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Sample records for genetic programming approach

  1. Genetic Programming Approach for Predicting Surface Subsidence Induced by Mining

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The surface subsidence induced by mining is a complex problem, which is related with many complex and uncertain factors.Genetic programming (GP) has a good ability to deal with complex and nonlinear problems, therefore genetic programming approach is proposed to predict mining induced surface subsidence in this article.First genetic programming technique is introduced, second, surface subsidence genetic programming model is set up by selecting its main affective factors and training relating to practical engineering data, and finally, predictions are made by the testing of data, whose results show that the relative error is approximately less than 10%, which can meet the engineering needs, and therefore, this proposed approach is valid and applicable in predicting mining induced surface subsidence.The model offers a novel method to predict surface subsidence in mining.

  2. A novel genetic programming approach for epileptic seizure detection.

    Science.gov (United States)

    Bhardwaj, Arpit; Tiwari, Aruna; Krishna, Ramesh; Varma, Vishaal

    2016-02-01

    The human brain is a delicate mix of neurons (brain cells), electrical impulses and chemicals, known as neurotransmitters. Any damage has the potential to disrupt the workings of the brain and cause seizures. These epileptic seizures are the manifestations of epilepsy. The electroencephalograph (EEG) signals register average neuronal activity from the cerebral cortex and label changes in activity over large areas. A detailed analysis of these electroencephalograph (EEG) signals provides valuable insights into the mechanisms instigating epileptic disorders. Moreover, the detection of interictal spikes and epileptic seizures in an EEG signal plays an important role in the diagnosis of epilepsy. Automatic seizure detection methods are required, as these epileptic seizures are volatile and unpredictable. This paper deals with an automated detection of epileptic seizures in EEG signals using empirical mode decomposition (EMD) for feature extraction and proposes a novel genetic programming (GP) approach for classifying the EEG signals. Improvements in the standard GP approach are made using a Constructive Genetic Programming (CGP) in which constructive crossover and constructive subtree mutation operators are introduced. A hill climbing search is integrated in crossover and mutation operators to remove the destructive nature of these operators. A new concept of selecting the Globally Prime offspring is also presented to select the best fitness offspring generated during crossover. To decrease the time complexity of GP, a new dynamic fitness value computation (DFVC) is employed to increase the computational speed. We conducted five different sets of experiments to evaluate the performance of the proposed model in the classification of different mixtures of normal, interictal and ictal signals, and the accuracies achieved are outstandingly high. The experimental results are compared with the existing methods on same datasets, and these results affirm the potential use of

  3. Genetic programming approach to evaluate complexity of texture images

    Science.gov (United States)

    Ciocca, Gianluigi; Corchs, Silvia; Gasparini, Francesca

    2016-11-01

    We adopt genetic programming (GP) to define a measure that can predict complexity perception of texture images. We perform psychophysical experiments on three different datasets to collect data on the perceived complexity. The subjective data are used for training, validation, and test of the proposed measure. These data are also used to evaluate several possible candidate measures of texture complexity related to both low level and high level image features. We select four of them (namely roughness, number of regions, chroma variance, and memorability) to be combined in a GP framework. This approach allows a nonlinear combination of the measures and could give hints on how the related image features interact in complexity perception. The proposed complexity measure M exhibits Pearson correlation coefficients of 0.890 on the training set, 0.728 on the validation set, and 0.724 on the test set. M outperforms each of all the single measures considered. From the statistical analysis of different GP candidate solutions, we found that the roughness measure evaluated on the gray level image is the most dominant one, followed by the memorability, the number of regions, and finally the chroma variance.

  4. Empirical valence bond models for reactive potential energy surfaces: a parallel multilevel genetic program approach.

    Science.gov (United States)

    Bellucci, Michael A; Coker, David F

    2011-07-28

    We describe a new method for constructing empirical valence bond potential energy surfaces using a parallel multilevel genetic program (PMLGP). Genetic programs can be used to perform an efficient search through function space and parameter space to find the best functions and sets of parameters that fit energies obtained by ab initio electronic structure calculations. Building on the traditional genetic program approach, the PMLGP utilizes a hierarchy of genetic programming on two different levels. The lower level genetic programs are used to optimize coevolving populations in parallel while the higher level genetic program (HLGP) is used to optimize the genetic operator probabilities of the lower level genetic programs. The HLGP allows the algorithm to dynamically learn the mutation or combination of mutations that most effectively increase the fitness of the populations, causing a significant increase in the algorithm's accuracy and efficiency. The algorithm's accuracy and efficiency is tested against a standard parallel genetic program with a variety of one-dimensional test cases. Subsequently, the PMLGP is utilized to obtain an accurate empirical valence bond model for proton transfer in 3-hydroxy-gamma-pyrone in gas phase and protic solvent.

  5. A Genetic-Algorithms-Based Approach for Programming Linear and Quadratic Optimization Problems with Uncertainty

    Directory of Open Access Journals (Sweden)

    Weihua Jin

    2013-01-01

    Full Text Available This paper proposes a genetic-algorithms-based approach as an all-purpose problem-solving method for operation programming problems under uncertainty. The proposed method was applied for management of a municipal solid waste treatment system. Compared to the traditional interactive binary analysis, this approach has fewer limitations and is able to reduce the complexity in solving the inexact linear programming problems and inexact quadratic programming problems. The implementation of this approach was performed using the Genetic Algorithm Solver of MATLAB (trademark of MathWorks. The paper explains the genetic-algorithms-based method and presents details on the computation procedures for each type of inexact operation programming problems. A comparison of the results generated by the proposed method based on genetic algorithms with those produced by the traditional interactive binary analysis method is also presented.

  6. A Domain-Independent Window Approach to Multiclass Object Detection Using Genetic Programming

    Directory of Open Access Journals (Sweden)

    Mengjie Zhang

    2003-07-01

    Full Text Available This paper describes a domain-independent approach to the use of genetic programming for object detection problems in which the locations of small objects of multiple classes in large images must be found. The evolved program is scanned over the large images to locate the objects of interest. The paper develops three terminal sets based on domain-independent pixel statistics and considers two different function sets. The fitness function is based on the detection rate and the false alarm rate. We have tested the method on three object detection problems of increasing difficulty. This work not only extends genetic programming to multiclass-object detection problems, but also shows how to use a single evolved genetic program for both object classification and localisation. The object classification map developed in this approach can be used as a general classification strategy in genetic programming for multiple-class classification problems.

  7. A genetic programming approach to oral cancer prognosis

    Directory of Open Access Journals (Sweden)

    Mei Sze Tan

    2016-09-01

    Full Text Available Background The potential of genetic programming (GP on various fields has been attained in recent years. In bio-medical field, many researches in GP are focused on the recognition of cancerous cells and also on gene expression profiling data. In this research, the aim is to study the performance of GP on the survival prediction of a small sample size of oral cancer prognosis dataset, which is the first study in the field of oral cancer prognosis. Method GP is applied on an oral cancer dataset that contains 31 cases collected from the Malaysia Oral Cancer Database and Tissue Bank System (MOCDTBS. The feature subsets that is automatically selected through GP were noted and the influences of this subset on the results of GP were recorded. In addition, a comparison between the GP performance and that of the Support Vector Machine (SVM and logistic regression (LR are also done in order to verify the predictive capabilities of the GP. Result The result shows that GP performed the best (average accuracy of 83.87% and average AUROC of 0.8341 when the features selected are smoking, drinking, chewing, histological differentiation of SCC, and oncogene p63. In addition, based on the comparison results, we found that the GP outperformed the SVM and LR in oral cancer prognosis. Discussion Some of the features in the dataset are found to be statistically co-related. This is because the accuracy of the GP prediction drops when one of the feature in the best feature subset is excluded. Thus, GP provides an automatic feature selection function, which chooses features that are highly correlated to the prognosis of oral cancer. This makes GP an ideal prediction model for cancer clinical and genomic data that can be used to aid physicians in their decision making stage of diagnosis or prognosis.

  8. On the Predictability of Risk Box Approach by Genetic Programming Method for Bankruptcy Prediction

    Directory of Open Access Journals (Sweden)

    Alireza Bahiraie

    2009-01-01

    Full Text Available Problem statement: Theoretical based data representation is an important tool for model selection and interpretations in bankruptcy analysis since the numerical representation are much less transparent. Some methodological problems concerning financial ratios such as non-proportionality, non-asymetricity, non-scalicity are solved in this study and we presented a complementary technique for empirical analysis of financial ratios and bankruptcy risk. Approach: This study presented new geometric technique for empirical analysis of bankruptcy risk using financial ratios. Within this framework, we proposed the use of a new ratio representation which named Risk Box measure (RB. We demonstrated the application of this geometric approach for variable representation, data visualization and financial ratios at different stages of corporate bankruptcy prediction models based on financial balance sheet ratios. These stages were the selection of variables (predictors, accuracy of each estimation model and the representation of each model for transformed and common ratios. Results: We provided evidence of extent to which changes in values of this index were associated with changes in each axis values and how this may alter our economic interpretation of changes in the patterns and direction of risk components. Results of Genetic Programming (GP models were compared as different classification models and results showed the classifiers outperform by modified ratios. Conclusion/Recommendations: In this study, a new dimension to risk measurement and data representation with the advent of the Share Risk method (SR was proposed. Genetic programming method is substantially superior to the traditional methods such as MDA or Logistic method. It was strongly suggested the use of SR methodology for ratio analysis, which provided a conceptual and complimentary methodological solution to many problems associated with the use of ratios. Respectively, GP will provide

  9. Genetic programming-based approach to elucidate biochemical interaction networks from data.

    Science.gov (United States)

    Kandpal, Manoj; Kalyan, Chakravarthy Mynampati; Samavedham, Lakshminarayanan

    2013-02-01

    Biochemical systems are characterised by cyclic/reversible reciprocal actions, non-linear interactions and a mixed relationship structures (linear and non-linear; static and dynamic). Deciphering the architecture of such systems using measured data to provide quantitative information regarding the nature of relationships that exist between the measured variables is a challenging proposition. Causality detection is one of the methodologies that are applied to elucidate biochemical networks from such data. Autoregressive-based modelling approach such as granger causality, partial directed coherence, directed transfer function and canonical variate analysis have been applied on different systems for deciphering such interactions, but with limited success. In this study, the authors propose a genetic programming-based causality detection (GPCD) methodology which blends evolutionary computation-based procedures along with parameter estimation methods to derive a mathematical model of the system. Application of the GPCD methodology on five data sets that contained the different challenges mentioned above indicated that GPCD performs better than the other methods in uncovering the exact structure with less false positives. On a glycolysis data set, GPCD was able to fill the 'interaction gaps' which were missed by other methods.

  10. Genetic programming approach on evaporation losses and its effect on climate change for Vaipar Basin

    Directory of Open Access Journals (Sweden)

    K.S.Kasiviswanathan

    2011-09-01

    Full Text Available Climate change is the major problem that every human being is facing over the world. The rise in fossil fuel usage increases the emission of `greenhouse' gases, particularly carbon dioxide continuously into the earth's atmosphere. This causes a rise in the amount of heat from the sun withheld in the earth's atmosphere that would normally radiated back into space. This increase in heat has led to the greenhouse effect, resulting in climate change and rise in temperature along with other climatological parameters directly affects evaporation losses. Accurate modelling and forecasting of these evaporation losses are important for preventing further effects due to climate change. Evaporation is purely non-linear and varying both spatially and temporally. This needs suitable data driven approach to model and should have the ability to take care of all these non-linear behaviour of the system. As such, though there are many empirical and analytical models suggested in the literature for the estimation of evaporation losses, such models should be used with care and caution. Further, difficulties arise in obtaining all the climatological data used in a given analytical or empirical model. Genetic programming (GP is one such technique applied where the non-linearity exist. GP has the flexible mathematical structure which is capable of identifying the non-linear relationship between input and output data sets. Thus, it is easy to construct 'local' models for estimating evaporation losses. The performance of GP model is compared with Thornthwaite method, and results from the study indicate that the GP model performed better than the Thornthwaite method. Forecasting of meteorological parameters such as temperature, relative humidity and wind velocity has been performed using Markovian chain series analysis subsequently it is used to estimate the future evaporation losses using developed GP model. Finally the effect of possible future climate change on

  11. Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain)

    NARCIS (Netherlands)

    Shiri, J.; Kisi, O.; Landeras, G.; Lopez, J.J.; Nazemi, A.H.; Stuyt, L.C.P.M.

    2012-01-01

    Evapotranspiration, as a major component of the hydrological cycle, is of importance for water resources management and development, as well as for estimating the water budget of irrigation schemes. This study presents a Gene Expression Programming (GEP) approach, for estimating daily reference evap

  12. Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain)

    NARCIS (Netherlands)

    Shiri, J.; Kisi, O.; Landeras, G.; Lopez, J.J.; Nazemi, A.H.; Stuyt, L.C.P.M.

    2012-01-01

    Evapotranspiration, as a major component of the hydrological cycle, is of importance for water resources management and development, as well as for estimating the water budget of irrigation schemes. This study presents a Gene Expression Programming (GEP) approach, for estimating daily reference

  13. Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain)

    NARCIS (Netherlands)

    Shiri, J.; Kisi, O.; Landeras, G.; Lopez, J.J.; Nazemi, A.H.; Stuyt, L.C.P.M.

    2012-01-01

    Evapotranspiration, as a major component of the hydrological cycle, is of importance for water resources management and development, as well as for estimating the water budget of irrigation schemes. This study presents a Gene Expression Programming (GEP) approach, for estimating daily reference evap

  14. Wavelet-linear genetic programming: A new approach for modeling monthly streamflow

    Science.gov (United States)

    Ravansalar, Masoud; Rajaee, Taher; Kisi, Ozgur

    2017-06-01

    The streamflows are important and effective factors in stream ecosystems and its accurate prediction is an essential and important issue in water resources and environmental engineering systems. A hybrid wavelet-linear genetic programming (WLGP) model, which includes a discrete wavelet transform (DWT) and a linear genetic programming (LGP) to predict the monthly streamflow (Q) in two gauging stations, Pataveh and Shahmokhtar, on the Beshar River at the Yasuj, Iran were used in this study. In the proposed WLGP model, the wavelet analysis was linked to the LGP model where the original time series of streamflow were decomposed into the sub-time series comprising wavelet coefficients. The results were compared with the single LGP, artificial neural network (ANN), a hybrid wavelet-ANN (WANN) and Multi Linear Regression (MLR) models. The comparisons were done by some of the commonly utilized relevant physical statistics. The Nash coefficients (E) were found as 0.877 and 0.817 for the WLGP model, for the Pataveh and Shahmokhtar stations, respectively. The comparison of the results showed that the WLGP model could significantly increase the streamflow prediction accuracy in both stations. Since, the results demonstrate a closer approximation of the peak streamflow values by the WLGP model, this model could be utilized for the simulation of cumulative streamflow data prediction in one month ahead.

  15. Behavioral program synthesis with genetic programming

    CERN Document Server

    Krawiec, Krzysztof

    2016-01-01

    Genetic programming (GP) is a popular heuristic methodology of program synthesis with origins in evolutionary computation. In this generate-and-test approach, candidate programs are iteratively produced and evaluated. The latter involves running programs on tests, where they exhibit complex behaviors reflected in changes of variables, registers, or memory. That behavior not only ultimately determines program output, but may also reveal its `hidden qualities' and important characteristics of the considered synthesis problem. However, the conventional GP is oblivious to most of that information and usually cares only about the number of tests passed by a program. This `evaluation bottleneck' leaves search algorithm underinformed about the actual and potential qualities of candidate programs. This book proposes behavioral program synthesis, a conceptual framework that opens GP to detailed information on program behavior in order to make program synthesis more efficient. Several existing and novel mechanisms subs...

  16. Binary Image Classification: A Genetic Programming Approach to the Problem of Limited Training Instances.

    Science.gov (United States)

    Al-Sahaf, Harith; Zhang, Mengjie; Johnston, Mark

    2016-01-01

    In the computer vision and pattern recognition fields, image classification represents an important yet difficult task. It is a challenge to build effective computer models to replicate the remarkable ability of the human visual system, which relies on only one or a few instances to learn a completely new class or an object of a class. Recently we proposed two genetic programming (GP) methods, one-shot GP and compound-GP, that aim to evolve a program for the task of binary classification in images. The two methods are designed to use only one or a few instances per class to evolve the model. In this study, we investigate these two methods in terms of performance, robustness, and complexity of the evolved programs. We use ten data sets that vary in difficulty to evaluate these two methods. We also compare them with two other GP and six non-GP methods. The results show that one-shot GP and compound-GP outperform or achieve results comparable to competitor methods. Moreover, the features extracted by these two methods improve the performance of other classifiers with handcrafted features and those extracted by a recently developed GP-based method in most cases.

  17. Applications of Genetic Programming

    DEFF Research Database (Denmark)

    Gaunholt, Hans; Toma, Laura

    1996-01-01

    In this report a study of genetic programming (GP) has been performed with respect to a number of applications such as Symbolic function regression, Solving Symbolic Differential Equations, Image encoding, the ant problem etc.......In this report a study of genetic programming (GP) has been performed with respect to a number of applications such as Symbolic function regression, Solving Symbolic Differential Equations, Image encoding, the ant problem etc....

  18. Applications of Genetic Programming

    DEFF Research Database (Denmark)

    Gaunholt, Hans; Toma, Laura

    1996-01-01

    In this report a study of genetic programming (GP) has been performed with respect to a number of applications such as Symbolic function regression, Solving Symbolic Differential Equations, Image encoding, the ant problem etc.......In this report a study of genetic programming (GP) has been performed with respect to a number of applications such as Symbolic function regression, Solving Symbolic Differential Equations, Image encoding, the ant problem etc....

  19. On the Performance of Different Genetic Programming Approaches for the SORTING Problem.

    Science.gov (United States)

    Wagner, Markus; Neumann, Frank; Urli, Tommaso

    2015-01-01

    In genetic programming, the size of a solution is typically not specified in advance, and solutions of larger size may have a larger benefit. The flexibility often comes at the cost of the so-called bloat problem: individuals grow without providing additional benefit to the quality of solutions, and the additional elements can block the optimization process. Consequently, problems that are relatively easy to optimize cannot be handled by variable-length evolutionary algorithms. In this article, we analyze different single- and multiobjective algorithms on the sorting problem, a problem that typically lacks independent and additive fitness structures. We complement the theoretical results with comprehensive experiments to indicate the tightness of existing bounds, and to indicate bounds where theoretical results are missing.

  20. Cartesian genetic programming

    CERN Document Server

    Miller, Julian F

    2011-01-01

    Cartesian Genetic Programming (CGP) is a highly effective and increasingly popular form of genetic programming. It represents programs in the form of directed graphs, and a particular characteristic is that it has a highly redundant genotype - phenotype mapping, in that genes can be noncoding. It has spawned a number of new forms, each improving on the efficiency, among them modular, or embedded, CGP, and self-modifying CGP. It has been applied to many problems in both computer science and applied sciences. This book contains chapters written by the leading figures in the development and appli

  1. Genetic Programming and Genetic Algorithms for Propositions

    Directory of Open Access Journals (Sweden)

    Nabil M. HEWAHI

    2012-01-01

    Full Text Available In this paper we propose a mechanism to discover the compound proposition solutions for a given truth table without knowing the compound propositions that lead to the truth table results. The approach is based on two proposed algorithms, the first is called Producing Formula (PF algorithm which is based on the genetic programming idea, to find out the compound proposition solutions for the given truth table. The second algorithm is called the Solutions Optimization (SO algorithm which is based on genetic algorithms idea, to find a list of the optimum compound propositions that can solve the truth table. The obtained list will depend on the solutions obtained from the PF algorithm. Various types of genetic operators have been introduced to obtain the solutions either within the PF algorithm or SO algorithm.

  2. Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.

    Science.gov (United States)

    Barton, Alan J; Valdés, Julio J; Orchard, Robert

    2009-01-01

    Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.

  3. A CAL Program to Teach the Basic Principles of Genetic Engineering--A Change from the Traditional Approach.

    Science.gov (United States)

    Dewhurst, D. G.; And Others

    1989-01-01

    An interactive computer-assisted learning program written for the BBC microcomputer to teach the basic principles of genetic engineering is described. Discussed are the hardware requirements software, use of the program, and assessment. (Author/CW)

  4. A CAL Program to Teach the Basic Principles of Genetic Engineering--A Change from the Traditional Approach.

    Science.gov (United States)

    Dewhurst, D. G.; And Others

    1989-01-01

    An interactive computer-assisted learning program written for the BBC microcomputer to teach the basic principles of genetic engineering is described. Discussed are the hardware requirements software, use of the program, and assessment. (Author/CW)

  5. Tree-based genetic programming approach to infer microphysical parameters of the DSDs from the polarization diversity measurements

    Science.gov (United States)

    Islam, Tanvir; Rico-Ramirez, Miguel A.; Han, Dawei

    2012-11-01

    The use of polarization diversity measurements to infer the microphysical parametrization has remained an active goal in the radar remote sensing community. In view of this, the tree-based genetic programming (GP) as a novel approach has been presented for retrieving the governing microphysical parameters of a normalized gamma drop size distribution model D0 (median drop diameter), Nw (concentration parameter), and μ (shape parameter) from the polarization diversity measurements. A large number of raindrop spectra acquired from a Joss-Waldvogel disdrometer has been utilized to develop the GP models, relating the microphysical parameters to the T-matrix scattering simulated polarization measurements. Several functional formulations retrieving the microphysical parameters-D0 [f(ZDR), f(ZH, ZDR)], log10Nw [f(ZH, D0), f(ZH, ZDR, D0), and μ[f(ZDR, D0), f(ZH, ZDR, D0)], where ZH represents reflectivity and ZDR represents differential reflectivity, have been investigated, and applied to a S-band polarimetric radar (CAMRA) for evaluation. It has been shown that the GP model retrieved microphysical parameters from the polarization measurements are in a reasonable agreement with disdrometer observations. The calculated root mean squared errors (RMSE) are noted as 0.23-0.25 mm for D0, 0.74-0.85 for log10Nw (Nw in mm-1 mm-3), and 3.30-3.36 for μ. The GP model based microphysical retrieval procedure is further compared with a physically based constrained gamma model for D0 and log10Nw estimates. The close agreement of the retrieval results between the GP and the constrained gamma models supports the suitability of the proposed genetic programming approach to infer microphysical parameterization.

  6. A Hybrid Computational Intelligence Approach Combining Genetic Programming And Heuristic Classification for Pap-Smear Diagnosis

    DEFF Research Database (Denmark)

    Tsakonas, Athanasios; Dounias, Georgios; Jantzen, Jan

    2001-01-01

    The paper suggests the combined use of different computational intelligence (CI) techniques in a hybrid scheme, as an effective approach to medical diagnosis. Getting to know the advantages and disadvantages of each computational intelligence technique in the recent years, the time has come for p...

  7. Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach

    Science.gov (United States)

    Moeeni, Hamid; Bonakdari, Hossein; Ebtehaj, Isa

    2017-03-01

    Forecasting reservoir inflow is one of the most important components of water resources and hydroelectric systems operation management. Seasonal autoregressive integrated moving average (SARIMA) models have been frequently used for predicting river flow. SARIMA models are linear and do not consider the random component of statistical data. To overcome this shortcoming, monthly inflow is predicted in this study based on a combination of seasonal autoregressive integrated moving average (SARIMA) and gene expression programming (GEP) models, which is a new hybrid method (SARIMA-GEP). To this end, a four-step process is employed. First, the monthly inflow datasets are pre-processed. Second, the datasets are modelled linearly with SARIMA and in the third stage, the non-linearity of residual series caused by linear modelling is evaluated. After confirming the non-linearity, the residuals are modelled in the fourth step using a gene expression programming (GEP) method. The proposed hybrid model is employed to predict the monthly inflow to the Jamishan Dam in west Iran. Thirty years' worth of site measurements of monthly reservoir dam inflow with extreme seasonal variations are used. The results of this hybrid model (SARIMA-GEP) are compared with SARIMA, GEP, artificial neural network (ANN) and SARIMA-ANN models. The results indicate that the SARIMA-GEP model ( R 2=78.8, VAF =78.8, RMSE =0.89, MAPE =43.4, CRM =0.053) outperforms SARIMA and GEP and SARIMA-ANN ( R 2=68.3, VAF =66.4, RMSE =1.12, MAPE =56.6, CRM =0.032) displays better performance than the SARIMA and ANN models. A comparison of the two hybrid models indicates the superiority of SARIMA-GEP over the SARIMA-ANN model.

  8. Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach

    Indian Academy of Sciences (India)

    Hamid Moeeni; Hossein Bonakdari; Isa Ebtehaj

    2017-03-01

    Forecasting reservoir inflow is one of the most important components of water resources and hydroelectric systems operation management. Seasonal autoregressive integrated moving average (SARIMA) models have been frequently used for predicting river flow. SARIMA models are linear and do not consider the random component of statistical data. To overcome this shortcoming, monthly inflow is predicted in this study based on a combination of seasonal autoregressive integrated moving average (SARIMA) andgene expression programming (GEP) models, which is a new hybrid method (SARIMA–GEP). To this end, a four-step process is employed. First, the monthly inflow datasets are pre-processed. Second, the datasets are modelled linearly with SARIMA and in the third stage, the non-linearity of residual seriescaused by linear modelling is evaluated. After confirming the non-linearity, the residuals are modelled in the fourth step using a gene expression programming (GEP) method. The proposed hybrid model is employed to predict the monthly inflow to the Jamishan Dam in west Iran. Thirty years’ worth of site measurements of monthly reservoir dam inflow with extreme seasonal variations are used. The results of this hybrid model (SARIMA–GEP) are compared with SARIMA, GEP, artificial neural network (ANN) and SARIMA–ANN models. The results indicate that the SARIMA–GEP model (R2=78.8, VAF=78.8, RMSE=0.89, MAPE=43.4, CRM=0.053) outperforms SARIMA and GEP and SARIMA– ANN (R2=68.3, VAF=66.4, RMSE=1.12, MAPE=56.6, CRM=0.032) displays better performance than the SARIMA and ANN models. A comparison of the two hybrid models indicates the superiority of SARIMA–GEP over the SARIMA–ANN model.

  9. A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

    Directory of Open Access Journals (Sweden)

    Chandra Nagasuma R

    2009-02-01

    Full Text Available Abstract Background A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN from transcript profiling data. Results The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting problem and solved finally by formulating a Linear Program (LP. A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known

  10. Recognition of Objects by Using Genetic Programming

    Directory of Open Access Journals (Sweden)

    Nerses Safaryan

    2013-01-01

    Full Text Available This document is devoted to the task of object detection and recognition in digital images by using genetic programming. The goal was to improve and simplify existing approaches. The detection and recognition are achieved by means of extracting the features. A genetic program is used to extract and classify features of objects. Simple features and primitive operators are processed in genetic programming operations. We are trying to detect and to recognize objects in SAR images. Due to the new approach described in this article, five and seven types of objects were recognized with good recognition results.

  11. A Hybrid Computational Intelligence Approach Combining Genetic Programming And Heuristic Classification for Pap-Smear Diagnosis

    DEFF Research Database (Denmark)

    Tsakonas, Athanasios; Dounias, Georgios; Jantzen, Jan;

    2001-01-01

    The paper suggests the combined use of different computational intelligence (CI) techniques in a hybrid scheme, as an effective approach to medical diagnosis. Getting to know the advantages and disadvantages of each computational intelligence technique in the recent years, the time has come...... diagnoses. The final result is a short but robust rule based classification scheme, achieving high degree of classification accuracy (exceeding 90% of accuracy for most classes) in a meaningful and user-friendly representation form for the medical expert. The domain of application analyzed through the paper...... is the well-known Pap-Test problem, corresponding to a numerical database, which consists of 450 medical records, 25 diagnostic attributes and 5 different diagnostic classes. Experimental data are divided in two equal parts for the training and testing phase, and 8 mutually dependent rules for diagnosis...

  12. Hierarchical On-line Scheduling of Multiproduct Batch Plants with a Combined Approach of Mathematical Programming and Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    陈理; 王克峰; 徐霄羽; 姚平经

    2004-01-01

    In this contribution we present an online scheduling algorithm for a real world multiproduct batch plant. The overall mixed integer nonlinear programming (MINLP) problem is hierarchically structured into a mixed integer linear programming (MILP) problem first and then a reduced dimensional MINLP problem, which are optimized by mathematical programming (MP) and genetic algorithm (GA) respectively. The basis idea relies on combining MP with GA to exploit their complementary capacity. The key features of the hierarchical model are explained and illustrated with some real world cases from the multiproduct batch plants.

  13. A case-based approach to the development of practice-based competencies for accreditation of and training in graduate programs in genetic counseling.

    Science.gov (United States)

    Fiddler, M B; Fine, B A; Baker, D L

    1996-09-01

    The American Board of Genetic Counseling (ABGC) sponsored a consensus development conference with participation from directors of graduate programs in genetic counseling, board members, and expert consultants. Using a collective, narrative, and case-based approach, 27 competencies were identified as embedded in the practice of genetic counseling. These competencies were organized into four domains of skills: Communication; Critical Thinking; Interpersonal, Counseling, and Psychosocial Assessment; and Professional Ethics and Values. The adoption of a competency framework for accreditation has a variety of implications for curriculum design and implementation. We report here the process by which a set of practice-based genetic counseling competencies have been derived; and in an accompanying article, the competencies themselves are provided. We also discuss the application of the competencies to graduate program accreditation as well as some of the implications competency-based standards may have for education and the genetic counseling profession. These guidelines may also serve as a basis for the continuing education of practicing genetic counselors and a performance evaluation tool in the workplace.

  14. Genetic Parallel Programming: design and implementation.

    Science.gov (United States)

    Cheang, Sin Man; Leung, Kwong Sak; Lee, Kin Hong

    2006-01-01

    This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach to evolving a feasible problem solution in parallel program form and then serializes it into a sequential program if required. The effectiveness and efficiency of GPP are investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially.

  15. Pragmatic approaches to genetic screening.

    NARCIS (Netherlands)

    Mallia, P.; Have, H.A.M.J. ten

    2005-01-01

    Pragmatic approaches to genetic testing are discussed and appraised. Whilst there are various schools of pragmatism, the Deweyan approach seems to be the most appreciated in bioethics as it allows a historical approach indebted to Hegel. This in turn allows the pragmatist to specify and balance prin

  16. Scientific discovery using genetic programming

    DEFF Research Database (Denmark)

    Keijzer, Maarten

    2001-01-01

    programming paradigm. The induction of mathematical expressions based on data is called symbolic regression. In this work, genetic programming is extended to not just fit the data i.e., get the numbers right, but also to get the dimensions right. For this units of measurement are used. The main contribution...... in this work can be summarized as: The symbolic expressions produced by genetic programming can be made suitable for analysis and interpretation by using units of measurements to guide or restrict the search. To achieve this, the following has been accomplished: A standard genetic programming system...... that are numerically stable and correct. A case study using four real-world problems in the induction of dimensionally correct empirical equations on data using the two different methods is presented to illustrate to use and limitations of these methods in a framework of scientific discovery....

  17. Genetic Programming with Simple Loops

    Institute of Scientific and Technical Information of China (English)

    QI Yuesheng; WANG Baozhong; KANG Lishan

    1999-01-01

    A kind of loop function LoopN inGenetic Programming (GP) is proposed.Different from other forms of loopfunction, such as While-Do and Repeat-Until, LoopNtakes only oneargument as its loop body and makes its loop body simply run N times,soinfinite loops will never happen. The problem of how to avoid too manylayers ofloops in Genetic Programming is also solved. The advantage ofLoopN in GP is shown bythe computational results in solving the mowerproblem.

  18. Optimizing a multi-echelon supply chain network flow using nonlinear fuzzy multi-objective integer programming: Genetic algorithm approach

    Directory of Open Access Journals (Sweden)

    Mohammad Ali Afshari

    2012-10-01

    Full Text Available The aim of this paper is to present mathematical models optimizing all materials flows in supply chain. In this research a fuzzy multi-objective nonlinear mixed- integer programming model with piecewise linear membership function is applied to design a multi echelon supply chain network (SCN by considering total transportation costs and capacities of all echelons with fuzzy objectives. The model that is proposed in this study has 4 fuzzy functions. The first function is minimizing the total transportation costs between all echelons (suppliers, factories, distribution centers (DCs and customers. The second one is minimizing holding and ordering cost on DCs. The third objective is minimizing the unnecessary and unused capacity of factories and DCs via decreasing variance of transported amounts between echelons. The forth is minimizing the number of total vehicles that ship the materials and products along with SCN. For solving such a problem, as nodes increases in SCN, the traditional method does not have ability to solve large scale problem. So, we applied a Meta heuristic method called Genetic Algorithm. The numerical example is real world applied and compared the results with each other demonstrate the feasibility of applying the proposed model to given problem, and also its advantages are discussed.

  19. Genetic Algorithm Approaches for Actuator Placement

    Science.gov (United States)

    Crossley, William A.

    2000-01-01

    This research investigated genetic algorithm approaches for smart actuator placement to provide aircraft maneuverability without requiring hinged flaps or other control surfaces. The effort supported goals of the Multidisciplinary Design Optimization focus efforts in NASA's Aircraft au program. This work helped to properly identify various aspects of the genetic algorithm operators and parameters that allow for placement of discrete control actuators/effectors. An improved problem definition, including better definition of the objective function and constraints, resulted from this research effort. The work conducted for this research used a geometrically simple wing model; however, an increasing number of potential actuator placement locations were incorporated to illustrate the ability of the GA to determine promising actuator placement arrangements. This effort's major result is a useful genetic algorithm-based approach to assist in the discrete actuator/effector placement problem.

  20. An Ensemble Empirical Mode Decomposition, Self-Organizing Map, and Linear Genetic Programming Approach for Forecasting River Streamflow

    Directory of Open Access Journals (Sweden)

    Jonathan T. Barge

    2016-06-01

    Full Text Available This study focused on employing Linear Genetic Programming (LGP, Ensemble Empirical Mode Decomposition (EEMD, and the Self-Organizing Map (SOM in modeling the rainfall–runoff relationship in a mid-size catchment. Models were assessed with regard to their ability to capture daily discharge at Lock and Dam 10 along the Kentucky River as well as the hybrid design of EEM-SOM-LGP to make predictions multiple time-steps ahead. Different model designs were implemented to demonstrate the improvements of hybrid designs compared to LGP as a standalone application. Additionally, LGP was utilized to gain a better understanding of the catchment in question and to assess its ability to capture different aspects of the flow hydrograph. As a standalone application, LGP was able to outperform published Artificial Neural Network (ANN results over the same dataset, posting an average absolute relative error (AARE of 17.118 and Nash-Sutcliff (E of 0.937. Utilizing EEMD derived IMF runoff subcomponents for forecasting daily discharge resulted in an AARE of 14.232 and E of 0.981. Clustering the EEMD-derived input space through an SOM before LGP application returned the strongest results, posting an AARE of 10.122 and E of 0.987. Applying LGP to the distinctive low and high flow seasons demonstrated a loss in correlation for the low flow season with an under-predictive nature signified by a normalized mean biased error (NMBE of −2.353. Separating the rising and falling trends of the hydrograph showed that the falling trends were more easily captured with an AARE of 8.511 and E of 0.968 compared to the rising trends AARE of 38.744 and E of 0.948. Utilizing the EEMD-SOM-LGP design to make predictions multiple-time-steps ahead resulted in a AARE of 43.365 and E of 0.902 for predicting streamflow three days ahead. The results demonstrate the effectiveness of utilizing EEMD and an SOM in conjunction with LGP for streamflow forecasting.

  1. A genetic engineering approach to genetic algorithms.

    Science.gov (United States)

    Gero, J S; Kazakov, V

    2001-01-01

    We present an extension to the standard genetic algorithm (GA), which is based on concepts of genetic engineering. The motivation is to discover useful and harmful genetic materials and then execute an evolutionary process in such a way that the population becomes increasingly composed of useful genetic material and increasingly free of the harmful genetic material. Compared to the standard GA, it provides some computational advantages as well as a tool for automatic generation of hierarchical genetic representations specifically tailored to suit certain classes of problems.

  2. Evolving evolutionary algorithms using linear genetic programming.

    Science.gov (United States)

    Oltean, Mihai

    2005-01-01

    A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

  3. Dynamical genetic programming in XCSF.

    Science.gov (United States)

    Preen, Richard J; Bull, Larry

    2013-01-01

    A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to artificial neural networks. This paper presents results from an investigation into using a temporally dynamic symbolic representation within the XCSF learning classifier system. In particular, dynamical arithmetic networks are used to represent the traditional condition-action production system rules to solve continuous-valued reinforcement learning problems and to perform symbolic regression, finding competitive performance with traditional genetic programming on a number of composite polynomial tasks. In addition, the network outputs are later repeatedly sampled at varying temporal intervals to perform multistep-ahead predictions of a financial time series.

  4. Engaging nurses in genetics: the strategic approach of the NHS National Genetics Education and Development Centre.

    Science.gov (United States)

    Kirk, Maggie; Tonkin, Emma; Burke, Sarah

    2008-04-01

    The UK government announced the establishment of an NHS National Genetics Education and Development Centre in its Genetics White Paper. The Centre aims to lead and coordinate developments to enhance genetics literacy of health professionals. The nursing program takes a strategic approach based on Ajzen's Theory of Planned Behavior, using the UK nursing genetics competences as the platform for development. The program team uses innovative approaches to raise awareness of the relevance of genetics, working collaboratively with policy stakeholders, as key agents of change in promoting competence. Providing practical help in preparing learning and teaching resources lends further encouragement. Evaluation of the program is dependent on gathering baseline data, and the program has been informed by an education needs analysis. The challenges faced are substantial and necessitate international collaboration where expertise and resources can be shared to produce a global system of influence to facilitate the engagement of non-genetic nurses.

  5. Reverse Genetic Approaches in Zebrafish

    Institute of Scientific and Technical Information of China (English)

    Peng Huang; Zuoyan Zhu; Shuo Lin; Bo Zhang

    2012-01-01

    Zebrafish (Danio rerio) is a well-established vertebrate animal model.A comprehensive collection of reverse genetics tools has been developed for studying gene function in this useful organism.Morpholino is the most widely used reagent to knock down target gene expression post-transcriptionally.For a long time,targeted genome modification has been heavily relied on large-scale traditional forward genetic screens,such as ENU (N-ethyl-N-nitrosourea) mutagenesis derived TILLING (Targeting Induced Local Lesions IN Genomes)strategy and pseudo-typed retrovirus mediated insertional mutagenesis.Recently,engineered endonucleases,including ZFNs (zinc finger nucleases) and TALENs (transcription activator-like effector nucleases),provide new and efficient strategies to directly generate sitespecific indel mutations by inducing double strand breaks in target genes.Here we summarize the major reverse genetic approaches for loss-of-function studies used and emerging in zebrafish,including strategies based on genome-wide mutagenesis and methods for sitespecific gene targeting.Future directions and expectations will also be discussed.

  6. Atmospheric Downscaling using Genetic Programming

    Science.gov (United States)

    Zerenner, Tanja; Venema, Victor; Simmer, Clemens

    2013-04-01

    Coupling models for the different components of the Soil-Vegetation-Atmosphere-System requires up-and downscaling procedures. Subject of our work is the downscaling scheme used to derive high resolution forcing data for land-surface and subsurface models from coarser atmospheric model output. The current downscaling scheme [Schomburg et. al. 2010, 2012] combines a bi-quadratic spline interpolation, deterministic rules and autoregressive noise. For the development of the scheme, training and validation data sets have been created by carrying out high-resolution runs of the atmospheric model. The deterministic rules in this scheme are partly based on known physical relations and partly determined by an automated search for linear relationships between the high resolution fields of the atmospheric model output and high resolution data on surface characteristics. Up to now deterministic rules are available for downscaling surface pressure and partially, depending on the prevailing weather conditions, for near surface temperature and radiation. Aim of our work is to improve those rules and to find deterministic rules for the remaining variables, which require downscaling, e.g. precipitation or near surface specifc humidity. To accomplish that, we broaden the search by allowing for interdependencies between different atmospheric parameters, non-linear relations, non-local and time-lagged relations. To cope with the vast number of possible solutions, we use genetic programming, a method from machine learning, which is based on the principles of natural evolution. We are currently working with GPLAB, a Genetic Programming toolbox for Matlab. At first we have tested the GP system to retrieve the known physical rule for downscaling surface pressure, i.e. the hydrostatic equation, from our training data. We have found this to be a simple task to the GP system. Furthermore we have improved accuracy and efficiency of the GP solution by implementing constant variation and

  7. Genetic programming and serial processing for time series classification.

    Science.gov (United States)

    Alfaro-Cid, Eva; Sharman, Ken; Esparcia-Alcázar, Anna I

    2014-01-01

    This work describes an approach devised by the authors for time series classification. In our approach genetic programming is used in combination with a serial processing of data, where the last output is the result of the classification. The use of genetic programming for classification, although still a field where more research in needed, is not new. However, the application of genetic programming to classification tasks is normally done by considering the input data as a feature vector. That is, to the best of our knowledge, there are not examples in the genetic programming literature of approaches where the time series data are processed serially and the last output is considered as the classification result. The serial processing approach presented here fills a gap in the existing literature. This approach was tested in three different problems. Two of them are real world problems whose data were gathered for online or conference competitions. As there are published results of these two problems this gives us the chance to compare the performance of our approach against top performing methods. The serial processing of data in combination with genetic programming obtained competitive results in both competitions, showing its potential for solving time series classification problems. The main advantage of our serial processing approach is that it can easily handle very large datasets.

  8. Grammar Based Genetic Programming Using Linear Representations

    Institute of Scientific and Technical Information of China (English)

    ZHANGHong; LUYinan; WANGFei

    2003-01-01

    In recent years,there has been a great interest in genetic programming(GP),which is used to solve many applications such as data mining,electronic engineering and pattern recognition etc.. Genetic programming paradigm as a from of adaptive learning is a functional approach to many problems that require a nonfixed representation and GP typically operates on a population of parse which usually represent computer programs whose nodes have single data type.In this paper GP using context-free grammars(CFGs) is described.This technique separates search space from solution space through a genotype to phenotype mapping.The genotypes and phenotypes of the individuals both act on different linear representations.A phenotype is postfix expression,a new method of representing which is described by making use of the definition and related features of a context-free grammar,i.e.a genotype is a variable length,linear valid genome determined by a simplifled derivation tree(SDT) generated from a context-free grammar.A CFG is used to specify how the possible solutions are created according to experiential knowledge and to direct legal crossover(ormutation)operations without any explicit reference to the process of program generation and parsing,and automatically ensuring typing and syntax correctness.Some related definitions involving genetic operators are described.Fitness evaluation is given.This technique is applied to a symbol regression problem-the identification of nonlinear dynamic characteristics of cushioning packaging.Experimental results show this method can flnd good relations between variables and is better than basic GP without a grammar.Future research on it is outlined.

  9. LIGO detector characterization with genetic programming

    Science.gov (United States)

    Cavaglia, Marco; Staats, Kai; Errico, Luciano; Mogushi, Kentaro; Gabbard, Hunter

    2017-01-01

    Genetic Programming (GP) is a supervised approach to Machine Learning. GP has for two decades been applied to a diversity of problems, from predictive and financial modelling to data mining, from code repair to optical character recognition and product design. GP uses a stochastic search, tournament, and fitness function to explore a solution space. GP evolves a population of individual programs, through multiple generations, following the principals of biological evolution (mutation and reproduction) to discover a model that best fits or categorizes features in a given data set. We apply GP to categorization of LIGO noise and show that it can effectively be used to characterize the detector non-astrophysical noise both in low latency and offline searches. National Science Foundation award PHY-1404139.

  10. Genetic programming theory and practice XII

    CERN Document Server

    Riolo, Rick; Kotanchek, Mark

    2015-01-01

    These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: gene expression regulation, novel genetic models for glaucoma, inheritable epigenetics, combinators in genetic programming, sequential symbolic regression, system dynamics, sliding window symbolic regression, large feature problems, alignment in the error space, HUMIE winners, Boolean multiplexer funct

  11. Genetic Programming Modeling and Complexity Analysis of the Magnetoencephalogram of Epileptic Patients

    Science.gov (United States)

    Georgopoulos, Efstratios F.; Adamopoulos, Adam V.; Likothanassis, Spiridon D.

    In this work MagnetoEncephaloGram (MEG) recordings of epileptic patients are modeled using a genetic programming approach. This is the first time that genetic programming is used to model MEG signal. Numerous experiments were conducted giving highly successful results. It is demonstrated that genetic programming can produce very simple nonlinear models that fit with great accuracy the observed data of MEG.

  12. Applied genetic programming and machine learning

    CERN Document Server

    Iba, Hitoshi; Paul, Topon Kumar

    2009-01-01

    What do financial data prediction, day-trading rule development, and bio-marker selection have in common? They are just a few of the tasks that could potentially be resolved with genetic programming and machine learning techniques. Written by leaders in this field, Applied Genetic Programming and Machine Learning delineates the extension of Genetic Programming (GP) for practical applications. Reflecting rapidly developing concepts and emerging paradigms, this book outlines how to use machine learning techniques, make learning operators that efficiently sample a search space, navigate the searc

  13. Multi-Center Genetic Study of Hypertension: The Family Blood Pressure Program (FBPP)

    National Research Council Canada - National Science Library

    The FBPP Investigators

    2002-01-01

    The Family Blood Pressure Program (FBPP) consists of 4 independently established multicenter networks of investigators who have complementary approaches to the genetics of blood pressure levels and hypertension...

  14. Programming cells: towards an automated 'Genetic Compiler'.

    Science.gov (United States)

    Clancy, Kevin; Voigt, Christopher A

    2010-08-01

    One of the visions of synthetic biology is to be able to program cells using a language that is similar to that used to program computers or robotics. For large genetic programs, keeping track of the DNA on the level of nucleotides becomes tedious and error prone, requiring a new generation of computer-aided design (CAD) software. To push the size of projects, it is important to abstract the designer from the process of part selection and optimization. The vision is to specify genetic programs in a higher-level language, which a genetic compiler could automatically convert into a DNA sequence. Steps towards this goal include: defining the semantics of the higher-level language, algorithms to select and assemble parts, and biophysical methods to link DNA sequence to function. These will be coupled to graphic design interfaces and simulation packages to aid in the prediction of program dynamics, optimize genes, and scan projects for errors.

  15. [Incest--forensic genetic approach].

    Science.gov (United States)

    Raczek, Ewa

    2012-01-01

    The paper presents intimate relationships between biologically and legally close relatives, complicated in the social, culture and religion perspective. (art. 201 of the Penal Code), but it chiefly addresses problems associated with giving opinion on the fatherhood towards the incestuous child. The report calls for a broader interest in this issue from expert witnesses in forensic genetics, as well as encourages them to publish examples taken from their own professional experience that may unquestionably be helpful to other practitioners in this field and above all will lead to extending educational methods related to widely understood DNA analysis in giving an opinion on arguable fatherhood.

  16. Genetic Programming Transforms in Linear Regression Situations

    Science.gov (United States)

    Castillo, Flor; Kordon, Arthur; Villa, Carlos

    The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.

  17. Template learning of cellular neural network using genetic programming.

    Science.gov (United States)

    Radwan, Elsayed; Tazaki, Eiichiro

    2004-08-01

    A new learning algorithm for space invariant Uncoupled Cellular Neural Network is introduced. Learning is formulated as an optimization problem. Genetic Programming has been selected for creating new knowledge because they allow the system to find new rules both near to good ones and far from them, looking for unknown good control actions. According to the lattice Cellular Neural Network architecture, Genetic Programming will be used in deriving the Cloning Template. Exploration of any stable domain is possible by the current approach. Details of the algorithm are discussed and several application results are shown.

  18. An adaptive genetic algorithm for solving bilevel linear programming problem

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Bilevel linear programming, which consists of the objective functions of the upper level and lower level, is a useful tool for modeling decentralized decision problems.Various methods are proposed for solving this problem. Of all the algorithms, the genetic algorithm is an alternative to conventional approaches to find the solution of the bilevel linear programming. In this paper, we describe an adaptive genetic algorithm for solving the bilevel linear programming problem to overcome the difficulty of determining the probabilities of crossover and mutation. In addition, some techniques are adopted not only to deal with the difficulty that most of the chromosomes may be infeasible in solving constrained optimization problem with genetic algorithm but also to improve the efficiency of the algorithm. The performance of this proposed algorithm is illustrated by the examples from references.

  19. The calculus a genetic approach

    CERN Document Server

    Toeplitz, Otto

    2007-01-01

    When first published posthumously in 1963, this book presented a radically different approach to the teaching of calculus.  In sharp contrast to the methods of his time, Otto Toeplitz did not teach calculus as a static system of techniques and facts to be memorized. Instead, he drew on his knowledge of the history of mathematics and presented calculus as an organic evolution of ideas beginning with the discoveries of Greek scholars, such as Archimedes, Pythagoras, and Euclid, and developing through the centuries in the work of Kepler, Galileo, Fermat, Newton, and Leibniz. Through this unique a

  20. Genetic Programming for Automatic Hydrological Modelling

    Science.gov (United States)

    Chadalawada, Jayashree; Babovic, Vladan

    2017-04-01

    One of the recent challenges for the hydrologic research community is the need for the development of coupled systems that involves the integration of hydrologic, atmospheric and socio-economic relationships. This poses a requirement for novel modelling frameworks that can accurately represent complex systems, given, the limited understanding of underlying processes, increasing volume of data and high levels of uncertainity. Each of the existing hydrological models vary in terms of conceptualization and process representation and is the best suited to capture the environmental dynamics of a particular hydrological system. Data driven approaches can be used in the integration of alternative process hypotheses in order to achieve a unified theory at catchment scale. The key steps in the implementation of integrated modelling framework that is influenced by prior understanding and data, include, choice of the technique for the induction of knowledge from data, identification of alternative structural hypotheses, definition of rules, constraints for meaningful, intelligent combination of model component hypotheses and definition of evaluation metrics. This study aims at defining a Genetic Programming based modelling framework that test different conceptual model constructs based on wide range of objective functions and evolves accurate and parsimonious models that capture dominant hydrological processes at catchment scale. In this paper, GP initializes the evolutionary process using the modelling decisions inspired from the Superflex framework [Fenicia et al., 2011] and automatically combines them into model structures that are scrutinized against observed data using statistical, hydrological and flow duration curve based performance metrics. The collaboration between data driven and physical, conceptual modelling paradigms improves the ability to model and manage hydrologic systems. Fenicia, F., D. Kavetski, and H. H. Savenije (2011), Elements of a flexible approach

  1. Genetic Programming for Medicinal Plant Family Identification System

    Directory of Open Access Journals (Sweden)

    Indra Laksmana

    2014-11-01

    Full Text Available Information about medicinal plants that is available in text documents is generally quite easy to access, however, one needs some efforts to use it. This research was aimed at utilizing crucial information taken from a text document to identify the family of several species of medicinal plants using a heuristic approach, i.e. genetic programming. Each of the species has its unique features. The genetic program puts the characteristics or special features of each family into a tree form. There are a number of processes involved in the investigated method, i.e. data acquisition, booleanization, grouping of training and test data, evaluation, and analysis. The genetic program uses a training process to select the best individual, initializes a generate-rule process to create several individuals and then executes a fitness evaluation. The next procedure is a genetic operation process, which consists of tournament selection to choose the best individual based on a fitness value, the crossover operation and the mutation operation. These operations have the purpose of complementing the individual. The best individual acquired is the expected solution, which is a rule for classifying medicinal plants. This process produced three rules, one for each plant family, displaying a feature structure that distinguishes each of the families from each other. The genetic program then used these rules to identify the medicinal plants, achieving an average accuracy of 86.47%.

  2. Genetic programming applied to RFI mitigation in radio astronomy

    Science.gov (United States)

    Staats, K.

    2016-12-01

    Genetic Programming is a type of machine learning that employs a stochastic search of a solutions space, genetic operators, a fitness function, and multiple generations of evolved programs to resolve a user-defined task, such as the classification of data. At the time of this research, the application of machine learning to radio astronomy was relatively new, with a limited number of publications on the subject. Genetic Programming had never been applied, and as such, was a novel approach to this challenging arena. Foundational to this body of research, the application Karoo GP was developed in the programming language Python following the fundamentals of tree-based Genetic Programming described in "A Field Guide to Genetic Programming" by Poli, et al. Karoo GP was tasked with the classification of data points as signal or radio frequency interference (RFI) generated by instruments and machinery which makes challenging astronomers' ability to discern the desired targets. The training data was derived from the output of an observation run of the KAT-7 radio telescope array built by the South African Square Kilometre Array (SKA-SA). Karoo GP, kNN, and SVM were comparatively employed, the outcome of which provided noteworthy correlations between input parameters, the complexity of the evolved hypotheses, and performance of raw data versus engineered features. This dissertation includes description of novel approaches to GP, such as upper and lower limits to the size of syntax trees, an auto-scaling multiclass classifier, and a Numpy array element manager. In addition to the research conducted at the SKA-SA, it is described how Karoo GP was applied to fine-tuning parameters of a weather prediction model at the South African Astronomical Observatory (SAAO), to glitch classification at the Laser Interferometer Gravitational-wave Observatory (LIGO), and to astro-particle physics at The Ohio State University.

  3. genomic and transcriptomic approaches towards the genetic ...

    African Journals Online (AJOL)

    USER

    to the complex nature of these stresses, and the genotype x environment interaction (GxE). .... collection (Azam-Ali et al., 2001); (vi) biological .... Integrative platform to study gene function and gene evolution in legumes ..... a powerful dissection of the genetic control of ... complemented by a new approach called genomic.

  4. A programming approach to computability

    CERN Document Server

    Kfoury, A J; Arbib, Michael A

    1982-01-01

    Computability theory is at the heart of theoretical computer science. Yet, ironically, many of its basic results were discovered by mathematical logicians prior to the development of the first stored-program computer. As a result, many texts on computability theory strike today's computer science students as far removed from their concerns. To remedy this, we base our approach to computability on the language of while-programs, a lean subset of PASCAL, and postpone consideration of such classic models as Turing machines, string-rewriting systems, and p. -recursive functions till the final chapter. Moreover, we balance the presentation of un solvability results such as the unsolvability of the Halting Problem with a presentation of the positive results of modern programming methodology, including the use of proof rules, and the denotational semantics of programs. Computer science seeks to provide a scientific basis for the study of information processing, the solution of problems by algorithms, and the design ...

  5. Long Term Energy Consumption Forecasting Using Genetic Programming

    OpenAIRE

    KARABULUT, Korhan; Alkan, Ahmet; YILMAZ, Ahmet

    2008-01-01

    Managing electrical energy supply is a complex task. The most important part of electric utility resource planning is forecasting of the future load demand in the regional or national service area. This is usually achieved by constructing models on relative information, such as climate and previous load demand data. In this paper, a genetic programming approach is proposed to forecast long term electrical power consumption in the area covered by a utility situated in the southeast of Turkey. ...

  6. Genetic Programming Framework for Fingerprint Matching

    CERN Document Server

    Ismail, Ismail A; Abd-ElWahid, Mohammed A; ElKafrawy, Passent M; Nasef, Mohammed M

    2009-01-01

    A fingerprint matching is a very difficult problem. Minutiae based matching is the most popular and widely used technique for fingerprint matching. The minutiae points considered in automatic identification systems are based normally on termination and bifurcation points. In this paper we propose a new technique for fingerprint matching using minutiae points and genetic programming. The goal of this paper is extracting the mathematical formula that defines the minutiae points.

  7. Deterministic Pattern Classifier Based on Genetic Programming

    Institute of Scientific and Technical Information of China (English)

    LI Jian-wu; LI Min-qiang; KOU Ji-song

    2001-01-01

    This paper proposes a supervised training-test method with Genetic Programming (GP) for pattern classification. Compared and contrasted with traditional methods with regard to deterministic pattern classifiers, this method is true for both linear separable problems and linear non-separable problems. For specific training samples, it can formulate the expression of discriminate function well without any prior knowledge. At last, an experiment is conducted, and the result reveals that this system is effective and practical.

  8. Genetic programming theory and practice X

    CERN Document Server

    Riolo, Rick; Ritchie, Marylyn D; Moore, Jason H

    2013-01-01

    These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: evolutionary constraints, relaxation of selection mechanisms, diversity preservation strategies, flexing fitness evaluation, evolution in dynamic environments, multi-objective and multi-modal selection, foundations of evolvability, evolvable and adaptive evolutionary operators, foundation of  injecting

  9. New Approaches to Establish Genetic Causality

    Science.gov (United States)

    McNally, Elizabeth M.; George, Alfred L.

    2015-01-01

    Cardiovascular medicine has evolved rapidly in the era of genomics with many diseases having primary genetic origins becoming the subject of intense investigation. The resulting avalanche of information on the molecular causes of these disorders has prompted a revolution in our understanding of disease mechanisms and provided new avenues for diagnoses. At the heart of this revolution is the need to correctly classify genetic variants discovered during the course of research or reported from clinical genetic testing. This review will address current concepts related to establishing the cause and effect relationship between genomic variants and heart diseases. A survey of general approaches used for functional annotation of variants will also be presented. PMID:25864169

  10. Fuzzy Multi-objective Linear Programming Approach

    Directory of Open Access Journals (Sweden)

    Amna Rehmat

    2007-07-01

    Full Text Available Traveling salesman problem (TSP is one of the challenging real-life problems, attracting researchers of many fields including Artificial Intelligence, Operations Research, and Algorithm Design and Analysis. The problem has been well studied till now under different headings and has been solved with different approaches including genetic algorithms and linear programming. Conventional linear programming is designed to deal with crisp parameters, but information about real life systems is often available in the form of vague descriptions. Fuzzy methods are designed to handle vague terms, and are most suited to finding optimal solutions to problems with vague parameters. Fuzzy multi-objective linear programming, an amalgamation of fuzzy logic and multi-objective linear programming, deals with flexible aspiration levels or goals and fuzzy constraints with acceptable deviations. In this paper, a methodology, for solving a TSP with imprecise parameters, is deployed using fuzzy multi-objective linear programming. An example of TSP with multiple objectives and vague parameters is discussed.

  11. Reverse Genetics Approaches to Control Arenavirus.

    Science.gov (United States)

    Martínez-Sobrido, Luis; Cheng, Benson Yee Hin; de la Torre, Juan Carlos

    2016-01-01

    Several arenavirus cause hemorrhagic fever disease in humans and pose a significant public health problem in their endemic regions. To date, no licensed vaccines are available to combat human arenavirus infections, and anti-arenaviral drug therapy is limited to an off-label use of ribavirin that is only partially effective. The development of arenavirus reverse genetics approaches provides investigators with a novel and powerful approach for the investigation of the arenavirus molecular and cell biology. The use of cell-based minigenome systems has allowed examining the cis- and trans-acting factors involved in arenavirus replication and transcription and the identification of novel anti-arenaviral drug targets without requiring the use of live forms of arenaviruses. Likewise, it is now feasible to rescue infectious arenaviruses entirely from cloned cDNAs containing predetermined mutations in their genomes to investigate virus-host interactions and mechanisms of pathogenesis, as well as to facilitate screens to identify anti-arenaviral drugs and development of novel live-attenuated arenavirus vaccines. Recently, reverse genetics have also allowed the generation of tri-segmented arenaviruses expressing foreign genes, facilitating virus detection and opening the possibility of implementing live-attenuated arenavirus-based vaccine vector approaches. Likewise, the development of single-cycle infectious, reporter-expressing, arenaviruses has provided a new experimental method to study some aspects of the biology of highly pathogenic arenaviruses without the requirement of high-security biocontainment required to study HF-causing arenaviruses. In this chapter we summarize the current knowledge on arenavirus reverse genetics and the implementation of plasmid-based reverse genetics techniques for the development of arenavirus vaccines and vaccine vectors.

  12. Genetic Evolutionary Approach for Cutting Forces Prediction in Hard Milling

    Science.gov (United States)

    Taylan, Fatih; Kayacan, Cengiz

    2011-11-01

    Hard milling is a very common used machining procedure in the last years. Therefore the prediction of cutting forces is important. The paper deals with this prediction using genetic evolutionary programming (GEP) approach to set mathematical expression for out cutting forces. In this study, face milling was performed using DIN1.2842 (90MnCrV8) cold work tool steel, with a hardness of 61 HRC. Experimental parameters were selected using stability measurements and simulations. In the hard milling experiments, cutting force data in a total of three axes were collected. Feed direction (Fx) and tangential direction (Fy) cutting forces generated using genetic evolutionary programming were modelled. Cutting speed and feed rate values were treated as inputs in the models, and average cutting force values as output. Mathematical expressions were created to predict average Fxand Fy forces that can be generated in hard material milling.

  13. Multigene Genetic Programming for Estimation of Elastic Modulus of Concrete

    Directory of Open Access Journals (Sweden)

    Alireza Mohammadi Bayazidi

    2014-01-01

    Full Text Available This paper presents a new multigene genetic programming (MGGP approach for estimation of elastic modulus of concrete. The MGGP technique models the elastic modulus behavior by integrating the capabilities of standard genetic programming and classical regression. The main aim is to derive precise relationships between the tangent elastic moduli of normal and high strength concrete and the corresponding compressive strength values. Another important contribution of this study is to develop a generalized prediction model for the elastic moduli of both normal and high strength concrete. Numerous concrete compressive strength test results are obtained from the literature to develop the models. A comprehensive comparative study is conducted to verify the performance of the models. The proposed models perform superior to the existing traditional models, as well as those derived using other powerful soft computing tools.

  14. A molecular-genetic approach to studying source-sink interactions in Arabidopsis thalian. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Gibson, S. I.

    2000-06-01

    This is a final report describing the results of the research funded by the DOE Energy Biosciences Program grant entitled ''A Molecular-Genetic Approach to Studying Source-Sink Interactions in Arabidiopsis thaliana''.

  15. Functional Localization of Genetic Network Programming

    Science.gov (United States)

    Eto, Shinji; Hirasawa, Kotaro; Hu, Jinglu

    According to the knowledge of brain science, it is suggested that there exists cerebral functional localization, which means that a specific part of the cerebrum is activated depending on various kinds of information human receives. The aim of this paper is to build an artificial model to realize functional localization based on Genetic Network Programming (GNP), a new evolutionary computation method recently developed. GNP has a directed graph structure suitable for realizing functional localization. We studied the basic characteristics of the proposed system by making GNP work in a functionally localized way.

  16. Bias-variance decomposition in Genetic Programming

    Directory of Open Access Journals (Sweden)

    Kowaliw Taras

    2016-01-01

    Full Text Available We study properties of Linear Genetic Programming (LGP through several regression and classification benchmarks. In each problem, we decompose the results into bias and variance components, and explore the effect of varying certain key parameters on the overall error and its decomposed contributions. These parameters are the maximum program size, the initial population, and the function set used. We confirm and quantify several insights into the practical usage of GP, most notably that (a the variance between runs is primarily due to initialization rather than the selection of training samples, (b parameters can be reasonably optimized to obtain gains in efficacy, and (c functions detrimental to evolvability are easily eliminated, while functions well-suited to the problem can greatly improve performance—therefore, larger and more diverse function sets are always preferable.

  17. Improving Search Properties in Genetic Programming

    Science.gov (United States)

    Janikow, Cezary Z.; DeWeese, Scott

    1997-01-01

    With the advancing computer processing capabilities, practical computer applications are mostly limited by the amount of human programming required to accomplish a specific task. This necessary human participation creates many problems, such as dramatically increased cost. To alleviate the problem, computers must become more autonomous. In other words, computers must be capable to program/reprogram themselves to adapt to changing environments/tasks/demands/domains. Evolutionary computation offers potential means, but it must be advanced beyond its current practical limitations. Evolutionary algorithms model nature. They maintain a population of structures representing potential solutions to the problem at hand. These structures undergo a simulated evolution by means of mutation, crossover, and a Darwinian selective pressure. Genetic programming (GP) is the most promising example of an evolutionary algorithm. In GP, the structures that evolve are trees, which is a dramatic departure from previously used representations such as strings in genetic algorithms. The space of potential trees is defined by means of their elements: functions, which label internal nodes, and terminals, which label leaves. By attaching semantic interpretation to those elements, trees can be interpreted as computer programs (given an interpreter), evolved architectures, etc. JSC has begun exploring GP as a potential tool for its long-term project on evolving dextrous robotic capabilities. Last year we identified representation redundancies as the primary source of inefficiency in GP. Subsequently, we proposed a method to use problem constraints to reduce those redundancies, effectively reducing GP complexity. This method was implemented afterwards at the University of Missouri. This summer, we have evaluated the payoff from using problem constraints to reduce search complexity on two classes of problems: learning boolean functions and solving the forward kinematics problem. We have also

  18. A new crossover operator in genetic programming for object classification.

    Science.gov (United States)

    Zhang, Mengjie; Gao, Xiaoying; Lou, Weijun

    2007-10-01

    The crossover operator has been considered "the centre of the storm" in genetic programming (GP). However, many existing GP approaches to object recognition suggest that the standard GP crossover is not sufficiently powerful in producing good child programs due to the totally random choice of the crossover points. To deal with this problem, this paper introduces an approach with a new crossover operator in GP for object recognition, particularly object classification. In this approach, a local hill-climbing search is used in constructing good building blocks, a weight called looseness is introduced to identify the good building blocks in individual programs, and the looseness values are used as heuristics in choosing appropriate crossover points to preserve good building blocks. This approach is examined and compared with the standard crossover operator and the headless chicken crossover (HCC) method on a sequence of object classification problems. The results suggest that this approach outperforms the HCC, the standard crossover, and the standard crossover operator with hill climbing on all of these problems in terms of the classification accuracy. Although this approach spends a bit longer time than the standard crossover operator, it significantly improves the system efficiency over the HCC method.

  19. Evolving Rule-Based Systems in two Medical Domains using Genetic Programming

    DEFF Research Database (Denmark)

    Tsakonas, A.; Dounias, G.; Jantzen, Jan

    2004-01-01

    We demonstrate, compare and discuss the application of two genetic programming methodologies for the construction of rule-based systems in two medical domains: the diagnosis of Aphasia's subtypes and the classification of Pap-Smear Test examinations. The first approach consists of a scheme...... that combines genetic programming and heuristic hierarchical crisp rule-base construction. The second model is composed by a grammar driven genetic programming system for the generation of fuzzy rule-based systems. Results are also compared for their efficiency, accuracy and comprehensibility, to those...... of a standard entropy based machine learning approach and to those of a standard genetic programming symbolic expression approach. In the diagnosis of subtypes of Aphasia, two models for crisp rule-bases are presented. The first one discriminates between four major types and the second attempts...

  20. On Using Surrogates with Genetic Programming.

    Science.gov (United States)

    Hildebrandt, Torsten; Branke, Jürgen

    2015-01-01

    One way to accelerate evolutionary algorithms with expensive fitness evaluations is to combine them with surrogate models. Surrogate models are efficiently computable approximations of the fitness function, derived by means of statistical or machine learning techniques from samples of fully evaluated solutions. But these models usually require a numerical representation, and therefore cannot be used with the tree representation of genetic programming (GP). In this paper, we present a new way to use surrogate models with GP. Rather than using the genotype directly as input to the surrogate model, we propose using a phenotypic characterization. This phenotypic characterization can be computed efficiently and allows us to define approximate measures of equivalence and similarity. Using a stochastic, dynamic job shop scenario as an example of simulation-based GP with an expensive fitness evaluation, we show how these ideas can be used to construct surrogate models and improve the convergence speed and solution quality of GP.

  1. A novel mating approach for genetic algorithms.

    Science.gov (United States)

    Galán, Severino F; Mengshoel, Ole J; Pinter, Rafael

    2013-01-01

    Genetic algorithms typically use crossover, which relies on mating a set of selected parents. As part of crossover, random mating is often carried out. A novel approach to parent mating is presented in this work. Our novel approach can be applied in combination with a traditional similarity-based criterion to measure distance between individuals or with a fitness-based criterion. We introduce a parameter called the mating index that allows different mating strategies to be developed within a uniform framework: an exploitative strategy called best-first, an explorative strategy called best-last, and an adaptive strategy called self-adaptive. Self-adaptive mating is defined in the context of the novel algorithm, and aims to achieve a balance between exploitation and exploration in a domain-independent manner. The present work formally defines the novel mating approach, analyzes its behavior, and conducts an extensive experimental study to quantitatively determine its benefits. In the domain of real function optimization, the experiments show that, as the degree of multimodality of the function at hand grows, increasing the mating index improves performance. In the case of the self-adaptive mating strategy, the experiments give strong results for several case studies.

  2. Genetic Programming Neural Networks: A Powerful Bioinformatics Tool for Human Genetics.

    Science.gov (United States)

    Ritchie, Marylyn D; Motsinger, Alison A; Bush, William S; Coffey, Christopher S; Moore, Jason H

    2007-01-01

    The identification of genes that influence the risk of common, complex disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions. We have previously introduced a genetic programming neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of genetic and gene-environment combinations associated with disease risk. Previous empirical studies suggest GPNN has excellent power for identifying gene-gene and gene-environment interactions. The goal of this study was to compare the power of GPNN to stepwise logistic regression (SLR) and classification and regression trees (CART) for identifying gene-gene and gene-environment interactions. SLR and CART are standard methods of analysis for genetic association studies. Using simulated data, we show that GPNN has higher power to identify gene-gene and gene-environment interactions than SLR and CART. These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions in studies of human disease.

  3. A Unified Approach to Modeling and Programming

    DEFF Research Database (Denmark)

    Madsen, Ole Lehrmann; Møller-Pedersen, Birger

    2010-01-01

    of this paper is to go back to the future and get inspiration from SIMULA and propose a unied approach. In addition to reintroducing the contributions of SIMULA and the Scandinavian approach to object-oriented programming, we do this by discussing a number of issues in modeling and programming and argue3 why we......SIMULA was a language for modeling and programming and provided a unied approach to modeling and programming in contrast to methodologies based on structured analysis and design. The current development seems to be going in the direction of separation of modeling and programming. The goal...

  4. An Integrated Approach to Crop Genetic Improvement

    Institute of Scientific and Technical Information of China (English)

    Martin A. J. Parry; Malcolm J. Hawkesford

    2012-01-01

    The balance between the supply and demand of the major food crops is fragile,fueling concerns for long-term global food security.The rising population,increasing wealth and a proliferation of nonfood uses (e.g.bioenergy) has led to growing demands on agriculture,while increased production is limited by greater urbanization,and the degradation of land.Furthermore,global climate change with increasing temperatures and lower,more erratic rainfall is projected to decrease agricultural yields.There is a predicted need to increase food production by at least 70% by 2050 and therefore an urgent need to develop novel and integrated approaches,incorporating high-throughput phenotyping that will both increase production per unit area and simultaneously improve the resource use efficiency of crops.Yield potential,yield stability,nutrient and water use are all complex multigenic traits and while there is genetic variability,their complexity makes such traits difficult to breed for directly.Nevertheless molecular plant breeding has the potential to deliver substantial improvements,once the component traits and the genes underlying these traits have been identified.In addition,interactions between the individual traits must also be taken into account,a demand that is difficult to fulfill with traditional screening approaches.Identified traits will be incorporated into new cultivars using conventional or biotechnological tools.In order to better understand the relationship between genotype,component traits,and environment over time,a multidisciplinary approach must be adopted to both understand the underlying processes and identify candidate genes,QTLs and traits that can be used to develop improved crops.

  5. Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-Objective Genetic Programming

    Science.gov (United States)

    2004-03-01

    In Genetic Programming 1997: Proceedings of the Second Annual Conference, pages 398–406, 1997. [23] Emilio Frazzoli. Maneuver-based motion planning...Evolutionary approaches to neural control of rolling, walking, swimming and flying animats or robots. In Richard J. Duro, Jose Santos, and Manuel Grana...objective genetic programming. In Proceedings of the Congress on Evolutionary Computation, Portland, OR, June 2004. [66] Peter Pacheco . Parallel

  6. Fuzzy Inspired Hybrid Genetic Approach to Optimize Travelling Salesman Problem

    Directory of Open Access Journals (Sweden)

    Bindu

    2012-06-01

    Full Text Available One of the category of algorithm Problems are basically exponential problems. These problems are basically exponential problems and take time to find the solution. In the present work we are optimising one of the common NP complete problem called Travelling Salesman Problem. In our work we have defined a genetic approach by combining fuzzy approach along with genetics. In this work we have implemented the modified DPX crossover to improve genetic approach. The work is implemented in MATLAB environment and obtained results shows the define approach has optimized the existing genetic algorithm results

  7. Genetic Approaches to Develop Salt Tolerant Germplasm

    KAUST Repository

    Tester, Mark A.

    2015-08-19

    Forty percent of the world\\'s food is produced under irrigation, and this is directly threatened by over-exploitation and changes in the global environment. One way to address this threat is to develop systems for increasing our ability to use lower quality water, in particular saline water. Low cost partial desalination of brackish water, use of saline water for cooling and increases in the salinity tolerance of crops can all contribute to the development of this new agricultural system. In this talk, the focus will be on the use of forward genetic approaches for discovery of genes related to salinity tolerance in barley and tomatoes. Rather than studying salinity tolerance as a trait in itself, we dissect salinity tolerance into a series of components that are hypothesised to contribute to overall salinity tolerance (following the paradigm of Munns & Tester, 2008). For example, one significant component of tolerance of most crop plants to moderate soil salinity is due to the ability to maintain low concentrations of Na+ in the leaves, and much analysis of this aspect has been done (e.g. Roy et al., 2013, 2014). A major site for the control of shoot Na+ accumulation is at the plasma membrane of the mature stele of the root. Alleles of HKT, a major gene underlying this transport process have been characterized and, in work led by Dr Rana Munns (CSIRO), have now been introgressed into commercial durum wheat and led to significantly increased yields in saline field conditions (Munns et al., 2012). The genotyping of mapping populations is now highly efficient. However, the ability to quantitatively phenotype these populations is now commonly limiting forward progress in plant science. The increasing power of digital imaging and computational technologies offers the opportunity to relieve this phenotyping bottleneck. The Plant Accelerator is a 4500m2 growth facility that provides non-destructive phenotyping of large populations of plants (http

  8. Improved Evolvability in Genetic Programming with Polyandry

    Directory of Open Access Journals (Sweden)

    Anisa Waganda Ragalo

    2013-12-01

    Full Text Available This paper proposes Polyandry, a new nature-inspired modification to canonical Genetic Programming (GP. Polyandry aims to improve evolvability in GP. Evolvability is a critically important GP trait, the maintenance of which determines the arrival of the GP at the global optimum solution. Specifically evolvability is defined as the ability of the genetic operators employed in GP to produce offspring that are fitter than their parents. When GP fails to exhibit evolvability, further adaptation of the GP individuals towards the global optimum solution becomes impossible. Polyandry improves evolvability by improving the typically disruptive standard GP crossover operator. The algorithm employs a dual strategy towards this goal. The chief part of this strategy is an incorporation of genetic material from multiple mating partners into broods of offspring. Given such a brood, the offspring in the brood then compete according to a culling function, which we make equivalent to the main GP fitness function. Polyandry’s incorporation of genetic material from multiple GP individuals into broods of offspring represents a more aggressive search for building block information. This characteristic of the algorithm leads to an advanced explorative capability in both GP structural space and fitness space. The second component of the Polyandry strategy is an attempt at multiple crossover points, in order to find crossover points that minimize building block disruption from parents to offspring. This strategy is employed by a similar algorithm, Brood Recombination. We conduct experiments to compare Polyandry with the canonical GP. Our experiments demonstrate that Polyandry consistently exhibits better evolvability than the canonical GP. As a consequence, Polyandry achieves higher success rates and finds solutions faster than the latter. The result of these observations is that given certain brood size settings, Polyandry requires less computational effort to

  9. Solving Classification Problems Using Genetic Programming Algorithms on GPUs

    Science.gov (United States)

    Cano, Alberto; Zafra, Amelia; Ventura, Sebastián

    Genetic Programming is very efficient in problem solving compared to other proposals but its performance is very slow when the size of the data increases. This paper proposes a model for multi-threaded Genetic Programming classification evaluation using a NVIDIA CUDA GPUs programming model to parallelize the evaluation phase and reduce computational time. Three different well-known Genetic Programming classification algorithms are evaluated using the parallel evaluation model proposed. Experimental results using UCI Machine Learning data sets compare the performance of the three classification algorithms in single and multithreaded Java, C and CUDA GPU code. Results show that our proposal is much more efficient.

  10. An Approach to Programming Based on Concepts

    CERN Document Server

    Savinov, Alexandr

    2007-01-01

    In this paper we describe a new approach to programming which generalizes object-oriented programming. It is based on using a new programming construct, called concept, which generalizes classes. Concept is defined as a pair of two classes: one reference class and one object class. Each concept has a parent concept which is specified using inclusion relation generalizing inheritance. We describe several important mechanisms such as reference resolution, context stack, dual methods and life-cycle management, inheritance and polymorphism. This approach to programming is positioned as a new programming paradigm and therefore we formulate its main principles and rules.

  11. Adaptable Constrained Genetic Programming: Extensions and Applications

    Science.gov (United States)

    Janikow, Cezary Z.

    2005-01-01

    An evolutionary algorithm applies evolution-based principles to problem solving. To solve a problem, the user defines the space of potential solutions, the representation space. Sample solutions are encoded in a chromosome-like structure. The algorithm maintains a population of such samples, which undergo simulated evolution by means of mutation, crossover, and survival of the fittest principles. Genetic Programming (GP) uses tree-like chromosomes, providing very rich representation suitable for many problems of interest. GP has been successfully applied to a number of practical problems such as learning Boolean functions and designing hardware circuits. To apply GP to a problem, the user needs to define the actual representation space, by defining the atomic functions and terminals labeling the actual trees. The sufficiency principle requires that the label set be sufficient to build the desired solution trees. The closure principle allows the labels to mix in any arity-consistent manner. To satisfy both principles, the user is often forced to provide a large label set, with ad hoc interpretations or penalties to deal with undesired local contexts. This unfortunately enlarges the actual representation space, and thus usually slows down the search. In the past few years, three different methodologies have been proposed to allow the user to alleviate the closure principle by providing means to define, and to process, constraints on mixing the labels in the trees. Last summer we proposed a new methodology to further alleviate the problem by discovering local heuristics for building quality solution trees. A pilot system was implemented last summer and tested throughout the year. This summer we have implemented a new revision, and produced a User's Manual so that the pilot system can be made available to other practitioners and researchers. We have also designed, and partly implemented, a larger system capable of dealing with much more powerful heuristics.

  12. [Molecular genetic bases of adaptation processes and approaches to their analysis].

    Science.gov (United States)

    Salmenkova, E A

    2013-01-01

    Great interest in studying the molecular genetic bases of the adaptation processes is explained by their importance in understanding evolutionary changes, in the development ofintraspecific and interspecific genetic diversity, and in the creation of approaches and programs for maintaining and restoring the population. The article examines the sources and conditions for generating adaptive genetic variability and contribution of neutral and adaptive genetic variability to the population structure of the species; methods for identifying the adaptive genetic variability on the genome level are also described. Considerable attention is paid to the potential of new technologies of genome analysis, including next-generation sequencing and some accompanying methods. In conclusion, the important role of the joint use of genomics and proteomics approaches in understanding the molecular genetic bases of adaptation is emphasized.

  13. A genetic algorithm approach to recognition and data mining

    Energy Technology Data Exchange (ETDEWEB)

    Punch, W.F.; Goodman, E.D.; Min, Pei [Michigan State Univ., East Lansing, MI (United States)] [and others

    1996-12-31

    We review here our use of genetic algorithm (GA) and genetic programming (GP) techniques to perform {open_quotes}data mining,{close_quotes} the discovery of particular/important data within large datasets, by finding optimal data classifications using known examples. Our first experiments concentrated on the use of a K-nearest neighbor algorithm in combination with a GA. The GA selected weights for each feature so as to optimize knn classification based on a linear combination of features. This combined GA-knn approach was successfully applied to both generated and real-world data. We later extended this work by substituting a GP for the GA. The GP-knn could not only optimize data classification via linear combinations of features but also determine functional relationships among the features. This allowed for improved performance and new information on important relationships among features. We review the effectiveness of the overall approach on examples from biology and compare the effectiveness of the GA and GP.

  14. Alternative Living Kidney Donation Programs Boost Genetically Unrelated Donation

    Directory of Open Access Journals (Sweden)

    Rosalie A. Poldervaart

    2015-01-01

    Full Text Available Donor-recipient ABO and/or HLA incompatibility used to lead to donor decline. Development of alternative transplantation programs enabled transplantation of incompatible couples. How did that influence couple characteristics? Between 2000 and 2014, 1232 living donor transplantations have been performed. In conventional and ABO-incompatible transplantation the willing donor becomes an actual donor for the intended recipient. In kidney-exchange and domino-donation the donor donates indirectly to the intended recipient. The relationship between the donor and intended recipient was studied. There were 935 conventional and 297 alternative program transplantations. There were 66 ABO-incompatible, 68 domino-paired, 62 kidney-exchange, and 104 altruistic donor transplantations. Waiting list recipients (n=101 were excluded as they did not bring a living donor. 1131 couples remained of whom 196 participated in alternative programs. Genetically unrelated donors (486 were primarily partners. Genetically related donors (645 were siblings, parents, children, and others. Compared to genetically related couples, almost three times as many genetically unrelated couples were incompatible and participated in alternative programs (P<0.001. 62% of couples were genetically related in the conventional donation program versus 32% in alternative programs (P<0.001. Patient and graft survival were not significantly different between recipient programs. Alternative donation programs increase the number of transplantations by enabling genetically unrelated donors to donate.

  15. Using genetic programming to discover nonlinear variable interactions.

    Science.gov (United States)

    Westbury, Chris; Buchanan, Lori; Sanderson, Michael; Rhemtulla, Mijke; Phillips, Leah

    2003-05-01

    Psychology has to deal with many interacting variables. The analyses usually used to uncover such relationships have many constraints that limit their utility. We briefly discuss these and describe recent work that uses genetic programming to evolve equations to combine variables in nonlinear ways in a number of different domains. We focus on four studies of interactions from lexical access experiments and psychometric problems. In all cases, genetic programming described nonlinear combinations of items in a manner that was subsequently independently verified. We discuss the general implications of genetic programming and related computational methods for multivariate problems in psychology.

  16. Whole genome approaches to quantitative genetics.

    Science.gov (United States)

    Visscher, Peter M

    2009-06-01

    Apart from parent-offspring pairs and clones, relative pairs vary in the proportion of the genome that they share identical by descent. In the past, quantitative geneticists have used the expected value of sharing genes by descent to estimate genetic parameters and predict breeding values. With the possibility to genotype individuals for many markers across the genome it is now possible to empirically estimate the actual relationship between relatives. We review some of the theory underlying the variation in genetic identity, show applications to estimating genetic variance for height in humans and discuss other applications.

  17. GENETIC PROGRAMMING TO PREDICT SKI-JUMP BUCKET SPILLWAY SCOUR

    Institute of Scientific and Technical Information of China (English)

    AZAMATHULLA H. MD; GHANI A. AB; ZAKARIA N. A; LAI S. H; CHANG C. K; LEOW C. S; ABUHASAN Z

    2008-01-01

    Researchers in the past had noticed that application of Artificial Neural Networks (ANN) in place of conventional statistics on the basis of data mining techniques predicts more accurate results in hydraulic predictions. Mostly these works pertained to applications of ANN. Recently, another tool of soft computing, namely, Genetic Programming (GP) has caught the attention of researchers in civil engineering computing. This article examines the usefulness of the GP based approach to predict the relative scour depth downstream of a common type of ski-jump bucket spillway. Actual field measurements were used to develop the GP model. The GP based estimations were found to be equally and more accurate than the ANN based ones, especially, when the underlying cause-effect relationship became more uncertain to model.

  18. Reversible circuit synthesis by genetic programming using dynamic gate libraries

    Science.gov (United States)

    Abubakar, Mustapha Y.; Jung, Low Tang; Zakaria, Nordin; Younes, Ahmed; Abdel-Aty, Abdel-Haleem

    2017-06-01

    We have defined a new method for automatic construction of reversible logic circuits by using the genetic programming approach. The choice of the gate library is 100% dynamic. The algorithm is capable of accepting all possible combinations of the following gate types: NOT TOFFOLI, NOT PERES, NOT CNOT TOFFOLI, NOT CNOT SWAP FREDKIN, NOT CNOT TOFFOLI SWAP FREDKIN, NOT CNOT PERES, NOT CNOT SWAP FREDKIN PERES, NOT CNOT TOFFOLI PERES and NOT CNOT TOFFOLI SWAP FREDKIN PERES. Our method produced near optimum circuits in some cases when a particular subset of gate types was used in the library. Meanwhile, in some cases, optimal circuits were produced due to the heuristic nature of the algorithm. We compared the outcomes of our method with several existing synthesis methods, and it was shown that our algorithm performed relatively well compared to the previous synthesis methods in terms of the output efficiency of the algorithm and execution time as well.

  19. EVOLVING RETRIEVAL ALGORITHMS WITH A GENETIC PROGRAMMING SCHEME

    Energy Technology Data Exchange (ETDEWEB)

    J. THEILER; ET AL

    1999-06-01

    The retrieval of scene properties (surface temperature, material type, vegetation health, etc.) from remotely sensed data is the ultimate goal of many earth observing satellites. The algorithms that have been developed for these retrievals are informed by physical models of how the raw data were generated. This includes models of radiation as emitted and/or rejected by the scene, propagated through the atmosphere, collected by the optics, detected by the sensor, and digitized by the electronics. To some extent, the retrieval is the inverse of this ''forward'' modeling problem. But in contrast to this forward modeling, the practical task of making inferences about the original scene usually requires some ad hoc assumptions, good physical intuition, and a healthy dose of trial and error. The standard MTI data processing pipeline will employ algorithms developed with this traditional approach. But we will discuss some preliminary research on the use of a genetic programming scheme to ''evolve'' retrieval algorithms. Such a scheme cannot compete with the physical intuition of a remote sensing scientist, but it may be able to automate some of the trial and error. In this scenario, a training set is used, which consists of multispectral image data and the associated ''ground truth;'' that is, a registered map of the desired retrieval quantity. The genetic programming scheme attempts to combine a core set of image processing primitives to produce an IDL (Interactive Data Language) program which estimates this retrieval quantity from the raw data.

  20. Artificial intelligence programming with LabVIEW: genetic algorithms for instrumentation control and optimization.

    Science.gov (United States)

    Moore, J H

    1995-06-01

    A genetic algorithm for instrumentation control and optimization was developed using the LabVIEW graphical programming environment. The usefulness of this methodology for the optimization of a closed loop control instrument is demonstrated with minimal complexity and the programming is presented in detail to facilitate its adaptation to other LabVIEW applications. Closed loop control instruments have variety of applications in the biomedical sciences including the regulation of physiological processes such as blood pressure. The program presented here should provide a useful starting point for those wishing to incorporate genetic algorithm approaches to LabVIEW mediated optimization of closed loop control instruments.

  1. An Approach to Programming Based on Concepts

    OpenAIRE

    Savinov, Alexandr

    2007-01-01

    In this paper we describe a new approach to programming which generalizes object-oriented programming. It is based on using a new programming construct, called concept, which generalizes classes. Concept is defined as a pair of two classes: one reference class and one object class. Each concept has a parent concept which is specified using inclusion relation generalizing inheritance. We describe several important mechanisms such as reference resolution, context stack, dual methods and life-cy...

  2. Simulation Approach for Timing Analysis of Genetic Logic Circuits.

    Science.gov (United States)

    Baig, Hasan; Madsen, Jan

    2017-02-01

    Constructing genetic logic circuits is an application of synthetic biology in which parts of the DNA of a living cell are engineered to perform a dedicated Boolean function triggered by an appropriate concentration of certain proteins or by different genetic components. These logic circuits work in a manner similar to electronic logic circuits, but they are much more stochastic and hence much harder to characterize. In this article, we introduce an approach to analyze the threshold value and timing of genetic logic circuits. We show how this approach can be used to analyze the timing behavior of single and cascaded genetic logic circuits. We further analyze the timing sensitivity of circuits by varying the degradation rates and concentrations. Our approach can be used not only to characterize the timing behavior but also to analyze the timing constraints of cascaded genetic logic circuits, a capability that we believe will be important for design automation in synthetic biology.

  3. Cellular and genetic approaches to myocardial regeneration

    NARCIS (Netherlands)

    Tuyn, John van

    2008-01-01

    Injection of (stem) cells into the damaged heart has a positive effect on cardiac function. In this thesis two strategies for improving myocardial regeneration over classical cell therapy were investigated. The first is to induce cardiomyogenic differentiation by genetically engineering cells to ex

  4. Neuroimaging genetic approaches to Posttraumatic Stress Disorder.

    Science.gov (United States)

    Lebois, Lauren A M; Wolff, Jonathan D; Ressler, Kerry J

    2016-10-01

    Neuroimaging genetic studies that associate genetic and epigenetic variation with neural activity or structure provide an opportunity to link genes to psychiatric disorders, often before psychopathology is discernable in behavior. Here we review neuroimaging genetics studies with participants who have Posttraumatic Stress Disorder (PTSD). Results show that genes related to the physiological stress response (e.g., glucocorticoid receptor and activity, neuroendocrine release), learning and memory (e.g., plasticity), mood, and pain perception are tied to neural intermediate phenotypes associated with PTSD. These genes are associated with and sometimes predict neural structure and function in areas involved in attention, executive function, memory, decision-making, emotion regulation, salience of potential threats, and pain perception. Evidence suggests these risk polymorphisms and neural intermediate phenotypes are vulnerabilities toward developing PTSD in the aftermath of trauma, or vulnerabilities toward particular symptoms once PTSD has developed. Work distinguishing between the re-experiencing and dissociative sub-types of PTSD, and examining other PTSD symptom clusters in addition to the re-experiencing and hyperarousal symptoms, will further clarify neurobiological mechanisms and inconsistent findings. Furthermore, an exciting possibility is that genetic associations with PTSD may eventually be understood through differential intermediate phenotypes of neural circuit structure and function, possibly underlying the different symptom clusters seen within PTSD. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Comparing approaches to generic programming in Haskell

    NARCIS (Netherlands)

    Hinze, R.; Jeuring, J.T.; Löh, A.

    2007-01-01

    The last decade has seen a number of approaches to data- type-generic programming: PolyP, Functorial ML, `Scrap Your Boiler- plate', Generic Haskell, `Generics for the Masses', etc. The approaches vary in sophistication and target audience: some propose full-blown pro- gramming languages, some sugge

  6. A Novel Approach for Solving Semidefinite Programs

    Directory of Open Access Journals (Sweden)

    Hong-Wei Jiao

    2014-01-01

    Full Text Available A novel linearizing alternating direction augmented Lagrangian approach is proposed for effectively solving semidefinite programs (SDP. For every iteration, by fixing the other variables, the proposed approach alternatively optimizes the dual variables and the dual slack variables; then the primal variables, that is, Lagrange multipliers, are updated. In addition, the proposed approach renews all the variables in closed forms without solving any system of linear equations. Global convergence of the proposed approach is proved under mild conditions, and two numerical problems are given to demonstrate the effectiveness of the presented approach.

  7. 1 Hierarchical Approaches to the Analysis of Genetic Diversity in ...

    African Journals Online (AJOL)

    2015-04-14

    Apr 14, 2015 ... Keywords: Genetic diversity, Hierarchical approach, Plant, Clustering,. Descriptive ... utilization) or by clustering (based on a phonetic analysis of individual ...... Improvement of Food Crop Preservatives for the next Millennium.

  8. Stego-audio Using Genetic Algorithm Approach

    Directory of Open Access Journals (Sweden)

    V. Santhi

    2014-06-01

    Full Text Available With the rapid development of digital multimedia applications, the secure data transmission becomes the main issue in data communication system. So the multimedia data hiding techniques have been developed to ensure the secured data transfer. Steganography is an art of hiding a secret message within an image/audio/video file in such a way that the secret message cannot be perceived by hacker/intruder. In this study, we use RSA encryption algorithm to encrypt the message and Genetic Algorithm (GA to encode the message in the audio file. This study presents a method to access the negative audio bytes and includes the negative audio bytes in the message encoding and position embedding process. This increases the capacity of encoding message in the audio file. The use of GA operators in Genetic Algorithm reduces the noise distortions.

  9. Biotech 101: an educational outreach program in genetics and biotechnology.

    Science.gov (United States)

    East, Kelly M; Hott, Adam M; Callanan, Nancy P; Lamb, Neil E

    2012-10-01

    Recent advances in research and biotechnology are making genetics and genomics increasingly relevant to the lives and health of the general public. For the public to make informed healthcare and public policy decisions relating to genetic information, there is a need for increased genetic literacy. Biotech 101 is a free, short-course for the local community introducing participants to topics in genetics, genomics, and biotechnology, created at the HudsonAlpha Institute for Biotechnology. This study evaluated the effectiveness of Biotech 101 in increasing the genetic literacy of program participants through pre-and-post surveys. Genetic literacy was measured through increases in self-perceived knowledge for each content area covered through the course and the self-reported impact the course had on various aspects of participants' lives. Three hundred ninety-two individuals attended Biotech 101 during the first three course offerings. Participants reported a significant increase in self-perceived knowledge for each content area (p biotechnology.

  10. Genetic & epigenetic approach to human obesity

    Directory of Open Access Journals (Sweden)

    K Rajender Rao

    2014-01-01

    Full Text Available Obesity is an important clinical and public health challenge, epitomized by excess adipose tissue accumulation resulting from an imbalance in energy intake and energy expenditure. It is a forerunner for a variety of other diseases such as type-2-diabetes (T2D, cardiovascular diseases, some types of cancer, stroke, hyperlipidaemia and can be fatal leading to premature death. Obesity is highly heritable and arises from the interplay of multiple genes and environmental factors. Recent advancements in Genome-wide association studies (GWAS have shown important steps towards identifying genetic risks and identification of genetic markers for lifestyle diseases, especially for a metabolic disorder like obesity. According to the 12 th u0 pdate of Human Obesity Gene Map there are 253 quantity trait loci (QTL for obesity related phenotypes from 61 genome wide scan studies. Contribution of genetic propensity of individual ethnic and racial variations in obesity is an active area of research. Further, understanding its complexity as to how these variations could influence ones susceptibility to become or remain obese will lead us to a greater understanding of how obesity occurs and hopefully, how to prevent and treat this condition. In this review, various strategies adapted for such an analysis based on the recent advances in genome wide and functional variations in human obesity are discussed.

  11. Genetic & epigenetic approach to human obesity.

    Science.gov (United States)

    Rao, K Rajender; Lal, Nirupama; Giridharan, N V

    2014-11-01

    Obesity is an important clinical and public health challenge, epitomized by excess adipose tissue accumulation resulting from an imbalance in energy intake and energy expenditure. It is a forerunner for a variety of other diseases such as type-2-diabetes (T2D), cardiovascular diseases, some types of cancer, stroke, hyperlipidaemia and can be fatal leading to premature death. Obesity is highly heritable and arises from the interplay of multiple genes and environmental factors. Recent advancements in Genome-wide association studies (GWAS) have shown important steps towards identifying genetic risks and identification of genetic markers for lifestyle diseases, especially for a metabolic disorder like obesity. According to the 12th Update of Human Obesity Gene Map there are 253 quantity trait loci (QTL) for obesity related phenotypes from 61 genome wide scan studies. Contribution of genetic propensity of individual ethnic and racial variations in obesity is an active area of research. Further, understanding its complexity as to how these variations could influence ones susceptibility to become or remain obese will lead us to a greater understanding of how obesity occurs and hopefully, how to prevent and treat this condition. In this review, various strategies adapted for such an analysis based on the recent advances in genome wide and functional variations in human obesity are discussed.

  12. Genetic & epigenetic approach to human obesity

    Science.gov (United States)

    Rao, K. Rajender; Lal, Nirupama; Giridharan, N.V.

    2014-01-01

    Obesity is an important clinical and public health challenge, epitomized by excess adipose tissue accumulation resulting from an imbalance in energy intake and energy expenditure. It is a forerunner for a variety of other diseases such as type-2-diabetes (T2D), cardiovascular diseases, some types of cancer, stroke, hyperlipidaemia and can be fatal leading to premature death. Obesity is highly heritable and arises from the interplay of multiple genes and environmental factors. Recent advancements in Genome-wide association studies (GWAS) have shown important steps towards identifying genetic risks and identification of genetic markers for lifestyle diseases, especially for a metabolic disorder like obesity. According to the 12th Update of Human Obesity Gene Map there are 253 quantity trait loci (QTL) for obesity related phenotypes from 61 genome wide scan studies. Contribution of genetic propensity of individual ethnic and racial variations in obesity is an active area of research. Further, understanding its complexity as to how these variations could influence ones susceptibility to become or remain obese will lead us to a greater understanding of how obesity occurs and hopefully, how to prevent and treat this condition. In this review, various strategies adapted for such an analysis based on the recent advances in genome wide and functional variations in human obesity are discussed. PMID:25579139

  13. Concurrency-based approaches to parallel programming

    Science.gov (United States)

    Kale, L.V.; Chrisochoides, N.; Kohl, J.; Yelick, K.

    1995-01-01

    The inevitable transition to parallel programming can be facilitated by appropriate tools, including languages and libraries. After describing the needs of applications developers, this paper presents three specific approaches aimed at development of efficient and reusable parallel software for irregular and dynamic-structured problems. A salient feature of all three approaches in their exploitation of concurrency within a processor. Benefits of individual approaches such as these can be leveraged by an interoperability environment which permits modules written using different approaches to co-exist in single applications.

  14. The "Genetic Program": Behind the Genesis of an Influential Metaphor.

    Science.gov (United States)

    Peluffo, Alexandre E

    2015-07-01

    The metaphor of the "genetic program," indicating the genome as a set of instructions required to build a phenotype, has been very influential in biology despite various criticisms over the years. This metaphor, first published in 1961, is thought to have been invented independently in two different articles, one by Ernst Mayr and the other by François Jacob and Jacques Monod. Here, after a detailed analysis of what both parties meant by "genetic program," I show, using unpublished archives, the strong resemblance between the ideas of Mayr and Monod and suggest that their idea of genetic program probably shares a common origin. I explore the possibility that the two men met before 1961 and also exchanged their ideas through common friends and colleagues in the field of molecular biology. Based on unpublished correspondence of Jacob and Monod, I highlight the important events that influenced the preparation of their influential paper, which introduced the concept of the genetic program. Finally, I suggest that the genetic program metaphor may have preceded both papers and that it was probably used informally before 1961.

  15. Automating the packing heuristic design process with genetic programming.

    Science.gov (United States)

    Burke, Edmund K; Hyde, Matthew R; Kendall, Graham; Woodward, John

    2012-01-01

    The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains.

  16. Cognitive Radio — Genetic Algorithm Approach

    Science.gov (United States)

    Reddy, Y. B.

    2005-03-01

    Cognitive Radio (CR) is relatively a new technology, which intelligently detects a particular segment of the radio spectrum currently in use and selects unused spectrum quickly without interfering the transmission of authorized users. Cognitive Radios can learn about current use of spectrum in their operating area, make intelligent decisions, and react to immediate changes in the use of spectrum by other authorized users. The goal of CR technology is to relieve radio spectrum overcrowding, which actually translates to a lack of access to full radio spectrum utilization. Due to this adaptive behavior, the CR can easily avoid the interference of signals in a crowded radio frequency spectrum. In this research, we discuss the possible application of genetic algorithms (GA) to create a CR that can respond intelligently in changing and unanticipated circumstances and in the presence of hostile jammers and interferers. Genetic algorithms are problem solving techniques based on evolution and natural selection. GA models adapt Charles Darwin's evolutionary theory for analysis of data and interchanging design elements in hundreds of thousands of different combinations. Only the best-performing combinations are permitted to survive, and those combinations "reproduce" further, progressively yielding better and better results.

  17. Mendelian Genetics: Paradigm, Conjecture, or Research Program.

    Science.gov (United States)

    Oldham, V.; Brouwer, W.

    1984-01-01

    Applies Kuhn's model of the structure of scientific revolutions, Popper's hypothetic-deductive model of science, and Lakatos' methodology of competing research programs to a historical biological episode. Suggests using Kuhn's model (emphasizing the nonrational basis of science) and Popper's model (emphasizing the rational basis of science) in…

  18. Mendelian Genetics: Paradigm, Conjecture, or Research Program.

    Science.gov (United States)

    Oldham, V.; Brouwer, W.

    1984-01-01

    Applies Kuhn's model of the structure of scientific revolutions, Popper's hypothetic-deductive model of science, and Lakatos' methodology of competing research programs to a historical biological episode. Suggests using Kuhn's model (emphasizing the nonrational basis of science) and Popper's model (emphasizing the rational basis of science) in…

  19. Maximizing lifetime of wireless sensor networks using genetic approach

    DEFF Research Database (Denmark)

    Wagh, Sanjeev; Prasad, Ramjee

    2014-01-01

    the cluster head intelligently using auction data of node i.e. its local battery power, topology strength and external battery support. The network lifetime is the centre focus of the research paper which explores intelligently selection of cluster head using auction based approach. The multi......-objective parameters are considered to solve the problem using genetic algorithm of evolutionary approach....

  20. Simulation Approach for Timing Analysis of Genetic Logic Circuits

    DEFF Research Database (Denmark)

    Baig, Hasan; Madsen, Jan

    2017-01-01

    in a manner similar to electronic logic circuits, but they are much more stochastic and hence much harder to characterize. In this article, we introduce an approach to analyze the threshold value and timing of genetic logic circuits. We show how this approach can be used to analyze the timing behavior...

  1. Primer on Molecular Genetics; DOE Human Genome Program

    Science.gov (United States)

    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.

  2. Primer on molecular genetics. DOE Human Genome Program

    Energy Technology Data Exchange (ETDEWEB)

    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.

  3. Genetic Algorithm Approaches to Prebiobiotic Chemistry Modeling

    Science.gov (United States)

    Lohn, Jason; Colombano, Silvano

    1997-01-01

    We model an artificial chemistry comprised of interacting polymers by specifying two initial conditions: a distribution of polymers and a fixed set of reversible catalytic reactions. A genetic algorithm is used to find a set of reactions that exhibit a desired dynamical behavior. Such a technique is useful because it allows an investigator to determine whether a specific pattern of dynamics can be produced, and if it can, the reaction network found can be then analyzed. We present our results in the context of studying simplified chemical dynamics in theorized protocells - hypothesized precursors of the first living organisms. Our results show that given a small sample of plausible protocell reaction dynamics, catalytic reaction sets can be found. We present cases where this is not possible and also analyze the evolved reaction sets.

  4. Cellular biosensing: chemical and genetic approaches.

    Science.gov (United States)

    Haruyama, Tetsuya

    2006-05-24

    Biosensors have been developed to determine the concentration of specific compounds in situ. They are already widely employed as a practical technology in the clinical and healthcare fields. Recently, another concept of biosensing has been receiving attention: biosensing for the evaluation of molecular potency. The development of this novel concept has been supported by the development of related technologies, as such as molecular design, molecular biology (genetic engineering) and cellular/tissular engineering. This review is addresses this new concept of biosensing and its application to the evaluation of the potency of chemicals in biological systems, in the field of cellular/tissular engineering. Cellular biosensing may provide information on both pharmaceutical and chemical safety, and on drug efficacy in vitro as a screening tool.

  5. Manufacturing Resource Planning Technology Based on Genetic Programming Simulation

    Institute of Scientific and Technical Information of China (English)

    GAO Shiwen; LIAO Wenhe; GUO Yu; LIU Jinshan; SU Yan

    2009-01-01

    Network-based manufacturing is a kind of distributed system, which enables manufacturers to finish production tasks as well as to grasp the opportunities in the market, even if manufacturing resources are insufficient. One of the main problems in network-based manufacturing is the allocation of resources and the assignment of tasks rationally, according to flexible resource distribution. The mapping rules and relations between production techniques and resources are proposed, followed by the definition of the resource unit. Ultimately, the genetic programming method for the optimization of the manufacturing system is put forward. A set of software for the optimization system of simulation process using genetic programming techniques has been developed, and the problems of manufacturing resource planning in network-based manufacturing are solved with the simulation of optimizing methods by genetic programming. The optimum proposal of hardware planning, selection of company and scheduling will be obtained in theory to help company managers in scientific decision-making.

  6. Polyglot programming in applications used for genetic data analysis.

    Science.gov (United States)

    Nowak, Robert M

    2014-01-01

    Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development.

  7. Genetic Evolution of Shape-Altering Programs for Supersonic Aerodynamics

    Science.gov (United States)

    Kennelly, Robert A., Jr.; Bencze, Daniel P. (Technical Monitor)

    2002-01-01

    Two constrained shape optimization problems relevant to aerodynamics are solved by genetic programming, in which a population of computer programs evolves automatically under pressure of fitness-driven reproduction and genetic crossover. Known optimal solutions are recovered using a small, naive set of elementary operations. Effectiveness is improved through use of automatically defined functions, especially when one of them is capable of a variable number of iterations, even though the test problems lack obvious exploitable regularities. An attempt at evolving new elementary operations was only partially successful.

  8. Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming

    Directory of Open Access Journals (Sweden)

    Wafa Abdelmalek

    2009-01-01

    Full Text Available The volatility is a crucial variable in option pricing and hedging strategies. The aim of this paper is to provide some initial evidence of the empirical relevance of genetic programming to volatility's forecasting. By using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes-implied volatility is compared between time series samples and moneyness-time to maturity classes. Total and out-of-sample mean squared errors are used as forecasting's performance measures. Comparisons reveal that the time series model seems to be more accurate in forecasting-implied volatility than moneyness time to maturity models. Overall, results are strongly encouraging and suggest that the genetic programming approach works well in solving financial problems.

  9. Genetic Programming for the Generation of Crisp and Fuzzy Rule Bases in Classification and Diagnosis of Medical Data

    DEFF Research Database (Denmark)

    Dounias, George; Tsakonas, Athanasios; Jantzen, Jan

    2002-01-01

    This paper demonstrates two methodologies for the construction of rule-based systems in medical decision making. The first approach consists of a method combining genetic programming and heuristic hierarchical rule-base construction. The second model is composed by a strongly-typed genetic progra...

  10. Reverse genetics approaches to combat pathogenic arenaviruses.

    Science.gov (United States)

    de la Torre, Juan C

    2008-12-01

    Several arenaviruses cause hemorrhagic fever (HF) in humans, and evidence indicates that the worldwide-distributed prototypic arenavirus lymphocytic choriomeningitis virus (LCMV) is a neglected human pathogen of clinical significance. Moreover, arenaviruses pose a biodefense threat. No licensed anti-arenavirus vaccines are available, and current anti-arenavirus therapy is limited to the use of ribavirin, which is only partially effective and is associated with anemia and other side effects. Therefore, it is important to develop effective vaccines and better antiviral drugs to combat the dual threats of naturally occurring and intentionally introduced arenavirus infections. The development of arenavirus reverse genetic systems is allowing investigators to conduct a detailed molecular characterization of the viral cis-acting signals and trans-acting factors that control each of the steps of the arenavirus life cycle, including RNA synthesis, packaging and budding. Knowledge derived from these studies is uncovering potential novel targets for therapeutic intervention, as well as facilitating the establishment of assays to identify and characterize candidate antiviral drugs capable of interfering with specific steps of the virus life cycle. Likewise, the ability to generate predetermined specific mutations within the arenavirus genome and analyze their phenotypic expression would significantly contribute to the elucidation of arenavirus-host interactions, including the basis of their ability to cause severe HF. This, in turn, could lead to the development of novel, potent and safe arenavirus vaccines.

  11. A New Approach to Solving Nonlinear Programming

    Institute of Scientific and Technical Information of China (English)

    SHEN Jie; CHEN Ling

    2002-01-01

    A method for solving nonlinear programming using genetic algorithm is presented. In the operations of crossover and mutation in each generation, to ensure the new solutions are all feasible, we present a method in which the bounds of every variable in the solution are estimated beforehand according to the constrained conditions. For the operation of mutation, we present two methods of cube bounding and variable bounding. The experimental results are given and analyzed. They show that the method is efficient and can obtain the results in less generation.

  12. Genetic program based data mining to reverse engineer digital logic

    Science.gov (United States)

    Smith, James F., III; Nguyen, Thanh Vu H.

    2006-04-01

    A data mining based procedure for automated reverse engineering and defect discovery has been developed. The data mining algorithm for reverse engineering uses a genetic program (GP) as a data mining function. A genetic program is an algorithm based on the theory of evolution that automatically evolves populations of computer programs or mathematical expressions, eventually selecting one that is optimal in the sense it maximizes a measure of effectiveness, referred to as a fitness function. The system to be reverse engineered is typically a sensor. Design documents for the sensor are not available and conditions prevent the sensor from being taken apart. The sensor is used to create a database of input signals and output measurements. Rules about the likely design properties of the sensor are collected from experts. The rules are used to create a fitness function for the genetic program. Genetic program based data mining is then conducted. This procedure incorporates not only the experts' rules into the fitness function, but also the information in the database. The information extracted through this process is the internal design specifications of the sensor. Uncertainty related to the input-output database and the expert based rule set can significantly alter the reverse engineering results. Significant experimental and theoretical results related to GP based data mining for reverse engineering will be provided. Methods of quantifying uncertainty and its effects will be presented. Finally methods for reducing the uncertainty will be examined.

  13. Integrating Genetic Algorithm, Tabu Search Approach for Job Shop Scheduling

    CERN Document Server

    Thamilselvan, R

    2009-01-01

    This paper presents a new algorithm based on integrating Genetic Algorithms and Tabu Search methods to solve the Job Shop Scheduling problem. The idea of the proposed algorithm is derived from Genetic Algorithms. Most of the scheduling problems require either exponential time or space to generate an optimal answer. Job Shop scheduling (JSS) is the general scheduling problem and it is a NP-complete problem, but it is difficult to find the optimal solution. This paper applies Genetic Algorithms and Tabu Search for Job Shop Scheduling problem and compares the results obtained by each. With the implementation of our approach the JSS problems reaches optimal solution and minimize the makespan.

  14. Multiple comparisons in genetic association studies: a hierarchical modeling approach.

    Science.gov (United States)

    Yi, Nengjun; Xu, Shizhong; Lou, Xiang-Yang; Mallick, Himel

    2014-02-01

    Multiple comparisons or multiple testing has been viewed as a thorny issue in genetic association studies aiming to detect disease-associated genetic variants from a large number of genotyped variants. We alleviate the problem of multiple comparisons by proposing a hierarchical modeling approach that is fundamentally different from the existing methods. The proposed hierarchical models simultaneously fit as many variables as possible and shrink unimportant effects towards zero. Thus, the hierarchical models yield more efficient estimates of parameters than the traditional methods that analyze genetic variants separately, and also coherently address the multiple comparisons problem due to largely reducing the effective number of genetic effects and the number of statistically "significant" effects. We develop a method for computing the effective number of genetic effects in hierarchical generalized linear models, and propose a new adjustment for multiple comparisons, the hierarchical Bonferroni correction, based on the effective number of genetic effects. Our approach not only increases the power to detect disease-associated variants but also controls the Type I error. We illustrate and evaluate our method with real and simulated data sets from genetic association studies. The method has been implemented in our freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/).

  15. A novel holistic framework for genetic-based captive-breeding and reintroduction programs.

    Science.gov (United States)

    Attard, C R M; Möller, L M; Sasaki, M; Hammer, M P; Bice, C M; Brauer, C J; Carvalho, D C; Harris, J O; Beheregaray, L B

    2016-10-01

    Research in reintroduction biology has provided a greater understanding of the often limited success of species reintroductions and highlighted the need for scientifically rigorous approaches in reintroduction programs. We examined the recent genetic-based captive-breeding and reintroduction literature to showcase the underuse of the genetic data gathered. We devised a framework that takes full advantage of the genetic data through assessment of the genetic makeup of populations before (past component of the framework), during (present component), and after (future component) captive-breeding and reintroduction events to understand their conservation potential and maximize their success. We empirically applied our framework to two small fishes: Yarra pygmy perch (Nannoperca obscura) and southern pygmy perch (Nannoperca australis). Each of these species has a locally adapted and geographically isolated lineage that is endemic to the highly threatened lower Murray-Darling Basin in Australia. These two populations were rescued during Australia's recent decade-long Millennium Drought, when their persistence became entirely dependent on captive-breeding and subsequent reintroduction efforts. Using historical demographic analyses, we found differences and similarities between the species in the genetic impacts of past natural and anthropogenic events that occurred in situ, such as European settlement (past component). Subsequently, successful maintenance of genetic diversity in captivity-despite skewed brooder contribution to offspring-was achieved through carefully managed genetic-based breeding (present component). Finally, genetic monitoring revealed the survival and recruitment of released captive-bred offspring in the wild (future component). Our holistic framework often requires no additional data collection to that typically gathered in genetic-based breeding programs, is applicable to a wide range of species, advances the genetic considerations of reintroduction

  16. Evaluating Pain Education Programs: An Integrated Approach

    Directory of Open Access Journals (Sweden)

    Adam Dubrowski

    2011-01-01

    Full Text Available Evaluation of educational programs and assessment of learning are essential to maintain high-standard health science education, which includes pain education. Current models of program evaluations applied to the education of the health professions, such as the Kirkpatrick model, are mainly outcome based. More recently, efforts have been made to examine other process-based models such as the Context Input Process Product model. The present article proposes an approach that integrates both outcome- and process-based models with models of clinical performance assessment to provide a deeper understanding of a program function. Because assessment instruments are a critical part of program evaluation, it is suggested that standardization and rigour should be used in their selection, development and adaptation. The present article suggests an alternative to currently used models in pain education evaluation.

  17. Transcription factories: genetic programming in three dimensions.

    Science.gov (United States)

    Edelman, Lucas Brandon; Fraser, Peter

    2012-04-01

    Among the most intensively studied systems in molecular biology is the eukaryotic transcriptional apparatus, which expresses genes in a regulated manner across hundreds of different cell types. Several studies over the past few years have added weight to the concept that transcription takes place within discrete 'transcription factories' assembled inside the cell nucleus. These studies apply innovative technical approaches to gain insights into the molecular constituents, dynamical behaviour and organizational regulators of transcription factories, providing exciting insights into the spatial dimension of transcriptional control.

  18. A multiobjective approach to the genetic code adaptability problem.

    Science.gov (United States)

    de Oliveira, Lariza Laura; de Oliveira, Paulo S L; Tinós, Renato

    2015-02-19

    The organization of the canonical code has intrigued researches since it was first described. If we consider all codes mapping the 64 codes into 20 amino acids and one stop codon, there are more than 1.51×10(84) possible genetic codes. The main question related to the organization of the genetic code is why exactly the canonical code was selected among this huge number of possible genetic codes. Many researchers argue that the organization of the canonical code is a product of natural selection and that the code's robustness against mutations would support this hypothesis. In order to investigate the natural selection hypothesis, some researches employ optimization algorithms to identify regions of the genetic code space where best codes, according to a given evaluation function, can be found (engineering approach). The optimization process uses only one objective to evaluate the codes, generally based on the robustness for an amino acid property. Only one objective is also employed in the statistical approach for the comparison of the canonical code with random codes. We propose a multiobjective approach where two or more objectives are considered simultaneously to evaluate the genetic codes. In order to test our hypothesis that the multiobjective approach is useful for the analysis of the genetic code adaptability, we implemented a multiobjective optimization algorithm where two objectives are simultaneously optimized. Using as objectives the robustness against mutation with the amino acids properties polar requirement (objective 1) and robustness with respect to hydropathy index or molecular volume (objective 2), we found solutions closer to the canonical genetic code in terms of robustness, when compared with the results using only one objective reported by other authors. Using more objectives, more optimal solutions are obtained and, as a consequence, more information can be used to investigate the adaptability of the genetic code. The multiobjective approach

  19. Feature extraction from multiple data sources using genetic programming.

    Energy Technology Data Exchange (ETDEWEB)

    Szymanski, J. J. (John J.); Brumby, Steven P.; Pope, P. A. (Paul A.); Eads, D. R. (Damian R.); Galassi, M. C. (Mark C.); Harvey, N. R. (Neal R.); Perkins, S. J. (Simon J.); Porter, R. B. (Reid B.); Theiler, J. P. (James P.); Young, A. C. (Aaron Cody); Bloch, J. J. (Jeffrey J.); David, N. A. (Nancy A.); Esch-Mosher, D. M. (Diana M.)

    2002-01-01

    Feature extration from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. The tool used is the GENetic Imagery Exploitation (GENIE) software, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land-cover features including towns, grasslands, wild fire burn scars, and several types of forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.

  20. Considering genetic characteristics in German Holstein breeding programs.

    Science.gov (United States)

    Segelke, D; Täubert, H; Reinhardt, F; Thaller, G

    2016-01-01

    Recently, several research groups have demonstrated that several haplotypes may cause embryonic loss in the homozygous state. Up to now, carriers of genetic disorders were often excluded from mating, resulting in a decrease of genetic gain and a reduced number of sires available for the breeding program. Ongoing research is very likely to identify additional genetic defects causing embryonic loss and calf mortality by genotyping a large proportion of the female cattle population and sequencing key ancestors. Hence, a clear demand is present to develop a method combining selection against recessive defects (e.g., Holstein haplotypes HH1-HH5) with selection for economically beneficial traits (e.g., polled) for mating decisions. Our proposed method is a genetic index that accounts for the allele frequencies in the population and the economic value of the genetic characteristic without excluding carriers from breeding schemes. Fertility phenotypes from routine genetic evaluations were used to determine the economic value per embryo lost. Previous research has shown that embryo loss caused by HH1 and HH2 occurs later than the loss for HH3, HH4, and HH5. Therefore, an economic value of € 97 was used against HH1 and HH2 and € 70 against HH3, HH4, and HH5. For polled, € 7 per polled calf was considered. Minor allele frequencies of the defects ranged between 0.8 and 3.3%. The polled allele has a frequency of 4.1% in the German Holstein population. A genomic breeding program was simulated to study the effect of changing the selection criteria from assortative mating based on breeding values to selecting the females using the genetic index. Selection for a genetic index on the female path is a useful method to control the allele frequencies by reducing undesirable alleles and simultaneously increasing economical beneficial characteristics maintaining most of the genetic gain in production and functional traits. Additionally, we applied the genetic index to real data and

  1. Population Genetic Diversity in the Australian 'Seascape': A Bioregion Approach.

    Directory of Open Access Journals (Sweden)

    Lisa C Pope

    Full Text Available Genetic diversity within species may promote resilience to environmental change, yet little is known about how such variation is distributed at broad geographic scales. Here we develop a novel Bayesian methodology to analyse multi-species genetic diversity data in order to identify regions of high or low genetic diversity. We apply this method to co-distributed taxa from Australian marine waters. We extracted published summary statistics of population genetic diversity from 118 studies of 101 species and > 1000 populations from the Australian marine economic zone. We analysed these data using two approaches: a linear mixed model for standardised data, and a mixed beta-regression for unstandardised data, within a Bayesian framework. Our beta-regression approach performed better than models using standardised data, based on posterior predictive tests. The best model included region (Integrated Marine and Coastal Regionalisation of Australia (IMCRA bioregions, latitude and latitude squared. Removing region as an explanatory variable greatly reduced model performance (delta DIC 23.4. Several bioregions were identified as possessing notably high genetic diversity. Genetic diversity increased towards the equator with a 'hump' in diversity across the range studied (-9.4 to -43.7°S. Our results suggest that factors correlated with both region and latitude play a role in shaping intra-specific genetic diversity, and that bioregion can be a useful management unit for intra-specific as well as species biodiversity. Our novel statistical model should prove useful for future analyses of within species genetic diversity at broad taxonomic and geographic scales.

  2. Genetic and genomic approaches to understanding macrophage identity and function.

    Science.gov (United States)

    Glass, Christopher K

    2015-04-01

    A major goal of our laboratory is to understand the molecular mechanisms that underlie the development and functions of diverse macrophage phenotypes in health and disease. Recent studies using genetic and genomic approaches suggest a relatively simple model of collaborative and hierarchical interactions between lineage-determining and signal-dependent transcription factors that enable selection and activation of transcriptional enhancers that specify macrophage identity and function. In addition, we have found that it is possible to use natural genetic variation as a powerful tool for advancing our understanding of how the macrophage deciphers the information encoded by the genome to attain specific phenotypes in a context-dependent manner. Here, I will describe our recent efforts to extend genetic and genomic approaches to investigate the roles of distinct tissue environments in determining the phenotypes of different resident populations of macrophages.

  3. Review: domestic animal forensic genetics - biological evidence, genetic markers, analytical approaches and challenges.

    Science.gov (United States)

    Kanthaswamy, S

    2015-10-01

    This review highlights the importance of domestic animal genetic evidence sources, genetic testing, markers and analytical approaches as well as the challenges this field is facing in view of the de facto 'gold standard' human DNA identification. Because of the genetic similarity between humans and domestic animals, genetic analysis of domestic animal hair, saliva, urine, blood and other biological material has generated vital investigative leads that have been admitted into a variety of court proceedings, including criminal and civil litigation. Information on validated short tandem repeat, single nucleotide polymorphism and mitochondrial DNA markers and public access to genetic databases for forensic DNA analysis is becoming readily available. Although the fundamental aspects of animal forensic genetic testing may be reliable and acceptable, animal forensic testing still lacks the standardized testing protocols that human genetic profiling requires, probably because of the absence of monetary support from government agencies and the difficulty in promoting cooperation among competing laboratories. Moreover, there is a lack in consensus about how to best present the results and expert opinion to comply with court standards and bear judicial scrutiny. This has been the single most persistent challenge ever since the earliest use of domestic animal forensic genetic testing in a criminal case in the mid-1990s. Crime laboratory accreditation ensures that genetic test results have the courts' confidence. Because accreditation requires significant commitments of effort, time and resources, the vast majority of animal forensic genetic laboratories are not accredited nor are their analysts certified forensic examiners. The relevance of domestic animal forensic genetics in the criminal justice system is undeniable. However, further improvements are needed in a wide range of supporting resources, including standardized quality assurance and control protocols for sample

  4. Understanding the Science-Learning Environment: A Genetically Sensitive Approach

    Science.gov (United States)

    Haworth, Claire M. A.; Davis, Oliver S. P.; Hanscombe, Ken B.; Kovas, Yulia; Dale, Philip S.; Plomin, Robert

    2013-01-01

    Previous studies have shown that environmental influences on school science performance increase in importance from primary to secondary school. Here we assess for the first time the relationship between the science-learning environment and science performance using a genetically sensitive approach to investigate the aetiology of this link. 3000…

  5. Genetic testing and Alzheimer disease: recommendations of the Stanford Program in Genomics, Ethics, and Society.

    Science.gov (United States)

    McConnell, L M; Koenig, B A; Greely, H T; Raffin, T A

    1999-01-01

    Several genes associated with Alzheimer disease (AD) have been localized and cloned; two genetic tests are already commercially available, and new tests are being developed. Genetic testing for AD--either for disease prediction or for diagnosis--raises critical ethical concerns. The multidisciplinary Alzheimer Disease Working Group of the Stanford Program in Genomics, Ethics, and Society (PGES) presents comprehensive recommendations on genetic testing for AD. The Group concludes that under current conditions, genetic testing for AD prediction or diagnosis is only rarely appropriate. Criteria for judging the readiness of a test for introduction into routine clinical practice typically rely heavily on evaluation of technical efficacy. PGES recommends a broader and more comprehensive approach, considering: 1) the unique social and historical meanings of AD; 2) the availability of procedures to promote good surrogate decision making for incompetent patients and to safeguard confidentiality; 3) access to sophisticated genetic counselors able to communicate complex risk information and effectively convey the social costs and psychological burdens of testing, such as unintentional disclosure of predictive genetic information to family members; 4) protection from inappropriate advertising and marketing of genetic tests; and 5) recognition of the need for public education about the meaning and usefulness of predictive and diagnostic tests for AD. In this special issue of Genetic Testing, the PGES recommendations are published along with comprehensive background papers authored by Working Group members.

  6. A semidefinite programming approach for solving Multiobjective Linear Programming

    CERN Document Server

    Blanco, Víctor; Ben-Ali, Safae El-Haj

    2011-01-01

    Several algorithms are available in the literature for finding the entire set of Pareto-optimal solutions in MultiObjective Linear Programming (MOLP). However, it has not been proposed so far an interior point algorithm that finds all Pareto-optimal solutions of MOLP. We present an explicit construction, based on a transformation of any MOLP into a finite sequence of SemiDefinite Programs (SDP), the solutions of which give the entire set of Pareto-optimal extreme points solutions of MOLP. These SDP problems are solved by interior point methods; thus our approach provides a pseudo-polynomial interior point methodology to find the set of Pareto-optimal solutions of MOLP.

  7. Initialization Method for Grammar-Guided Genetic Programming

    Science.gov (United States)

    García-Arnau, M.; Manrique, D.; Ríos, J.; Rodríguez-Patón, A.

    This paper proposes a new tree-generation algorithm for grammarguided genetic programming that includes a parameter to control the maximum size of the trees to be generated. An important feature of this algorithm is that the initial populations generated are adequately distributed in terms of tree size and distribution within the search space. Consequently, genetic programming systems starting from the initial populations generated by the proposed method have a higher convergence speed. Two different problems have been chosen to carry out the experiments: a laboratory test involving searching for arithmetical equalities and the real-world task of breast cancer prognosis. In both problems, comparisons have been made to another five important initialization methods.

  8. Mathematical Modeling of Intestinal Iron Absorption Using Genetic Programming

    Science.gov (United States)

    Colins, Andrea; Gerdtzen, Ziomara P.; Nuñez, Marco T.; Salgado, J. Cristian

    2017-01-01

    Iron is a trace metal, key for the development of living organisms. Its absorption process is complex and highly regulated at the transcriptional, translational and systemic levels. Recently, the internalization of the DMT1 transporter has been proposed as an additional regulatory mechanism at the intestinal level, associated to the mucosal block phenomenon. The short-term effect of iron exposure in apical uptake and initial absorption rates was studied in Caco-2 cells at different apical iron concentrations, using both an experimental approach and a mathematical modeling framework. This is the first report of short-term studies for this system. A non-linear behavior in the apical uptake dynamics was observed, which does not follow the classic saturation dynamics of traditional biochemical models. We propose a method for developing mathematical models for complex systems, based on a genetic programming algorithm. The algorithm is aimed at obtaining models with a high predictive capacity, and considers an additional parameter fitting stage and an additional Jackknife stage for estimating the generalization error. We developed a model for the iron uptake system with a higher predictive capacity than classic biochemical models. This was observed both with the apical uptake dataset used for generating the model and with an independent initial rates dataset used to test the predictive capacity of the model. The model obtained is a function of time and the initial apical iron concentration, with a linear component that captures the global tendency of the system, and a non-linear component that can be associated to the movement of DMT1 transporters. The model presented in this paper allows the detailed analysis, interpretation of experimental data, and identification of key relevant components for this complex biological process. This general method holds great potential for application to the elucidation of biological mechanisms and their key components in other complex

  9. Genetic variability of broodstocks of restocking programs in Brazil

    Directory of Open Access Journals (Sweden)

    Nelson Lopera-Barrero

    2015-09-01

    Full Text Available Objective. The aim of this study was evaluate the genetic diversity of the following broodstocks: piapara (Leporinus elongatus, dourado (Salminus brasiliensis, jundiá (Rhamdia quelen and cachara (Pseudoplatystoma fasciatum already useful for restocking programs in the Paranapanema, Iguaçu and Paraná Brazilian Rivers. Materials and methods. Samples from the caudal fin of 122 fish were analyzed. DNA was extracted by NaCl protocol. PCR products were separated by a horizontal agarose gel electrophoresis. The fragments were visualized by staining with ethidium bromide. Results. The amplification of 25 primers generated different fragments in studied species that allowed characterizing 440 fragments of 100-2900 bp. High percentage of polymorphic fragments (66.67 to 86.29, Shannon index (0.365 to 0.486 and genetic diversity of Nei (0.248 to 0.331 were detected. Conclusions. The level of genetic variability in the broodstocks was adequate for allowing their use in restocking programs in the studied Rivers. However, periodical monitoring studies of genetic variability in these stocks, the mating system, reproductive system and general management must be made to guarantee the preservation of wild populations.

  10. Finding Approximate Analytic Solutions to Differential Equations by Seed Selection Genetic Programming

    Institute of Scientific and Technical Information of China (English)

    侯进军

    2007-01-01

    @@ 1 Seed Selection Genetic Programming In Genetic Programming, each tree in population shows an algebraic or surmounting expression, and each algebraic or surmounting expression shows an approximate analytic solution to differential equations.

  11. WONOEP appraisal: new genetic approaches to study epilepsy

    Science.gov (United States)

    Rossignol, Elsa; Kobow, Katja; Simonato, Michele; Loeb, Jeffrey A.; Grisar, Thierry; Gilby, Krista L.; Vinet, Jonathan; Kadam, Shilpa D.; Becker, Albert J.

    2014-01-01

    Objective New genetic investigation techniques, including next-generation sequencing, epigenetic profiling, cell lineage mapping, targeted genetic manipulation of specific neuronal cell types, stem cell reprogramming and optogenetic manipulations within epileptic networks are progressively unravelling the mysteries of epileptogenesis and ictogenesis. These techniques have opened new avenues to discover the molecular basis of epileptogenesis and to study the physiological impacts of mutations in epilepsy-associated genes on a multilayer level, from cells to circuits. Methods This manuscript reviews recently published applications of these new genetic technologies in the study of epilepsy, as well as work presented by the authors at the genetic session of the XII Workshop on the Neurobiology of Epilepsy in Quebec, Canada. Results Next-generation sequencing is providing investigators with an unbiased means to assess the molecular causes of sporadic forms of epilepsy and have revealed the complexity and genetic heterogeneity of sporadic epilepsy disorders. To assess the functional impact of mutations in these newly identified genes on specific neuronal cell-types during brain development, new modeling strategies in animals, including conditional genetics in mice and in utero knockdown approaches, are enabling functional validation with exquisite cell-type and temporal specificity. In addition, optogenetics, using cell-type specific Cre recombinase driver lines, is enabling investigators to dissect networks involved in epilepsy. Genetically-encoded cell-type labeling is also providing new means to assess the role of the non-neuronal components of epileptic networks such as glial cells. Furthermore, beyond its role in revealing coding variants involved in epileptogenesis, next-generation sequencing can be used to assess the epigenetic modifications that lead to sustained network hyperexcitability in epilepsy, including methylation changes in gene promoters and non

  12. Genetic regulation of programmed cell death in Drosophila

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Programmed cell death plays an important role in maintaining homeostasis during animal development, and has been conserved in animals as different as nematodes and humans. Recent studies of Drosophila have provided valuable information toward our understanding of genetic regulation of death. Different signals trigger the novel death regulators rpr, hid, and grim, that utilize the evolutionarily conserved iap and ark genes to modulate caspase function. Subsequent removal of dying cells also appears to be accomplished by conserved mechanisms. The similarity between Drosophila and human in cell death signaling pathways illustrate the promise of fruit flies as a model system to elucidate the mechanisms underlying regulation of programmed cell death.

  13. Prediction of jet engine parameters for control design using genetic programming

    OpenAIRE

    Martínez-Arellano, G; Cant, R; Nolle, L

    2014-01-01

    The simulation of a jet engine behavior is widely used in many different aspects of the engine development and maintenance. Achieving high quality jet engine control systems requires the iterative use of these simulations to virtually test the performance of the engine avoiding any possible damage on the real engine. Jet engine simulations involve the use of mathematical models which are complex and may not always be available. This paper introduces an approach based on Genetic Programming (G...

  14. Genetic programming-based chaotic time series modeling

    Institute of Scientific and Technical Information of China (English)

    张伟; 吴智铭; 杨根科

    2004-01-01

    This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.

  15. Malformations of cortical development: genetic mechanisms and diagnostic approach

    Science.gov (United States)

    2017-01-01

    Malformations of cortical development are rare congenital anomalies of the cerebral cortex, wherein patients present with intractable epilepsy and various degrees of developmental delay. Cases show a spectrum of anomalous cortical formations with diverse anatomic and morphological abnormalities, a variety of genetic causes, and different clinical presentations. Brain magnetic resonance imaging has been of great help in determining the exact morphologies of cortical malformations. The hypothetical mechanisms of malformation include interruptions during the formation of cerebral cortex in the form of viral infection, genetic causes, and vascular events. Recent remarkable developments in genetic analysis methods have improved our understanding of these pathological mechanisms. The present review will discuss normal cortical development, the current proposed malformation classifications, and the diagnostic approach for malformations of cortical development. PMID:28203254

  16. Tracking the Genetic Stability of a Honey Bee (Hymenoptera: Apidae) Breeding Program With Genetic Markers.

    Science.gov (United States)

    Bourgeois, Lelania; Beaman, Lorraine

    2017-08-01

    A genetic stock identification (GSI) assay was developed in 2008 to distinguish Russian honey bees from other honey bee stocks that are commercially produced in the United States. Probability of assignment (POA) values have been collected and maintained since the stock release in 2008 to the Russian Honey Bee Breeders Association. These data were used to assess stability of the breeding program and the diversity levels of the contemporary breeding stock through comparison of POA values and genetic diversity parameters from the initial release to current values. POA values fluctuated throughout 2010-2016, but have recovered to statistically similar levels in 2016 (POA(2010) = 0.82, POA(2016) = 0.74; P = 0.33). Genetic diversity parameters (i.e., allelic richness and gene diversity) in 2016 also remained at similar levels when compared to those in 2010. Estimates of genetic structure revealed stability (FST(2009/2016) = 0.0058) with a small increase in the estimate of the inbreeding coefficient (FIS(2010) = 0.078, FIS(2016) = 0.149). The relationship among breeding lines, based on genetic distance measurement, was similar in 2008 and 2016 populations, but with increased homogeneity among lines (i.e., decreased genetic distance). This was expected based on the closed breeding system used for Russian honey bees. The successful application of the GSI assay in a commercial breeding program demonstrates the utility and stability of such technology to contribute to and monitor the genetic integrity of a breeding stock of an insect species. Published by Oxford University Press on behalf of Entomological Society of America 2017. This work is written by US Government employees and is in the public domain in the US.

  17. Reverse Genetics Approaches for the Development of Influenza Vaccines

    Directory of Open Access Journals (Sweden)

    Aitor Nogales

    2016-12-01

    Full Text Available Influenza viruses cause annual seasonal epidemics and occasional pandemics of human respiratory disease. Influenza virus infections represent a serious public health and economic problem, which are most effectively prevented through vaccination. However, influenza viruses undergo continual antigenic variation, which requires either the annual reformulation of seasonal influenza vaccines or the rapid generation of vaccines against potential pandemic virus strains. The segmented nature of influenza virus allows for the reassortment between two or more viruses within a co-infected cell, and this characteristic has also been harnessed in the laboratory to generate reassortant viruses for their use as either inactivated or live-attenuated influenza vaccines. With the implementation of plasmid-based reverse genetics techniques, it is now possible to engineer recombinant influenza viruses entirely from full-length complementary DNA copies of the viral genome by transfection of susceptible cells. These reverse genetics systems have provided investigators with novel and powerful approaches to answer important questions about the biology of influenza viruses, including the function of viral proteins, their interaction with cellular host factors and the mechanisms of influenza virus transmission and pathogenesis. In addition, reverse genetics techniques have allowed the generation of recombinant influenza viruses, providing a powerful technology to develop both inactivated and live-attenuated influenza vaccines. In this review, we will summarize the current knowledge of state-of-the-art, plasmid-based, influenza reverse genetics approaches and their implementation to provide rapid, convenient, safe and more effective influenza inactivated or live-attenuated vaccines.

  18. Reverse Genetics Approaches for the Development of Influenza Vaccines

    Science.gov (United States)

    Nogales, Aitor; Martínez-Sobrido, Luis

    2016-01-01

    Influenza viruses cause annual seasonal epidemics and occasional pandemics of human respiratory disease. Influenza virus infections represent a serious public health and economic problem, which are most effectively prevented through vaccination. However, influenza viruses undergo continual antigenic variation, which requires either the annual reformulation of seasonal influenza vaccines or the rapid generation of vaccines against potential pandemic virus strains. The segmented nature of influenza virus allows for the reassortment between two or more viruses within a co-infected cell, and this characteristic has also been harnessed in the laboratory to generate reassortant viruses for their use as either inactivated or live-attenuated influenza vaccines. With the implementation of plasmid-based reverse genetics techniques, it is now possible to engineer recombinant influenza viruses entirely from full-length complementary DNA copies of the viral genome by transfection of susceptible cells. These reverse genetics systems have provided investigators with novel and powerful approaches to answer important questions about the biology of influenza viruses, including the function of viral proteins, their interaction with cellular host factors and the mechanisms of influenza virus transmission and pathogenesis. In addition, reverse genetics techniques have allowed the generation of recombinant influenza viruses, providing a powerful technology to develop both inactivated and live-attenuated influenza vaccines. In this review, we will summarize the current knowledge of state-of-the-art, plasmid-based, influenza reverse genetics approaches and their implementation to provide rapid, convenient, safe and more effective influenza inactivated or live-attenuated vaccines. PMID:28025504

  19. Network medicine approaches to the genetics of complex diseases.

    Science.gov (United States)

    Silverman, Edwin K; Loscalzo, Joseph

    2012-08-01

    Complex diseases are caused by perturbations of biological networks. Genetic analysis approaches focused on individual genetic determinants are unlikely to characterize the network architecture of complex diseases comprehensively. Network medicine, which applies systems biology and network science to complex molecular networks underlying human disease, focuses on identifying the interacting genes and proteins which lead to disease pathogenesis. The long biological path between a genetic risk variant and development of a complex disease involves a range of biochemical intermediates, including coding and non-coding RNA, proteins, and metabolites. Transcriptomics, proteomics, metabolomics, and other -omics technologies have the potential to provide insights into complex disease pathogenesis, especially if they are applied within a network biology framework. Most previous efforts to relate genetics to -omics data have focused on a single -omics platform; the next generation of complex disease genetics studies will require integration of multiple types of -omics data sets in a network context. Network medicine may also provide insight into complex disease heterogeneity, serve as the basis for new disease classifications that reflect underlying disease pathogenesis, and guide rational therapeutic and preventive strategies.

  20. Chemical genetics approaches for selective intervention in epigenetics.

    Science.gov (United States)

    Runcie, Andrew C; Chan, Kwok-Ho; Zengerle, Michael; Ciulli, Alessio

    2016-08-01

    Chemical genetics is the use of biologically active small molecules (chemical probes) to investigate the functions of gene products, through the modulation of protein activity. Recent years have seen significant progress in the application of chemical genetics to study epigenetics, following the development of new chemical probes, a growing appreciation of the role of epigenetics in disease and a recognition of the need and utility of high-quality, cell-active chemical probes. In this review, we single out the bromodomain reader domains as a prime example of both the success, and challenges facing chemical genetics. The difficulty in generating single-target selectivity has long been a thorn in the side of chemical genetics, however, recent developments in advanced forms of chemical genetics promise to bypass this, and other, limitations. The 'bump-and-hole' approach has now been used to probe - for the first time - the BET bromodomain subfamily with single-target selectivity and may be applicable to other epigenetic domains. Meanwhile, PROTAC compounds have been shown to be significantly more efficacious than standard domain inhibitors, and have the potential to enhance target selectivity.

  1. OPTIMIZING LOCALIZATION ROUTE USING PARTICLE SWARM-A GENETIC APPROACH

    Directory of Open Access Journals (Sweden)

    L. Lakshmanan

    2014-01-01

    Full Text Available One of the most key problems in wireless sensor networks is finding optimal algorithms for sending packets from source node to destination node. Several algorithms exist in literature, since some are in vital role other may not. Since WSN focus on low power consumption during packet transmission and receiving, finally we adopt by merging swarm particle based algorithm with genetic approach. Initially we order the nodes based on their energy criterion and then focusing towards node path; this can be done using Proactive route algorithm for finding optimal path between Source-Destination (S-D nodes. Fast processing and pre traversal can be done using selective flooding approach and results are in genetic. We have improved our results with high accuracy and optimality in rendering routes.

  2. A genetic algorithm approach to routine gamma spectra analysis

    Energy Technology Data Exchange (ETDEWEB)

    Carlevaro, C M [Instituto de FIsica de LIquidos y Sistemas Biologicos, Calle 59 No 789, B1900BTE La Plata (Argentina); Wilkinson, M V [Autoridad Regulatoria Nuclear, Avda. del Libertador 8250, C1429BNP Buenos Aires (Argentina); Barrios, L A [Autoridad Regulatoria Nuclear, Avda. del Libertador 8250, C1429BNP Buenos Aires (Argentina)

    2008-01-15

    In this work we present an alternative method for performing routine gamma spectra analysis based on genetic algorithm techniques. The main idea is to search for patterns of single nuclide spectra obtained by simulation in a sample spectrum targeted for analysis. We show how this approach is applied to the analysis of simulated and real target spectra, and also to the study of interference resolution.

  3. DNA enrichment approaches to identify unauthorized genetically modified organisms (GMOs).

    Science.gov (United States)

    Arulandhu, Alfred J; van Dijk, Jeroen P; Dobnik, David; Holst-Jensen, Arne; Shi, Jianxin; Zel, Jana; Kok, Esther J

    2016-07-01

    With the increased global production of different genetically modified (GM) plant varieties, chances increase that unauthorized GM organisms (UGMOs) may enter the food chain. At the same time, the detection of UGMOs is a challenging task because of the limited sequence information that will generally be available. PCR-based methods are available to detect and quantify known UGMOs in specific cases. If this approach is not feasible, DNA enrichment of the unknown adjacent sequences of known GMO elements is one way to detect the presence of UGMOs in a food or feed product. These enrichment approaches are also known as chromosome walking or gene walking (GW). In recent years, enrichment approaches have been coupled with next generation sequencing (NGS) analysis and implemented in, amongst others, the medical and microbiological fields. The present review will provide an overview of these approaches and an evaluation of their applicability in the identification of UGMOs in complex food or feed samples.

  4. A Genetic Programming Method for the Identification of Signal Peptides and Prediction of Their Cleavage Sites

    Directory of Open Access Journals (Sweden)

    David Lennartsson

    2004-01-01

    Full Text Available A novel approach to signal peptide identification is presented. We use an evolutionary algorithm for automatic evolution of classification programs, so-called programmatic motifs. The variant of evolutionary algorithm used is called genetic programming where a population of solution candidates in the form of full computer programs is evolved, based on training examples consisting of signal peptide sequences. The method is compared with a previous work using artificial neural network (ANN approaches. Some advantages compared to ANNs are noted. The programmatic motif can perform computational tasks beyond that of feed-forward neural networks and has also other advantages such as readability. The best motif evolved was analyzed and shown to detect the h-region of the signal peptide. A powerful parallel computer cluster was used for the experiment.

  5. Feedback Control of Turbulent Shear Flows by Genetic Programming

    CERN Document Server

    Duriez, Thomas; von Krbek, Kai; Bonnet, Jean-Paul; Cordier, Laurent; Noack, Bernd R; Segond, Marc; Abel, Markus; Gautier, Nicolas; Aider, Jean-Luc; Raibaudo, Cedric; Cuvier, Christophe; Stanislas, Michel; Debien, Antoine; Mazellier, Nicolas; Kourta, Azeddine; Brunton, Steven L

    2015-01-01

    Turbulent shear flows have triggered fundamental research in nonlinear dynamics, like transition scenarios, pattern formation and dynamical modeling. In particular, the control of nonlinear dynamics is subject of research since decades. In this publication, actuated turbulent shear flows serve as test-bed for a nonlinear feedback control strategy which can optimize an arbitrary cost function in an automatic self-learning manner. This is facilitated by genetic programming providing an analytically treatable control law. Unlike control based on PID laws or neural networks, no structure of the control law needs to be specified in advance. The strategy is first applied to low-dimensional dynamical systems featuring aspects of turbulence and for which linear control methods fail. This includes stabilizing an unstable fixed point of a nonlinearly coupled oscillator model and maximizing mixing, i.e.\\ the Lyapunov exponent, for forced Lorenz equations. For the first time, we demonstrate the applicability of genetic p...

  6. [Current methods in genetic analysis : an approach for genetics-based preventive medicine].

    Science.gov (United States)

    Klein, Hans-Georg; Rost, Imma

    2015-02-01

    Modern genetic analysis methods such as DNA arrays (gene chips) or high-throughput DNA sequencing of the next generation (Next Generation Sequencing, NGS) have once again accelerated the pace of innovation that has been powered by genome research over the past 10 years of the "post-genomic era". The present paper introduces array and NGS methods as two important innovation driving methods and provides examples for their application in large-scale scientific projects. However, a broad application of these very powerful technologies for genetic screening for the purpose of disease prevention is currently not yet in sight. The complexity of the interaction of genes, gene products and the environment has so far exceeded all expectations, suggesting that reliable statements about the medical relevance of common genetic variants can presently only be made in a few areas such as pharmacogenetics and oncology. We also discuss ethical issues raised by genetic population screening. The aim of this paper is to provide a brief outline of the development of methods in molecular genetics to the now dominant modern technologies and present their applications in research, in the diagnosis of rare diseases, and in terms of screening approaches.

  7. Impact of genomics approaches on plant genetics and physiology.

    Science.gov (United States)

    Tabata, Satoshi

    2002-08-01

    Comprehensive analysis of genetic information in higher plants is under way for several plants of biological and agronomical importance. Among them, Arabidopsis thaliana, a member of Brassica family, and Oryza sativa(rice) have been chosen as model plants most suitable for genome analysis. Sequencing of the genome of A. thaliana was completed in December 2000, and rice genome sequencing is in progress. The accumulated genome sequences, together with the hundreds of thousands of ESTs from several tens of plant species, have drastically changed the strategy of plant genetics. By utilizing the information on the genome and gene structures, comprehensive approaches for genome-wide functional analysis of the genes, including transcriptome analysis using microarray systems and a comprehensive analysis of a large number of insertion mutant lines, have been widely adopted. As a consequence, a large quantity of information on both the structure and function of genes in these model plants has been accumulated. However, other plant species may have their own characteristics and advantages to study individual phenomena. Application of knowledge from the model plants to other plant species and vice versa through the common language, namely the genome information, should facilitate understanding of the genetic systems underlying a variety of biological phenomena. Introduction of this common language may not be very simple, especially in the case of complex pathways such as a process of cell-covering formation. Nevertheless, it should be emphasized that genomics approaches are the most promising way to understand these processes.

  8. Extracting classification rules from an informatic security incidents repository by genetic programming

    Directory of Open Access Journals (Sweden)

    Carlos Javier Carvajal Montealegre

    2015-04-01

    Full Text Available This paper describes the data mining process to obtain classification rules over an information security incident data collection, explaining in detail the use of genetic programming as a mean to model the incidents behavior and representing such rules as decision trees. The described mining process includes several tasks, such as the GP (Genetic Programming approach evaluation, the individual's representation and the algorithm parameters tuning to upgrade the performance. The paper concludes with the result analysis and the description of the rules obtained, suggesting measures to avoid the occurrence of new informatics attacks. This paper is a part of the thesis work degree: Information Security Incident Analytics by Data Mining for Behavioral Modeling and Pattern Recognition (Carvajal, 2012.

  9. An Airborne Conflict Resolution Approach Using a Genetic Algorithm

    Science.gov (United States)

    Mondoloni, Stephane; Conway, Sheila

    2001-01-01

    An airborne conflict resolution approach is presented that is capable of providing flight plans forecast to be conflict-free with both area and traffic hazards. This approach is capable of meeting constraints on the flight plan such as required times of arrival (RTA) at a fix. The conflict resolution algorithm is based upon a genetic algorithm, and can thus seek conflict-free flight plans meeting broader flight planning objectives such as minimum time, fuel or total cost. The method has been applied to conflicts occurring 6 to 25 minutes in the future in climb, cruise and descent phases of flight. The conflict resolution approach separates the detection, trajectory generation and flight rules function from the resolution algorithm. The method is capable of supporting pilot-constructed resolutions, cooperative and non-cooperative maneuvers, and also providing conflict resolution on trajectories forecast by an onboard FMC.

  10. Genetic Programming for the Generation of Crisp and Fuzzy Rule Bases in Classification and Diagnosis of Medical Data

    DEFF Research Database (Denmark)

    Dounias, George; Tsakonas, Athanasios; Jantzen, Jan;

    2002-01-01

    This paper demonstrates two methodologies for the construction of rule-based systems in medical decision making. The first approach consists of a method combining genetic programming and heuristic hierarchical rule-base construction. The second model is composed by a strongly-typed genetic progra...... systems. Comparisons on the system's comprehensibility and the transparency are included. These comparisons include for the Aphasia domain, previous work consisted of two neural network models....

  11. System Response Analysis and Model Order Reduction, Using Conventional Method, Bond Graph Technique and Genetic Programming

    Directory of Open Access Journals (Sweden)

    Lubna Moin

    2009-04-01

    Full Text Available This research paper basically explores and compares the different modeling and analysis techniques and than it also explores the model order reduction approach and significance. The traditional modeling and simulation techniques for dynamic systems are generally adequate for single-domain systems only, but the Bond Graph technique provides new strategies for reliable solutions of multi-domain system. They are also used for analyzing linear and non linear dynamic production system, artificial intelligence, image processing, robotics and industrial automation. This paper describes a unique technique of generating the Genetic design from the tree structured transfer function obtained from Bond Graph. This research work combines bond graphs for model representation with Genetic programming for exploring different ideas on design space tree structured transfer function result from replacing typical bond graph element with their impedance equivalent specifying impedance lows for Bond Graph multiport. This tree structured form thus obtained from Bond Graph is applied for generating the Genetic Tree. Application studies will identify key issues and importance for advancing this approach towards becoming on effective and efficient design tool for synthesizing design for Electrical system. In the first phase, the system is modeled using Bond Graph technique. Its system response and transfer function with conventional and Bond Graph method is analyzed and then a approach towards model order reduction is observed. The suggested algorithm and other known modern model order reduction techniques are applied to a 11th order high pass filter [1], with different approach. The model order reduction technique developed in this paper has least reduction errors and secondly the final model retains structural information. The system response and the stability analysis of the system transfer function taken by conventional and by Bond Graph method is compared and

  12. System Response Analysis and Model Order Reduction, Using Conventional Method, Bond Graph Technique and Genetic Programming

    Directory of Open Access Journals (Sweden)

    Shahid Ali

    2009-04-01

    Full Text Available This research paper basically explores and compares the different modeling and analysis techniques and than it also explores the model order reduction approach and significance. The traditional modeling and simulation techniques for dynamic systems are generally adequate for single-domain systems only, but the Bond Graph technique provides new strategies for reliable solutions of multi-domain system. They are also used for analyzing linear and non linear dynamic production system, artificial intelligence, image processing, robotics and industrial automation. This paper describes a unique technique of generating the Genetic design from the tree structured transfer function obtained from Bond Graph. This research work combines bond graphs for model representation with Genetic programming for exploring different ideas on design space tree structured transfer function result from replacing typical bond graph element with their impedance equivalent specifying impedance lows for Bond Graph multiport. This tree structured form thus obtained from Bond Graph is applied for generating the Genetic Tree. Application studies will identify key issues and importance for advancing this approach towards becoming on effective and efficient design tool for synthesizing design for Electrical system. In the first phase, the system is modeled using Bond Graph technique. Its system response and transfer function with conventional and Bond Graph method is analyzed and then a approach towards model order reduction is observed. The suggested algorithm and other known modern model order reduction techniques are applied to a 11th order high pass filter [1], with different approach. The model order reduction technique developed in this paper has least reduction errors and secondly the final model retains structural information. The system response and the stability analysis of the system transfer function taken by conventional and by Bond Graph method is compared and

  13. Stream Flow Prediction by Remote Sensing and Genetic Programming

    Science.gov (United States)

    Chang, Ni-Bin

    2009-01-01

    A genetic programming (GP)-based, nonlinear modeling structure relates soil moisture with synthetic-aperture-radar (SAR) images to present representative soil moisture estimates at the watershed scale. Surface soil moisture measurement is difficult to obtain over a large area due to a variety of soil permeability values and soil textures. Point measurements can be used on a small-scale area, but it is impossible to acquire such information effectively in large-scale watersheds. This model exhibits the capacity to assimilate SAR images and relevant geoenvironmental parameters to measure soil moisture.

  14. Genetic programming-based chaotic time series modeling

    Institute of Scientific and Technical Information of China (English)

    张伟; 吴智铭; 杨根科

    2004-01-01

    This paper proposes a Genetic Programming-Based Modeling(GPM)algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space,and the Particle Swarm Optimization(PSO)algorithm is used for Nonlinear Parameter Estimation(NPE)of dynamic model structures. In addition,GPM integrates the results of Nonlinear Time Series Analysis(NTSA)to adjust the parameters and takes them as the criteria of established models.Experiments showed the effectiveness of such improvements on chaotic time series modeling.

  15. An analytical approach to the implementation of genetically modified crops

    DEFF Research Database (Denmark)

    Borch, K.; Rasmussen, B.

    2000-01-01

    Public scepticism towards genetically modified (GM) crops is increasing. To address this, the risks and benefits of GM crops must be examined across scientific disciplines, and be discussed with the authorities, the agricultural industry and the consumers. In a feasibility study we have...... systematically analysed the challenges of the development and marketing of GM crops in Europe. A life-cycle inventory was used together with established technology foresight techniques in an interdisciplinary and empirical framework. The approach taken in this study established a dialogue between stakeholders...... and provided a framework for discussions about the future direction of GM crops....

  16. A reverse genetics approach to study feline infectious peritonitis.

    Science.gov (United States)

    Tekes, Gergely; Spies, Danica; Bank-Wolf, Barbara; Thiel, Volker; Thiel, Heinz-Jürgen

    2012-06-01

    Feline infectious peritonitis (FIP) is a lethal immunopathological disease caused by feline coronaviruses (FCoVs). Here, we describe a reverse genetics approach to study FIP by assessing the pathogenicity of recombinant type I and type II and chimeric type I/type II FCoVs. All recombinant FCoVs established productive infection in cats, and recombinant type II FCoV (strain 79-1146) induced FIP. Virus sequence analyses from FIP-diseased cats revealed that the 3c gene stop codon of strain 79-1146 has changed to restore a full-length open reading frame (ORF).

  17. Combining classifiers generated by multi-gene genetic programming for protein fold recognition using genetic algorithm.

    Science.gov (United States)

    Bardsiri, Mahshid Khatibi; Eftekhari, Mahdi; Mousavi, Reza

    2015-01-01

    In this study the problem of protein fold recognition, that is a classification task, is solved via a hybrid of evolutionary algorithms namely multi-gene Genetic Programming (GP) and Genetic Algorithm (GA). Our proposed method consists of two main stages and is performed on three datasets taken from the literature. Each dataset contains different feature groups and classes. In the first step, multi-gene GP is used for producing binary classifiers based on various feature groups for each class. Then, different classifiers obtained for each class are combined via weighted voting so that the weights are determined through GA. At the end of the first step, there is a separate binary classifier for each class. In the second stage, the obtained binary classifiers are combined via GA weighting in order to generate the overall classifier. The final obtained classifier is superior to the previous works found in the literature in terms of classification accuracy.

  18. Discovering Fuzzy Censored Classification Rules (Fccrs: A Genetic Algorithm Approach

    Directory of Open Access Journals (Sweden)

    Renu Bala

    2012-08-01

    Full Text Available Classification Rules (CRs are often discovered in the form of ‘If-Then’ Production Rules (PRs. PRs, beinghigh level symbolic rules, are comprehensible and easy to implement. However, they are not capable ofdealing with cognitive uncertainties like vagueness and ambiguity imperative to real word decision makingsituations. Fuzzy Classification Rules (FCRs based on fuzzy logic provide a framework for a flexiblehuman like reasoning involving linguistic variables. Moreover, a classification system consisting of simple‘If-Then’ rules is not competent in handling exceptional circumstances. In this paper, we propose aGenetic Algorithm approach to discover Fuzzy Censored Classification Rules (FCCRs. A FCCR is aFuzzy Classification Rule (FCRs augmented with censors. Here, censors are exceptional conditions inwhich the behaviour of a rule gets modified. The proposed algorithm works in two phases. In the firstphase, the Genetic Algorithm discovers Fuzzy Classification Rules. Subsequently, these FuzzyClassification Rules are mutated to produce FCCRs in the second phase. The appropriate encodingscheme, fitness function and genetic operators are designed for the discovery of FCCRs. The proposedapproach for discovering FCCRs is then illustrated on a synthetic dataset.

  19. A Knowledge—Based Approach to Program Synthesis from Examples

    Institute of Scientific and Technical Information of China (English)

    朱鸿; 金凌紫

    1991-01-01

    This paper proposes an approach to synthesize functional programs of Backus' FP system[1,2] from input/output instances.Based on a theory of orthogonal expansion of programs3[,4],the task of program synthesis is expressed in program equations,and fulfilled by solving them according to the knowledge about the equivalence between programs.Some general knowledge of solving program equations with a number of examples are given in the paper.

  20. Controlling Risk Exposure in Periodic Environments: A Genetic Algorithm Approach

    CERN Document Server

    Navarro, Emeterio

    2007-01-01

    In this paper, we compare the performance of different agent's investment strategies in an investment scenario with periodic returns and different types and levels of noise. We consider an investment model, where an agent decides the percentage of budget to risk at each time step. Afterwards, agent's investment is evaluated in the market via a return on investment (RoI), which we assume is a stochastic process with unknown periodicities and different levels of noise. To control the risk exposure, we investigate approaches based on: technical analysis (Moving Least Squares, MLS), and evolutionary computation (Genetic Algorithms, GA). In our comparison, we also consider two reference strategies for zero-knowledge and complete-knowledge behaviors, respectively. In our approach, the performance of a strategy corresponds to the average budget that can be obtained with this strategy over a certain number of time steps. To this end, we perform some computer experiments, where for each strategy the budget obtained af...

  1. Which Introductory Programming Approach Is Most Suitable for Students: Procedural or Visual Programming?

    Science.gov (United States)

    Eid, Chaker; Millham, Richard

    2012-01-01

    In this paper, we discuss the visual programming approach to teaching introductory programming courses and then compare this approach with that of procedural programming. The involved cognitive levels of students, as beginning students are introduced to different types of programming concepts, are correlated to the learning processes of…

  2. Identifying human disease genes: advances in molecular genetics and computational approaches.

    Science.gov (United States)

    Bakhtiar, S M; Ali, A; Baig, S M; Barh, D; Miyoshi, A; Azevedo, V

    2014-07-04

    The human genome project is one of the significant achievements that have provided detailed insight into our genetic legacy. During the last two decades, biomedical investigations have gathered a considerable body of evidence by detecting more than 2000 disease genes. Despite the imperative advances in the genetic understanding of various diseases, the pathogenesis of many others remains obscure. With recent advances, the laborious methodologies used to identify DNA variations are replaced by direct sequencing of genomic DNA to detect genetic changes. The ability to perform such studies depends equally on the development of high-throughput and economical genotyping methods. Currently, basically for every disease whose origen is still unknown, genetic approaches are available which could be pedigree-dependent or -independent with the capacity to elucidate fundamental disease mechanisms. Computer algorithms and programs for linkage analysis have formed the foundation for many disease gene detection projects, similarly databases of clinical findings have been widely used to support diagnostic decisions in dysmorphology and general human disease. For every disease type, genome sequence variations, particularly single nucleotide polymorphisms are mapped by comparing the genetic makeup of case and control groups. Methods that predict the effects of polymorphisms on protein stability are useful for the identification of possible disease associations, whereas structural effects can be assessed using methods to predict stability changes in proteins using sequence and/or structural information.

  3. A Dynamic Programming Approach to Adaptive Fractionation

    CERN Document Server

    Ramakrishnan, Jagdish; Bortfeld, Thomas; Tsitsiklis, John

    2011-01-01

    We formulate a previously introduced adaptive fractionation problem in a dynamic programming (DP) framework and explore various solution techniques. The two messages of this paper are: (i) the DP model is a useful framework for studying adaptive radiation therapy, particularly adaptive fractionation, and (ii) there is a potential for substantial decrease in dose to the primary organ-at-risk (OAR), or equivalently increase in tumor escalation, when using an adaptive fraction size. The essence of adaptive fractionation is to increase the fraction size when observing a "favorable" anatomy or when the tumor and OAR are far apart and to decrease the fraction size when they are close together. Given that a fixed prescribed dose must be delivered to the tumor over the course of the treatment, such an approach results in a lower cumulative dose to the OAR when compared to that resulting from standard fractionation. We first establish a benchmark by using the DP algorithm to solve the problem exactly. In this case, we...

  4. Applying new genetic approaches to improve quality of population assessment of green and loggerhead turtles

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — As the NOAA-Fisheries? National Sea Turtle Genetics Lab, the SWFSC Marine Turtle Genetics Program has the lead responsibility for generating, analyzing and...

  5. Modeling the Isentropic Head Value of Centrifugal Gas Compressor using Genetic Programming

    Directory of Open Access Journals (Sweden)

    Safiyullah Ferozkhan

    2016-01-01

    Full Text Available Gas compressor performance is vital in oil and gas industry because of the equipment criticality which requires continuous operations. Plant operators often face difficulties in predicting appropriate time for maintenance and would usually rely on time based predictive maintenance intervals as recommended by original equipment manufacturer (OEM. The objective of this work is to develop the computational model to find the isentropic head value using genetic programming. The isentropic head value is calculated from the OEM performance chart. Inlet mass flow rate and speed of the compressor are taken as the input value. The obtained results from the GP computational models show good agreement with experimental and target data with the average prediction error of 1.318%. The genetic programming computational model will assist machinery engineers to quantify performance deterioration of gas compressor and the results from this study will be then utilized to estimate future maintenance requirements based on the historical data. In general, this genetic programming modelling provides a powerful solution for gas compressor operators to realize predictive maintenance approach in their operations.

  6. A novel formal approach to program slicing

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Program slicing is a well-known program analysis technique that extracts the elements of a program related to a particular computation. The current slicing methods, however, are singular (mainly based on a program or system dependence graph), and lack good reusability and flexibility. In this paper, we present a novel formal method for program slicing, modular monadic program slicing, which abstracts the computation of program slicing as a slice monad transformer, and applies it to semantic descriptions of the program analyzed in a modular way, forming the corresponding monadic slicing algorithms. The modular abstraction mechanism allows our slicing method to possess excellent modularity and language-flexibility properties. We also give the related axioms of our slice monad transformer, the proof of the correctness and the implementation of monadic slicing algorithms. We reveal the relations of our algorithms and graph-reachable slicing algorithms.

  7. Cognitive agent programming : A semantic approach

    NARCIS (Netherlands)

    Riemsdijk, M.B. van

    2006-01-01

    In this thesis we are concerned with the design and investigation of dedicated programming languages for programming agents. We focus in particular on programming languages for rational agents, i.e., flexibly behaving computing entities that are able to make "good" decisions about what to do. An imp

  8. Exponential distribution-based genetic algorithm for solving mixed-integer bilevel programming problems

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Two classes of mixed-integer nonlinear bilevel programming problems are discussed. One is that the follower's functions are separable with respect to the follower's variables, and the other is that the follower's functions are convex if the follower's variables are not restricted to integers. A genetic algorithm based on an exponential distribution is proposed for the aforementioned problems. First, for each fixed leader's variable x, it is proved that the optimal solution y of the follower's mixed-integer programming can be obtained by solving associated relaxed problems, and according to the convexity of the functions involved, a simplified branch and bound approach is given to solve the follower's programming for the second class of problems. Furthermore, based on an exponential distribution with a parameter A, a new crossover operator is designed in which the best individuals are used to generate better offspring of crossover. The simulation results illustrate that the proposed algorithm is efficient and robust.

  9. Genetic counseling services and development of training programs in Malaysia.

    Science.gov (United States)

    Lee, Juliana Mei-Har; Thong, Meow-Keong

    2013-12-01

    Genetic counseling service is urgently required in developing countries. In Malaysia, the first medical genetic service was introduced in 1994 at one of the main teaching hospitals in Kuala Lumpur. Two decades later, the medical genetic services have improved with the availability of genetic counseling, genetic testing and diagnosis, for both paediatric conditions and adult-onset inherited conditions, at four main centers of medical genetic services in Malaysia. Prenatal diagnosis services and assisted reproductive technologies are available at tertiary centres and private medical facilities. Positive developments include governmental recognition of Clinical Genetics as a subspecialty, increased funding for genetics services, development of medical ethics guidelines, and establishment of support groups. However, the country lacked qualified genetic counselors. Proposals were presented to policy-makers to develop genetic counseling courses. Challenges encountered included limited resources and public awareness, ethical dilemmas such as religious and social issues and inadequate genetic health professionals especially genetic counselors.

  10. Learning to solve planning problems efficiently by means of genetic programming.

    Science.gov (United States)

    Aler, R; Borrajo, D; Isasi, P

    2001-01-01

    Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming (GP). There have been recent attempts to apply GP to planning that fit two approaches: (a) using GP to search in plan space or (b) to evolve a planner. In this article, we propose to evolve only the heuristics to make a particular planner more efficient. This approach is more feasible than (b) because it does not have to build a planner from scratch but can take advantage of already existing planning systems. It is also more efficient than (a) because once the heuristics have been evolved, they can be used to solve a whole class of different planning problems in a planning domain, instead of running GP for every new planning problem. Empirical results show that our approach (EvoCK) is able to evolve heuristics in two planning domains (the blocks world and the logistics domain) that improve PRODIGY4.0 performance. Additionally, we experiment with a new genetic operator --Instance-Based Crossover--that is able to use traces of the base planner as raw genetic material to be injected into the evolving population.

  11. Practice-based competencies for accreditation of and training in graduate programs in genetic counseling.

    Science.gov (United States)

    Fine, B A; Baker, D L; Fiddler, M B

    1996-09-01

    In January 1996, the American Board of Genetic Counseling (ABGC) adopted 27 practice-based competencies as a standard for assessing the training of graduate students in genetic counseling. These competencies were identified and refined through a collective, narrative process that took place from January through November 1994, and included directors of graduate programs in genetic counseling, ABGC board members and expert consultants. These competencies now form the basis of the document "Requirements for Graduate Programs in Genetic Counseling Seeking Accreditation by the American Board of Genetic Counseling" (American Board of Genetic Counseling, 1996). The competencies are organized into four domains and are presented and discussed in this article.

  12. Concept of automatic programming of NC machine for metal plate cutting by genetic algorithm method

    Directory of Open Access Journals (Sweden)

    B. Vaupotic

    2005-12-01

    Full Text Available Purpose: In this paper the concept of automatic programs of the NC machine for metal plate cutting by genetic algorithm method has been presented.Design/methodology/approach: The paper was limited to automatic creation of NC programs for two-dimensional cutting of material by means of adaptive heuristic search algorithms.Findings: Automatic creation of NC programs in laser cutting of materials combines the CAD concepts, the recognition of features and creation and optimization of NC programs. The proposed intelligent system is capable to recognize automatically the nesting of products in the layout, to determine the incisions and sequences of cuts forming the laid out products. Position of incisions is determined at the relevant places on the cut. The system is capable to find the shortest path between individual cuts and to record the NC program.Research limitations/implications: It would be appropriate to orient future researches towards conceiving an improved system for three-dimensional cutting with optional determination of positions of incisions, with the capability to sense collisions and with optimization of the speed and acceleration during cutting.Practical implications: The proposed system assures automatic preparation of NC program without NC programer.Originality/value: The proposed concept shows a high degree of universality, efficiency and reliability and it can be simply adapted to other NC-machines.

  13. Integer programming model for optimizing bus timetable using genetic algorithm

    Science.gov (United States)

    Wihartiko, F. D.; Buono, A.; Silalahi, B. P.

    2017-01-01

    Bus timetable gave an information for passengers to ensure the availability of bus services. Timetable optimal condition happened when bus trips frequency could adapt and suit with passenger demand. In the peak time, the number of bus trips would be larger than the off-peak time. If the number of bus trips were more frequent than the optimal condition, it would make a high operating cost for bus operator. Conversely, if the number of trip was less than optimal condition, it would make a bad quality service for passengers. In this paper, the bus timetabling problem would be solved by integer programming model with modified genetic algorithm. Modification was placed in the chromosomes design, initial population recovery technique, chromosomes reconstruction and chromosomes extermination on specific generation. The result of this model gave the optimal solution with accuracy 99.1%.

  14. A Comparison of Genetic Programming Variants for Hyper-Heuristics

    Energy Technology Data Exchange (ETDEWEB)

    Harris, Sean [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-03-01

    Modern society is faced with ever more complex problems, many of which can be formulated as generate-and-test optimization problems. General-purpose optimization algorithms are not well suited for real-world scenarios where many instances of the same problem class need to be repeatedly and efficiently solved, such as routing vehicles over highways with constantly changing traffic flows, because they are not targeted to a particular scenario. Hyper-heuristics automate the design of algorithms to create a custom algorithm for a particular scenario. Hyper-heuristics typically employ Genetic Programming (GP) and this project has investigated the relationship between the choice of GP and performance in Hyper-heuristics. Results are presented demonstrating the existence of problems for which there is a statistically significant performance differential between the use of different types of GP.

  15. Forecasting tourist arrivals to balearic islands using genetic programming

    Directory of Open Access Journals (Sweden)

    Rosselló-Nadal, Jaume

    2007-01-01

    Full Text Available Traditionally, univariate time-series models have largely dominated forecasting for international tourism demand. In this paper, the ability of a Genetic Program (GP to predict monthly tourist arrivals from UK and Germany to Balearic Islands (Spain is explored. GP has already been employed satisfactorily in different scientific areas, including economics. The technique shows different advantages regarding to other forecasting methods. Firstly, it does not assume a priori a rigid functional form of the model. Secondly, it is more robust and easy-to-use than other non-parametric methods. Finally, it provides explicitly a mathematical equation which allows a simple ad hoc interpretation of the results. Comparing the performance of the proposed technique against other method commonly used in tourism forecasting (no-change model, Moving Average and ARIMA, the empirical results reveal that GP can be a valuable tool in this field.

  16. Comprehensive bidding strategies with genetic programming/finite state automata

    Energy Technology Data Exchange (ETDEWEB)

    Richter, C.W. Jr.; Sheble, G.B.; Ashlock, D.

    1999-11-01

    This research is an extension of the authors' previous work in double auctions aimed at developing bidding strategies for electric utilities which trade electricity competitively. The improvements detailed in this paper come from using data structures which combine genetic programming and finite state automata termed GP-Automata. The strategies developed by the method described here are adaptive--reacting to inputs--whereas the previously developed strategies were only suitable in the particular scenario for which they had been designed. The strategies encoded in the GP-Automata are tested in an auction simulator. The simulator pits them against other distribution companies (distcos) and generation companies (gencos), buying and selling power via double auctions implemented in regional commodity exchanges. The GP-Automata are evolved with a genetic algorithm so that they possess certain characteristics. In addition to designing successful bidding strategies (whose usage would result in higher profits) the resulting strategies can also be designed to imitate certain types of trading behaviors. The resulting strategies can be implemented directly in on-line trading, or can be used as realistic competitors in an off-line trading simulator.

  17. Accurate construction of consensus genetic maps via integer linear programming.

    Science.gov (United States)

    Wu, Yonghui; Close, Timothy J; Lonardi, Stefano

    2011-01-01

    We study the problem of merging genetic maps, when the individual genetic maps are given as directed acyclic graphs. The computational problem is to build a consensus map, which is a directed graph that includes and is consistent with all (or, the vast majority of) the markers in the input maps. However, when markers in the individual maps have ordering conflicts, the resulting consensus map will contain cycles. Here, we formulate the problem of resolving cycles in the context of a parsimonious paradigm that takes into account two types of errors that may be present in the input maps, namely, local reshuffles and global displacements. The resulting combinatorial optimization problem is, in turn, expressed as an integer linear program. A fast approximation algorithm is proposed, and an additional speedup heuristic is developed. Our algorithms were implemented in a software tool named MERGEMAP which is freely available for academic use. An extensive set of experiments shows that MERGEMAP consistently outperforms JOINMAP, which is the most popular tool currently available for this task, both in terms of accuracy and running time. MERGEMAP is available for download at http://www.cs.ucr.edu/~yonghui/mgmap.html.

  18. A Boolean Approach to Interactive Program Planning

    OpenAIRE

    George Abonyi; Nigel Howard

    1980-01-01

    Program planning refers to the process of selecting an integrated set of projects for implementation. Ultimately, decisions tend to be "political" in the sense that they involve compromises and tradeoffs among different interests, though the arguments may be couched in technical terms. Program planning may be structured as a multi-stage process. One stage involves the assessment of various alternatives for the project components of the program. A second stage then involves the selection of th...

  19. Using a Hybrid Approach to Facilitate Learning Introductory Programming

    Science.gov (United States)

    Cakiroglu, Unal

    2013-01-01

    In order to facilitate students' understanding in introductory programming courses, different types of teaching approaches were conducted. In this study, a hybrid approach including comment first coding (CFC), analogy and template approaches were used. The goal was to investigate the effect of such a hybrid approach on students' understanding in…

  20. Identifying genetic risk variants for coronary heart disease in familial hypercholesterolemia: an extreme genetics approach

    Science.gov (United States)

    Versmissen, Jorie; Oosterveer, Daniëlla M; Yazdanpanah, Mojgan; Dehghan, Abbas; Hólm, Hilma; Erdman, Jeanette; Aulchenko, Yurii S; Thorleifsson, Gudmar; Schunkert, Heribert; Huijgen, Roeland; Vongpromek, Ranitha; Uitterlinden, André G; Defesche, Joep C; van Duijn, Cornelia M; Mulder, Monique; Dadd, Tony; Karlsson, Hróbjartur D; Ordovas, Jose; Kindt, Iris; Jarman, Amelia; Hofman, Albert; van Vark-van der Zee, Leonie; Blommesteijn-Touw, Adriana C; Kwekkeboom, Jaap; Liem, Anho H; van der Ouderaa, Frans J; Calandra, Sebastiano; Bertolini, Stefano; Averna, Maurizio; Langslet, Gisle; Ose, Leiv; Ros, Emilio; Almagro, Fátima; de Leeuw, Peter W; Civeira, Fernando; Masana, Luis; Pintó, Xavier; Simoons, Maarten L; Schinkel, Arend FL; Green, Martin R; Zwinderman, Aeilko H; Johnson, Keith J; Schaefer, Arne; Neil, Andrew; Witteman, Jacqueline CM; Humphries, Steve E; Kastelein, John JP; Sijbrands, Eric JG

    2015-01-01

    Mutations in the low-density lipoprotein receptor (LDLR) gene cause familial hypercholesterolemia (FH), a disorder characterized by coronary heart disease (CHD) at young age. We aimed to apply an extreme sampling method to enhance the statistical power to identify novel genetic risk variants for CHD in individuals with FH. We selected cases and controls with an extreme contrast in CHD risk from 17 000 FH patients from the Netherlands, whose functional LDLR mutation was unequivocally established. The genome-wide association (GWA) study was performed on 249 very young FH cases with CHD and 217 old FH controls without CHD (above 65 years for males and 70 years of age for females) using the Illumina HumanHap550K chip. In the next stage, two independent samples (one from the Netherlands and one from Italy, Norway, Spain, and the United Kingdom) of FH patients were used as replication samples. In the initial GWA analysis, we identified 29 independent single nucleotide polymorphisms (SNPs) with suggestive associations with premature CHD (P<1 × 10−4). We examined the association of these SNPs with CHD risk in the replication samples. After Bonferroni correction, none of the SNPs either replicated or reached genome-wide significance after combining the discovery and replication samples. Therefore, we conclude that the genetics of CHD risk in FH is complex and even applying an ‘extreme genetics' approach we did not identify new genetic risk variants. Most likely, this method is not as effective in leveraging effect size as anticipated, and may, therefore, not lead to significant gains in statistical power. PMID:24916650

  1. Reverse Pathway Genetic Approach Identifies Epistasis in Autism Spectrum Disorders

    Science.gov (United States)

    Traglia, Michela; Tsang, Kathryn; Bearden, Carrie E.; Rauen, Katherine A.

    2017-01-01

    Although gene-gene interaction, or epistasis, plays a large role in complex traits in model organisms, genome-wide by genome-wide searches for two-way interaction have limited power in human studies. We thus used knowledge of a biological pathway in order to identify a contribution of epistasis to autism spectrum disorders (ASDs) in humans, a reverse-pathway genetic approach. Based on previous observation of increased ASD symptoms in Mendelian disorders of the Ras/MAPK pathway (RASopathies), we showed that common SNPs in RASopathy genes show enrichment for association signal in GWAS (P = 0.02). We then screened genome-wide for interactors with RASopathy gene SNPs and showed strong enrichment in ASD-affected individuals (P < 2.2 x 10−16), with a number of pairwise interactions meeting genome-wide criteria for significance. Finally, we utilized quantitative measures of ASD symptoms in RASopathy-affected individuals to perform modifier mapping via GWAS. One top region overlapped between these independent approaches, and we showed dysregulation of a gene in this region, GPR141, in a RASopathy neural cell line. We thus used orthogonal approaches to provide strong evidence for a contribution of epistasis to ASDs, confirm a role for the Ras/MAPK pathway in idiopathic ASDs, and to identify a convergent candidate gene that may interact with the Ras/MAPK pathway. PMID:28076348

  2. Genetic Approach for the Fast Discovery of Phenazine Producing Bacteria

    Directory of Open Access Journals (Sweden)

    Johannes F. Imhoff

    2011-05-01

    Full Text Available A fast and efficient approach was established to identify bacteria possessing the potential to biosynthesize phenazines, which are of special interest regarding their antimicrobial activities. Sequences of phzE genes, which are part of the phenazine biosynthetic pathway, were used to design one universal primer system and to analyze the ability of bacteria to produce phenazine. Diverse bacteria from different marine habitats and belonging to six major phylogenetic lines were investigated. Bacteria exhibiting phzE gene fragments affiliated to Firmicutes, Alpha- and Gammaproteobacteria, and Actinobacteria. Thus, these are the first primers for amplifying gene fragments from Firmicutes and Alphaproteobacteria. The genetic potential for phenazine production was shown for four type strains belonging to the genera Streptomyces and Pseudomonas as well as for 13 environmental isolates from marine habitats. For the first time, the genetic ability of phenazine biosynthesis was verified by analyzing the metabolite pattern of all PCR-positive strains via HPLC-UV/MS. Phenazine production was demonstrated for the type strains known to produce endophenazines, 2-hydroxy-phenazine, phenazine-1-carboxylic acid, phenazine-1,6-dicarboxylic acid, and chlororaphin as well as for members of marine Actinobacteria. Interestingly, a number of unidentified phenazines possibly represent new phenazine structures.

  3. Estimating Sampling Selection Bias in Human Genetics: A Phenomenological Approach

    Science.gov (United States)

    Risso, Davide; Taglioli, Luca; De Iasio, Sergio; Gueresi, Paola; Alfani, Guido; Nelli, Sergio; Rossi, Paolo; Paoli, Giorgio; Tofanelli, Sergio

    2015-01-01

    This research is the first empirical attempt to calculate the various components of the hidden bias associated with the sampling strategies routinely-used in human genetics, with special reference to surname-based strategies. We reconstructed surname distributions of 26 Italian communities with different demographic features across the last six centuries (years 1447–2001). The degree of overlapping between "reference founding core" distributions and the distributions obtained from sampling the present day communities by probabilistic and selective methods was quantified under different conditions and models. When taking into account only one individual per surname (low kinship model), the average discrepancy was 59.5%, with a peak of 84% by random sampling. When multiple individuals per surname were considered (high kinship model), the discrepancy decreased by 8–30% at the cost of a larger variance. Criteria aimed at maximizing locally-spread patrilineages and long-term residency appeared to be affected by recent gene flows much more than expected. Selection of the more frequent family names following low kinship criteria proved to be a suitable approach only for historically stable communities. In any other case true random sampling, despite its high variance, did not return more biased estimates than other selective methods. Our results indicate that the sampling of individuals bearing historically documented surnames (founders' method) should be applied, especially when studying the male-specific genome, to prevent an over-stratification of ancient and recent genetic components that heavily biases inferences and statistics. PMID:26452043

  4. A Full Bayesian Approach for Boolean Genetic Network Inference

    Science.gov (United States)

    Han, Shengtong; Wong, Raymond K. W.; Lee, Thomas C. M.; Shen, Linghao; Li, Shuo-Yen R.; Fan, Xiaodan

    2014-01-01

    Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data. PMID:25551820

  5. A full bayesian approach for boolean genetic network inference.

    Directory of Open Access Journals (Sweden)

    Shengtong Han

    Full Text Available Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data.

  6. Human Metabolic Enzymes Deficiency: A Genetic Mutation Based Approach

    Science.gov (United States)

    Chaturvedi, Swati; Singh, Ashok K.; Maity, Siddhartha; Sarkar, Srimanta

    2016-01-01

    One of the extreme challenges in biology is to ameliorate the understanding of the mechanisms which emphasize metabolic enzyme deficiency (MED) and how these pretend to have influence on human health. However, it has been manifested that MED could be either inherited as inborn error of metabolism (IEM) or acquired, which carries a high risk of interrupted biochemical reactions. Enzyme deficiency results in accumulation of toxic compounds that may disrupt normal organ functions and cause failure in producing crucial biological compounds and other intermediates. The MED related disorders cover widespread clinical presentations and can involve almost any organ system. To sum up the causal factors of almost all the MED-associated disorders, we decided to embark on a less traveled but nonetheless relevant direction, by focusing our attention on associated gene family products, regulation of their expression, genetic mutation, and mutation types. In addition, the review also outlines the clinical presentations as well as diagnostic and therapeutic approaches. PMID:27051561

  7. A genetic epidemiology approach to cyber-security.

    Science.gov (United States)

    Gil, Santiago; Kott, Alexander; Barabási, Albert-László

    2014-07-16

    While much attention has been paid to the vulnerability of computer networks to node and link failure, there is limited systematic understanding of the factors that determine the likelihood that a node (computer) is compromised. We therefore collect threat log data in a university network to study the patterns of threat activity for individual hosts. We relate this information to the properties of each host as observed through network-wide scans, establishing associations between the network services a host is running and the kinds of threats to which it is susceptible. We propose a methodology to associate services to threats inspired by the tools used in genetics to identify statistical associations between mutations and diseases. The proposed approach allows us to determine probabilities of infection directly from observation, offering an automated high-throughput strategy to develop comprehensive metrics for cyber-security.

  8. A Deductive Approach to Programming Methodology.

    Science.gov (United States)

    1981-12-01

    of the Workshop on Logics of Programs (Yorktown-Heights, NY), Springer- Verlag Lecture Notes in Computer Science , 1981. [3] Z. Manna, "Verification of...Specifications" Proceedings of the Workshop on Logics of Programs (Yorktown-Heights), NY, Springer-Verlag Lecture Notes in Computer Science , 1981. (5] Z

  9. A Novel Approach for Collaborative Pair Programming

    Science.gov (United States)

    Goel, Sanjay; Kathuria, Vanshi

    2010-01-01

    The majority of an engineer's time in the software industry is spent working with other programmers. Agile methods of software development like eXtreme Programming strongly rely upon practices like daily meetings and pair programming. Hence, the need to learn the skill of working collaboratively is of primary importance for software developers.…

  10. Structural health monitoring feature design by genetic programming

    Science.gov (United States)

    Harvey, Dustin Y.; Todd, Michael D.

    2014-09-01

    Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems.

  11. A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction

    Science.gov (United States)

    Danandeh Mehr, Ali; Kahya, Ercan

    2017-06-01

    Genetic programming (GP) is able to systematically explore alternative model structures of different accuracy and complexity from observed input and output data. The effectiveness of GP in hydrological system identification has been recognized in recent studies. However, selecting a parsimonious (accurate and simple) model from such alternatives still remains a question. This paper proposes a Pareto-optimal moving average multigene genetic programming (MA-MGGP) approach to develop a parsimonious model for single-station streamflow prediction. The three main components of the approach that take us from observed data to a validated model are: (1) data pre-processing, (2) system identification and (3) system simplification. The data pre-processing ingredient uses a simple moving average filter to diminish the lagged prediction effect of stand-alone data-driven models. The multigene ingredient of the model tends to identify the underlying nonlinear system with expressions simpler than classical monolithic GP and, eventually simplification component exploits Pareto front plot to select a parsimonious model through an interactive complexity-efficiency trade-off. The approach was tested using the daily streamflow records from a station on Senoz Stream, Turkey. Comparing to the efficiency results of stand-alone GP, MGGP, and conventional multi linear regression prediction models as benchmarks, the proposed Pareto-optimal MA-MGGP model put forward a parsimonious solution, which has a noteworthy importance of being applied in practice. In addition, the approach allows the user to enter human insight into the problem to examine evolved models and pick the best performing programs out for further analysis.

  12. Mini core germplasm collections for infusing genetic diversity in plant breeding programs

    Directory of Open Access Journals (Sweden)

    Hari D Upadhyaya*, Devvart Yadav, Naresh Dronavalli, CLL Gowda, and Sube Singh

    2010-07-01

    Full Text Available Plant genetic resources are essential components to meet future food security needs of world. Crop germplasm diversitycontributes to developing improved crop cultivars aimed at increasing crop productivity. The large size of germplasmcollections, coupled with unavailability of detailed data and information, has resulted in low use (<1% of germplasmleading to a narrow genetic base in many crops. The miniaturization of crop collections with almost full representation ofgenetic diversity in the form of mini core (~1% of the entire collection approach is an effective methodology to enrichand enhance crop improvement programs. The concept and process of developing mini core at The International CropsResearch Institute for the Semi-Arid Tropics (ICRISAT has been recognized worldwide as an “International PublicGood” (IPG. The mini core provides a means for accessing the larger collections for further exploration and also helps inproper assessment of genetic diversity and population structure and for association mapping and targeted gene mining.Use of mini core approach will lead to greater utilization of diverse germplasm for developing broad-based cultivars,especially in the context of climate change. Many national programs have shown immense interest in evaluating minicore as reflected by the supply of 114 sets of mini core of chickpea, groundnut, pigeonpea, sorghum, pearl millet, foxtailmillet and finger millet to researchers in 14 countries. Scientists have been able to identify new and diverse sources ofvariation for morpho-agronomic, quality, biotic, and abiotic stress resistance traits in various crops. The molecularcharacterization of the mini core will further enhance its use in plant breeding programs.

  13. Next-generation sequencing approaches in genetic rodent model systems to study functional effects of human genetic variation

    NARCIS (Netherlands)

    Guryev, Victor; Cuppen, Edwin

    2009-01-01

    Rapid advances in DNA sequencing improve existing techniques and enable new approaches in genetics and functional genomics, bringing about unprecedented coverage, resolution and sensitivity. Enhanced toolsets can facilitate the untangling of connections between genomic variation, environmental

  14. Next-generation sequencing approaches in genetic rodent model systems to study functional effects of human genetic variation.

    NARCIS (Netherlands)

    Guryev, V.; Cuppen, E.

    2009-01-01

    Rapid advances in DNA sequencing improve existing techniques and enable new approaches in genetics and functional genomics, bringing about unprecedented coverage, resolution and sensitivity. Enhanced toolsets can facilitate the untangling of connections between genomic variation, environmental facto

  15. Next-generation sequencing approaches in genetic rodent model systems to study functional effects of human genetic variation

    NARCIS (Netherlands)

    Guryev, Victor; Cuppen, Edwin

    2009-01-01

    Rapid advances in DNA sequencing improve existing techniques and enable new approaches in genetics and functional genomics, bringing about unprecedented coverage, resolution and sensitivity. Enhanced toolsets can facilitate the untangling of connections between genomic variation, environmental facto

  16. Graphical approach to evaluate genetic estimates of calf survival.

    Science.gov (United States)

    Schlesser, H N; Shanks, R D; Berger, P J; Healey, M H

    2009-05-01

    Genetic variation and resemblance among relatives are fundamentals of quantitative genetics. Our purpose was to identify bulls with a bimodal pattern of inheritance in the quest for new discoveries about the inheritance of calf survival. A bimodal pattern of inheritance for calf survival was identified in sons of Holstein bulls. A bimodal pattern of inheritance indicates 2 groups of sons resulting from an allele effect, a grandsire effect, or some other common factor. Different combinations (AA, Aa, aa) of 2 alleles at a locus cause varying phenotypes to be expressed. Bulls that are heterozygous for loci affecting reproductive performance may have a bimodal pattern of inheritance if the difference in effect of the 2 alleles is large. If the bimodal pattern is caused by an allele effect, then molecular markers can be identified for use in marker-assisted selection breeding programs. Data on predicted transmitting ability for perinatal survival for the first parity of 8,678 sons of 599 sires were collected from 1984 through 1997 from the National Association of Animal Breeders calving ease database, which included 7 Midwestern states. Sixteen bulls were identified with a potential bimodal pattern of inheritance because they had 2 distinct groups of sons. The 2 groups of sons were separated by calculating the coefficient of variation for each possible combination of sons; the combination that gave the smallest coefficient of variation difference between the 2 groups was considered the correct distribution of the sons into those groups. Bulls with a bimodal distribution were analyzed to determine the distribution of the grandsons among the maternal grandsires (MGS) of the 2 groups of the bimodal distribution. The bimodal distribution may be a result of heterozygous sires or MGS that are homozygous for low or high survival. If the bimodal distribution is caused by a MGS effect, then marker-assisted selection can still be used by evaluating the MGS instead of the sires.

  17. Solving bilevel programs with the KKT-approach

    OpenAIRE

    Bouza Allende, Gemayqzel; Still, Georg

    2013-01-01

    Bilevel programs (BL) form a special class of optimization problems. They appear in many models in economics, game theory and mathematical physics. BL programs show a more complicated structure than standard finite problems. We study the so-called KKT-approach for solving bilevel problems, where the lower level minimality condition is replaced by the KKT- or the FJ-condition. This leads to a special structured mathematical program with complementarity constraints. We analyze the KKT-approach ...

  18. An Object Oriented Approach to Semidefinite Programming

    OpenAIRE

    Ge, Yuzhen; Watson, Layne; Collins, Emmanuel

    1998-01-01

    An object-oriented design and implementation of a primal-dual algorithm for solving the semidefinite programming problem is presented. The advantages of applying the object-oriented methodology to numerical computations, in particular to an interior point algorithm for semidefinite programming, or for solving other types of linear matrix inequalities are discussed. One object-oriented design of the primal-dual algorithm and its implementation using C++ is presented. The performance of the ...

  19. Modeling the MagnetoencephaloGram (MEG) Of Epileptic Patients Using Genetic Programming and Minimizing the Derived Models Using Genetic Algorithms

    Science.gov (United States)

    Theofilatos, Konstantinos; Georgopoulos, Efstratios; Likothanassis, Spiridon

    2009-09-01

    In this paper, a variation of traditional Genetic Programming(GP) is used to model the MagnetoencephaloGram(MEG) of Epileptic Patients. This variation is Linear Genetic Programming(LGP). LGP is a particular subset of GP wherein computer programs in population are represented as a sequence of instructions from imperative programming language or machine language. The derived models from this method were simplified using genetic algorithms. The proposed method was used to model the MEG signal of epileptic patients using 6 different datasets. Each dataset uses different number of previous values of MEG to predict the next value. The models were tested in datasets different from the ones which were used to produce them and the results were very promising.

  20. Genetics in psychosomatic medicine : research designs and statistical approaches

    NARCIS (Netherlands)

    McCaffery, Jeanne M.; Snieder, Harold; Dong, Yanbin; de Geus, Eco

    2007-01-01

    It has become increasingly clear that genetic factors influence many of the behaviors and disease endpoints of interest to psychosomatic medicine researchers. There has been increasing interest in incorporating genetic variation markers into psychosomatic research. In this Statistical Corner article

  1. Productive Parallel Programming: The PCN Approach

    Directory of Open Access Journals (Sweden)

    Ian Foster

    1992-01-01

    Full Text Available We describe the PCN programming system, focusing on those features designed to improve the productivity of scientists and engineers using parallel supercomputers. These features include a simple notation for the concise specification of concurrent algorithms, the ability to incorporate existing Fortran and C code into parallel applications, facilities for reusing parallel program components, a portable toolkit that allows applications to be developed on a workstation or small parallel computer and run unchanged on supercomputers, and integrated debugging and performance analysis tools. We survey representative scientific applications and identify problem classes for which PCN has proved particularly useful.

  2. A cultural evolutionary programming approach to automatic analytical modeling of electrochemical phenomena through impedance spectroscopy

    CERN Document Server

    Arpaia, Pasquale

    2009-01-01

    An approach to automatic analytical modeling of electrochemical impedance spectroscopy data by evolutionary programming based on cultural algorithms is proposed. A solution-search strategy based on a cultural mechanism is exploited for defining the equivalent-circuit model automatically: information on search advance is transmitted to all potential solutions, rather than only to a small inheriting subset, such as in a traditional genetic approach. Moreover, with respect to the state of the art, also specific information related to constraints on the application physics knowledge is transferred. Experimental results of the proposed approach implementation in impedance spectroscopy for general-purpose electrochemical circuit analysis and for corrosion monitoring and diagnosing are presented.

  3. Population genetics analysis using R and the Geneland program

    DEFF Research Database (Denmark)

    Guillot, Gilles; Santos, Filipe; Estoup, Arnaud

    2011-01-01

    Geneland program documentation 2011 Program distributed under GNU license as an R package on the Comprehensive R Archive Network.......Geneland program documentation 2011 Program distributed under GNU license as an R package on the Comprehensive R Archive Network....

  4. Modeling of Drilling Forces Based on Twist Drill Point Angles Using Multigene Genetic Programming

    Directory of Open Access Journals (Sweden)

    Myong-Il Kim

    2016-01-01

    Full Text Available The mathematical model was developed for predicting the influence of the drill point angles on the cutting forces in drilling with the twist drills, which was used to optimize those angles for reducing drilling forces. The approach was based on multigene genetic programming, for the training data, the grinding tests of twist drill were firstly conducted for the different drill point angles in Biglide parallel machine, and then drilling tests were performed on carbon fiber reinforced plastics using the grinded drills. The effectiveness of the proposed approach was verified through comparing with published data. It was found that the proposed model agreed well with the experimental data and was useful for improving the performance of twist drill.

  5. Comparison of two approaches to dynamic programming

    NARCIS (Netherlands)

    Broek, van den Pim; Noppen, Joost

    2004-01-01

    Both in mathematics and in computer science Dynamic Programming is a well known concept. It is an algorithmic technique, which can be used to write efficient algorithms, based on the avoidance of multiple executions of identical subcomputations. Its definition in both disciplines is however quite di

  6. A Collaborative Approach to International Programs.

    Science.gov (United States)

    Godbey, Galen C.; Turlington, Barbara

    2002-01-01

    Discusses how collaboration among institutions of higher education in developing international programs and globalizing the curriculum is becoming an effective strategy in postsecondary education. Especially for small- and medium-sized institutions, collaboration can provide the scale and quality of resources needed to sustain cost-effective,…

  7. Flexible approaches for teaching computational genomics in a health information management program.

    Science.gov (United States)

    Zhou, Leming; Watzlaf, Valerie; Abdelhak, Mervat

    2013-01-01

    The astonishing improvement of high-throughput biotechnologies in recent years makes it possible to access a huge amount of genomic data. The association between genomic data and genetic disease has already been and will continue to be applied to personalized healthcare. Health information management (HIM) professionals are the ones who will handle personal genetic information and provide solid evidence to support physicians' diagnoses and personalized treatment strategies, and therefore they will need to have the knowledge and skills to process genomic data. In this paper, we describe flexible approaches for teaching a computational genomics course in the HIM program at the University of Pittsburgh. HIM programs at other universities may choose an appropriate approach to fit into their own curriculum.

  8. Genetic Programming Based Ensemble System for Microarray Data Classification

    Directory of Open Access Journals (Sweden)

    Kun-Hong Liu

    2015-01-01

    Full Text Available Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP based new ensemble system (named GPES, which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved.

  9. A Program Recognition and Auto-Testing Approach

    Directory of Open Access Journals (Sweden)

    Wen C. Pai

    2003-06-01

    Full Text Available The goals of the software testing are to assess and improve the quality of the software. An important problem in software testing is to determine whether a program has been tested enough with a testing criterion. To raise a technology to reconstruct the program structure and generating test data automatically will help software developers to improve software quality efficiently. Program recognition and transformation is a technology that can help maintainers to recover the programs' structure and consequently make software testing properly. In this paper, a methodology to follow the logic of a program and transform to the original program graph is proposed. An approach to derive testing paths automatically for a program to test every blocks of the program is provided. A real example is presented to illustrate and prove that the methodology is practicable. The proposed methodology allows developers to recover the programs' design and makes software maintenance properly.

  10. Electrochemical impedance spectroscopy of supercapacitors: A novel analysis approach using evolutionary programming

    Science.gov (United States)

    Oz, Alon; Hershkovitz, Shany; Tsur, Yoed

    2014-11-01

    In this contribution we present a novel approach to analyze impedance spectroscopy measurements of supercapacitors. Transforming the impedance data into frequency-dependent capacitance allows us to use Impedance Spectroscopy Genetic Programming (ISGP) in order to find the distribution function of relaxation times (DFRT) of the processes taking place in the tested device. Synthetic data was generated in order to demonstrate this technique and a model for supercapacitor ageing process has been obtained.

  11. Sediment Analysis Using a Structured Programming Approach

    Directory of Open Access Journals (Sweden)

    Daniela Arias-Madrid

    2012-12-01

    Full Text Available This paper presents an algorithm designed for the analysis of a sedimentary sample of unconsolidated material and seeks to identify very quickly the main features that occur in a sediment and thus classify them fast and efficiently. For this purpose, it requires that the weight of each particle size to be entered in the program and using the method of Moments, which is based on four equations representing the mean, standard deviation, skewness and kurtosis, is found the attributes of the sample in few seconds. With the program these calculations are performed in an effective and more accurately way, obtaining also the explanations of the results of the features such as grain size, sorting, symmetry and origin, which helps to improve the study of sediments and in general the study of sedimentary rocks.

  12. Genetic braid optimization: A heuristic approach to compute quasiparticle braids

    Science.gov (United States)

    McDonald, Ross B.; Katzgraber, Helmut G.

    2013-02-01

    In topologically protected quantum computation, quantum gates can be carried out by adiabatically braiding two-dimensional quasiparticles, reminiscent of entangled world lines. Bonesteel [Phys. Rev. Lett.10.1103/PhysRevLett.95.140503 95, 140503 (2005)], as well as Leijnse and Flensberg [Phys. Rev. B10.1103/PhysRevB.86.104511 86, 104511 (2012)], recently provided schemes for computing quantum gates from quasiparticle braids. Mathematically, the problem of executing a gate becomes that of finding a product of the generators (matrices) in that set that approximates the gate best, up to an error. To date, efficient methods to compute these gates only strive to optimize for accuracy. We explore the possibility of using a generic approach applicable to a variety of braiding problems based on evolutionary (genetic) algorithms. The method efficiently finds optimal braids while allowing the user to optimize for the relative utilities of accuracy and/or length. Furthermore, when optimizing for error only, the method can quickly produce efficient braids.

  13. An unbiased systems genetics approach to mapping genetic loci modulating susceptibility to severe streptococcal sepsis.

    Directory of Open Access Journals (Sweden)

    Nourtan F Abdeltawab

    2008-04-01

    Full Text Available Striking individual differences in severity of group A streptococcal (GAS sepsis have been noted, even among patients infected with the same bacterial strain. We had provided evidence that HLA class II allelic variation contributes significantly to differences in systemic disease severity by modulating host responses to streptococcal superantigens. Inasmuch as the bacteria produce additional virulence factors that participate in the pathogenesis of this complex disease, we sought to identify additional gene networks modulating GAS sepsis. Accordingly, we applied a systems genetics approach using a panel of advanced recombinant inbred mice. By analyzing disease phenotypes in the context of mice genotypes we identified a highly significant quantitative trait locus (QTL on Chromosome 2 between 22 and 34 Mb that strongly predicts disease severity, accounting for 25%-30% of variance. This QTL harbors several polymorphic genes known to regulate immune responses to bacterial infections. We evaluated candidate genes within this QTL using multiple parameters that included linkage, gene ontology, variation in gene expression, cocitation networks, and biological relevance, and identified interleukin1 alpha and prostaglandin E synthases pathways as key networks involved in modulating GAS sepsis severity. The association of GAS sepsis with multiple pathways underscores the complexity of traits modulating GAS sepsis and provides a powerful approach for analyzing interactive traits affecting outcomes of other infectious diseases.

  14. Optimal in silico target gene deletion through nonlinear programming for genetic engineering.

    Science.gov (United States)

    Hong, Chung-Chien; Song, Mingzhou

    2010-02-24

    Optimal selection of multiple regulatory genes, known as targets, for deletion to enhance or suppress the activities of downstream genes or metabolites is an important problem in genetic engineering. Such problems become more feasible to address in silico due to the availability of more realistic dynamical system models of gene regulatory and metabolic networks. The goal of the computational problem is to search for a subset of genes to knock out so that the activity of a downstream gene or a metabolite is optimized. Based on discrete dynamical system modeling of gene regulatory networks, an integer programming problem is formulated for the optimal in silico target gene deletion problem. In the first result, the integer programming problem is proved to be NP-hard and equivalent to a nonlinear programming problem. In the second result, a heuristic algorithm, called GKONP, is designed to approximate the optimal solution, involving an approach to prune insignificant terms in the objective function, and the parallel differential evolution algorithm. In the third result, the effectiveness of the GKONP algorithm is demonstrated by applying it to a discrete dynamical system model of the yeast pheromone pathways. The empirical accuracy and time efficiency are assessed in comparison to an optimal, but exhaustive search strategy. Although the in silico target gene deletion problem has enormous potential applications in genetic engineering, one must overcome the computational challenge due to its NP-hardness. The presented solution, which has been demonstrated to approximate the optimal solution in a practical amount of time, is among the few that address the computational challenge. In the experiment on the yeast pheromone pathways, the identified best subset of genes for deletion showed advantage over genes that were selected empirically. Once validated in vivo, the optimal target genes are expected to achieve higher genetic engineering effectiveness than a trial

  15. Optimal in silico target gene deletion through nonlinear programming for genetic engineering.

    Directory of Open Access Journals (Sweden)

    Chung-Chien Hong

    Full Text Available BACKGROUND: Optimal selection of multiple regulatory genes, known as targets, for deletion to enhance or suppress the activities of downstream genes or metabolites is an important problem in genetic engineering. Such problems become more feasible to address in silico due to the availability of more realistic dynamical system models of gene regulatory and metabolic networks. The goal of the computational problem is to search for a subset of genes to knock out so that the activity of a downstream gene or a metabolite is optimized. METHODOLOGY/PRINCIPAL FINDINGS: Based on discrete dynamical system modeling of gene regulatory networks, an integer programming problem is formulated for the optimal in silico target gene deletion problem. In the first result, the integer programming problem is proved to be NP-hard and equivalent to a nonlinear programming problem. In the second result, a heuristic algorithm, called GKONP, is designed to approximate the optimal solution, involving an approach to prune insignificant terms in the objective function, and the parallel differential evolution algorithm. In the third result, the effectiveness of the GKONP algorithm is demonstrated by applying it to a discrete dynamical system model of the yeast pheromone pathways. The empirical accuracy and time efficiency are assessed in comparison to an optimal, but exhaustive search strategy. SIGNIFICANCE: Although the in silico target gene deletion problem has enormous potential applications in genetic engineering, one must overcome the computational challenge due to its NP-hardness. The presented solution, which has been demonstrated to approximate the optimal solution in a practical amount of time, is among the few that address the computational challenge. In the experiment on the yeast pheromone pathways, the identified best subset of genes for deletion showed advantage over genes that were selected empirically. Once validated in vivo, the optimal target genes are

  16. Forward Genetic Approaches for Elucidation of Novel Regulators of Lyme Arthritis Severity

    Directory of Open Access Journals (Sweden)

    Kenneth K.C. Bramwell

    2014-06-01

    Full Text Available Patients experiencing natural infection with Borrelia burgdorferi display a spectrum of associated symptoms and severity, strongly implicating the impact of genetically determined host factors in the pathogenesis of Lyme disease. Herein, we provide a summary of the host genetic factors that have been demonstrated to influence the severity and chronicity of Lyme arthritis symptoms, and a review of the resources available, current progress, and added value of a forward genetic approach for identification of novel genetic regulators.

  17. A Dynamic Programming Approach to Constrained Portfolios

    DEFF Research Database (Denmark)

    Kraft, Holger; Steffensen, Mogens

    2013-01-01

    This paper studies constrained portfolio problems that may involve constraints on the probability or the expected size of a shortfall of wealth or consumption. Our first contribution is that we solve the problems by dynamic programming, which is in contrast to the existing literature that applies...... the martingale method. More precisely, we construct the non-separable value function by formalizing the optimal constrained terminal wealth to be a (conjectured) contingent claim on the optimal non-constrained terminal wealth. This is relevant by itself, but also opens up the opportunity to derive new solutions...

  18. A New Approach to Recursive Programs

    Science.gov (United States)

    1975-12-01

    condtions. The main part of this program is the functional f IF]: if «-a then y tlit F (F ««.y-l I .F (K-1 ty») , in which the symbol F is...involvec n the procese of proving propertiee of fixedpointei A function f , a domain 0 , and a deeired property Q Any one of these...holds. Thia method can be justified by the following argument! By part (t), any fixedpoint fcS haa proparty 0.(0

  19. Controller design approach based on linear programming.

    Science.gov (United States)

    Tanaka, Ryo; Shibasaki, Hiroki; Ogawa, Hiromitsu; Murakami, Takahiro; Ishida, Yoshihisa

    2013-11-01

    This study explains and demonstrates the design method for a control system with a load disturbance observer. Observer gains are determined by linear programming (LP) in terms of the Routh-Hurwitz stability criterion and the final-value theorem. In addition, the control model has a feedback structure, and feedback gains are determined to be the linear quadratic regulator. The simulation results confirmed that compared with the conventional method, the output estimated by our proposed method converges to a reference input faster when a load disturbance is added to a control system. In addition, we also confirmed the effectiveness of the proposed method by performing an experiment with a DC motor.

  20. An approach for solving linear fractional programming problems ...

    African Journals Online (AJOL)

    An approach for solving linear fractional programming problems. ... Journal of the Nigerian Association of Mathematical Physics. Journal Home · ABOUT · Advanced Search ... Open Access DOWNLOAD FULL TEXT Subscription or Fee Access ...

  1. Representing genetic variation as continuous surfaces: An approach for identifying spatial dependency in landscape genetic studies

    Science.gov (United States)

    Melanie A. Murphy; Jeffrey S. Evans; Samuel A. Cushman; Andrew Storfer

    2008-01-01

    Landscape genetics, an emerging field integrating landscape ecology and population genetics, has great potential to influence our understanding of habitat connectivity and distribution of organisms. Whereas typical population genetics studies summarize gene flow as pairwise measures between sampling localities, landscape characteristics that influence population...

  2. The Genetic Blues: Understanding Genetic Principles Using a Practical Approach and a Historical Perspective.

    Science.gov (United States)

    Mysliwiec, Tami H.

    2003-01-01

    Incorporates history and genetics to explain how genetic traits are passed on to the next generation by focusing on methemoglobinemia, a rare genetic disease, and discusses how oxygen is carried by hemoglobin. Includes individual pedigree analysis and class pedigree analysis. (YDS)

  3. A general approach to belief change in answer set programming

    CERN Document Server

    Delgrande, James; Tompits, Hans; Woltran, Stefan

    2009-01-01

    We address the problem of belief change in (nonmonotonic) logic programming under answer set semantics. Unlike previous approaches to belief change in logic programming, our formal techniques are analogous to those of distance-based belief revision in propositional logic. In developing our results, we build upon the model theory of logic programs furnished by SE models. Since SE models provide a formal, monotonic characterisation of logic programs, we can adapt techniques from the area of belief revision to belief change in logic programs. We introduce methods for revising and merging logic programs, respectively. For the former, we study both subset-based revision as well as cardinality-based revision, and we show that they satisfy the majority of the AGM postulates for revision. For merging, we consider operators following arbitration merging and IC merging, respectively. We also present encodings for computing the revision as well as the merging of logic programs within the same logic programming framework...

  4. Research on teacher education programs: logic model approach.

    Science.gov (United States)

    Newton, Xiaoxia A; Poon, Rebecca C; Nunes, Nicole L; Stone, Elisa M

    2013-02-01

    Teacher education programs in the United States face increasing pressure to demonstrate their effectiveness through pupils' learning gains in classrooms where program graduates teach. The link between teacher candidates' learning in teacher education programs and pupils' learning in K-12 classrooms implicit in the policy discourse suggests a one-to-one correspondence. However, the logical steps leading from what teacher candidates have learned in their programs to what they are doing in classrooms that may contribute to their pupils' learning are anything but straightforward. In this paper, we argue that the logic model approach from scholarship on evaluation can enhance research on teacher education by making explicit the logical links between program processes and intended outcomes. We demonstrate the usefulness of the logic model approach through our own work on designing a longitudinal study that focuses on examining the process and impact of an undergraduate mathematics and science teacher education program.

  5. Estimating soil wetting patterns for drip irrigation using genetic programming

    Energy Technology Data Exchange (ETDEWEB)

    Samadianfard, S.; Sadraddini, A. A.; Nazemi, A. H.; Provenzano, G.; Kisi, O.

    2012-07-01

    Drip irrigation is considered as one of the most efficient irrigation systems. Knowledge of the soil wetted perimeter arising from infiltration of water from drippers is important in the design and management of efficient irrigation systems. To this aim, numerical models can represent a powerful tool to analyze the evolution of the wetting pattern during irrigation, in order to explore drip irrigation management strategies, to set up the duration of irrigation, and finally to optimize water use efficiency. This paper examines the potential of genetic programming (GP) in simulating wetting patterns of drip irrigation. First by considering 12 different soil textures of USDA-SCS soil texture triangle, different emitter discharge and duration of irrigation, soil wetting patterns have been simulated by using HYDRUS 2D software. Then using the calculated values of depth and radius of wetting pattern as target outputs, two different GP models have been considered. Finally, the capability of GP for simulating wetting patterns was analyzed using some values of data set that were not used in training. Results showed that the GP method had good agreement with results of HYDRUS 2D software in the case of considering full set of operators with R{sup 2} of 0.99 and 0.99 and root mean squared error of 2.88 and 4.94 in estimation of radius and depth of wetting patterns, respectively. Also, field experimental results in a sandy loam soil with emitter discharge of 4 L h{sup -}1 showed reasonable agreement with GP results. As a conclusion, the results of the study demonstrate the usefulness of the GP method for estimating wetting patterns of drip irrigation. (Author) 40 refs.

  6. Evaluation of Slum Upgrading Programs: Literature Review and Methodological Approaches

    OpenAIRE

    José Brakarz; Laura Jaitman

    2013-01-01

    This technical note analyzes the methodologies used to evaluate neighborhood upgrading programs, describes their results, and suggests approaches for future evaluations. Local and central governments are increasingly utilizing slum or neighborhood upgrading programs to deal with the multiple problems of urban poverty. These programs employ a methodology of integral interventions, combining of both infrastructure works and social services targeted to specific neighborhoods. Due to this variety...

  7. A Constraint programming-based genetic algorithm for capacity output optimization

    Directory of Open Access Journals (Sweden)

    Kate Ean Nee Goh

    2014-10-01

    Full Text Available Purpose: The manuscript presents an investigation into a constraint programming-based genetic algorithm for capacity output optimization in a back-end semiconductor manufacturing company.Design/methodology/approach: In the first stage, constraint programming defining the relationships between variables was formulated into the objective function. A genetic algorithm model was created in the second stage to optimize capacity output. Three demand scenarios were applied to test the robustness of the proposed algorithm.Findings: CPGA improved both the machine utilization and capacity output once the minimum requirements of a demand scenario were fulfilled. Capacity outputs of the three scenarios were improved by 157%, 7%, and 69%, respectively.Research limitations/implications: The work relates to aggregate planning of machine capacity in a single case study. The constraints and constructed scenarios were therefore industry-specific.Practical implications: Capacity planning in a semiconductor manufacturing facility need to consider multiple mutually influenced constraints in resource availability, process flow and product demand. The findings prove that CPGA is a practical and an efficient alternative to optimize the capacity output and to allow the company to review its capacity with quick feedback.Originality/value: The work integrates two contemporary computational methods for a real industry application conventionally reliant on human judgement.

  8. Genetic testing in congenital heart disease:A clinical approach

    Institute of Scientific and Technical Information of China (English)

    Marie A Chaix; Gregor Andelfinger; Paul Khairy

    2016-01-01

    Congenital heart disease(CHD) is the most common type of birth defect. Traditionally, a polygenic model defined by the interaction of multiple genes and environmental factors was hypothesized to account for different forms of CHD. It is now understood that the contribution of genetics to CHD extends beyond a single unified paradigm. For example, monogenic models and chromosomal abnormalities have been associated with various syndromic and non-syndromic forms of CHD. In such instances, genetic investigation and testing may potentially play an important role in clinical care. A family tree with a detailed phenotypic description serves as the initial screening tool to identify potentially inherited defects and to guide further genetic investigation. The selection of a genetic test is contingent upon the particular diagnostic hypothesis generated by clinical examination. Genetic investigation in CHD may carry the potential to improve prognosis by yielding valuable information with regards to personalized medical care, confidence in the clinical diagnosis, and/or targeted patient followup. Moreover, genetic assessment may serve as a tool to predict recurrence risk, define the pattern of inheritance within a family, and evaluate the need for further family screening. In some circumstances, prenatal or preimplantation genetic screening could identify fetuses or embryos at high risk for CHD. Although genetics may appear to constitute a highly specialized sector of cardiology, basic knowledge regarding inheritance patterns, recurrence risks, and available screening and diagnostic tools, including their strengths and limitations, could assist the treating physician in providing sound counsel.

  9. A Genetic Predictive Model for Canine Hip Dysplasia: Integration of Genome Wide Association Study (GWAS) and Candidate Gene Approaches

    Science.gov (United States)

    Bartolomé, Nerea; Segarra, Sergi; Artieda, Marta; Francino, Olga; Sánchez, Elisenda; Szczypiorska, Magdalena; Casellas, Joaquim; Tejedor, Diego; Cerdeira, Joaquín; Martínez, Antonio; Velasco, Alfonso; Sánchez, Armand

    2015-01-01

    Canine hip dysplasia is one of the most prevalent developmental orthopedic diseases in dogs worldwide. Unfortunately, the success of eradication programs against this disease based on radiographic diagnosis is low. Adding the use of diagnostic genetic tools to the current phenotype-based approach might be beneficial. The aim of this study was to develop a genetic prognostic test for early diagnosis of hip dysplasia in Labrador Retrievers. To develop our DNA test, 775 Labrador Retrievers were recruited. For each dog, a blood sample and a ventrodorsal hip radiograph were taken. Dogs were divided into two groups according to their FCI hip score: control (A/B) and case (D/E). C dogs were not included in the sample. Genetic characterization combining a GWAS and a candidate gene strategy using SNPs allowed a case-control population association study. A mathematical model which included 7 SNPs was developed using logistic regression. The model showed a good accuracy (Area under the ROC curve = 0.85) and was validated in an independent population of 114 dogs. This prognostic genetic test represents a useful tool for choosing the most appropriate therapeutic approach once genetic predisposition to hip dysplasia is known. Therefore, it allows a more individualized management of the disease. It is also applicable during genetic selection processes, since breeders can benefit from the information given by this test as soon as a blood sample can be collected, and act accordingly. In the authors’ opinion, a shift towards genomic screening might importantly contribute to reducing canine hip dysplasia in the future. In conclusion, based on genetic and radiographic information from Labrador Retrievers with hip dysplasia, we developed an accurate predictive genetic test for early diagnosis of hip dysplasia in Labrador Retrievers. However, further research is warranted in order to evaluate the validity of this genetic test in other dog breeds. PMID:25874693

  10. A genetic predictive model for canine hip dysplasia: integration of Genome Wide Association Study (GWAS and candidate gene approaches.

    Directory of Open Access Journals (Sweden)

    Nerea Bartolomé

    Full Text Available Canine hip dysplasia is one of the most prevalent developmental orthopedic diseases in dogs worldwide. Unfortunately, the success of eradication programs against this disease based on radiographic diagnosis is low. Adding the use of diagnostic genetic tools to the current phenotype-based approach might be beneficial. The aim of this study was to develop a genetic prognostic test for early diagnosis of hip dysplasia in Labrador Retrievers. To develop our DNA test, 775 Labrador Retrievers were recruited. For each dog, a blood sample and a ventrodorsal hip radiograph were taken. Dogs were divided into two groups according to their FCI hip score: control (A/B and case (D/E. C dogs were not included in the sample. Genetic characterization combining a GWAS and a candidate gene strategy using SNPs allowed a case-control population association study. A mathematical model which included 7 SNPs was developed using logistic regression. The model showed a good accuracy (Area under the ROC curve = 0.85 and was validated in an independent population of 114 dogs. This prognostic genetic test represents a useful tool for choosing the most appropriate therapeutic approach once genetic predisposition to hip dysplasia is known. Therefore, it allows a more individualized management of the disease. It is also applicable during genetic selection processes, since breeders can benefit from the information given by this test as soon as a blood sample can be collected, and act accordingly. In the authors' opinion, a shift towards genomic screening might importantly contribute to reducing canine hip dysplasia in the future. In conclusion, based on genetic and radiographic information from Labrador Retrievers with hip dysplasia, we developed an accurate predictive genetic test for early diagnosis of hip dysplasia in Labrador Retrievers. However, further research is warranted in order to evaluate the validity of this genetic test in other dog breeds.

  11. A genetic predictive model for canine hip dysplasia: integration of Genome Wide Association Study (GWAS) and candidate gene approaches.

    Science.gov (United States)

    Bartolomé, Nerea; Segarra, Sergi; Artieda, Marta; Francino, Olga; Sánchez, Elisenda; Szczypiorska, Magdalena; Casellas, Joaquim; Tejedor, Diego; Cerdeira, Joaquín; Martínez, Antonio; Velasco, Alfonso; Sánchez, Armand

    2015-01-01

    Canine hip dysplasia is one of the most prevalent developmental orthopedic diseases in dogs worldwide. Unfortunately, the success of eradication programs against this disease based on radiographic diagnosis is low. Adding the use of diagnostic genetic tools to the current phenotype-based approach might be beneficial. The aim of this study was to develop a genetic prognostic test for early diagnosis of hip dysplasia in Labrador Retrievers. To develop our DNA test, 775 Labrador Retrievers were recruited. For each dog, a blood sample and a ventrodorsal hip radiograph were taken. Dogs were divided into two groups according to their FCI hip score: control (A/B) and case (D/E). C dogs were not included in the sample. Genetic characterization combining a GWAS and a candidate gene strategy using SNPs allowed a case-control population association study. A mathematical model which included 7 SNPs was developed using logistic regression. The model showed a good accuracy (Area under the ROC curve = 0.85) and was validated in an independent population of 114 dogs. This prognostic genetic test represents a useful tool for choosing the most appropriate therapeutic approach once genetic predisposition to hip dysplasia is known. Therefore, it allows a more individualized management of the disease. It is also applicable during genetic selection processes, since breeders can benefit from the information given by this test as soon as a blood sample can be collected, and act accordingly. In the authors' opinion, a shift towards genomic screening might importantly contribute to reducing canine hip dysplasia in the future. In conclusion, based on genetic and radiographic information from Labrador Retrievers with hip dysplasia, we developed an accurate predictive genetic test for early diagnosis of hip dysplasia in Labrador Retrievers. However, further research is warranted in order to evaluate the validity of this genetic test in other dog breeds.

  12. CONTEMPORARY APPROACH TO DIAGNOSIS OF GENETIC CAUSES OF INTELLECTUAL DISABILITY

    Directory of Open Access Journals (Sweden)

    Ana PETERLIN

    2016-09-01

    Full Text Available Intellectual disability is a lifelong debilitating developmental disorder with important genetic contribution, which remains challenging to investigate due to high clinical and genetic variability. Finding the etiologic diagnosis of ID, however comes with great benefits for patients and their families, therefore establishing a genetic diagnostic pathway with right combination and succession of diagnostic tools is crucial for both prevention and appropriate treatment and/or rehabilitation of patients with ID. New diagnostic tools in genetics such as array comparative genomic hybridization (aCGH and next-generation sequencing (NGS have much higher detection rate for genetic aberrations responsible for ID and have enormous potential to shorten the path to diagnosis, as early diagnosis is a cornerstone for medical and non-medical management of persons suffering from ID.

  13. Genetic programming as alternative for predicting development effort of individual software projects.

    Directory of Open Access Journals (Sweden)

    Arturo Chavoya

    Full Text Available Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects. Accuracy obtained from a genetic programming model was compared against one generated from the application of a statistical regression model. A sample of 219 projects developed by 71 practitioners was used for generating the two models, whereas another sample of 130 projects developed by 38 practitioners was used for validating them. The models used two kinds of lines of code as well as programming language experience as independent variables. Accuracy results from the model obtained with genetic programming suggest that it could be used to predict the software development effort of individual projects when these projects have been developed in a disciplined manner within a development-controlled environment.

  14. Process planning optimization on turning machine tool using a hybrid genetic algorithm with local search approach

    Directory of Open Access Journals (Sweden)

    Yuliang Su

    2015-04-01

    Full Text Available A turning machine tool is a kind of new type of machine tool that is equipped with more than one spindle and turret. The distinctive simultaneous and parallel processing abilities of turning machine tool increase the complexity of process planning. The operations would not only be sequenced and satisfy precedence constraints, but also should be scheduled with multiple objectives such as minimizing machining cost, maximizing utilization of turning machine tool, and so on. To solve this problem, a hybrid genetic algorithm was proposed to generate optimal process plans based on a mixed 0-1 integer programming model. An operation precedence graph is used to represent precedence constraints and help generate a feasible initial population of hybrid genetic algorithm. Encoding strategy based on data structure was developed to represent process plans digitally in order to form the solution space. In addition, a local search approach for optimizing the assignments of available turrets would be added to incorporate scheduling with process planning. A real-world case is used to prove that the proposed approach could avoid infeasible solutions and effectively generate a global optimal process plan.

  15. CDPOP: A spatially explicit cost distance population genetics program

    Science.gov (United States)

    Erin L. Landguth; S. A. Cushman

    2010-01-01

    Spatially explicit simulation of gene flow in complex landscapes is essential to explain observed population responses and provide a foundation for landscape genetics. To address this need, we wrote a spatially explicit, individual-based population genetics model (CDPOP). The model implements individual-based population modelling with Mendelian inheritance and k-allele...

  16. SAM: The "Search and Match" Computer Program of the Escherichia coli Genetic Stock Center

    Science.gov (United States)

    Bachmann, B. J.; And Others

    1973-01-01

    Describes a computer program used at a genetic stock center to locate particular strains of bacteria. The program can match up to 30 strain descriptions requested by a researcher with the records on file. Uses of this particular program can be made in many fields. (PS)

  17. Beginning Java programming the object-oriented approach

    CERN Document Server

    Baesens, Bart; vanden Broucke, Seppe

    2015-01-01

    A comprehensive Java guide, with samples, exercises, case studies, and step-by-step instruction Beginning Java Programming: The Object Oriented Approach is a straightforward resource for getting started with one of the world's most enduringly popular programming languages. Based on classes taught by the authors, the book starts with the basics and gradually builds into more advanced concepts. The approach utilizes an integrated development environment that allows readers to immediately apply what they learn, and includes step-by-step instruction with plenty of sample programs. Each chapter c

  18. Mathematical-programming approaches to test item pool design

    NARCIS (Netherlands)

    Veldkamp, Bernard P.; van der Linden, Willem J.; Ariel, A.

    2002-01-01

    This paper presents an approach to item pool design that has the potential to improve on the quality of current item pools in educational and psychological testing andhence to increase both measurement precision and validity. The approach consists of the application of mathematical programming

  19. [A novel approach to techniques in genetic testing for cancer].

    Science.gov (United States)

    Kato, Jun-ichi

    2014-04-01

    In molecular targeted drug therapy, genetic screening is carried out to identify the existence of target genes that are specifically expressed in cancer cells. Conventional methods for detecting the mutation of genes in cancer cells through the use of purified DNA is time consuming, especially in the case of the enzymatic treatment of pathological specimens, and it is difficult to finish all these protocols on the same day. Also, depending on the condition of the patients, it may be difficult to perform surgery or biopsy, and pathological specimens are not always obtainable. Thus, sometimes genetic screening using purified DNA and the enzymatic treatment of pathological specimens cannot be performed. We have successfully solved these problems using i-densy, a genetic analysis device, and two different methods of genetic testing for cancer. The first is a method which, without extracting DNA, uses simply pretreated pathological specimens for genetic screening. Using deparaffinized specimens that have only been heat-treated for a short period of time, we were able to obtain the exact same results as if we had extracted DNA. The second is the highly specific genetic screening technique, the MBP-QP method. Using this method, we were able to confirm the detection of genetic mutation from the DNA of blood plasma. It is now possible to screen for the mutation of genes in cancer cells using just a blood sample from patients without using tissue or cells, which also has little burden on the patient.

  20. Neurolinguistic programming: a systematic approach to change.

    Science.gov (United States)

    Steinbach, A M

    1984-01-01

    Neurolinguistic programming (NLP) integrates advances in cybernetics, psychophysiology, linguistics, and information services. It has been used in business, education, law, medicine and psychotherapy to alter people's responses to stimuli, so they are better able to regulate their environment and themselves. There are five steps to an effective NLP interaction. They include 1. establishing rapport; the therapist must match his verbal and non-verbal behaviors to the patient's, 2. gathering information about the patient's present problem and goals by noting his verbal patterns and non-verbal responses, 3. considering the impact that achieving the patient's goals will have on him, his work, family and friends, and retaining any positive aspects of his current situation, 4. helping the patient achieve his goals by using specific techniques to alter his responses to various stimuli, and 5. ensuring the altered responses achieved in therapy are integrated into the patient's daily life. NLP has been used to help patients with medical problems ranging from purely psychological to complex organic ones.

  1. A Unified Approach for Developing Efficient Algorithmic Programs

    Institute of Scientific and Technical Information of China (English)

    薛锦云

    1997-01-01

    A unified approach called partition-and-recur for developing efficient and correct algorithmic programs is presented.An algorithm(represented by recurrence and initiation)is separated from program,and special attention is paid to algorithm manipulation rather than proram calculus.An algorithm is exactly a set of mathematical formulae.It is easier for formal erivation and proof.After getting efficient and correct algorithm,a trivial transformation is used to get a final rogram,The approach covers several known algorithm design techniques,e.g.dynamic programming,greedy,divide-and-conquer and enumeration,etc.The techniques of partition and recurrence are not new.Partition is a general approach for dealing with complicated objects and is typically used in divide-and-conquer approach.Recurrence is used in algorithm analysis,in developing loop invariants and dynamic programming approach.The main contribution is combining two techniques used in typical algorithm development into a unified and systematic approach to develop general efficient algorithmic programs and presenting a new representation of algorithm that is easier for understanding and demonstrating the correctness and ingenuity of algorithmicprograms.

  2. 多产品间歇过程的分层次在线调度--一种数学规划与遗传算法的混合算法%Hierarchical On-line Scheduling of Multiproduct Batch Plants with a Combined Approach of Mathematical Programming and Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    陈理; 王克峰; 徐霄羽; 姚平经

    2004-01-01

    In this contribution we present an online scheduling algorithm for a real world multiproduct batch plant.The overall mixed integer nonlinear programming (MINLP) problem is hierarchically structured into a mixed integer linear programming (MILP) problem first and then a reduced dimensional MINLP problem, which are optimized by mathematical programming (MP) and genetic algorithm (GA) respectively. The basis idea relies on combining MP with GA to exploit their complementary capacity. The key features of the hierarchical model are explained and illustrated with some real world cases from the multiproduct batch plants.

  3. Investigating an Ethical Approach to Genetically Modified Crops in ...

    African Journals Online (AJOL)

    4carolinebell@gmail.com

    Genetically modified (GM) crops gained attention in southern Africa in the ... cited in Webster, 1999:414) states that political institutions find themselves .... means that risks are socially invisible and must clearly be brought to consciousness, ...

  4. Mobile transporter path planning using a genetic algorithm approach

    Science.gov (United States)

    Baffes, Paul; Wang, Lui

    1988-01-01

    The use of an optimization technique known as a genetic algorithm for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the Space Station which must be able to reach any point of the structure autonomously. Specific elements of the genetic algorithm are explored in both a theoretical and experimental sense. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research. However, trajectory planning problems are common in space systems and the genetic algorithm provides an attractive alternative to the classical techniques used to solve these problems.

  5. Congenital hydrocephalus in clinical practice : A genetic diagnostic approach

    NARCIS (Netherlands)

    Verhagen, J. M. A.; Schrander-Stumpel, C. T. R. M.; Krapels, P. C.; de Die-Smulders, C. E. M.; van Lint, F. H. M.; Willekes, C.; Weber, J. W.; Gavilanes, A. W. D.; Macville, M. V. E.; Stegmann, A. P. A.; Engelen, J. J. M.; Bakker, J.; Vos, Y. J.; Frints, S. G. M.

    2011-01-01

    Congenital hydrocephalus is a common and often disabling disorder. The etiology is very heterogeneous. Little is known about the genetic causes of congenital hydrocephalus. A retrospective survey was performed including patients with primary congenital hydrocephalus referred to the Department of

  6. Integrating demographic and genetic approaches in plant conservation

    NARCIS (Netherlands)

    Oostermeijer, J.G.B.; Luijten, S.H.; den Nijs, J.C.M.

    2003-01-01

    We summarize the problems that populations of formerly common plants may encounter when habitat fragmentation isolates them and reduces population size. Genetic erosion, inbreeding depression, Allee-effects on reproductive success, catastrophes and environmental stochasticity are illustrated with st

  7. Potential International Approaches to Ownership/Control of Human Genetic Resources.

    Science.gov (United States)

    Rhodes, Catherine

    2016-09-01

    In its governance activities for genetic resources, the international community has adopted various approaches to their ownership, including: free access; common heritage of mankind; intellectual property rights; and state sovereign rights. They have also created systems which combine elements of these approaches. While governance of plant and animal genetic resources is well-established internationally, there has not yet been a clear approach selected for human genetic resources. Based on assessment of the goals which international governance of human genetic resources ought to serve, and the implications for how they will be accessed and utilised, it is argued that common heritage of mankind will be the most appropriate approach to adopt to their ownership/control. It does this with the aim of stimulating discussion in this area and providing a starting point for deeper consideration of how a common heritage of mankind, or similar, regime for human genetic resources would function and be implemented.

  8. Introduction to Focus Issue: Quantitative Approaches to Genetic Networks

    Science.gov (United States)

    Albert, Réka; Collins, James J.; Glass, Leon

    2013-06-01

    All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks

  9. Combining Single (Mixed) Metric Approach and Genetic Algorithm for QoS Routing Problem

    Institute of Scientific and Technical Information of China (English)

    胡世余; 谢剑英

    2004-01-01

    A hybrid algorithm for the delay constrained least cost path problem is proposed through combination of single (mixed) metric approach and genetic algorithm. Compared with the known genetic algorithm for the same problem, the new algorithm adopts integral coding scheme and new genetic operator, which reduces the search space and improves the efficiency of genetic operation. Meanwhile, the single (mixed) approach accelerates the convergence speed. Simulation results indicate that the proposed algorithm can find near-optimal even optimal solutions within moderate numbers of generations.

  10. Performance of Cost Assessment on Reusable Components for Software Development using Genetic Programming

    Directory of Open Access Journals (Sweden)

    T.Tejaswini

    2015-08-01

    Full Text Available Reusability is the quality of a piece of software, which enables it to be used again, be it partial, modified or complete. A wide range of modeling techniques have been proposed and applied for software quality predictions. Complexity and size metrics have been used to predict the number of defects in software components. Estimation of cost is important, during the process of software development. There are two main types of cost estimation approaches: algorithmic methods and non-algorithmic methods. In this work, using genetic programming which is a branch of evolutionary algorithms, a new algorithmic method is presented for software development cost estimation, using the implementation of this method; new formulas were obtained for software development cost estimation in which reusability of components is given priority. After evaluation of these formulas, the mean and standard deviation of the magnitude of relative error is better than related algorithmic methods such as COCOMO formulas.

  11. Evolving Rule-Based Systems in two Medical Domains using Genetic Programming

    DEFF Research Database (Denmark)

    Tsakonas, A.; Dounias, G.; Jantzen, Jan;

    2004-01-01

    We demonstrate, compare and discuss the application of two genetic programming methodologies for the construction of rule-based systems in two medical domains: the diagnosis of Aphasia's subtypes and the classification of Pap-Smear Test examinations. The first approach consists of a scheme...... the classification between all common types. A third model consisting of a GP-generated fuzzy rule-based system is tested on the same field. In the classification of Pap-Smear Test examinations, a crisp rule-based system is constructed. Results denote the effectiveness of the proposed systems. Comments...... and comparisons are made between the proposed methods and previous attempts on the selected fields of application....

  12. Developing close combat behaviors for simulated soldiers using genetic programming techniques.

    Energy Technology Data Exchange (ETDEWEB)

    Pryor, Richard J.; Schaller, Mark J.

    2003-10-01

    Genetic programming is a powerful methodology for automatically producing solutions to problems in a variety of domains. It has been used successfully to develop behaviors for RoboCup soccer players and simple combat agents. We will attempt to use genetic programming to solve a problem in the domain of strategic combat, keeping in mind the end goal of developing sophisticated behaviors for compound defense and infiltration. The simplified problem at hand is that of two armed agents in a small room, containing obstacles, fighting against each other for survival. The base case and three changes are considered: a memory of positions using stacks, context-dependent genetic programming, and strongly typed genetic programming. Our work demonstrates slight improvements from the first two techniques, and no significant improvement from the last.

  13. Bioethical – Theological and Legal approach in genetic testing of adult persons

    Directory of Open Access Journals (Sweden)

    George Katsimigas

    2012-07-01

    Full Text Available Thorough genetic testing gives possibility's diagnosis of genetic diseases or identity individuals, who genetic predisposed for disease outbreak Aims: To present/identify the ethical and religious issues, which arise from the application of genetic testing in humans. Furthermore, the principles from the European and Greek legislation regarding genetic testing will be discussed. Materials & Methods: A literature review based on both review and research literature, conducted during the period of (1993-2010, derived from MEDLINE, SCOPUS and ΙΑΤΡΟΤΕΚ databases using as key words: Bioethics, genetic testing, bioethics, access, genetic information, orthodox ethics, Legislation. Results: Genetic testing for disease prevention is of primary importance. The main ethical concerns however, are related to the dissemination/ disclosure and use of this information from insurance companies, healthcare authorities, scientists, forensic departments/services and employers. Similarly, the orthodox religion accepts the use of genetic testing for the prevention and treatment of diseases as long as there is no break of confidentiality. Finally, considering the legal issues, it is apparent that genetic information is regarded as personal information and as such it is protected from the national (Greek and international law. Conclusions: It is necessary to ensure that the public authorities protect the rights of their citizens regarding genetic testing and all insurance companies, employers, schools etc. should not be allowed to have access to genetic information. Such an approach will ensure that social discrimination, obstructions or other inequalities between people on the basis of genetic information is avoided.

  14. National Survey of Genetics Content in Basic Nursing Preparatory Programs in the United States.

    Science.gov (United States)

    Hetteberg, Carol G.; Prows, Cynthia A.; Deets, Carol; Monsen, Rita B.; Kenner, Carole A.

    1999-01-01

    A sample of 879 basic nursing programs was used to identify the type and amount of genetics content in curricula. Recommendations were made for increasing genetics content as a result of the synthesis of the survey data with previously collected data. (25 references) (Author/JOW)

  15. Effects of genotype x environment interaction on genetic gain in breeding programs

    NARCIS (Netherlands)

    Mulder, H.A.; Bijma, P.

    2005-01-01

    Genotype x environment interaction (G x E) is increasingly important, because breeding programs tend to be more internationally oriented. The aim of this theoretical study was to investigate the effects of G x E on genetic gain in sib-testing and progeny-testing schemes. Loss of genetic gain due to

  16. Approach of generating parallel programs from parallelized algorithm design strategies

    Institute of Scientific and Technical Information of China (English)

    WAN Jian-yi; LI Xiao-ying

    2008-01-01

    Today, parallel programming is dominated by message passing libraries, such as message passing interface (MPI). This article intends to simplify parallel programming by generating parallel programs from parallelized algorithm design strategies. It uses skeletons to abstract parallelized algorithm design strategies, as well as parallel architectures. Starting from problem specification, an abstract parallel abstract programming language+ (Apla+) program is generated from parallelized algorithm design strategies and problem-specific function definitions. By combining with parallel architectures, implicity of parallelism inside the parallelized algorithm design strategies is exploited. With implementation and transformation, C++ and parallel virtual machine (CPPVM) parallel program is finally generated. Parallelized branch and bound (B&B) algorithm design strategy and parallelized divide and conquer (D & C) algorithm design strategy are studied in this article as examples. And it also illustrates the approach with a case study.

  17. A propensity score approach to correction for bias due to population stratification using genetic and non-genetic factors.

    Science.gov (United States)

    Zhao, Huaqing; Rebbeck, Timothy R; Mitra, Nandita

    2009-12-01

    Confounding due to population stratification (PS) arises when differences in both allele and disease frequencies exist in a population of mixed racial/ethnic subpopulations. Genomic control, structured association, principal components analysis (PCA), and multidimensional scaling (MDS) approaches have been proposed to address this bias using genetic markers. However, confounding due to PS can also be due to non-genetic factors. Propensity scores are widely used to address confounding in observational studies but have not been adapted to deal with PS in genetic association studies. We propose a genomic propensity score (GPS) approach to correct for bias due to PS that considers both genetic and non-genetic factors. We compare the GPS method with PCA and MDS using simulation studies. Our results show that GPS can adequately adjust and consistently correct for bias due to PS. Under no/mild, moderate, and severe PS, GPS yielded estimated with bias close to 0 (mean=-0.0044, standard error=0.0087). Under moderate or severe PS, the GPS method consistently outperforms the PCA method in terms of bias, coverage probability (CP), and type I error. Under moderate PS, the GPS method consistently outperforms the MDS method in terms of CP. PCA maintains relatively high power compared to both MDS and GPS methods under the simulated situations. GPS and MDS are comparable in terms of statistical properties such as bias, type I error, and power. The GPS method provides a novel and robust tool for obtaining less-biased estimates of genetic associations that can consider both genetic and non-genetic factors.

  18. A linear programming approach for optimal contrast-tone mapping.

    Science.gov (United States)

    Wu, Xiaolin

    2011-05-01

    This paper proposes a novel algorithmic approach of image enhancement via optimal contrast-tone mapping. In a fundamental departure from the current practice of histogram equalization for contrast enhancement, the proposed approach maximizes expected contrast gain subject to an upper limit on tone distortion and optionally to other constraints that suppress artifacts. The underlying contrast-tone optimization problem can be solved efficiently by linear programming. This new constrained optimization approach for image enhancement is general, and the user can add and fine tune the constraints to achieve desired visual effects. Experimental results demonstrate clearly superior performance of the new approach over histogram equalization and its variants.

  19. Marketing: an approach to successful energy-conservation information programs

    Energy Technology Data Exchange (ETDEWEB)

    Hutton, R. B.; McNeill, D. L.

    1980-08-01

    This monograph shows how the adoption of a marketing approach can improve the quality of the development and delivery of energy-conservation programs. Several factors make the use of such a marketing approach to conservation particularly beneficial, namely: (1) goals of conservation programs can be quantified (e.g., specified amount of energy to be saved); in addition, intermediate effects necessary for program success are also measureable (e.g., knowledge, attitude change, etc); (2) there is an apparent and increasing need for conservation by different parts (or sectors) of the population; however, it is clear that the desire for conservation is not the same for all sectors; (3) conservation programs can be thought of in much the same way as products with benefits and costs; this necessitates an understanding of how the population makes conservation decisions so that the program can fit into that decision process; (4) the need to tailor programs to the needs of the population is heightened by the general competition for the consumer dollar; it is necessary to design and present programs in a way that the individual will view conservation as an attractive choice among many (e.g., bank savings, buying clothes, furniture, car, etc.); and (5) the population's response to, and need for conservation is constantly changing; consequently, it is important to realize that these changes may need to be reflected in the conservation programs themselves (both ongoing and new).

  20. A genetic algorithm approach in interface and surface structure optimization

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Jian [Iowa State Univ., Ames, IA (United States)

    2010-01-01

    The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the material structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.

  1. Horizontal symmetry in the algebraic approach of genetic code

    CERN Document Server

    Godina-Nava, J J

    2013-01-01

    Using concepts of physics of elementary particles concerning the breaking of symmetry and grannd unified theory we propose to study with the algebraic approximation the degeneracy finded in the genetic code with the incorporation of a horizontal symmetry used in gauge theories to fit the contents of the multiplets of the genetic code. It is used the algebraic approch of Hornos et. al. \\cite{main,PRL71,PRE,MPLB}. We propose an example for the incorporation of horizontal symmetry to study mixtures of elements of the multiplets.

  2. Horizontal symmetry in the algebraic approach of genetic code

    OpenAIRE

    Godina-Nava, J. J.

    2013-01-01

    Using concepts of physics of elementary particles concerning the breaking of symmetry and grannd unified theory we propose to study with the algebraic approximation the degeneracy finded in the genetic code with the incorporation of a horizontal symmetry used in gauge theories to fit the contents of the multiplets of the genetic code. It is used the algebraic approch of Hornos et. al. \\cite{main,PRL71,PRE,MPLB}. We propose an example for the incorporation of horizontal symmetry to study mixtu...

  3. Concurrent object-oriented programming: The MP-Eiffel approach

    OpenAIRE

    Silva, Miguel Augusto Mendes Oliveira e

    2004-01-01

    This article evaluates several possible approaches for integrating concurrency into object-oriented programming languages, presenting afterwards, a new language named MP-Eiffel. MP-Eiffel was designed attempting to include all the essential properties of both concurrent and object-oriented programming with simplicity and safety. A special care was taken to achieve the orthogonality of all the language mechanisms, allowing their joint use without unsafe side-effects (such as inh...

  4. DYNAMIC RELOCATION OF PLANT/WAREHOUSE FACILITIES:A FAST COMPACT GENETIC ALGORITHM APPROACH

    Institute of Scientific and Technical Information of China (English)

    Li Shugang; Wu Zhiming; Pang Xiaohong

    2004-01-01

    The problem of dynamic relocation and phase-out of combined manufacturing plant and warehousing facilities in the supply chain are concerned.A multiple time/multiple objective model is proposed to maximize total profit during the time horizon, minimize total access time from the plant/warehouse facilities to its suppliers and customers and maximize aggregated local incentives during the time horizon.The relocation problem keeps the feature of NP-hard and with the traditional method the optimal result cannot be got easily.So a compact genetic algorithm (CGA) is introduced to solve the problem.In order to accelerate the convergence speed of the CGA, the least square approach is introduced and a fast compact genetic algorithm (fCGA) is proposed.Finally, simulation results with the fCGA are compared with the CGA and classical integer programming (IP).The results show that the fCGA proposed is of high efficiency for Pareto optimality problem.

  5. Rethink, Reform, Reenter: An Entrepreneurial Approach to Prison Programming.

    Science.gov (United States)

    Keena, Linda; Simmons, Chris

    2015-07-01

    The purpose of this article was to present a description and first-stage evaluation of the impact of the Ice House Entrepreneurship Program on the learning experience of participating prerelease inmates at a Mississippi maximum-security prison and their perception of the transfer of skills learned in program into securing employment upon reentry. The Ice House Entrepreneurship Program is a 12-week program facilitated by volunteer university professors to inmates in a prerelease unit of a maximum-security prison in Mississippi. Participants' perspectives were examined through content analysis of inmates' answers to program Reflection and Response Assignments and interviews. The analyses were conducted according to the constant comparative method. Findings reveal the emergent of eight life-lessons and suggest that this is a promising approach to prison programming for prerelease inmates. This study discusses three approaches to better prepare inmates for a mindset change. The rethink, reform, and reenter approaches help break the traditional cycle of release, reoffend, and return. © The Author(s) 2014.

  6. DNA enrichment approaches to identify unauthorized genetically modified organisms (GMOs)

    NARCIS (Netherlands)

    Arulandhu, Alfred J.; Dijk, van Jeroen P.; Dobnik, David; Holst-Jensen, Arne; Shi, Jianxin; Zel, Jana; Kok, Esther J.

    2016-01-01

    With the increased global production of different genetically modified (GM) plant varieties, chances increase that unauthorized GM organisms (UGMOs) may enter the food chain. At the same time, the detection of UGMOs is a challenging task because of the limited sequence information that will gener

  7. Congenital hydrocephalus in clinical practice : A genetic diagnostic approach

    NARCIS (Netherlands)

    Verhagen, J. M. A.; Schrander-Stumpel, C. T. R. M.; Krapels, P. C.; de Die-Smulders, C. E. M.; van Lint, F. H. M.; Willekes, C.; Weber, J. W.; Gavilanes, A. W. D.; Macville, M. V. E.; Stegmann, A. P. A.; Engelen, J. J. M.; Bakker, J.; Vos, Y. J.; Frints, S. G. M.

    2011-01-01

    Congenital hydrocephalus is a common and often disabling disorder. The etiology is very heterogeneous. Little is known about the genetic causes of congenital hydrocephalus. A retrospective survey was performed including patients with primary congenital hydrocephalus referred to the Department of Cli

  8. The characterization of goat genetic diversity : Towards a genomic approach

    NARCIS (Netherlands)

    Ajmone-Marsan, P.; Colli, L.; Han, J. L.; Achilli, A.; Lancioni, H.; Joost, S.; Crepaldi, P.; Pilla, F.; Stella, A.; Taberlet, P.; Boettcher, P.; Negrini, R.; Lenstra, J. A.

    2014-01-01

    The investigation of genetic diversity at molecular level has been proposed as a valuable complement and sometimes proxy to phenotypic diversity of local breeds and is presently considered as one of the FAO priorities for breed characterization. By recommending a set of selected molecular markers fo

  9. The characterization of goat genetic diversity : Towards a genomic approach

    NARCIS (Netherlands)

    Ajmone-Marsan, P.; Colli, L.; Han, J. L.; Achilli, A.; Lancioni, H.; Joost, S.; Crepaldi, P.; Pilla, F.; Stella, A.; Taberlet, P.; Boettcher, P.; Negrini, R.; Lenstra, J. A.|info:eu-repo/dai/nl/067852335

    2014-01-01

    The investigation of genetic diversity at molecular level has been proposed as a valuable complement and sometimes proxy to phenotypic diversity of local breeds and is presently considered as one of the FAO priorities for breed characterization. By recommending a set of selected molecular markers

  10. DISTRIBUTED APPROACH to WEB PAGE CATEGORIZATION USING MAPREDUCE PROGRAMMING MODEL

    Directory of Open Access Journals (Sweden)

    P.Malarvizhi

    2011-12-01

    Full Text Available The web is a large repository of information and to facilitate the search and retrieval of pages from it,categorization of web documents is essential. An effective means to handle the complexity of information retrieval from the internet is through automatic classification of web pages. Although lots of automatic classification algorithms and systems have been presented, most of the existing approaches are computationally challenging. In order to overcome this challenge, we have proposed a parallel algorithm, known as MapReduce programming model to automatically categorize the web pages. This approach incorporates three concepts. They are web crawler, MapReduce programming model and the proposed web page categorization approach. Initially, we have utilized web crawler to mine the World Wide Web and the crawled web pages are then directly given as input to the MapReduce programming model. Here the MapReduce programming model adapted to our proposed web page categorization approach finds the appropriate category of the web page according to its content. The experimental results show that our proposed parallel web page categorization approach achieves satisfactory results in finding the right category for any given web page.

  11. A Fuzzy Genetic Algorithm Approach to an Adaptive Information Retrieval Agent.

    Science.gov (United States)

    Martin-Bautista, Maria J.; Vila, Maria-Amparo; Larsen, Henrik Legind

    1999-01-01

    Presents an approach to a Genetic Information Retrieval Agent Filter (GIRAF) that filters and ranks documents retrieved from the Internet according to users' preferences by using a Genetic Algorithm and fuzzy set theory to handle the imprecision of users' preferences and users' evaluation of the retrieved documents. (Author/LRW)

  12. Food Control and a Citizen Science Approach for Improving Teaching of Genetics in Universities

    Science.gov (United States)

    Borrell, Y. J.; Muñoz-Colmenero, A. M.; Dopico, E.; Miralles, L.; Garcia-Vazquez, E.

    2016-01-01

    A Citizen Science approach was implemented in the laboratory practices of Genetics at the University of Oviedo, related with the engaging topic of Food Control. Real samples of food products consumed by students at home ("students as samplers") were employed as teaching material in three different courses of Genetics during the academic…

  13. An MC2 Linear Programming Approaches to Portfolios

    Institute of Scientific and Technical Information of China (English)

    ZHOU Zong-fang; TANG Xiao-wo; SHI Yong

    2002-01-01

    Portfolios is a well-known investment technique of handling multiple stocks, bonds and securities. However, the previous portfolios investment lack of incorporating the various possible opinions from several experts on an given portfolios investment problem. This paper proposes an MC2 linear programming approach to determining weighted coefficients of portfolios that involves multiple experts. The numerical example of the paper shows that the proposed approach likely outperforms the current techniques of portfolios in dealing with the case of multiple experts.

  14. The potential use of genetics to increase the effectiveness of treatment programs for criminal offenders.

    Science.gov (United States)

    Beaver, Kevin M; Jackson, Dylan B; Flesher, Dillon

    2014-01-01

    During the past couple of decades, the amount of research examining the genetic underpinnings to antisocial behaviors, including crime, has exploded. Findings from this body of work have generated a great deal of information linking genetics to criminal involvement. As a partial result, there is now a considerable amount of interest in how these findings should be integrated into the criminal justice system. In the current paper, we outline the potential ways that genetic information can be used to increase the effectiveness of treatment programs designed to reduce recidivism among offenders. We conclude by drawing attention to how genetic information can be used by rehabilitation programs to increase program effectiveness, reduce offender recidivism rates, and enhance public safety.

  15. A systems genetics approach provides a bridge from discovered genetic variants to biological pathways in rheumatoid arthritis.

    Directory of Open Access Journals (Sweden)

    Hirofumi Nakaoka

    Full Text Available Genome-wide association studies (GWAS have yielded novel genetic loci underlying common diseases. We propose a systems genetics approach to utilize these discoveries for better understanding of the genetic architecture of rheumatoid arthritis (RA. Current evidence of genetic associations with RA was sought through PubMed and the NHGRI GWAS catalog. The associations of 15 single nucleotide polymorphisms and HLA-DRB1 alleles were confirmed in 1,287 cases and 1,500 controls of Japanese subjects. Among these, HLA-DRB1 alleles and eight SNPs showed significant associations and all but one of the variants had the same direction of effect as identified in the previous studies, indicating that the genetic risk factors underlying RA are shared across populations. By receiver operating characteristic curve analysis, the area under the curve (AUC for the genetic risk score based on the selected variants was 68.4%. For seropositive RA patients only, the AUC improved to 70.9%, indicating good but suboptimal predictive ability. A simulation study shows that more than 200 additional loci with similar effect size as recent GWAS findings or 20 rare variants with intermediate effects are needed to achieve AUC = 80.0%. We performed the random walk with restart (RWR algorithm to prioritize genes for future mapping studies. The performance of the algorithm was confirmed by leave-one-out cross-validation. The RWR algorithm pointed to ZAP70 in the first rank, in which mutation causes RA-like autoimmune arthritis in mice. By applying the hierarchical clustering method to a subnetwork comprising RA-associated genes and top-ranked genes by the RWR, we found three functional modules relevant to RA etiology: "leukocyte activation and differentiation", "pattern-recognition receptor signaling pathway", and "chemokines and their receptors".These results suggest that the systems genetics approach is useful to find directions of future mapping strategies to illuminate

  16. Human-competitive evolution of quantum computing artefacts by Genetic Programming.

    Science.gov (United States)

    Massey, Paul; Clark, John A; Stepney, Susan

    2006-01-01

    We show how Genetic Programming (GP) can be used to evolve useful quantum computing artefacts of increasing sophistication and usefulness: firstly specific quantum circuits, then quantum programs, and finally system-independent quantum algorithms. We conclude the paper by presenting a human-competitive Quantum Fourier Transform (QFT) algorithm evolved by GP.

  17. Energy Efficient Routing in Wireless Sensor Networks: A Genetic Approach

    CERN Document Server

    Chakraborty, Ayon; Naskar, Mrinal Kanti

    2011-01-01

    The key parameters that need to be addressed while designing protocols for sensor networks are its energy awareness and computational feasibility in resource constrained sensor nodes. Variation in the distances of nodes from the Base Station and differences in inter-nodal distances are primary factors causing unequal energy dissipation among the nodes. Thus energy difference among the nodes increases with time resulting in degraded network performance. The LEACH and PEGASIS schemes which provided elegant solutions to the problem suffer due to randomization of cluster heads and greedy chain formation respectively. In this paper, we propose a Genetic algorithm inspired ROUting Protocol (GROUP) which shows enhanced performance in terms of energy efficiency and network lifetime over other schemes. GROUP increases the network performance by ensuring a sub-optimal energy dissipation of the individual nodes despite their random deployment. It employs modern heuristics like Genetic Algorithms along with Simulated Ann...

  18. Genetic Approach to Elucidation of Sasang Constitutional Medicine

    OpenAIRE

    Bu-Yeo Kim; Seongwon Cha; Hee-Jeong Jin; Sangkyun Jeong

    2009-01-01

    Sasang Constitutional Medicine (SCM) offers a medical principle that classifies humans into four constitution groups and guides their treatment with constitution-matched medical assistance. The principle of this traditional medicine, although requires significant scientific support, appears to suggest a genetic influence on constitution type. The relative frequency of constitution types in a population, for instance, has remained relatively constant since Jema Lee first described them from hi...

  19. Hierarchical Genetic Algorithm Approach to Determine Pulse Sequences in NMR

    CERN Document Server

    Ajoy, Ashok

    2009-01-01

    We develop a new class of genetic algorithm that computationally determines efficient pulse sequences to implement a quantum gate U in a three-qubit system. The method is shown to be quite general, and the same algorithm can be used to derive efficient sequences for a variety of target matrices. We demonstrate this by implementing the inversion-on-equality gate efficiently when the spin-spin coupling constants $J_{12}=J_{23}=J$ and $J_{13}=0$. We also propose new pulse sequences to implement the Parity gate and Fanout gate, which are about 50% more efficient than the previous best efforts. Moreover, these sequences are shown to require significantly less RF power for their implementation. The proposed algorithm introduces several new features in the conventional genetic algorithm framework. We use matrices instead of linear chains, and the columns of these matrices have a well defined hierarchy. The algorithm is a genetic algorithm coupled to a fast local optimizer, and is hence a hybrid GA. It shows fast con...

  20. Genetic Programming Applied to Base-Metal Prospectivity Mapping in the Aravalli Province, India

    Science.gov (United States)

    Lewkowski, Christopher; Porwal, Alok; González-Álvarez, Ignacio

    2010-05-01

    Genetic Programming Applied to Base-Metal Prospectivity Mapping in the Aravalli Province, India Mineral prospectivity mapping of an area involves demarcation of potentially mineralized zones based on geologic features associated with the targeted mineral deposits. These features are sometimes directly observable and mapped; more often, their presence is inferred from their responses in various geoscience datasets, which are appropriately processed, generally in a GIS software environment, to derive their spatial proxies, also called predictor maps layers. Most approaches to mineral prospectivity mapping use mathematical models to approximate the relation between predictor map layers and the presence (or absence) of the targeted mineral deposits and to label unique combinations of spatially coincident predictor map layers as mineralized or barren. Essentially, the procedure involves recognizing and distinguishing the patterns of predictor map layers associated with mineralized locations from those associated with barren locations. Machine learning algorithms such as neural networks, support vector machines, and Bayesian classifiers are highly efficient pattern recognizers and classifiers. They are being increasingly applied to mineral prospectivity mapping, within or outside a GIS environment. However, most of these algorithms have a black-box-type implementation, that is, the output of these models do not generate new conceptual geological knowledge about the relative importance of various variables and their mutual relationships. Genetic Programming (GP) is a category of machine learning algorithms that address this problem effectively. In addition to generating the output classification map, GP also generates a set of rules that reveal the mutual relationships of the predictor variables, based on empirical analyses. These rules can be used to validate conceptual knowledge against empirical data, and also reveal new patterns in the data, resulting in new

  1. Interdisciplinary approach to a total knee replacement program.

    Science.gov (United States)

    Seemann, S

    2000-06-01

    An orthopedic clinical nurse specialist facilitated an interdisciplinary evaluation, design, and implementation of best practice initiatives for the total knee replacement patient population using the Center for Advanced Nursing Practice's Evidence-Based Practice Model. The interdisciplinary team approach enhanced the total joint program by achieving positive patient outcomes, demonstrating financial stewardship of resources, and facilitating inter- and intradisciplinary communication.

  2. Dynamic Programming Approach for Exact Decision Rule Optimization

    KAUST Repository

    Amin, Talha

    2013-01-01

    This chapter is devoted to the study of an extension of dynamic programming approach that allows sequential optimization of exact decision rules relative to the length and coverage. It contains also results of experiments with decision tables from UCI Machine Learning Repository. © Springer-Verlag Berlin Heidelberg 2013.

  3. A Relational Approach to International Education through Homestay Programs

    Science.gov (United States)

    Kobayashi, Junko; Viswat, Linda

    2015-01-01

    This paper identifies and analyzes intercultural problems through surveys of homestay programs with Japanese students and American host mothers. Given that participants need to go beyond their cognitive knowledge to interact effectively with people from other cultures, a relational approach may be more effective than traditional intercultural…

  4. Training Program Handbook: A systematic approach to training

    Energy Technology Data Exchange (ETDEWEB)

    1994-08-01

    This DOE handbook describes a systematic method for establishing and maintaining training programs that meet the requirements and expectations of DOE Orders 5480.18B and 5480.20. The systematic approach to training includes 5 phases: Analysis, design, development, implementation, and evaluation.

  5. A Dialogue Game Approach to Multi-Agent System Programming

    NARCIS (Netherlands)

    Lebbink, Henk-Jan; Witteman, Cilia; Meyer, John-Jules Ch.

    2005-01-01

    This paper approaches multi-agent system programming with dialogue games allowing the semantics of communicative acts to be a component in multi-agent architectures. We present a dialogue game for enquiry enabling agents to answer questions in a distributed fashion. In addition, we propose a reasoni

  6. On the path to genetic novelties: insights from programmed DNA elimination and RNA splicing.

    Science.gov (United States)

    Catania, Francesco; Schmitz, Jürgen

    2015-01-01

    Understanding how genetic novelties arise is a central goal of evolutionary biology. To this end, programmed DNA elimination and RNA splicing deserve special consideration. While programmed DNA elimination reshapes genomes by eliminating chromatin during organismal development, RNA splicing rearranges genetic messages by removing intronic regions during transcription. Small RNAs help to mediate this class of sequence reorganization, which is not error-free. It is this imperfection that makes programmed DNA elimination and RNA splicing excellent candidates for generating evolutionary novelties. Leveraging a number of these two processes' mechanistic and evolutionary properties, which have been uncovered over the past years, we present recently proposed models and empirical evidence for how splicing can shape the structure of protein-coding genes in eukaryotes. We also chronicle a number of intriguing similarities between the processes of programmed DNA elimination and RNA splicing, and highlight the role that the variation in the population-genetic environment may play in shaping their target sequences.

  7. A statistical approach to quantification of genetically modified organisms (GMO) using frequency distributions.

    Science.gov (United States)

    Gerdes, Lars; Busch, Ulrich; Pecoraro, Sven

    2014-12-14

    According to Regulation (EU) No 619/2011, trace amounts of non-authorised genetically modified organisms (GMO) in feed are tolerated within the EU if certain prerequisites are met. Tolerable traces must not exceed the so-called 'minimum required performance limit' (MRPL), which was defined according to the mentioned regulation to correspond to 0.1% mass fraction per ingredient. Therefore, not yet authorised GMO (and some GMO whose approvals have expired) have to be quantified at very low level following the qualitative detection in genomic DNA extracted from feed samples. As the results of quantitative analysis can imply severe legal and financial consequences for producers or distributors of feed, the quantification results need to be utterly reliable. We developed a statistical approach to investigate the experimental measurement variability within one 96-well PCR plate. This approach visualises the frequency distribution as zygosity-corrected relative content of genetically modified material resulting from different combinations of transgene and reference gene Cq values. One application of it is the simulation of the consequences of varying parameters on measurement results. Parameters could be for example replicate numbers or baseline and threshold settings, measurement results could be for example median (class) and relative standard deviation (RSD). All calculations can be done using the built-in functions of Excel without any need for programming. The developed Excel spreadsheets are available (see section 'Availability of supporting data' for details). In most cases, the combination of four PCR replicates for each of the two DNA isolations already resulted in a relative standard deviation of 15% or less. The aims of the study are scientifically based suggestions for minimisation of uncertainty of measurement especially in -but not limited to- the field of GMO quantification at low concentration levels. Four PCR replicates for each of the two DNA isolations

  8. Linear decomposition approach for a class of nonconvex programming problems.

    Science.gov (United States)

    Shen, Peiping; Wang, Chunfeng

    2017-01-01

    This paper presents a linear decomposition approach for a class of nonconvex programming problems by dividing the input space into polynomially many grids. It shows that under certain assumptions the original problem can be transformed and decomposed into a polynomial number of equivalent linear programming subproblems. Based on solving a series of liner programming subproblems corresponding to those grid points we can obtain the near-optimal solution of the original problem. Compared to existing results in the literature, the proposed algorithm does not require the assumptions of quasi-concavity and differentiability of the objective function, and it differs significantly giving an interesting approach to solving the problem with a reduced running time.

  9. A Matheuristic Approach Combining Local Search and Mathematical Programming

    Directory of Open Access Journals (Sweden)

    Carolina Lagos

    2016-01-01

    Full Text Available A novel matheuristic approach is presented and tested on a well-known optimisation problem, namely, capacitated facility location problem (CFLP. The algorithm combines local search and mathematical programming. While the local search algorithm is used to select a subset of promising facilities, mathematical programming strategies are used to solve the subproblem to optimality. Proposed local search is influenced by instance-specific information such as installation cost and the distance between customers and facilities. The algorithm is tested on large instances of the CFLP, where neither local search nor mathematical programming is able to find good quality solutions within acceptable computational times. Our approach is shown to be a very competitive alternative to solve large-scale instances for the CFLP.

  10. Food control and a citizen science approach for improving teaching of Genetics in universities.

    Science.gov (United States)

    Borrell, Y J; Muñoz-Colmenero, A M; Dopico, E; Miralles, L; Garcia-Vazquez, E

    2016-09-10

    A Citizen Science approach was implemented in the laboratory practices of Genetics at the University of Oviedo, related with the engaging topic of Food Control. Real samples of food products consumed by students at home (students as samplers) were employed as teaching material in three different courses of Genetics during the academic year 2014-2015: Experimental Methods in Food Production (MBTA) (Master level), and Applied Molecular Biology (BMA) and Conservation Genetics and Breeding (COMGE) (Bachelor/Degree level). Molecular genetics based on PCR amplification of DNA markers was employed for species identification of 22 seafood products in COMGE and MBTA, and for detection of genetically modified (GM) maize from nine products in BMA. In total six seafood products incorrectly labeled (27%), and two undeclared GM maize (22%) were found. A post-Laboratory survey was applied for assessing the efficacy of the approach for improving motivation in the Laboratory Practices of Genetics. Results confirmed that students that worked on their own samples from local markets were significantly more motivated and better evaluated their Genetic laboratory practices than control students (χ(2)  = 12.11 p = 0.033). Our results suggest that citizen science approaches could not be only useful for improving teaching of Genetics in universities but also to incorporate students and citizens as active agents in food control. © 2016 by The International Union of Biochemistry and Molecular Biology, 44(5):450-462, 2016.

  11. Runtime software adaptation: approaches and a programming tool

    Directory of Open Access Journals (Sweden)

    Jarosław Rudy

    2012-03-01

    Full Text Available Software systems steadily tend to be bigger and more complex, making it more difficult to change them, especially during runtime. Several types of runtime software adaptation approaches were proposed to increase the adaptation capability of applications and turn them into an evolution software. Many of these approaches (using software architectural models for example are implemented during the design phase of software development life cycle, making them ineffective or difficult to use in case of already existing applications. Moreover, the overhead caused by the use of these approaches has not been determined in many cases. In this paper author presents the taxonomy of high- and low-level approaches to runtime software adaptation and then introduces a lightweight prototype programming tool used to add runtime code modification capability (via function hotswapping to existing applications written in C++ and run under Linux. The tool also enables to replace a defective function by its older or corrected version at runtime. Several tests were prepared to compare traditional C++ applications with the same applications developed with the aforementioned programming tool. Applications were compared in terms of execution time, size of executable code and memory usage. Different size and number of functions have been considered. The paper also researches the constant overhead caused by the programming tool regardless of the target application. The paper ends with the summary of presented approaches and their characteristics, including effects on the targeted systems, capabilities, ease of use, level of abstraction etc.

  12. Forecasting Shaharchay River Flow in Lake Urmia Basin using Genetic Programming and M5 Model Tree

    Directory of Open Access Journals (Sweden)

    S. Samadianfard

    2017-01-01

    Full Text Available Introduction: Precise prediction of river flows is the key factor for proper planning and management of water resources. Thus, obtaining the reliable methods for predicting river flows has great importance in water resource engineering. In the recent years, applications of intelligent methods such as artificial neural networks, fuzzy systems and genetic programming in water science and engineering have been grown extensively. These mentioned methods are able to model nonlinear process of river flows without any need to geometric properties. A huge number of studies have been reported in the field of using intelligent methods in water resource engineering. For example, Noorani and Salehi (23 presented a model for predicting runoff in Lighvan basin using adaptive neuro-fuzzy network and compared the performance of it with neural network and fuzzy inference methods in east Azerbaijan, Iran. Nabizadeh et al. (21 used fuzzy inference system and adaptive neuro-fuzzy inference system in order to predict river flow in Lighvan river. Khalili et al. (13 proposed a BL-ARCH method for prediction of flows in Shaharchay River in Urmia. Khu et al. (16 used genetic programming for runoff prediction in Orgeval catchment in France. Firat and Gungor (11 evaluated the fuzzy-neural model for predicting Mendes river flow in Turkey. The goal of present study is comparing the performance of genetic programming and M5 model trees for prediction of Shaharchay river flow in the basin of Lake Urmia and obtaining a comprehensive insight of their abilities. Materials and Methods: Shaharchay river as a main source of providing drinking water of Urmia city and agricultural needs of surrounding lands and finally one of the main input sources of Lake Urmia is quite important in the region. For obtaining the predetermined goals of present study, average monthly flows of Shaharchay River in Band hydrometric station has been gathered from 1951 to 2011. Then, two third of mentioned

  13. Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming

    Directory of Open Access Journals (Sweden)

    Hongguang Liu

    2016-12-01

    Full Text Available Technical analysis has been proved to be capable of exploiting short-term fluctuations in financial markets. Recent results indicate that the market timing approach beats many traditional buy-and-hold approaches in most of the short-term trading periods. Genetic programming (GP was used to generate short-term trade rules on the stock markets during the last few decades. However, few of the related studies on the analysis of financial time series with genetic programming considered the non-stationary and noisy characteristics of the time series. In this paper, to de-noise the original financial time series and to search profitable trading rules, an integrated method is proposed based on the Wavelet Threshold (WT method and GP. Since relevant information that affects the movement of the time series is assumed to be fully digested during the market closed periods, to avoid the jumping points of the daily or monthly data, in this paper, intra-day high-frequency time series are used to fully exploit the short-term forecasting advantage of technical analysis. To validate the proposed integrated approach, an empirical study is conducted based on the China Securities Index (CSI 300 futures in the emerging China Financial Futures Exchange (CFFEX market. The analysis outcomes show that the wavelet de-noise approach outperforms many comparative models.

  14. The practice of genetic counselling: a Ccmparative approach to understanding genetic counselling in China

    NARCIS (Netherlands)

    Suli, S.

    2009-01-01

    This article provides an empirical account of the application of genetic counselling in China based on interviews, clinical observation and literature research during a field study from September 2008 to February 2009, carried out mainly in China and partly in Hong Kong and the United Kingdom.

  15. The practice of genetic counselling: a Ccmparative approach to understanding genetic counselling in China

    NARCIS (Netherlands)

    Suli, S.

    2009-01-01

    This article provides an empirical account of the application of genetic counselling in China based on interviews, clinical observation and literature research during a field study from September 2008 to February 2009, carried out mainly in China and partly in Hong Kong and the United Kingdom. Makin

  16. Invasion success in Cogongrass (Imperata cylindrica): A population genetic approach exploring genetic diversity and historical introductions

    Science.gov (United States)

    Rima D. Lucardi; Lisa E. Wallace; Gary N. Ervin

    2014-01-01

    Propagule pressure significantly contributes to and limits the potential success of a biological invasion, especially during transport, introduction, and establishment. Events such as multiple introductions of foreign parent material and gene flow among them can increase genetic diversity in founding populations, often leading to greater invasion success. We applied...

  17. Linkage intensity learning approach with genetic algorithm for causality diagram

    Institute of Scientific and Technical Information of China (English)

    WANG Cheng-liang; CHEN Juan-juan

    2007-01-01

    The causality diagram theory, which adopts graphical expression of knowledge and direct intensity of causality, overcomes some shortages in belief network and has evolved into a mixed causality diagram methodology for discrete and continuous variable. But to give linkage intensity of causality diagram is difficult, particularly in many working conditions in which sampling data are limited or noisy. The classic learning algorithm is hard to be adopted. We used genetic algorithm to learn linkage intensity from limited data. The simulation results demonstrate that this algorithm is more suitable than the classic algorithm in the condition of sample shortage such as space shuttle's fault diagnoisis.

  18. A genetic algorithm approach for assessing soil liquefaction potential based on reliability method

    Indian Academy of Sciences (India)

    M H Bagheripour; I Shooshpasha; M Afzalirad

    2012-02-01

    Deterministic approaches are unable to account for the variations in soil’s strength properties, earthquake loads, as well as source of errors in evaluations of liquefaction potential in sandy soils which make them questionable against other reliability concepts. Furthermore, deterministic approaches are incapable of precisely relating the probability of liquefaction and the factor of safety (FS). Therefore, the use of probabilistic approaches and especially, reliability analysis is considered since a complementary solution is needed to reach better engineering decisions. In this study, Advanced First-Order Second-Moment (AFOSM) technique associated with genetic algorithm (GA) and its corresponding sophisticated optimization techniques have been used to calculate the reliability index and the probability of liquefaction. The use of GA provides a reliable mechanism suitable for computer programming and fast convergence. A new relation is developed here, by which the liquefaction potential can be directly calculated based on the estimated probability of liquefaction (), cyclic stress ratio (CSR) and normalized standard penetration test (SPT) blow counts while containing a mean error of less than 10% from the observational data. The validity of the proposed concept is examined through comparison of the results obtained by the new relation and those predicted by other investigators. A further advantage of the proposed relation is that it relates and FS and hence it provides possibility of decision making based on the liquefaction risk and the use of deterministic approaches. This could be beneficial to geotechnical engineers who use the common methods of FS for evaluation of liquefaction. As an application, the city of Babolsar which is located on the southern coasts of Caspian Sea is investigated for liquefaction potential. The investigation is based primarily on in situ tests in which the results of SPT are analysed.

  19. Classical mechanics approach applied to analysis of genetic oscillators.

    Science.gov (United States)

    Vasylchenkova, Anastasiia; Mraz, Miha; Zimic, Nikolaj; Moskon, Miha

    2016-04-05

    Biological oscillators present a fundamental part of several regulatory mechanisms that control the response of various biological systems. Several analytical approaches for their analysis have been reported recently. They are, however, limited to only specific oscillator topologies and/or to giving only qualitative answers, i.e., is the dynamics of an oscillator given the parameter space oscillatory or not. Here we present a general analytical approach that can be applied to the analysis of biological oscillators. It relies on the projection of biological systems to classical mechanics systems. The approach is able to provide us with relatively accurate results in the meaning of type of behaviour system reflects (i.e. oscillatory or not) and periods of potential oscillations without the necessity to conduct expensive numerical simulations. We demonstrate and verify the proposed approach on three different implementations of amplified negative feedback oscillator.

  20. Introduction: integrating genetic and cultural evolutionary approaches to language.

    Science.gov (United States)

    Mesoudi, Alex; McElligott, Alan G; Adger, David

    2011-04-01

    The papers in this special issue of Human Biology address recent research in the field of language evolution, both the genetic evolution of the language faculty and the cultural evolution of specific languages. While both of these areas have received increasing interest in recent years, there is also a need to integrate these somewhat separate efforts and explore the relevant gene-culture coevolutionary interactions. Here we summarize the individual contributions, set them in the context of the wider literature, and identify outstanding future research questions. The first set of papers concerns the comparative study of nonhuman communication in primates and birds from both a behavioral and neurobiological perspective, revealing evidence for several common language-related traits in various nonhuman species and providing clues as to the evolutionary origin and function of the human language faculty. The second set of papers discusses the consequences of viewing language as a culturally evolving system in its own right, including claims that this removes the need for strong genetic biases for language acquisition, and that phylogenetic evolutionary methods can be used to reconstruct language histories. We conclude by highlighting outstanding areas for future research, including identifying the precise selection pressures that gave rise to the language faculty in ancestral hominin species, and determining the strength, domain specificity, and origin of the cultural transmission biases that shape languages as they pass along successive generations of language learners.

  1. A Bootstrap Approach to an Affordable Exploration Program

    Science.gov (United States)

    Oeftering, Richard C.

    2011-01-01

    This paper examines the potential to build an affordable sustainable exploration program by adopting an approach that requires investing in technologies that can be used to build a space infrastructure from very modest initial capabilities. Human exploration has had a history of flight programs that have high development and operational costs. Since Apollo, human exploration has had very constrained budgets and they are expected be constrained in the future. Due to their high operations costs it becomes necessary to consider retiring established space facilities in order to move on to the next exploration challenge. This practice may save cost in the near term but it does so by sacrificing part of the program s future architecture. Human exploration also has a history of sacrificing fully functional flight hardware to achieve mission objectives. An affordable exploration program cannot be built when it involves billions of dollars of discarded space flight hardware, instead, the program must emphasize preserving its high value space assets and building a suitable permanent infrastructure. Further this infrastructure must reduce operational and logistics cost. The paper examines the importance of achieving a high level of logistics independence by minimizing resource consumption, minimizing the dependency on external logistics, and maximizing the utility of resources available. The approach involves the development and deployment of a core suite of technologies that have minimum initial needs yet are able expand upon initial capability in an incremental bootstrap fashion. The bootstrap approach incrementally creates an infrastructure that grows and becomes self sustaining and eventually begins producing the energy, products and consumable propellants that support human exploration. The bootstrap technologies involve new methods of delivering and manipulating energy and materials. These technologies will exploit the space environment, minimize dependencies, and

  2. Building enterprise reuse program--A model-based approach

    Institute of Scientific and Technical Information of China (English)

    梅宏; 杨芙清

    2002-01-01

    Reuse is viewed as a realistically effective approach to solving software crisis. For an organization that wants to build a reuse program, technical and non-technical issues must be considered in parallel. In this paper, a model-based approach to building systematic reuse program is presented. Component-based reuse is currently a dominant approach to software reuse. In this approach, building the right reusable component model is the first important step. In order to achieve systematic reuse, a set of component models should be built from different perspectives. Each of these models will give a specific view of the components so as to satisfy different needs of different persons involved in the enterprise reuse program. There already exist some component models for reuse from technical perspectives. But less attention is paid to the reusable components from a non-technical view, especially from the view of process and management. In our approach, a reusable component model--FLP model for reusable component--is introduced. This model describes components from three dimensions (Form, Level, and Presentation) and views components and their relationships from the perspective of process and management. It determines the sphere of reusable components, the time points of reusing components in the development process, and the needed means to present components in terms of the abstraction level, logic granularity and presentation media. Being the basis on which the management and technical decisions are made, our model will be used as the kernel model to initialize and normalize a systematic enterprise reuse program.

  3. Solution for integer linear bilevel programming problems using orthogonal genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    Hong Li; Li Zhang; Yongchang Jiao

    2014-01-01

    An integer linear bilevel programming problem is firstly transformed into a binary linear bilevel programming problem, and then converted into a single-level binary implicit program-ming. An orthogonal genetic algorithm is developed for solving the binary linear implicit programming problem based on the ortho-gonal design. The orthogonal design with the factor analysis, an experimental design method is applied to the genetic algorithm to make the algorithm more robust, statistical y sound and quickly convergent. A crossover operator formed by the orthogonal array and the factor analysis is presented. First, this crossover operator can generate a smal but representative sample of points as off-spring. After al of the better genes of these offspring are selected, a best combination among these offspring is then generated. The simulation results show the effectiveness of the proposed algo-rithm.

  4. AGROBACTERIUM-MEDIATED GENETIC TRANSFORMATION OF SORGHUM USING TISSUE CULTURE-BASED AND POLLEN-MEDIATED APPROACHES

    OpenAIRE

    Elkonin L.A.; O.N. Nosova; J.V. Italianskaya

    2012-01-01

    Genetic transformation is a powerful tool for genetic improvement of arable crops. Genetic engineering approaches are especially important for modification of starch and protein contents, vitamin and micronutrient concentration, improvement of nutritive value of protein fractions, and increase tolerance to environmental stresses. Application of transgenic technologies for genetic improvement of sorghum, a highly productive heat tolerant and drought resistant crop, is extremely important since...

  5. An epidemiologic approach to computerized medical diagnosis--AEDMI program.

    Science.gov (United States)

    Ferrer Salvans, P; Alonso Vallès, L

    1990-01-01

    A program called "An Epidemiological Approach to Computerized Medical Diagnosis" (AEDMI) is presented. Using an interactive questionnaire, physician-patient interviews are conducted and a summary of the relevant clinical data is provided. Standard items, obtained on a multi-centre basis, form a large-scale data base. Simultaneously, the reasoning of clinical experts in each real case is analyzed to obtain a knowledge-rules data base. The methodology of the program combines Bayesian systems, expert systems, and other new lines of research such as neural networks or case-based reasoning. The general concepts of clinical decision making aid systems are reviewed. This publication is aimed at obtaining international cooperation.

  6. The Sociopolitical Importance of Genetic, Phenomenological Approaches to Science Teaching and Learning

    Science.gov (United States)

    Bazzul, Jesse

    2015-01-01

    This article discusses Wolff-Michael Roth's theoretical framework for a phenomenological, genetic approach to science teaching and learning based on the work of Edmund Husserl. This approach advocates the inclusion of student lifeworlds in science education and underlines the importance of thinking about subjectivity in both science and science…

  7. A Genetic Algorithms-based Approach for Optimized Self-protection in a Pervasive Service Middleware

    DEFF Research Database (Denmark)

    Zhang, Weishan; Ingstrup, Mads; Hansen, Klaus Marius

    2009-01-01

    the constraints of heterogeneous devices and networks. In this paper, we present a Genetic Algorithms-based approach for obtaining optimized security configurations at run time, supported by a set of security OWL ontologies and an event-driven framework. This approach has been realized as a prototype for self...

  8. The Sociopolitical Importance of Genetic, Phenomenological Approaches to Science Teaching and Learning

    Science.gov (United States)

    Bazzul, Jesse

    2015-01-01

    This article discusses Wolff-Michael Roth's theoretical framework for a phenomenological, genetic approach to science teaching and learning based on the work of Edmund Husserl. This approach advocates the inclusion of student lifeworlds in science education and underlines the importance of thinking about subjectivity in both science and science…

  9. Evaluating a physician leadership development program - a mixed methods approach.

    Science.gov (United States)

    Throgmorton, Cheryl; Mitchell, Trey; Morley, Tom; Snyder, Marijo

    2016-05-16

    Purpose - With the extent of change in healthcare today, organizations need strong physician leaders. To compensate for the lack of physician leadership education, many organizations are sending physicians to external leadership programs or developing in-house leadership programs targeted specifically to physicians. The purpose of this paper is to outline the evaluation strategy and outcomes of the inaugural year of a Physician Leadership Academy (PLA) developed and implemented at a Michigan-based regional healthcare system. Design/methodology/approach - The authors applied the theoretical framework of Kirkpatrick's four levels of evaluation and used surveys, observations, activity tracking, and interviews to evaluate the program outcomes. The authors applied grounded theory techniques to the interview data. Findings - The program met targeted outcomes across all four levels of evaluation. Interview themes focused on the significance of increasing self-awareness, building relationships, applying new skills, and building confidence. Research limitations/implications - While only one example, this study illustrates the importance of developing the evaluation strategy as part of the program design. Qualitative research methods, often lacking from learning evaluation design, uncover rich themes of impact. The study supports how a PLA program can enhance physician learning, engagement, and relationship building throughout and after the program. Physician leaders' partnership with organization development and learning professionals yield results with impact to individuals, groups, and the organization. Originality/value - Few studies provide an in-depth review of evaluation methods and outcomes of physician leadership development programs. Healthcare organizations seeking to develop similar in-house programs may benefit applying the evaluation strategy outlined in this study.

  10. An approach for in vitro genetic networks assembly

    Science.gov (United States)

    Noireaux, Vincent; Bar-Ziv, Roy; Libchaber, Albert

    2004-03-01

    A cell-free expression extract has been used to assemble genetic circuits in vitro. The extract, which does not contained endogenous DNA and RNA, is used as a battery to carry out transcription and translation of genes inserted into plasmids. We engineered transcriptional activation and repression cascades, in which the protein product of each stage is the input required to drive or block the following stage. Although we can find regions of linear response for single stages, cascading to subsequent stages requires working in non-linear regimes. Substantial time delays and dramatic decreases in output production are incurred with each additional stage, due to a bottleneck at the translation machinery. Faster turnover of RNA message can relieve competition between genes and stabilize output against variations in input and parameters.

  11. Data Mining Using Neural–Genetic Approach: A Review

    Directory of Open Access Journals (Sweden)

    Parvez Rahi

    2014-04-01

    Full Text Available In the advance age of technology, there is an increasing availability of digital documents in various languages in various fields. Data mining is gaining popularity in field of knowledge discovery. Data mining is the knowledge discovery process by which we can analyze the large amounts of data from various data repositories and summarizing it into information useful to us. Due to its importance of extracting information/ knowledge from the large data repositories, data mining has become an essential part of human life in various fields. Data mining has a very wide area of applications, and these applications have enriched the human life in various fields including scientific, medical, business, education etc. Here in this paper we will discuss the emphasis of Neural Network and Genetic Algorithm in the field of data mining.

  12. A genetic approach to understanding asthma and lung function development

    DEFF Research Database (Denmark)

    Kreiner-Møller, Eskil

    2014-01-01

    Asthma is a common heritable disease of the airways with recurrent episodes of symptoms and reversible airflow obstruction that has increased dramatically in prevalence. The disease is highly heterogeneous with varying age at onset and clinical presentation and most likely represents several...... different subtypes of disease associated with distinct clinical features, divergent underlying molecular mechanisms, and individual treatment responses. Information obtained from genetic studies may be an important way of understanding underlying disease subtypes. Genome wide association studies (GWAS) have......, related traits and objective measures in order to disentangle the underlying pathophysiological disease mechanisms for the subtypes of disease. Several genes and loci have been found to be associated with adult lung function in GWAS, but it is currently unknown at what time in life these genes exert...

  13. [Mendelian randomisation - a genetic approach to an epidemiological method].

    Science.gov (United States)

    Stensrud, Mats Julius

    2016-06-01

    BACKGROUND Genetic information is becoming more easily available, and rapid progress is being made in developing methods of illuminating issues of interest. Mendelian randomisation makes it possible to study causes of disease using observational data. The name refers to the random distribution of gene variants in meiosis. The methodology makes use of genes that influence a risk factor for a disease, without influencing the disease itself. In this review article I explain the principles behind Mendelian randomisation and present the areas of application for this methodology.MATERIAL AND METHOD Methodology articles describing Mendelian randomisation were reviewed. The articles were found through a search in PubMed with the combination «mendelian randomization» OR «mendelian randomisation», and a search in McMaster Plus with the combination «mendelian randomization». A total of 15 methodology articles were read in full text. Methodology articles were supplemented by clinical studies found in the PubMed search.RESULTS In contrast to traditional observational studies, Mendelian randomisation studies are not affected by two important sources of error: conventional confounding variables and reverse causation. Mendelian randomisation is therefore a promising tool for studying causality. Mendelian randomisation studies have already provided valuable knowledge on the risk factors for a wide range of diseases. It is nevertheless important to be aware of the limitations of the methodology. As a result of the rapid developments in genetics research, Mendelian randomisation will probably be widely used in future years.INTERPRETATION If Mendelian randomisation studies are conducted correctly, they may help to reveal both modifiable and non-modifiable causes of disease.

  14. Academic training: From Evolution Theory to Parallel and Distributed Genetic Programming

    CERN Multimedia

    2007-01-01

    2006-2007 ACADEMIC TRAINING PROGRAMME LECTURE SERIES 15, 16 March From 11:00 to 12:00 - Main Auditorium, bldg. 500 From Evolution Theory to Parallel and Distributed Genetic Programming F. FERNANDEZ DE VEGA / Univ. of Extremadura, SP Lecture No. 1: From Evolution Theory to Evolutionary Computation Evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) involving combinatorial optimization problems, which are based to some degree on the evolution of biological life in the natural world. In this tutorial we will review the source of inspiration for this metaheuristic and its capability for solving problems. We will show the main flavours within the field, and different problems that have been successfully solved employing this kind of techniques. Lecture No. 2: Parallel and Distributed Genetic Programming The successful application of Genetic Programming (GP, one of the available Evolutionary Algorithms) to optimization problems has encouraged an ...

  15. QUADRATIC BI-LEVEL PROGRAMMING PROBLEM BASED ON FUZZY GOAL PROGRAMMING APPROACH

    Directory of Open Access Journals (Sweden)

    Partha Pratim Dey

    2011-11-01

    Full Text Available This paper presents fuzzy goal programming approach to quadratic bi-level programming problem. Inthe model formulation of the problem, we construct the quadratic membership functions by determiningindividual best solutions of the quadratic objective functions subject to the system constraints. Thequadratic membership functions are then transformed into equivalent linear membership functions byfirst order Taylor series approximation at the individual best solution point. Since the objectives of upperand lower level decision makers are potentially conflicting in nature, a possible relaxation of each leveldecisions are considered by providing preference bounds on the decision variables for avoiding decisiondeadlock. Then fuzzy goal programming approach is used for achieving highest degree of each of themembership goals by minimizing deviational variables. Numerical examples are provided in order todemonstrate the efficiency of the proposed approach.

  16. Genetic Approaches to Appearance and Ancestry : Improving Forensic DNA Analysis

    NARCIS (Netherlands)

    L.C. Chaitanya (Lakshmi)

    2016-01-01

    textabstractTraditionally, routine forensic casework is based on comparative grounds. DNA profiles obtained from crime-scenes are compared with those of potential suspects or DNA profiles deposited in forensic DNA databases. The principal limitation of such comparative approach is that trace donors

  17. Molecular Genetic Approaches to Human Diseases Involving Mental Retardation.

    Science.gov (United States)

    Latt, Samuel A.; And Others

    1984-01-01

    Recombinant DNA techniques provide new approaches to the diagnosis and analysis of inherited human diseases associated with mental retardation, such as Lesch-Nyhan syndrome, phenylketonauria, the Fragile X syndrome, Down syndrome, and those associated with deletions or duplications of subchromosomal regions. (Author/CL)

  18. Genetic and metabolomic approaches for coronary heart disease risk prediction

    NARCIS (Netherlands)

    Vaarhorst, Anika Antoinette Maria

    2014-01-01

    The prediction of coronary heart disease (CHD) risk is currently based on traditional risk factors (TRFs) like age, sex, lipid levels, blood pressure. Here we investigated, using the CAREMA cohort, whether this prediction can potentially be improved by applying a metabolomics approach and by includi

  19. Penalized interior point approach for constrained nonlinear programming

    Institute of Scientific and Technical Information of China (English)

    LU Wen-ting; YAO Yi-rong; ZHANG Lian-sheng

    2009-01-01

    A penalized interior point approach for constrained nonlinear programming is examined in this work. To overcome the difficulty of initialization for the interior point method, a problem equivalent to the primal problem via incorporating an auxiliary variable is constructed. A combined approach of logarithm barrier and quadratic penalty function is proposed to solve the problem. Based on Newton's method, the global convergence of interior point and line search algorithm is proven.Only a finite number of iterations is required to reach an approximate optimal solution. Numerical tests are given to show the effectiveness of the method.

  20. Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer

    Directory of Open Access Journals (Sweden)

    Mauro Castelli

    2015-01-01

    Full Text Available Energy consumption forecasting (ECF is an important policy issue in today’s economies. An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs. The paper proposes a semantic based genetic programming framework to address the ECF problem. In particular, we propose a system that finds (quasi-perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data. The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. Experimental results confirm the suitability of the proposed method in predicting the energy consumption. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data.

  1. Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer.

    Science.gov (United States)

    Castelli, Mauro; Trujillo, Leonardo; Vanneschi, Leonardo

    2015-01-01

    Energy consumption forecasting (ECF) is an important policy issue in today's economies. An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs. The paper proposes a semantic based genetic programming framework to address the ECF problem. In particular, we propose a system that finds (quasi-)perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data. The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. Experimental results confirm the suitability of the proposed method in predicting the energy consumption. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data.

  2. Analysis of genetic structure and relationship among nine indigenous Chinese chicken populations by the Structure program

    Indian Academy of Sciences (India)

    H. F. Li; W. Han; Y. F. Zhu; J. T. Shu; X. Y. Zhang; K. W. Chen

    2009-08-01

    The multi-locus model-based clustering method Structure program was used to infer the genetic structure of nine indigenous Chinese chicken (Gallus gallus) populations based on 16 microsatellite markers. Twenty runs were carried out at each chosen value of predefined cluster numbers $(K)$ under admixture model. The Structure program properly inferred the presence of genetic structure with 0.999 probabilities. The genetic structure not only indicated that the nine kinds of chicken populations were defined actually by their locations, phenotypes or culture, but also reflected the underlying genetic variations. At $K = 2$, nine chicken populations were divided into two main clusters, one light-body type, including Chahua chicken (CHA), Tibet chicken (TIB), Xianju chicken (XIA), Gushi chicken (GUS) and Baier chicken (BAI); and the other heavy-body type, including Beijing You chicken (YOU), Xiaoshan chicken (XIA), Luyuan chicken (LUY) and Dagu chicken (DAG). GUS and DAG were divided into independent clusters respectively when equaled 4, 5, or 6. XIA and BIA chicken, XIA and LUY chicken, TIB and CHA chicken still clustered together when equaled 6, 7, and 8, respectively. These clustering results were consistent with the breeding directions of the nine chicken populations. The Structure program also identified migrants or admixed individuals. The admixed individuals were distributed in all the nine chicken populations, while migrants were only distributed in TIB, XIA and LUY populations. These results indicated that the clustering analysis using the Structure program might provide an accurate representation of the genetic relationship among the breeds.

  3. Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer

    Science.gov (United States)

    Vanneschi, Leonardo

    2015-01-01

    Energy consumption forecasting (ECF) is an important policy issue in today's economies. An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs. The paper proposes a semantic based genetic programming framework to address the ECF problem. In particular, we propose a system that finds (quasi-)perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data. The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. Experimental results confirm the suitability of the proposed method in predicting the energy consumption. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data. PMID:26106410

  4. The underlying mechanisms of genetic innovation and speciation in the family Corynebacteriaceae: A phylogenomics approach.

    Science.gov (United States)

    Zhi, Xiao-Yang; Jiang, Zhao; Yang, Ling-Ling; Huang, Ying

    2017-02-01

    The pangenome of a bacterial species population is formed by genetic reduction and genetic expansion over the long course of evolution. Gene loss is a pervasive source of genetic reduction, and (exogenous and endogenous) gene gain is the main driver of genetic expansion. To understand the genetic innovation and speciation of the family Corynebacteriaceae, which cause a wide range of serious infections in humans and animals, we analyzed the pangenome of this family, and reconstructed its phylogeny using a phylogenomics approach. Genetic variations have occurred throughout the whole evolutionary history of the Corynebacteriaceae. Gene loss has been the primary force causing genetic changes, not only in terms of the number of protein families affected, but also because of its continuity on the time series. The variation in metabolism caused by these genetic changes mainly occurred for membrane transporters, two-component systems, and metabolism related to amino acids and carbohydrates. Interestingly, horizontal gene transfer (HGT) not only caused changes related to pathogenicity, but also triggered the acquisition of antimicrobial resistance. The Darwinian theory of evolution did not adequately explain the effects of dispersive HGT and/or gene loss in the evolution of the Corynebacteriaceae. These findings provide new insight into the evolution and speciation of Corynebacteriaceae and advance our understanding of the genetic innovation in microbial populations.

  5. A large health system's approach to utilization of the genetic counselor CPT® 96040 code.

    Science.gov (United States)

    Gustafson, Shanna L; Pfeiffer, Gail; Eng, Charis

    2011-12-01

    : In 2007, CPT® code 96040 was approved for genetic counseling services provided by nonphysician providers. Because of professional recognition and licensure limitations, experiences in direct billing by genetic counselors for these services are limited. A minority of genetics clinics report using this code because of limitations, including perceived denial of the code and confusion regarding compliant use of this code. We present results of our approach to 96040 billing for genetic counseling services under a supervising physicians National Provider ID number in a strategy for integration of genetics services within nongenetics specialty departments of a large academic medical center. : The 96040 billing encounters were tracked for a 14-month period and analyzed for reimbursement by private payers. Association of denial by diagnosis code or specialty of genetics service was statistically analyzed. Descriptive data regarding appointment availability are also summarized. : Of 350 encounters January 2008 to February 2009, 289 (82%) were billed to private payers. Of these, 62.6% received some level of reimbursement. No association was seen for denial when analyzed by the diagnosis code or by genetics focus. Through this model, genetics appointment availability minimally doubled. : Using 96040 allowed for expanding access to genetics services, increased appointment availability, and was successful in obtaining reimbursement for more than half of encounters billed.

  6. Program Management Approach to the Territorial Development of Small Business

    Directory of Open Access Journals (Sweden)

    Natalia Aleksandrovna Knysh

    2016-06-01

    Full Text Available This article presents the results of the research of the application on a state level of the program management approach to the territorial development of small business. Studying the main mechanism of the state policy implementation in the sphere of small business on a regional level, the authors have revealed the necessity to take into account the territorial specificity while the government programs of small business development are being formed. The analysis of the national practice of utilizing the program management mechanism in the regional system of the government support of small entrepreneurship was conducted on the example of Omsk region. The results of the analysis have shown the inefficiency of the current support system for small business and have determined the need to create an integrated model of territorial programming, which would not only contribute to the qualitative development of small business, but also provide the functioning efficiency of program management mechanism. As a result, the authors have created the two-level model of the programming of the territorial development of small business, which allows to satisfy purposefully the needs of entrepreneurship taking into account the specificity of the internal and external environment of the region. The first level of the model is methodological one and it is based on the marketing approach (the concepts of place marketing and relationship marketing to the operation of the program management mechanism. The second level of the model is methodical one. It offers the combination of the flexible methods of management of programming procedure (benchmarking, foresight, crowdsourcing and outsourcing. The given model raises the efficiency of the management decisions of the state structures in the sphere of small business. Therefore, it is interesting for the government authorities, which are responsible for the regional and municipal support programs of small business, as well

  7. A survey of application: genomics and genetic programming, a new frontier.

    Science.gov (United States)

    Khan, Mohammad Wahab; Alam, Mansaf

    2012-08-01

    The aim of this paper is to provide an introduction to the rapidly developing field of genetic programming (GP). Particular emphasis is placed on the application of GP to genomics. First, the basic methodology of GP is introduced. This is followed by a review of applications in the areas of gene network inference, gene expression data analysis, SNP analysis, epistasis analysis and gene annotation. Finally this paper concluded by suggesting potential avenues of possible future research on genetic programming, opportunities to extend the technique, and areas for possible practical applications.

  8. A dialogue game approach to multi-agent system programming

    OpenAIRE

    Lebbink, Henk-Jan; Witteman, Cilia; Meyer, John-Jules Ch.

    2004-01-01

    This paper approaches multi-agent system programming with dialogue games allowing the semantics of communicative acts to be a component in multi-agent architectures. We present a dialogue game for enquiry enabling agents to answer questions in a distributed fashion. In addition, we propose a reasoning game that defines when agents are allowed to make decisions, in the current case, decisions to accept to believe propositions. These games are brought together in a deliberation cycle and are im...

  9. Dynamic Programming Approach for Construction of Association Rule Systems

    KAUST Repository

    Alsolami, Fawaz

    2016-11-18

    In the paper, an application of dynamic programming approach for optimization of association rules from the point of view of knowledge representation is considered. The association rule set is optimized in two stages, first for minimum cardinality and then for minimum length of rules. Experimental results present cardinality of the set of association rules constructed for information system and lower bound on minimum possible cardinality of rule set based on the information obtained during algorithm work as well as obtained results for length.

  10. An algebraic approach to analysis of recursive and concurrent programs

    DEFF Research Database (Denmark)

    Terepeta, Michal Tomasz

    This thesis focuses on formal techniques based on static program analysis, model checking and abstract interpretation that offer means for reasoning about software, verification of its properties and discovering potential bugs. First, we investigate an algebraic approach to static analysis...... the soundness or completeness results. Moreover, we present a new application of pushdown systems in the context of an aspect-oriented process calculus. The addition of aspect-oriented features makes it possible for a process to exhibit a recursive structure. We show how one can faithfully model and analyze...... present a compact data structure as well as efficient algorithms for the semiring operations. Apart from that, we discuss an improvement to Pre* and Post* algorithms for pushdown systems, making it possible to directly use program representations such as program graphs. We present a modular library...

  11. Genetic approaches to the molecular/neuronal mechanisms underlying learning and memory in the mouse.

    Science.gov (United States)

    Nakajima, Akira; Tang, Ya-Ping

    2005-09-01

    Learning and memory is an essential component of human intelligence. To understand its underlying molecular and neuronal mechanisms is currently an extensive focus in the field of cognitive neuroscience. We have employed advanced mouse genetic approaches to analyze the molecular and neuronal bases for learning and memory, and our results showed that brain region-specific genetic manipulations (including transgenic and knockout), inducible/reversible knockout, genetic/chemical kinase inactivation, and neuronal-based genetic approach are very powerful tools for studying the involvements of various molecules or neuronal substrates in the processes of learning and memory. Studies using these techniques may eventually lead to the understanding of how new information is acquired and how learned information is memorized in the brain.

  12. Linear-programming approach to electricity demand-curtailment planning

    Energy Technology Data Exchange (ETDEWEB)

    Allentuck, J; Carroll, O; Schnader, M

    1980-05-01

    Curtailment planning at a generally rudimentary level has been undertaken by the governments of some twenty states. Many utilities have demand-curtailment plans, however, these are often incorporated in plans for meeting capacity shortages. In at least five states, there are apparently no curtailment plans either at the state-government level or at the utility level. Moreover, none of the existing electricity demand curtailment plans examined included an explicit statement of the planners' objective in arriving at a specified sharing of the burdens of curtailment among consumer classes. Yet clearly the actual allocations of such burdens will affect the cost of the shortage. Since a study of state planning failed to yield a clear-cut indication of which of many possible curtailment allocation schemes would best serve as a point of departure for the design of an optimal curtailment strategy to deal with prolonged supply deficiencies, it was then decided to use a linear-programming approach. The advantages of such an approach are examined first, after which some important conceptual and practical problems in the design of a specific linear-programming model are addressed. A mathematical statement of the model is then followed by a brief review the principal methodological shortcomings of the linear-programming approach. Finally, the authors discuss how the analysis might be expanded to account for inter-regional and other secondary effects of curtailment.

  13. Search for genetic virulence markers in viral haemorrhagic septicaemia virus (VHSV) using a reverse genetics approach

    DEFF Research Database (Denmark)

    Stegmann, Anders; Biacchesi, S.; Bremont, M.

    2011-01-01

    VHSV is a negative strand RNA virus causing serious disease in farmed rainbow trout. Although VHSV has been eradicated by stamping out procedures in several fresh water bodies, recently including all streams in Denmark, the wildlife marine reservoir still represents a threat against rainbow trout...... farming. Particularly in Scandinavia, outbreaks of VHS in sea reared rainbow trout have demonstrated that although marine variants of VHSV are considered to be avirulent to rainbow trout, the virus is potentially able to adapt to this host and cause disease. Limited knowledge about the genetic background...

  14. Constraint Logic Programming approach to protein structure prediction

    Directory of Open Access Journals (Sweden)

    Fogolari Federico

    2004-11-01

    Full Text Available Abstract Background The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Results Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. Conclusions The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

  15. A Detailed look of Audio Steganography Techniques using LSB and Genetic Algorithm Approach

    OpenAIRE

    Gunjan Nehru; Puja Dhar

    2012-01-01

    This paper is the study of various techniques of audio steganography using different algorithmis like genetic algorithm approach and LSB approach. We have tried some approaches that helps in audio steganography. As we know it is the art and science of writing hidden messages in such a way that no one, apart from the sender and intended recipient, suspects the existence of the message, a form of security through obscurity. In steganography, the message used to hide secret message is called hos...

  16. A Genetic Algorithms Based Approach for Group Multicast Routing

    Directory of Open Access Journals (Sweden)

    Luca Sanna Randaccio

    2006-08-01

    Full Text Available Whereas multicast transmission in one-to-many communications allows the operator to drastically save network resources, it also makes the routing of the traffic flows more complex then in unicast transmissions. A huge amount of possible trees have to be considered and analyzed to find the appropriate routing paths. To address this problem, we propose the use of the genetic algorithms (GA, which considerably reduce the number of solutions to be evaluated. A heuristic procedure is first used to discern a set of possible trees for each multicast session in isolation. Then, the GA are applied to find the appropriate combination of the trees to comply with the bandwidth needs of the group of multicast sessions simultaneously. The goodness of each solution is assessed by means of an expression that weights both network bandwidth allocation and one-way delay. The resulting cost function is guided by few parameters that can be easily tuned during traffic engineering operations; an appropriate setting of these parameters allows the operator to configure the desired balance between network resource utilization and provided quality of service. Simulations have been performed to compare the proposed algorithm with alternative solutions in terms of bandwidth utilization and transmission delay.

  17. Hybrid genetic algorithm approach for selective harmonic control

    Energy Technology Data Exchange (ETDEWEB)

    Dahidah, Mohamed S.A. [Faculty of Engineering, Multimedia University, 63100, Jalan Multimedia-Cyberjaya, Selangor (Malaysia); Agelidis, Vassilios G. [School of Electrical and Information Engineering, The University of Sydney, NSW (Australia); Rao, Machavaram V. [Faculty of Engineering and Technology, Multimedia University, 75450, Jalan Ayer Keroh Lama-Melaka (Malaysia)

    2008-02-15

    The paper presents an optimal solution for a selective harmonic elimination pulse width modulated (SHE-PWM) technique suitable for a high power inverter used in constant frequency utility applications. The main challenge of solving the associated non-linear equations, which are transcendental in nature and, therefore, have multiple solutions, is the convergence, and therefore, an initial point selected considerably close to the exact solution is required. The paper discusses an efficient hybrid real coded genetic algorithm (HRCGA) that reduces significantly the computational burden, resulting in fast convergence. An objective function describing a measure of the effectiveness of eliminating selected orders of harmonics while controlling the fundamental, namely a weighted total harmonic distortion (WTHD) is derived, and a comparison of different operating points is reported. It is observed that the method was able to find the optimal solution for a modulation index that is higher than unity. The theoretical considerations reported in this paper are verified through simulation and experimentally on a low power laboratory prototype. (author)

  18. Structural modification of polysaccharides: A biochemical-genetic approach

    Science.gov (United States)

    Kern, Roger G.; Petersen, Gene R.

    1991-01-01

    Polysaccharides have a wide range of industrial and biomedical applications. An industry trend is underway towards the increased use of bacteria to produce polysaccharides. Long term goals of this work are the adaptation and enhancement of saccharide properties for electronic and optic applications. In this report we illustrate the application of enzyme-bearing bacteriophage on strains of the enteric bacterium Klebsiella pneumoniae, which produces a polysaccharide with the relatively rare rheological property of drag-reduction. This has resulted in the production of new polysaccharides with enhanced rheological properties. Our laboratory is developing techniques for processing and structurally modifying bacterial polysaccharides and oligosaccharides which comprise their basic polymeric repeat units. Our research has focused on bacteriophage which produce specific polysaccharide degrading enzymes. This has lead to the development of enzymes generated by bacteriophage as tools for polysaccharide modification and purification. These enzymes were used to efficiently convert the native material to uniform-sized high molecular weight polymers, or alternatively into high-purity oligosaccharides. Enzyme-bearing bacteriophage also serve as genetic selection tools for bacteria that produce new families of polysaccharides with modified structures.

  19. Diagnostic/genetic sreening - approach for genetic diagnoses and prevention of cleft lip and/or palate.

    Science.gov (United States)

    Natsume, Nagato; Kato, Tomoki; Hayakawa, Toko; Imura, Hideto

    2013-01-01

    The treatment, research and volunteer work for cleft lip and/or palate (CL/P) has been led for over 30 years by our team. Within this period, more than 4,000 cases of CL/P were treated and at the same time, and approximately 400 papers were published as the first or partner researcher in Nature Genetics, New England Journal of Medicine and others. In addition, with $20 million that was donated from companies and laypeople, and the grant from the Japanese government, CL/P centres in many countries and in Japan, the oral and craniofacial congenital anomaly gene bank in our CL/P centre was established by our leadership. In the bank there are genes from approximately more than 8,000 cases. The genes were mapped with Professor Jeffery Murray of Iowa University in the United States, the findings about genetic syndromes such as Van der Woude Syndrome and basal cell nevus syndrome were applied in clinical settings. The genetic counselling section that specialises in the oral and maxillofacial field was established by our effort for the first time in Japan. In this review, our clinical experience and approach for genetic diagnoses and prevention of cleft lip and/or palate will be discussed.

  20. [Approach to depressogenic genes from genetic analyses of animal models].

    Science.gov (United States)

    Yoshikawa, Takeo

    2004-01-01

    Human depression or mood disorder is defined as a complex disease, making positional cloning of susceptibility genes a formidable task. We have undertaken genetic analyses of three different animal models for depression, comparing our results with advanced database resources. We first performed quantitative trait loci (QTL) analysis on two mouse models of "despair", namely, the forced swim test (FST) and tail suspension test (TST), and detected multiple chromosomal loci that control immobility time in these tests. Since one QTL detected on mouse chromosome 11 harbors the GABA A receptor subunit genes, we tested these genes for association in human mood disorder patients. We obtained significant associations of the alpha 1 and alpha 6 subunit genes with the disease, particularly in females. This result was striking, because we had previously detected an epistatic interaction between mouse chromosomes 11 and X that regulates immobility time in these animals. Next, we performed genome-wide expression analyses using a rat model of depression, learned helplessness (LH). We found that in the frontal cortex of LH rats, a disease implicated region, the LIM kinase 1 gene (Limk 1) showed greatest alteration, in this case down-regulation. By combining data from the QTL analysis of FST/TST and DNA microarray analysis of mouse frontal cortex, we identified adenylyl cyclase-associated CAP protein 1 (Cap 1) as another candidate gene for depression susceptibility. Both Limk 1 and Cap 1 are key players in the modulation of actin G-F conversion. In summary, our current study using animal models suggests disturbances of GABAergic neurotransmission and actin turnover as potential pathophysiologies for mood disorder.

  1. Genetics

    Science.gov (United States)

    ... 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 ...

  2. Effects of Maternal Obesity on Fetal Programming: Molecular Approaches.

    Science.gov (United States)

    Neri, Caterina; Edlow, Andrea G

    2015-09-03

    Maternal obesity has become a worldwide epidemic. Obesity and a high-fat diet have been shown to have deleterious effects on fetal programming, predisposing offspring to adverse cardiometabolic and neurodevelopmental outcomes. Although large epidemiological studies have shown an association between maternal obesity and adverse outcomes for offspring, the underlying mechanisms remain unclear. Molecular approaches have played a key role in elucidating the mechanistic underpinnings of fetal malprogramming in the setting of maternal obesity. These approaches include, among others, characterization of epigenetic modifications, microRNA expression, the gut microbiome, the transcriptome, and evaluation of specific mRNA expression via quantitative reverse transcription polmerase chain reaction (RT-qPCR) in fetuses and offspring of obese females. This work will review the data from animal models and human fluids/cells regarding the effects of maternal obesity on fetal and offspring neurodevelopment and cardiometabolic outcomes, with a particular focus on molecular approaches.

  3. Genetic algorithm based image binarization approach and its quantitative evaluation via pooling

    Science.gov (United States)

    Hu, Huijun; Liu, Ya; Liu, Maofu

    2015-12-01

    The binarized image is very critical to image visual feature extraction, especially shape feature, and the image binarization approaches have been attracted more attentions in the past decades. In this paper, the genetic algorithm is applied to optimizing the binarization threshold of the strip steel defect image. In order to evaluate our genetic algorithm based image binarization approach in terms of quantity, we propose the novel pooling based evaluation metric, motivated by information retrieval community, to avoid the lack of ground-truth binary image. Experimental results show that our genetic algorithm based binarization approach is effective and efficiency in the strip steel defect images and our quantitative evaluation metric on image binarization via pooling is also feasible and practical.

  4. [Modern evolutional developmental biology: mechanical and molecular genetic or phenotypic approaches?].

    Science.gov (United States)

    Vorob'eva, É I

    2010-01-01

    Heightened interest in the evolutionary problems of developmental biology in the 1980s was due to the success of molecular genetics and disappointment in the synthetic theory of evolution, where the chapters of embryology and developmental biology seem to have been left out. Modern evo-devo, which turned out to be antipodean to the methodology of the synthetic theory of evolution, propagandized in the development of evolutionary problems only the mechanical and molecular genetic approach to the evolution of ontogenesis, based on cellular and intercellular interactions. The phonotypical approach to the evaluation of evolutionary occurrences in ontogenesis, which aids in the joining of the genetic and epigenetic levels of research, the theory of natural selection, the nomogenetic conception, and the problem of the wholeness of the organism in onto- and phylogenesis may be against this. The phenotypic approach to ontogenesis is methodologically the most perspective for evolutionary developmental biology.

  5. Genetic algorithms and genetic programming for multiscale modeling: Applications in materials science and chemistry and advances in scalability

    Science.gov (United States)

    Sastry, Kumara Narasimha

    2007-03-01

    Effective and efficient rnultiscale modeling is essential to advance both the science and synthesis in a, wide array of fields such as physics, chemistry, materials science; biology, biotechnology and pharmacology. This study investigates the efficacy and potential of rising genetic algorithms for rnultiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately. In particular, this thesis demonstrates the use of genetic algorithms (GAs) and genetic programming (GP) in multiscale modeling with the help of two non-trivial case studies in materials science and chemistry. The first case study explores the utility of genetic programming (GP) in multi-timescaling alloy kinetics simulations. In essence, GP is used to bridge molecular dynamics and kinetic Monte Carlo methods to span orders-of-magnitude in simulation time. Specifically, GP is used to regress symbolically an inline barrier function from a limited set of molecular dynamics simulations to enable kinetic Monte Carlo that simulate seconds of real time. Results on a non-trivial example of vacancy-assisted migration on a surface of a face-centered cubic (fcc) Copper-Cobalt (CuxCo 1-x) alloy show that GP predicts all barriers with 0.1% error from calculations for less than 3% of active configurations, independent of type of potentials used to obtain the learning set of barriers via molecular dynamics. The resulting method enables 2--9 orders-of-magnitude increase in real-time dynamics simulations taking 4--7 orders-of-magnitude less CPU time. The second case study presents the application of multiobjective genetic algorithms (MOGAs) in multiscaling quantum chemistry simulations. Specifically, MOGAs are used to bridge high-level quantum chemistry and semiempirical methods to provide accurate representation of complex molecular excited-state and ground-state behavior. Results on ethylene and benzene---two common

  6. Application of genetic programming in shape optimization of concrete gravity dams by metaheuristics

    Directory of Open Access Journals (Sweden)

    Abdolhossein Baghlani

    2014-12-01

    Full Text Available A gravity dam maintains its stability against the external loads by its massive size. Hence, minimization of the weight of the dam can remarkably reduce the construction costs. In this paper, a procedure for finding optimal shape of concrete gravity dams with a computationally efficient approach is introduced. Genetic programming (GP in conjunction with metaheuristics is used for this purpose. As a case study, shape optimization of the Bluestone dam is presented. Pseudo-dynamic analysis is carried out on a total number of 322 models in order to establish a database of the results. This database is then used to find appropriate relations based on GP for design criteria of the dam. This procedure eliminates the necessity of the time-consuming process of structural analyses in evolutionary optimization methods. The method is hybridized with three different metaheuristics, including particle swarm optimization, firefly algorithm (FA, and teaching–learning-based optimization, and a comparison is made. The results show that although all algorithms are very suitable, FA is slightly superior to other two algorithms in finding a lighter structure in less number of iterations. The proposed method reduces the weight of dam up to 14.6% with very low computational effort.

  7. Discovering link communities in complex networks by an integer programming model and a genetic algorithm.

    Science.gov (United States)

    Li, Zhenping; Zhang, Xiang-Sun; Wang, Rui-Sheng; Liu, Hongwei; Zhang, Shihua

    2013-01-01

    Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some recent studies start partitioning links to find overlapping communities straightforwardly. In this paper, we propose a new quantity function for link community identification in complex networks. Based on this quantity function we formulate the link community partition problem into an integer programming model which allows us to partition a complex network into overlapping communities. We further propose a genetic algorithm for link community detection which can partition a network into overlapping communities without knowing the number of communities. We test our model and algorithm on both artificial networks and real-world networks. The results demonstrate that the model and algorithm are efficient in detecting overlapping community structure in complex networks.

  8. Mixed model approaches for the identification of QTLs within a maize hybrid breeding program.

    Science.gov (United States)

    van Eeuwijk, Fred A; Boer, Martin; Totir, L Radu; Bink, Marco; Wright, Deanne; Winkler, Christopher R; Podlich, Dean; Boldman, Keith; Baumgarten, Andy; Smalley, Matt; Arbelbide, Martin; ter Braak, Cajo J F; Cooper, Mark

    2010-01-01

    Two outlines for mixed model based approaches to quantitative trait locus (QTL) mapping in existing maize hybrid selection programs are presented: a restricted maximum likelihood (REML) and a Bayesian Markov Chain Monte Carlo (MCMC) approach. The methods use the in-silico-mapping procedure developed by Parisseaux and Bernardo (2004) as a starting point. The original single-point approach is extended to a multi-point approach that facilitates interval mapping procedures. For computational and conceptual reasons, we partition the full set of relationships from founders to parents of hybrids into two types of relations by defining so-called intermediate founders. QTL effects are defined in terms of those intermediate founders. Marker based identity by descent relationships between intermediate founders define structuring matrices for the QTL effects that change along the genome. The dimension of the vector of QTL effects is reduced by the fact that there are fewer intermediate founders than parents. Furthermore, additional reduction in the number of QTL effects follows from the identification of founder groups by various algorithms. As a result, we obtain a powerful mixed model based statistical framework to identify QTLs in genetic backgrounds relevant to the elite germplasm of a commercial breeding program. The identification of such QTLs will provide the foundation for effective marker assisted and genome wide selection strategies. Analyses of an example data set show that QTLs are primarily identified in different heterotic groups and point to complementation of additive QTL effects as an important factor in hybrid performance.

  9. Genetic Algorithm Based on Duality Principle for Bilevel Programming Problem in Steel-making Production

    Institute of Scientific and Technical Information of China (English)

    Shuo Lin; Fangjun Luan; Zhonghua Han; Xisheng Lü; Xiaofeng Zhou; Wei Liu

    2014-01-01

    Steel-making and continuous/ingot casting are the key processes of modern iron and steel enterprises. Bilevel programming problems (BLPPs) are the optimization problems with hierarchical structure. In steel-making pro-duction, the plan is not only decided by the steel-making scheduling, but also by the transportation equipment. This paper proposes a genetic algorithm to solve continuous and ingot casting scheduling problems. Based on the characteristics of the problems involved, a genetic algorithm is proposed for solving the bilevel programming problem in steel-making production. Furthermore, based on the simplex method, a new crossover operator is designed to improve the efficiency of the genetic algorithm. Finally, the convergence is analyzed. Using actual data the validity of the proposed algorithm is proved and the application results in the steel plant are analyzed.

  10. Innate and adaptive immunity in bacteria: mechanisms of programmed genetic variation to fight bacteriophages.

    Science.gov (United States)

    Bikard, David; Marraffini, Luciano A

    2012-02-01

    Bacteria are constantly challenged by bacteriophages (viruses that infect bacteria), the most abundant microorganism on earth. Bacteria have evolved a variety of immunity mechanisms to resist bacteriophage infection. In response, bacteriophages can evolve counter-resistance mechanisms and launch a 'virus versus host' evolutionary arms race. In this context, rapid evolution is fundamental for the survival of the bacterial cell. Programmed genetic variation mechanisms at loci involved in immunity against bacteriophages generate diversity at a much faster rate than random point mutation and enable bacteria to quickly adapt and repel infection. Diversity-generating retroelements (DGRs) and phase variation mechanisms enhance the generic (innate) immune response against bacteriophages. On the other hand, the integration of small bacteriophage sequences in CRISPR loci provide bacteria with a virus-specific and sequence-specific adaptive immune response. Therefore, although using different molecular mechanisms, both prokaryotes and higher organisms rely on programmed genetic variation to increase genetic diversity and fight rapidly evolving infectious agents.

  11. Optimization of Turning Operations by Using a Hybrid Genetic Algorithm with Sequential Quadratic Programming

    Directory of Open Access Journals (Sweden)

    A. Belloufi*

    2013-01-01

    Full Text Available The determination of optimal cutting parameters is one of the most important elements in any process planning ofmetal parts. In this paper, a new hybrid genetic algorithm by using sequential quadratic programming is used for theoptimization of cutting conditions. It is used for the resolution of a multipass turning optimization case by minimizingthe production cost under a set of machining constraints. The genetic algorithm (GA is the main optimizer of thisalgorithm whereas SQP Is used to fine tune the results obtained from the GA. Furthermore, the convergencecharacteristics and robustness of the proposed method have been explored through comparisons with resultsreported in literature. The obtained results indicate that the proposed hybrid genetic algorithm by using a sequentialquadratic programming is effective compared to other techniques carried out by different researchers.

  12. A linear programming approach for placement of applicants to academic programs.

    Science.gov (United States)

    Kassa, Biniyam Asmare

    2013-01-01

    This paper reports a linear programming approach for placement of applicants to study programs developed and implemented at the college of Business & Economics, Bahir Dar University, Bahir Dar, Ethiopia. The approach is estimated to significantly streamline the placement decision process at the college by reducing required man hour as well as the time it takes to announce placement decisions. Compared to the previous manual system where only one or two placement criteria were considered, the new approach allows the college's management to easily incorporate additional placement criteria, if needed. Comparison of our approach against manually constructed placement decisions based on actual data for the 2012/13 academic year suggested that about 93 percent of the placements from our model concur with the actual placement decisions. For the remaining 7 percent of placements, however, the actual placements made by the manual system display inconsistencies of decisions judged against the very criteria intended to guide placement decisions by the college's program management office. Overall, the new approach proves to be a significant improvement over the manual system in terms of efficiency of the placement process and the quality of placement decisions.

  13. Model-based problem solving through symbolic regression via pareto genetic programming

    NARCIS (Netherlands)

    Vladislavleva, E.

    2008-01-01

    Pareto genetic programming methodology is extended by additional generic model selection and generation strategies that (1) drive the modeling engine to creation of models of reduced non-linearity and increased generalization capabilities, and (2) improve the effectiveness of the search for robust m

  14. Application of Genetic Programming in Predicting Infinite Dilution Activity Coefficients of Organic Compounds in Water

    Institute of Scientific and Technical Information of China (English)

    Yi Lin CAO; Huan Ying LI

    2003-01-01

    In this paper, we calculated 37 structural descriptors of 174 organic compounds. The154 molecules were used to derive quantitative structure-infinite dilution activity coefficientrelationship by genetic programming, the other 20 compounds were used to test the model. Theresult showed that molecular partition property and three-dimensional structural descriptors havesignificant influence on the infinite dilution activity coefficients.

  15. Genetic Diversity of Some Tomato Cultivars and Breeding Lines Commonly Used in Pakistani Breeding Program

    Directory of Open Access Journals (Sweden)

    Muhammad Azhar Shah

    2014-10-01

    Full Text Available Genetic diversity present in gene pool is an important determination for breeding programs, and characterization is useful of building crop plant collections primarily based on the knowledge of the presence of valuable genes and traits. Developing successful varieties for increasing the future yield and quality of tomato depend mainly on the genetic diversity of parents used in the breeding program. Molecular characterization of 21 tomato genotypes used in in Pakistani breeding program was studied using random amplified polymorphic DNA (RAPD markers. Total 102 bands were amplified among 21 genotypes using 20 RAPD primers. Overall 73.5% polymorphism was shown as 75 out of 102 loci were polymorphic. High degree of divergence between varieties was indicated by low level of monomorphic bands. The number of PCR products per primer varied from 2-8 with an average of 5.1 bands per primer. Primer GL J-20 and GL C-09 produced maximum number of bands whereas the primers GL A-09 produced the lowest. The polymorphism per RAPD primer ranged from 50% to 100% with an average of 73.5%. The accumulative analysis of amplified products generated by RAPD’s was enough to assess the genetic diversity among the genotypes. The information would be helpful for formulating future breeding and genome mapping programs. This study will also work as an indicator for tomato breeders to evolve varieties with genetic diverse back ground to achieve sustainability in tomato production in the country.

  16. Behaviour of genetically modified amylose free potato clones as progenitors in a breeding program.

    NARCIS (Netherlands)

    Heeres, P.; Jacobsen, E.; Visser, R.G.F.

    1997-01-01

    Three amylose-free genetically modified potato clones were used both as male and female parents in a breeding program with non-GMO potato clones. Segregation data on the expression of the inserted antisense gene construct in tubers of progeny plants were in agreement with previous molecular analysis

  17. A Neurodynamic Optimization Approach to Bilevel Quadratic Programming.

    Science.gov (United States)

    Qin, Sitian; Le, Xinyi; Wang, Jun

    2016-08-19

    This paper presents a neurodynamic optimization approach to bilevel quadratic programming (BQP). Based on the Karush-Kuhn-Tucker (KKT) theorem, the BQP problem is reduced to a one-level mathematical program subject to complementarity constraints (MPCC). It is proved that the global solution of the MPCC is the minimal one of the optimal solutions to multiple convex optimization subproblems. A recurrent neural network is developed for solving these convex optimization subproblems. From any initial state, the state of the proposed neural network is convergent to an equilibrium point of the neural network, which is just the optimal solution of the convex optimization subproblem. Compared with existing recurrent neural networks for BQP, the proposed neural network is guaranteed for delivering the exact optimal solutions to any convex BQP problems. Moreover, it is proved that the proposed neural network for bilevel linear programming is convergent to an equilibrium point in finite time. Finally, three numerical examples are elaborated to substantiate the efficacy of the proposed approach.

  18. Portfolio optimization in enhanced index tracking with goal programming approach

    Science.gov (United States)

    Siew, Lam Weng; Jaaman, Saiful Hafizah Hj.; Ismail, Hamizun bin

    2014-09-01

    Enhanced index tracking is a popular form of passive fund management in stock market. Enhanced index tracking aims to generate excess return over the return achieved by the market index without purchasing all of the stocks that make up the index. This can be done by establishing an optimal portfolio to maximize the mean return and minimize the risk. The objective of this paper is to determine the portfolio composition and performance using goal programming approach in enhanced index tracking and comparing it to the market index. Goal programming is a branch of multi-objective optimization which can handle decision problems that involve two different goals in enhanced index tracking, a trade-off between maximizing the mean return and minimizing the risk. The results of this study show that the optimal portfolio with goal programming approach is able to outperform the Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index because of higher mean return and lower risk without purchasing all the stocks in the market index.

  19. Fetal metabolic programming and epigenetic modifications: a systems biology approach.

    Science.gov (United States)

    Sookoian, Silvia; Gianotti, Tomas Fernández; Burgueño, Adriana L; Pirola, Carlos J

    2013-04-01

    A growing body of evidence supports the notion that epigenetic changes such as DNA methylation and histone modifications, both involving chromatin remodeling, contribute to fetal metabolic programming. We use a combination of gene-protein enrichment analysis resources along with functional annotations and protein interaction networks for an integrative approach to understanding the mechanisms underlying fetal metabolic programming. Systems biology approaches suggested that fetal adaptation to an impaired nutritional environment presumes profound changes in gene expression that involve regulation of tissue-specific patterns of methylated cytosine residues, modulation of the histone acetylation-deacetylation switch, cell differentiation, and stem cell pluripotency. The hypothalamus and the liver seem to be differently involved. In addition, new putative explanations have emerged about the question of whether in utero overnutrition modulates fetal metabolic programming in the same fashion as that of a maternal environment of undernutrition, suggesting that the mechanisms behind these two fetal nutritional imbalances are different. In conclusion, intrauterine growth restriction is most likely to be associated with the induction of persistent changes in tissue structure and functionality. Conversely, a maternal obesogenic environment is most probably associated with metabolic reprogramming of glucose and lipid metabolism, as well as future risk of metabolic syndrome (MS), fatty liver, and insulin (INS) resistance.

  20. Dynamic Programming and Genetic Algorithm for Business Processes Optimisation

    Directory of Open Access Journals (Sweden)

    Mateusz Wibig

    2012-12-01

    Full Text Available There are many business process modelling techniques, which allow to capture features of those processes, but graphical, diagrammatic models seems to be used most in companies and organizations. Although the modelling notations are more and more mature and can be used not only to visualise the process idea but also to implement it in the workflow solution and although modern software allows us to gather a lot of data for analysis purposes, there is still not much commercial used business process optimisation methods. In this paper the scheduling / optimisation method for automatic task scheduling in business processes models is described. The Petri Net model is used, but it can be easily applied to any other modelling notation, where the process is presented as a set of tasks, i.e. BPMN (Business Process Modelling Notation. The method uses Petri Nets’, business processes’ scalability and dynamic programming concept to reduce the necessary computations, by revising only those parts of the model, to which the change was applied.

  1. An innovative approach of risk planning for space programs.

    Science.gov (United States)

    Ray, P

    2000-07-01

    According to the current rule-based risk management approach at the National Aeronautics and Space Administration (NASA), the effort is directed to contain all identified risks of a program. The identification of hazards and mitigation effort proceeds along with the development of the system hardware, till all the tradable resources for a program is exhausted. In this process, no conscious effort is made to evaluate risks and associated cost, and the final design is likely to have undesirable residual risks. This approach also results in allocating a significant amount of resources to gain only marginal mitigation of hazard and leave some undesirable hazards in the system due to the budget limitation. The approach in the proposed knowledge-based risk planning system makes a conscious attempt to trade risk with other resources, e.g., schedule, cost, reliability, performance, and others in a judicious and cost-effective way. A knowledge of the feasible option sets requiring high incremental cost for a marginal gain in hazard reduction helps the management to make decision for residual risk that falls within an acceptable range for an option set.

  2. Genetic and Modeling Approaches Reveal Distinct Components of Impulsive Behavior.

    Science.gov (United States)

    Nautiyal, Katherine M; Wall, Melanie M; Wang, Shuai; Magalong, Valerie M; Ahmari, Susanne E; Balsam, Peter D; Blanco, Carlos; Hen, René

    2017-01-18

    Impulsivity is an endophenotype found in many psychiatric disorders including substance use disorders, pathological gambling, and attention deficit hyperactivity disorder. Two behavioral features often considered in impulsive behavior are behavioral inhibition (impulsive action) and delayed gratification (impulsive choice). However, the extent to which these behavioral constructs represent distinct facets of behavior with discrete biological bases is unclear. To test the hypothesis that impulsive action and impulsive choice represent statistically independent behavioral constructs in mice, we collected behavioral measures of impulsivity in a single cohort of mice using well-validated operant behavioral paradigms. Mice with manipulation of serotonin 1B receptor (5-HT1BR) expression were included as a model of disordered impulsivity. A factor analysis was used to characterize correlations between the measures of impulsivity and to identify covariates. Using two approaches, we dissociated impulsive action from impulsive choice. First, the absence of 5-HT1BRs caused increased impulsive action, but not impulsive choice. Second, based on an exploratory factor analysis, a two-factor model described the data well, with measures of impulsive action and choice separating into two independent factors. A multiple-indicator multiple-causes analysis showed that 5-HT1BR expression and sex were significant covariates of impulsivity. Males displayed increased impulsivity in both dimensions, whereas 5-HT1BR expression was a predictor of increased impulsive action only. These data support the conclusion that impulsive action and impulsive choice are distinct behavioral phenotypes with dissociable biological influences that can be modeled in mice. Our work may help inform better classification, diagnosis, and treatment of psychiatric disorders, which present with disordered impulsivity.Neuropsychopharmacology advance online publication, 18 January 2017; doi:10.1038/npp.2016.277.

  3. A Constraint Programming Approach for Solving a Queueing Control Problem

    CERN Document Server

    Terekhov, Daria; 10.1613/jair.2446

    2011-01-01

    In a facility with front room and back room operations, it is useful to switch workers between the rooms in order to cope with changing customer demand. Assuming stochastic customer arrival and service times, we seek a policy for switching workers such that the expected customer waiting time is minimized while the expected back room staffing is sufficient to perform all work. Three novel constraint programming models and several shaving procedures for these models are presented. Experimental results show that a model based on closed-form expressions together with a combination of shaving procedures is the most efficient. This model is able to find and prove optimal solutions for many problem instances within a reasonable run-time. Previously, the only available approach was a heuristic algorithm. Furthermore, a hybrid method combining the heuristic and the best constraint programming method is shown to perform as well as the heuristic in terms of solution quality over time, while achieving the same performanc...

  4. An Integer Programming Approach to Solving Tantrix on Fixed Boards

    Directory of Open Access Journals (Sweden)

    Yushi Uno

    2012-03-01

    Full Text Available Tantrix (Tantrix R ⃝ is a registered trademark of Colour of Strategy Ltd. in New Zealand, and of TANTRIX JAPAN in Japan, respectively, under the license of M. McManaway, the inventor. is a puzzle to make a loop by connecting lines drawn on hexagonal tiles, and the objective of this research is to solve it by a computer. For this purpose, we first give a problem setting of solving Tantrix as making a loop on a given fixed board. We then formulate it as an integer program by describing the rules of Tantrix as its constraints, and solve it by a mathematical programming solver to have a solution. As a result, we establish a formulation that can solve Tantrix of moderate size, and even when the solutions are invalid only by elementary constraints, we achieved it by introducing additional constraints and re-solve it. By this approach we succeeded to solve Tantrix of size up to 60.

  5. Improving the accuracy of density-functional theory calculation: the genetic algorithm and neural network approach.

    Science.gov (United States)

    Li, Hui; Shi, LiLi; Zhang, Min; Su, Zhongmin; Wang, XiuJun; Hu, LiHong; Chen, GuanHua

    2007-04-14

    The combination of genetic algorithm and neural network approach (GANN) has been developed to improve the calculation accuracy of density functional theory. As a demonstration, this combined quantum mechanical calculation and GANN correction approach has been applied to evaluate the optical absorption energies of 150 organic molecules. The neural network approach reduces the root-mean-square (rms) deviation of the calculated absorption energies of 150 organic molecules from 0.47 to 0.22 eV for the TDDFTB3LYP6-31G(d) calculation, and the newly developed GANN correction approach reduces the rms deviation to 0.16 eV.

  6. Non-Genetic Engineering Approaches for Isolating and Generating Novel Yeasts for Industrial Applications

    Science.gov (United States)

    Chambers, P. J.; Bellon, J. R.; Schmidt, S. A.; Varela, C.; Pretorius, I. S.

    Generating novel yeast strains for industrial applications should be quite straightforward; after all, research into the genetics, biochemistry and physiology of Baker's Yeast, Saccharomyces cerevisiae, has paved the way for many advances in the modern biological sciences. We probably know more about this humble eukaryote than any other, and it is the most tractable of organisms for manipulation using modern genetic engineering approaches. In many countries, however, there are restrictions on the use of genetically-modified organisms (GMOs), particularly in foods and beverages, and the level of consumer acceptance of GMOs is, at best, variable. Thus, many researchers working with industrial yeasts use genetic engineering techniques primarily as research tools, and strain development continues to rely on non-GM technologies. This chapter explores the non-GM tools and strategies available to such researchers.

  7. Using Balanced Scorecard (BSC) approach to improve ergonomics programs.

    Science.gov (United States)

    Fernandes, Marcelo Vicente Forestieri

    2012-01-01

    The purpose of this paper is to propose foundations for a theory of using the Balanced Scorecard (BSC) methodology to improve the strategic view of ergonomics inside the organizations. This approach may help to promote a better understanding of investing on an ergonomic program to obtain good results in quality and production, as well as health maintenance. It is explained the basics of balanced scorecard, and how ergonomists could use this to work with strategic enterprises demand. Implications of this viewpoint for the development of a new methodology for ergonomics strategy views are offered.

  8. Solving an aggregate production planning problem by using multi-objective genetic algorithm (MOGA approach

    Directory of Open Access Journals (Sweden)

    Ripon Kumar Chakrabortty

    2013-01-01

    Full Text Available In hierarchical production planning system, Aggregate Production Planning (APP falls between the broad decisions of long-range planning and the highly specific and detailed short-range planning decisions. This study develops an interactive Multi-Objective Genetic Algorithm (MOGA approach for solving the multi-product, multi-period aggregate production planning (APP with forecasted demand, related operating costs, and capacity. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, overtime, subcontracting and backordering levels, and labor, machine and warehouse capacity. Here several genetic algorithm parameters are considered for solving NP-hard problem (APP problem and their relative comparisons are focused to choose the most auspicious combination for solving multiple objective problems. An industrial case demonstrates the feasibility of applying the proposed approach to real APP decision problems. Consequently, the proposed MOGA approach yields an efficient APP compromise solution for large-scale problems.

  9. Stochastic Control of Energy Efficient Buildings: A Semidefinite Programming Approach

    Energy Technology Data Exchange (ETDEWEB)

    Ma, Xiao [ORNL; Dong, Jin [ORNL; Djouadi, Seddik M [ORNL; Nutaro, James J [ORNL; Kuruganti, Teja [ORNL

    2015-01-01

    The key goal in energy efficient buildings is to reduce energy consumption of Heating, Ventilation, and Air- Conditioning (HVAC) systems while maintaining a comfortable temperature and humidity in the building. This paper proposes a novel stochastic control approach for achieving joint performance and power control of HVAC. We employ a constrained Stochastic Linear Quadratic Control (cSLQC) by minimizing a quadratic cost function with a disturbance assumed to be Gaussian. The problem is formulated to minimize the expected cost subject to a linear constraint and a probabilistic constraint. By using cSLQC, the problem is reduced to a semidefinite optimization problem, where the optimal control can be computed efficiently by Semidefinite programming (SDP). Simulation results are provided to demonstrate the effectiveness and power efficiency by utilizing the proposed control approach.

  10. Telegenetics: application of a tele-education program in genetic syndromes for Brazilian students.

    Science.gov (United States)

    Maximino, Luciana Paula; Picolini-Pereira, Mirela Machado; Carvalho, José Luiz Brito

    2014-01-01

    With the high occurrence of genetic anomalies in Brazil and the manifestations of communication disorders associated with these conditions, the development of educative actions that comprise these illnesses can bring unique benefits in the identification and appropriate treatment of these clinical pictures. Objective The aim of this study was to develop and analyze an educational program in genetic syndromes for elementary students applied in two Brazilian states, using an Interactive Tele-education model. Material and Methods The study was carried out in 4 schools: two in the state of São Paulo, Southeast Region, Brazil, and two in the state of Amazonas, North Region, Brazil. Forty-five students, both genders, aged between 13 and 14 years, of the 9th grade of the basic education of both public and private system, were divided into two groups: 21 of São Paulo Group (SPG) and 24 of Amazonas Group (AMG). The educational program lasted about 3 months and was divided into two stages including both classroom and distance activities on genetic syndromes. The classroom activity was carried out separately in each school, with expository lessons, graphs and audiovisual contents. In the activity at a distance the educational content was presented to students by means of the Interactive Tele-education model. In this stage, the students had access a Cybertutor, using the Young Doctor Project methodology. In order to measure the effectiveness of the educational program, the Problem Situation Questionnaire (PSQ) and the Web Site Motivational Analysis Checklist adapted (FPM) were used. Results The program developed was effective for knowledge acquisition in 80% of the groups. FPM showed a high satisfaction index from the participants in relation to the Interactive Tele-education, evaluating the program as "awesome course". No statistically significant differences between the groups regarding type of school or state were observed. Conclusion Thus, the Tele-Education Program can

  11. Genetic and Molecular Approaches to Study Neuronal Migration in the Developing Cerebral Cortex.

    Science.gov (United States)

    Dudok, Jacobus J; Leonards, Pim E G; Wijnholds, Jan

    2017-05-05

    The migration of neuronal cells in the developing cerebral cortex is essential for proper development of the brain and brain networks. Disturbances in this process, due to genetic abnormalities or exogenous factors, leads to aberrant brain formation, brain network formation, and brain function. In the last decade, there has been extensive research in the field of neuronal migration. In this review, we describe different methods and approaches to assess and study neuronal migration in the developing cerebral cortex. First, we discuss several genetic methods, techniques and genetic models that have been used to study neuronal migration in the developing cortex. Second, we describe several molecular approaches to study aberrant neuronal migration in the cortex which can be used to elucidate the underlying mechanisms of neuronal migration. Finally, we describe model systems to investigate and assess the potential toxicity effect of prenatal exposure to environmental chemicals on proper brain formation and neuronal migration.

  12. On Generating Optimal Signal Probabilities for Random Tests: A Genetic Approach

    Directory of Open Access Journals (Sweden)

    M. Srinivas

    1996-01-01

    Full Text Available Genetic Algorithms are robust search and optimization techniques. A Genetic Algorithm based approach for determining the optimal input distributions for generating random test vectors is proposed in the paper. A cost function based on the COP testability measure for determining the efficacy of the input distributions is discussed. A brief overview of Genetic Algorithms (GAs and the specific details of our implementation are described. Experimental results based on ISCAS-85 benchmark circuits are presented. The performance of our GAbased approach is compared with previous results. While the GA generates more efficient input distributions than the previous methods which are based on gradient descent search, the overheads of the GA in computing the input distributions are larger.

  13. Potential of gene drives with genome editing to increase genetic gain in livestock breeding programs.

    Science.gov (United States)

    Gonen, Serap; Jenko, Janez; Gorjanc, Gregor; Mileham, Alan J; Whitelaw, C Bruce A; Hickey, John M

    2017-01-04

    This paper uses simulation to explore how gene drives can increase genetic gain in livestock breeding programs. Gene drives are naturally occurring phenomena that cause a mutation on one chromosome to copy itself onto its homologous chromosome. We simulated nine different breeding and editing scenarios with a common overall structure. Each scenario began with 21 generations of selection, followed by 20 generations of selection based on true breeding values where the breeder used selection alone, selection in combination with genome editing, or selection with genome editing and gene drives. In the scenarios that used gene drives, we varied the probability of successfully incorporating the gene drive. For each scenario, we evaluated genetic gain, genetic variance [Formula: see text], rate of change in inbreeding ([Formula: see text]), number of distinct quantitative trait nucleotides (QTN) edited, rate of increase in favourable allele frequencies of edited QTN and the time to fix favourable alleles. Gene drives enhanced the benefits of genome editing in seven ways: (1) they amplified the increase in genetic gain brought about by genome editing; (2) they amplified the rate of increase in the frequency of favourable alleles and reduced the time it took to fix them; (3) they enabled more rapid targeting of QTN with lesser effect for genome editing; (4) they distributed fixed editing resources across a larger number of distinct QTN across generations; (5) they focussed editing on a smaller number of QTN within a given generation; (6) they reduced the level of inbreeding when editing a subset of the sires; and (7) they increased the efficiency of converting genetic variation into genetic gain. Genome editing in livestock breeding results in short-, medium- and long-term increases in genetic gain. The increase in genetic gain occurs because editing increases the frequency of favourable alleles in the population. Gene drives accelerate the increase in allele frequency

  14. Approaches for the Identification of Genetic Modifiers of Nutrient Dependent Phenotypes: Examples from Folate

    OpenAIRE

    MacFarlane, Amanda J.; Ian eZinck

    2014-01-01

    By combining the sciences of nutrition, bioinformatics, genomics, population genetics, and epidemiology, nutrigenomics is improving our understanding of how diet and nutrient intake can interact with or modify gene expression and disease risk. In this review, we explore various approaches to examine gene–nutrient interactions and the modifying role of nutrient consumption, as they relate to nutrient status and disease risk in human populations. Two common approaches include the use of SNPs in...

  15. Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier.

    Science.gov (United States)

    Pandey, Daya Shankar; Pan, Indranil; Das, Saptarshi; Leahy, James J; Kwapinski, Witold

    2015-03-01

    A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well.

  16. CAFFEINE: Template-Free Symbolic Model Generation of Analog Circuits via Canonical Form Functions and Genetic Programming

    CERN Document Server

    Mcconaghy, Trent; Gielen, Georges

    2011-01-01

    This paper presents a method to automatically generate compact symbolic performance models of analog circuits with no prior specification of an equation template. The approach takes SPICE simulation data as input, which enables modeling of any nonlinear circuits and circuit characteristics. Genetic programming is applied as a means of traversing the space of possible symbolic expressions. A grammar is specially designed to constrain the search to a canonical form for functions. Novel evolutionary search operators are designed to exploit the structure of the grammar. The approach generates a set of symbolic models which collectively provide a tradeoff between error and model complexity. Experimental results show that the symbolic models generated are compact and easy to understand, making this an effective method for aiding understanding in analog design. The models also demonstrate better prediction quality than posynomials.

  17. Potential benefits of genomic selection on genetic gain of small ruminant breeding programs.

    Science.gov (United States)

    Shumbusho, F; Raoul, J; Astruc, J M; Palhiere, I; Elsen, J M

    2013-08-01

    In conventional small ruminant breeding programs, only pedigree and phenotype records are used to make selection decisions but prospects of including genomic information are now under consideration. The objective of this study was to assess the potential benefits of genomic selection on the genetic gain in French sheep and goat breeding designs of today. Traditional and genomic scenarios were modeled with deterministic methods for 3 breeding programs. The models included decisional variables related to male selection candidates, progeny testing capacity, and economic weights that were optimized to maximize annual genetic gain (AGG) of i) a meat sheep breeding program that improved a meat trait of heritability (h(2)) = 0.30 and a maternal trait of h(2) = 0.09 and ii) dairy sheep and goat breeding programs that improved a milk trait of h(2) = 0.30. Values of ±0.20 of genetic correlation between meat and maternal traits were considered to study their effects on AGG. The Bulmer effect was accounted for and the results presented here are the averages of AGG after 10 generations of selection. Results showed that current traditional breeding programs provide an AGG of 0.095 genetic standard deviation (σa) for meat and 0.061 σa for maternal trait in meat breed and 0.147 σa and 0.120 σa in sheep and goat dairy breeds, respectively. By optimizing decisional variables, the AGG with traditional selection methods increased to 0.139 σa for meat and 0.096 σa for maternal traits in meat breeding programs and to 0.174 σa and 0.183 σa in dairy sheep and goat breeding programs, respectively. With a medium-sized reference population (nref) of 2,000 individuals, the best genomic scenarios gave an AGG that was 17.9% greater than with traditional selection methods with optimized values of decisional variables for combined meat and maternal traits in meat sheep, 51.7% in dairy sheep, and 26.2% in dairy goats. The superiority of genomic schemes increased with the size of the

  18. Order batching in warehouses by minimizing total tardiness: a hybrid approach of weighted association rule mining and genetic algorithms.

    Science.gov (United States)

    Azadnia, Amir Hossein; Taheri, Shahrooz; Ghadimi, Pezhman; Saman, Muhamad Zameri Mat; Wong, Kuan Yew

    2013-01-01

    One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach.

  19. Order Batching in Warehouses by Minimizing Total Tardiness: A Hybrid Approach of Weighted Association Rule Mining and Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Amir Hossein Azadnia

    2013-01-01

    Full Text Available One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach.

  20. A Genetic Algorithm Approach for Group Formation in Collaborative Learning Considering Multiple Student Characteristics

    Science.gov (United States)

    Moreno, Julian; Ovalle, Demetrio A.; Vicari, Rosa M.

    2012-01-01

    Considering that group formation is one of the key processes in collaborative learning, the aim of this paper is to propose a method based on a genetic algorithm approach for achieving inter-homogeneous and intra-heterogeneous groups. The main feature of such a method is that it allows for the consideration of as many student characteristics as…

  1. Integrating Genetic, Psychopharmacological and Neuroimaging Studies: A Converging Methods Approach to Understanding the Neurobiology of ADHD

    Science.gov (United States)

    Durston, Sarah; Konrad, Kerstin

    2007-01-01

    This paper aims to illustrate how combining multiple approaches can inform us about the neurobiology of ADHD. Converging evidence from genetic, psychopharmacological and functional neuroimaging studies has implicated dopaminergic fronto-striatal circuitry in ADHD. However, while the observation of converging evidence from multiple vantage points…

  2. A Genetic Algorithm Approach for Group Formation in Collaborative Learning Considering Multiple Student Characteristics

    Science.gov (United States)

    Moreno, Julian; Ovalle, Demetrio A.; Vicari, Rosa M.

    2012-01-01

    Considering that group formation is one of the key processes in collaborative learning, the aim of this paper is to propose a method based on a genetic algorithm approach for achieving inter-homogeneous and intra-heterogeneous groups. The main feature of such a method is that it allows for the consideration of as many student characteristics as…

  3. Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

    Science.gov (United States)

    Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R

    2012-08-01

    A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three

  4. Epigenetic-genetic chromosome dosage approach for fetal trisomy 21 detection using an autosomal genetic reference marker.

    Directory of Open Access Journals (Sweden)

    Yu K Tong

    Full Text Available BACKGROUND: The putative promoter of the holocarboxylase synthetase (HLCS gene on chromosome 21 is hypermethylated in placental tissues and could be detected as a fetal-specific DNA marker in maternal plasma. Detection of fetal trisomy 21 (T21 has been demonstrated by an epigenetic-genetic chromosome dosage approach where the amount of hypermethylated HLCS in maternal plasma is normalized using a fetal genetic marker on the Y chromosome as a chromosome dosage reference marker. We explore if this method can be applied on both male and female fetuses with the use of a paternally-inherited fetal single nucleotide polymorphism (SNP allele on a reference chromosome for chromosome dosage normalization. METHODOLOGY: We quantified hypermethylated HLCS molecules using methylation-sensitive restriction endonuclease digestion followed by real-time or digital PCR analyses. For chromosome dosage analysis, we compared the amount of digestion-resistant HLCS to that of a SNP allele (rs6636, a C/G SNP that the fetus has inherited from the father but absent in the pregnant mother. PRINCIPAL FINDINGS: Using a fetal-specific SNP allele on a reference chromosome, we analyzed 20 euploid and nine T21 placental tissue samples. All samples with the fetal-specific C allele were correctly classified. One sample from each of the euploid and T21 groups were misclassified when the fetal-specific G allele was used as the reference marker. We then analyzed 33 euploid and 14 T21 maternal plasma samples. All but one sample from each of the euploid and T21 groups were correctly classified using the fetal-specific C allele, while correct classification was achieved for all samples using the fetal-specific G allele as the reference marker. CONCLUSIONS: As a proof-of-concept study, we have demonstrated that the epigenetic-genetic chromosome dosage approach can be applied to the prenatal diagnosis of trisomy 21 for both male and female fetuses.

  5. Multi-Objective Genetic Programming with Redundancy-Regulations for Automatic Construction of Image Feature Extractors

    Science.gov (United States)

    Watchareeruetai, Ukrit; Matsumoto, Tetsuya; Takeuchi, Yoshinori; Kudo, Hiroaki; Ohnishi, Noboru

    We propose a new multi-objective genetic programming (MOGP) for automatic construction of image feature extraction programs (FEPs). The proposed method was originated from a well known multi-objective evolutionary algorithm (MOEA), i.e., NSGA-II. The key differences are that redundancy-regulation mechanisms are applied in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity as well as convergence rate. Experimental results indicate that the proposed MOGP-based FEP construction system outperforms the two conventional MOEAs (i.e., NSGA-II and SPEA2) for a test problem. Moreover, we compared the programs constructed by the proposed MOGP with four human-designed object recognition programs. The results show that the constructed programs are better than two human-designed methods and are comparable with the other two human-designed methods for the test problem.

  6. National Swine Genetic Improvement: An overview of essential program components and organizational structure needed for success

    Institute of Scientific and Technical Information of China (English)

    John; MABRY

    2005-01-01

    The swine industry in China is a thrivingand evolving industry that has shown phenome-nal growth over the past10years.Newand mod-ern swine farms have been started in locationsacross the country.Genetics has been importedfrom many different countries in an effort to up-grade the quality and efficiency of the traditionalbreeds of swine.But to insure long term successand viability in a worldwide competitive industrysuch as pork,there is need for a National SwineGenetic Improvement Program.This programneeds to ...

  7. Dynamic Simulations of Nonlinear Multi-Domain Systems Based on Genetic Programming and Bond Graphs

    Institute of Scientific and Technical Information of China (English)

    DI Wenhui; SUN Bo; XU Lixin

    2009-01-01

    A dynamic simulation method for non-linear systems based on genetic programming (GP) and bond graphs (BG) was developed to improve the design of nonlinear multi-domain energy conversion sys-tems. The genetic operators enable the embryo bond graph to evolve towards the target graph according to the fitness function. Better simulation requires analysis of the optimization of the eigenvalue and the filter circuit evolution. The open topological design and space search ability of this method not only gives a more optimized convergence for the operation, but also reduces the generation time for the new circuit graph for the design of nonlinear multi-domain systems.

  8. Developing Novel Therapeutic Approaches in Small Cell Lung Carcinoma Using Genetically Engineered Mouse Models and Human Circulating Tumor Cells

    Science.gov (United States)

    2015-10-01

    Using Genetically Engineered Mouse Models and Human Circulating Tumor Cells PRINCIPAL INVESTIGATOR: Jeffrey Engelman MD PhD CONTRACTING...SUBTITLE Developiing Novel Therapeutic Approaches in Small Cell Lung 5a. CONTRACT NUMBER Carcinoma Using Genetically Engineered Mouse Models and 5b...biomarkers. 15. SUBJECT TERMS Small cell lung cancer (SCLC), Genetically engineered mouse model (GEMM), BH3 mimetic, TORC inhibitor, Apoptosis

  9. A DC programming approach for finding communities in networks.

    Science.gov (United States)

    Le Thi, Hoai An; Nguyen, Manh Cuong; Dinh, Tao Pham

    2014-12-01

    Automatic discovery of community structures in complex networks is a fundamental task in many disciplines, including physics, biology, and the social sciences. The most used criterion for characterizing the existence of a community structure in a network is modularity, a quantitative measure proposed by Newman and Girvan (2004). The discovery community can be formulated as the so-called modularity maximization problem that consists of finding a partition of nodes of a network with the highest modularity. In this letter, we propose a fast and scalable algorithm called DCAM, based on DC (difference of convex function) programming and DCA (DC algorithms), an innovative approach in nonconvex programming framework for solving the modularity maximization problem. The special structure of the problem considered here has been well exploited to get an inexpensive DCA scheme that requires only a matrix-vector product at each iteration. Starting with a very large number of communities, DCAM furnishes, as output results, an optimal partition together with the optimal number of communities [Formula: see text]; that is, the number of communities is discovered automatically during DCAM's iterations. Numerical experiments are performed on a variety of real-world network data sets with up to 4,194,304 nodes and 30,359,198 edges. The comparative results with height reference algorithms show that the proposed approach outperforms them not only on quality and rapidity but also on scalability. Moreover, it realizes a very good trade-off between the quality of solutions and the run time.

  10. wisepair: a computer program for individual matching in genetic tracking studies.

    Science.gov (United States)

    Rothstein, Andrew P; McLaughlin, Ryan; Acevedo-Gutiérrez, Alejandro; Schwarz, Dietmar

    2017-03-01

    Individual-based data sets tracking organisms over space and time are fundamental to answering broad questions in ecology and evolution. A 'permanent' genetic tag circumvents a need to invasively mark or tag animals, especially if there are little phenotypic differences among individuals. However, genetic tracking of individuals does not come without its limits; correctly matching genotypes and error rates associated with laboratory work can make it difficult to parse out matched individuals. In addition, defining a sampling design that effectively matches individuals in the wild can be a challenge for researchers. Here, we combine the two objectives of defining sampling design and reducing genotyping error through an efficient Python-based computer-modelling program, wisepair. We describe the methods used to develop the computer program and assess its effectiveness through three empirical data sets, with and without reference genotypes. Our results show that wisepair outperformed similar genotype matching programs using previously published from reference genotype data of diurnal poison frogs (Allobates femoralis) and without-reference (faecal) genotype sample data sets of harbour seals (Phoca vitulina) and Eurasian otters (Lutra lutra). In addition, due to limited sampling effort in the harbour seal data, we present optimal sampling designs for future projects. wisepair allows for minimal sacrifice in the available methods as it incorporates sample rerun error data, allelic pairwise comparisons and probabilistic simulations to determine matching thresholds. Our program is the lone tool available to researchers to define parameters a priori for genetic tracking studies.

  11. Multi-choice stochastic bi-level programming problem in cooperative nature via fuzzy programming approach

    Science.gov (United States)

    Maiti, Sumit Kumar; Roy, Sankar Kumar

    2016-05-01

    In this paper, a Multi-Choice Stochastic Bi-Level Programming Problem (MCSBLPP) is considered where all the parameters of constraints are followed by normal distribution. The cost coefficients of the objective functions are multi-choice types. At first, all the probabilistic constraints are transformed into deterministic constraints using stochastic programming approach. Further, a general transformation technique with the help of binary variables is used to transform the multi-choice type cost coefficients of the objective functions of Decision Makers(DMs). Then the transformed problem is considered as a deterministic multi-choice bi-level programming problem. Finally, a numerical example is presented to illustrate the usefulness of the paper.

  12. Understanding the complex etiologies of developmental disorders: behavioral and molecular genetic approaches.

    Science.gov (United States)

    Willcutt, Erik G; Pennington, Bruce F; Duncan, Laramie; Smith, Shelley D; Keenan, Janice M; Wadsworth, Sally; Defries, John C; Olson, Richard K

    2010-09-01

    This article has 2 primary goals. First, a brief tutorial on behavioral and molecular genetic methods is provided for readers without extensive training in these areas. To illustrate the application of these approaches to developmental disorders, etiologically informative studies of reading disability (RD), math disability (MD), and attention-deficit hyperactivity disorder (ADHD) are then reviewed. Implications of the results for these specific disorders and for developmental disabilities as a whole are discussed, and novel directions for future research are highlighted. Previous family and twin studies of RD, MD, and ADHD are reviewed systematically, and the extensive molecular genetic literatures on each disorder are summarized. To illustrate 4 novel extensions of these etiologically informative approaches, new data are presented from the Colorado Learning Disabilities Research Center, an ongoing twin study of the etiology of RD, ADHD, MD, and related disorders. RD, MD, and ADHD are familial and heritable, and co-occur more frequently than expected by chance. Molecular genetic studies suggest that all 3 disorders have complex etiologies, with multiple genetic and environmental risk factors each contributing to overall risk for each disorder. Neuropsychological analyses indicate that the 3 disorders are each associated with multiple neuropsychological weaknesses, and initial evidence suggests that comorbidity between the 3 disorders is due to common genetic risk factors that lead to slow processing speed.

  13. Better Crunching: Recommendations for Multivariate Data Analysis Approaches for Program Impact Evaluations

    Science.gov (United States)

    Braverman, Marc T.

    2016-01-01

    Extension program evaluations often present opportunities to analyze data in multiple ways. This article suggests that program evaluations can involve more sophisticated data analysis approaches than are often used. On the basis of a hypothetical program scenario and corresponding data set, two approaches to testing for evidence of program impact…

  14. A cellular genetics approach identifies gene-drug interactions and pinpoints drug toxicity pathway nodes

    Directory of Open Access Journals (Sweden)

    Oscar Takeo Suzuki

    2014-08-01

    Full Text Available New approaches to toxicity testing have incorporated high-throughput screening across a broad-range of in vitro assays to identify potential key events in response to chemical or drug treatment. To date, these approaches have primarily utilized repurposed drug discovery assays. In this study, we describe an approach that combines in vitro screening with genetic approaches for the experimental identification of genes and pathways involved in chemical or drug toxicity. Primary embryonic fibroblasts isolated from 32 genetically-characterized inbred mouse strains were treated in concentration-response format with 65 compounds, including pharmaceutical drugs, environmental chemicals, and compounds with known modes-of-action. Integrated cellular responses were measured at 24 and 72 hours using high-content imaging and included cell loss, membrane permeability, mitochondrial function, and apoptosis. Genetic association analysis of cross-strain differences in the cellular responses resulted in a collection of candidate loci potentially underlying the variable strain response to each chemical. As a demonstration of the approach, one candidate gene involved in rotenone sensitivity, Cybb, was experimentally validated in vitro and in vivo. Pathway analysis on the combined list of candidate loci across all chemicals identified a number of over-connected nodes that may serve as core regulatory points in toxicity pathways.

  15. A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems

    Energy Technology Data Exchange (ETDEWEB)

    Sahoo, N.C. [Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450 Melaka (Malaysia); Prasad, K. [Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450 Melaka (Malaysia)

    2006-11-15

    This paper presents a fuzzy genetic approach for reconfiguration of radial distribution systems (RDS) so as to maximize the voltage stability of the network for a specific set of loads. The network reconfiguration involves a mechanism for selection of the best set of branches to be opened, one from each loop, such that the reconfigured RDS possesses desired performance characteristics. This discrete solution space is better handled by the proposed scheme, which maximizes a suitable optimizing function (computed using two different approaches). In the first approach, this function is chosen as the average of a voltage stability index of all the buses in the RDS, while in the second approach, the complete RDS is reduced to a two bus equivalent system and the optimizing function is the voltage stability index of this reduced two bus system. The fuzzy genetic algorithm uses a suitable coding and decoding scheme for maintaining the radial nature of the network at every stage of genetic evolution, and it also uses a fuzzy rule based mutation controller for efficient search of the solution space. This method, tested on 69 bus and 33 bus RDSs, shows promising results for the both approaches. It is also observed that the network losses are reduced when the voltage stability is enhanced by the network reconfiguration. (author)

  16. Arbitrariness is not enough: towards a functional approach to the genetic code.

    Science.gov (United States)

    Lacková, Ľudmila; Matlach, Vladimír; Faltýnek, Dan

    2017-05-09

    Arbitrariness in the genetic code is one of the main reasons for a linguistic approach to molecular biology: the genetic code is usually understood as an arbitrary relation between amino acids and nucleobases. However, from a semiotic point of view, arbitrariness should not be the only condition for definition of a code, consequently it is not completely correct to talk about "code" in this case. Yet we suppose that there exist a code in the process of protein synthesis, but on a higher level than the nucleic bases chains. Semiotically, a code should be always associated with a function and we propose to define the genetic code not only relationally (in basis of relation between nucleobases and amino acids) but also in terms of function (function of a protein as meaning of the code). Even if the functional definition of meaning in the genetic code has been discussed in the field of biosemiotics, its further implications have not been considered. In fact, if the function of a protein represents the meaning of the genetic code (the sign's object), then it is crucial to reconsider the notion of its expression (the sign) as well. In our contribution, we will show that the actual model of the genetic code is not the only possible and we will propose a more appropriate model from a semiotic point of view.

  17. [Recent progress in gene mapping through high-throughput sequencing technology and forward genetic approaches].

    Science.gov (United States)

    Lu, Cairui; Zou, Changsong; Song, Guoli

    2015-08-01

    Traditional gene mapping using forward genetic approaches is conducted primarily through construction of a genetic linkage map, the process of which is tedious and time-consuming, and often results in low accuracy of mapping and large mapping intervals. With the rapid development of high-throughput sequencing technology and decreasing cost of sequencing, a variety of simple and quick methods of gene mapping through sequencing have been developed, including direct sequencing of the mutant genome, sequencing of selective mutant DNA pooling, genetic map construction through sequencing of individuals in population, as well as sequencing of transcriptome and partial genome. These methods can be used to identify mutations at the nucleotide level and has been applied in complex genetic background. Recent reports have shown that sequencing mapping could be even done without the reference of genome sequence, hybridization, and genetic linkage information, which made it possible to perform forward genetic study in many non-model species. In this review, we summarized these new technologies and their application in gene mapping.

  18. Genetics of schizophrenia and smoking: an approach to studying their comorbidity based on epidemiological findings

    Science.gov (United States)

    de Leon, Jose; Diaz, Francisco J.

    2012-01-01

    The association between schizophrenia and tobacco smoking has been described in more than 1,000 articles, many with inadequate methodology. The studies on this association can focus on: (1) current smoking, ever smoking or smoking cessation; (2) non-psychiatric controls or controls with severe mental illness (e.g., bipolar disorder); and (3) higher smoking frequency or greater usage in smokers. The association with the most potential for genetic studies is that between ever daily smoking and schizophrenia; it may reflect a shared genetic vulnerability. To reduce the number of false-positive genes, we propose a three-stage approach derived from epidemiological knowledge. In the first stage, only genetic variations associated with ever daily smoking that are simultaneously significant within the non-psychiatric controls, the bipolar disorder controls and the schizophrenia cases will be selected. Only those genetic variations that are simultaneously significant in the three hypothesis tests will be tested in the second stage, where the prevalence of the genes must be significantly higher in schizophrenia than in bipolar disorder, and significantly higher in bipolar disorder than in controls. The genes simultaneously significant in the second stage will be included in a third stage where the gene variations must be significantly more frequent in schizophrenia patients who did not start smoking daily until their 20s (late start) versus those who had an early start. Any genetic approach to psychiatric disorders may fail if attention is not given to comorbidity and epidemiological studies that suggest which comorbidities are likely to be explained by genetics and which are not. Our approach, which examines the results of epidemiological studies on comorbidities and then looks for genes that simultaneously satisfy epidemiologically suggested sets of hypotheses, may also apply to the study of other major illnesses. PMID:22190153

  19. Gene Prioritization for Imaging Genetics Studies Using Gene Ontology and a Stratified False Discovery Rate Approach.

    Science.gov (United States)

    Patel, Sejal; Park, Min Tae M; Chakravarty, M Mallar; Knight, Jo

    2016-01-01

    Imaging genetics is an emerging field in which the association between genes and neuroimaging-based quantitative phenotypes are used to explore the functional role of genes in neuroanatomy and neurophysiology in the context of healthy function and neuropsychiatric disorders. The main obstacle for researchers in the field is the high dimensionality of the data in both the imaging phenotypes and the genetic variants commonly typed. In this article, we develop a novel method that utilizes Gene Ontology, an online database, to select and prioritize certain genes, employing a stratified false discovery rate (sFDR) approach to investigate their associations with imaging phenotypes. sFDR has the potential to increase power in genome wide association studies (GWAS), and is quickly gaining traction as a method for multiple testing correction. Our novel approach addresses both the pressing need in genetic research to move beyond candidate gene studies, while not being overburdened with a loss of power due to multiple testing. As an example of our methodology, we perform a GWAS of hippocampal volume using both the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA2) and the Alzheimer's Disease Neuroimaging Initiative datasets. The analysis of ENIGMA2 data yielded a set of SNPs with sFDR values between 10 and 20%. Our approach demonstrates a potential method to prioritize genes based on biological systems impaired in a disease.

  20. Genetic evaluation of early egg production and maturation traits using two different approaches in Japanese quail.

    Science.gov (United States)

    Abou Khadiga, G; Mahmoud, B Y F; El-Full, E A

    2016-04-01

    The objective of the current study was to evaluate a multi-trait selection program based on aggregated breeding values using an animal model Best Linear Unbiased Prediction (BLUP) in Japanese quail. The estimated genetic gain was compared by both mixed model and least squares methods. Data of 1,682 female Japanese quails were collected through four consecutive generations to estimate genetic gain, depending on aggregated breeding values, for age at first egg (AFE), body weight at sexual maturity (BWSM), and days needed to produce the first ten eggs (DN10). Estimates of cumulative selection response were favorable for all the studied traits and significant for AFE (-3.03) and BWSM(10.38), but not significant for DN10(-0.15). Estimates of direct heritability were moderate for AFE (0.21) and BWSM(0.25) but low for DN10(0.08), while estimates of maternal heritability were moderate for AFE (0.19) but low for BWSM(0.04) and DN10(0.01). High (0.45 to 0.56) genetic and low (-0.01 to -0.18) phenotypic correlations were observed among the studied traits. Negative (-0.23 to -0.95) correlations between additive genetic and maternal genetic effects were observed for all traits. Genetic trends were -0.76 (P=0.031), 2.54 (P=0.037), and -0.06 (P=0.052) with calculated product-moment correlations between breeding values, estimated by BLUP and phenotypic selection methods, of 0.78 (P=0.002), 0.77 (P=0.004), and 0.61 (P=0.007) for AFE, BWSM, and DN10, respectively. Aggregated breeding value estimation based on animal model BLUP could be an effective method of constructing a selection program to achieve a favorable selection response in egg production traits in Japanese quail.

  1. A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

    Directory of Open Access Journals (Sweden)

    Dalton Meitei Thounaojam

    2016-01-01

    Full Text Available This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.

  2. A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection.

    Science.gov (United States)

    Thounaojam, Dalton Meitei; Khelchandra, Thongam; Manglem Singh, Kh; Roy, Sudipta

    2016-01-01

    This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.

  3. The genetic diversity of triticale genotypes involved in Polish breeding programs.

    Science.gov (United States)

    Niedziela, Agnieszka; Orłowska, Renata; Machczyńska, Joanna; Bednarek, Piotr T

    2016-01-01

    Genetic diversity analysis of triticale populations is useful for breeding programs, as it helps to select appropriate genetic material for classifying the parental lines, heterotic groups and predicting hybrid performance. In our study 232 breeding forms were analyzed using diversity arrays technology markers. Principal coordinate analysis followed by model-based Bayesian analysis of population structure revealed the presence of weak data structuring with three groups of data. In the first group, 17 spring and 17 winter forms were clustered. The second and the third groups were represented by 101 and 26 winter forms, respectively. Polymorphic information content values, as well as Shannon's Information Index, were higher for the first (0.319) and second (0.309) than for third (0.234) group. AMOVA analysis demonstrated a higher level of within variation (86 %) than among populations (14 %). This study provides the basic information on the presence of structure within a genetic pool of triticale breeding forms.

  4. Interactive computer program for learning genetic principles of segregation and independent assortment through meiosis.

    Science.gov (United States)

    Yang, Xiaoli; Ge, Rong; Yang, Yufei; Shen, Hao; Li, Yingjie; Tseng, Charles C

    2009-01-01

    Teaching fundamental principles of genetics such as segregation and independent assortment of genes could be challenging for high school and college biology instructors. Students without thorough knowledge in meiosis often end up of frustration and failure in genetics courses. Although all textbooks and laboratory manuals have excellent graphic demonstrations and photographs of meiotic process, students may not always master the concept due to the lack of hands-on exercise. In response to the need for an effective lab exercise to understand the segregation of allelic genes and the independent assortment of the unlinked genes, we developed an interactive program for students to manually manipulate chromosome models and visualize each major step of meiosis so that these two genetic principles can be thoroughly understood.

  5. Application of the CUDA programming model in the simulation of genetic sequences evolution

    Directory of Open Access Journals (Sweden)

    Freddy Yasmany Chávez

    2017-03-01

    Full Text Available Simulation is a powerful approach in the study of the molecular evolution of genetic sequences and their divergence over time; there are different procedures for the simulation of molecular evolution, but all of them have high computational complexity, and in most cases the genetic sequences have large size, increasing the execution time of the implementations of those procedures. Based on this problem, this paper describes a proposal of parallelization model using CUDA technology and the results of this proposal are compared with its sequential equivalent.

  6. Using AFLP markers and the Geneland program for the inference of population genetic structure

    DEFF Research Database (Denmark)

    Guillot, Gilles; Santos, Filipe

    2010-01-01

    The use of dominant markers such as amplified fragment length polymorphism (AFLP) for population genetics analyses is often impeded by the lack of appropriate computer programs and rarely motivated by objective considerations. The point of the present note is twofold: (i) we describe how the comp......The use of dominant markers such as amplified fragment length polymorphism (AFLP) for population genetics analyses is often impeded by the lack of appropriate computer programs and rarely motivated by objective considerations. The point of the present note is twofold: (i) we describe how...... such as single nucleotide polymorphisms (SNP) markers but this difference becomes negligible for data sets of common size (number of individuals n≥100, number of markers L≥200). The latest Geneland version (3.2.1) handling dominant markers is freely available as an R package with a fully clickable graphical...

  7. The influence of genetic background versus commercial breeding programs on chicken immunocompetence.

    Science.gov (United States)

    Emam, Mehdi; Mehrabani-Yeganeh, Hassan; Barjesteh, Neda; Nikbakht, Gholamreza; Thompson-Crispi, Kathleen; Charkhkar, Saeid; Mallard, Bonnie

    2014-01-01

    Immunocompetence of livestock plays an important role in farm profitability because it directly affects health maintenance. Genetics significantly influences the immune system, and the genotypic structure of modern fast-growing chickens has been changed, particularly after decades of breeding for higher production. Therefore, this study was designed to help determine if intensive breeding programs have adversely affected immunocompetence or whether the immune response profiles are controlled to greater extent by genetic background. Thus, 3 indigenous chicken populations from different genetic backgrounds and 2 globally available modern broiler strains, Ross 308 and Cobb 500, were evaluated for various aspects of immune response. These included antibody responses against sheep red blood cells and Brucella abortus antigen, as well as some aspects of cell-mediated immunocompetence by toe web swelling test and in vitro blood mononuclear cell proliferation. Significant differences (P chickens is most likely due to differences in the genetic background between each strain of chicken rather than by commercial selection programs for high production.

  8. An integrated biochemistry and genetics outreach program designed for elementary school students.

    Science.gov (United States)

    Ross, Eric D; Lee, Sarah K; Radebaugh, Catherine A; Stargell, Laurie A

    2012-02-01

    Exposure to genetic and biochemical experiments typically occurs late in one's academic career. By the time students have the opportunity to select specialized courses in these areas, many have already developed negative attitudes toward the sciences. Given little or no direct experience with the fields of genetics and biochemistry, it is likely that many young people rule these out as potential areas of study or career path. To address this problem, we developed a 7-week (~1 hr/week) hands-on course to introduce fifth grade students to basic concepts in genetics and biochemistry. These young students performed a series of investigations (ranging from examining phenotypic variation, in vitro enzymatic assays, and yeast genetic experiments) to explore scientific reasoning through direct experimentation. Despite the challenging material, the vast majority of students successfully completed each experiment, and most students reported that the experience increased their interest in science. Additionally, the experiments within the 7-week program are easily performed by instructors with basic skills in biological sciences. As such, this program can be implemented by others motivated to achieve a broader impact by increasing the accessibility of their university and communicating to a young audience a positive impression of the sciences and the potential for science as a career.

  9. Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

    OpenAIRE

    Palika Chopra; Rajendra Kumar Sharma; Maneek Kumar

    2016-01-01

    An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks (ANNs) and Genetic Programming (GP). The data for analysis and model development was collected at 28-, 56-, and 91-day curing periods through experiments conducted in the laboratory under standard controlled conditions. The developed models have also been tested on in situ concrete data taken from literature. A comparison o...

  10. Lasing from Escherichia coli bacteria genetically programmed to express green fluorescent protein

    Science.gov (United States)

    Gather, Malte C.; Yun, Seok Hyun

    2011-08-01

    We report on lasing action from colonies of Escherichia coli bacteria that are genetically programmed to synthesize the green fluorescent protein (GFP). When embedded in a Fabry--Perot type cavity and excited by ns-pulses of blue light (465nm), the bacteria generate green laser emission (˜520nm). Broad illumination of pump light yields simultaneous lasing over a large area in bacterial colonies.

  11. Statistical studies in genetic toxicology: a perspective from the U.S. National Toxicology Program.

    OpenAIRE

    Margolin, B H

    1985-01-01

    This paper surveys recent, as yet unpublished, statistical studies arising from research in genetic toxicology within the U.S. National Toxicology Program (NTP). These studies all involve analyses of data from Ames Salmonella/microsome mutagenicity tests, but the statistical methodologies are broadly applicable. Three issues are addressed: First, what is a tenable sampling model for Ames test data, and how does one best test the adequacy of the Poisson sampling assumption? Second, given that ...

  12. Optimization of decision rules based on dynamic programming approach

    KAUST Repository

    Zielosko, Beata

    2014-01-14

    This chapter is devoted to the study of an extension of dynamic programming approach which allows optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure that is the difference between number of rows in a given decision table and the number of rows labeled with the most common decision for this table divided by the number of rows in the decision table. We fix a threshold γ, such that 0 ≤ γ < 1, and study so-called γ-decision rules (approximate decision rules) that localize rows in subtables which uncertainty is at most γ. Presented algorithm constructs a directed acyclic graph Δ γ T which nodes are subtables of the decision table T given by pairs "attribute = value". The algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The chapter contains also results of experiments with decision tables from UCI Machine Learning Repository. © 2014 Springer International Publishing Switzerland.

  13. A High Precision Comprehensive Evaluation Method for Flood Disaster Loss Based on Improved Genetic Programming

    Institute of Scientific and Technical Information of China (English)

    ZHOU Yuliang; LU Guihua; JIN Juliang; TONG Fang; ZHOU Ping

    2006-01-01

    Precise comprehensive evaluation of flood disaster loss is significant for the prevention and mitigation of flood disasters. Here, one of the difficulties involved is how to establish a model capable of describing the complex relation between the input and output data of the system of flood disaster loss. Genetic programming (GP) solves problems by using ideas from genetic algorithm and generates computer programs automatically. In this study a new method named the evaluation of the grade of flood disaster loss (EGFD) on the basis of improved genetic programming (IGP) is presented (IGPEGFD). The flood disaster area and the direct economic loss are taken as the evaluation indexes of flood disaster loss. Obviously that the larger the evaluation index value, the larger the corresponding value of the grade of flood disaster loss is. Consequently the IGP code is designed to make the value of the grade of flood disaster be an increasing function of the index value. The result of the application of the IGP-EGFD model to Henan Province shows that a good function expression can be obtained within a bigger searched function space; and the model is of high precision and considerable practical significance.Thus, IGP-EGFD can be widely used in automatic modeling and other evaluation systems.

  14. Synthetic biology and genetic causation.

    Science.gov (United States)

    Oftedal, Gry; Parkkinen, Veli-Pekka

    2013-06-01

    Synthetic biology research is often described in terms of programming cells through the introduction of synthetic genes. Genetic material is seemingly attributed with a high level of causal responsibility. We discuss genetic causation in synthetic biology and distinguish three gene concepts differing in their assumptions of genetic control. We argue that synthetic biology generally employs a difference-making approach to establishing genetic causes, and that this approach does not commit to a specific notion of genetic program or genetic control. Still, we suggest that a strong program concept of genetic material can be used as a successful heuristic in certain areas of synthetic biology. Its application requires control of causal context, and may stand in need of a modular decomposition of the target system. We relate different modularity concepts to the discussion of genetic causation and point to possible advantages of and important limitations to seeking modularity in synthetic biology systems. Copyright © 2013 Elsevier Ltd. All rights reserved.

  15. Genetic variability and population structure of Salvia lachnostachys: implications for breeding and conservation programs.

    Science.gov (United States)

    Erbano, Marianna; Schühli, Guilherme Schnell E; Santos, Élide Pereira Dos

    2015-04-08

    The genetic diversity and population structure of Salvia lachnostachys Benth were assessed. Inter Simple Sequence Repeat (ISSR) molecular markers were used to investigate the restricted distribution of S. lachnostachys in Parana State, Brazil. Leaves of 73 individuals representing three populations were collected. DNA was extracted and submitted to PCR-ISSR amplification with nine tested primers. Genetic diversity parameters were evaluated. Our analysis indicated 95.6% polymorphic loci (stress value 0.02) with a 0.79 average Simpson's index. The Nei-Li distance dendrogram and principal component analysis largely recovered the geographical origin of each sample. Four major clusters were recognized representing each collected population. Nei's gene diversity and Shannon's information index were 0.25 and 0.40 respectively. As is typical for outcrossing herbs, the majority of genetic variation occurred at the population level (81.76%). A high gene flow (Nm = 2.48) was observed with a correspondingly low fixation index. These values were generally similar to previous studies on congeneric species. The results of principal coordinate analysis (PCA) and of arithmetic average (UPGMA) were consistent and all three populations appear distinct as in STRUCTURE analysis. In addition, this analysis indicated a majority intrapopulation genetic variation. Despite the human pressure on natural populations our study found high levels of genetic diversity for S. lachnostachys. This was the first molecular assessment for this endemic species with medicinal proprieties and the results can guide for subsequent bioprospection, breeding programs or conservation actions.

  16. A nearest neighbour approach by genetic distance to the assignment of individual trees to geographic origin.

    Science.gov (United States)

    Degen, Bernd; Blanc-Jolivet, Céline; Stierand, Katrin; Gillet, Elizabeth

    2017-03-01

    During the past decade, the use of DNA for forensic applications has been extensively implemented for plant and animal species, as well as in humans. Tracing back the geographical origin of an individual usually requires genetic assignment analysis. These approaches are based on reference samples that are grouped into populations or other aggregates and intend to identify the most likely group of origin. Often this grouping does not have a biological but rather a historical or political justification, such as "country of origin". In this paper, we present a new nearest neighbour approach to individual assignment or classification within a given but potentially imperfect grouping of reference samples. This method, which is based on the genetic distance between individuals, functions better in many cases than commonly used methods. We demonstrate the operation of our assignment method using two data sets. One set is simulated for a large number of trees distributed in a 120km by 120km landscape with individual genotypes at 150 SNPs, and the other set comprises experimental data of 1221 individuals of the African tropical tree species Entandrophragma cylindricum (Sapelli) genotyped at 61 SNPs. Judging by the level of correct self-assignment, our approach outperformed the commonly used frequency and Bayesian approaches by 15% for the simulated data set and by 5-7% for the Sapelli data set. Our new approach is less sensitive to overlapping sources of genetic differentiation, such as genetic differences among closely-related species, phylogeographic lineages and isolation by distance, and thus operates better even for suboptimal grouping of individuals. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  17. A mathematical programming approach for sequential clustering of dynamic networks

    Science.gov (United States)

    Silva, Jonathan C.; Bennett, Laura; Papageorgiou, Lazaros G.; Tsoka, Sophia

    2016-02-01

    A common analysis performed on dynamic networks is community structure detection, a challenging problem that aims to track the temporal evolution of network modules. An emerging area in this field is evolutionary clustering, where the community structure of a network snapshot is identified by taking into account both its current state as well as previous time points. Based on this concept, we have developed a mixed integer non-linear programming (MINLP) model, SeqMod, that sequentially clusters each snapshot of a dynamic network. The modularity metric is used to determine the quality of community structure of the current snapshot and the historical cost is accounted for by optimising the number of node pairs co-clustered at the previous time point that remain so in the current snapshot partition. Our method is tested on social networks of interactions among high school students, college students and members of the Brazilian Congress. We show that, for an adequate parameter setting, our algorithm detects the classes that these students belong more accurately than partitioning each time step individually or by partitioning the aggregated snapshots. Our method also detects drastic discontinuities in interaction patterns across network snapshots. Finally, we present comparative results with similar community detection methods for time-dependent networks from the literature. Overall, we illustrate the applicability of mathematical programming as a flexible, adaptable and systematic approach for these community detection problems. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.

  18. A type-driven approach to concrete meta programming.

    NARCIS (Netherlands)

    Vinju, J.J.

    2005-01-01

    Applications that manipulate programs as data are called meta programs. Examples of meta programs are compilers, source-to-source translators and code generators. Meta programming can be supported by the ability to represent program fragments in concrete syntax instead of abstract syntax. The result

  19. Landscape genetic approaches to guide native plant restoration in the Mojave Desert.

    Science.gov (United States)

    Shryock, Daniel F; Havrilla, Caroline A; DeFalco, Lesley A; Esque, Todd C; Custer, Nathan A; Wood, Troy E

    2017-03-01

    Restoring dryland ecosystems is a global challenge due to synergistic drivers of disturbance coupled with unpredictable environmental conditions. Dryland plant species have evolved complex life-history strategies to cope with fluctuating resources and climatic extremes. Although rarely quantified, local adaptation is likely widespread among these species and potentially influences restoration outcomes. The common practice of reintroducing propagules to restore dryland ecosystems, often across large spatial scales, compels evaluation of adaptive divergence within these species. Such evaluations are critical to understanding the consequences of large-scale manipulation of gene flow and to predicting success of restoration efforts. However, genetic information for species of interest can be difficult and expensive to obtain through traditional common garden experiments. Recent advances in landscape genetics offer marker-based approaches for identifying environmental drivers of adaptive genetic variability in non-model species, but tools are still needed to link these approaches with practical aspects of ecological restoration. Here, we combine spatially explicit landscape genetics models with flexible visualization tools to demonstrate how cost-effective evaluations of adaptive genetic divergence can facilitate implementation of different seed sourcing strategies in ecological restoration. We apply these methods to Amplified Fragment Length Polymorphism (AFLP) markers genotyped in two Mojave Desert shrub species of high restoration importance: the long-lived, wind-pollinated gymnosperm Ephedra nevadensis, and the short-lived, insect-pollinated angiosperm Sphaeralcea ambigua. Mean annual temperature was identified as an important driver of adaptive genetic divergence for both species. Ephedra showed stronger adaptive divergence with respect to precipitation variability, while temperature variability and precipitation averages explained a larger fraction of adaptive

  20. Landscape genetic approaches to guide native plant restoration in the Mojave Desert

    Science.gov (United States)

    Shryock, Daniel F.; Havrilla, Caroline A.; DeFalco, Lesley; Esque, Todd; Custer, Nathan; Wood, Troy E.

    2016-01-01

    Restoring dryland ecosystems is a global challenge due to synergistic drivers of disturbance coupled with unpredictable environmental conditions. Dryland plant species have evolved complex life-history strategies to cope with fluctuating resources and climatic extremes. Although rarely quantified, local adaptation is likely widespread among these species and potentially influences restoration outcomes. The common practice of reintroducing propagules to restore dryland ecosystems, often across large spatial scales, compels evaluation of adaptive divergence within these species. Such evaluations are critical to understanding the consequences of large-scale manipulation of gene flow and to predicting success of restoration efforts. However, genetic information for species of interest can be difficult and expensive to obtain through traditional common garden experiments. Recent advances in landscape genetics offer marker-based approaches for identifying environmental drivers of adaptive genetic variability in non-model species, but tools are still needed to link these approaches with practical aspects of ecological restoration. Here, we combine spatially-explicit landscape genetics models with flexible visualization tools to demonstrate how cost-effective evaluations of adaptive genetic divergence can facilitate implementation of different seed sourcing strategies in ecological restoration. We apply these methods to Amplified Fragment Length Polymorphism (AFLP) markers genotyped in two Mojave Desert shrub species of high restoration importance: the long-lived, wind-pollinated gymnosperm Ephedra nevadensis, and the short-lived, insect-pollinated angiosperm Sphaeralcea ambigua. Mean annual temperature was identified as an important driver of adaptive genetic divergence for both species. Ephedra showed stronger adaptive divergence with respect to precipitation variability, while temperature variability and precipitation averages explained a larger fraction of adaptive

  1. Deciphering genetic diversity and inheritance of tomato fruit weight and composition through a systems biology approach.

    Science.gov (United States)

    Pascual, Laura; Xu, Jiaxin; Biais, Benoît; Maucourt, Mickaël; Ballias, Patricia; Bernillon, Stéphane; Deborde, Catherine; Jacob, Daniel; Desgroux, Aurore; Faurobert, Mireille; Bouchet, Jean-Paul; Gibon, Yves; Moing, Annick; Causse, Mathilde

    2013-12-01

    Integrative systems biology proposes new approaches to decipher the variation of phenotypic traits. In an effort to link the genetic variation and the physiological and molecular bases of fruit composition, the proteome (424 protein spots), metabolome (26 compounds), enzymatic profile (26 enzymes), and phenotypes of eight tomato accessions, covering the genetic diversity of the species, and four of their F1 hybrids, were characterized at two fruit developmental stages (cell expansion and orange-red). The contents of metabolites varied among the genetic backgrounds, while enzyme profiles were less variable, particularly at the cell expansion stage. Frequent genotype by stage interactions suggested that the trends observed for one accession at a physiological level may change in another accession. In agreement with this, the inheritance modes varied between crosses and stages. Although additivity was predominant, 40% of the traits were non-additively inherited. Relationships among traits revealed associations between different levels of expression and provided information on several key proteins. Notably, the role of frucktokinase, invertase, and cysteine synthase in the variation of metabolites was highlighted. Several stress-related proteins also appeared related to fruit weight differences. These key proteins might be targets for improving metabolite contents of the fruit. This systems biology approach provides better understanding of networks controlling the genetic variation of tomato fruit composition. In addition, the wide data sets generated provide an ideal framework to develop innovative integrated hypothesis and will be highly valuable for the research community.

  2. A pooling-based approach to mapping genetic variants associated with DNA methylation.

    Science.gov (United States)

    Kaplow, Irene M; MacIsaac, Julia L; Mah, Sarah M; McEwen, Lisa M; Kobor, Michael S; Fraser, Hunter B

    2015-06-01

    DNA methylation is an epigenetic modification that plays a key role in gene regulation. Previous studies have investigated its genetic basis by mapping genetic variants that are associated with DNA methylation at specific sites, but these have been limited to microarrays that cover map of DNA methylation. Compared to methods that do not account for ASM, our approach increases statistical power to detect associations while sharply reducing cost, effort, and experimental variability. As a proof of concept, we generated deep sequencing data from a pool of 60 human cell lines; we evaluated almost twice as many CpGs as the largest microarray studies and identified more than 2000 genetic variants associated with DNA methylation. We found that these variants are highly enriched for associations with chromatin accessibility and CTCF binding but are less likely to be associated with traits indirectly linked to DNA, such as gene expression and disease phenotypes. In summary, our approach allows genome-wide mapping of genetic variants associated with DNA methylation in any tissue of any species, without the need for individual-level genotype or methylation data.

  3. GPA-MDS: A Visualization Approach to Investigate Genetic Architecture among Phenotypes Using GWAS Results.

    Science.gov (United States)

    Wei, Wei; Ramos, Paula S; Hunt, Kelly J; Wolf, Bethany J; Hardiman, Gary; Chung, Dongjun

    2016-01-01

    Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. Recently, there has been accumulating evidence suggesting that different complex traits share a common risk basis, namely, pleiotropy. Previously, a statistical method, namely, GPA (Genetic analysis incorporating Pleiotropy and Annotation), was developed to improve identification of risk variants and to investigate pleiotropic structure through a joint analysis of multiple GWAS datasets. While GPA provides a statistically rigorous framework to evaluate pleiotropy between phenotypes, it is still not trivial to investigate genetic relationships among a large number of phenotypes using the GPA framework. In order to address this challenge, in this paper, we propose a novel approach, GPA-MDS, to visualize genetic relationships among phenotypes using the GPA algorithm and multidimensional scaling (MDS). This tool will help researchers to investigate common etiology among diseases, which can potentially lead to development of common treatments across diseases. We evaluate the proposed GPA-MDS framework using a simulation study and apply it to jointly analyze GWAS datasets examining 18 unique phenotypes, which helps reveal the shared genetic architecture of these phenotypes.

  4. New Genetic Approaches to AD: Lessons from APOE-TOMM40 Phylogenetics.

    Science.gov (United States)

    Lutz, Michael W; Crenshaw, Donna; Welsh-Bohmer, Kathleen A; Burns, Daniel K; Roses, Allen D

    2016-05-01

    Clinical trials for Alzheimer's disease are now focusing on the earliest stages of the disease with the goal of delaying dementia onset. There is great utility in using genetic variants to identify individuals at high age-dependent risk when the goal is to begin treatment before the development of any cognitive symptoms. Genetic variants identified through large-scale genome-wide association studies have not substantially improved the accuracy provided by APOE genotype to identify people at high risk of late-onset Alzheimer's disease (LOAD). We describe novel approaches, focused on molecular phylogenetics, to finding genetic variants that predict age at LOAD onset with sufficient accuracy and precision to be useful. We highlight the discovery of a polymorphism in TOMM40 that, in addition to APOE, may improve risk prediction and review how TOMM40 genetic variants may impact the develop of LOAD independently from APOE. The analysis methods described in this review may be useful for other genetically complex human diseases.

  5. Genetic parameters for carcass traits and body weight using a Bayesian approach in the Canchim cattle.

    Science.gov (United States)

    Meirelles, S L C; Mokry, F B; Espasandín, A C; Dias, M A D; Baena, M M; de A Regitano, L C

    2016-06-10

    Correlation between genetic parameters and factors such as backfat thickness (BFT), rib eye area (REA), and body weight (BW) were estimated for Canchim beef cattle raised in natural pastures of Brazil. Data from 1648 animals were analyzed using multi-trait (BFT, REA, and BW) animal models by the Bayesian approach. This model included the effects of contemporary group, age, and individual heterozygosity as covariates. In addition, direct additive genetic and random residual effects were also analyzed. Heritability estimated for BFT (0.16), REA (0.50), and BW (0.44) indicated their potential for genetic improvements and response to selection processes. Furthermore, genetic correlations between BW and the remaining traits were high (P > 0.50), suggesting that selection for BW could improve REA and BFT. On the other hand, genetic correlation between BFT and REA was low (P = 0.39 ± 0.17), and included considerable variations, suggesting that these traits can be jointly included as selection criteria without influencing each other. We found that REA and BFT responded to the selection processes, as measured by ultrasound. Therefore, selection for yearling weight results in changes in REA and BFT.

  6. Infant development in family context: Call for a genetically informed approach

    Directory of Open Access Journals (Sweden)

    Stephanie H. Parade

    2012-09-01

    Full Text Available We call for a genetically informed approach in the examination of infant social and emotional development in family context. We recommend that scholars conceptualize family functioning as occurring on three unique levels: the parent-child dyad, the inter-parental dyad, and whole family functioning. Although advances in the area of understanding genetic variation in infants as a potential moderator of the influence of parent-child dyadic functioning have been made over the past decade, it is time to widen this inquiry to consider genetic variation in infants as a potential moderator of the influence of inter-parental dyadic and whole family functioning as well. A critical review of the literature also calls for additional examination of genetic variation in infants as a moderator of positive contextual influences, the integration of unique temperament variables with studies of infant genotype, consideration of the role of the gene-environment correlation, and epigenetic effects. Furthermore, we call for the application of genetically-informed research methods to these questions. Expanding knowledge in this area has the potential to refine treatment and prevention efforts aimed at promoting infant social and emotional development.

  7. An integrative systems genetics approach reveals potential causal genes and pathways related to obesity

    DEFF Research Database (Denmark)

    Kogelman, Lisette; Zhernakova, Daria V.; Westra, Harm-Jan

    2015-01-01

    BACKGROUND: Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about...... the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from...... a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach. METHODS: Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential...

  8. The introduction history of invasive garden ants in Europe: integrating genetic, chemical and behavioural approaches

    DEFF Research Database (Denmark)

    Ugelvig, Line; Drijfhout, Falko; Kronauer, Daniel;

    2008-01-01

    BACKGROUND: The invasive garden ant, Lasius neglectus, is the most recently detected pest ant and the first known invasive ant able to become established and thrive in the temperate regions of Eurasia. In this study, we aim to reconstruct the invasion history of this ant in Europe analysing 14...... populations with three complementary approaches: genetic microsatellite analysis, chemical analysis of cuticular hydrocarbon profiles and behavioural observations of aggression behaviour. We evaluate the relative informative power of the three methodological approaches and estimate both the number...... of independent introduction events from a yet unknown native range somewhere in the Black Sea area, and the invasive potential of the existing introduced populations. RESULTS: Three clusters of genetically similar populations were detected, and all but one population had a similar chemical profile. Aggression...

  9. Discovering Pair-Wise Genetic Interactions: An Information Theory-Based Approach

    Science.gov (United States)

    Ignac, Tomasz M.; Skupin, Alexander; Sakhanenko, Nikita A.; Galas, David J.

    2014-01-01

    Phenotypic variation, including that which underlies health and disease in humans, results in part from multiple interactions among both genetic variation and environmental factors. While diseases or phenotypes caused by single gene variants can be identified by established association methods and family-based approaches, complex phenotypic traits resulting from multi-gene interactions remain very difficult to characterize. Here we describe a new method based on information theory, and demonstrate how it improves on previous approaches to identifying genetic interactions, including both synthetic and modifier kinds of interactions. We apply our measure, called interaction distance, to previously analyzed data sets of yeast sporulation efficiency, lipid related mouse data and several human disease models to characterize the method. We show how the interaction distance can reveal novel gene interaction candidates in experimental and simulated data sets, and outperforms other measures in several circumstances. The method also allows us to optimize case/control sample composition for clinical studies. PMID:24670935

  10. Genetic engineering of industrial Saccharomyces cerevisiae strains using a selection/counter-selection approach.

    Science.gov (United States)

    Kutyna, Dariusz R; Cordente, Antonio G; Varela, Cristian

    2014-01-01

    Gene modification of laboratory yeast strains is currently a very straightforward task thanks to the availability of the entire yeast genome sequence and the high frequency with which yeast can incorporate exogenous DNA into its genome. Unfortunately, laboratory strains do not perform well in industrial settings, indicating the need for strategies to modify industrial strains to enable strain development for industrial applications. Here we describe approaches we have used to genetically modify industrial strains used in winemaking.

  11. An integrated approach to structural design of buildings using genetic algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Rafiq, M.Y.; Mathews, J.D. [Univ. of Plymouth (United Kingdom)

    1996-12-31

    This paper presents an evolutionary approach to the integration of design activities, in the area of structural design of buildings, using Genetic Algorithms (GA). Integration process is viewed in two contexts: (i) Integration across the design activities within a particular discipline, and (ii) Integration across of the disciplines involved in the design. Particular advantages of the integration of design activities during the conceptual stage of the design process are highlighted.

  12. The introduction history of invasive garden ants in Europe: Integrating genetic, chemical and behavioural approaches

    OpenAIRE

    Boomsma Jacobus J; Kronauer Daniel JC; Drijfhout Falko P; Ugelvig Line V; Pedersen Jes S; Cremer Sylvia

    2008-01-01

    Abstract Background The invasive garden ant, Lasius neglectus, is the most recently detected pest ant and the first known invasive ant able to become established and thrive in the temperate regions of Eurasia. In this study, we aim to reconstruct the invasion history of this ant in Europe analysing 14 populations with three complementary approaches: genetic microsatellite analysis, chemical analysis of cuticular hydrocarbon profiles and behavioural observations of aggression behaviour. We eva...

  13. A New Approach to Programming Language Education for Beginners with Top-Down Learning

    Directory of Open Access Journals (Sweden)

    Daisuke Saito

    2013-12-01

    Full Text Available There are two basic approaches in learning new programming language: a bottom-up approach and a top-down approach. It has been said that if a learner has already acquired one language, the top-down approach is more efficient to learn another while, for a person who has absolutely no knowledge of any programming languages; the bottom-up approach is preferable. The major problem of the bottom-up approach is that it requires longer period to acquire the language. For quicker learning, this paper applies a top-down approach for a beginners who has not yet acquired any programming languages.

  14. A chemical genetics approach for specific differentiation of stem cells to somatic cells: a new promising therapeutical approach.

    Science.gov (United States)

    Sachinidis, Agapios; Sotiriadou, Isaia; Seelig, Bianca; Berkessel, Albrecht; Hescheler, Jürgen

    2008-01-01

    Cell replacement therapy of severe degenerative diseases such as diabetes, myocardial infarction and Parkinson's disease through transplantation of somatic cells generated from embryonic stem (ES) cells is currently receiving considerable attention for the therapeutic applications. ES cells harvested from the inner cell mass (ICM) of the early embryo, can proliferate indefinitely in vitro while retaining the ability to differentiate into all somatic cells thereby providing an unlimited renewable source of somatic cells. In this context, identifying soluble factors, in particular chemically synthesized small molecules, and signal cascades involved in specific differentiation processes toward a defined tissue specific cell type are crucial for optimizing the generation of somatic cells in vitro for therapeutic approaches. However, experimental models are required allowing rapid and "easy-to-handle" parallel screening of chemical libraries to achieve this goal. Recently, the forward chemical genetic screening strategy has been postulated to screen small molecules in cellular systems for a specific desired phenotypic effect. The current review is focused on the progress of ES cell research in the context of the chemical genetics to identify small molecules promoting specific differentiation of ES cells to desired cell phenotype. Chemical genetics in the context of the cell ES-based cell replacement therapy remains a challenge for the near future for several scientific fields including chemistry, molecular biology, medicinal physics and robotic technologies.

  15. Kernel Approach for Modeling Interaction Effects in Genetic Association Studies of Complex Quantitative Traits.

    Science.gov (United States)

    Broadaway, K Alaine; Duncan, Richard; Conneely, Karen N; Almli, Lynn M; Bradley, Bekh; Ressler, Kerry J; Epstein, Michael P

    2015-07-01

    The etiology of complex traits likely involves the effects of genetic and environmental factors, along with complicated interaction effects between them. Consequently, there has been interest in applying genetic association tests of complex traits that account for potential modification of the genetic effect in the presence of an environmental factor. One can perform such an analysis using a joint test of gene and gene-environment interaction. An optimal joint test would be one that remains powerful under a variety of models ranging from those of strong gene-environment interaction effect to those of little or no gene-environment interaction effect. To fill this demand, we have extended a kernel machine based approach for association mapping of multiple SNPs to consider joint tests of gene and gene-environment interaction. The kernel-based approach for joint testing is promising, because it incorporates linkage disequilibrium information from multiple SNPs simultaneously in analysis and permits flexible modeling of interaction effects. Using simulated data, we show that our kernel machine approach typically outperforms the traditional joint test under strong gene-environment interaction models and further outperforms the traditional main-effect association test under models of weak or no gene-environment interaction effects. We illustrate our test using genome-wide association data from the Grady Trauma Project, a cohort of highly traumatized, at-risk individuals, which has previously been investigated for interaction effects. © 2015 WILEY PERIODICALS, INC.

  16. The introduction history of invasive garden ants in Europe: Integrating genetic, chemical and behavioural approaches

    Directory of Open Access Journals (Sweden)

    Boomsma Jacobus J

    2008-02-01

    Full Text Available Abstract Background The invasive garden ant, Lasius neglectus, is the most recently detected pest ant and the first known invasive ant able to become established and thrive in the temperate regions of Eurasia. In this study, we aim to reconstruct the invasion history of this ant in Europe analysing 14 populations with three complementary approaches: genetic microsatellite analysis, chemical analysis of cuticular hydrocarbon profiles and behavioural observations of aggression behaviour. We evaluate the relative informative power of the three methodological approaches and estimate both the number of independent introduction events from a yet unknown native range somewhere in the Black Sea area, and the invasive potential of the existing introduced populations. Results Three clusters of genetically similar populations were detected, and all but one population had a similar chemical profile. Aggression between populations could be predicted from their genetic and chemical distance, and two major clusters of non-aggressive groups of populations were found. However, populations of L. neglectus did not separate into clear supercolonial associations, as is typical for other invasive ants. Conclusion The three methodological approaches gave consistent and complementary results. All joint evidence supports the inference that the 14 introduced populations of L. neglectus in Europe likely arose from only very few independent introductions from the native range, and that new infestations were typically started through introductions from other invasive populations. This indicates that existing introduced populations have a very high invasive potential when the ants are inadvertently spread by human transport.

  17. Approaches for the identification of genetic modifiers of nutrient dependent phenotypes: Examples from folate

    Directory of Open Access Journals (Sweden)

    Amanda J. Macfarlane

    2014-07-01

    Full Text Available By combining the sciences of nutrition, bioinformatics, genomics, population genetics and epidemiology, nutrigenomics is improving our understanding of how diet and nutrient intake can interact with or modify gene expression and disease risk. In this review, we explore various approaches to examine gene-nutrient interactions and the modifying role of nutrient consumption, as they relate to nutrient status and disease risk in human populations. Two common approaches include the use of SNPs in candidate genes to identify their association with nutritional status or disease outcomes, or genome wide association studies to identify genetic polymorphisms associated with a given phenotype. Here, we examine the results of various gene-nutrient interaction studies, the association of genetic polymorphisms with disease expression and the identification of nutritional factors that modify gene-dependent disease phenotypes. We have focussed on specific examples from investigations of the interactions of folate and B-vitamin consumption and polymorphisms in the genes of B vitamin dependent enzymes and their association with disease risk, followed by an examination of the strengths and limitations of the methods employed. We also present suggestions for future studies, including an approach from an on-going large scale study, to examine the interaction of nutrient intake and genotypic variation and their impact on nutritional status.

  18. Developmental psychopathology in an era of molecular genetics and neuroimaging: A developmental neurogenetics approach.

    Science.gov (United States)

    Hyde, Luke W

    2015-05-01

    The emerging field of neurogenetics seeks to model the complex pathways from gene to brain to behavior. This field has focused on imaging genetics techniques that examine how variability in common genetic polymorphisms predict differences in brain structure and function. These studies are informed by other complimentary techniques (e.g., animal models and multimodal imaging) and have recently begun to incorporate the environment through examination of Imaging Gene × Environment interactions. Though neurogenetics has the potential to inform our understanding of the development of psychopathology, there has been little integration between principles of neurogenetics and developmental psychopathology. The paper describes a neurogenetics and Imaging Gene × Environment approach and how these approaches have been usefully applied to the study of psychopathology. Six tenets of developmental psychopathology (the structure of phenotypes, the importance of exploring mechanisms, the conditional nature of risk, the complexity of multilevel pathways, the role of development, and the importance of who is studied) are identified, and how these principles can further neurogenetics applications to understanding the development of psychopathology is discussed. A major issue of this piece is how neurogenetics and current imaging and molecular genetics approaches can be incorporated into developmental psychopathology perspectives with a goal of providing models for better understanding pathways from among genes, environments, the brain, and behavior.

  19. Telegenetics: application of a tele-education program in genetic syndromes for Brazilian students

    Directory of Open Access Journals (Sweden)

    Luciana Paula MAXIMINO

    2014-12-01

    Full Text Available With the high occurrence of genetic anomalies in Brazil and the manifestations of communication disorders associated with these conditions, the development of educative actions that comprise these illnesses can bring unique benefits in the identification and appropriate treatment of these clinical pictures. Objective The aim of this study was to develop and analyze an educational program in genetic syndromes for elementary students applied in two Brazilian states, using an Interactive Tele-education model. Material and Methods The study was carried out in 4 schools: two in the state of São Paulo, Southeast Region, Brazil, and two in the state of Amazonas, North Region, Brazil. Forty-five students, both genders, aged between 13 and 14 years, of the 9th grade of the basic education of both public and private system, were divided into two groups: 21 of São Paulo Group (SPG and 24 of Amazonas Group (AMG. The educational program lasted about 3 months and was divided into two stages including both classroom and distance activities on genetic syndromes. The classroom activity was carried out separately in each school, with expository lessons, graphs and audiovisual contents. In the activity at a distance the educational content was presented to students by means of the Interactive Tele-education model. In this stage, the students had access a Cybertutor, using the Young Doctor Project methodology. In order to measure the effectiveness of the educational program, the Problem Situation Questionnaire (PSQ and the Web Site Motivational Analysis Checklist adapted (FPM were used. Results The program developed was effective for knowledge acquisition in 80% of the groups. FPM showed a high satisfaction index from the participants in relation to the Interactive Tele-education, evaluating the program as "awesome course". No statistically significant differences between the groups regarding type of school or state were observed. Conclusion Thus, the Tele

  20. Potential for cell therapy in Parkinson's disease using genetically programmed human embryonic stem cell-derived neural progenitor cells.

    Science.gov (United States)

    Ambasudhan, Rajesh; Dolatabadi, Nima; Nutter, Anthony; Masliah, Eliezer; Mckercher, Scott R; Lipton, Stuart A

    2014-08-15

    Neural transplantation is a promising strategy for restoring dopaminergic dysfunction and modifying disease progression in Parkinson's disease (PD). Human embryonic stem cells (hESCs) are a potential resource in this regard because of their ability to provide a virtually limitless supply of homogenous dopaminergic progenitors and neurons of appropriate lineage. The recent advances in developing robust cell culture protocols for directed differentiation of hESCs to near pure populations of ventral mesencephalic (A9-type) dopaminergic neurons has heightened the prospects for PD cell therapy. Here, we focus our review on current state-of-the-art techniques for harnessing hESC-based strategies toward development of a stem cell therapeutic for PD. Importantly, we also briefly describe a novel genetic-programming approach that may address many of the key challenges that remain in the field and that may hasten clinical translation. © 2014 Wiley Periodicals, Inc.

  1. The Mouse House: A brief history of the ORNL mouse-genetics program, 1947–2009

    Energy Technology Data Exchange (ETDEWEB)

    Russell, Liane B.

    2013-10-01

    The large mouse genetics program at the Oak Ridge National Lab is often re-membered chiefly for the germ-cell mutation-rate data it generated and their uses in estimating the risk of heritable radiation damage. In fact, it soon became a multi-faceted research effort that, over a period of almost 60 years, generated a wealth of information in the areas of mammalian mutagenesis, basic genetics (later enriched by molecular techniques), cytogenetics, reproductive biology, biochemistry of germ cells, and teratology. Research in the area of germ-cell mutagenesis explored the important physical and biological factors that affect the frequency and nature of induced mutations and made several unexpected discoveries, such as the major importance of the perigametic interval (the zygote stage) for the origin of spontaneous mutations and for the sensitivity to induced genetic change. Of practical value was the discovery that ethylnitrosourea was a supermutagen for point mutations, making high-efficiency mutagenesis in the mouse feasible worldwide. Teratogenesis findings resulted in recommendations still generally accepted in radiological practice. Studies supporting the mutagenesis research added whole bodies of information about mammalian germ-cell development and about molecular targets in germ cells. The early decision to not merely count but propagate genetic variants of all sorts made possible further discoveries, such as the Y-Chromosome s importance in mammalian sex determination and the identification of rare X-autosome translocations, which, in turn, led to the formulation of the single-active-X hypothesis and provided tools for studies of functional mosaicism for autosomal genes, male sterility, and chromosome-pairing mechanism. Extensive genetic and then molecular analyses of large numbers of induced specific-locus mutants resulted in fine-structure physical and correlated functional mapping of significant portions of the mouse genome and constituted a valuable

  2. Pharmacologically regulated induction of silent mutations (PRISM): combined pharmacological and genetic approaches for learning and memory.

    Science.gov (United States)

    Frankland, Paul W; Ohno, Masuo; Takahashi, Eiki; Chen, Adele R; Costa, Rui M; Kushner, Steven A; Silva, Alcino J

    2003-04-01

    Mouse transgenic and knock-out approaches have made fundamental contributions to our understanding of the molecular and cellular bases of learning and memory. These approaches have successfully identified a large number of molecules with either a central or modulatory role in learning and memory. However, there are limitations associated with first-generation mutant mice, which include, for example, the lack of temporal control over the mutation. Recent technical developments have started to address some of these shortcomings. Here, the authors review a newly developed inducible approach that takes advantage of synergistic interactions between subthreshold genetic and pharmacological manipulations. This approach is easily set up and can be used to study the functional interactions between molecules in signaling pathways.

  3. DNAStat, version 2.1--a computer program for processing genetic profile databases and biostatistical calculations.

    Science.gov (United States)

    Berent, Jarosław

    2010-01-01

    This paper presents the new DNAStat version 2.1 for processing genetic profile databases and biostatistical calculations. The popularization of DNA studies employed in the judicial system has led to the necessity of developing appropriate computer programs. Such programs must, above all, address two critical problems, i.e. the broadly understood data processing and data storage, and biostatistical calculations. Moreover, in case of terrorist attacks and mass natural disasters, the ability to identify victims by searching related individuals is very important. DNAStat version 2.1 is an adequate program for such purposes. The DNAStat version 1.0 was launched in 2005. In 2006, the program was updated to 1.1 and 1.2 versions. There were, however, slight differences between those versions and the original one. The DNAStat version 2.0 was launched in 2007 and the major program improvement was an introduction of the group calculation options with the potential application to personal identification of mass disasters and terrorism victims. The last 2.1 version has the option of language selection--Polish or English, which will enhance the usage and application of the program also in other countries.

  4. An ICA with reference approach in identification of genetic variation and associated brain networks

    Directory of Open Access Journals (Sweden)

    Jingyu eLiu

    2012-02-01

    Full Text Available To address the statistical challenges associated with genome-wide association studies, we present an independent component analysis (ICA with reference approach to target a specific genetic variation and associated brain networks. First, a small set of single nucleotide polymorphisms (SNPs are empirically chosen to reflect a feature of interest and these SNPs are used as a reference when applying ICA to a full genomic SNP array. After extracting the genetic component maximally representing the characteristics of the reference, we test its association with brain networks in functional magnetic resonance imaging (fMRI data. The method was evaluated on both real and simulated datasets. Simulation demonstrates that ICA with reference can extract a specific genetic factor, even when the variance accounted for by such a factor is so small that a regular ICA fails. Our real data application from 48 schizophrenia patients and 40 healthy controls include 300K SNPs and fMRI images in an auditory oddball task. Using SNPs with allelic frequency difference in two groups as a reference, we extracted a genetic component that maximally differentiates patients from controls (p<4×10-17, and discovered a brain functional network that was significantly associated with this genetic component (p<1×10-4. The regions in the functional network mainly locate in the thalamus, anterior and posterior cingulate gyri. The contributing SNPs in the genetic factor mainly fall into two clusters centered at chromosome 7q21 and chromosome 5q35. The findings from the schizophrenia application are in concordance with previous knowledge about brain regions and gene function. All together, the results suggest that the ICA with reference can be particularly useful to explore the whole genome to find a specific factor of interest and further study its effect on brain.

  5. Genetic shift in local rice populations during rice breeding programs in the northern limit of rice cultivation in the world.

    Science.gov (United States)

    Fujino, Kenji; Obara, Mari; Ikegaya, Tomohito; Tamura, Kenichi

    2015-09-01

    The rapid accumulation of pre-existing mutations may play major roles in the establishment and shaping of adaptability for local regions in current rice breeding programs. The cultivated rice, Oryza sativa L., which originated from tropical regions, is now grown worldwide due to the concerted efforts of breeding programs. However, the process of establishing local populations and their origins remain unclear. In the present study, we characterized DNA polymorphisms in the rice variety KITAAKE from Hokkaido, one of the northern limits of rice cultivation in the world. Indel polymorphisms were attributed to transposable element-like insertions, tandem duplications, and non-TE deletions as the original mutation events in the NIPPONBARE and KITAAKE genomes. The allele frequencies of the KITAAKE alleles markedly shifted to the current variety types among the local population from Hokkaido in the last two decades. The KITAAKE alleles widely distributed throughout wild rice and cultivated rice over the world. These have accumulated in the local population from Hokkaido via Japanese landraces as the ancestral population of Hokkaido. These results strongly suggested that combinations of pre-existing mutations played a role in the establishment of adaptability. This approach using the re-sequencing of local varieties in unique environmental conditions will be useful as a genetic resource in plant breeding programs in local regions.

  6. Comparative genetic approaches to the evolution of human brain and behavior.

    Science.gov (United States)

    Vallender, Eric J

    2011-01-01

    With advances in genomic technologies, the amount of genetic data available to scientists today is vast. Genomes are now available or planned for 14 different primate species and complete resequencing of numerous human individuals from numerous populations is underway. Moreover, high-throughput deep sequencing is quickly making whole genome efforts within the reach of single laboratories allowing for unprecedented studies. Comparative genetic approaches to the identification of the underlying basis of human brain, behavior, and cognitive ability are moving to the forefront. Two approaches predominate: inter-species divergence comparisons and intra-species polymorphism studies. These methodological differences are useful for different time scales of evolution and necessarily focus on different evolutionary events in the history of primate and hominin evolution. Inter-species divergence is more useful in studying large scale primate, or hominoid, evolution whereas intra-species polymorphism can be more illuminating of recent hominin evolution. These differences in methodological utility also extend to studies of differing genetic substrates; current divergence studies focus primarily on protein evolution whereas polymorphism studies are substrate ambivalent. Some of the issues inherent in these studies can be ameliorated by current sequencing capabilities whereas others remain intractable. New avenues are also being opened that allow for the incorporation of novel substrates and approaches. In the post-genomic era, the study of human evolution, specifically as it relates to the brain, is becoming more complete focusing increasingly on the totality of the system and better conceptualizing the entirety of the genetic changes that have lead to the human phenotype today.

  7. On the Calibration of Multigene Genetic Programming to Simulate Low Flows in the Moselle River

    Directory of Open Access Journals (Sweden)

    Ali DANANDEH MEHR

    2016-12-01

    Full Text Available The aim of this paper is to calibrate a data-driven model to simulate Moselle River flows and compare the performance with three different hydrologic models from a previous study. For consistency a similar set up and error metric are used to evaluate the model results. Precipitation, potential evapotranspiration and streamflow from previous day have been used as inputs. Based on the calibration and validation results, the proposed multigene genetic programming model is the best performing model among four models. The timing and the magnitude of extreme low flow events could be captured even when we use root mean squared error as the objective function for model calibration. Although the model is developed and calibrated for Moselle River flows, the multigene genetic algorithm offers a great opportunity for hydrologic prediction and forecast problems in the river basins with scarce data issues.

  8. Dynamic programming approach to optimization of approximate decision rules

    KAUST Repository

    Amin, Talha

    2013-02-01

    This paper is devoted to the study of an extension of dynamic programming approach which allows sequential optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure R(T) which is the number of unordered pairs of rows with different decisions in the decision table T. For a nonnegative real number β, we consider β-decision rules that localize rows in subtables of T with uncertainty at most β. Our algorithm constructs a directed acyclic graph Δβ(T) which nodes are subtables of the decision table T given by systems of equations of the kind "attribute = value". This algorithm finishes the partitioning of a subtable when its uncertainty is at most β. The graph Δβ(T) allows us to describe the whole set of so-called irredundant β-decision rules. We can describe all irredundant β-decision rules with minimum length, and after that among these rules describe all rules with maximum coverage. We can also change the order of optimization. The consideration of irredundant rules only does not change the results of optimization. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository. © 2012 Elsevier Inc. All rights reserved.

  9. Dynamic programming approach for partial decision rule optimization

    KAUST Repository

    Amin, Talha

    2012-10-04

    This paper is devoted to the study of an extension of dynamic programming approach which allows optimization of partial decision rules relative to the length or coverage. We introduce an uncertainty measure J(T) which is the difference between number of rows in a decision table T and number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules (partial decision rules) that localize rows in subtables of T with uncertainty at most γ. Presented algorithm constructs a directed acyclic graph Δ γ(T) which nodes are subtables of the decision table T given by systems of equations of the kind "attribute = value". This algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The graph Δ γ(T) allows us to describe the whole set of so-called irredundant γ-decision rules. We can optimize such set of rules according to length or coverage. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.

  10. Improving the Held and Karp Approach with Constraint Programming

    Science.gov (United States)

    Benchimol, Pascal; Régin, Jean-Charles; Rousseau, Louis-Martin; Rueher, Michel; van Hoeve, Willem-Jan

    Held and Karp have proposed, in the early 1970s, a relaxation for the Traveling Salesman Problem (TSP) as well as a branch-and-bound procedure that can solve small to modest-size instances to optimality [4, 5]. It has been shown that the Held-Karp relaxation produces very tight bounds in practice, and this relaxation is therefore applied in TSP solvers such as Concorde [1]. In this short paper we show that the Held-Karp approach can benefit from well-known techniques in Constraint Programming (CP) such as domain filtering and constraint propagation. Namely, we show that filtering algorithms developed for the weighted spanning tree constraint [3, 8] can be adapted to the context of the Held and Karp procedure. In addition to the adaptation of existing algorithms, we introduce a special-purpose filtering algorithm based on the underlying mechanisms used in Prim's algorithm [7]. Finally, we explored two different branching schemes to close the integrality gap. Our initial experimental results indicate that the addition of the CP techniques to the Held-Karp method can be very effective.

  11. Dynamic programming approach for newborn's incubator humidity control.

    Science.gov (United States)

    Bouattoura, D; Villon, P; Farges, G

    1998-01-01

    The anatomy, physiology, and biochemistry of the human skin have been studied for a long time. A special interest has been shown in the water permeability of the premature infant's skin, which is known to be an important factor in the maintenance of a controlled water and heat balance. The rate of evaporative heat exchange between the skin surface of a very premature infant and the surrounding incubator air may be so high that evaporative heat loss alone may exceed the infant's total metabolic heat production. However, it has been demonstrated in several investigations published in recent years that basal evaporative water loss can be consistently reduced by increasing the ambient humidity. Nevertheless, the passive humidification system (water reservoir) used in most incubators cannot achieve high and steady humidity levels. In this paper, we propose an active humidification system. The algorithm is based on a combination of optimal control theory and dynamic programming approach. The relative-humidity (R.H.) regulation is performed in range of 35-90% at 33 degrees C with small oscillations (+/- 0.5% R.H.) around the reference value (i.e., prescribed R.H.).

  12. Replacement model of city bus: A dynamic programming approach

    Science.gov (United States)

    Arifin, Dadang; Yusuf, Edhi

    2017-06-01

    This paper aims to develop a replacement model of city bus vehicles operated in Bandung City. This study is driven from real cases encountered by the Damri Company in the efforts to improve services to the public. The replacement model propounds two policy alternatives: First, to maintain or keep the vehicles, and second is to replace them with new ones taking into account operating costs, revenue, salvage value, and acquisition cost of a new vehicle. A deterministic dynamic programming approach is used to solve the model. The optimization process was heuristically executed using empirical data of Perum Damri. The output of the model is to determine the replacement schedule and the best policy if the vehicle has passed the economic life. Based on the results, the technical life of the bus is approximately 20 years old, while the economic life is an average of 9 (nine) years. It means that after the bus is operated for 9 (nine) years, managers should consider the policy of rejuvenation.

  13. Lazy evaluation of FP programs: A data-flow approach

    Energy Technology Data Exchange (ETDEWEB)

    Wei, Y.H. [International Business Machines Corp., Yorktown Heights, NY (United States). Thomas J. Watson Research Center; Gaudiot, J.L. [University of Southern California, Los Angeles, CA (United States). Computer Research Inst.

    1988-12-31

    This paper presents a lazy evaluation system for the list-based functional language, Backus` FP in data-driven environment. A superset language of FP, called DFP (Demand-driven FP), is introduced. FP eager programs are transformed into DFP lazy programs which contain the notions of demands. The data-driven execution of DFP programs has the same effects of lazy evaluation. DFP lazy programs have the property of always evaluating a sufficient and necessary result. The infinite sequence generator is used to demonstrate the eager-lazy program transformation and the execution of the lazy programs.

  14. Endometriosis: A New Cellular and Molecular Genetic Approach for understanding the pathogenesis and evolutivity

    Directory of Open Access Journals (Sweden)

    Jean eBouquet De Joliniere

    2014-05-01

    Full Text Available ABSTRACT. Endometriosis is a benign disease with high prevalence in women of reproductive age estimated between 10 and 15% and is associated with considerable morbidity. Its etiology and pathogenesis are controversial but it is believed to involve multiple genetic, environmental, immunological, angiogenic and endocrine processes. Altered expressions of growth factors, cytokines, adhesion molecules, matrix metalloproteinases, and enzymes for estrogen synthesis and metabolism have been frequently observed in this condition. The possibility of genetic basis of endometriosis is demonstrated in studies of familial disease, in which the incidence of endometriosis is higher for first-degree relatives of probands as compared to controls. This review describes mainly the cellular, cytochemical, cytogenetic and molecular genetic features of endometriotic lesions and cultured endometriotic cells. In attempts to identify candidate gene (s involved in the pathogenesis of endometriosis, a tissue-based approaches including conventional cytogenetics (RHG-banding, loss of heterozygosity (LOH and Comparative Genomic Hybridization (CGH were employed. In addition to the karyotipic anomalies, consistent chromosome instability was confirmed by CGH and Fluorescence in Situ Hybridization (FISH. The nature and significance of the molecular genetic aberrations in relation to the locations and function of oncogenes and tumor supressor genes will be discussed. At last, a possible pathogenic role of embryonic duct remnants was observed in 7 female foetal reproductive tract in endometriosis and may induce a discussion about the begining of ovarian tumors and malignant proliferations

  15. Gene networks associated with conditional fear in mice identified using a systems genetics approach

    Directory of Open Access Journals (Sweden)

    Eskin Eleazar

    2011-03-01

    Full Text Available Abstract Background Our understanding of the genetic basis of learning and memory remains shrouded in mystery. To explore the genetic networks governing the biology of conditional fear, we used a systems genetics approach to analyze a hybrid mouse diversity panel (HMDP with high mapping resolution. Results A total of 27 behavioral quantitative trait loci were mapped with a false discovery rate of 5%. By integrating fear phenotypes, transcript profiling data from hippocampus and striatum and also genotype information, two gene co-expression networks correlated with context-dependent immobility were identified. We prioritized the key markers and genes in these pathways using intramodular connectivity measures and structural equation modeling. Highly connected genes in the context fear modules included Psmd6, Ube2a and Usp33, suggesting an important role for ubiquitination in learning and memory. In addition, we surveyed the architecture of brain transcript regulation and demonstrated preservation of gene co-expression modules in hippocampus and striatum, while also highlighting important differences. Rps15a, Kif3a, Stard7, 6330503K22RIK, and Plvap were among the individual genes whose transcript abundance were strongly associated with fear phenotypes. Conclusion Application of our multi-faceted mapping strategy permits an increasingly detailed characterization of the genetic networks underlying behavior.

  16. Multiplicity of experimental approaches to therapy for genetic muscle diseases and necessity for population screening.

    Science.gov (United States)

    Laing, Nigel G

    2008-01-01

    Currently a multiplicity of experimental approaches to therapy for genetic muscle diseases is being investigated. These include replacement of the missing gene, manipulation of the gene message, repair of the mutation, upregulation of an alternative gene and pharmacological interventions targeting a number of systems. A number of these approaches are in current clinical trials. There is considerable anticipation that perhaps more than one of the approaches will finally prove of clinical benefit, but there are many voices of caution. No matter which approaches might ultimately prove effective, there is a consensus that for most benefit to the patients it will be necessary to start treatment as early as possible. A consensus is also developing that the only way to do this is to implement population-based newborn screening to identify affected children shortly after birth. Population-based newborn screening is currently practised in very few places in the world and it brings with it implications for prevention rather than cure of genetic muscle diseases.

  17. Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices

    Science.gov (United States)

    Chen, Yan; Mabu, Shingo; Shimada, Kaoru; Hirasawa, Kotaro

    The key in stock trading model is to take the right actions for trading at the right time, primarily based on the accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to creating a stock trading model. In this paper, we propose a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are three important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the candlestick charts according to the real time stock prices. Second, we combine RTU-GNP with a Sarsa learning algorithm to create the programs efficiently. Also, sub-nodes are introduced in each judgment and processing node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. Third, a Real Time Updating system has been firstly introduced in our paper considering the change of the trend of stock prices. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without real time updating. We also compared the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.

  18. On the local optimal solutions of metabolic regulatory networks using information guided genetic algorithm approach and clustering analysis.

    Science.gov (United States)

    Zheng, Ying; Yeh, Chen-Wei; Yang, Chi-Da; Jang, Shi-Shang; Chu, I-Ming

    2007-08-31

    Biological information generated by high-throughput technology has made systems approach feasible for many biological problems. By this approach, optimization of metabolic pathway has been successfully applied in the amino acid production. However, in this technique, gene modifications of metabolic control architecture as well as enzyme expression levels are coupled and result in a mixed integer nonlinear programming problem. Furthermore, the stoichiometric complexity of metabolic pathway, along with strong nonlinear behaviour of the regulatory kinetic models, directs a highly rugged contour in the whole optimization problem. There may exist local optimal solutions wherein the same level of production through different flux distributions compared with global optimum. The purpose of this work is to develop a novel stochastic optimization approach-information guided genetic algorithm (IGA) to discover the local optima with different levels of modification of the regulatory loop and production rates. The novelties of this work include the information theory, local search, and clustering analysis to discover the local optima which have physical meaning among the qualified solutions.

  19. Novel Approaches to Cellular Transplantation from the US Space Program

    Science.gov (United States)

    Pellis, Neal R.; Homick, Jerry L. (Technical Monitor)

    1999-01-01

    Research in the treatment of type I diabetes is entering a new era that takes advantage of our knowledge in an ever increasing variety of scientific disciplines. Some may originate from very diverse sources, one of which is the Space Program at National Aeronautics and Space Administration (NASA). The Space Program contributes to diabetes-related research in several treatment modalities. As an ongoing effort for medical monitoring of personnel involved in space exploration activities NASA and the extramural scientific community investigate strategies for noninvasive estimation of blood glucose levels. Part of the effort in the space protein crystal growth program is high-resolution structural analysis insulin as a means to better understand the interaction with its receptor and with host immune components and as a basis for rational design of a "better" insulin molecule. The Space Program is also developing laser technology for potential early cataract detection as well as a noninvasive analyses for addressing preclinical diabetic retinopathy. Finally, NASA developed an exciting cell culture system that affords some unique advantages in the propagation and maintenance of mammalian cells in vitro. The cell culture system was originally designed to maintain cell suspensions with a minimum of hydrodynamic and mechanical sheer while awaiting launch into microgravity. Currently the commercially available NASA bioreactor (Synthecon, Inc., Houston, TX) is used as a research tool in basic and applied cell biology. In recent years there is continued strong interest in cellular transplantation as treatment for type I diabetes. The advantages are the potential for successful long-term amelioration and a minimum risk for morbidity in the event of rejection of the transplanted cells. The pathway to successful application of this strategy is accompanied by several substantial hurdles: (1) isolation and propagation of a suitable uniform donor cell population; (2) management of

  20. Genetic Variability and Population Structure of Salvia lachnostachys: Implications for Breeding and Conservation Programs

    Directory of Open Access Journals (Sweden)

    Marianna Erbano

    2015-04-01

    Full Text Available The genetic diversity and population structure of Salvia lachnostachys Benth were assessed. Inter Simple Sequence Repeat (ISSR molecular markers were used to investigate the restricted distribution of S. lachnostachys in Parana State, Brazil. Leaves of 73 individuals representing three populations were collected. DNA was extracted and submitted to PCR-ISSR amplification with nine tested primers. Genetic diversity parameters were evaluated. Our analysis indicated 95.6% polymorphic loci (stress value 0.02 with a 0.79 average Simpson’s index. The Nei-Li distance dendrogram and principal component analysis largely recovered the geographical origin of each sample. Four major clusters were recognized representing each collected population. Nei’s gene diversity and Shannon’s information index were 0.25 and 0.40 respectively. As is typical for outcrossing herbs, the majority of genetic variation occurred at the population level (81.76%. A high gene flow (Nm = 2.48 was observed with a correspondingly low fixation index. These values were generally similar to previous studies on congeneric species. The results of principal coordinate analysis (PCA and of arithmetic average (UPGMA were consistent and all three populations appear distinct as in STRUCTURE analysis. In addition, this analysis indicated a majority intrapopulation genetic variation. Despite the human pressure on natural populations our study found high levels of genetic diversity for S. lachnostachys. This was the first molecular assessment for this endemic species with medicinal proprieties and the results can guide for subsequent bioprospection, breeding programs or conservation actions.

  1. From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming.

    Science.gov (United States)

    Mendyk, Aleksander; Güres, Sinan; Jachowicz, Renata; Szlęk, Jakub; Polak, Sebastian; Wiśniowska, Barbara; Kleinebudde, Peter

    2015-01-01

    The purpose of this work was to develop a mathematical model of the drug dissolution (Q) from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modeling of Q versus extrudate diameter (d) and the time variable (t) and (2) indirect modeling through Weibull equation. ANNs provided also information about minimum achievable generalization error and the way to enhance the original dataset used for adjustment of the equations' parameters. Two inputs were found important for the drug dissolution: d and t. The extrudates length (L) was found not important. Both GP modeling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modeling of Q versus d and t resulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs' black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modeling strategies.

  2. A Detailed look of Audio Steganography Techniques using LSB and Genetic Algorithm Approach

    Directory of Open Access Journals (Sweden)

    Gunjan Nehru

    2012-01-01

    Full Text Available This paper is the study of various techniques of audio steganography using different algorithmis like genetic algorithm approach and LSB approach. We have tried some approaches that helps in audio steganography. As we know it is the art and science of writing hidden messages in such a way that no one, apart from the sender and intended recipient, suspects the existence of the message, a form of security through obscurity. In steganography, the message used to hide secret message is called host message or cover message. Once the contents of the host message or cover message are modified, the resultant message is known as stego message. In other words, stego message is combination of host message and secret message. Audio steganography requires a text or audio secret message to be embedded within a cover audio message. Due to availability of redundancy, the cover audio message before steganography, stego message after steganography remains same. for information hiding.

  3. A Fuzzy Linear Programming Approach for Aggregate Production Planning

    DEFF Research Database (Denmark)

    Iris, Cagatay; Cevikcan, Emre

    2014-01-01

    a mathematical programming framework for aggregate production planning problem under imprecise data environment. After providing background information about APP problem, together with fuzzy linear programming, the fuzzy linear programming model of APP is solved on an illustrative example for different a...

  4. An Approach to Theory-Based Youth Programming

    Science.gov (United States)

    Duerden, Mat D.; Gillard, Ann

    2011-01-01

    A key but often overlooked aspect of intentional, out-of-school-time programming is the integration of a guiding theoretical framework. The incorporation of theory in programming can provide practitioners valuable insights into essential processes and principles of successful programs. While numerous theories exist that relate to youth development…

  5. An Approach to Theory-Based Youth Programming

    Science.gov (United States)

    Duerden, Mat D.; Gillard, Ann

    2011-01-01

    A key but often overlooked aspect of intentional, out-of-school-time programming is the integration of a guiding theoretical framework. The incorporation of theory in programming can provide practitioners valuable insights into essential processes and principles of successful programs. While numerous theories exist that relate to youth development…

  6. Employee Assistance Programs: A New Human Resource Approach.

    Science.gov (United States)

    Gould, Gary M.; Schneider, John H.

    1983-01-01

    After reviewing industrial sector program results, University of Southern California administration officials agreed to finance a counseling and consulting program for USC employees. The program serves as an adjunct to the personnel office in areas such as outplacement, sexual harassment, and conflict resolution. (MLW)

  7. Towards HPC++: A Unified Approach to Parallel Programming in C++

    Science.gov (United States)

    1998-10-30

    Compositional C++ or CC++, is a general purpose parallel programming language designed to support a wide range of parallel programming styles. By...appropriate for parallelizing the range of applications that one would write in C++. CC++ supports the integration of different parallel programming styles

  8. Employee Assistance Programs: A New Human Resource Approach.

    Science.gov (United States)

    Gould, Gary M.; Schneider, John H.

    1983-01-01

    After reviewing industrial sector program results, University of Southern California administration officials agreed to finance a counseling and consulting program for USC employees. The program serves as an adjunct to the personnel office in areas such as outplacement, sexual harassment, and conflict resolution. (MLW)

  9. Buying and Selling Stocks of Multi Brands Using Genetic Network Programming with Control Nodes

    Science.gov (United States)

    Ohkawa, Etsushi; Chen, Yan; Bao, Zhiguo; Mabu, Shingo; Shimada, Kaoru; Hirasawa, Kotaro

    A new evolutionary method named “Genetic Network Programming with control nodes, GNPcn” has been applied to determine the timing of buying or selling stocks. GNPcn represents its solutions as directed graph structures which has some useful features inherently. For example, GNPcn has an implicit memory function which memorizes the past action sequences of agents and GNPcn can re-use nodes repeatedly in the network flow, so very compact graph structures can be made. GNPcn can determine the strategy of buying and selling stocks of multi issues. The effectiveness of the proposed method is confirmed by simulations.

  10. Evaluation of a non-targeted "Omic"' approach in the safety assessment of genetically modified plants

    DEFF Research Database (Denmark)

    Metzdorff, Stine Broeng; Kok, E. J.; Knuthsen, Pia;

    2006-01-01

    Genetically modified plants must be approved before release in the European Union, and the approval is generally based upon a comparison of various characteristics between the transgenic plant and a conventional counterpart. As a case study, focusing on safety assessment of genetically modified...... plants, we here report the development and characterisation of six independently transformed Arabidopsis thaliana lines modified in the flavonoid biosynthesis. Analyses of integration events and comparative analysis for characterisation of the intended effects were performed by PCR, quantitative Real......, no unintended effects were identified. However, we found that the majority of genes showing differential expression were identified as stress-related genes and that environmental conditions had a large impact on the expression of several genes, proteins, and metabolites. We suggest that the microarray approach...

  11. Branch-pipe-routing approach for ships using improved genetic algorithm

    Science.gov (United States)

    Sui, Haiteng; Niu, Wentie

    2016-09-01

    Branch-pipe routing plays fundamental and critical roles in ship-pipe design. The branch-pipe-routing problem is a complex combinatorial optimization problem and is thus difficult to solve when depending only on human experts. A modified genetic-algorithm-based approach is proposed in this paper to solve this problem. The simplified layout space is first divided into threedimensional (3D) grids to build its mathematical model. Branch pipes in layout space are regarded as a combination of several two-point pipes, and the pipe route between two connection points is generated using an improved maze algorithm. The coding of branch pipes is then defined, and the genetic operators are devised, especially the complete crossover strategy that greatly accelerates the convergence speed. Finally, simulation tests demonstrate the performance of proposed method.

  12. A genetic approach for the identification of exosporium assembly determinants of Bacillus anthracis

    Science.gov (United States)

    Spreng, Krista A.; Thompson, Brian M.; Stewart, George C.

    2013-01-01

    The exosporium is the outermost layer of spores of the zoonotic pathogen Bacillus anthracis. The composition of the exosporium and its functions are only partly understood. Because this outer spore layer is refractive to traditional biochemical analysis, a genetic approach is needed in order to define the proteins which comprise this important spore layer and its assembly pathway. We have created a novel genetic screening system for the identification and isolation of mutants with defects in exosporium assembly during B. anthracis spore maturation. The system is based on the targeting sequence of the BclA exosporium nap layer glycoprotein and a fluorescent reporter. By utilizing this screening system and gene inactivation with Tn916, several novel putative exosporium-associated determinants were identified. A sampling of the mutants obtained was further characterized, confirming their exosporium defect and validating the utility of this screen to identify novel spore determinants in the genome of this pathogen. PMID:23411372

  13. Landscape genetics of raccoons (Procyon lotor) associated with ridges and valleys of Pennsylvania: implications for oral rabies vaccination programs.

    Science.gov (United States)

    Root, J Jeffrey; Puskas, Robert B; Fischer, Justin W; Swope, Craig B; Neubaum, Melissa A; Reeder, Serena A; Piaggio, Antoinette J

    2009-12-01

    Raccoons are the reservoir for the raccoon rabies virus variant in the United States. To combat this threat, oral rabies vaccination (ORV) programs are conducted in many eastern states. To aid in these efforts, the genetic structure of raccoons (Procyon lotor) was assessed in southwestern Pennsylvania to determine if select geographic features (i.e., ridges and valleys) serve as corridors or hindrances to raccoon gene flow (e.g., movement) and, therefore, rabies virus trafficking in this physiographic region. Raccoon DNA samples (n = 185) were collected from one ridge site and two adjacent valleys in southwestern Pennsylvania (Westmoreland, Cambria, Fayette, and Somerset counties). Raccoon genetic structure within and among these study sites was characterized at nine microsatellite loci. Results indicated that there was little population subdivision among any sites sampled. Furthermore, analyses using a model-based clustering approach indicated one essentially panmictic population was present among all the raccoons sampled over a reasonably broad geographic area (e.g., sites up to 36 km apart). However, a signature of isolation by distance was detected, suggesting that widths of ORV zones are critical for success. Combined, these data indicate that geographic features within this landscape influence raccoon gene flow only to a limited extent, suggesting that ridges of this physiographic system will not provide substantial long-term natural barriers to rabies virus trafficking. These results may be of value for future ORV efforts in Pennsylvania and other eastern states with similar landscapes.

  14. The Genetics of Obsessive-Compulsive Disorder and Tourette Syndrome: An Epidemiological and Pathway-Based Approach for Gene Discovery

    Science.gov (United States)

    Grados, Marco A.

    2010-01-01

    Objective: To provide a contemporary perspective on genetic discovery methods applied to obsessive-compulsive disorder (OCD) and Tourette syndrome (TS). Method: A review of research trends in genetics research in OCD and TS is conducted, with emphasis on novel approaches. Results: Genome-wide association studies (GWAS) are now in progress in OCD…

  15. At risk, or not at risk: Epidemiological approaches for assessing psychiatric (genetic) risk factors in the general population

    NARCIS (Netherlands)

    Breetvelt, E.J.

    2013-01-01

    This thesis “At risk, or not at risk” describes several approaches - cross-sectional, prospective, phenotype mining and forward genetics - for assessing psychiatric (genetic) risk factors in a general population study. The aims were 1) to investigate how routine and follow-up data from populationbas

  16. The Genetics of Obsessive-Compulsive Disorder and Tourette Syndrome: An Epidemiological and Pathway-Based Approach for Gene Discovery

    Science.gov (United States)

    Grados, Marco A.

    2010-01-01

    Objective: To provide a contemporary perspective on genetic discovery methods applied to obsessive-compulsive disorder (OCD) and Tourette syndrome (TS). Method: A review of research trends in genetics research in OCD and TS is conducted, with emphasis on novel approaches. Results: Genome-wide association studies (GWAS) are now in progress in OCD…

  17. A Genetic Programming Framework for Topic Discovery from Online Digital Library

    Directory of Open Access Journals (Sweden)

    Yinxing Li

    2010-11-01

    Full Text Available Various topic extraction techniques for digital libraries have been proposed over the past decade. Generally the topic extraction system requires a large number of features and complicated lexical analysis. While these features and analysis are effective to represent the statistical characteristics of the document, they didn't capture the high level semantics. In this paper, we present a new approach for topic extraction. Our approach combines user's click stream data with traditional lexical analysis. From our point of view, the user's click stream directly reflects human understanding of the high-level semantics in the document. Furthermore, a simple, yet effective, piece-wise linear model for topic evolution is proposed. We apply genetic algorithm to estimate the model and extract topics. Experiments on the set of US congress digital library documents demonstrate that our approach achieves better accuracy for the topic extraction than traditional methods.

  18. A New Approach to Commercialization of NASA's Human Research Program Technologies Project

    Data.gov (United States)

    National Aeronautics and Space Administration — This Phase I SBIR proposal describes, "A New Approach to Commercialization of NASA's Human Research Program Technologies." NASA has a powerful research...

  19. Better-than-classical circuits for OR and AND/OR found using genetic programming

    CERN Document Server

    Barnum, H N; Spector, L; Barnum, Howard; Bernstein, Herbert J.; Spector, Lee

    1999-01-01

    We present a quantum circuit evolved using genetic programming. From it we derive the first better-than-classical one-query bounded-error circuit for OR of one-bit black-box functions. The larger evolved circuit calculates, with error probability lower than any possible classical one-query algorithm, the property defined by a depth-two binary AND/OR tree with the four possible function input values as leaves. We analyze it as a kind of recursive application of the OR circuit. Since the OR and AND/OR trees have fan-in 2, these circuits may be useful in investigating the uniform binary AND/OR tree in the large-N asymptotic regime, a problem whose classical query complexity is completely understood and which has applications in game tree evaluation, logic programming, theorem-proving, and many other areas.

  20. Enhancement of lipid production using biochemical, genetic and transcription factor engineering approaches.

    Science.gov (United States)

    Courchesne, Noémie Manuelle Dorval; Parisien, Albert; Wang, Bei; Lan, Christopher Q

    2009-04-20

    This paper compares three possible strategies for enhanced lipid overproduction in microalgae: the biochemical engineering (BE) approaches, the genetic engineering (GE) approaches, and the transcription factor engineering (TFE) approaches. The BE strategy relies on creating a physiological stress such as nutrient-starvation or high salinity to channel metabolic fluxes to lipid accumulation. The GE strategy exploits our understanding to the lipid metabolic pathway, especially the rate-limiting enzymes, to create a channelling of metabolites to lipid biosynthesis by overexpressing one or more key enzymes in recombinant microalgal strains. The TFE strategy is an emerging technology aiming at enhancing the production of a particular metabolite by means of overexpressing TFs regulating the metabolic pathways involved in the accumulation of target metabolites. Currently, BE approaches are the most established in microalgal lipid production. The TFE is a very promising strategy because it may avoid the inhibitive effects of the BE approaches and the limitation of "secondary bottlenecks" as commonly observed in the GE approaches. However, it is still a novel concept to be investigated systematically.