Enable Javascript to view the expand/collapse boxes. Print All Open All Close All Description Hemophilia is a bleeding disorder that slows the blood ...

  16. 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

  17. 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 ...

  18. 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.

  19. 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.

  20. Genetic obesity syndromes.

    PubMed

    Goldstone, Anthony P; Beales, Philip L

    2008-01-01

    There are numerous reports of multi-system genetic disorders with obesity. Many have a characteristic presentation and several, an overlapping phenotype indicating the likelihood of a shared common underlying mechanism or pathway. By understanding the genetic causes and functional perturbations of such syndromes we stand to gain tremendous insight into obesogenic pathways. In this review we focus particularly on Bardet-Biedl syndrome, whose molecular genetics and cell biology has been elucidated recently, and Prader-Willi syndrome, the commonest obesity syndrome due to loss of imprinted genes on 15q11-13. We also discuss highlights of other genetic obesity syndromes including Alstrom syndrome, Cohen syndrome, Albright's hereditary osteodystrophy (pseudohypoparathyroidism), Carpenter syndrome, MOMO syndrome, Rubinstein-Taybi syndrome, cases with deletions of 6q16, 1p36, 2q37 and 9q34, maternal uniparental disomy of chromosome 14, fragile X syndrome and Börjeson-Forssman-Lehman syndrome. PMID:18230893

  1. 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 ...

  2. [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

  3. 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 ...

  4. Genetic associations with brain cortical thickness in multiple sclerosis

    PubMed Central

    Matsushita, T.; Madireddy, L.; Sprenger, T.; Khankhanian, P.; Magon, S.; Naegelin, Y.; Caverzasi, E.; Lindberg, R. L. P.; Kappos, L.; Hauser, S. L.; Oksenberg, J. R.; Henry, R.; Pelletier, D.; Baranzini, S. E.

    2016-01-01

    Multiple sclerosis (MS) is characterized by temporal and spatial dissemination of demyelinating lesions in the central nervous system. Associated neurodegenerative changes contributing to disability have been recognized even at early disease stages. Recent studies show the importance of gray matter damage for the accrual of clinical disability rather than white matter where demyelination is easily visualized by magnetic resonance imaging (MRI). The susceptibility to MS is influenced by genetic risk, but genetic factors associated with the disability are not known. We used MRI data to determine cortical thickness in 557 MS cases and 75 controls and in another cohort of 219 cases. We identified nine areas showing different thickness between cases and controls (regions of interest, ROI) (eight of them were negatively correlated with Kurtzke’s expanded disability status scale, EDSS) and conducted genome-wide association studies (GWAS) in 464 and 211 cases available from the two data sets. No marker exceeded genome-wide significance in the discovery cohort. We next combined nominal statistical evidence of association with physical evidence of interaction from a curated human protein interaction network, and searched for subnetworks enriched with nominally associated genes and for commonalities between the two data sets. This network-based pathway analysis of GWAS detected gene sets involved in glutamate signaling, neural development and an adjustment of intracellular calcium concentration. We report here for the first time gene sets associated with cortical thinning of MS. These genes are potentially correlated with disability of MS. PMID:25684059

  5. 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.

  6. 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…

  7. Synchronization of genetic oscillators

    NASA Astrophysics Data System (ADS)

    Zhou, Tianshou; Zhang, Jiajun; Yuan, Zhanjiang; Chen, Luonan

    2008-09-01

    Synchronization of genetic or cellular oscillators is a central topic in understanding the rhythmicity of living organisms at both molecular and cellular levels. Here, we show how a collective rhythm across a population of genetic oscillators through synchronization-induced intercellular communication is achieved, and how an ensemble of independent genetic oscillators is synchronized by a common noisy signaling molecule. Our main purpose is to elucidate various synchronization mechanisms from the viewpoint of dynamics, by investigating the effects of various biologically plausible couplings, several kinds of noise, and external stimuli. To have a comprehensive understanding on the synchronization of genetic oscillators, we consider three classes of genetic oscillators: smooth oscillators (exhibiting sine-like oscillations), relaxation oscillators (displaying jump dynamics), and stochastic oscillators (noise-induced oscillation). For every class, we further study two cases: with intercellular communication (including phase-attractive and repulsive coupling) and without communication between cells. We find that an ensemble of smooth oscillators has different synchronization phenomena from those in the case of relaxation oscillators, where noise plays a different but key role in synchronization. To show differences in synchronization between them, we make comparisons in many aspects. We also show that a population of genetic stochastic oscillators have their own synchronization mechanisms. In addition, we present interesting phenomena, e.g., for relaxation-type stochastic oscillators coupled to a quorum-sensing mechanism, different noise intensities can induce different periodic motions (i.e., inhomogeneous limit cycles).

  8. 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

  9. 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

  10. Early Alzheimer's disease genetics.

    PubMed

    Schellenberg, Gerard D

    2006-01-01

    The genetics community working on Alzheimer's disease and related dementias has made remarkable progress in the past 20 years. The cumulative efforts by multiple groups have lead to the identification of three autosomal dominant genes for early onset AD. These are the amyloid-beta protein precursor gene (APP), and the genes encoding presenilin1 and 2. The knowledge derived from this work has firmly established Abeta as a critical disease molecule and lead to candidate drugs currently in treatment trials. Work on a related disease, frontotemporal dementia with parkinsonism - chromosome 17 type has also added to our understanding of pathogenesis by revealing that tau, the protein component of neurofibrillary tangles, is also a critical molecule in neurodegeneration. Lessons learned that still influence work on human genetics include the need to recognize and deal with genetic heterogeneity, a feature common to many genetic disorders. Genetic heterogeneity, if recognized, can be source of information. Another critical lesson is that clinical, molecular, and statistical scientists need to work closely on disease projects to succeed in solving the complex problems of common genetic disorders. PMID:16914874

  11. What was classical genetics?

    PubMed

    Waters, C Kenneth

    2004-12-01

    I present an account of classical genetics to challenge theory-biased approaches in the philosophy of science. Philosophers typically assume that scientific knowledge is ultimately structured by explanatory reasoning and that research programs in well-established sciences are organized around efforts to fill out a central theory and extend its explanatory range. In the case of classical genetics, philosophers assume that the knowledge was structured by T. H. Morgan's theory of transmission and that research throughout the later 1920s, 30s, and 40s was organized around efforts to further validate, develop, and extend this theory, I show that classical genetics was structured by an integration of explanatory reasoning (associated with the transmission theory) and investigative strategies (such as the 'genetic approach'). The investigative strategies, which have been overlooked in historical and philosophical accounts, were as important as the so-called laws of Mendelian genetics. By the later 1920s, geneticists of the Morgan school were no longer organizing research around the goal of explaining inheritance patterns; rather, they were using genetics to investigate a range of biological phenomena that extended well beyond the explanatory domain of transmission theories. Theory-biased approaches in history and philosophy of science fail to reveal the overall structure of scientific knowledge and obscure the way it functions. PMID:15682554

  12. 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)

  13. Genetic of uveitis.

    PubMed

    Pichi, Francesco; Carrai, Paola; Srivastava, Sunil K; Lowder, Careen Y; Nucci, Paolo; Neri, Piergiorgio

    2016-06-01

    Immune-mediated uveitis may be associated with a systemic disease or may be localized to the eye. T-cell-dependent immunological events are increasingly being regarded as extremely important in the pathogenesis of uveitis. Several studies have also shown that macrophages are major effectors of tissue damage in uveitis. Uveitis phenotypes can differ substantially, and most uveitis diseases are considered polygenic with complex inheritance patterns. This review attempts to present the current state of knowledge from in vitro and in vivo research on the role of genetics in the development and clinical course of uveitis. A review of the literature in the PubMed, MEDLINE, and Cochrane databases was conducted to identify clinical trials, comparative studies, case series, and case reports describing host genetic factors as well as immune imbalance which contribute to the development of uveitis. The search was limited to primary reports published in English with human subjects from 1990 to the present, yielding 3590 manuscripts. In addition, referenced articles from the initial searches were hand searched to identify additional relevant reports. After title and abstract selection, duplicate elimination, and manual search, 55 papers were selected for analysis and reviewed by the authors for inclusion in this review. Studies have demonstrated associations between various genetic factors and the development and clinical course of intraocular inflammatory conditions. Genes involved included genes expressing interleukins, chemokines, chemokine receptors, and tumor necrosis factor and genes involved in complement system. When considering the genetics of uveitis, common threads can be identified. Genome-wide scans and other genetic methods are becoming increasingly successful in identifying genetic loci and candidate genes in many inflammatory disorders that have a uveitic component. It will be important to test these findings as uveitis-specific genetic factors. Therefore, the

  14. Genetics of otitis media.

    PubMed

    Post, J Christopher

    2011-01-01

    There is a growing body of evidence, both from animal and human studies, that host genetic factors can influence the risk of developing otitis media (OM). The role of genetics in OM has been elucidated through studies with monozygotic and dizygotic twins, analyses linking genetic polymorphisms to OM susceptibility, and genome scans. Several twin studies have shown a strong genetic component to middle ear effusion risk, with the estimate of the role of heredity for the proportion of time with middle ear effusions being around 0.7. Genetic polymorphisms in plasminogen activator inhibitor-1, interleukin-6, tumor necrosis factor-α, human leukocyte antigen, and mannose-binding lectin have been variously linked with OM and upper respiratory infection susceptibility. Several genome linkage studies have identified chromosomal regions associated with chronic OM, including 3p, 10q, 10q22.3, 17q12 and 19q. A number of candidate genes are associated with these sites. Given the current state of understanding of the role of genetics in OM, a family history of OM should be ascertained for all patients. Children with a strong family history of OM should be considered as candidates for a more aggressive early treatment of OM, particularly if other risk factors are present. These children may be earlier candidates for the placement of tympanostomy tubes and/or adenoidectomy. Existing data do not support routine genetic testing to determine a child's susceptibility to OM; however, given the advances in whole genome sequencing, such testing may someday play a role in the management of the OM patient. PMID:21358196

  15. 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. PMID:25823041

  16. Individual 3D region-of-interest atlas of the human brain: automatic training point extraction for neural-network-based classification of brain tissue types

    NASA Astrophysics Data System (ADS)

    Wagenknecht, Gudrun; Kaiser, Hans-Juergen; Obladen, Thorsten; Sabri, Osama; Buell, Udalrich

    2000-04-01

    Individual region-of-interest atlas extraction consists of two main parts: T1-weighted MRI grayscale images are classified into brain tissues types (gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), scalp/bone (SB), background (BG)), followed by class image analysis to define automatically meaningful ROIs (e.g., cerebellum, cerebral lobes, etc.). The purpose of this algorithm is the automatic detection of training points for neural network-based classification of brain tissue types. One transaxial slice of the patient data set is analyzed. Background separation is done by simple region growing. A random generator extracts spatially uniformly distributed training points of class BG from that region. For WM training point extraction (TPE), the homogeneity operator is the most important. The most homogeneous voxels define the region for WM TPE. They are extracted by analyzing the cumulative histogram of the homogeneity operator response. Assuming a Gaussian gray value distribution in WM, a random number is used as a probabilistic threshold for TPE. Similarly, non-white matter and non-background regions are analyzed for GM and CSF training points. For SB TPE, the distance from the BG region is an additional feature. Simulated and real 3D MRI images are analyzed and error rates for TPE and classification calculated.

  17. 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.

  18. 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

  19. 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

  20. Efficiency improvement in multi-sensor wireless network based estimation algorithms for distributed parameter systems with application at the heat transfer

    NASA Astrophysics Data System (ADS)

    Volosencu, Constantin; Curiac, Daniel-Ioan

    2013-12-01

    This paper gives a technical solution to improve the efficiency in multi-sensor wireless network based estimation for distributed parameter systems. A complex structure based on some estimation algorithms, with regression and autoregression, implemented using linear estimators, neural estimators and ANFIS estimators, is developed for this purpose. The three kinds of estimators are working with precision on different parts of the phenomenon characteristic. A comparative study of three methods - linear and nonlinear based on neural networks and adaptive neuro-fuzzy inference system - to implement these algorithms is made. The intelligent wireless sensor networks are taken in consideration as an efficient tool for measurement, data acquisition and communication. They are seen as a "distributed sensor", placed in the desired positions in the measuring field. The algorithms are based on regression using values from adjacent and also on auto-regression using past values from the same sensor. A modelling and simulation for a case study is presented. The quality of estimation is validated using a quadratic criterion. A practical implementation is made using virtual instrumentation. Applications of this complex estimation system are in fault detection and diagnosis of distributed parameter systems and discovery of malicious nodes in wireless sensor networks.

  1. 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 ...

  2. 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

  3. 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

  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. Genetic Susceptibility to Lymphoma

    PubMed Central

    Skibola, Christine F.; Curry, John D.; Nieters, Alexandra

    2010-01-01

    BACKGROUND Genetic susceptibility studies of lymphoma may serve to identify at risk populations and to elucidate important disease mechanisms. METHODS This review considered all studies published through October 2006 on the contribution of genetic polymorphisms in the risk of lymphoma. RESULTS Numerous studies implicate the role of genetic variants that promote B-cell survival and growth with increased risk of lymphoma. Several reports including a large pooled study by InterLymph, an international consortium of non-Hodgkin lymphoma (NHL) case-control studies, found positive associations between variant alleles in TNF -308G>A and IL10 -3575T>A genes and risk of diffuse large B-cell lymphoma. Four studies reported positive associations between a GSTT1 deletion and risk of Hodgkin and non-Hodgkin lymphoma. Genetic studies of folate-metabolizing genes implicate folate in NHL risk, but further studies that include folate and alcohol assessments are needed. Links between NHL and genes involved in energy regulation and hormone production and metabolism may provide insights into novel mechanisms implicating neuro- and endocrine-immune cross-talk with lymphomagenesis, but will need replication in larger populations. CONCLUSIONS Numerous studies suggest that common genetic variants with low penetrance influence lymphoma risk, though replication studies will be needed to eliminate false positive associations. PMID:17606447

  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. 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

  8. Network-Based Electronic Serials.

    ERIC Educational Resources Information Center

    Bailey, Charles W., Jr.

    1992-01-01

    Discusses electronic serials that are available on noncommercial international computer networks such as BITNET and Internet. Issues affecting libraries are discussed, including access and ownership; computer conferences are considered; examples of electronic newsletters and electronic journals are described; and the possible future of electronic…

  9. Network based sky Brightness Monitor

    NASA Astrophysics Data System (ADS)

    McKenna, Dan; Pulvermacher, R.; Davis, D. R.

    2009-01-01

    We have developed and are currently testing an autonomous 2 channel photometer designed to measure the night sky brightness in the visual wavelengths over a multi-year campaign. The photometer uses a robust silicon sensor filtered with Hoya CM500 glass. The Sky brightness is measured every minute at two elevation angles typically zenith and 20 degrees to monitor brightness and transparency. The Sky Brightness monitor consists of two units, the remote photometer and a network interface. Currently these devices use 2.4 Ghz transceivers with a free space range of 100 meters. The remote unit is battery powered with day time recharging using a solar panel. Data received by the network interface transmits data via standard POP Email protocol. A second version is under development for radio sensitive areas using an optical fiber for data transmission. We will present the current comparison with the National Park Service sky monitoring camera. We will also discuss the calibration methods used for standardization and temperature compensation. This system is expected to be deployed in the next year and be operated by the International Dark Sky Association SKYMONITOR project.

  10. Genetic diversity, structure, and breed relationships in Iberian cattle.

    PubMed

    Martín-Burriel, I; Rodellar, C; Cañón, J; Cortés, O; Dunner, S; Landi, V; Martínez-Martínez, A; Gama, L T; Ginja, C; Penedo, M C T; Sanz, A; Zaragoza, P; Delgado, J V

    2011-04-01

    In Iberia there are 51 officially recognized cattle breeds of which 15 are found in Portugal and 38 in Spain. We present here a comprehensive analysis of the genetic diversity and structure of Iberian cattle. Forty of these breeds were genotyped with 19 highly polymorphic microsatellite markers. Asturiana de los Valles displayed the greatest allelic diversity and Mallorquina the least. Unbiased heterozygosity values ranged from 0.596 to 0.787. The network based on Reynolds distances was star-shaped with few pairs of interrelated breeds and a clear cluster of 4 breeds (Alistana/Arouquesa/Marinhoa/Mirandesa). The analysis of the genetic structure of Iberian cattle indicated that the most probable number of population clusters included in the study would be 36. Distance results were supported by the STRUCTURE software indicating a relatively recent origin or possible crossbreeding or both between pairs or small groups of breeds. Five clusters included 2 different breeds (Betizu/Pirenaica, Morucha/Avileña, Parda de Montaña/Bruna de los Pirineos, Barrosã/Cachena, and Toro de Lidia/Brava de Lide), 3 breeds (Berrenda en Negro, Negra Andaluza, and Mertolenga) were divided in 2 independent clusters each, and 2 breeds were considered admixed (Asturiana de los Valles and Berrenda en Colorado). Individual assignation to breeds was not possible in the 2 admixed breeds and the pair Parda de Montaña/Bruna de los Pirineos. The relationship between Iberian cattle reflects their geographical origin rather than their morphotypes. Exceptions to this geographic clustering are most probably a consequence of crossbreeding with foreign breeds. The relative genetic isolation within their geographical origin, the consequent genetic drift, the adaptation to specific environment and production systems, and the influence of African and European cattle have contributed to the current genetic status of Iberian cattle, which are grouped according to their geographical origin. The greater

  11. 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

  12. Genetic Epidemiology of Psoriasis

    PubMed Central

    Gupta, Rashmi; Debbaneh, Maya G.; Liao, Wilson

    2014-01-01

    Psoriasis is a chronic, inflammatory, immune-mediated skin condition with a prevalence of 0-11.8% across the world. It is associated with a number of cardiovascular, metabolic, and autoimmune disease co-morbidities. Psoriasis is a multifactorial disorder, influenced by both genetic and environmental factors. Its genetic basis has long been established through twin studies and familial clustering. The association of psoriasis with the HLA-Cw6 allele has been shown in many studies. Recent genome-wide association studies have identified a large number of other genes associated with psoriasis. Many of these genes regulate the innate and adaptive immune system. These findings indicate that a dysregulated immune system may play a major role in the pathogenesis of psoriasis. In this article, we review the clinical and genetic epidemiology of psoriasis with a brief description of the pathogenesis of disease. PMID:25580373

  13. 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. PMID:11201326

  14. 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

  15. Darwin's contributions to genetics.

    PubMed

    Liu, Y-S; Zhou, X-M; Zhi, M-X; Li, X-J; Wang, Q-L

    2009-01-01

    Darwin's contributions to evolutionary biology are well known, but his contributions to genetics are much less known. His main contribution was the collection of a tremendous amount of genetic data, and an attempt to provide a theoretical framework for its interpretation. Darwin clearly described almost all genetic phenomena of fundamental importance, such as prepotency (Mendelian inheritance), bud variation (mutation), heterosis, reversion (atavism), graft hybridization (Michurinian inheritance), sex-limited inheritance, the direct action of the male element on the female (xenia and telegony), the effect of use and disuse, the inheritance of acquired characters (Lamarckian inheritance), and many other observations pertaining to variation, heredity and development. To explain all these observations, Darwin formulated a developmental theory of heredity - Pangenesis - which not only greatly influenced many subsequent theories, but also is supported by recent evidence. PMID:19638672

  16. 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. PMID:15641921

  17. Molecular Genetics in Glaucoma

    PubMed Central

    Liu, Yutao

    2015-01-01

    Glaucoma is a family of diseases whose pathology is defined by the progressive loss of retinal ganglion cells. Clinically, glaucoma presents as a distinctive optic neuropathy with associated visual field loss. Primary open-angle glaucoma (POAG), chronic angle closure glaucoma (ACG), and exfoliation glaucoma (XFG) are the most prevalent forms of glaucoma globally and are the most common causes of glaucoma-related blindness worldwide. A host of genetic and environmental factors contribute to glaucoma phenotypes. This review examines the current status of genetic investigations of POAG, ACG, XFG, including the less common forms of glaucoma primary congenital glaucoma (PCG), the developmental glaucomas, and pigment dispersion glaucoma. PMID:21871452

  18. Genetic control of mosquitoes.

    PubMed

    Alphey, Luke

    2014-01-01

    Genetics can potentially provide new, species-specific, environmentally friendly methods for mosquito control. Genetic control strategies aim either to suppress target populations or to introduce a harm-reducing novel trait. Different approaches differ considerably in their properties, especially between self-limiting strategies, where the modification has limited persistence, and self-sustaining strategies, which are intended to persist indefinitely in the target population and may invade other populations. Several methods with different molecular biology are under development and the first field trials have been completed successfully. PMID:24160434

  19. 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.

  20. Genetics for the General Internist

    PubMed Central

    Laukaitis, Christina M.

    2011-01-01

    The internist’s goal is to determine a patient’s disease risk and to implement preventative interventions. Genetic evaluation is a powerful risk assessment tool and new interventions target previously untreatable genetic disorders. The purpose of this review is to educate the general internist about common genetic conditions affecting adult patients with special emphasis on diagnoses with an effective intervention, including hereditary cancer syndromes and cardiovascular disorders. Basic tenets of genetic counseling, complex genetic disease and management of adults with genetic diagnoses are also discussed. PMID:22079017

  1. A neural network-based four-band model for estimating the total absorption coefficients from the global oceanic and coastal waters

    NASA Astrophysics Data System (ADS)

    Chen, Jun; Cui, Tingwei; Quan, Wenting

    2015-01-01

    this study, a neural network-based four-band model (NNFM) for the global oceanic and coastal waters has been developed in order to retrieve the total absorption coefficients a(λ). The applicability of the quasi-analytical algorithm (QAA) and NNFM models is evaluated by five independent data sets. Based on the comparison of a(λ) predicted by these two models with the field measurements taken from the global oceanic and coastal waters, it was found that both the QAA and NNFM models had good performances in deriving a(λ), but that the NNFM model works better than the QAA model. The results of the QAA model-derived a(λ), especially in highly turbid waters with strong backscattering properties of optical activity, was found to be lower than the field measurements. The QAA and NNFM models-derived a(λ) could be obtained from the MODIS data after atmospheric corrections. When compared with the field measurements, the NNFM model decreased by a 0.86-24.15% uncertainty (root-mean-square relative error) of the estimation from the QAA model in deriving a(λ) from the Bohai, Yellow, and East China seas. Finally, the NNFM model was applied to map the global climatological seasonal mean a(443) for the time range of July 2002 to May 2014. As expected, the a(443) value around the coastal regions was always larger than the open ocean around the equator. Viewed on a global scale, the oceans at a high latitude exhibited higher a(443) values than those at a low latitude.

  2. 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.

  3. 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.

  4. 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

  5. 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. PMID:26464321

  6. 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

  7. 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...

  8. 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

  9. Genetics Home Reference: SADDAN

    MedlinePlus

    ... particular ethnic groups? Genetic Changes Mutations in the FGFR3 gene cause SADDAN . The FGFR3 gene provides instructions for making a protein that ... A mutation in this gene may cause the FGFR3 protein to be overly active, which leads to ...

  10. 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.

  11. 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 ...

  12. 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…

  13. 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…

  14. 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…

  15. Genetics Home Reference: cherubism

    MedlinePlus

    ... About Genetics Home Reference Site Map Contact Us Selection Criteria for Links Copyright Privacy Accessibility FOIA Viewers & Players U.S. Department of Health & Human Services National Institutes of Health National Library of Medicine Lister Hill National Center for Biomedical Communications 8600 ...

  16. 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.

  17. 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…

  18. Genetics of disease resistance

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Genetic resistance is alluring from both the industrial and academic viewpoints. With respect to poultry companies, losses due to diseases induced by infectious pathogens continue to be a significant issue and can be the key factor in determining economic viability. This is because pathogens lead ...

  19. Genetically engineering milk.

    PubMed

    Whitelaw, C Bruce A; Joshi, Akshay; Kumar, Satish; Lillico, Simon G; Proudfoot, Chris

    2016-02-01

    It has been thirty years since the first genetically engineered animal with altered milk composition was reported. During the intervening years, the world population has increased from 5bn to 7bn people. An increasing demand for protein in the human diet has followed this population expansion, putting huge stress on the food supply chain. Many solutions to the grand challenge of food security for all have been proposed and are currently under investigation and study. Amongst these, genetics still has an important role to play, aiming to continually enable the selection of livestock with enhanced traits. Part of the geneticist's tool box is the technology of genetic engineering. In this Invited Review, we indicate that this technology has come a long way, we focus on the genetic engineering of dairy animals and we argue that the new strategies for precision breeding demand proper evaluation as to how they could contribute to the essential increases in agricultural productivity our society must achieve. PMID:26869106

  20. Chapter 2. Genetic Resources

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In this chapter, four categories of plant genetic resources (PGR) are identified as important for breeding: Wild relatives, ecotypes, landraces, and cultivars. Fodder crops and amenity grasses differ from field crops in the relative importance of these categories, as well as in the relative importan...

  1. Association genetics in barley

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Applied and basic barley geneticists have begun to use association genetics as a tool to identify and fine map polymorphisms directly in breeding populations or diversity panels. Barley presents an ideal system because its populations present different extents of LD, from long-range LD in elite cult...

  2. 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…

  3. 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

  4. 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…

  5. Genetic resources for phenotyping

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Phenotyping of structured populations, along with molecular genotyping, will be essential for marker development in peanut. This research is essential for making the peanut genome sequence and genomic tools useful to breeders because it makes the connection between genes, gene markers, genetic maps...

  6. Genetics of diabetes complications.

    PubMed

    Alkayyali, Sami; Lyssenko, Valeriya

    2014-10-01

    Chronic hyperglycemia and duration of diabetes are the major risk factors associated with development of micro- and macrovascular complications of diabetes. Although it is believed that hyperglycemia induces damage to the particular cell subtypes, e.g., mesangial cells in the renal glomerulus, capillary endothelial cells in the retina, and neurons and Schwann cells in peripheral nerves, the exact mechanisms underlying these damaging defects are not yet well understood. Clustering of micro- and macrovascular complications in families of patients with diabetes suggests a strong genetic susceptibility. However, until now only a handful number of genetic variants were reported to be associated with either nephropathy (ACE, ELMO1, FRMD3, and AKR1B1) or retinopathy (VEGF, AKR1B1, and EPO), and only a few studies were carried out for genetic susceptibility to cardiovascular diseases (ADIPOQ, GLUL) in patients with diabetes. It is, therefore, obvious that the accumulation of more data from larger studies and better phenotypically characterized cohorts is needed to facilitate genetic discoveries and unravel novel insights into the pathogenesis of diabetic complications. PMID:25169573

  7. 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...

  8. Genetic variability in Macadamia

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A genetic variability analysis involving 45 accessions of Macadamia including four species, M. integrifolia, M. tetraphylla, M. ternifolia, and M. hildebrandii and a wild relative, Hicksbeachia pinnatifolia was performed usingeight enzyme systems encoded by 16 loci (Gpi-1 and 2, Idh-1 and 2, Lap, Md...

  9. The genetics of Tamarix

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Genetic studies have helped us gain basic knowledge of the Tamarix invasion. We now have a better understanding of the species identities involved in the invasion, their evolutionary relationships, and the contribution of hybridization to the invasion. This information can be used to enhance the eff...

  10. Genetics of Candida albicans.

    PubMed Central

    Scherer, S; Magee, P T

    1990-01-01

    Candida albicans is among the most common fungal pathogens. Infections caused by C. albicans and other Candida species can be life threatening in individuals with impaired immune function. Genetic analysis of C. albicans pathogenesis is complicated by the diploid nature of the species and the absence of a known sexual cycle. Through a combination of parasexual techniques and molecular approaches, an effective genetic system has been developed. The close relationship of C. albicans to the more extensively studied Saccharomyces cerevisiae has been of great utility in the isolation of Candida genes and development of the C. albicans DNA transformation system. Molecular methods have been used for clarification of taxonomic relationships and more precise epidemiologic investigations. Analysis of the physical and genetic maps of C. albicans and the closely related Candida stellatoidea has provided much information on the highly fluid nature of the Candida genome. The genetic system is seeing increased application to biological questions such as drug resistance, virulence determinants, and the phenomenon of phenotypic variation. Although most molecular analysis to data has been with C. albicans, the same methodologies are proving highly effective with other Candida species. Images PMID:2215421

  11. 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

  12. BACTERIOPHAGE: BIOLOGY AND GENETICS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Bacteriophage are viruses that infect bacteria. Bacteriophage are very small and made up of a protein coat with an inner core containing their genetic material. They infect bacterium, by attaching to the bacterial cell and injecting their nucleic acids into the bacteria. The phages then use the bac...

  13. General cardinality genetic algorithms

    PubMed

    Koehler; Bhattacharyya; Vose

    1997-01-01

    A complete generalization of the Vose genetic algorithm model from the binary to higher cardinality case is provided. Boolean AND and EXCLUSIVE-OR operators are replaced by multiplication and addition over rings of integers. Walsh matrices are generalized with finite Fourier transforms for higher cardinality usage. Comparison of results to the binary case are provided. PMID:10021767

  14. 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.

  15. 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

  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. New sunflower genetics

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Genetic research of the sunflower research unit, USDA-ARS, in Fargo, ND, was discussed in a presentation to a group of Canadian producers, industry representatives, and scientists. Because this was an international audience, I introduced the audience to ARS and the structure of the sunflower unit, a...

  18. GENETIC EVALUATION OF STILLBIRTH

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A new stillbirth (SB) evaluation has been developed for Holstein bulls, and will be available beginning in August 2006. The data set includes 6 million stillbirth records from calves born since 1980. The genetic analysis includes effects for herd-year, year-season, parity-gender, sire birth year, ma...

  19. 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

  20. 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…