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Sample records for genetic programming approach

  1. A genetic programming approach for Burkholderia Pseudomallei diagnostic pattern discovery

    Science.gov (United States)

    Yang, Zheng Rong; Lertmemongkolchai, Ganjana; Tan, Gladys; Felgner, Philip L.; Titball, Richard

    2009-01-01

    Motivation: Finding diagnostic patterns for fighting diseases like Burkholderia pseudomallei using biomarkers involves two key issues. First, exhausting all subsets of testable biomarkers (antigens in this context) to find a best one is computationally infeasible. Therefore, a proper optimization approach like evolutionary computation should be investigated. Second, a properly selected function of the antigens as the diagnostic pattern which is commonly unknown is a key to the diagnostic accuracy and the diagnostic effectiveness in clinical use. Results: A conversion function is proposed to convert serum tests of antigens on patients to binary values based on which Boolean functions as the diagnostic patterns are developed. A genetic programming approach is designed for optimizing the diagnostic patterns in terms of their accuracy and effectiveness. During optimization, it is aimed to maximize the coverage (the rate of positive response to antigens) in the infected patients and minimize the coverage in the non-infected patients while maintaining the fewest number of testable antigens used in the Boolean functions as possible. The final coverage in the infected patients is 96.55% using 17 of 215 (7.4%) antigens with zero coverage in the non-infected patients. Among these 17 antigens, BPSL2697 is the most frequently selected one for the diagnosis of Burkholderia Pseudomallei. The approach has been evaluated using both the cross-validation and the Jack–knife simulation methods with the prediction accuracy as 93% and 92%, respectively. A novel approach is also proposed in this study to evaluate a model with binary data using ROC analysis. Contact: z.r.yang@ex.ac.uk PMID:19561021

  2. 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. © 2011 American Institute of Physics

  3. 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.

  4. 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.

  5. A genetic programming approach to oral cancer prognosis.

    Science.gov (United States)

    Tan, Mei Sze; Tan, Jing Wei; Chang, Siow-Wee; Yap, Hwa Jen; Abdul Kareem, Sameem; Zain, Rosnah Binti

    2016-01-01

    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. 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. 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. 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.

  6. Genetic programming in microorganisms

    Energy Technology Data Exchange (ETDEWEB)

    Hopwood, D A

    1981-11-01

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

  7. Evaluation of two-year Jewish genetic disease screening program in Atlanta: insight into community genetic screening approaches.

    Science.gov (United States)

    Shao, Yunru; Liu, Shuling; Grinzaid, Karen

    2015-04-01

    Improvements in genetic testing technologies have led to the development of expanded carrier screening panels for the Ashkenazi Jewish population; however, there are major inconsistencies in current screening practices. A 2-year pilot program was launched in Atlanta in 2010 to promote and facilitate screening for 19 Jewish genetic diseases. We analyzed data from this program, including participant demographics and outreach efforts. This retrospective analysis is based on a de-identified dataset of 724 screenees. Data were obtained through medical chart review and questionnaires and included demographic information, screening results, response to outreach efforts, and follow-up behavior and preferences. We applied descriptive analysis, chi-square tests, and logistic regression to analyze the data and compare findings with published literature. The majority of participants indicated that they were not pregnant or did not have a partner who was pregnant were affiliated with Jewish organizations and reported 100 % AJ ancestry. Overall, carrier frequency was 1 in 3.9. Friends, rabbis, and family members were the most common influencers of the decision to receive screening. People who were older, had a history of pregnancy, and had been previously screened were more likely to educate others (all p influencers who then encouraged screening in the target population. Educating influencers and increasing overall awareness were the most effective outreach strategies.

  8. 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.

  9. 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....

  10. 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...

  11. Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach.

    Science.gov (United States)

    Mridula, Meenu R; Nair, Ashalatha S; Kumar, K Satheesh

    2018-02-01

    In this paper, we compared the efficacy of observation based modeling approach using a genetic algorithm with the regular statistical analysis as an alternative methodology in plant research. Preliminary experimental data on in vitro rooting was taken for this study with an aim to understand the effect of charcoal and naphthalene acetic acid (NAA) on successful rooting and also to optimize the two variables for maximum result. Observation-based modelling, as well as traditional approach, could identify NAA as a critical factor in rooting of the plantlets under the experimental conditions employed. Symbolic regression analysis using the software deployed here optimised the treatments studied and was successful in identifying the complex non-linear interaction among the variables, with minimalistic preliminary data. The presence of charcoal in the culture medium has a significant impact on root generation by reducing basal callus mass formation. Such an approach is advantageous for establishing in vitro culture protocols as these models will have significant potential for saving time and expenditure in plant tissue culture laboratories, and it further reduces the need for specialised background.

  12. Linear genetic programming

    CERN Document Server

    Brameier, Markus

    2007-01-01

    Presents a variant of Genetic Programming that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees. This book serves as a reference for researchers, but also contains sufficient introduction for students and those who are new to the field

  13. 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)

  14. 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...

  15. 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

  16. 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.

  17. 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......Genetic Programming is capable of automatically inducing symbolic computer programs on the basis of a set of examples or their performance in a simulation. Mathematical expressions are a well-defined subset of symbolic computer programs and are also suitable for optimization using the genetic...... 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...

  18. Water Curtain System Pre-design for Crude Oil Storage URCs : A Numerical Modeling and Genetic Programming Approach

    NARCIS (Netherlands)

    Ghotbi Ravandi, Ebrahim; Rahmannejad, Reza; Karimi-Nasab, Saeed; Sarrafi, Amir; Raoof, Amir

    In this paper the main criteria of the water curtain system for unlined rock caverns (URCs) is described. By the application of numerical modeling and genetic programming (GP), a method for water curtain system pre-design for Iranian crude oil storage URCs (common dimension worldwide) is presented.

  19. Therapeutic approaches to genetic disorders

    African Journals Online (AJOL)

    salah

    Although prevention is the ideal goal for genetic disorders, various types of therapeutic ... The patient being ... pirical or aimed at controlling or mediating signs and symptoms without care. ... plications and gene therapy approaches .... genes family, have opened a wide and .... cancer where nanoparticles are used to.

  20. Studying Air Quality Dynamics using A Linear Genetic Programming Approach over Remotely Sensed Atmospheric Parameters: case study (Cairo, Egypt)

    Science.gov (United States)

    El-Askary, H. M.; Sheta, W.; Prasad, A. K.; Ali, H.; Abdel rahman, M.; El-Desouki, A.; Kafatos, M.

    2011-12-01

    Water Vapor Low Mean, Atmospheric Water Vapor Mean, Mass Concentration Land Mean, Optical Depth Ratio Small Land and Ocean Mean, Small Mode Optical Depth Land and Ocean Mean, Cloud Top Pressure Day Mean, Cloud Top Pressure Mean, Cloud Top Temperature Mean. The suggested linear Genetic approach detected hidden anomalies and relationships that cannot be observed from the conventional statistical methods. A well-established model as an important contribution to show the relationships between particle size and the physical and chemical aerosols properties has been designed. Such coupling will provide insight into the micro physics of the phenomenon. The proposed research will reveal previously uncharacterized yet fundamental relations and dependencies among aerosols, cloud and meteorological related parameters. Moreover, it would aid in filling gaps of missing satellite parameters using other available ones.

  1. Genetic Network Programming with Reconstructed Individuals

    Science.gov (United States)

    Ye, Fengming; Mabu, Shingo; Wang, Lutao; Eto, Shinji; Hirasawa, Kotaro

    A lot of research on evolutionary computation has been done and some significant classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Programming (EP), and Evolution Strategies (ES) have been studied. Recently, a new approach named Genetic Network Programming (GNP) has been proposed. GNP can evolve itself and find the optimal solution. It is based on the idea of Genetic Algorithm and uses the data structure of directed graphs. Many papers have demonstrated that GNP can deal with complex problems in the dynamic environments very efficiently and effectively. As a result, recently, GNP is getting more and more attentions and is used in many different areas such as data mining, extracting trading rules of stock markets, elevator supervised control systems, etc., and GNP has obtained some outstanding results. Focusing on the GNP's distinguished expression ability of the graph structure, this paper proposes a method named Genetic Network Programming with Reconstructed Individuals (GNP-RI). The aim of GNP-RI is to balance the exploitation and exploration of GNP, that is, to strengthen the exploitation ability by using the exploited information extensively during the evolution process of GNP and finally obtain better performances than that of GNP. In the proposed method, the worse individuals are reconstructed and enhanced by the elite information before undergoing genetic operations (mutation and crossover). The enhancement of worse individuals mimics the maturing phenomenon in nature, where bad individuals can become smarter after receiving a good education. In this paper, GNP-RI is applied to the tile-world problem which is an excellent bench mark for evaluating the proposed architecture. The performance of GNP-RI is compared with that of the conventional GNP. The simulation results show some advantages of GNP-RI demonstrating its superiority over the conventional GNPs.

  2. 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.

  3. 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

  4. Basic Genetics: A Human Approach.

    Science.gov (United States)

    Biological Sciences Curriculum Study, Colorado Springs, CO. Center for Education in Human and Medical Genetics.

    This document (which has the form of a magazine) provides a variety of articles, stories, editorials, letters, interviews, and other types of magazine features (such as book reviews) which focus on human genetics. In addition to providing information about the principles of genetics, nearly all of the sections in the "magazine" address moral,…

  5. Comparative Approaches to Genetic Discrimination: Chasing Shadows?

    Science.gov (United States)

    Joly, Yann; Feze, Ida Ngueng; Song, Lingqiao; Knoppers, Bartha M

    2017-05-01

    Genetic discrimination (GD) is one of the most pervasive issues associated with genetic research and its large-scale implementation. An increasing number of countries have adopted public policies to address this issue. Our research presents a worldwide comparative review and typology of these approaches. We conclude with suggestions for public policy development. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Dynamic traffic assignment : genetic algorithms approach

    Science.gov (United States)

    1997-01-01

    Real-time route guidance is a promising approach to alleviating congestion on the nations highways. A dynamic traffic assignment model is central to the development of guidance strategies. The artificial intelligence technique of genetic algorithm...

  7. Hybrid of Genetic Programming with PBIL

    International Nuclear Information System (INIS)

    Caldas, Gustavo Henrique Flores; Schirru, Roberto

    2005-01-01

    Genetic programming and PBIL (Population-Based Incremental Learning) are evolutionary algorithms that have found applications in several fields of application. The Genetic Programming searches a solution allowing that the individuals of a population modify, mainly, its structures. The PBIL, on the other hand, works with individuals of fixed structure and is particularly successful in finding numerical solutions. There are problems where the simultaneous adjustment of the structure and numerical constants in a solution is essential. The Symbolic Regression is an example where both the form and the constants of a mathematical expression must be found. Although the traditional Genetic Programming is capable to solve this problem by itself, it is interesting to explore a cooperation with the PBIL, allowing each algorithm to do only that they do best: the Genetic Programming tries to find a structure while the PBIL adjust the constants that will be enclosed in the structure. In this work, the benchmark 'the sextic polynomial regression problem' is used to compare some traditional techniques of Genetic Programming with the proposed Hybrid of Genetic Programming with PBIL. The results are presented and discussed. (author)

  8. 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

  9. Algorithmic Trading with Developmental and Linear Genetic Programming

    Science.gov (United States)

    Wilson, Garnett; Banzhaf, Wolfgang

    A developmental co-evolutionary genetic programming approach (PAM DGP) and a standard linear genetic programming (LGP) stock trading systemare applied to a number of stocks across market sectors. Both GP techniques were found to be robust to market fluctuations and reactive to opportunities associated with stock price rise and fall, with PAMDGP generating notably greater profit in some stock trend scenarios. Both algorithms were very accurate at buying to achieve profit and selling to protect assets, while exhibiting bothmoderate trading activity and the ability to maximize or minimize investment as appropriate. The content of the trading rules produced by both algorithms are also examined in relation to stock price trend scenarios.

  10. Genetically programmed chiral organoborane synthesis

    Science.gov (United States)

    Kan, S. B. Jennifer; Huang, Xiongyi; Gumulya, Yosephine; Chen, Kai; Arnold, Frances H.

    2017-12-01

    Recent advances in enzyme engineering and design have expanded nature’s catalytic repertoire to functions that are new to biology. However, only a subset of these engineered enzymes can function in living systems. Finding enzymatic pathways that form chemical bonds that are not found in biology is particularly difficult in the cellular environment, as this depends on the discovery not only of new enzyme activities, but also of reagents that are both sufficiently reactive for the desired transformation and stable in vivo. Here we report the discovery, evolution and generalization of a fully genetically encoded platform for producing chiral organoboranes in bacteria. Escherichia coli cells harbouring wild-type cytochrome c from Rhodothermus marinus (Rma cyt c) were found to form carbon-boron bonds in the presence of borane-Lewis base complexes, through carbene insertion into boron-hydrogen bonds. Directed evolution of Rma cyt c in the bacterial catalyst provided access to 16 novel chiral organoboranes. The catalyst is suitable for gram-scale biosynthesis, providing up to 15,300 turnovers, a turnover frequency of 6,100 h-1, a 99:1 enantiomeric ratio and 100% chemoselectivity. The enantiopreference of the biocatalyst could also be tuned to provide either enantiomer of the organoborane products. Evolved in the context of whole-cell catalysts, the proteins were more active in the whole-cell system than in purified forms. This study establishes a DNA-encoded and readily engineered bacterial platform for borylation; engineering can be accomplished at a pace that rivals the development of chemical synthetic methods, with the ability to achieve turnovers that are two orders of magnitude (over 400-fold) greater than those of known chiral catalysts for the same class of transformation. This tunable method for manipulating boron in cells could expand the scope of boron chemistry in living systems.

  11. Using genetic programming to find Lyapunov functions

    NARCIS (Netherlands)

    Soute, I.A.C.; Molengraft, van de M.J.G.; Angelis, G.Z.; Ryan, C; Spector, L.

    2001-01-01

    In this paper Genetic Programming is used to find Lyapunov functions for (non)linear dif ferential equations of autonomous systems. As Lyapunov functions can be difficult to find, we use OP to make the decisions concerning the form of the Lyapunov function. As an e5cample two systems are taken to

  12. 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%.

  13. Genetic approaches in comparative and evolutionary physiology

    Science.gov (United States)

    Bridgham, Jamie T.; Kelly, Scott A.; Garland, Theodore

    2015-01-01

    Whole animal physiological performance is highly polygenic and highly plastic, and the same is generally true for the many subordinate traits that underlie performance capacities. Quantitative genetics, therefore, provides an appropriate framework for the analysis of physiological phenotypes and can be used to infer the microevolutionary processes that have shaped patterns of trait variation within and among species. In cases where specific genes are known to contribute to variation in physiological traits, analyses of intraspecific polymorphism and interspecific divergence can reveal molecular mechanisms of functional evolution and can provide insights into the possible adaptive significance of observed sequence changes. In this review, we explain how the tools and theory of quantitative genetics, population genetics, and molecular evolution can inform our understanding of mechanism and process in physiological evolution. For example, lab-based studies of polygenic inheritance can be integrated with field-based studies of trait variation and survivorship to measure selection in the wild, thereby providing direct insights into the adaptive significance of physiological variation. Analyses of quantitative genetic variation in selection experiments can be used to probe interrelationships among traits and the genetic basis of physiological trade-offs and constraints. We review approaches for characterizing the genetic architecture of physiological traits, including linkage mapping and association mapping, and systems approaches for dissecting intermediary steps in the chain of causation between genotype and phenotype. We also discuss the promise and limitations of population genomic approaches for inferring adaptation at specific loci. We end by highlighting the role of organismal physiology in the functional synthesis of evolutionary biology. PMID:26041111

  14. 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

  15. 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.

  16. Multiobjective genetic algorithm approaches to project scheduling under risk

    OpenAIRE

    Kılıç, Murat; Kilic, Murat

    2003-01-01

    In this thesis, project scheduling under risk is chosen as the topic of research. Project scheduling under risk is defined as a biobjective decision problem and is formulated as a 0-1 integer mathematical programming model. In this biobjective formulation, one of the objectives is taken as the expected makespan minimization and the other is taken as the expected cost minimization. As the solution approach to this biobjective formulation genetic algorithm (GA) is chosen. After carefully invest...

  17. Applications of genetic programming in cancer research.

    Science.gov (United States)

    Worzel, William P; Yu, Jianjun; Almal, Arpit A; Chinnaiyan, Arul M

    2009-02-01

    The theory of Darwinian evolution is the fundamental keystones of modern biology. Late in the last century, computer scientists began adapting its principles, in particular natural selection, to complex computational challenges, leading to the emergence of evolutionary algorithms. The conceptual model of selective pressure and recombination in evolutionary algorithms allow scientists to efficiently search high dimensional space for solutions to complex problems. In the last decade, genetic programming has been developed and extensively applied for analysis of molecular data to classify cancer subtypes and characterize the mechanisms of cancer pathogenesis and development. This article reviews current successes using genetic programming and discusses its potential impact in cancer research and treatment in the near future.

  18. 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

  19. 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 ...

  20. Genetic Programming for Sea Level Predictions in an Island Environment

    Directory of Open Access Journals (Sweden)

    M.A. Ghorbani

    2010-03-01

    Full Text Available Accurate predictions of sea-level are important for geodetic applications, navigation, coastal, industrial and tourist activities. In the current work, the Genetic Programming (GP and artificial neural networks (ANNs were applied to forecast half-daily and daily sea-level variations from 12 hours to 5 days ahead. The measurements at the Cocos (Keeling Islands in the Indian Ocean were used for training and testing of the employed artificial intelligence techniques. A comparison was performed of the predictions from the GP model and the ANN simulations. Based on the comparison outcomes, it was found that the Genetic Programming approach can be successfully employed in forecasting of sea level variations.

  1. 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.

  2. Genetic programming over context-free languages with linear constraints for the knapsack problem: first results.

    Science.gov (United States)

    Bruhn, Peter; Geyer-Schulz, Andreas

    2002-01-01

    In this paper, we introduce genetic programming over context-free languages with linear constraints for combinatorial optimization, apply this method to several variants of the multidimensional knapsack problem, and discuss its performance relative to Michalewicz's genetic algorithm with penalty functions. With respect to Michalewicz's approach, we demonstrate that genetic programming over context-free languages with linear constraints improves convergence. A final result is that genetic programming over context-free languages with linear constraints is ideally suited to modeling complementarities between items in a knapsack problem: The more complementarities in the problem, the stronger the performance in comparison to its competitors.

  3. A NEW MUTATION OPERATOR IN GENETIC PROGRAMMING

    Directory of Open Access Journals (Sweden)

    Anuradha Purohit

    2013-01-01

    Full Text Available This paper proposes a new type of mutation operator, FEDS (Fitness, Elitism, Depth, and Size mutation in genetic programming. The concept behind the new mutation operator is inspired from already introduced FEDS crossover operator to handle the problem of code bloating. FEDS mutation operates by using local elitism replacement in combination with depth limit and size of the trees to reduce bloat with a subsequent improvement in the performance of trees (program structures. We have designed a multiclass classifier for some benchmark datasets to test the performance of proposed mutation. The results show that when the initial run uses FEDS crossover and the concluding run uses FEDS mutation, then not only is the final result significantly improved but there is reduction in bloat also.

  4. 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.

  5. Testing the structure of a hydrological model using Genetic Programming

    Science.gov (United States)

    Selle, Benny; Muttil, Nitin

    2011-01-01

    SummaryGenetic Programming is able to systematically explore many alternative model structures of different complexity from available input and response data. We hypothesised that Genetic Programming can be used to test the structure of hydrological models and to identify dominant processes in hydrological systems. To test this, Genetic Programming was used to analyse a data set from a lysimeter experiment in southeastern Australia. The lysimeter experiment was conducted to quantify the deep percolation response under surface irrigated pasture to different soil types, watertable depths and water ponding times during surface irrigation. Using Genetic Programming, a simple model of deep percolation was recurrently evolved in multiple Genetic Programming runs. This simple and interpretable model supported the dominant process contributing to deep percolation represented in a conceptual model that was published earlier. Thus, this study shows that Genetic Programming can be used to evaluate the structure of hydrological models and to gain insight about the dominant processes in hydrological systems.

  6. Portfolio optimization by using linear programing models based on genetic algorithm

    Science.gov (United States)

    Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

    2018-01-01

    In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

  7. Guidelines on the use of molecular genetics in reintroduction programs

    Science.gov (United States)

    Michael K. Schwartz

    2005-01-01

    The use of molecular genetics can play a key role in reintroduction efforts. Prior to the introduction of any individuals, molecular genetics can be used to identify the most appropriate source population for the reintroduction, ensure that no relic populations exist in the reintroduction area, and guide captive breeding programs. The use of molecular genetics post-...

  8. 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

  9. A Heuristics Approach for Classroom Scheduling Using Genetic Algorithm Technique

    Science.gov (United States)

    Ahmad, Izah R.; Sufahani, Suliadi; Ali, Maselan; Razali, Siti N. A. M.

    2018-04-01

    Reshuffling and arranging classroom based on the capacity of the audience, complete facilities, lecturing time and many more may lead to a complexity of classroom scheduling. While trying to enhance the productivity in classroom planning, this paper proposes a heuristic approach for timetabling optimization. A new algorithm was produced to take care of the timetabling problem in a university. The proposed of heuristics approach will prompt a superior utilization of the accessible classroom space for a given time table of courses at the university. Genetic Algorithm through Java programming languages were used in this study and aims at reducing the conflicts and optimizes the fitness. The algorithm considered the quantity of students in each class, class time, class size, time accessibility in each class and lecturer who in charge of the classes.

  10. Implementing corporate wellness programs: a business approach to program planning.

    Science.gov (United States)

    Helmer, D C; Dunn, L M; Eaton, K; Macedonio, C; Lubritz, L

    1995-11-01

    1. Support of key decision makers is critical to the successful implementation of a corporate wellness program. Therefore, the program implementation plan must be communicated in a format and language readily understood by business people. 2. A business approach to corporate wellness program planning provides a standardized way to communicate the implementation plan. 3. A business approach incorporates the program planning components in a format that ranges from general to specific. This approach allows for flexibility and responsiveness to changes in program planning. 4. Components of the business approach are the executive summary, purpose, background, ground rules, approach, requirements, scope of work, schedule, and financials.

  11. Experimental control of a fluidic pinball using genetic programming

    Science.gov (United States)

    Raibaudo, Cedric; Zhong, Peng; Noack, Bernd R.; Martinuzzi, Robert J.

    2017-11-01

    The wake stabilization of a triangular cluster of three rotating cylinders was investigated in the present study. Experiments were performed at Reynolds number Re 6000, and compared with URANS-2D simulations at same flow conditions. 2D2C PIV measurements and constant temperature anemometry were used to characterize the flow without and with actuation. Open-loop actuation was first considered for the identification of particular control strategies. Machine learning control was also implemented for the experimental study. Linear genetic programming has been used for the optimization of open-loop parameters and closed-loop controllers. Considering a cost function J based on the fluctuations of the velocity measured by the hot-wire sensor, significant performances were achieved using the machine learning approach. The present work is supported by the senior author's (R. J. Martinuzzi) NSERC discovery Grant. C. Raibaudo acknowledges the financial support of the University of Calgary Eyes-High PDF program.

  12. Testing the Structure of Hydrological Models using Genetic Programming

    Science.gov (United States)

    Selle, B.; Muttil, N.

    2009-04-01

    Genetic Programming is able to systematically explore many alternative model structures of different complexity from available input and response data. We hypothesised that genetic programming can be used to test the structure hydrological models and to identify dominant processes in hydrological systems. To test this, genetic programming was used to analyse a data set from a lysimeter experiment in southeastern Australia. The lysimeter experiment was conducted to quantify the deep percolation response under surface irrigated pasture to different soil types, water table depths and water ponding times during surface irrigation. Using genetic programming, a simple model of deep percolation was consistently evolved in multiple model runs. This simple and interpretable model confirmed the dominant process contributing to deep percolation represented in a conceptual model that was published earlier. Thus, this study shows that genetic programming can be used to evaluate the structure of hydrological models and to gain insight about the dominant processes in hydrological systems.

  13. Developing robotic behavior using a genetic programming model

    International Nuclear Information System (INIS)

    Pryor, R.J.

    1998-01-01

    This report describes the methodology for using a genetic programming model to develop tracking behaviors for autonomous, microscale robotic vehicles. The use of such vehicles for surveillance and detection operations has become increasingly important in defense and humanitarian applications. Through an evolutionary process similar to that found in nature, the genetic programming model generates a computer program that when downloaded onto a robotic vehicle's on-board computer will guide the robot to successfully accomplish its task. Simulations of multiple robots engaged in problem-solving tasks have demonstrated cooperative behaviors. This report also discusses the behavior model produced by genetic programming and presents some results achieved during the study

  14. Genetics Education in Nurse Residency Programs: A Natural Fit.

    Science.gov (United States)

    Hamilton, Nalo M; Stenman, Christina W; Sang, Elaine; Palmer, Christina

    2017-08-01

    Scientific advances are shedding light on the genetic underpinning of common diseases. With such insight, the entire health care team is faced with the need to address patient questions regarding genetic risk, testing, and the psychosocial aspects of genetics information. Nurses are in a prime position to help with patient education about genetic conditions, yet they often lack adequate genetics education within their nursing curriculum to address patient questions and provide resources. One mechanism to address this knowledge deficit is the incorporation of a genetics-based curriculum into nurse residency programs. This article describes a novel genetics-based curriculum designed and implemented in the UCLA Health System Nurse Residency Program. J Contin Educ Nurs. 2017;48(8):379-384. Copyright 2017, SLACK Incorporated.

  15. 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.

  16. Programming peptidomimetic syntheses by translating genetic codes designed de novo.

    Science.gov (United States)

    Forster, Anthony C; Tan, Zhongping; Nalam, Madhavi N L; Lin, Hening; Qu, Hui; Cornish, Virginia W; Blacklow, Stephen C

    2003-05-27

    Although the universal genetic code exhibits only minor variations in nature, Francis Crick proposed in 1955 that "the adaptor hypothesis allows one to construct, in theory, codes of bewildering variety." The existing code has been expanded to enable incorporation of a variety of unnatural amino acids at one or two nonadjacent sites within a protein by using nonsense or frameshift suppressor aminoacyl-tRNAs (aa-tRNAs) as adaptors. However, the suppressor strategy is inherently limited by compatibility with only a small subset of codons, by the ways such codons can be combined, and by variation in the efficiency of incorporation. Here, by preventing competing reactions with aa-tRNA synthetases, aa-tRNAs, and release factors during translation and by using nonsuppressor aa-tRNA substrates, we realize a potentially generalizable approach for template-encoded polymer synthesis that unmasks the substantially broader versatility of the core translation apparatus as a catalyst. We show that several adjacent, arbitrarily chosen sense codons can be completely reassigned to various unnatural amino acids according to de novo genetic codes by translating mRNAs into specific peptide analog polymers (peptidomimetics). Unnatural aa-tRNA substrates do not uniformly function as well as natural substrates, revealing important recognition elements for the translation apparatus. Genetic programming of peptidomimetic synthesis should facilitate mechanistic studies of translation and may ultimately enable the directed evolution of small molecules with desirable catalytic or pharmacological properties.

  17. Evolving rule-based systems in two medical domains using genetic programming.

    Science.gov (United States)

    Tsakonas, Athanasios; Dounias, Georgios; Jantzen, Jan; Axer, Hubertus; Bjerregaard, Beth; von Keyserlingk, Diedrich Graf

    2004-11-01

    To demonstrate and compare the application of different genetic programming (GP) based intelligent methodologies for the construction of rule-based systems in two medical domains: the diagnosis of aphasia's subtypes and the classification of pap-smear examinations. Past data representing (a) successful diagnosis of aphasia's subtypes from collaborating medical experts through a free interview per patient, and (b) correctly classified smears (images of cells) by cyto-technologists, previously stained using the Papanicolaou method. Initially a hybrid approach is proposed, which combines standard genetic programming and heuristic hierarchical crisp rule-base construction. Then, genetic programming for the production of crisp rule based systems is attempted. Finally, another hybrid intelligent model is composed by a grammar driven genetic programming system for the generation of fuzzy rule-based systems. Results denote the effectiveness of the proposed systems, while they are also compared for their efficiency, accuracy and comprehensibility, to those of an inductive machine learning approach as well as to those of a standard genetic programming symbolic expression approach. The proposed GP-based intelligent methodologies are able to produce accurate and comprehensible results for medical experts performing competitive to other intelligent approaches. The aim of the authors was the production of accurate but also sensible decision rules that could potentially help medical doctors to extract conclusions, even at the expense of a higher classification score achievement.

  18. 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...... 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...

  19. 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...

  20. Mechanisms of Bunyavirus Virulence: A Genetic Approach.

    Science.gov (United States)

    1984-12-01

    of canine parvovirus Type-2, feline panleukopenia virus and mink enteritis virus. Virology 129,401-414. Partner A., Webster, R. G., and Bean W. J...CM, and Webster RG. Procedures for the characterization of the genetic material of candidate vaccine strains. Develop Biol Standard 39:15-24, 1977

  1. A “genetics first” approach to selection

    Science.gov (United States)

    A different approach for using genomic information in genetic improvement is proposed. Past research in population genetics and animal breeding combined with information on sequence variants suggest the possibility that selection might be able to capture a portion of inbreeding and heterosis effect...

  2. Genetic programs can be compressed and autonomously decompressed in live cells

    Science.gov (United States)

    Lapique, Nicolas; Benenson, Yaakov

    2018-04-01

    Fundamental computer science concepts have inspired novel information-processing molecular systems in test tubes1-13 and genetically encoded circuits in live cells14-21. Recent research has shown that digital information storage in DNA, implemented using deep sequencing and conventional software, can approach the maximum Shannon information capacity22 of two bits per nucleotide23. In nature, DNA is used to store genetic programs, but the information content of the encoding rarely approaches this maximum24. We hypothesize that the biological function of a genetic program can be preserved while reducing the length of its DNA encoding and increasing the information content per nucleotide. Here we support this hypothesis by describing an experimental procedure for compressing a genetic program and its subsequent autonomous decompression and execution in human cells. As a test-bed we choose an RNAi cell classifier circuit25 that comprises redundant DNA sequences and is therefore amenable for compression, as are many other complex gene circuits15,18,26-28. In one example, we implement a compressed encoding of a ten-gene four-input AND gate circuit using only four genetic constructs. The compression principles applied to gene circuits can enable fitting complex genetic programs into DNA delivery vehicles with limited cargo capacity, and storing compressed and biologically inert programs in vivo for on-demand activation.

  3. 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.

  4. 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.

  5. Designing Genetics Instruction for a Socratic Approach

    Science.gov (United States)

    Idros, Sharifah Norhaidah Syed

    2004-01-01

    Science is at heart a rational activity. Reasoning, being an important component of critical thinking has been successfully taught using Socratic methods. As an approach, the instructor or designer of instruction models an inquiring and probing mind focusing on providing questions and not answers. The main aim has been to allow learners to…

  6. 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...

  7. 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.

  8. 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.

  9. 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…

  10. 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

  11. 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

  12. 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...

  13. A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules.

    Science.gov (United States)

    Nguyen, Su; Mei, Yi; Xue, Bing; Zhang, Mengjie

    2018-06-04

    Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This paper develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

  14. 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.

  15. A holistic approach to genetic conservation of Pinus strobiformis

    Science.gov (United States)

    K.M. Waring; R. Sniezko; B.A. Goodrich; C. Wehenkel; J.J. Jacobs

    2017-01-01

    Pinus strobiformis (southwestern white pine) is threatened by both a rapidly changing climate and the tree disease white pine blister rust, caused by an introduced fungal pathogen, Cronartium ribicola. We began a proactive program in ~2009 to sustain P. strobiformis that includes genetic conservation, research, and management strategies. Research...

  16. Comparing approaches to generic programming in Haskell

    NARCIS (Netherlands)

    Hinze, R.; Jeuring, J.T.; Löh, A.

    2006-01-01

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

  17. Comparing approaches to generic programming in Haskell

    NARCIS (Netherlands)

    Hinze, R.; Jeuring, J.T.; Löh, A.

    2006-01-01

    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

  18. The use of genetic programming to develop a predictor of swash excursion on sandy beaches

    OpenAIRE

    M. Passarella; E. B. Goldstein; S. De Muro; G. Coco

    2018-01-01

    We use genetic programming (GP), a type of machine learning (ML) approach, to predict the total and infragravity swash excursion using previously published data sets that have been used extensively in swash prediction studies. Three previously published works with a range of new conditions are added to this data set to extend the range of measured swash conditions. Using this newly compiled data set we demonstrate that a ML approach can reduce the prediction errors compared ...

  19. Genetic similarity of polyploids - A new version of the computer program POPDIST (ver. 1.2.0) considers intraspecific genetic differentiation

    DEFF Research Database (Denmark)

    Tomiuk, Jürgen; Guldbrandtsen, Bernt; Loeschcke, Volker

    2009-01-01

    For evolutionary studies of polyploid species estimates of the genetic identity between species with different degrees of ploidy are particularly required because gene counting in samples of polyploid individuals often cannot be done, e.g., in triploids the phenotype AB can be genotypically either...... ABB or AAB. We recently suggested a genetic distance measure that is based on phenotype counting and made available the computer program POPDIST. The program provides maximum-likelihood estimates of the genetic identities and distances between polyploid populations, but this approach...

  20. 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.

  1. A reliability program approach to operational safety

    International Nuclear Information System (INIS)

    Mueller, C.J.; Bezella, W.A.

    1985-01-01

    A Reliability Program (RP) model based on proven reliability techniques is being formulated for potential application in the nuclear power industry. Methods employed under NASA and military direction, commercial airline and related FAA programs were surveyed and a review of current nuclear risk-dominant issues conducted. The need for a reliability approach to address dependent system failures, operating and emergency procedures and human performance, and develop a plant-specific performance data base for safety decision making is demonstrated. Current research has concentrated on developing a Reliability Program approach for the operating phase of a nuclear plant's lifecycle. The approach incorporates performance monitoring and evaluation activities with dedicated tasks that integrate these activities with operation, surveillance, and maintenance of the plant. The detection, root-cause evaluation and before-the-fact correction of incipient or actual systems failures as a mechanism for maintaining plant safety is a major objective of the Reliability Program. (orig./HP)

  2. Non-linear nuclear engineering models as genetic programming application

    International Nuclear Information System (INIS)

    Domingos, Roberto P.; Schirru, Roberto; Martinez, Aquilino S.

    1997-01-01

    This work presents a Genetic Programming paradigm and a nuclear application. A field of Artificial Intelligence, based on the concepts of Species Evolution and Natural Selection, can be understood as a self-programming process where the computer is the main agent responsible for the discovery of a program able to solve a given problem. In the present case, the problem was to find a mathematical expression in symbolic form, able to express the existent relation between equivalent ratio of a fuel cell, the enrichment of fuel elements and the multiplication factor. Such expression would avoid repeatedly reactor physics codes execution for core optimization. The results were compared with those obtained by different techniques such as Neural Networks and Linear Multiple Regression. Genetic Programming has shown to present a performance as good as, and under some features superior to Neural Network and Linear Multiple Regression. (author). 10 refs., 8 figs., 1 tabs

  3. 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.

  4. A Hybrid Genetic Algorithm Approach for Optimal Power Flow

    Directory of Open Access Journals (Sweden)

    Sydulu Maheswarapu

    2011-08-01

    Full Text Available This paper puts forward a reformed hybrid genetic algorithm (GA based approach to the optimal power flow. In the approach followed here, continuous variables are designed using real-coded GA and discrete variables are processed as binary strings. The outcomes are compared with many other methods like simple genetic algorithm (GA, adaptive genetic algorithm (AGA, differential evolution (DE, particle swarm optimization (PSO and music based harmony search (MBHS on a IEEE30 bus test bed, with a total load of 283.4 MW. Its found that the proposed algorithm is found to offer lowest fuel cost. The proposed method is found to be computationally faster, robust, superior and promising form its convergence characteristics.

  5. 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

  6. Flow discharge prediction in compound channels using linear genetic programming

    Science.gov (United States)

    Azamathulla, H. Md.; Zahiri, A.

    2012-08-01

    SummaryFlow discharge determination in rivers is one of the key elements in mathematical modelling in the design of river engineering projects. Because of the inundation of floodplains and sudden changes in river geometry, flow resistance equations are not applicable for compound channels. Therefore, many approaches have been developed for modification of flow discharge computations. Most of these methods have satisfactory results only in laboratory flumes. Due to the ability to model complex phenomena, the artificial intelligence methods have recently been employed for wide applications in various fields of water engineering. Linear genetic programming (LGP), a branch of artificial intelligence methods, is able to optimise the model structure and its components and to derive an explicit equation based on the variables of the phenomena. In this paper, a precise dimensionless equation has been derived for prediction of flood discharge using LGP. The proposed model was developed using published data compiled for stage-discharge data sets for 394 laboratories, and field of 30 compound channels. The results indicate that the LGP model has a better performance than the existing models.

  7. 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…

  8. An imaging genetics approach to understanding social influence

    Directory of Open Access Journals (Sweden)

    Emily eFalk

    2012-06-01

    Full Text Available Normative social influences shape nearly every aspect of our lives, yet the biological processes mediating the impact of these social influences on behavior remain incompletely understood. In this Hypothesis, we outline a theoretical framework and an integrative research approach to the study of social influences on the brain and genetic moderators of such effects. First, we review neuroimaging evidence linking social influence and conformity to the brain’s reward system. We next review neuroimaging evidence linking social punishment (exclusion to brain systems involved in the experience of pain, as well as evidence linking exclusion to conformity. We suggest that genetic variants that increase sensitivity to social cues may predispose individuals to be more sensitive to either social rewards or punishments (or potentially both, which in turn increases conformity and susceptibility to normative social influences more broadly. To this end, we review evidence for genetic moderators of neurochemical responses in the brain, and suggest ways in which genes and pharmacology may modulate sensitivity to social influences. We conclude by proposing an integrative imaging genetics approach to the study of brain mediators and genetic modulators of a variety of social influences on human attitudes, beliefs, and actions.

  9. An imaging genetics approach to understanding social influence.

    Science.gov (United States)

    Falk, Emily B; Way, Baldwin M; Jasinska, Agnes J

    2012-01-01

    Normative social influences shape nearly every aspect of our lives, yet the biological processes mediating the impact of these social influences on behavior remain incompletely understood. In this Hypothesis, we outline a theoretical framework and an integrative research approach to the study of social influences on the brain and genetic moderators of such effects. First, we review neuroimaging evidence linking social influence and conformity to the brain's reward system. We next review neuroimaging evidence linking social punishment (exclusion) to brain systems involved in the experience of pain, as well as evidence linking exclusion to conformity. We suggest that genetic variants that increase sensitivity to social cues may predispose individuals to be more sensitive to either social rewards or punishments (or potentially both), which in turn increases conformity and susceptibility to normative social influences more broadly. To this end, we review evidence for genetic moderators of neurochemical responses in the brain, and suggest ways in which genes and pharmacology may modulate sensitivity to social influences. We conclude by proposing an integrative imaging genetics approach to the study of brain mediators and genetic modulators of a variety of social influences on human attitudes, beliefs, and actions.

  10. 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

  11. A genetic ensemble approach for gene-gene interaction identification

    Directory of Open Access Journals (Sweden)

    Ho Joshua WK

    2010-10-01

    Full Text Available Abstract Background It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging. Methods In this paper, we propose a new approach for identifying such gene-gene and gene-environment interactions underlying complex diseases. This is a hybrid algorithm and it combines genetic algorithm (GA and an ensemble of classifiers (called genetic ensemble. Using this approach, the original problem of SNP interaction identification is converted into a data mining problem of combinatorial feature selection. By collecting various single nucleotide polymorphisms (SNP subsets as well as environmental factors generated in multiple GA runs, patterns of gene-gene and gene-environment interactions can be extracted using a simple combinatorial ranking method. Also considered in this study is the idea of combining identification results obtained from multiple algorithms. A novel formula based on pairwise double fault is designed to quantify the degree of complementarity. Conclusions Our simulation study demonstrates that the proposed genetic ensemble algorithm has comparable identification power to Multifactor Dimensionality Reduction (MDR and is slightly better than Polymorphism Interaction Analysis (PIA, which are the two most popular methods for gene-gene interaction identification. More importantly, the identification results generated by using our genetic ensemble algorithm are highly complementary to those obtained by PIA and MDR. Experimental results from our simulation studies and real world data application also confirm the effectiveness of the proposed genetic ensemble algorithm, as well as the potential benefits of

  12. Developmental Programming of Renal Function and Re-Programming Approaches.

    Science.gov (United States)

    Nüsken, Eva; Dötsch, Jörg; Weber, Lutz T; Nüsken, Kai-Dietrich

    2018-01-01

    Chronic kidney disease affects more than 10% of the population. Programming studies have examined the interrelationship between environmental factors in early life and differences in morbidity and mortality between individuals. A number of important principles has been identified, namely permanent structural modifications of organs and cells, long-lasting adjustments of endocrine regulatory circuits, as well as altered gene transcription. Risk factors include intrauterine deficiencies by disturbed placental function or maternal malnutrition, prematurity, intrauterine and postnatal stress, intrauterine and postnatal overnutrition, as well as dietary dysbalances in postnatal life. This mini-review discusses critical developmental periods and long-term sequelae of renal programming in humans and presents studies examining the underlying mechanisms as well as interventional approaches to "re-program" renal susceptibility toward disease. Clinical manifestations of programmed kidney disease include arterial hypertension, proteinuria, aggravation of inflammatory glomerular disease, and loss of kidney function. Nephron number, regulation of the renin-angiotensin-aldosterone system, renal sodium transport, vasomotor and endothelial function, myogenic response, and tubuloglomerular feedback have been identified as being vulnerable to environmental factors. Oxidative stress levels, metabolic pathways, including insulin, leptin, steroids, and arachidonic acid, DNA methylation, and histone configuration may be significantly altered by adverse environmental conditions. Studies on re-programming interventions focused on dietary or anti-oxidative approaches so far. Further studies that broaden our understanding of renal programming mechanisms are needed to ultimately develop preventive strategies. Targeted re-programming interventions in animal models focusing on known mechanisms will contribute to new concepts which finally will have to be translated to human application. Early

  13. Applying genetic algorithms for programming manufactoring cell tasks

    Directory of Open Access Journals (Sweden)

    Efredy Delgado

    2005-05-01

    Full Text Available This work was aimed for developing computational intelligence for scheduling a manufacturing cell's tasks, based manily on genetic algorithms. The manufacturing cell was modelled as beign a production-line; the makespan was calculated by using heuristics adapted from several libraries for genetic algorithms computed in C++ builder. Several problems dealing with small, medium and large list of jobs and machinery were resolved. The results were compared with other heuristics. The approach developed here would seem to be promising for future research concerning scheduling manufacturing cell tasks involving mixed batches.

  14. 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.

  15. 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

  16. Applications of a formal approach to decipher discrete genetic networks.

    Science.gov (United States)

    Corblin, Fabien; Fanchon, Eric; Trilling, Laurent

    2010-07-20

    A growing demand for tools to assist the building and analysis of biological networks exists in systems biology. We argue that the use of a formal approach is relevant and applicable to address questions raised by biologists about such networks. The behaviour of these systems being complex, it is essential to exploit efficiently every bit of experimental information. In our approach, both the evolution rules and the partial knowledge about the structure and the behaviour of the network are formalized using a common constraint-based language. In this article our formal and declarative approach is applied to three biological applications. The software environment that we developed allows to specifically address each application through a new class of biologically relevant queries. We show that we can describe easily and in a formal manner the partial knowledge about a genetic network. Moreover we show that this environment, based on a constraint algorithmic approach, offers a wide variety of functionalities, going beyond simple simulations, such as proof of consistency, model revision, prediction of properties, search for minimal models relatively to specified criteria. The formal approach proposed here deeply changes the way to proceed in the exploration of genetic and biochemical networks, first by avoiding the usual trial-and-error procedure, and second by placing the emphasis on sets of solutions, rather than a single solution arbitrarily chosen among many others. Last, the constraint approach promotes an integration of model and experimental data in a single framework.

  17. 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.

  18. An imaging genetics approach to understanding social influence

    OpenAIRE

    Emily eFalk; Emily eFalk; Baldwin eWay; Agnes eJasinska

    2012-01-01

    Normative social influences shape nearly every aspect of our lives, yet the biological processes mediating the impact of these social influences on behavior remain incompletely understood. In this Hypothesis, we outline a theoretical framework and an integrative research approach to the study of social influences on the brain and genetic moderators of such effects. First, we review neuroimaging evidence linking social influence and conformity to the brain’s reward system. We next review neur...

  19. An imaging genetics approach to understanding social influence

    OpenAIRE

    Falk, Emily B.; Way, Baldwin M.; Jasinska, Agnes J.

    2012-01-01

    Normative social influences shape nearly every aspect of our lives, yet the biological processes mediating the impact of these social influences on behavior remain incompletely understood. In this Hypothesis, we outline a theoretical framework and an integrative research approach to the study of social influences on the brain and genetic moderators of such effects. First, we review neuroimaging evidence linking social influence and conformity to the brain's reward system. We next review neuro...

  20. Geometric Semantic Genetic Programming Algorithm and Slump Prediction

    OpenAIRE

    Xu, Juncai; Shen, Zhenzhong; Ren, Qingwen; Xie, Xin; Yang, Zhengyu

    2017-01-01

    Research on the performance of recycled concrete as building material in the current world is an important subject. Given the complex composition of recycled concrete, conventional methods for forecasting slump scarcely obtain satisfactory results. Based on theory of nonlinear prediction method, we propose a recycled concrete slump prediction model based on geometric semantic genetic programming (GSGP) and combined it with recycled concrete features. Tests show that the model can accurately p...

  1. Genetic diversity of tambaqui broodstocks in stock enhancement programs

    Directory of Open Access Journals (Sweden)

    Americo Moraes Neto

    2017-06-01

    Full Text Available Natural populations of tambaqui (Colossoma macropomum have significantly decreased in recent decades especially due to human extraction activities. So that the environmental impact may be reduced, the restocking of fish and increase in fish production are enhanced. Genetic evaluations using molecular markers are essential for this purpose. Current study evaluates the genetic variability of two tambaqui broodstocks used in restocking programs. Sixty-five samples (33 samples from broodstock A and 32 samples from broodstock B were collected. DNA was extracted from caudal fin samples, with the amplification of four microsatellite loci: Cm1A11 (EU685307 Cm1C8 (EU685308 Cm1F4 (EU685311 and Cm1H8 (EU685315. Fourteen alleles in the stock of broodstock A were produced, five alleles for Cm1A11 locus (230, 255, 260, 270 and 276 bp, three alleles Cm1C8 (239, 260, and 273 bp, two alleles Cm1F4 (211 and 245 bp, four alleles for Cm1H8 (275, 290, 320 and 331 bp and two unique alleles were found for Cm1A11 loci (alleles 270 and 276 bp and Cm1H8 (alleles 275 and 331 bp. In broodstock B, ten alleles were produced, the same alleles of the first stock except for alleles 270 and 276 bp in Cm1A11 locus and 275 and 331 bp in Cm1H8 locus. Broodstock A revealed low frequency alleles in Cm1A11 loci, Cm1C8, Cm1F4 and Cm1H8, whereas broodstock B had no locus with low allelic frequency. Loci Cm1A11, Cm1C8 and Cm1H8 exhibited significant deficit of heterozygotes in both broodstocks, revealing changes in Hardy-Weinberg equilibrium. Genetic diversity between stocks was 0.1120, whilst genetic similarity was 0.894, with FST rate = 0.05, and Nm = 3.93, indicating gene flow between the two broodstocks. Results show that broodstocks are genetically closely related, with no great genetic variability. Strategies such as a previous genetic analysis of breeding with its marking, use of a large Ne crossing between the most genetically divergent specimens, and the introduction of new

  2. Developmental Programming of Renal Function and Re-Programming Approaches

    Directory of Open Access Journals (Sweden)

    Eva Nüsken

    2018-02-01

    Full Text Available Chronic kidney disease affects more than 10% of the population. Programming studies have examined the interrelationship between environmental factors in early life and differences in morbidity and mortality between individuals. A number of important principles has been identified, namely permanent structural modifications of organs and cells, long-lasting adjustments of endocrine regulatory circuits, as well as altered gene transcription. Risk factors include intrauterine deficiencies by disturbed placental function or maternal malnutrition, prematurity, intrauterine and postnatal stress, intrauterine and postnatal overnutrition, as well as dietary dysbalances in postnatal life. This mini-review discusses critical developmental periods and long-term sequelae of renal programming in humans and presents studies examining the underlying mechanisms as well as interventional approaches to “re-program” renal susceptibility toward disease. Clinical manifestations of programmed kidney disease include arterial hypertension, proteinuria, aggravation of inflammatory glomerular disease, and loss of kidney function. Nephron number, regulation of the renin–angiotensin–aldosterone system, renal sodium transport, vasomotor and endothelial function, myogenic response, and tubuloglomerular feedback have been identified as being vulnerable to environmental factors. Oxidative stress levels, metabolic pathways, including insulin, leptin, steroids, and arachidonic acid, DNA methylation, and histone configuration may be significantly altered by adverse environmental conditions. Studies on re-programming interventions focused on dietary or anti-oxidative approaches so far. Further studies that broaden our understanding of renal programming mechanisms are needed to ultimately develop preventive strategies. Targeted re-programming interventions in animal models focusing on known mechanisms will contribute to new concepts which finally will have to be translated

  3. Developmental Programming of Renal Function and Re-Programming Approaches

    Science.gov (United States)

    Nüsken, Eva; Dötsch, Jörg; Weber, Lutz T.; Nüsken, Kai-Dietrich

    2018-01-01

    Chronic kidney disease affects more than 10% of the population. Programming studies have examined the interrelationship between environmental factors in early life and differences in morbidity and mortality between individuals. A number of important principles has been identified, namely permanent structural modifications of organs and cells, long-lasting adjustments of endocrine regulatory circuits, as well as altered gene transcription. Risk factors include intrauterine deficiencies by disturbed placental function or maternal malnutrition, prematurity, intrauterine and postnatal stress, intrauterine and postnatal overnutrition, as well as dietary dysbalances in postnatal life. This mini-review discusses critical developmental periods and long-term sequelae of renal programming in humans and presents studies examining the underlying mechanisms as well as interventional approaches to “re-program” renal susceptibility toward disease. Clinical manifestations of programmed kidney disease include arterial hypertension, proteinuria, aggravation of inflammatory glomerular disease, and loss of kidney function. Nephron number, regulation of the renin–angiotensin–aldosterone system, renal sodium transport, vasomotor and endothelial function, myogenic response, and tubuloglomerular feedback have been identified as being vulnerable to environmental factors. Oxidative stress levels, metabolic pathways, including insulin, leptin, steroids, and arachidonic acid, DNA methylation, and histone configuration may be significantly altered by adverse environmental conditions. Studies on re-programming interventions focused on dietary or anti-oxidative approaches so far. Further studies that broaden our understanding of renal programming mechanisms are needed to ultimately develop preventive strategies. Targeted re-programming interventions in animal models focusing on known mechanisms will contribute to new concepts which finally will have to be translated to human application

  4. Molecular genetics and livestock selection. Approaches, opportunities and risks

    International Nuclear Information System (INIS)

    Williams, J.L.

    2005-01-01

    Following domestication, livestock were selected both naturally through adaptation to their environments and by man so that they would fulfil a particular use. As selection methods have become more sophisticated, rapid progress has been made in improving those traits that are easily measured. However, selection has also resulted in decreased diversity. In some cases, improved breeds have replaced local breeds, risking the loss of important survival traits. The advent of molecular genetics provides the opportunity to identify the genes that control particular traits by a gene mapping approach. However, as with selection, the early mapping studies focused on traits that are easy to measure. Where molecular genetics can play a valuable role in livestock production is by providing the means to select effectively for traits that are difficult to measure. Identifying the genes underpinning particular traits requires a population in which these traits are segregating. Fortunately, several experimental populations have been created that have allowed a wide range of traits to be studied. Gene mapping work in these populations has shown that the role of particular genes in controlling variation in a given trait can depend on the genetic background. A second finding is that the most favourable alleles for a trait may in fact. be present in animals that perform poorly for the trait. In the long term, knowledge of -the genes controlling particular traits, and the way they interact with the genetic background, will allow introgression between breeds and the assembly of genotypes that are best suited to particular environments, producing animals with the desired characteristics. If used wisely, this approach will maintain genetic diversity while improving performance over a wide range of desired traits. (author)

  5. Rationale for an integrated approach to genetic epidemiology.

    Science.gov (United States)

    Laberge, Claude M; Knoppers, Bartha Maria

    1992-10-01

    Genetic knowledge is now in the public domain and its interpretation by the media and the citizens brings the issues into the public forum of discussion for the necessary ethical, legal and socio-cultural evaluation of its application. Science is being perceived by some as dangerous and as requiring international regulation. Others feel that genetic knowledge will be the breakthrough that will permit medical progress and individual autonomy with regards to personal health and lifestyle choices. The mapping of the human genome has already yielded valuable information on an increasing number of diseases and their variants. Prevailing popular and journalistic archetypes ("imaginaires") used in the media are perceived by the producers as slowing down the possible application of genetic knowledge. The answers to these dilemmas are not readily apparent nor are they prescribed by classical philosophy of medicine. Since genetic knowledge eventually resides with the individual who carries the genes of disease and/or susceptibility, a logical approach to integration of this knowledge at a societal level would seem to reside with individual education and decision-making. The politics of the ensuing social debate could transform the current social contract since an individual's interests need to be balanced against those of his or her immediate family in the sharing of information. The ethical foundations of such a contract requires the genetic education of "Everyone" as a matter of urgent priority. Genetic education should not serve ideological power struggles between the medical establishment and the ethical-legal alliance. Instead, it should ensure the transfer of knowledge to physicians, to patients, to users, to planners, to social science and humanities researchers and to politicians, so that they may make "informed" and free decisions....

  6. Mouse genetic approaches applied to the normal tissue radiation response

    International Nuclear Information System (INIS)

    Haston, Christina K.

    2012-01-01

    The varying responses of inbred mouse models to radiation exposure present a unique opportunity to dissect the genetic basis of radiation sensitivity and tissue injury. Such studies are complementary to human association studies as they permit both the analysis of clinical features of disease, and of specific variants associated with its presentation, in a controlled environment. Herein I review how animal models are studied to identify specific genetic variants influencing predisposition to radiation-induced traits. Among these radiation-induced responses are documented strain differences in repair of DNA damage and in extent of tissue injury (in the lung, skin, and intestine) which form the base for genetic investigations. For example, radiation-induced DNA damage is consistently greater in tissues from BALB/cJ mice, than the levels in C57BL/6J mice, suggesting there may be an inherent DNA damage level per strain. Regarding tissue injury, strain specific inflammatory and fibrotic phenotypes have been documented for principally, C57BL/6 C3H and A/J mice but a correlation among responses such that knowledge of the radiation injury in one tissue informs of the response in another is not evident. Strategies to identify genetic differences contributing to a trait based on inbred strain differences, which include linkage analysis and the evaluation of recombinant congenic (RC) strains, are presented, with a focus on the lung response to irradiation which is the only radiation-induced tissue injury mapped to date. Such approaches are needed to reveal genetic differences in susceptibility to radiation injury, and also to provide a context for the effects of specific genetic variation uncovered in anticipated clinical association studies. In summary, mouse models can be studied to uncover heritable variation predisposing to specific radiation responses, and such variations may point to pathways of importance to phenotype development in the clinic.

  7. Genetic approaches refine ex situ lowland tapir (Tapirus terrestris) conservation.

    Science.gov (United States)

    Gonçalves da Silva, Anders; Lalonde, Danielle R; Quse, Viviana; Shoemaker, Alan; Russello, Michael A

    2010-01-01

    Ex situ conservation management remains an important tool in the face of continued habitat loss and global environmental change. Here, we use microsatellite marker variation to evaluate conventional assumptions of pedigree-based ex situ population management and directly inform a captive lowland tapir breeding program within a range country. We found relatively high levels of genetic variation (N(total) = 41; mean H(E) = 0.67 across 10 variable loci) and little evidence for relatedness among founder individuals (N(founders) = 10; mean relatedness = -0.05). Seven of 29 putative parent-offspring relationships were excluded by parentage analysis based on allele sharing, and we identified 2 individuals of high genetic value to the population (mk program. Traditional assumptions of founders being unrelated and individuals of unknown origin being highly related led to overestimates of mean kinship and inbreeding, and underestimates of gene diversity, when compared with values found when genetic markers were used to inform kinship. We discuss our results within the context of recent studies that have assessed the utility of neutral molecular markers for ex situ conservation.

  8. 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.

  9. The sheep blowfly genetic control program in Australia

    International Nuclear Information System (INIS)

    Foster, Geoffrey G.

    1989-01-01

    The blowfly Lucilia cuprina is the most important myiasis pet of sheep in Australia. Other species are associated with sheep myiasis, but L. cuprina is probably responsible for initiating more than 90% of infestations. Annual costs of production losses, prevention and treatment have been estimated at $149 millions in 1985. Prevention and treatment encompass both insecticidal applications to sheep and non-chemical management practices. In the absence of effective preventive measures, the sheep industry would be non-viable over much of Australia. Insecticide usage against L. cuprina has been marked by the appearance of widespread resistance to cyclodienes in 1956, the organophosphates in 1965, and carbamates in 1966. Resistance has not yet been reported against the triazine compounds introduced for blowfly control in 1981. The most effective non-chemical control measures are surgical (removal of skin from the breech in certain breeds of sheep, and tail-docking). They protect sheep by reducing favourable oviposition sites (dung and urine-stained wool). The spectre of insecticide resistance and the early success of the sterile insect technique (SIT) against screwworm fly in the U.S.A., led this Division to consider SIT and other autocidal methods in the 1960s. The L. cuprina genetics research program was established in 1966 and subsequently expanded in 1971. More recently, lobbying by animal welfare groups against surgical blowfly control practices, as well as increasing consumer awareness of insecticide residues in animal products, have accelerated the search for alternatives to chemical control. When SIT was first considered for L. cuprina control in 1960, little was known about the population dynamics of L. cuprina. There were insufficient ecological data to evaluate the prospects of alternative strategies such as suppression or containment. The number of flies which would have to be released in a SIT program was unknown, as were the costs. Assuming that the cost of

  10. 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.

  11. Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem

    Science.gov (United States)

    Wihartiko, F. D.; Wijayanti, H.; Virgantari, F.

    2018-03-01

    Genetic Algorithm (GA) is a common algorithm used to solve optimization problems with artificial intelligence approach. Similarly, the Particle Swarm Optimization (PSO) algorithm. Both algorithms have different advantages and disadvantages when applied to the case of optimization of the Model Integer Programming for Bus Timetabling Problem (MIPBTP), where in the case of MIPBTP will be found the optimal number of trips confronted with various constraints. The comparison results show that the PSO algorithm is superior in terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.

  12. Modeling AEC—New Approaches to Study Rare Genetic Disorders

    Science.gov (United States)

    Koch, Peter J.; Dinella, Jason; Fete, Mary; Siegfried, Elaine C.; Koster, Maranke I.

    2015-01-01

    Ankyloblepharon-ectodermal defects-cleft lip/palate (AEC) syndrome is a rare monogenetic disorder that is characterized by severe abnormalities in ectoderm-derived tissues, such as skin and its appendages. A major cause of morbidity among affected infants is severe and chronic skin erosions. Currently, supportive care is the only available treatment option for AEC patients. Mutations in TP63, a gene that encodes key regulators of epidermal development, are the genetic cause of AEC. However, it is currently not clear how mutations in TP63 lead to the various defects seen in the patients’ skin. In this review, we will discuss current knowledge of the AEC disease mechanism obtained by studying patient tissue and genetically engineered mouse models designed to mimic aspects of the disorder. We will then focus on new approaches to model AEC, including the use of patient cells and stem cell technology to replicate the disease in a human tissue culture model. The latter approach will advance our understanding of the disease and will allow for the development of new in vitro systems to identify drugs for the treatment of skin erosions in AEC patients. Further, the use of stem cell technology, in particular induced pluripotent stem cells (iPSC), will enable researchers to develop new therapeutic approaches to treat the disease using the patient’s own cells (autologous keratinocyte transplantation) after correction of the disease-causing mutations. PMID:24665072

  13. Maximizing lifetime of wireless sensor networks using genetic approach

    DEFF Research Database (Denmark)

    Wagh, Sanjeev; Prasad, Ramjee

    2014-01-01

    The wireless sensor networks are designed to install the smart network applications or network for emergency solutions, where human interaction is not possible. The nodes in wireless sensor networks have to self organize as per the users requirements through monitoring environments. As the sensor......-objective parameters are considered to solve the problem using genetic algorithm of evolutionary approach.......The wireless sensor networks are designed to install the smart network applications or network for emergency solutions, where human interaction is not possible. The nodes in wireless sensor networks have to self organize as per the users requirements through monitoring environments. As the sensor...

  14. 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).

  15. 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

  16. 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.

  17. 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.

  18. Epileptic MEG Spike Detection Using Statistical Features and Genetic Programming with KNN

    Directory of Open Access Journals (Sweden)

    Turky N. Alotaiby

    2017-01-01

    Full Text Available Epilepsy is a neurological disorder that affects millions of people worldwide. Monitoring the brain activities and identifying the seizure source which starts with spike detection are important steps for epilepsy treatment. Magnetoencephalography (MEG is an emerging epileptic diagnostic tool with high-density sensors; this makes manual analysis a challenging task due to the vast amount of MEG data. This paper explores the use of eight statistical features and genetic programing (GP with the K-nearest neighbor (KNN for interictal spike detection. The proposed method is comprised of three stages: preprocessing, genetic programming-based feature generation, and classification. The effectiveness of the proposed approach has been evaluated using real MEG data obtained from 28 epileptic patients. It has achieved a 91.75% average sensitivity and 92.99% average specificity.

  19. 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.

  20. Dating Antarctic ice sheet collapse: Proposing a molecular genetic approach

    Science.gov (United States)

    Strugnell, Jan M.; Pedro, Joel B.; Wilson, Nerida G.

    2018-01-01

    Sea levels at the end of this century are projected to be 0.26-0.98 m higher than today. The upper end of this range, and even higher estimates, cannot be ruled out because of major uncertainties in the dynamic response of polar ice sheets to a warming climate. Here, we propose an ecological genetics approach that can provide insight into the past stability and configuration of the West Antarctic Ice Sheet (WAIS). We propose independent testing of the hypothesis that a trans-Antarctic seaway occurred at the last interglacial. Examination of the genomic signatures of bottom-dwelling marine species using the latest methods can provide an independent window into the integrity of the WAIS more than 100,000 years ago. Periods of connectivity facilitated by trans-Antarctic seaways could be revealed by dating coalescent events recorded in DNA. These methods allow alternative scenarios to be tested against a fit to genomic data. Ideal candidate taxa for this work would need to possess a circumpolar distribution, a benthic habitat, and some level of genetic structure indicated by phylogeographical investigation. The purpose of this perspective piece is to set out an ecological genetics method to help resolve when the West Antarctic Ice Shelf last collapsed.

  1. Nuclear power plant maintenance scheduling dilemma: a genetic algorithm approach

    International Nuclear Information System (INIS)

    Mahdavi, M.H.; Modarres, M.

    2004-01-01

    There are huge numbers of components scheduled for maintenance when a nuclear power plant is shut down. Among these components, a number of them are safety related which their operability as well as reliability when plant becomes up is main concerns. Not performing proper maintenance on this class of components/system would impose substantial risk on operating the NPP. In this paper a new approach based on genetic algorithms is presented to optimize the NPP maintenance schedule during shutdown. following this approach the cost incurred by maintenance activities for each schedule is balanced with the risk imposed by the maintenance scheduling plan to the plant operation status when it is up. The risk model implemented in the GA scheduler as its evaluation function is developed on the basis of the probabilistic risk assessment methodology. the Ga optimizers itself is shown to be superior compared to other optimization methods such as the monte carlo technique

  2. Production of amino acids - Genetic and metabolic engineering approaches.

    Science.gov (United States)

    Lee, Jin-Ho; Wendisch, Volker F

    2017-12-01

    The biotechnological production of amino acids occurs at the million-ton scale and annually about 6milliontons of l-glutamate and l-lysine are produced by Escherichia coli and Corynebacterium glutamicum strains. l-glutamate and l-lysine production from starch hydrolysates and molasses is very efficient and access to alternative carbon sources and new products has been enabled by metabolic engineering. This review focusses on genetic and metabolic engineering of amino acid producing strains. In particular, rational approaches involving modulation of transcriptional regulators, regulons, and attenuators will be discussed. To address current limitations of metabolic engineering, this article gives insights on recent systems metabolic engineering approaches based on functional tools and method such as genome reduction, amino acid sensors based on transcriptional regulators and riboswitches, CRISPR interference, small regulatory RNAs, DNA scaffolding, and optogenetic control, and discusses future prospects. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. 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...

  4. Towards Merging Binary Integer Programming Techniques with Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Reza Zamani

    2017-01-01

    Full Text Available This paper presents a framework based on merging a binary integer programming technique with a genetic algorithm. The framework uses both lower and upper bounds to make the employed mathematical formulation of a problem as tight as possible. For problems whose optimal solutions cannot be obtained, precision is traded with speed through substituting the integrality constrains in a binary integer program with a penalty. In this way, instead of constraining a variable u with binary restriction, u is considered as real number between 0 and 1, with the penalty of Mu(1-u, in which M is a large number. Values not near to the boundary extremes of 0 and 1 make the component of Mu(1-u large and are expected to be avoided implicitly. The nonbinary values are then converted to priorities, and a genetic algorithm can use these priorities to fill its initial pool for producing feasible solutions. The presented framework can be applied to many combinatorial optimization problems. Here, a procedure based on this framework has been applied to a scheduling problem, and the results of computational experiments have been discussed, emphasizing the knowledge generated and inefficiencies to be circumvented with this framework in future.

  5. Genetic and neural approaches to nuclear transient identification

    International Nuclear Information System (INIS)

    Almeida, Jose Carlos Soares de; Mol, Antonio Carlos de Abreu; Pereira, Claudio Marcio Nascimento Abreu; Lapa, Celso Marcelo Franklin

    2005-01-01

    This work presents two approaches for pattern recognition to the same set of reactor signals. The first one describes a possibilistic approach optimized by genetic algorithm. The use of a possibilistic classification provides a natural and consistent classification rules, leading naturally to a good heuristic to handle the 'don't know' response, in case of unrecognized transient, which is fairly desirable in transient classification systems where safety is critical, since wrong or not reliable classifications can be catastrophic. Application of the proposed approach to a nuclear transient identification problem reveals good capability of the genetic algorithm in learning optimized possibilistic classification rules for efficient diagnosis including 'don't know' response. The second one uses two multilayer neural networks (NN). The first NN is responsible for the dynamic identification. This NN uses, as input, a short set (in a moving time window) of recent measurements of each variable avoiding the necessity of using starting events. The second NN is used to validate the instantaneous identification (from the first net) through the validation of each variable. This net is responsible for allowing the system to provide a 'don't know' response. In order to validate both methods, a Nuclear Power Plant (NPP) transient identification problem comprising postulated accidents, simulated for a pressurized water reactor, was proposed in the validation process it has been considered noisy data in order to evaluate the method robustness. Obtained results reveal the ability of the methods in dealing with both dynamic identification of transients and correct 'don't know' response. (author)

  6. Extraction of Static and Dynamic Reservoir Operation Rules by Genetic Programming

    Directory of Open Access Journals (Sweden)

    Habib Akbari Alashti

    2014-11-01

    Full Text Available Considering the necessity of desirable operation of limited water resources and assuming the significant role of dams in controlling and consuming the surface waters, highlights the advantageous of suitable operation rules for optimal and sustainable operation of dams. This study investigates the hydroelectric supply of a one-reservoir system of Karoon3 using nonlinear programming (NLP, genetic algorithm (GA, genetic programming (GP and fixed length gen GP (FLGGP in real-time operation of dam considering two approaches of static and dynamic operation rules. In static operation rule, only one rule curve is extracted for all months in a year whereas in dynamic operation rule, monthly rule curves (12 rules are extracted for each month of a year. In addition, nonlinear decision rule (NLDR curves are considered, and the total deficiency function as the target (objective function have been used for evaluating the performance of each method and approach. Results show appropriate efficiency of GP and FLGGP methods in extracting operation rules in both approaches. Superiority of these methods to operation methods yielded by GA and NLP is 5%. Moreover, according to the results, it can be remarked that, FLGGP method is an alternative for GP method, whereas the GP method cannot be used due to its limitations. Comparison of two approaches of static and dynamic operation rules demonstrated the superiority of dynamic operation rule to static operation rule (about 10% and therefore this method has more capabilities in real-time operation of the reservoirs systems.

  7. School District Program Cost Accounting: An Alternative Approach

    Science.gov (United States)

    Hentschke, Guilbert C.

    1975-01-01

    Discusses the value for school districts of a program cost accounting system and examines different approaches to generating program cost data, with particular emphasis on the "cost allocation to program system" (CAPS) and the traditional "transaction-based system." (JG)

  8. 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.

  9. A genetic approach to shape reconstruction in limited data tomography

    International Nuclear Information System (INIS)

    Turcanu, C.; Craciunescu, T.

    2001-01-01

    The paper proposes a new method for shape reconstruction in computerized tomography. Unlike nuclear medicine applications, in physical science problems we are often confronted with limited data sets: constraints in the number of projections or limited view angles . The problem of image reconstruction from projection may be considered as a problem of finding an image (solution) having projections that match the experimental ones. In our approach, we choose a statistical correlation coefficient to evaluate the fitness of any potential solution. The optimization process is carried out by a genetic algorithm. The algorithm has some features common to all genetic algorithms but also some problem-oriented characteristics. One of them is that a chromosome, representing a potential solution, is not linear but coded as a matrix of pixels corresponding to a two-dimensional image. This kind of internal representation reflects the genuine manifestation and slight differences between two points situated in the original problem space give rise to similar differences once they become coded. Another particular feature is a newly built crossover operator: the grid-based crossover, suitable for high dimension two-dimensional chromosomes. Except for the population size and the dimension of the cutting grid for the grid-based crossover, all the other parameters of the algorithm are independent of the geometry of the tomographic reconstruction. The performances of the method are evaluated on a phantom typical for an application with limited data sets: the determination of the neutron energy spectra with time resolution in case of short-pulsed neutron emission. A genetic reconstruction is presented. The qualitative judgement and also the quantitative one, based on some figures of merit, point out that the proposed method ensures an improved reconstruction of shapes, sizes and resolution in the image, even in the presence of noise. (authors)

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

    Directory of Open Access Journals (Sweden)

    Swati Chaturvedi

    2016-01-01

    Full Text Available 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.

  11. 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.

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

    International Nuclear Information System (INIS)

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

    1999-01-01

    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

  13. 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.

  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. Genetic Programming and Standardization in Water Temperature Modelling

    Directory of Open Access Journals (Sweden)

    Maritza Arganis

    2009-01-01

    Full Text Available An application of Genetic Programming (an evolutionary computational tool without and with standardization data is presented with the aim of modeling the behavior of the water temperature in a river in terms of meteorological variables that are easily measured, to explore their explanatory power and to emphasize the utility of the standardization of variables in order to reduce the effect of those with large variance. Recorded data corresponding to the water temperature behavior at the Ebro River, Spain, are used as analysis case, showing a performance improvement on the developed model when data are standardized. This improvement is reflected in a reduction of the mean square error. Finally, the models obtained in this document were applied to estimate the water temperature in 2004, in order to provide evidence about their applicability to forecasting purposes.

  16. The use of genetic programming to develop a predictor of swash excursion on sandy beaches

    Directory of Open Access Journals (Sweden)

    M. Passarella

    2018-02-01

    Full Text Available We use genetic programming (GP, a type of machine learning (ML approach, to predict the total and infragravity swash excursion using previously published data sets that have been used extensively in swash prediction studies. Three previously published works with a range of new conditions are added to this data set to extend the range of measured swash conditions. Using this newly compiled data set we demonstrate that a ML approach can reduce the prediction errors compared to well-established parameterizations and therefore it may improve coastal hazards assessment (e.g. coastal inundation. Predictors obtained using GP can also be physically sound and replicate the functionality and dependencies of previous published formulas. Overall, we show that ML techniques are capable of both improving predictability (compared to classical regression approaches and providing physical insight into coastal processes.

  17. The use of genetic programming to develop a predictor of swash excursion on sandy beaches

    Science.gov (United States)

    Passarella, Marinella; Goldstein, Evan B.; De Muro, Sandro; Coco, Giovanni

    2018-02-01

    We use genetic programming (GP), a type of machine learning (ML) approach, to predict the total and infragravity swash excursion using previously published data sets that have been used extensively in swash prediction studies. Three previously published works with a range of new conditions are added to this data set to extend the range of measured swash conditions. Using this newly compiled data set we demonstrate that a ML approach can reduce the prediction errors compared to well-established parameterizations and therefore it may improve coastal hazards assessment (e.g. coastal inundation). Predictors obtained using GP can also be physically sound and replicate the functionality and dependencies of previous published formulas. Overall, we show that ML techniques are capable of both improving predictability (compared to classical regression approaches) and providing physical insight into coastal processes.

  18. Surrogate-Assisted Genetic Programming With Simplified Models for Automated Design of Dispatching Rules.

    Science.gov (United States)

    Nguyen, Su; Zhang, Mengjie; Tan, Kay Chen

    2017-09-01

    Automated design of dispatching rules for production systems has been an interesting research topic over the last several years. Machine learning, especially genetic programming (GP), has been a powerful approach to dealing with this design problem. However, intensive computational requirements, accuracy and interpretability are still its limitations. This paper aims at developing a new surrogate assisted GP to help improving the quality of the evolved rules without significant computational costs. The experiments have verified the effectiveness and efficiency of the proposed algorithms as compared to those in the literature. Furthermore, new simplification and visualisation approaches have also been developed to improve the interpretability of the evolved rules. These approaches have shown great potentials and proved to be a critical part of the automated design system.

  19. Structural health monitoring feature design by genetic programming

    International Nuclear Information System (INIS)

    Harvey, Dustin Y; Todd, Michael D

    2014-01-01

    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. (paper)

  20. 76 FR 72424 - Submission for OMB Review; Comment Request Information Program on the Genetic Testing Registry

    Science.gov (United States)

    2011-11-23

    ... particular tests; and (3) facilitating genetic and genomic data-sharing for research and new scientific...; Comment Request Information Program on the Genetic Testing Registry AGENCY: National Institutes of Health... currently valid OMB control number. Proposed Collection: Title: The Genetic Testing Registry; Type of...

  1. 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.

  2. Molecular genetic approaches to the study of cellular senescence.

    Science.gov (United States)

    Goletz, T J; Smith, J R; Pereira-Smith, O M

    1994-01-01

    Cellular senescence is an inability of cells to synthesize DNA and divide, which results in a terminal loss of proliferation despite the maintenance of basic metabolic processes. Senescence has been proposed as a model for the study of aging at the cellular level, and the basis for this model system and its features have been summarized. Although strong experimental evidence exists to support the hypothesis that cellular senescence is a dominant active process, the mechanisms responsible for this phenomenon remain a mystery. Investigators have taken several approaches to gain a better understanding of senescence. Several groups have documented the differences between young and senescent cells, and others have identified changes that occur during the course of a cell's in vitro life span. Using molecular and biochemical approaches, important changes in gene expression and function of cell-cycle-associated products have been identified. The active production of an inhibitor of DNA synthesis has been demonstrated. This may represent the final step in a cascade of events governing senescence. The study of immortal cells which have escaped senescence has also provided useful information, particularly with regard to the genes governing the senescence program. These studies have identified four complementation groups for indefinite division, which suggests that there are at least four genes or gene pathways in the senescence program. Through the use of microcell-mediated chromosome transfer, chromosomes encoding senescence genes have been identified; efforts to clone these genes are ongoing.(ABSTRACT TRUNCATED AT 250 WORDS)

  3. Translation and genetic criticism : genetic and editorial approaches to the 'untranslatable' in Joyce and Beckett

    OpenAIRE

    Hulle, Van, Dirk

    2015-01-01

    Abstract: Genetics of translation may suggest a unidirectional link between two fields of research (genetic criticism applied to translation), but there are many ways in which translation and genetic criticism interact. This article's research hypothesis is that an exchange of ideas between translation studies and genetic criticism can be mutually beneficial in more than one way. The main function of this exchange is to enhance a form of textual awareness, and to realize this enhanced textual...

  4. Control of Angra 1' PZR by a fuzzy rule base build through genetic programming

    International Nuclear Information System (INIS)

    Caldas, Gustavo Henrique Flores; Schirru, Roberto

    2002-01-01

    There is an optimum pressure for the normal operation of nuclear power plant reactors and thresholds that must be respected during transients, what make the pressurizer an important control mechanism. Inside a pressurizer there are heaters and a shower. From their actuation levels, they control the vapor pressure inside the pressurizer and, consequently, inside the primary circuit. Therefore, the control of the pressurizer consists in controlling the actuation levels of the heaters and of the shower. In the present work this function is implemented through a fuzzy controller. Besides the efficient way of exerting control, this approach presents the possibility of extracting knowledge of how this control is been made. A fuzzy controller consists basically in an inference machine and a rule base, the later been constructed with specialized knowledge. In some circumstances, however, this knowledge is not accurate, and may lead to non-efficient results. With the development of artificial intelligence techniques, there wore found methods to substitute specialists, simulating its knowledge. Genetic programming is an evolutionary algorithm particularly efficient in manipulating rule base structures. In this work genetic programming was used as a substitute for the specialist. The goal is to test if an irrational object, a computer, is capable, by it self, to find out a rule base reproducing a pre-established actuation levels profile. The result is positive, with the discovery of a fuzzy rule base presenting an insignificant error. A remarkable result that proves the efficiency of the approach. (author)

  5. Genetic approaches to understanding post-traumatic stress disorder

    Science.gov (United States)

    Almli, Lynn M.; Fani, Negar; Smith, Alicia K.; Ressler, Kerry J.

    2015-01-01

    Post-traumatic stress disorder (PTSD) is increasingly recognized as both a disorder of enormous mental health and societal burden, but also as an anxiety disorder that may be particularly understandable from a scientific perspective. Specifically, PTSD can be conceptualized as a disorder of fear and stress dysregulation, and the neural circuitry underlying these pathways in both animals and humans are becoming increasingly well understood. Furthermore, PTSD is the only disorder in psychiatry in which the initiating factor, the trauma exposure, can be identified. Thus, the pathophysiology of the fear and stress response underlying PTSD can be examined and potentially interrupted. Twin studies have shown that the development of PTSD following a trauma is heritable, and that genetic risk factors may account for up to 30–40% of this heritability. A current goal is to understand the gene pathways that are associated with PTSD, and how those genes act on the fear/stress circuitry to mediate risk vs. resilience for PTSD. This review will examine gene pathways that have recently been analysed, primarily through candidate gene studies (including neuroimaging studies of candidate genes), in addition to genome-wide associations and the epigenetic regulation of PTSD. Future and on-going studies are utilizing larger and collaborative cohorts to identify novel gene candidates through genome-wide association and other powerful genomic approaches. Identification of PTSD biological pathways strengthens the hope of progress in the mechanistic understanding of a model psychiatric disorder and allows for the development of targeted treatments and interventions. PMID:24103155

  6. Genetic approaches to understanding post-traumatic stress disorder.

    Science.gov (United States)

    Almli, Lynn M; Fani, Negar; Smith, Alicia K; Ressler, Kerry J

    2014-02-01

    Post-traumatic stress disorder (PTSD) is increasingly recognized as both a disorder of enormous mental health and societal burden, but also as an anxiety disorder that may be particularly understandable from a scientific perspective. Specifically, PTSD can be conceptualized as a disorder of fear and stress dysregulation, and the neural circuitry underlying these pathways in both animals and humans are becoming increasingly well understood. Furthermore, PTSD is the only disorder in psychiatry in which the initiating factor, the trauma exposure, can be identified. Thus, the pathophysiology of the fear and stress response underlying PTSD can be examined and potentially interrupted. Twin studies have shown that the development of PTSD following a trauma is heritable, and that genetic risk factors may account for up to 30-40% of this heritability. A current goal is to understand the gene pathways that are associated with PTSD, and how those genes act on the fear/stress circuitry to mediate risk vs. resilience for PTSD. This review will examine gene pathways that have recently been analysed, primarily through candidate gene studies (including neuroimaging studies of candidate genes), in addition to genome-wide associations and the epigenetic regulation of PTSD. Future and on-going studies are utilizing larger and collaborative cohorts to identify novel gene candidates through genome-wide association and other powerful genomic approaches. Identification of PTSD biological pathways strengthens the hope of progress in the mechanistic understanding of a model psychiatric disorder and allows for the development of targeted treatments and interventions.

  7. An MDE Approach for Modular Program Analyses

    NARCIS (Netherlands)

    Yildiz, Bugra Mehmet; Bockisch, Christoph; Aksit, Mehmet; Rensink, Arend

    Program analyses are an important tool to check if a system fulfills its specification. A typical implementation strategy for program analyses is to use an imperative, general-purpose language like Java, and access the program to be analyzed through libraries that offer an API for reading, writing

  8. 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

  9. 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…

  10. 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.

  11. Swarm, genetic and evolutionary programming algorithms applied to multiuser detection

    Directory of Open Access Journals (Sweden)

    Paul Jean Etienne Jeszensky

    2005-02-01

    Full Text Available In this paper, the particles swarm optimization technique, recently published in the literature, and applied to Direct Sequence/Code Division Multiple Access systems (DS/CDMA with multiuser detection (MuD is analyzed, evaluated and compared. The Swarm algorithm efficiency when applied to the DS-CDMA multiuser detection (Swarm-MuD is compared through the tradeoff performance versus computational complexity, being the complexity expressed in terms of the number of necessary operations in order to reach the performance obtained through the optimum detector or the Maximum Likelihood detector (ML. The comparison is accomplished among the genetic algorithm, evolutionary programming with cloning and Swarm algorithm under the same simulation basis. Additionally, it is proposed an heuristics-MuD complexity analysis through the number of computational operations. Finally, an analysis is carried out for the input parameters of the Swarm algorithm in the attempt to find the optimum parameters (or almost-optimum for the algorithm applied to the MuD problem.

  12. 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....

  13. 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.

  14. Modelling Autistic Features in Mice Using Quantitative Genetic Approaches

    NARCIS (Netherlands)

    Molenhuis, Remco T; Bruining, Hilgo; Kas, Martien J

    2017-01-01

    Animal studies provide a unique opportunity to study the consequences of genetic variants at the behavioural level. Human studies have identified hundreds of risk genes for autism spectrum disorder (ASD) that can lead to understanding on how genetic variation contributes to individual differences in

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

    Science.gov (United States)

    Chaix, Marie A; Andelfinger, Gregor; Khairy, Paul

    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 follow-up. 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. PMID:26981213

  16. Ethical genetic research in Indigenous communities: challenges and successful approaches.

    Science.gov (United States)

    McWhirter, Rebekah E; Mununggirritj, Djapirri; Marika, Dipililnga; Dickinson, Joanne L; Condon, John R

    2012-12-01

    Indigenous populations, in common with all populations, stand to benefit from the potential of genetic research to lead to improvements in diagnostic and therapeutic tools for a wide range of complex diseases. However, many Indigenous communities, especially ones that are isolated, are not included in genetic research efforts. This situation is largely a consequence of the challenges of ethically conducting genetic research in Indigenous communities and compounded by Indigenous peoples' negative past experiences with genetic issues. To examine ways of addressing these challenges, we review one investigation of a cancer cluster in remote Aboriginal communities in Arnhem Land, Australia. Our experiences demonstrate that genetic research can be both ethically and successfully conducted with Indigenous communities by respecting the authority of the community, involving community members, and including regular community review throughout the research process. Copyright © 2012 Elsevier Ltd. All rights reserved.

  17. Multi-objective genetic algorithm for solving N-version program design problem

    International Nuclear Information System (INIS)

    Yamachi, Hidemi; Tsujimura, Yasuhiro; Kambayashi, Yasushi; Yamamoto, Hisashi

    2006-01-01

    N-version programming (NVP) is a programming approach for constructing fault tolerant software systems. Generally, an optimization model utilized in NVP selects the optimal set of versions for each module to maximize the system reliability and to constrain the total cost to remain within a given budget. In such a model, while the number of versions included in the obtained solution is generally reduced, the budget restriction may be so rigid that it may fail to find the optimal solution. In order to ameliorate this problem, this paper proposes a novel bi-objective optimization model that maximizes the system reliability and minimizes the system total cost for designing N-version software systems. When solving multi-objective optimization problem, it is crucial to find Pareto solutions. It is, however, not easy to obtain them. In this paper, we propose a novel bi-objective optimization model that obtains many Pareto solutions efficiently. We formulate the optimal design problem of NVP as a bi-objective 0-1 nonlinear integer programming problem. In order to overcome this problem, we propose a Multi-objective genetic algorithm (MOGA), which is a powerful, though time-consuming, method to solve multi-objective optimization problems. When implementing genetic algorithm (GA), the use of an appropriate genetic representation scheme is one of the most important issues to obtain good performance. We employ random-key representation in our MOGA to find many Pareto solutions spaced as evenly as possible along the Pareto frontier. To pursue improve further performance, we introduce elitism, the Pareto-insertion and the Pareto-deletion operations based on distance between Pareto solutions in the selection process. The proposed MOGA obtains many Pareto solutions along the Pareto frontier evenly. The user of the MOGA can select the best compromise solution among the candidates by controlling the balance between the system reliability and the total cost

  18. Multi-objective genetic algorithm for solving N-version program design problem

    Energy Technology Data Exchange (ETDEWEB)

    Yamachi, Hidemi [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan) and Department of Production and Information Systems Engineering, Tokyo Metropolitan Institute of Technology, Hino, Tokyo 191-0065 (Japan)]. E-mail: yamachi@nit.ac.jp; Tsujimura, Yasuhiro [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan)]. E-mail: tujimr@nit.ac.jp; Kambayashi, Yasushi [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan)]. E-mail: yasushi@nit.ac.jp; Yamamoto, Hisashi [Department of Production and Information Systems Engineering, Tokyo Metropolitan Institute of Technology, Hino, Tokyo 191-0065 (Japan)]. E-mail: yamamoto@cc.tmit.ac.jp

    2006-09-15

    N-version programming (NVP) is a programming approach for constructing fault tolerant software systems. Generally, an optimization model utilized in NVP selects the optimal set of versions for each module to maximize the system reliability and to constrain the total cost to remain within a given budget. In such a model, while the number of versions included in the obtained solution is generally reduced, the budget restriction may be so rigid that it may fail to find the optimal solution. In order to ameliorate this problem, this paper proposes a novel bi-objective optimization model that maximizes the system reliability and minimizes the system total cost for designing N-version software systems. When solving multi-objective optimization problem, it is crucial to find Pareto solutions. It is, however, not easy to obtain them. In this paper, we propose a novel bi-objective optimization model that obtains many Pareto solutions efficiently. We formulate the optimal design problem of NVP as a bi-objective 0-1 nonlinear integer programming problem. In order to overcome this problem, we propose a Multi-objective genetic algorithm (MOGA), which is a powerful, though time-consuming, method to solve multi-objective optimization problems. When implementing genetic algorithm (GA), the use of an appropriate genetic representation scheme is one of the most important issues to obtain good performance. We employ random-key representation in our MOGA to find many Pareto solutions spaced as evenly as possible along the Pareto frontier. To pursue improve further performance, we introduce elitism, the Pareto-insertion and the Pareto-deletion operations based on distance between Pareto solutions in the selection process. The proposed MOGA obtains many Pareto solutions along the Pareto frontier evenly. The user of the MOGA can select the best compromise solution among the candidates by controlling the balance between the system reliability and the total cost.

  19. 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.…

  20. Stigmatization of carrier status: social implications of heterozygote genetic screening programs.

    Science.gov (United States)

    Kenen, R H; Schmidt, R M

    1978-01-01

    Possible latent psychological and social consequences ensuing from genetic screening programs need to be investigated during the planning phase of national genetic screening programs. The relatively few studies which have been performed to determine psychological, social, and economic consequences resulting from a genetic screening program are reviewed. Stigmatization of carrier-status, having major psychosocial implications in heterozygote genetic screening programs, is discussed and related to Erving Goffman's work in the area of stigmatization. Questions are raised regarding the relationship between such variables as religiosity and sex of the individual and acceptance of the status of newly identified carrier of a mutant gene. Severity of the deleterious gene and visibility of the carrier status are two important factors to consider in an estimation of potential stigma. Specific implications are discussed for four genetic diseases: Tay-Sachs, Sickle-Cell Anemia, Huntington's disease and Hemophilia. PMID:152585

  1. 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.

  2. 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...

  3. What next for preimplantation genetic screening? A polar body approach!

    NARCIS (Netherlands)

    Geraedts, Joep; Collins, John; Gianaroli, Luca; Goossens, Veerle; Handyside, Alan; Harper, Joyce; Montag, Markus; Repping, Sjoerd; Schmutzler, Andreas

    2010-01-01

    Screening of human preimplantation embryos for numerical chromosome abnormalities has been conducted mostly at the preimplantation stage using fluorescence in situ hybridization. However, it is clear that preimplantation genetic screening (PGS) as it is currently practiced does not improve live

  4. 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

  5. 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

  6. 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.

  7. Amount of Genetics Education is Low Among Didactic Programs in Dietetics.

    Science.gov (United States)

    Beretich, Kaitlan; Pope, Janet; Erickson, Dawn; Kennedy, Angela

    2017-01-01

    Nutritional genomics is a growing area of research. Research has shown registered dietitian nutritionists (RDNs) have limited knowledge of genetics. Limited research is available regarding how didactic programs in dietetics (DPDs) meet the genetics knowledge requirement of the Accreditation Council for Education in Nutrition and Dietetics (ACEND®). The purpose of this study was to determine the extent to which the study of nutritional genomics is incorporated into undergraduate DPDs in response to the Academy of Nutrition and Dietetics position statement on nutritional genomics. The sample included 62 DPD directors in the U.S. Most programs (63.9%) reported the ACEND genetics knowledge requirement was being met by integrating genetic information into the current curriculum. However, 88.7% of programs reported devoting only 1-10 clock hours to genetics education. While 60.3% of directors surveyed reported they were confident in their program's ability to teach information related to genetics, only 6 directors reported having specialized training in genetics. The overall amount of clock hours devoted to genetics education is low. DPD directors, faculty, and instructors are not adequately trained to provide this education to students enrolled in DPDs. Therefore, the primary recommendation of this study is the development of a standardized curriculum for genetics education in DPDs.

  8. Genetic algorithm and neural network hybrid approach for job-shop scheduling

    OpenAIRE

    Zhao, Kai; Yang, Shengxiang; Wang, Dingwei

    1998-01-01

    Copyright @ 1998 ACTA Press This paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate for searching optimal solutions, CSANN is used to obtain feasible solutions during the iteration of genetic algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop scheduling with respect to the quality of solutions and ...

  9. Improving feature ranking for biomarker discovery in proteomics mass spectrometry data using genetic programming

    Science.gov (United States)

    Ahmed, Soha; Zhang, Mengjie; Peng, Lifeng

    2014-07-01

    Feature selection on mass spectrometry (MS) data is essential for improving classification performance and biomarker discovery. The number of MS samples is typically very small compared with the high dimensionality of the samples, which makes the problem of biomarker discovery very hard. In this paper, we propose the use of genetic programming for biomarker detection and classification of MS data. The proposed approach is composed of two phases: in the first phase, feature selection and ranking are performed. In the second phase, classification is performed. The results show that the proposed method can achieve better classification performance and biomarker detection rate than the information gain- (IG) based and the RELIEF feature selection methods. Meanwhile, four classifiers, Naive Bayes, J48 decision tree, random forest and support vector machines, are also used to further test the performance of the top ranked features. The results show that the four classifiers using the top ranked features from the proposed method achieve better performance than the IG and the RELIEF methods. Furthermore, GP also outperforms a genetic algorithm approach on most of the used data sets.

  10. 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. 2009 Wiley-Liss, Inc.

  11. 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.

  12. 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.

  13. PSHED: a simplified approach to developing parallel programs

    International Nuclear Information System (INIS)

    Mahajan, S.M.; Ramesh, K.; Rajesh, K.; Somani, A.; Goel, M.

    1992-01-01

    This paper presents a simplified approach in the forms of a tree structured computational model for parallel application programs. An attempt is made to provide a standard user interface to execute programs on BARC Parallel Processing System (BPPS), a scalable distributed memory multiprocessor. The interface package called PSHED provides a basic framework for representing and executing parallel programs on different parallel architectures. The PSHED package incorporates concepts from a broad range of previous research in programming environments and parallel computations. (author). 6 refs

  14. 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...

  15. A Practical Approach to Program Evaluation.

    Science.gov (United States)

    Lee, Linda J.; Sampson, John F.

    1990-01-01

    The Research and Evaluation Support Services Unit of the New South Wales (Australia) Department of Education conducts program evaluations to provide information to senior management for decision making. The 10-step system used is described, which provides for planning, evaluation, and staff development. (TJH)

  16. Fuzzy linear programming approach for solving transportation

    Indian Academy of Sciences (India)

    Transportation problem (TP) is an important network structured linear programming problem that arises in several contexts and has deservedly received a great deal of attention in the literature. The central concept in this problem is to find the least total transportation cost of a commodity in order to satisfy demands at ...

  17. Genes, Culture and Conservatism-A Psychometric-Genetic Approach.

    Science.gov (United States)

    Schwabe, Inga; Jonker, Wilfried; van den Berg, Stéphanie M

    2016-07-01

    The Wilson-Patterson conservatism scale was psychometrically evaluated using homogeneity analysis and item response theory models. Results showed that this scale actually measures two different aspects in people: on the one hand people vary in their agreement with either conservative or liberal catch-phrases and on the other hand people vary in their use of the "?" response category of the scale. A 9-item subscale was constructed, consisting of items that seemed to measure liberalism, and this subscale was subsequently used in a biometric analysis including genotype-environment interaction, correcting for non-homogeneous measurement error. Biometric results showed significant genetic and shared environmental influences, and significant genotype-environment interaction effects, suggesting that individuals with a genetic predisposition for conservatism show more non-shared variance but less shared variance than individuals with a genetic predisposition for liberalism.

  18. Genetic algorithm approach to thin film optical parameters determination

    International Nuclear Information System (INIS)

    Jurecka, S.; Jureckova, M.; Muellerova, J.

    2003-01-01

    Optical parameters of thin film are important for several optical and optoelectronic applications. In this work the genetic algorithm proposed to solve optical parameters of thin film values. The experimental reflectance is modelled by the Forouhi - Bloomer dispersion relations. The refractive index, the extinction coefficient and the film thickness are the unknown parameters in this model. Genetic algorithm use probabilistic examination of promissing areas of the parameter space. It creates a population of solutions based on the reflectance model and then operates on the population to evolve the best solution by using selection, crossover and mutation operators on the population individuals. The implementation of genetic algorithm method and the experimental results are described too (Authors)

  19. Improved Genetic and Simulating Annealing Algorithms to Solve the Traveling Salesman Problem Using Constraint Programming

    Directory of Open Access Journals (Sweden)

    M. Abdul-Niby

    2016-04-01

    Full Text Available The Traveling Salesman Problem (TSP is an integer programming problem that falls into the category of NP-Hard problems. As the problem become larger, there is no guarantee that optimal tours will be found within reasonable computation time. Heuristics techniques, like genetic algorithm and simulating annealing, can solve TSP instances with different levels of accuracy. Choosing which algorithm to use in order to get a best solution is still considered as a hard choice. This paper suggests domain reduction as a tool to be combined with any meta-heuristic so that the obtained results will be almost the same. The hybrid approach of combining domain reduction with any meta-heuristic encountered the challenge of choosing an algorithm that matches the TSP instance in order to get the best results.

  20. 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.

  1. 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.

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

    African Journals Online (AJOL)

    Genetically modified (GM) crops gained attention in southern Africa in the context of broader debates about the struggle for food security and poverty alleviation to achieve sustainable development. The prospects of GM crops as a technological innovation have provoked numerous debates and environmental concern ...

  3. Genetic bases of the nutritional approach to migraine.

    Science.gov (United States)

    De Marchis, Maria Laura; Guadagni, Fiorella; Silvestris, Erica; Lovero, Domenica; Della-Morte, David; Ferroni, Patrizia; Barbanti, Piero; Palmirotta, Raffaele

    2018-03-08

    Migraine is a common multifactorial and polygenic neurological disabling disorder characterized by a genetic background and associated to environmental, hormonal and food stimulations. A large series of evidence suggest a strong correlation between nutrition and migraine and indicates several commonly foods, food additives and beverages that may be involved in the mechanisms triggering the headache attack in migraine-susceptible persons. There are foods and drinks, or ingredients of the same, that can trigger the migraine crisis as well as some foods play a protective function depending on the specific genetic sensitivity of the subject. The recent biotechnological advances have enhanced the identification of some genetic factors involved in onset diseases and the identification of sequence variants of genes responsible for the individual sensitivity to migraine trigger-foods. Therefore many studies are aimed at the analysis of polymorphisms of genes coding for the enzymes involved in the metabolism of food factors in order to clarify the different ways in which people respond to foods based on their genetic constitution. This review discusses the latest knowledge and scientific evidence of the role of gene variants and nutrients, food additives and nutraceuticals interactions in migraine.

  4. 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

  5. 1 Hierarchical Approaches to the Analysis of Genetic Diversity in ...

    African Journals Online (AJOL)

    2015-04-14

    Apr 14, 2015 ... Study of genetic diversity is the process by which variation ... diversity tree is available, a core collection can be selected by .... are numerous natural factors, such as hybridization and clinical .... to develop a deeper understanding of the topic .... This can be detected by southern hybridization after running ...

  6. Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks

    International Nuclear Information System (INIS)

    Zameer, Aneela; Arshad, Junaid; Khan, Asifullah; Raja, Muhammad Asif Zahoor

    2017-01-01

    Highlights: • Genetic programming based ensemble of neural networks is employed for short term wind power prediction. • Proposed predictor shows resilience against abrupt changes in weather. • Genetic programming evolves nonlinear mapping between meteorological measures and wind-power. • Proposed approach gives mathematical expressions of wind power to its independent variables. • Proposed model shows relatively accurate and steady wind-power prediction performance. - Abstract: The inherent instability of wind power production leads to critical problems for smooth power generation from wind turbines, which then requires an accurate forecast of wind power. In this study, an effective short term wind power prediction methodology is presented, which uses an intelligent ensemble regressor that comprises Artificial Neural Networks and Genetic Programming. In contrast to existing series based combination of wind power predictors, whereby the error or variation in the leading predictor is propagated down the stream to the next predictors, the proposed intelligent ensemble predictor avoids this shortcoming by introducing Genetical Programming based semi-stochastic combination of neural networks. It is observed that the decision of the individual base regressors may vary due to the frequent and inherent fluctuations in the atmospheric conditions and thus meteorological properties. The novelty of the reported work lies in creating ensemble to generate an intelligent, collective and robust decision space and thereby avoiding large errors due to the sensitivity of the individual wind predictors. The proposed ensemble based regressor, Genetic Programming based ensemble of Artificial Neural Networks, has been implemented and tested on data taken from five different wind farms located in Europe. Obtained numerical results of the proposed model in terms of various error measures are compared with the recent artificial intelligence based strategies to demonstrate the

  7. 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

  8. 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…

  9. Safety of genetically engineered foods: approaches to assessing unintended health effects

    National Research Council Canada - National Science Library

    Committee on Identifying and Assessing Unintended Effects of Genetically Engineered Foods on Human Health, National Research Council

    2004-01-01

    .... It identifies and recommends several pre- and post-market approaches to guide the assessment of unintended compositional changes that could result from genetically modified foods and research avenues...

  10. Dynamic programming approach to optimization of approximate decision rules

    KAUST Repository

    Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

    2013-01-01

    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

  11. Dynamic Programming Approach for Exact Decision Rule Optimization

    KAUST Repository

    Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

    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

  12. 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. © 2013 ISA. Published by ISA. All rights reserved.

  13. 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. © 2016 The International Union of Biochemistry and Molecular Biology.

  14. An Approach for Solving Linear Fractional Programming Problems

    OpenAIRE

    Andrew Oyakhobo Odior

    2012-01-01

    Linear fractional programming problems are useful tools in production planning, financial and corporate planning, health care and hospital planning and as such have attracted considerable research interest. The paper presents a new approach for solving a fractional linear programming problem in which the objective function is a linear fractional function, while the constraint functions are in the form of linear inequalities. The approach adopted is based mainly upon solving the problem algebr...

  15. An Improved Dynamic Programming Decomposition Approach for Network Revenue Management

    OpenAIRE

    Dan Zhang

    2011-01-01

    We consider a nonlinear nonseparable functional approximation to the value function of a dynamic programming formulation for the network revenue management (RM) problem with customer choice. We propose a simultaneous dynamic programming approach to solve the resulting problem, which is a nonlinear optimization problem with nonlinear constraints. We show that our approximation leads to a tighter upper bound on optimal expected revenue than some known bounds in the literature. Our approach can ...

  16. 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.

  17. 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.

  18. 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...

  19. A cutting- plane approach for semi- infinite mathematical programming

    African Journals Online (AJOL)

    Many situations ranging from industrial to social via economic and environmental problems may be cast into a Semi-infinite mathematical program. In this paper, the cutting-plane approach which lends itself better for standard non-linear programs is exploited with good reasons for grappling with linear, convex and ...

  20. Genetic counseling for schizophrenia: a review of referrals to a provincial medical genetics program from 1968–2007

    Science.gov (United States)

    Hunter, MJ; Hippman, Catriona; Honer, William G; Austin, Jehannine C.

    2014-01-01

    Purpose Recent studies have shown that individuals with schizophrenia and their family members are interested in genetic counseling, but few have received this service. We conducted an exploratory, retrospective study to describe (a) the population of individuals who were referred to the provincial program for genetic counseling for a primary indication of schizophrenia, and (b) trends in number of referrals between 1968 and 2007. Methods Referrals for a primary indication of schizophrenia were identified through the provincial program database. Charts were reviewed and the following information was recorded: discipline of referring physician, demographics, psychiatric diagnosis, referred individual’s and partner’s (if applicable) family history, and any current pregnancy history. Data were characterized using descriptive statistics. Results Between 1968 and 2007, 288 referrals were made for a primary indication of schizophrenia. Most referrals were made: (a) for individuals who had a first-degree family member with schizophrenia, rather than for affected individuals, (b) for preconception counseling, and (c) by family physicians (69%), with only 2% by psychiatrists. Conclusions In British Columbia, individuals affected with schizophrenia and their family members are rarely referred for psychiatric genetic counseling. There is a need to identify barriers to psychiatric genetic counseling and develop strategies to improve access. PMID:20034078

  1. 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. PMID:21283502

  2. 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.

  3. 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

  4. A New Approach to Tuning Heuristic Parameters of Genetic Algorithms

    Czech Academy of Sciences Publication Activity Database

    Holeňa, Martin

    2006-01-01

    Roč. 3, č. 3 (2006), s. 562-569 ISSN 1790-0832. [AIKED'06. WSEAS International Conference on Artificial Intelligence , Knowledge Engineering and Data Bases. Madrid, 15.02.2006-17.02.2006] R&D Projects: GA ČR(CZ) GA201/05/0325; GA ČR(CZ) GA201/05/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : evolutionary optimization * genetic algorithms * heuristic parameters * parameter tuning * artificial neural networks * convergence speed * population diversity Subject RIV: IN - Informatics, Computer Science

  5. Theorising Masculinities and Crime: A Genetic-Social Approach

    OpenAIRE

    Owen, Timothy

    2012-01-01

    This paper examines competing notions of ‘masculinities’ in relation to crime, and the global nature of gendered inequalities. It is the contention here that social constructionist theories of male sexualities contain certain theoretical deficits. It is suggested that a post-Postmodern analysis of ‘masculinities’ might incorporate some of the insights from Owen’s Genetic-Social meta-theoretical framework. Owen’s ‘sensitising’ framework has been ‘applied’ to the sociological study of human bio...

  6. Molecular approaches for genetic improvement of seed quality and characterization of genetic diversity in soybean: a critical review.

    Science.gov (United States)

    Tripathi, Niraj; Khare, Dhirendra

    2016-10-01

    Soybean is an economically important leguminous crop. Genetic improvements of soybeans have focused on enhancement of seed and oil yield, development of varieties suited to different cropping systems, and breeding resistant/tolerant varieties for various biotic and abiotic stresses. Plant breeders have used conventional breeding techniques for the improvement of these traits in soybean. The conventional breeding process can be greatly accelerated through the application of molecular and genomic approaches. Molecular markers have proved to be a new tool in soybean breeding by enhancing selection efficiency in a rapid and time-bound manner. An overview of molecular approaches for the genetic improvement of soybean seed quality parameters, considering recent applications of marker-assisted selection and 'omics' research, is provided in this article.

  7. Multifaceted Approach to Designing an Online Masters Program.

    Science.gov (United States)

    McNeil, Sara G.; Chernish, William N.; DeFranco, Agnes L.

    At the Conrad N. Hilton College of Hotel and Restaurant Management at the University of Houston (Texas), the faculty and administrators made a conscious effort to take a broad, extensive approach to designing and implementing a fully online masters program. This approach was entered in a comprehensive needs assessment model and sought input from…

  8. 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

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

    African Journals Online (AJOL)

    The paper presents a new approach for solving a fractional linear programming problem in which the objective function is a linear fractional function, while the constraint functions are in the form of linear inequalities. The approach adopted is based mainly upon solving the problem algebraically using the concept of duality ...

  10. 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

  11. 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.

  12. Routine human-competitive machine intelligence by means of genetic programming

    Science.gov (United States)

    Koza, John R.; Streeter, Matthew J.; Keane, Martin

    2004-01-01

    Genetic programming is a systematic method for getting computers to automatically solve a problem. Genetic programming starts from a high-level statement of what needs to be done and automatically creates a computer program to solve the problem. The paper demonstrates that genetic programming (1) now routinely delivers high-return human-competitive machine intelligence; (2) is an automated invention machine; (3) can automatically create a general solution to a problem in the form of a parameterized topology; and (4) has delivered a progression of qualitatively more substantial results in synchrony with five approximately order-of-magnitude increases in the expenditure of computer time. Recent results involving the automatic synthesis of the topology and sizing of analog electrical circuits and controllers demonstrate these points.

  13. 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

  14. Genetic shifting: a novel approach for controlling vector-borne diseases

    OpenAIRE

    Powell, Jeffrey R.; Tabachnick, Walter J.

    2014-01-01

    Rendering populations of vectors of diseases incapable of transmitting pathogens through genetic methods has long been a goal of vector geneticists. We outline a method to achieve this goal that does not involve introduction of any new genetic variants to the target population. Rather we propose that shifting the frequencies of naturally occurring alleles that confer refractoriness to transmission can reduce transmission below a sustainable level. The program employs methods successfully used...

  15. A genetic-neural artificial intelligence approach to resins optimization

    International Nuclear Information System (INIS)

    Cabral, Denise C.; Barros, Marcio P.; Lapa, Celso M.F.; Pereira, Claudio M.N.A.

    2005-01-01

    This work presents a preliminary study about the viability and adequacy of a new methodology for the definition of one of the main properties of ion exchange resins used for isotopic separation. Basically, the main problem is the definition of pelicule diameter in case of pelicular ion exchange resins, in order to achieve the best performance in the shortest time. In order to achieve this, a methodology was developed, based in two classic techniques of Artificial Intelligence (AI). At first, an artificial neural network (NN) was trained to map the existing relations between the nucleus radius and the resin's efficiency associated with the exchange time. Later on, a genetic algorithm (GA) was developed in order to find the best pelicule dimension. Preliminary results seem to confirm the potential of the method, and this can be used in any chemical process employing ion exchange resins. (author)

  16. 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. Copyright © 2016 Elsevier Inc. All rights reserved.

  17. Genetic Complexity of Episodic Memory: A Twin Approach to Studies of Aging

    Science.gov (United States)

    Kremen, William S.; Spoon, Kelly M.; Jacobson, Kristen C.; Vasilopoulos, Terrie; McCaffery, Jeanne M.; Panizzon, Matthew S.; Franz, Carol E.; Vuoksimaa, Eero; Xian, Hong; Rana, Brinda K.; Toomey, Rosemary; McKenzie, Ruth; Lyons, Michael J.

    2016-01-01

    Episodic memory change is a central issue in cognitive aging, and understanding that process will require elucidation of its genetic underpinnings. A key limiting factor in genetically informed research on memory has been lack of attention to genetic and phenotypic complexity, as if “memory is memory” and all well-validated assessments are essentially equivalent. Here we applied multivariate twin models to data from late-middle-aged participants in the Vietnam Era Twin Study of Aging to examine the genetic architecture of 6 measures from 3 standard neuropsychological tests: the California Verbal Learning Test-2, and Wechsler Memory Scale-III Logical Memory (LM) and Visual Reproductions (VR). An advantage of the twin method is that it can estimate the extent to which latent genetic influences are shared or independent across different measures before knowing which specific genes are involved. The best-fitting model was a higher order common pathways model with a heritable higher order general episodic memory factor and three test-specific subfactors. More importantly, substantial genetic variance was accounted for by genetic influences that were specific to the latent LM and VR subfactors (28% and 30%, respectively) and independent of the general factor. Such unique genetic influences could partially account for replication failures. Moreover, if different genes influence different memory phenotypes, they could well have different age-related trajectories. This approach represents an important step toward providing critical information for all types of genetically informative studies of aging and memory. PMID:24956007

  18. Catecholaminergic systems in stress: structural and molecular genetic approaches.

    Science.gov (United States)

    Kvetnansky, Richard; Sabban, Esther L; Palkovits, Miklos

    2009-04-01

    Stressful stimuli evoke complex endocrine, autonomic, and behavioral responses that are extremely variable and specific depending on the type and nature of the stressors. We first provide a short overview of physiology, biochemistry, and molecular genetics of sympatho-adrenomedullary, sympatho-neural, and brain catecholaminergic systems. Important processes of catecholamine biosynthesis, storage, release, secretion, uptake, reuptake, degradation, and transporters in acutely or chronically stressed organisms are described. We emphasize the structural variability of catecholamine systems and the molecular genetics of enzymes involved in biosynthesis and degradation of catecholamines and transporters. Characterization of enzyme gene promoters, transcriptional and posttranscriptional mechanisms, transcription factors, gene expression and protein translation, as well as different phases of stress-activated transcription and quantitative determination of mRNA levels in stressed organisms are discussed. Data from catecholamine enzyme gene knockout mice are shown. Interaction of catecholaminergic systems with other neurotransmitter and hormonal systems are discussed. We describe the effects of homotypic and heterotypic stressors, adaptation and maladaptation of the organism, and the specificity of stressors (physical, emotional, metabolic, etc.) on activation of catecholaminergic systems at all levels from plasma catecholamines to gene expression of catecholamine enzymes. We also discuss cross-adaptation and the effect of novel heterotypic stressors on organisms adapted to long-term monotypic stressors. The extra-adrenal nonneuronal adrenergic system is described. Stress-related central neuronal regulatory circuits and central organization of responses to various stressors are presented with selected examples of regulatory molecular mechanisms. Data summarized here indicate that catecholaminergic systems are activated in different ways following exposure to distinct

  19. A Genetic Algorithm Approach to the Optimization of a Radioactive Waste Treatment System

    International Nuclear Information System (INIS)

    Yang, Yeongjin; Lee, Kunjai; Koh, Y.; Mun, J.H.; Kim, H.S.

    1998-01-01

    This study is concerned with the applications of goal programming and genetic algorithm techniques to the analysis of management and operational problems in the radioactive waste treatment system (RWTS). A typical RWTS is modeled and solved by goal program and genetic algorithm to study and resolve the effects of conflicting objectives such as cost, limitation of released radioactivity to the environment, equipment utilization and total treatable radioactive waste volume before discharge and disposal. The developed model is validated and verified using actual data obtained from the RWTS at Kyoto University in Japan. The solution by goal programming and genetic algorithm would show the optimal operation point which is to maximize the total treatable radioactive waste volume and minimize the released radioactivity of liquid waste even under the restricted resources. The comparison of two methods shows very similar results. (author)

  20. 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.

  1. Towards programming languages for genetic engineering of living cells.

    Science.gov (United States)

    Pedersen, Michael; Phillips, Andrew

    2009-08-06

    Synthetic biology aims at producing novel biological systems to carry out some desired and well-defined functions. An ultimate dream is to design these systems at a high level of abstraction using engineering-based tools and programming languages, press a button, and have the design translated to DNA sequences that can be synthesized and put to work in living cells. We introduce such a programming language, which allows logical interactions between potentially undetermined proteins and genes to be expressed in a modular manner. Programs can be translated by a compiler into sequences of standard biological parts, a process that relies on logic programming and prototype databases that contain known biological parts and protein interactions. Programs can also be translated to reactions, allowing simulations to be carried out. While current limitations on available data prevent full use of the language in practical applications, the language can be used to develop formal models of synthetic systems, which are otherwise often presented by informal notations. The language can also serve as a concrete proposal on which future language designs can be discussed, and can help to guide the emerging standard of biological parts which so far has focused on biological, rather than logical, properties of parts.

  2. Mining Context-Aware Association Rules Using Grammar-Based Genetic Programming.

    Science.gov (United States)

    Luna, Jose Maria; Pechenizkiy, Mykola; Del Jesus, Maria Jose; Ventura, Sebastian

    2017-09-25

    Real-world data usually comprise features whose interpretation depends on some contextual information. Such contextual-sensitive features and patterns are of high interest to be discovered and analyzed in order to obtain the right meaning. This paper formulates the problem of mining context-aware association rules, which refers to the search for associations between itemsets such that the strength of their implication depends on a contextual feature. For the discovery of this type of associations, a model that restricts the search space and includes syntax constraints by means of a grammar-based genetic programming methodology is proposed. Grammars can be considered as a useful way of introducing subjective knowledge to the pattern mining process as they are highly related to the background knowledge of the user. The performance and usefulness of the proposed approach is examined by considering synthetically generated datasets. A posteriori analysis on different domains is also carried out to demonstrate the utility of this kind of associations. For example, in educational domains, it is essential to identify and understand contextual and context-sensitive factors that affect overall and individual student behavior and performance. The results of the experiments suggest that the approach is feasible and it automatically identifies interesting context-aware associations from real-world datasets.

  3. 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

  4. Speed Bump Detection Using Accelerometric Features: A Genetic Algorithm Approach.

    Science.gov (United States)

    Celaya-Padilla, Jose M; Galván-Tejada, Carlos E; López-Monteagudo, F E; Alonso-González, O; Moreno-Báez, Arturo; Martínez-Torteya, Antonio; Galván-Tejada, Jorge I; Arceo-Olague, Jose G; Luna-García, Huizilopoztli; Gamboa-Rosales, Hamurabi

    2018-02-03

    Among the current challenges of the Smart City, traffic management and maintenance are of utmost importance. Road surface monitoring is currently performed by humans, but the road surface condition is one of the main indicators of road quality, and it may drastically affect fuel consumption and the safety of both drivers and pedestrians. Abnormalities in the road, such as manholes and potholes, can cause accidents when not identified by the drivers. Furthermore, human-induced abnormalities, such as speed bumps, could also cause accidents. In addition, while said obstacles ought to be signalized according to specific road regulation, they are not always correctly labeled. Therefore, we developed a novel method for the detection of road abnormalities (i.e., speed bumps). This method makes use of a gyro, an accelerometer, and a GPS sensor mounted in a car. After having the vehicle cruise through several streets, data is retrieved from the sensors. Then, using a cross-validation strategy, a genetic algorithm is used to find a logistic model that accurately detects road abnormalities. The proposed model had an accuracy of 0.9714 in a blind evaluation, with a false positive rate smaller than 0.018, and an area under the receiver operating characteristic curve of 0.9784. This methodology has the potential to detect speed bumps in quasi real-time conditions, and can be used to construct a real-time surface monitoring system.

  5. 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.

  6. Speed Bump Detection Using Accelerometric Features: A Genetic Algorithm Approach

    Directory of Open Access Journals (Sweden)

    Jose M. Celaya-Padilla

    2018-02-01

    Full Text Available Among the current challenges of the Smart City, traffic management and maintenance are of utmost importance. Road surface monitoring is currently performed by humans, but the road surface condition is one of the main indicators of road quality, and it may drastically affect fuel consumption and the safety of both drivers and pedestrians. Abnormalities in the road, such as manholes and potholes, can cause accidents when not identified by the drivers. Furthermore, human-induced abnormalities, such as speed bumps, could also cause accidents. In addition, while said obstacles ought to be signalized according to specific road regulation, they are not always correctly labeled. Therefore, we developed a novel method for the detection of road abnormalities (i.e., speed bumps. This method makes use of a gyro, an accelerometer, and a GPS sensor mounted in a car. After having the vehicle cruise through several streets, data is retrieved from the sensors. Then, using a cross-validation strategy, a genetic algorithm is used to find a logistic model that accurately detects road abnormalities. The proposed model had an accuracy of 0.9714 in a blind evaluation, with a false positive rate smaller than 0.018, and an area under the receiver operating characteristic curve of 0.9784. This methodology has the potential to detect speed bumps in quasi real-time conditions, and can be used to construct a real-time surface monitoring system.

  7. 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 ...

  8. A functional data analysis approach for genetic association studies

    OpenAIRE

    Reimherr, Matthew; Nicolae, Dan

    2014-01-01

    We present a new method based on Functional Data Analysis (FDA) for detecting associations between one or more scalar covariates and a longitudinal response, while correcting for other variables. Our methods exploit the temporal structure of longitudinal data in ways that are otherwise difficult with a multivariate approach. Our procedure, from an FDA perspective, is a departure from more established methods in two key aspects. First, the raw longitudinal phenotypes are assembled into functio...

  9. 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.

  10. A stochastic programming approach to manufacturing flow control

    OpenAIRE

    Haurie, Alain; Moresino, Francesco

    2012-01-01

    This paper proposes and tests an approximation of the solution of a class of piecewise deterministic control problems, typically used in the modeling of manufacturing flow processes. This approximation uses a stochastic programming approach on a suitably discretized and sampled system. The method proceeds through two stages: (i) the Hamilton-Jacobi-Bellman (HJB) dynamic programming equations for the finite horizon continuous time stochastic control problem are discretized over a set of sample...

  11. 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...

  12. Dynamic Programming Approaches for the Traveling Salesman Problem with Drone

    OpenAIRE

    Bouman, Paul; Agatz, Niels; Schmidt, Marie

    2017-01-01

    markdownabstractA promising new delivery model involves the use of a delivery truck that collaborates with a drone to make deliveries. Effectively combining a drone and a truck gives rise to a new planning problem that is known as the Traveling Salesman Problem with Drone (TSP-D). This paper presents an exact solution approach for the TSP-D based on dynamic programming and present experimental results of different dynamic programming based heuristics. Our numerical experiments show that our a...

  13. 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.

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

    OpenAIRE

    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 ...

  15. Genetic shifting: a novel approach for controlling vector-borne diseases.

    Science.gov (United States)

    Powell, Jeffrey R; Tabachnick, Walter J

    2014-06-01

    Rendering populations of vectors of diseases incapable of transmitting pathogens through genetic methods has long been a goal of vector geneticists. We outline a method to achieve this goal that does not involve the introduction of any new genetic variants to the target population. Rather we propose that shifting the frequencies of naturally occurring alleles that confer refractoriness to transmission can reduce transmission below a sustainable level. The program employs methods successfully used in plant and animal breeding. Because no artificially constructed genetically modified organisms (GMOs) are introduced into the environment, the method is minimally controversial. We use Aedes aegypti and dengue virus (DENV) for illustrative purposes but point out that the proposed program is generally applicable to vector-borne disease control. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Automated Feature Design for Time Series Classification by Genetic Programming

    OpenAIRE

    Harvey, Dustin Yewell

    2014-01-01

    Time series classification (TSC) methods discover and exploit patterns in time series and other one-dimensional signals. Although many accurate, robust classifiers exist for multivariate feature sets, general approaches are needed to extend machine learning techniques to make use of signal inputs. Numerous applications of TSC can be found in structural engineering, especially in the areas of structural health monitoring and non-destructive evaluation. Additionally, the fields of process contr...

  17. 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

  18. 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.

  19. 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.

  20. Comparison of weighting approaches for genetic risk scores in gene-environment interaction studies.

    Science.gov (United States)

    Hüls, Anke; Krämer, Ursula; Carlsten, Christopher; Schikowski, Tamara; Ickstadt, Katja; Schwender, Holger

    2017-12-16

    Weighted genetic risk scores (GRS), defined as weighted sums of risk alleles of single nucleotide polymorphisms (SNPs), are statistically powerful for detection gene-environment (GxE) interactions. To assign weights, the gold standard is to use external weights from an independent study. However, appropriate external weights are not always available. In such situations and in the presence of predominant marginal genetic effects, we have shown in a previous study that GRS with internal weights from marginal genetic effects ("GRS-marginal-internal") are a powerful and reliable alternative to single SNP approaches or the use of unweighted GRS. However, this approach might not be appropriate for detecting predominant interactions, i.e. interactions showing an effect stronger than the marginal genetic effect. In this paper, we present a weighting approach for such predominant interactions ("GRS-interaction-training") in which parts of the data are used to estimate the weights from the interaction terms and the remaining data are used to determine the GRS. We conducted a simulation study for the detection of GxE interactions in which we evaluated power, type I error and sign-misspecification. We compared this new weighting approach to the GRS-marginal-internal approach and to GRS with external weights. Our simulation study showed that in the absence of external weights and with predominant interaction effects, the highest power was reached with the GRS-interaction-training approach. If marginal genetic effects were predominant, the GRS-marginal-internal approach was more appropriate. Furthermore, the power to detect interactions reached by the GRS-interaction-training approach was only slightly lower than the power achieved by GRS with external weights. The power of the GRS-interaction-training approach was confirmed in a real data application to the Traffic, Asthma and Genetics (TAG) Study (N = 4465 observations). When appropriate external weights are unavailable, we

  1. 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.

  2. Population-genetic approach to standardization of radiation and non-radiation factors

    International Nuclear Information System (INIS)

    Telnov, I.

    2006-01-01

    Numerous studies demonstrate the importance of genetic predisposition in the development of wide range of pathologies and unfavorable effects caused by different factors. This prompts to account for genetic factors in the risk assessment of unfavorable effects. Current approaches used to solve this problem are far from perfect. On the one hand, recommendations on occupational selection bas ed on genetic signs are presently considered as human rights violation. On the other hand, to medically inform an individual with certain genetic characteristics about possible unfavorable health effects due to occupational hazard has little effect. Finally, a vast number of polymorphic genes in human genome (at least 30%) hampers accounting for all possible factors of genetic predisposition to the increasing number of environmental factors. Therefore, the current situation proves it appropriate to develop the new approach to account for genetic predisposition of individuals that would be free of flaws considered above. A possible basis for such an approach is the assessment of genotype specific relative risk (G.S.R.R.) that accounts for genetic predisposition (susceptibility) of individuals to the effects of unfavorable factors. The study used results from 65 studies. This effort was undertaken to study the association between 32 diseases and unfavorable effects and 17 genetic polymorphic systems. Data analysis included calculation of relative risk (R.R.) of specific diseases or effects development in individuals with different genotypes. Genotype-specific relative risk (G.S.R.R.) of diseases and unfavorable effects in individuals with 'sensitive' genotypes was calculated. Since about the third of genes in human genome are polymorphic, and therefore, a considerable number of genes can be involved in genetic predisposition of an individual to a specific unfavorable effect, an averaged G.S.R.R. of diseases and unfavorable effects was calculated for integral characteristics on

  3. 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.

  4. Linear genetic programming application for successive-station monthly streamflow prediction

    Science.gov (United States)

    Danandeh Mehr, Ali; Kahya, Ercan; Yerdelen, Cahit

    2014-09-01

    In recent decades, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological phenomena. A number of researches have been still comparing these techniques in order to find more effective approaches in terms of accuracy and applicability. In this study, we examined the ability of linear genetic programming (LGP) technique to model successive-station monthly streamflow process, as an applied alternative for streamflow prediction. A comparative efficiency study between LGP and three different artificial neural network algorithms, namely feed forward back propagation (FFBP), generalized regression neural networks (GRNN), and radial basis function (RBF), has also been presented in this study. For this aim, firstly, we put forward six different successive-station monthly streamflow prediction scenarios subjected to training by LGP and FFBP using the field data recorded at two gauging stations on Çoruh River, Turkey. Based on Nash-Sutcliffe and root mean squared error measures, we then compared the efficiency of these techniques and selected the best prediction scenario. Eventually, GRNN and RBF algorithms were utilized to restructure the selected scenario and to compare with corresponding FFBP and LGP. Our results indicated the promising role of LGP for successive-station monthly streamflow prediction providing more accurate results than those of all the ANN algorithms. We found an explicit LGP-based expression evolved by only the basic arithmetic functions as the best prediction model for the river, which uses the records of the both target and upstream stations.

  5. 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.

  6. A Generic multi-dimensional feature extraction method using multiobjective genetic programming.

    Science.gov (United States)

    Zhang, Yang; Rockett, Peter I

    2009-01-01

    In this paper, we present a generic feature extraction method for pattern classification using multiobjective genetic programming. This not only evolves the (near-)optimal set of mappings from a pattern space to a multi-dimensional decision space, but also simultaneously optimizes the dimensionality of that decision space. The presented framework evolves vector-to-vector feature extractors that maximize class separability. We demonstrate the efficacy of our approach by making statistically-founded comparisons with a wide variety of established classifier paradigms over a range of datasets and find that for most of the pairwise comparisons, our evolutionary method delivers statistically smaller misclassification errors. At very worst, our method displays no statistical difference in a few pairwise comparisons with established classifier/dataset combinations; crucially, none of the misclassification results produced by our method is worse than any comparator classifier. Although principally focused on feature extraction, feature selection is also performed as an implicit side effect; we show that both feature extraction and selection are important to the success of our technique. The presented method has the practical consequence of obviating the need to exhaustively evaluate a large family of conventional classifiers when faced with a new pattern recognition problem in order to attain a good classification accuracy.

  7. 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

  8. 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.

  9. Environmental Contamination Genetic Consequences Monitoring on the Former Semipalatinsk Test Site: General Approach

    International Nuclear Information System (INIS)

    Seisebaev, A.T.; Bakhtin, M.M.; Zhapbasov, R.Zh.

    1998-01-01

    genetic monitoring of natural populations of plants and animals and the theoretic approach for their fulfillment. We also consider the main issues of research work on assessment and forecast of the remote genetic consequences of nuclear tests at STS: 1) assessment of the environmental radiation situation; determination of the indicator species of plants and animals and the criteria encompassing the different levels from the molecular one through the genetic to the population one; 2) study of the dose dependence of the genetic effects under the chronic ionizing radiation; 3) analysis of mutation process dynamics in the following generations of population under various exposure condition; 4) study of the possible ways of population adaptation to the chronic impact of various radiation doses; 5) analysis of relation between different genetic changes in exposed population and ecology alterations, etc

  10. Zebrafish Functional Genetics Approach to the Pathogenesis of Well-Differentiated Liposarcoma

    Science.gov (United States)

    2015-12-01

    Roderick JE, LaBelle JL, Bird G, Mathieu R, Bodaar K, Colon D, Pyati U, Stevenson KE, Qi J, Harris M, Silverman LB, Sallan SE, Bradner JL, Neuberg DS...pathogenesis of high-risk T-cell acute lymphoblastic leukemia. Our approach combines human cancer genomics with functional genetics, biochemistry and

  11. Chapter 7. Management strategies for dwarf mistletoes: Biological, chemical, and genetic approaches

    Science.gov (United States)

    S. F. Shamoun; L. E. DeWald

    2002-01-01

    The opportunity and need for management of mistletoe populations with biological, chemical, and genetic approaches are greatest for application to the dwarf mistletoes. Although much information is available on these management strategies (see reviews by Hawksworth 1972, Knutson 1978), significant research and development are still required for these to become...

  12. Report on an Investigation into an Entry Level Clinical Doctorate for the Genetic Counseling Profession and a Survey of the Association of Genetic Counseling Program Directors.

    Science.gov (United States)

    Reiser, Catherine; LeRoy, Bonnie; Grubs, Robin; Walton, Carol

    2015-10-01

    The master's degree is the required entry-level degree for the genetic counseling profession in the US and Canada. In 2012 the Association of Genetic Counseling Program Directors (AGCPD) passed resolutions supporting retention of the master's as the entry-level and terminal degree and opposing introduction of an entry-level clinical doctorate (CD) degree. An AGCPD workgroup surveyed directors of all 34 accredited training programs with the objective of providing the Genetic Counseling Advanced Degrees Task Force (GCADTF) with information regarding potential challenges if master's programs were required to transition to an entry-level CD. Program demographics, projected ability to transition to an entry-level CD, factors influencing ability to transition, and potential effects of transition on programs, students and the genetic counseling workforce were characterized. Two programs would definitely be able to transition, four programs would close, thirteen programs would be at risk to close and fourteen programs would probably be able to transition with varying degrees of difficulty. The most frequently cited limiting factors were economic, stress on clinical sites, and administrative approval of a new degree/program. Student enrollment under an entry-level CD model was projected to decrease by 26.2 %, negatively impacting the workforce pipeline. The results further illuminate and justify AGCPD's position to maintain the master's as the entry-level degree.

  13. The genetic algorithm for the nonlinear programming of water pollution control system

    Energy Technology Data Exchange (ETDEWEB)

    Wei, J.; Zhang, J. [China University of Geosciences (China)

    1999-08-01

    In the programming of water pollution control system the combined method of optimization with simulation is used generally. It is not only laborious in calculation, but also the global optimum of the obtained solution is guaranteed difficult. In this paper, the genetic algorithm (GA) used in the nonlinear programming of water pollution control system is given, by which the preferred conception for the programming of waste water system is found in once-through operation. It is more succinct than the conventional method and the global optimum of the obtained solution could be ensured. 6 refs., 4 figs., 3 tabs.

  14. 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.

  15. [Genetic diagnostics of pathogenic splicing abnormalities in the clinical laboratory--pitfalls and screening approaches].

    Science.gov (United States)

    Niimi, Hideki; Ogawa, Tomomi; Note, Rhougou; Hayashi, Shirou; Ueno, Tomohiro; Harada, Kenu; Uji, Yoshinori; Kitajima, Isao

    2010-12-01

    In recent years, genetic diagnostics of pathogenic splicing abnormalities are increasingly recognized as critically important in the clinical genetic diagnostics. It is reported that approximately 10% of pathogenic mutations causing human inherited diseases are splicing mutations. Nonetheless, it is still difficult to identify splicing abnormalities in routine genetic diagnostic settings. Here, we studied two different kinds of cases with splicing abnormalities. The first case is a protein S deficiency. Nucleotide analyses revealed that the proband had a previously reported G to C substitution in the invariant AG dinucleotide at the splicing acceptor site of intronl/exon2, which produces multiple splicing abnormalities resulting in protein S deficiency. The second case is an antithrombin (AT) deficiency. This proband had a previously reported G to A substitution, at nucleotide position 9788 in intron 4, 14 bp in front of exon 5, which created a de novo exon 5 splice site and resulted in AT deficiency. From a practical standpoint, we discussed the pitfalls, attentions, and screening approaches in genetic diagnostics of pathogenic splicing abnormalities. Due to the difficulty with full-length sequence analysis of introns, and the lack of RNA samples, splicing mutations may escape identification. Although current genetic testing remains to be improved, to screen for splicing abnormalities more efficiently, it is significant to use an appropriate combination of various approaches such as DNA and/or RNA samples, splicing mutation databases, bioinformatic tools to detect splice sites and cis-regulatory elements, and in vitro and/or in vivo experimentally methods as needed.

  16. Arbitrariness is not enough: towards a functional approach to the genetic code.

    Science.gov (United States)

    Lacková, Ľudmila; Matlach, Vladimír; Faltýnek, Dan

    2017-12-01

    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. 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.

  18. 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. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. Implementation of genetic conservation practices in a muskellunge propagation and stocking program

    Science.gov (United States)

    Jennings, Martin J.; Sloss, Brian L.; Hatzenbeler, Gene R.; Kampa, Jeffrey M.; Simonson, Timothy D.; Avelallemant, Steven P.; Lindenberger, Gary A.; Underwood, Bruce D.

    2010-01-01

    Conservation of genetic resources is a challenging issue for agencies managing popular sport fishes. To address the ongoing potential for genetic risks, we developed a comprehensive set of recommendations to conserve genetic diversity of muskellunge (Esox masquinongy) in Wisconsin, and evaluated the extent to which the recommendations can be implemented. Although some details are specific to Wisconsin's muskellunge propagation program, many of the practical issues affecting implementation are applicable to other species and production systems. We developed guidelines to restrict future broodstock collection operations to lakes with natural reproduction and to develop a set of brood lakes to use on a rotational basis within regional stock boundaries, but implementation will require considering lakes with variable stocking histories. Maintaining an effective population size sufficient to minimize the risk of losing alleles requires limiting broodstock collection to large lakes. Recommendations to better approximate the temporal distribution of spawning in hatchery operations and randomize selection of brood fish are feasible. Guidelines to modify rearing and distribution procedures face some logistic constraints. An evaluation of genetic diversity of hatchery-produced fish during 2008 demonstrated variable success representing genetic variation of the source population. Continued evaluation of hatchery operations will optimize operational efficiency while moving toward genetic conservation goals.

  20. 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)

  1. Comparing targeted exome and whole exome approaches for genetic diagnosis of neuromuscular disorders

    Directory of Open Access Journals (Sweden)

    Svetlana Gorokhova

    2015-12-01

    Full Text Available Massively parallel sequencing is rapidly becoming a widely used method in genetic diagnostics. However, there is still no clear consensus as to which approach can most efficiently identify the pathogenic mutations carried by a given patient, while avoiding false negative and false positive results. We developed a targeted exome approach (MyoPanel2 in order to optimize genetic diagnosis of neuromuscular disorders. Using this approach, we were able to analyse 306 genes known to be mutated in myopathies as well as in related disorders, obtaining 98.8% target sequence coverage at 20×. Moreover, MyoPanel2 was able to detect 99.7% of 11,467 known mutations responsible for neuromuscular disorders. We have then used several quality control parameters to compare performance of the targeted exome approach with that of whole exome sequencing. The results of this pilot study of 140 DNA samples suggest that targeted exome sequencing approach is an efficient genetic diagnostic test for most neuromuscular diseases.

  2. A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems

    International Nuclear Information System (INIS)

    Sahoo, N.C.; Prasad, K.

    2006-01-01

    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

  3. Mathematical programming models for solving in equal-sized facilities layout problems. A genetic search method

    International Nuclear Information System (INIS)

    Tavakkoli-Moghaddam, R.

    1999-01-01

    This paper present unequal-sized facilities layout solutions generated by a genetic search program. named Layout Design using a Genetic Algorithm) 9. The generalized quadratic assignment problem requiring pre-determined distance and material flow matrices as the input data and the continuous plane model employing a dynamic distance measure and a material flow matrix are discussed. Computational results on test problems are reported as compared with layout solutions generated by the branch - and bound algorithm a hybrid method merging simulated annealing and local search techniques, and an optimization process of an enveloped block

  4. 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

  5. The integrated approach to teaching programming in secondary school

    Directory of Open Access Journals (Sweden)

    Martynyuk A.A.

    2018-02-01

    Full Text Available the article considers an integrated approach to teaching programming with the use of technologies of computer modeling and 3D-graphics, allowing to improve the quality of education. It is shown that this method will allow you to systematize knowledge, improve the level of motivation through the inclusion of relevant technologies, to develop skills of project activities, to strengthen interdisciplinary connections, and promotes professional and personal self-determination of students of secondary school.

  6. 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.

  7. Dynamic Programming Approach for Construction of Association Rule Systems

    KAUST Repository

    Alsolami, Fawaz; Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

    2016-01-01

    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.

  8. Practical approaches to implementing facility wide equipment strengthening programs

    International Nuclear Information System (INIS)

    Kincaid, R.H.; Smietana, E.A.

    1989-01-01

    Equipment strengthening programs typically focus on components required to ensure operability of safety related equipment or to prevent the release of toxic substances. Survival of non-safety related equipment may also be crucial to ensure rapid recovery and minimize business interruption losses. Implementing a strengthening program for non-safety related equipment can be difficult due to the large amounts of equipment involved and limited budget availability. EQE has successfully implemented comprehensive equipment strengthening programs for a number of California corporations. Many of the lessons learned from these projects are applicable to DOE facilities. These include techniques for prioritizing equipment and three general methodologies for anchoring equipment. Pros and cons of each anchorage approach are presented along with typical equipment strengthening costs

  9. 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.

  10. Constraint Logic Programming approach to protein structure prediction.

    Science.gov (United States)

    Dal Palù, Alessandro; Dovier, Agostino; Fogolari, Federico

    2004-11-30

    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. 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. 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.

  11. On the implicit programming approach in a class of mathematical programs with equilibrium constraints

    Czech Academy of Sciences Publication Activity Database

    Outrata, Jiří; Červinka, Michal

    2009-01-01

    Roč. 38, 4B (2009), s. 1557-1574 ISSN 0324-8569 R&D Projects: GA ČR GA201/09/1957 Institutional research plan: CEZ:AV0Z10750506 Keywords : mathematical problem with equilibrium constraint * state constraints * implicit programming * calmness * exact penalization Subject RIV: BA - General Mathematics Impact factor: 0.378, year: 2009 http://library.utia.cas.cz/separaty/2010/MTR/outrata-on the implicit programming approach in a class of mathematical programs with equilibrium constraints.pdf

  12. Neuro-genetic hybrid approach for the solution of non-convex economic dispatch problem

    International Nuclear Information System (INIS)

    Malik, T.N.; Asar, A.U.

    2009-01-01

    ED (Economic Dispatch) is non-convex constrained optimization problem, and is used for both on line and offline studies in power system operation. Conventionally, it is solved as convex problem using optimization techniques by approximating generator input/output characteristic. Curves of monotonically increasing nature thus resulting in an inaccurate dispatch. The GA (Genetic Algorithm) has been used for the solution of this problem owing to its inherent ability to address the convex and non-convex problems equally. This approach brings the solution to the global minimum region of search space in a short time and then takes longer time to converge to near optimal results. GA based hybrid approaches are used to fine tune the near optimal results produced by GA. This paper proposes NGH (Neuro Genetic Hybrid) approach to solve the economic dispatch with valve point effect. The proposed approach combines the GA with the ANN (Artificial Neural Network) using SI (Swarm Intelligence) learning rule. The GA acts as a global optimizer and the neural network fine tunes the GA results to the desired targets. Three machines standard test system has been tested for validation of the approach. Comparing the results with GA and NGH model based on back-propagation learning, the proposed approach gives contrast improvements showing the promise of the approach. (author)

  13. Genetic Approaches to Study Meiosis and Meiosis-Specific Gene Expression in Saccharomyces cerevisiae.

    Science.gov (United States)

    Kassir, Yona; Stuart, David T

    2017-01-01

    The budding yeast Saccharomyces cerevisiae has a long history as a model organism for studies of meiosis and the cell cycle. The popularity of this yeast as a model is in large part due to the variety of genetic and cytological approaches that can be effectively performed with the cells. Cultures of the cells can be induced to synchronously progress through meiosis and sporulation allowing large-scale gene expression and biochemical studies to be performed. Additionally, the spore tetrads resulting from meiosis make it possible to characterize the haploid products of meiosis allowing investigation of meiotic recombination and chromosome segregation. Here we describe genetic methods for analysis progression of S. cerevisiae through meiosis and sporulation with an emphasis on strategies for the genetic analysis of regulators of meiosis-specific genes.

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

    Science.gov (United States)

    Neri, Caterina; Edlow, Andrea G.

    2016-01-01

    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. PMID:26337113

  15. Maternal Smoking During Pregnancy and Offspring Birth Weight: A Genetically-Informed Approach Comparing Multiple Raters

    Science.gov (United States)

    Knopik, Valerie S.; Marceau, Kristine; Palmer, Rohan H. C.; Smith, Taylor F.; Heath, Andrew C.

    2016-01-01

    Maternal smoking during pregnancy (SDP) is a significant public health concern with adverse consequences to the health and well-being of the fetus. There is considerable debate about the best method of assessing SDP, including birth/medical records, timeline follow-back approaches, multiple reporters, and biological verification (e.g., cotinine). This is particularly salient for genetically-informed approaches where it is not always possible or practical to do a prospective study starting during the prenatal period when concurrent biological specimen samples can be collected with ease. In a sample of families (N = 173) specifically selected for sibling pairs discordant for prenatal smoking exposure, we: (1) compare rates of agreement across different types of report—maternal report of SDP, paternal report of maternal SDP, and SDP contained on birth records from the Department of Vital Statistics; (2) examine whether SDP is predictive of birth weight outcomes using our best SDP report as identified via step (1); and (3) use a sibling-comparison approach that controls for genetic and familial influences that siblings share in order to assess the effects of SDP on birth weight. Results show high agreement between reporters and support the utility of retrospective report of SDP. Further, we replicate a causal association between SDP and birth weight, wherein SDP results in reduced birth weight even when accounting for genetic and familial confounding factors via a sibling comparison approach. PMID:26494459

  16. Analysis of stock investment selection based on CAPM using covariance and genetic algorithm approach

    Science.gov (United States)

    Sukono; Susanti, D.; Najmia, M.; Lesmana, E.; Napitupulu, H.; Supian, S.; Putra, A. S.

    2018-03-01

    Investment is one of the economic growth factors of countries, especially in Indonesia. Stocks is a form of investment, which is liquid. In determining the stock investment decisions which need to be considered by investors is to choose stocks that can generate maximum returns with a minimum risk level. Therefore, we need to know how to allocate the capital which may give the optimal benefit. This study discusses the issue of stock investment based on CAPM which is estimated using covariance and Genetic Algorithm approach. It is assumed that the stocks analyzed follow the CAPM model. To do the estimation of beta parameter on CAPM equation is done by two approach, first is to be represented by covariance approach, and second with genetic algorithm optimization. As a numerical illustration, in this paper analyzed ten stocks traded on the capital market in Indonesia. The results of the analysis show that estimation of beta parameters using covariance and genetic algorithm approach, give the same decision, that is, six underpriced stocks with buying decision, and four overpriced stocks with a sales decision. Based on the analysis, it can be concluded that the results can be used as a consideration for investors buying six under-priced stocks, and selling four overpriced stocks.

  17. Marketing the dental hygiene program. A public relations approach.

    Science.gov (United States)

    Nielsen, C

    1989-09-01

    Since 1980 there has been a decline in dental hygiene enrollment and graduates. Marketing dental hygiene programs, a recognized component of organizational survival, is necessary to meet societal demands for dental hygiene care now and in the future. The purpose of this article is to examine theories on the marketing of education and to describe a systematic approach to marketing dental hygiene education. Upon examination of these theories, the importance of analysis, planning, implementation, and evaluation/control of a marketing program is found to be essential. Application of the four p's of marketing--product/service, price, place, and promotion--is necessary to achieve marketing's goals and objectives and ultimately the program's mission and goals. Moreover, projecting a quality image of the dental hygiene program and the profession of dental hygiene must be included in the overall marketing plan. Results of an effective marketing plan should increase the number of quality students graduating from the dental hygiene program, ultimately contributing to the quality of oral health care in the community.

  18. The INEL approach: Environmental Restoration Program management and implementation methodology

    International Nuclear Information System (INIS)

    1996-01-01

    The overall objectives of the INEL Environmental Restoration (ER) Program management approach are to facilitate meeting mission needs through the successful implementation of a sound, and effective project management philosophy. This paper outlines the steps taken to develop the ER program, and explains further the implementing tools and processes used to achieve what can be viewed as fundamental to a successful program. The various examples provided will demonstrate how the strategies for implementing these operating philosophies are actually present and at work throughout the program, in spite of budget drills and organizational changes within DOE and the implementing contractor. A few of the challenges and successes of the INEL Environmental Restoration Program have included: a) completion of all enforceable milestones to date, b) acceleration of enforceable milestones, c) managing funds to reduce uncosted obligations at year end by utilizing greater than 99% of FY-95 budget, d) an exemplary safety record, e) developing a strategy for partial Delisting of the INEL by the year 2000, f) actively dealing with Natural Resource Damages Assessment issues, g) the achievement of significant project cost reductions, h) and implementation of a partnering charter and application of front end quality principles

  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; Esque, Todd C.; 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

  20. Research on non-uniform strain profile reconstruction along fiber Bragg grating via genetic programming algorithm and interrelated experimental verification

    Science.gov (United States)

    Zheng, Shijie; Zhang, Nan; Xia, Yanjun; Wang, Hongtao

    2014-03-01

    A new heuristic strategy for the non-uniform strain profile reconstruction along Fiber Bragg Gratings is proposed in this paper, which is based on the modified transfer matrix and Genetic Programming(GP) algorithm. The present method uses Genetic Programming to determine the applied strain field as a function of position along the fiber length. The structures that undergo adaptation in genetic programming are hierarchical structures which are different from that of conventional genetic algorithm operating on strings. GP regress the strain profile function which matches the 'measured' spectrum best and makes space resolution of strain reconstruction arbitrarily high, or even infinite. This paper also presents an experimental verification of the reconstruction of non-homogeneous strain fields using GP. The results are compared with numerical calculations of finite element method. Both the simulation examples and experimental results demonstrate that Genetic Programming can effectively reconstruct continuous profile expression along the whole FBG, and greatly improves its computational efficiency and accuracy.

  1. 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.

  2. Mitigation of inbreeding while preserving genetic gain in genomic breeding programs for outbred plants.

    Science.gov (United States)

    Lin, Zibei; Shi, Fan; Hayes, Ben J; Daetwyler, Hans D

    2017-05-01

    Heuristic genomic inbreeding controls reduce inbreeding in genomic breeding schemes without reducing genetic gain. Genomic selection is increasingly being implemented in plant breeding programs to accelerate genetic gain of economically important traits. However, it may cause significant loss of genetic diversity when compared with traditional schemes using phenotypic selection. We propose heuristic strategies to control the rate of inbreeding in outbred plants, which can be categorised into three types: controls during mate allocation, during selection, and simultaneous selection and mate allocation. The proposed mate allocation measure GminF allocates two or more parents for mating in mating groups that minimise coancestry using a genomic relationship matrix. Two types of relationship-adjusted genomic breeding values for parent selection candidates ([Formula: see text]) and potential offspring ([Formula: see text]) are devised to control inbreeding during selection and even enabling simultaneous selection and mate allocation. These strategies were tested in a case study using a simulated perennial ryegrass breeding scheme. As compared to the genomic selection scheme without controls, all proposed strategies could significantly decrease inbreeding while achieving comparable genetic gain. In particular, the scenario using [Formula: see text] in simultaneous selection and mate allocation reduced inbreeding to one-third of the original genomic selection scheme. The proposed strategies are readily applicable in any outbred plant breeding program.

  3. Earthquake—explosion discrimination using genetic algorithm-based boosting approach

    Science.gov (United States)

    Orlic, Niksa; Loncaric, Sven

    2010-02-01

    An important and challenging problem in seismic data processing is to discriminate between natural seismic events such as earthquakes and artificial seismic events such as explosions. Many automatic techniques for seismogram classification have been proposed in the literature. Most of these methods have a similar approach to seismogram classification: a predefined set of features based on ad-hoc feature selection criteria is extracted from the seismogram waveform or spectral data and these features are used for signal classification. In this paper we propose a novel approach for seismogram classification. A specially formulated genetic algorithm has been employed to automatically search for a near-optimal seismogram feature set, instead of using ad-hoc feature selection criteria. A boosting method is added to the genetic algorithm when searching for multiple features in order to improve classification performance. A learning set of seismogram data is used by the genetic algorithm to discover a near-optimal feature set. The feature set identified by the genetic algorithm is then used for seismogram classification. The described method is developed to classify seismograms in two groups, whereas a brief overview of method extension for multiple group classification is given. For method verification, a learning set consisting of 40 local earthquake seismograms and 40 explosion seismograms was used. The method was validated on seismogram set consisting of 60 local earthquake seismograms and 60 explosion seismograms, with correct classification of 85%.

  4. Mathematical solution of multilevel fractional programming problem with fuzzy goal programming approach

    Science.gov (United States)

    Lachhwani, Kailash; Poonia, Mahaveer Prasad

    2012-08-01

    In this paper, we show a procedure for solving multilevel fractional programming problems in a large hierarchical decentralized organization using fuzzy goal programming approach. In the proposed method, the tolerance membership functions for the fuzzily described numerator and denominator part of the objective functions of all levels as well as the control vectors of the higher level decision makers are respectively defined by determining individual optimal solutions of each of the level decision makers. A possible relaxation of the higher level decision is considered for avoiding decision deadlock due to the conflicting nature of objective functions. Then, fuzzy goal programming approach is used for achieving the highest degree of each of the membership goal by minimizing negative deviational variables. We also provide sensitivity analysis with variation of tolerance values on decision vectors to show how the solution is sensitive to the change of tolerance values with the help of a numerical example.

  5. 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.

  6. A Proposal for the Common Safety Approach of Space Programs

    Science.gov (United States)

    Grimard, Max

    2002-01-01

    For all applications, business and systems related to Space programs, Quality is mandatory and is a key factor for the technical as well as the economical performances. Up to now the differences of applications (launchers, manned space-flight, sciences, telecommunications, Earth observation, planetary exploration, etc.) and the difference of technical culture and background of the leading countries (USA, Russia, Europe) have generally led to different approaches in terms of standards and processes for Quality. At a time where international cooperation is quite usual for the institutional programs and globalization is the key word for the commercial business, it is considered of prime importance to aim at common standards and approaches for Quality in Space Programs. For that reason, the International Academy of Astronautics has set up a Study Group which mandate is to "Make recommendations to improve the Quality, Reliability, Efficiency, and Safety of space programmes, taking into account the overall environment in which they operate : economical constraints, harsh environments, space weather, long life, no maintenance, autonomy, international co-operation, norms and standards, certification." The paper will introduce the activities of this Study Group, describing a first list of topics which should be addressed : Through this paper it is expected to open the discussion to update/enlarge this list of topics and to call for contributors to this Study Group.

  7. Empirical study of self-configuring genetic programming algorithm performance and behaviour

    International Nuclear Information System (INIS)

    KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" data-affiliation=" (Siberian State Aerospace University named after Academician M.F. Reshetnev 31 KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" >Semenkin, E; KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" data-affiliation=" (Siberian State Aerospace University named after Academician M.F. Reshetnev 31 KrasnoyarskiyRabochiy prospect, Krasnoyarsk, 660014 (Russian Federation))" >Semenkina, M

    2015-01-01

    The behaviour of the self-configuring genetic programming algorithm with a modified uniform crossover operator that implements a selective pressure on the recombination stage, is studied over symbolic programming problems. The operator's probabilistic rates interplay is studied and the role of operator variants on algorithm performance is investigated. Algorithm modifications based on the results of investigations are suggested. The performance improvement of the algorithm is demonstrated by the comparative analysis of suggested algorithms on the benchmark and real world problems

  8. 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.

  9. 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.

  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. Feature Selection using Multi-objective Genetic Algorith m: A Hybrid Approach

    OpenAIRE

    Ahuja, Jyoti; GJUST - Guru Jambheshwar University of Sciecne and Technology; Ratnoo, Saroj Dahiya; GJUST - Guru Jambheshwar University of Sciecne and Technology

    2015-01-01

    Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large search spaces like in the case of feature selection. Moreover, feature selection is an inherently multi-objective problem with many competing objectives involving size, predictive power and redundancy of the featu...

  12. Toward a global space exploration program: A stepping stone approach

    Science.gov (United States)

    Ehrenfreund, Pascale; McKay, Chris; Rummel, John D.; Foing, Bernard H.; Neal, Clive R.; Masson-Zwaan, Tanja; Ansdell, Megan; Peter, Nicolas; Zarnecki, John; Mackwell, Steve; Perino, Maria Antionetta; Billings, Linda; Mankins, John; Race, Margaret

    2012-01-01

    In response to the growing importance of space exploration in future planning, the Committee on Space Research (COSPAR) Panel on Exploration (PEX) was chartered to provide independent scientific advice to support the development of exploration programs and to safeguard the potential scientific assets of solar system objects. In this report, PEX elaborates a stepwise approach to achieve a new level of space cooperation that can help develop world-wide capabilities in space science and exploration and support a transition that will lead to a global space exploration program. The proposed stepping stones are intended to transcend cross-cultural barriers, leading to the development of technical interfaces and shared legal frameworks and fostering coordination and cooperation on a broad front. Input for this report was drawn from expertise provided by COSPAR Associates within the international community and via the contacts they maintain in various scientific entities. The report provides a summary and synthesis of science roadmaps and recommendations for planetary exploration produced by many national and international working groups, aiming to encourage and exploit synergies among similar programs. While science and technology represent the core and, often, the drivers for space exploration, several other disciplines and their stakeholders (Earth science, space law, and others) should be more robustly interlinked and involved than they have been to date. The report argues that a shared vision is crucial to this linkage, and to providing a direction that enables new countries and stakeholders to join and engage in the overall space exploration effort. Building a basic space technology capacity within a wider range of countries, ensuring new actors in space act responsibly, and increasing public awareness and engagement are concrete steps that can provide a broader interest in space exploration, worldwide, and build a solid basis for program sustainability. By engaging

  13. Using the hybrid fuzzy goal programming model and hybrid genetic algorithm to solve a multi-objective location routing problem for infectious waste disposaL

    Energy Technology Data Exchange (ETDEWEB)

    Wichapa, Narong; Khokhajaikiat, Porntep

    2017-07-01

    Disposal of infectious waste remains one of the most serious problems in the social and environmental domains of almost every nation. Selection of new suitable locations and finding the optimal set of transport routes to transport infectious waste, namely location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Design/methodology/approach: Due to the complexity of this problem, location routing problem for a case study, forty hospitals and three candidate municipalities in sub-Northeastern Thailand, was divided into two phases. The first phase is to choose suitable municipalities using hybrid fuzzy goal programming model which hybridizes the fuzzy analytic hierarchy process and fuzzy goal programming. The second phase is to find the optimal routes for each selected municipality using hybrid genetic algorithm which hybridizes the genetic algorithm and local searches including 2-Opt-move, Insertion-move and ?-interchange-move. Findings: The results indicate that the hybrid fuzzy goal programming model can guide the selection of new suitable municipalities, and the hybrid genetic algorithm can provide the optimal routes for a fleet of vehicles effectively. Originality/value: The novelty of the proposed methodologies, hybrid fuzzy goal programming model, is the simultaneous combination of both intangible and tangible factors in order to choose new suitable locations, and the hybrid genetic algorithm can be used to determine the optimal routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation, efficiently.

  14. Using the hybrid fuzzy goal programming model and hybrid genetic algorithm to solve a multi-objective location routing problem for infectious waste disposaL

    International Nuclear Information System (INIS)

    Wichapa, Narong; Khokhajaikiat, Porntep

    2017-01-01

    Disposal of infectious waste remains one of the most serious problems in the social and environmental domains of almost every nation. Selection of new suitable locations and finding the optimal set of transport routes to transport infectious waste, namely location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Design/methodology/approach: Due to the complexity of this problem, location routing problem for a case study, forty hospitals and three candidate municipalities in sub-Northeastern Thailand, was divided into two phases. The first phase is to choose suitable municipalities using hybrid fuzzy goal programming model which hybridizes the fuzzy analytic hierarchy process and fuzzy goal programming. The second phase is to find the optimal routes for each selected municipality using hybrid genetic algorithm which hybridizes the genetic algorithm and local searches including 2-Opt-move, Insertion-move and ?-interchange-move. Findings: The results indicate that the hybrid fuzzy goal programming model can guide the selection of new suitable municipalities, and the hybrid genetic algorithm can provide the optimal routes for a fleet of vehicles effectively. Originality/value: The novelty of the proposed methodologies, hybrid fuzzy goal programming model, is the simultaneous combination of both intangible and tangible factors in order to choose new suitable locations, and the hybrid genetic algorithm can be used to determine the optimal routes which provide a minimum number of vehicles and minimum transportation cost under the actual situation, efficiently.

  15. 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.

  16. A program approach for site safety at oil spills

    International Nuclear Information System (INIS)

    Whipple, F.L.; Glenn, S.P.; Ocken, J.J.; Ott, G.L.

    1993-01-01

    When OSHA developed the hazardous waste operations (Hazwoper) regulations (29 CFR 1910.120) members of the response community envisioned a separation of oil and open-quotes hazmatclose quotes response operations. Organizations that deal with oil spills have had difficulty applying Hazwoper regulations to oil spill operations. This hinders meaningful implementation of the standard for their personnel. We should approach oil spills with the same degree of caution that is applied to hazmat response. Training frequently does not address the safety of oil spill response operations. Site-specific safety and health plans often are neglected or omitted. Certain oils expose workers to carcinogens, as well as chronic and acute hazards. Significant physical hazards are most important. In responding to oil spills, the hazards must be addressed. It is the authors' contention that a need exists for safety program at oil spill sites. Gone are the days of labor pool hires cleaning up spills in jeans and sneakers. The key to meaningful programs for oil spills requires application of controls focused on relevant safety risks rather than minimal chemical exposure hazards. Working with concerned reviewers from other agencies and organizations, the authors have developed a general safety and health program for oil spill response. It is intended to serve as the basis for organizations to customize their own written safety and health program (required by OSHA). It also provides a separate generic site safety plan for emergency phase oil spill operations (check-list) and long term post-emergency phase operations

  17. Including nonadditive genetic effects in mating programs to maximize dairy farm profitability.

    Science.gov (United States)

    Aliloo, H; Pryce, J E; González-Recio, O; Cocks, B G; Goddard, M E; Hayes, B J

    2017-02-01

    We compared the outcome of mating programs based on different evaluation models that included nonadditive genetic effects (dominance and heterozygosity) in addition to additive effects. The additive and dominance marker effects and the values of regression on average heterozygosity were estimated using 632,003 single nucleotide polymorphisms from 7,902 and 7,510 Holstein cows with calving interval and production (milk, fat, and protein yields) records, respectively. Expected progeny values were computed based on the estimated genetic effects and genotype probabilities of hypothetical progeny from matings between the available genotyped cows and the top 50 young genomic bulls. An index combining the traits based on their economic values was developed and used to evaluate the performance of different mating scenarios in terms of dollar profit. We observed that mating programs with nonadditive genetic effects performed better than a model with only additive effects. Mating programs with dominance and heterozygosity effects increased milk, fat, and protein yields by up to 38, 1.57, and 1.21 kg, respectively. The inclusion of dominance and heterozygosity effects decreased calving interval by up to 0.70 d compared with random mating. The average reduction in progeny inbreeding by the inclusion of nonadditive genetic effects in matings compared with random mating was between 0.25 to 1.57 and 0.64 to 1.57 percentage points for calving interval and production traits, respectively. The reduction in inbreeding was accompanied by an average of A$8.42 (Australian dollars) more profit per mating for a model with additive, dominance, and heterozygosity effects compared with random mating. Mate allocations that benefit from nonadditive genetic effects can improve progeny performance only in the generation where it is being implemented, and the gain from specific combining abilities cannot be accumulated over generations. Continuous updating of genomic predictions and mate

  18. 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.

  19. Method of transient identification based on a possibilistic approach, optimized by genetic algorithm

    International Nuclear Information System (INIS)

    Almeida, Jose Carlos Soares de

    2001-02-01

    This work develops a method for transient identification based on a possible approach, optimized by Genetic Algorithm to optimize the number of the centroids of the classes that represent the transients. The basic idea of the proposed method is to optimize the partition of the search space, generating subsets in the classes within a partition, defined as subclasses, whose centroids are able to distinguish the classes with the maximum correct classifications. The interpretation of the subclasses as fuzzy sets and the possible approach provided a heuristic to establish influence zones of the centroids, allowing to achieve the 'don't know' answer for unknown transients, that is, outside the training set. (author)

  20. A probabilistic multi objective CLSC model with Genetic algorithm-ε_Constraint approach

    Directory of Open Access Journals (Sweden)

    Alireza TaheriMoghadam

    2014-05-01

    Full Text Available In this paper an uncertain multi objective closed-loop supply chain is developed. The first objective function is maximizing the total profit. The second objective function is minimizing the use of row materials. In the other word, the second objective function is maximizing the amount of remanufacturing and recycling. Genetic algorithm is used for optimization and for finding the pareto optimal line, Epsilon-constraint method is used. Finally a numerical example is solved with proposed approach and performance of the model is evaluated in different sizes. The results show that this approach is effective and useful for managerial decisions.

  1. 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.

  2. Gas contract portfolio management: a stochastic programming approach

    International Nuclear Information System (INIS)

    Haurie, A.; Smeers, Y.; Zaccour, G.

    1991-01-01

    This paper deals with a stochastic programming model which complements long range market simulation models generating scenarios concerning the evolution of demand and prices for gas in different market segments. Agas company has to negociate contracts with lengths going from one to twenty years. This stochastic model is designed to assess the risk associated with committing the gas production capacity of the company to these market segments. Different approaches are presented to overcome the difficulties associated with the very large size of the resulting optimization problem

  3. 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.

  4. Approaches to Education and Training for Kenya's Nuclear Power Program

    International Nuclear Information System (INIS)

    Kalambuka, H.A.

    2014-01-01

    1. Review of status and development of E and T for the nuclear power program in Kenya; 2. Review of challenges in nuclear E and T, and the initiatives being undertaken to mitigate them: • Recommendations for strategic action; 3. State of nuclear skills in the context of key drivers of the global revival in nuclear energy; 4. Point of view: Education in Applied Nuclear and Radiation physics at Nairobi: • Its growth has helped identify the gaps, and relevant practical approaches for realizing the broad spectrum of technical capacity to conduct a national NPP; 5. Proposed approach to support the E and T infrastructure necessary to allow the country to plan, construct, operate, regulate, and safely and securely handle nuclear facilities sustainably; 6. Specified E and T initiatives in the context of the national industrial development strategy and nuclear energy policy and funding for the complete life cycle and technology localization. (author)

  5. Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach

    DEFF Research Database (Denmark)

    Bothwell, H.; Bisbing, S.; Therkildsen, Nina Overgaard

    2013-01-01

    It is generally accepted that most plant populations are locally adapted. Yet, understanding how environmental forces give rise to adaptive genetic variation is a challenge in conservation genetics and crucial to the preservation of species under rapidly changing climatic conditions. Environmental...... loci, we compared outlier locus detection methods with a recently-developed landscape genetic approach. We analyzed 157 loci from samples of the alpine herb Gentiana nivalis collected across the European Alps. Principle coordinates of neighbor matrices (PCNM), eigenvectors that quantify multi...... variables identified eight more potentially adaptive loci than models run without spatial variables. 3) When compared to outlier detection methods, the landscape genetic approach detected four of the same loci plus 11 additional loci. 4) Temperature, precipitation, and solar radiation were the three major...

  6. Genetics

    International Nuclear Information System (INIS)

    Hubitschek, H.E.

    1975-01-01

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

  7. Area program in population genetics. Final report, November 1, 1975-August 31, 1982

    International Nuclear Information System (INIS)

    Chu, E.H.Y.; Gershowitz, H.; Meisler, M.H.; Mohrenweiser, H.W.; Neel, J.V.; Rothman, E.D.; Sing, C.S.

    1982-01-01

    Research results are summarized for the following task areas: (1) Amerindian mutation rates; (2) pilot study of monitoring populations for the frequency of mutation; (3) interdigitation with the biochemical genetics study of the Radiation Effects Research Foundation (Hiroshima, Japan); (4) intraindividual variation in erythrocyte blood group antigens as indicators of somatic mutation; (5) in vitro studies of somatic cell mutation rates; (6) development of approaches to the study of mutation rates; and (7) statistical problems associated with the study of mutation and selection

  8. 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

  9. The Mouse House: a brief history of the ORNL mouse-genetics program, 1947-2009.

    Science.gov (United States)

    Russell, Liane B

    2013-01-01

    The large mouse genetics program at the Oak Ridge National Laboratory (ORNL) is often remembered 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

  10. Hereditary breast and ovarian cancer: successful systematic implementation of a group approach to genetic counselling.

    Science.gov (United States)

    Benusiglio, Patrick R; Di Maria, Marina; Dorling, Leila; Jouinot, Anne; Poli, Antoine; Villebasse, Sophie; Le Mentec, Marine; Claret, Béatrice; Boinon, Diane; Caron, Olivier

    2017-01-01

    The increase in referrals to cancer genetics clinics, partially associated with the "Angelina Jolie effect", presents a challenge to existing services, many are already running at full capacity. More efficient ways to deliver genetic counselling are therefore urgently needed. We now systematically offer group instead of standard individual counselling to patients with suspected Hereditary Breast and Ovarian Cancer. Group sessions last 30 min. The first twenty consist of a presentation by the genetic counsellor, the next ten of a discussion involving a cancer geneticist and a psychologist. A short individual consultation ensues, where personal and family issues are addressed and consent obtained. Blood is drawn afterwards. Satisfaction and knowledge are evaluated. We report data for the Oct-2014-Aug-2015 period. 210 patients attended group counselling, up to eight simultaneously. We always fitted them within a 4-h time frame. Mean satisfaction score was 41/43. Knowledge scores increased from 3.1/6 to 4.9/6 post-counselling (p value group counselling, we have withstood increases in referrals without compromising care. The "Angelina Jolie effect" and rapid developments in personalized medicine threaten to overwhelm cancer genetics clinics. In this context, our innovative approach should ensure that all patients have access to approved services.

  11. Usher syndrome: an effective sequencing approach to establish a genetic and clinical diagnosis.

    Science.gov (United States)

    Lenarduzzi, S; Vozzi, D; Morgan, A; Rubinato, E; D'Eustacchio, A; Osland, T M; Rossi, C; Graziano, C; Castorina, P; Ambrosetti, U; Morgutti, M; Girotto, G

    2015-02-01

    Usher syndrome is an autosomal recessive disorder characterized by retinitis pigmentosa, sensorineural hearing loss and, in some cases, vestibular dysfunction. The disorder is clinically and genetically heterogeneous and, to date, mutations in 11 genes have been described. This finding makes difficult to get a precise molecular diagnosis and offer patients accurate genetic counselling. To overcome this problem and to increase our knowledge of the molecular basis of Usher syndrome, we designed a targeted resequencing custom panel. In a first validation step a series of 16 Italian patients with known molecular diagnosis were analysed and 31 out of 32 alleles were detected (97% of accuracy). After this step, 31 patients without a molecular diagnosis were enrolled in the study. Three out of them with an uncertain Usher diagnosis were excluded. One causative allele was detected in 24 out 28 patients (86%) while the presence of both causative alleles characterized 19 patients out 28 (68%). Sixteen novel and 27 known alleles were found in the following genes: USH2A (50%), MYO7A (7%), CDH23 (11%), PCDH15 (7%) and USH1G (2%). Overall, on the 44 patients the protocol was able to characterize 74 alleles out of 88 (84%). These results suggest that our panel is an effective approach for the genetic diagnosis of Usher syndrome leading to: 1) an accurate molecular diagnosis, 2) better genetic counselling, 3) more precise molecular epidemiology data fundamental for future interventional plans. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. Genetics

    DEFF Research Database (Denmark)

    Christensen, Kaare; McGue, Matt

    2016-01-01

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

  13. Implementation of the program for conservation and sustainable utilization of forest genetic resources in Republic of Serbia

    Directory of Open Access Journals (Sweden)

    Šijačić-Nikolić Mirjana

    2017-01-01

    Full Text Available Program for conservation and sustainable utilization of forest genetic resources has been defined for 2016-2025 period and it is a base for concrete activities in this field. This Program could be divided into several parts that deal with: the legal framework for the conservation and sustainable utilization of forest genetic resources; status of forest genetic resources in Serbia; previous activities on the conservation of forest genetic resources; and objectives, priorities and measures of conservation. The Program should have an impact on the development of the forestry sector through the following activities: conservation and sustainable utilization of the available gene pool; improving forest management in accordance with conservation principles; improving the production of reproductive material of forest trees; make the public awareness of the need for conservation and sustainable utilization of forest genetic resources; fulfillment of international obligations related to this field and the possibility of joining FAO activities related to forest genetic resources - development of the national report as a part of the publication The State of the World's Forest Genetic Resources. Implementation of the Program will depend upon raising the awareness on the importance, conservation and sustainable utilization of forest genetic resources, as a precondition for the forests survival; it will depend of funds that will be allocated for this purpose and enthusiasm of people who deal with these issues.

  14. Genetic evaluation of reproductive potential in the Zatorska goose under a conservation program.

    Science.gov (United States)

    Graczyk, Magdalena; Andres, Krzysztof; Kapkowska, Ewa; Szwaczkowski, Tomasz

    2018-05-01

    The aim of this study was to estimate the genetic parameters and inbreeding effect on the fertility, embryo mortality and hatchability traits in the Zatorska goose covered by the animal genetic resources conservation program. The material for this study contains information about results of hatching of 18 863 eggs from 721 dams and 168 sires, laid between 1998-2015. Genetic parameters were estimated based on the threshold animal model by the use of Restricted Maximum Likelihood and Gibbs sampling. The percentage of fertilized eggs ranged yearly between 37-80%. The percentage of embryo mortality was very low, ranging between 4.63-23.73%. The percentage of the hatched goslings from the total number of analyzed eggs was on average 33.18%, and 53.72% from fertilized eggs. On average based on both methods, the heritability estimates of the fertility, embryo mortality and hatchability reached 0.36, 0.07, 0.24 for males and 0.44, 0.11, 0.32 for females. The genetic trend had increasing tendency for fertility and hatchability and was stable for embryo mortality for both sexes. The obtained result shows that the Zatorska goose can be still maintained in the reserves of the local gene pool according to current rules and use in the local market as a breed with good reproductive potential. © 2018 Japanese Society of Animal Science.

  15. Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming

    Science.gov (United States)

    Devra, Amit; Prabhu, Prithviraj; Singh, Harpreet; Arvind; Dorai, Kavita

    2018-03-01

    We have designed efficient quantum circuits for the three-qubit Toffoli (controlled-controlled-NOT) and the Fredkin (controlled-SWAP) gate, optimized via genetic programming methods. The gates thus obtained were experimentally implemented on a three-qubit NMR quantum information processor, with a high fidelity. Toffoli and Fredkin gates in conjunction with the single-qubit Hadamard gates form a universal gate set for quantum computing and are an essential component of several quantum algorithms. Genetic algorithms are stochastic search algorithms based on the logic of natural selection and biological genetics and have been widely used for quantum information processing applications. We devised a new selection mechanism within the genetic algorithm framework to select individuals from a population. We call this mechanism the "Luck-Choose" mechanism and were able to achieve faster convergence to a solution using this mechanism, as compared to existing selection mechanisms. The optimization was performed under the constraint that the experimentally implemented pulses are of short duration and can be implemented with high fidelity. We demonstrate the advantage of our pulse sequences by comparing our results with existing experimental schemes and other numerical optimization methods.

  16. Ruminant Nutrition Symposium: a systems approach to integrating genetics, nutrition, and metabolic efficiency in dairy cattle.

    Science.gov (United States)

    McNamara, J P

    2012-06-01

    The role of the dairy cow is to help provide high-quality protein and other nutrients for humans. We must select and manage cows with the goal of reaching the greatest possible efficiency for any given environment. We have increased efficiency tremendously over the years, yet the variation in productive and reproductive efficiency among animals is still quite large. In part this is because of a lack of full integration of genetic, nutritional, and reproductive biology into management decisions. However, integration across these disciplines is increasing as biological research findings show more specific control points at which genetics, nutrition, and reproduction interact. An ordered systems biology approach that focuses on why and how cells regulate energy and N use and on how and why organs interact by endocrine and neurocrine mechanisms will speed improvements in efficiency. More sophisticated dairy managers will demand better information to improve the efficiency of their animals. Using genetic improvement and proper animal management to improve milk productive and reproductive efficiency requires a deeper understanding of metabolic processes during the transition period. Using existing metabolic models, we can design experiments specifically to integrate new data from transcriptional arrays into models that describe nutrient use in farm animals. A systems modeling approach can help focus our research to make faster and large advances in efficiency and show directly how this can be applied on the farms.

  17. Approaches to quality management and accreditation in a genetic testing laboratory

    Science.gov (United States)

    Berwouts, Sarah; Morris, Michael A; Dequeker, Elisabeth

    2010-01-01

    Medical laboratories, and specifically genetic testing laboratories, provide vital medical services to different clients: clinicians requesting a test, patients from whom the sample was collected, public health and medical-legal instances, referral laboratories and authoritative bodies. All expect results that are accurate and obtained in an efficient and effective manner, within a suitable time frame and at acceptable cost. There are different ways of achieving the end results, but compliance with International Organization for Standardization (ISO) 15189, the international standard for the accreditation of medical laboratories, is becoming progressively accepted as the optimal approach to assuring quality in medical testing. We present recommendations and strategies designed to aid genetic testing laboratories with the implementation of a quality management system, including key aspects such as document control, external quality assessment, internal quality control, internal audit, management review, validation, as well as managing the human side of change. The focus is on pragmatic approaches to attain the levels of quality management and quality assurance required for accreditation according to ISO 15189, within the context of genetic testing. Attention is also given to implementing efficient and effective quality improvement. PMID:20720559

  18. A non-traditional multinational approach to construction inspection program

    International Nuclear Information System (INIS)

    Ram, Srinivasan; Smith, M.E.; Walker, T.F.

    2007-01-01

    The next generation of nuclear plants would be fabricated, constructed and licensed in markedly different ways than the present light water reactors. Non-traditional commercial nuclear industry suppliers, shipyards in Usa and international fabricators, would be a source to supply major components and subsystems. The codes of construction may vary depending upon the prevailing codes and standards used by the respective supplier. Such codes and standards need to be reconciled with the applicable regulations (e.g., 10 CFR 52). A Construction Inspection Program is an integral part of the Quality Assurance Measures required during the Construction Phase of the power plant. In order to achieve the stated cost and schedule goals of the new build plants, a nontraditional multi-national approach would be required. In lieu of the traditional approach of individual utility inspecting the quality of fabrication and construction, a multi-utility team approach is a method that will be discussed. Likewise, a multinational cooperative licensing approach is suggested taking advantage of inspectors of the regulatory authority where the component would be built. The multi-national approach proposed here is based on the principle of forming teaming agreements between the utilities, vendors and the regulators. For instance, rather than sending Country A's inspectors all over the world, inspectors of the regulator in Country B where a particular component is being fabricated would in fact be performing the required inspections for Country A's regulator. Similarly teaming arrangements could be set up between utilities and vendors in different countries. The required oversight for the utility or the vendor could be performed by their counterparts in the country where a particular item is being fabricated

  19. 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…

  20. Dynamic Load Balanced Clustering using Elitism based Random Immigrant Genetic Approach for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    K. Mohaideen Pitchai

    2017-07-01

    Full Text Available Wireless Sensor Network (WSN consists of a large number of small sensors with restricted energy. Prolonged network lifespan, scalability, node mobility and load balancing are important needs for several WSN applications. Clustering the sensor nodes is an efficient technique to reach these goals. WSN have the characteristics of topology dynamics because of factors like energy conservation and node movement that leads to Dynamic Load Balanced Clustering Problem (DLBCP. In this paper, Elitism based Random Immigrant Genetic Approach (ERIGA is proposed to solve DLBCP which adapts to topology dynamics. ERIGA uses the dynamic Genetic Algorithm (GA components for solving the DLBCP. The performance of load balanced clustering process is enhanced with the help of this dynamic GA. As a result, the ERIGA achieves to elect suitable cluster heads which balances the network load and increases the lifespan of the network.

  1. 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.

  2. PhyloGeoViz: a web-based program that visualizes genetic data on maps.

    Science.gov (United States)

    Tsai, Yi-Hsin E

    2011-05-01

    The first step of many population genetic studies is the simple visualization of allele frequencies on a landscape. This basic data exploration can be challenging without proprietary software, and the manual plotting of data is cumbersome and unfeasible at large sample sizes. I present an open source, web-based program that plots any kind of frequency or count data as pie charts in Google Maps (Google Inc., Mountain View, CA). Pie polygons are then exportable to Google Earth (Google Inc.), a free Geographic Information Systems platform. Import of genetic data into Google Earth allows phylogeographers access to a wealth of spatial information layers integral to forming hypotheses and understanding patterns in the data. © 2010 Blackwell Publishing Ltd.

  3. 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

    With increasingly complex and heterogeneous systems in pervasive service computing, it becomes more and more important to provide self-protected services to end users. In order to achieve self-protection, the corresponding security should be provided in an optimized manner considering...... 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-protection...... in the Hydra middleware, and is integrated with a framework for enforcing the computed solution at run time using security obligations. The experiments with the prototype on configuring security strategies for a pervasive service middleware show that this approach has acceptable performance, and could be used...

  4. 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.

  5. 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.

  6. Approaches in Characterizing Genetic Structure and Mapping in a Rice Multiparental Population.

    Science.gov (United States)

    Raghavan, Chitra; Mauleon, Ramil; Lacorte, Vanica; Jubay, Monalisa; Zaw, Hein; Bonifacio, Justine; Singh, Rakesh Kumar; Huang, B Emma; Leung, Hei

    2017-06-07

    Multi-parent Advanced Generation Intercross (MAGIC) populations are fast becoming mainstream tools for research and breeding, along with the technology and tools for analysis. This paper demonstrates the analysis of a rice MAGIC population from data filtering to imputation and processing of genetic data to characterizing genomic structure, and finally quantitative trait loci (QTL) mapping. In this study, 1316 S6:8 indica MAGIC (MI) lines and the eight founders were sequenced using Genotyping by Sequencing (GBS). As the GBS approach often includes missing data, the first step was to impute the missing SNPs. The observable number of recombinations in the population was then explored. Based on this case study, a general outline of procedures for a MAGIC analysis workflow is provided, as well as for QTL mapping of agronomic traits and biotic and abiotic stress, using the results from both association and interval mapping approaches. QTL for agronomic traits (yield, flowering time, and plant height), physical (grain length and grain width) and cooking properties (amylose content) of the rice grain, abiotic stress (submergence tolerance), and biotic stress (brown spot disease) were mapped. Through presenting this extensive analysis in the MI population in rice, we highlight important considerations when choosing analytical approaches. The methods and results reported in this paper will provide a guide to future genetic analysis methods applied to multi-parent populations. Copyright © 2017 Raghavan et al.

  7. Approaches in Characterizing Genetic Structure and Mapping in a Rice Multiparental Population

    Directory of Open Access Journals (Sweden)

    Chitra Raghavan

    2017-06-01

    Full Text Available Multi-parent Advanced Generation Intercross (MAGIC populations are fast becoming mainstream tools for research and breeding, along with the technology and tools for analysis. This paper demonstrates the analysis of a rice MAGIC population from data filtering to imputation and processing of genetic data to characterizing genomic structure, and finally quantitative trait loci (QTL mapping. In this study, 1316 S6:8 indica MAGIC (MI lines and the eight founders were sequenced using Genotyping by Sequencing (GBS. As the GBS approach often includes missing data, the first step was to impute the missing SNPs. The observable number of recombinations in the population was then explored. Based on this case study, a general outline of procedures for a MAGIC analysis workflow is provided, as well as for QTL mapping of agronomic traits and biotic and abiotic stress, using the results from both association and interval mapping approaches. QTL for agronomic traits (yield, flowering time, and plant height, physical (grain length and grain width and cooking properties (amylose content of the rice grain, abiotic stress (submergence tolerance, and biotic stress (brown spot disease were mapped. Through presenting this extensive analysis in the MI population in rice, we highlight important considerations when choosing analytical approaches. The methods and results reported in this paper will provide a guide to future genetic analysis methods applied to multi-parent populations.

  8. Optimal planning approaches with multiple impulses for rendezvous based on hybrid genetic algorithm and control method

    Directory of Open Access Journals (Sweden)

    JingRui Zhang

    2015-03-01

    Full Text Available In this article, we focus on safe and effective completion of a rendezvous and docking task by looking at planning approaches and control with fuel-optimal rendezvous for a target spacecraft running on a near-circular reference orbit. A variety of existent practical path constraints are considered, including the constraints of field of view, impulses, and passive safety. A rendezvous approach is calculated by using a hybrid genetic algorithm with those constraints. Furthermore, a control method of trajectory tracking is adopted to overcome the external disturbances. Based on Clohessy–Wiltshire equations, we first construct the mathematical model of optimal planning approaches of multiple impulses with path constraints. Second, we introduce the principle of hybrid genetic algorithm with both stronger global searching ability and local searching ability. We additionally explain the application of this algorithm in the problem of trajectory planning. Then, we give three-impulse simulation examples to acquire an optimal rendezvous trajectory with the path constraints presented in this article. The effectiveness and applicability of the tracking control method are verified with the optimal trajectory above as control objective through the numerical simulation.

  9. Genetic Gain and Inbreeding from Genomic Selection in a Simulated Commercial Breeding Program for Perennial Ryegrass

    Directory of Open Access Journals (Sweden)

    Zibei Lin

    2016-03-01

    Full Text Available Genomic selection (GS provides an attractive option for accelerating genetic gain in perennial ryegrass ( improvement given the long cycle times of most current breeding programs. The present study used simulation to investigate the level of genetic gain and inbreeding obtained from GS breeding strategies compared with traditional breeding strategies for key traits (persistency, yield, and flowering time. Base population genomes were simulated through random mating for 60,000 generations at an effective population size of 10,000. The degree of linkage disequilibrium (LD in the resulting population was compared with that obtained from empirical studies. Initial parental varieties were simulated to match diversity of current commercial cultivars. Genomic selection was designed to fit into a company breeding program at two selection points in the breeding cycle (spaced plants and miniplot. Genomic estimated breeding values (GEBVs for productivity traits were trained with phenotypes and genotypes from plots. Accuracy of GEBVs was 0.24 for persistency and 0.36 for yield for single plants, while for plots it was lower (0.17 and 0.19, respectively. Higher accuracy of GEBVs was obtained for flowering time (up to 0.7, partially as a result of the larger reference population size that was available from the clonal row stage. The availability of GEBVs permit a 4-yr reduction in cycle time, which led to at least a doubling and trebling genetic gain for persistency and yield, respectively, than the traditional program. However, a higher rate of inbreeding per cycle among varieties was also observed for the GS strategy.

  10. Genetic Gain and Inbreeding from Genomic Selection in a Simulated Commercial Breeding Program for Perennial Ryegrass.

    Science.gov (United States)

    Lin, Zibei; Cogan, Noel O I; Pembleton, Luke W; Spangenberg, German C; Forster, John W; Hayes, Ben J; Daetwyler, Hans D

    2016-03-01

    Genomic selection (GS) provides an attractive option for accelerating genetic gain in perennial ryegrass () improvement given the long cycle times of most current breeding programs. The present study used simulation to investigate the level of genetic gain and inbreeding obtained from GS breeding strategies compared with traditional breeding strategies for key traits (persistency, yield, and flowering time). Base population genomes were simulated through random mating for 60,000 generations at an effective population size of 10,000. The degree of linkage disequilibrium (LD) in the resulting population was compared with that obtained from empirical studies. Initial parental varieties were simulated to match diversity of current commercial cultivars. Genomic selection was designed to fit into a company breeding program at two selection points in the breeding cycle (spaced plants and miniplot). Genomic estimated breeding values (GEBVs) for productivity traits were trained with phenotypes and genotypes from plots. Accuracy of GEBVs was 0.24 for persistency and 0.36 for yield for single plants, while for plots it was lower (0.17 and 0.19, respectively). Higher accuracy of GEBVs was obtained for flowering time (up to 0.7), partially as a result of the larger reference population size that was available from the clonal row stage. The availability of GEBVs permit a 4-yr reduction in cycle time, which led to at least a doubling and trebling genetic gain for persistency and yield, respectively, than the traditional program. However, a higher rate of inbreeding per cycle among varieties was also observed for the GS strategy. Copyright © 2016 Crop Science Society of America.

  11. Consensus statement understanding health and malnutrition through a systems approach: the ENOUGH program for early life.

    Science.gov (United States)

    Kaput, Jim; van Ommen, Ben; Kremer, Bas; Priami, Corrado; Monteiro, Jacqueline Pontes; Morine, Melissa; Pepping, Fre; Diaz, Zoey; Fenech, Michael; He, Yiwu; Albers, Ruud; Drevon, Christian A; Evelo, Chris T; Hancock, Robert E W; Ijsselmuiden, Carel; Lumey, L H; Minihane, Anne-Marie; Muller, Michael; Murgia, Chiara; Radonjic, Marijana; Sobral, Bruno; West, Keith P

    2014-01-01

    Nutrition research, like most biomedical disciplines, adopted and often uses experimental approaches based on Beadle and Tatum's one gene-one polypeptide hypothesis, thereby reducing biological processes to single reactions or pathways. Systems thinking is needed to understand the complexity of health and disease processes requiring measurements of physiological processes, as well as environmental and social factors, which may alter the expression of genetic information. Analysis of physiological processes with omics technologies to assess systems' responses has only become available over the past decade and remains costly. Studies of environmental and social conditions known to alter health are often not connected to biomedical research. While these facts are widely accepted, developing and conducting comprehensive research programs for health are often beyond financial and human resources of single research groups. We propose a new research program on essential nutrients for optimal underpinning of growth and health (ENOUGH) that will use systems approaches with more comprehensive measurements and biostatistical analysis of the many biological and environmental factors that influence undernutrition. Creating a knowledge base for nutrition and health is a necessary first step toward developing solutions targeted to different populations in diverse social and physical environments for the two billion undernourished people in developed and developing economies.

  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. Reactor Network Synthesis Using Coupled Genetic Algorithm with the Quasi-linear Programming Method

    OpenAIRE

    Soltani, H.; Shafiei, S.; Edraki, J.

    2016-01-01

    This research is an attempt to develop a new procedure for the synthesis of reactor networks (RNs) using a genetic algorithm (GA) coupled with the quasi-linear programming (LP) method. The GA is used to produce structural configuration, whereas continuous variables are handled using a quasi-LP formulation for finding the best objective function. Quasi-LP consists of LP together with a search loop to find the best reactor conversions (xi), as well as split and recycle ratios (yi). Quasi-LP rep...

  14. Genetic programming for evolving due-date assignment models in job shop environments.

    Science.gov (United States)

    Nguyen, Su; Zhang, Mengjie; Johnston, Mark; Tan, Kay Chen

    2014-01-01

    Due-date assignment plays an important role in scheduling systems and strongly influences the delivery performance of job shops. Because of the stochastic and dynamic nature of job shops, the development of general due-date assignment models (DDAMs) is complicated. In this study, two genetic programming (GP) methods are proposed to evolve DDAMs for job shop environments. The experimental results show that the evolved DDAMs can make more accurate estimates than other existing dynamic DDAMs with promising reusability. In addition, the evolved operation-based DDAMs show better performance than the evolved DDAMs employing aggregate information of jobs and machines.

  15. Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming

    Czech Academy of Sciences Publication Activity Database

    Křen, T.; Pilát, M.; Neruda, Roman

    2017-01-01

    Roč. 26, č. 5 (2017), č. článku 1760020. ISSN 0218-2130 R&D Projects: GA ČR GA15-19877S Grant - others:GA MŠk(CZ) LM2015042 Institutional support: RVO:67985807 Keywords : genetic programming * machine learning workflows * asynchronous evolutionary algorithm Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 0.778, year: 2016

  16. A Genetic Algorithm-based Antenna Selection Approach for Large-but-Finite MIMO Networks

    KAUST Repository

    Makki, Behrooz

    2016-12-29

    We study the performance of antenna selectionbased multiple-input-multiple-output (MIMO) networks with large but finite number of transmit antennas and receivers. Considering the continuous and bursty communication scenarios with different users’ data request probabilities, we develop an efficient antenna selection scheme using genetic algorithms (GA). As demonstrated, the proposed algorithm is generic in the sense that it can be used in the cases with different objective functions, precoding methods, levels of available channel state information and channel models. Our results show that the proposed GAbased algorithm reaches (almost) the same throughput as the exhaustive search-based optimal approach, with substantially less implementation complexity.

  17. A Genetic Algorithm-based Antenna Selection Approach for Large-but-Finite MIMO Networks

    KAUST Repository

    Makki, Behrooz; Ide, Anatole; Svensson, Tommy; Eriksson, Thomas; Alouini, Mohamed-Slim

    2016-01-01

    We study the performance of antenna selectionbased multiple-input-multiple-output (MIMO) networks with large but finite number of transmit antennas and receivers. Considering the continuous and bursty communication scenarios with different users’ data request probabilities, we develop an efficient antenna selection scheme using genetic algorithms (GA). As demonstrated, the proposed algorithm is generic in the sense that it can be used in the cases with different objective functions, precoding methods, levels of available channel state information and channel models. Our results show that the proposed GAbased algorithm reaches (almost) the same throughput as the exhaustive search-based optimal approach, with substantially less implementation complexity.

  18. Approach to estimation of level of information security at enterprise based on genetic algorithm

    Science.gov (United States)

    V, Stepanov L.; V, Parinov A.; P, Korotkikh L.; S, Koltsov A.

    2018-05-01

    In the article, the way of formalization of different types of threats of information security and vulnerabilities of an information system of the enterprise and establishment is considered. In a type of complexity of ensuring information security of application of any new organized system, the concept and decisions in the sphere of information security are expedient. One of such approaches is the method of a genetic algorithm. For the enterprises of any fields of activity, the question of complex estimation of the level of security of information systems taking into account the quantitative and qualitative factors characterizing components of information security is relevant.

  19. Parameter identification of the glazed photovoltaic thermal system using Genetic Algorithm–Fuzzy System (GA–FS) approach and its comparative study

    International Nuclear Information System (INIS)

    Singh, Sonveer; Agrawal, Sanjay

    2015-01-01

    Highlights: • Optimization using Genetic Algorithm–Fuzzy System approach. • Overall exergy efficiency has been evaluated with different optimization tools. • Comparative analysis has been done. • GA–FS is very efficient and fast technique. • Overall exergy efficiency has been improved. - Abstract: In this paper, Genetic Algorithm–Fuzzy System (GA–FS) approach is used to identify the optimized parameters of the glazed photovoltaic thermal (PVT) system and to improve its overall exergy efficiency. The fuzzy knowledge base is used to improve the efficiency of Genetic Algorithm (GA). It is observed that three GA parameters, namely: (i) crossover probability (P cross ), (ii) mutation probability (P mut ) and (iii) population size are changing dynamically during the program, according to fuzzy knowledge base to maximize the efficiency of the GA. Here, overall exergy efficiency is considered as an objective function during the optimization process for GA–FS approach. The effort has been made to identify the different optimized parameters like; length and depth of the channel, velocity of flowing fluid, overall heat transfer coefficient from solar cell to ambient and flowing fluid and overall back loss heat transfer coefficient from flowing fluid to the ambient to maximize the overall exergy efficiency using GA–FS approach. Performance of glazed PVT using GA–FS approach has been compared with performance using GA approach and without GA. It has also been observed that the GA–FS approach is a better approach as compared to GA approach because it converges faster as compare to GA because the use of the fuzzy knowledge base with GA and take less time for identification of optimized system parameters.

  20. 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.

  1. Specific Cell (Re-)Programming: Approaches and Perspectives.

    Science.gov (United States)

    Hausburg, Frauke; Jung, Julia Jeannine; David, Robert

    2018-01-01

    Many disorders are manifested by dysfunction of key cell types or their disturbed integration in complex organs. Thereby, adult organ systems often bear restricted self-renewal potential and are incapable of achieving functional regeneration. This underlies the need for novel strategies in the field of cell (re-)programming-based regenerative medicine as well as for drug development in vitro. The regenerative field has been hampered by restricted availability of adult stem cells and the potentially hazardous features of pluripotent embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs). Moreover, ethical concerns and legal restrictions regarding the generation and use of ESCs still exist. The establishment of direct reprogramming protocols for various therapeutically valuable somatic cell types has overcome some of these limitations. Meanwhile, new perspectives for safe and efficient generation of different specified somatic cell types have emerged from numerous approaches relying on exogenous expression of lineage-specific transcription factors, coding and noncoding RNAs, and chemical compounds.It should be of highest priority to develop protocols for the production of mature and physiologically functional cells with properties ideally matching those of their endogenous counterparts. Their availability can bring together basic research, drug screening, safety testing, and ultimately clinical trials. Here, we highlight the remarkable successes in cellular (re-)programming, which have greatly advanced the field of regenerative medicine in recent years. In particular, we review recent progress on the generation of cardiomyocyte subtypes, with a focus on cardiac pacemaker cells. Graphical Abstract.

  2. Impacts of the proposed program approach on waste stream characteristics

    International Nuclear Information System (INIS)

    King, J.F.; Fleming, M.E.

    1995-01-01

    The evolution of the U.S. Department of Energy's Civilian Radioactive Waste Management System (CRWMS) over the past few years has led to significant changes in key system scenario assumption. This paper describes the effects of two recent changes on waste stream characteristics focusing primarily on repository impacts. First, the multi-purpose canister (MPC) concept has been included in the Program baseline. The change from a bare fuel system to one including an MPC-based system forces the fuel assemblies initially loaded together in MPCs to remain together throughout the system. Second, current system analyses also assume a system without a monitored retrievable storage (MRS), with the understanding that an MRS would be reincorporated if a site becomes available. Together these two changes have significant impacts on waste stream characteristics. Those two changes create a class of scenarios referred to generally as Program Approach (PA) scenarios. Scenarios based on the previously assumed system, bare fuel with an MRS, are referred to here as the Previous Reference (PR) system scenarios. The analysis compares scenarios with otherwise consistent assumptions and presents summary comparisons. The number of disposal containers and the waste heat output are determined for eight PA and PR scenarios

  3. From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming

    Directory of Open Access Journals (Sweden)

    Aleksander Mendyk

    2015-01-01

    Full Text Available 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.

  4. Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic Programming

    Directory of Open Access Journals (Sweden)

    Krawiec Krzysztof

    2017-12-01

    Full Text Available Genetic programming (GP is a variant of evolutionary algorithm where the entities undergoing simulated evolution are computer programs. A fitness function in GP is usually based on a set of tests, each of which defines the desired output a correct program should return for an exemplary input. The outcomes of interactions between programs and tests in GP can be represented as an interaction matrix, with rows corresponding to programs in the current population and columns corresponding to tests. In previous work, we proposed SFIMX, a method that performs only a fraction of interactions and employs non-negative matrix factorization to estimate the outcomes of remaining ones, shortening GP’s runtime. In this paper, we build upon that work and propose three extensions of SFIMX, in which the subset of tests drawn to perform interactions is selected with respect to test difficulty. The conducted experiment indicates that the proposed extensions surpass the original SFIMX on a suite of discrete GP benchmarks.

  5. A combined genetic and multi medium approach revels new secondary metabolites in Aspergillus nidulans

    DEFF Research Database (Denmark)

    Klejnstrup, Marie Louise; Nielsen, Morten Thrane; Frisvad, Jens Christian

    Secondary metabolites are a diverse group of metabolites which serve as important natural sources of drugs for treating diseases. The availability of full genome sequences of several filamentous fungi has revealed a large genetic potential for production of secondary metabolites that are not obse......Secondary metabolites are a diverse group of metabolites which serve as important natural sources of drugs for treating diseases. The availability of full genome sequences of several filamentous fungi has revealed a large genetic potential for production of secondary metabolites...... that are not observed under standard laboratory conditions. Genetic approaches have proven a fruitfull strategy towards the production and identification of these unknown metabolites. Examples include deletion of the cclA1 and laeA2 genes in A. nidulans which affects the expression of secondary metabolites including...... monodictyphenone and terrequinone A respectively. We have deleted the cclA gene in A. nidulans and grown the mutants on several complex media to provoke the production of secondary metabolites. This resulted in the production of several metabolites not previously reported from A. nidulans. Some of these have been...

  6. Genetic engineering represents a safe approach for innovations improving nutritional contents of major food crops

    Directory of Open Access Journals (Sweden)

    Werner Arber

    2017-05-01

    Full Text Available About 70 years ago early microbial genetic research revealed that inherited phenotypic traits become determined by DNA filaments composed of 4 different nucleotides that are linearly arranged. In the meantime we know that genes, the determinants of specific life functions, are genomic segments of an average size of about 1000 nucleotides, i.e. a very small part of a genome. Fundamental insights into the structures and functions of selected genes can be reached by sorting out the relevant short DNA segment, splicing this fragment into a natural gene vector such as a viral genome or a fertility plasmid. This allows the researchers to transfer the genetic hybrid into an appropriate host cell in order to produce many copies that can then serve for functional and structural analysis. This research approach became efficient in the 1970s. On the request of involved researchers, safety guidelines became proposed 1975 at the Asilomar Conference on Recombinant DNA (Berg, Baltimore, Brenner, Roblin, & Singer, 1975, then generally introduced and still largely followed nowadays. Carefully carried out genetic engineering by horizontally transferring a selected and functionally well known DNA segment into the genome of another organism has in many published biosafety investigations never shown any unexpected harmful effect. We will present below selected examples of research contributions enabling innovations for the benefit of human life conditions.

  7. The challenges and promises of genetic approaches for ballast water management

    Science.gov (United States)

    Rey, Anaïs; Basurko, Oihane C.; Rodríguez-Ezpeleta, Naiara

    2018-03-01

    Ballast water is a main vector of introduction of Harmful Aquatic Organisms and Pathogens, which includes Non-Indigenous Species. Numerous and diversified organisms are transferred daily from a donor to a recipient port. Developed to prevent these introduction events, the International Convention for the Control and Management of Ships' Ballast Water and Sediments will enter into force in 2017. This international convention is asking for the monitoring of Harmful Aquatic Organisms and Pathogens. In this review, we highlight the urgent need to develop cost-effective methods to: (1) perform the biological analyses required by the convention; and (2) assess the effectiveness of two main ballast water management strategies, i.e. the ballast water exchange and the use of ballast water treatment systems. We have compiled the biological analyses required by the convention, and performed a comprehensive evaluation of the potential and challenges of the use of genetic tools in this context. Following an overview of the studies applying genetic tools to ballast water related research, we present metabarcoding as a relevant approach for early detection of Harmful Aquatic Organisms and Pathogens in general and for ballast water monitoring and port risk assessment in particular. Nonetheless, before implementation of genetic tools in the context of the ballast water management convention, benchmarked tests against traditional methods should be performed, and standard, reproducible and easy to apply protocols should be developed.

  8. An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations.

    Directory of Open Access Journals (Sweden)

    Arunabha Majumdar

    2018-02-01

    Full Text Available Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy. For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package 'CPBayes' implementing the proposed method.

  9. Evaluation of Average Life Expectancy of Exposed Individuals and their offspring: Population Genetic Approach

    International Nuclear Information System (INIS)

    Telnov, V. I.; Sotnik, N. V.

    2004-01-01

    Average life expectancy (ALE) is a significant integrating indicator of the population health. It can be affected by many factors such as radiation and hereditary ones. A population-genetic analysis of the average life expectancy (ALE) was performed for nuclear workers at the Mayak Production. Association exposed to external and internal radiation over a wide dose range and their offspring. A methodical approach was proposed to determine ALE for individuals with different genotypes and estimate ALE in the population based on genotype distribution. The analysis of a number of genetic markers revealed significant changes in the age-specific pattern of the Hp types in workers over 60 years. Such changes were caused by both radiation and non-radiation (cardiovascular pathology) factors. In the first case ALE decreased as Hp 1-1 > Hp 2-2> Hp2-1 (radiation). In the second case, it decreased as Hp 1-1> Hp-2-1> Hp2-2 (non-radiation). analysis of genetic markers in the workers offspring indicated significant shifts in distribution of the Hp types, especially an increase in the proportion of Hp 2-2 at doses from external γ-rays over 200 cGy to parents by the time of conception. Based on the non-radiation genotype differences in ALE in this group of offspring, the preliminary calculation of ALE was carried out, which indicated its reduction by 0.52 years in comparison with the control. (Author) 21 refs

  10. Leveraging ethnic group incidence variation to investigate genetic susceptibility to glioma: A novel candidate SNP approach

    Directory of Open Access Journals (Sweden)

    Daniel Ian Jacobs

    2012-10-01

    Full Text Available Objectives: Using a novel candidate SNP approach, we aimed to identify a possible genetic basis for the higher glioma incidence in Whites relative to East Asians and African-Americans. Methods: We hypothesized that genetic regions containing SNPs with extreme differences in allele frequencies across ethnicities are most likely to harbor susceptibility variants. We used International HapMap Project data to identify 3,961 candidate SNPs with the largest allele frequency differences in Whites compared to East Asians and Africans and tested these SNPs for association with glioma risk in a set of White cases and controls. Top SNPs identified in the discovery dataset were tested for association with glioma in five independent replication datasets. Results: No SNP achieved statistical significance in either the discovery or replication datasets after accounting for multiple testing. However, the most strongly associated SNP, rs879471, was found to be in linkage disequilibrium with a previously identified risk SNP, rs6010620, in RTEL1. We estimate rs6010620 to account for a glioma incidence rate ratio of 1.34 for Whites relative to East Asians. Conclusions: We explored genetic susceptibility to glioma using a novel candidate SNP method which may be applicable to other diseases with appropriate epidemiologic patterns.

  11. A type-driven approach to concrete meta programming.

    NARCIS (Netherlands)

    J.J. Vinju (Jurgen)

    2005-01-01

    textabstractApplications 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

  12. Electric generating capacity planning: A nonlinear programming approach

    Energy Technology Data Exchange (ETDEWEB)

    Yakin, M.Z.; McFarland, J.W.

    1987-02-01

    This paper presents a nonlinear programming approach for long-range generating capacity expansion planning in electrical power systems. The objective in the model is the minimization of total cost consisting of investment cost plus generation cost for a multi-year planning horizon. Reliability constraints are imposed by using standard and practical reserve margin requirements. State equations representing the dynamic aspect of the problem are included. The electricity demand (load) and plant availabilities are treated as random variables, and the method of cumulants is used to calculate the expected energy generated by each plant in each year of the planning horizon. The resulting model has a (highly) nonlinear objective function and linear constraints. The planning model is solved over the multiyear planning horizon instead of decomposing it into one-year period problems. This approach helps the utility decision maker to carry out extensive sensitivity analysis easily. A case study example is provided using EPRI test data. Relationships among the reserve margin, total cost and surplus energy generating capacity over the planning horizon are explored by analyzing the model.

  13. Internal combustion engine control for series hybrid electric vehicles by parallel and distributed genetic programming/multiobjective genetic algorithms

    Science.gov (United States)

    Gladwin, D.; Stewart, P.; Stewart, J.

    2011-02-01

    This article addresses the problem of maintaining a stable rectified DC output from the three-phase AC generator in a series-hybrid vehicle powertrain. The series-hybrid prime power source generally comprises an internal combustion (IC) engine driving a three-phase permanent magnet generator whose output is rectified to DC. A recent development has been to control the engine/generator combination by an electronically actuated throttle. This system can be represented as a nonlinear system with significant time delay. Previously, voltage control of the generator output has been achieved by model predictive methods such as the Smith Predictor. These methods rely on the incorporation of an accurate system model and time delay into the control algorithm, with a consequent increase in computational complexity in the real-time controller, and as a necessity relies to some extent on the accuracy of the models. Two complementary performance objectives exist for the control system. Firstly, to maintain the IC engine at its optimal operating point, and secondly, to supply a stable DC supply to the traction drive inverters. Achievement of these goals minimises the transient energy storage requirements at the DC link, with a consequent reduction in both weight and cost. These objectives imply constant velocity operation of the IC engine under external load disturbances and changes in both operating conditions and vehicle speed set-points. In order to achieve these objectives, and reduce the complexity of implementation, in this article a controller is designed by the use of Genetic Programming methods in the Simulink modelling environment, with the aim of obtaining a relatively simple controller for the time-delay system which does not rely on the implementation of real time system models or time delay approximations in the controller. A methodology is presented to utilise the miriad of existing control blocks in the Simulink libraries to automatically evolve optimal control

  14. Training programs for the systems approach to nuclear security

    International Nuclear Information System (INIS)

    Ellis, D.

    2005-01-01

    Full text: In support of United States Government (USG) and International Atomic Energy Agency (IAEA) nuclear security programs, Sandia National Laboratories (SNL) has advocated and practiced a risk-based, systematic approach to nuclear security. The risk equation has been developed and implemented as the basis for a performance-based methodology for the design and evaluation of physical protection systems against a design basis threat (DBT) for theft and sabotage of nuclear and/or radiological materials. Integrated systems must include technology, people, and the man-machine interface. A critical aspect of the human element is training on the systems-approach for all the stakeholders in nuclear security. Current training courses and workshops have been very beneficial but are still rather limited in scope. SNL has developed two primary international classes - the international training course on the physical protection of nuclear facilities and materials, and the design basis threat methodology workshop. SNL is also completing the development of three new courses that will be offered and presented in the near term. They are vital area identification methodology focused on nuclear power plants to aid in their protection against radiological sabotage, insider threat analysis methodology and protection schemes, and security foundations for competent authority and facility operator stakeholders who are not security professionals. In the long term, we envision a comprehensive nuclear security curriculum that spans policy and technology, regulators and operators, introductory and expert levels, classroom and laboratory/field, and local and offsite training options. This training curriculum will be developed in concert with a nuclear security series of guidance documents that is expected to be forthcoming from the IAEA. It is important to note that while appropriate implementation of systems based on such training and documentation can improve the risk reduction, such a

  15. Genetic diversity of Prochilodus lineatus stocks using in the stocking program of Tietê River, Brazil

    Directory of Open Access Journals (Sweden)

    Ricardo Ribeiro

    2013-11-01

    Full Text Available Objective. Assess the genetic diversity in four brood stocks and one juvenile stock of curimba Prochilodus lineatus in a Hydropower plant in São Paulo - Brazil, using the Tietê River stocking program. Materials and methods. Five RAPD primers were used to amplify the extracted DNA from 150 fin-clip samples. Results. Fifty-nine fragments were polymorphic, 52 had frequencies with significant differences (p<0.05, 45 had low frequencies, 54 were excluded, and two were fixed fragments. High values for polymorphic fragments (71.19% to 91.53% and Shannon index (0.327 to 0.428 were observed. The genetic divergence values within each stock were greater than 50%. Most of the genetic variation was found within the groups through the AMOVA analysis, which was confirmed by the results of the identity and genetic distance. High ancestry levels (FST among the groups value indicated high and moderate genetic differentiation. The estimates of number of migrants by generation (Nm indicated low levels of gene flow. High and moderate genetic divergence between groups (0.58 to 0.83 was observed. Conclusions. The results indicate high variability within the stocks, and genetic differentiation among them. The fish stocks analyzed represent a large genetic base that will allow the fish technicians to release juveniles without genetic risks to wild populations present in the river. These genetic procedures may be used as models for other migratory species, including those threatened by extinction.

  16. Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach.

    Directory of Open Access Journals (Sweden)

    Hyunseok Park

    Full Text Available The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method can dramatically reduce network complexity without missing any dominantly important patents. The main paths identified by our approach for two test cases are almost 10x less complex than the main paths identified by the existing approach. The proposed approach identifies all dominantly important patents on the main paths, but the main paths identified by the existing approach miss about 20% of dominantly important patents.

  17. New approaches to the treatment of orphan genetic disorders: Mitigating molecular pathologies using chemicals

    Directory of Open Access Journals (Sweden)

    RENATA V. VELHO

    2015-08-01

    Full Text Available With the advance and popularization of molecular techniques, the identification of genetic mutations that cause diseases has increased dramatically. Thus, the number of laboratories available to investigate a given disorder and the number of subsequent diagnosis have increased over time. Although it is necessary to identify mutations and provide diagnosis, it is also critical to develop specific therapeutic approaches based on this information. This review aims to highlight recent advances in mutation-targeted therapies with chemicals that mitigate mutational pathology at the molecular level, for disorders that, for the most part, have no effective treatment. Currently, there are several strategies being used to correct different types of mutations, including the following: the identification and characterization of translational readthrough compounds; antisense oligonucleotide-mediated splicing redirection; mismatch repair; and exon skipping. These therapies and other approaches are reviewed in this paper.

  18. New approaches to the treatment of orphan genetic disorders: Mitigating molecular pathologies using chemicals.

    Science.gov (United States)

    Velho, Renata V; Sperb-Ludwig, Fernanda; Schwartz, Ida V D

    2015-08-01

    With the advance and popularization of molecular techniques, the identification of genetic mutations that cause diseases has increased dramatically. Thus, the number of laboratories available to investigate a given disorder and the number of subsequent diagnosis have increased over time. Although it is necessary to identify mutations and provide diagnosis, it is also critical to develop specific therapeutic approaches based on this information. This review aims to highlight recent advances in mutation-targeted therapies with chemicals that mitigate mutational pathology at the molecular level, for disorders that, for the most part, have no effective treatment. Currently, there are several strategies being used to correct different types of mutations, including the following: the identification and characterization of translational readthrough compounds; antisense oligonucleotide-mediated splicing redirection; mismatch repair; and exon skipping. These therapies and other approaches are reviewed in this paper.

  19. Synthetic biology approaches in cancer immunotherapy, genetic network engineering, and genome editing.

    Science.gov (United States)

    Chakravarti, Deboki; Cho, Jang Hwan; Weinberg, Benjamin H; Wong, Nicole M; Wong, Wilson W

    2016-04-18

    Investigations into cells and their contents have provided evolving insight into the emergence of complex biological behaviors. Capitalizing on this knowledge, synthetic biology seeks to manipulate the cellular machinery towards novel purposes, extending discoveries from basic science to new applications. While these developments have demonstrated the potential of building with biological parts, the complexity of cells can pose numerous challenges. In this review, we will highlight the broad and vital role that the synthetic biology approach has played in applying fundamental biological discoveries in receptors, genetic circuits, and genome-editing systems towards translation in the fields of immunotherapy, biosensors, disease models and gene therapy. These examples are evidence of the strength of synthetic approaches, while also illustrating considerations that must be addressed when developing systems around living cells.

  20. Prediksi Nilai Tukar Rupiah Terhadap US Dollar Menggunakan Metode Genetic Programming

    Directory of Open Access Journals (Sweden)

    Daneswara Jauhari

    2016-12-01

    Exchange currency rate has a wide influence in the economy of a country, both domestically or internationally. The importance of knowing the pattern of exchange rate against the IDR to USD could help the economic growth due to foreign trade involves the use of currencies of different countries. Therefore, we need an application that can predict the value of IDR against the USD in the future. In this research, the authors use genetic programming (GP method which produces solutions (chromosome that obtained from the evaluation of exchange rate and then this solution used as an approximation or prediction of currency exchange rate in the future. These solutions formed from the combination of the set terminal and the set of function that generated randomly. After testing by the number popsize and different iterations, it was found that the GP algorithm can predict the value of the rupiah against the US Dollar with a very good, judging from the value of Mean Absolute Percentage Error (MAPE generated by 0.08%. This research can be developed even better by adding terminal parameters and operating parameters so they can add variation calculation results. Keywords:  prediction, exchange currency rate, genetic programming, MAPE.

  1. Drag reduction of a car model by linear genetic programming control

    Science.gov (United States)

    Li, Ruiying; Noack, Bernd R.; Cordier, Laurent; Borée, Jacques; Harambat, Fabien

    2017-08-01

    We investigate open- and closed-loop active control for aerodynamic drag reduction of a car model. Turbulent flow around a blunt-edged Ahmed body is examined at ReH≈ 3× 105 based on body height. The actuation is performed with pulsed jets at all trailing edges (multiple inputs) combined with a Coanda deflection surface. The flow is monitored with 16 pressure sensors distributed at the rear side (multiple outputs). We apply a recently developed model-free control strategy building on genetic programming in Dracopoulos and Kent (Neural Comput Appl 6:214-228, 1997) and Gautier et al. (J Fluid Mech 770:424-441, 2015). The optimized control laws comprise periodic forcing, multi-frequency forcing and sensor-based feedback including also time-history information feedback and combinations thereof. Key enabler is linear genetic programming (LGP) as powerful regression technique for optimizing the multiple-input multiple-output control laws. The proposed LGP control can select the best open- or closed-loop control in an unsupervised manner. Approximately 33% base pressure recovery associated with 22% drag reduction is achieved in all considered classes of control laws. Intriguingly, the feedback actuation emulates periodic high-frequency forcing. In addition, the control identified automatically the only sensor which listens to high-frequency flow components with good signal to noise ratio. Our control strategy is, in principle, applicable to all multiple actuators and sensors experiments.

  2. When Darwin meets Lorenz: Evolving new chaotic attractors through genetic programming

    International Nuclear Information System (INIS)

    Pan, Indranil; Das, Saptarshi

    2015-01-01

    Highlights: •New 3D continuous time chaotic systems with analytical expressions are obtained. •The multi-gene genetic programming (MGGP) paradigm is employed to achieve this. •Extends earlier works for evolving generalised family of Lorenz attractors. •Over one hundred of new chaotic attractors along with their parameters are reported. •The MGGP method have the potential for finding other similar chaotic attractors. -- Abstract: In this paper, we propose a novel methodology for automatically finding new chaotic attractors through a computational intelligence technique known as multi-gene genetic programming (MGGP). We apply this technique to the case of the Lorenz attractor and evolve several new chaotic attractors based on the basic Lorenz template. The MGGP algorithm automatically finds new nonlinear expressions for the different state variables starting from the original Lorenz system. The Lyapunov exponents of each of the attractors are calculated numerically based on the time series of the state variables using time delay embedding techniques. The MGGP algorithm tries to search the functional space of the attractors by aiming to maximise the largest Lyapunov exponent (LLE) of the evolved attractors. To demonstrate the potential of the proposed methodology, we report over one hundred new chaotic attractor structures along with their parameters, which are evolved from just the Lorenz system alone

  3. A Parallel Approach To Optimum Actuator Selection With a Genetic Algorithm

    Science.gov (United States)

    Rogers, James L.

    2000-01-01

    Recent discoveries in smart technologies have created a variety of aerodynamic actuators which have great potential to enable entirely new approaches to aerospace vehicle flight control. For a revolutionary concept such as a seamless aircraft with no moving control surfaces, there is a large set of candidate locations for placing actuators, resulting in a substantially larger number of combinations to examine in order to find an optimum placement satisfying the mission requirements. The placement of actuators on a wing determines the control effectiveness of the airplane. One approach to placement Maximizes the moments about the pitch, roll, and yaw axes, while minimizing the coupling. Genetic algorithms have been instrumental in achieving good solutions to discrete optimization problems, such as the actuator placement problem. As a proof of concept, a genetic has been developed to find the minimum number of actuators required to provide uncoupled pitch, roll, and yaw control for a simplified, untapered, unswept wing model. To find the optimum placement by searching all possible combinations would require 1,100 hours. Formulating the problem and as a multi-objective problem and modifying it to take advantage of the parallel processing capabilities of a multi-processor computer, reduces the optimization time to 22 hours.

  4. A systematic approach to assessing the clinical significance of genetic variants.

    Science.gov (United States)

    Duzkale, H; Shen, J; McLaughlin, H; Alfares, A; Kelly, M A; Pugh, T J; Funke, B H; Rehm, H L; Lebo, M S

    2013-11-01

    Molecular genetic testing informs diagnosis, prognosis, and risk assessment for patients and their family members. Recent advances in low-cost, high-throughput DNA sequencing and computing technologies have enabled the rapid expansion of genetic test content, resulting in dramatically increased numbers of DNA variants identified per test. To address this challenge, our laboratory has developed a systematic approach to thorough and efficient assessments of variants for pathogenicity determination. We first search for existing data in publications and databases including internal, collaborative and public resources. We then perform full evidence-based assessments through statistical analyses of observations in the general population and disease cohorts, evaluation of experimental data from in vivo or in vitro studies, and computational predictions of potential impacts of each variant. Finally, we weigh all evidence to reach an overall conclusion on the potential for each variant to be disease causing. In this report, we highlight the principles of variant assessment, address the caveats and pitfalls, and provide examples to illustrate the process. By sharing our experience and providing a framework for variant assessment, including access to a freely available customizable tool, we hope to help move towards standardized and consistent approaches to variant assessment. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  5. A practical approach to screen for authorised and unauthorised genetically modified plants.

    Science.gov (United States)

    Waiblinger, Hans-Ulrich; Grohmann, Lutz; Mankertz, Joachim; Engelbert, Dirk; Pietsch, Klaus

    2010-03-01

    In routine analysis, screening methods based on real-time PCR are most commonly used for the detection of genetically modified (GM) plant material in food and feed. In this paper, it is shown that the combination of five DNA target sequences can be used as a universal screening approach for at least 81 GM plant events authorised or unauthorised for placing on the market and described in publicly available databases. Except for maize event LY038, soybean events DP-305423 and BPS-CV127-9 and cotton event 281-24-236 x 3006-210-23, at least one of the five genetic elements has been inserted in these GM plants and is targeted by this screening approach. For the detection of these sequences, fully validated real-time PCR methods have been selected. A screening table is presented that describes the presence or absence of the target sequences for most of the listed GM plants. These data have been verified either theoretically according to available databases or experimentally using available reference materials. The screening table will be updated regularly by a network of German enforcement laboratories.

  6. Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using Genetic Programming

    Science.gov (United States)

    Kashid, Satishkumar S.; Maity, Rajib

    2012-08-01

    SummaryPrediction of Indian Summer Monsoon Rainfall (ISMR) is of vital importance for Indian economy, and it has been remained a great challenge for hydro-meteorologists due to inherent complexities in the climatic systems. The Large-scale atmospheric circulation patterns from tropical Pacific Ocean (ENSO) and those from tropical Indian Ocean (EQUINOO) are established to influence the Indian Summer Monsoon Rainfall. The information of these two large scale atmospheric circulation patterns in terms of their indices is used to model the complex relationship between Indian Summer Monsoon Rainfall and the ENSO as well as EQUINOO indices. However, extracting the signal from such large-scale indices for modeling such complex systems is significantly difficult. Rainfall predictions have been done for 'All India' as one unit, as well as for five 'homogeneous monsoon regions of India', defined by Indian Institute of Tropical Meteorology. Recent 'Artificial Intelligence' tool 'Genetic Programming' (GP) has been employed for modeling such problem. The Genetic Programming approach is found to capture the complex relationship between the monthly Indian Summer Monsoon Rainfall and large scale atmospheric circulation pattern indices - ENSO and EQUINOO. Research findings of this study indicate that GP-derived monthly rainfall forecasting models, that use large-scale atmospheric circulation information are successful in prediction of All India Summer Monsoon Rainfall with correlation coefficient as good as 0.866, which may appears attractive for such a complex system. A separate analysis is carried out for All India Summer Monsoon rainfall for India as one unit, and five homogeneous monsoon regions, based on ENSO and EQUINOO indices of months of March, April and May only, performed at end of month of May. In this case, All India Summer Monsoon Rainfall could be predicted with 0.70 as correlation coefficient with somewhat lesser Correlation Coefficient (C.C.) values for different

  7. 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.

  8. Trading Rules on Stock Markets Using Genetic Network Programming with Reinforcement Learning and Importance Index

    Science.gov (United States)

    Mabu, Shingo; Hirasawa, Kotaro; Furuzuki, Takayuki

    Genetic Network Programming (GNP) is an evolutionary computation which represents its solutions using graph structures. Since GNP can create quite compact programs and has an implicit memory function, it has been clarified that GNP works well especially in dynamic environments. In addition, a study on creating trading rules on stock markets using GNP with Importance Index (GNP-IMX) has been done. IMX is a new element which is a criterion for decision making. In this paper, we combined GNP-IMX with Actor-Critic (GNP-IMX&AC) and create trading rules on stock markets. Evolution-based methods evolve their programs after enough period of time because they must calculate fitness values, however reinforcement learning can change programs during the period, therefore the trading rules can be created efficiently. In the simulation, the proposed method is trained using the stock prices of 10 brands in 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. The simulation results show that the proposed method can obtain larger profits than GNP-IMX without AC and Buy&Hold.

  9. A New Spectral Shape-Based Record Selection Approach Using Np and Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Edén Bojórquez

    2013-01-01

    Full Text Available With the aim to improve code-based real records selection criteria, an approach inspired in a parameter proxy of spectral shape, named Np, is analyzed. The procedure is based on several objectives aimed to minimize the record-to-record variability of the ground motions selected for seismic structural assessment. In order to select the best ground motion set of records to be used as an input for nonlinear dynamic analysis, an optimization approach is applied using genetic algorithms focuse on finding the set of records more compatible with a target spectrum and target Np values. The results of the new Np-based approach suggest that the real accelerograms obtained with this procedure, reduce the scatter of the response spectra as compared with the traditional approach; furthermore, the mean spectrum of the set of records is very similar to the target seismic design spectrum in the range of interest periods, and at the same time, similar Np values are obtained for the selected records and the target spectrum.

  10. A Hybrid Genetic Programming Method in Optimization and Forecasting: A Case Study of the Broadband Penetration in OECD Countries

    Directory of Open Access Journals (Sweden)

    Konstantinos Salpasaranis

    2012-01-01

    Full Text Available The introduction of a hybrid genetic programming method (hGP in fitting and forecasting of the broadband penetration data is proposed. The hGP uses some well-known diffusion models, such as those of Gompertz, Logistic, and Bass, in the initial population of the solutions in order to accelerate the algorithm. The produced solutions models of the hGP are used in fitting and forecasting the adoption of broadband penetration. We investigate the fitting performance of the hGP, and we use the hGP to forecast the broadband penetration in OECD (Organisation for Economic Co-operation and Development countries. The results of the optimized diffusion models are compared to those of the hGP-generated models. The comparison indicates that the hGP manages to generate solutions with high-performance statistical indicators. The hGP cooperates with the existing diffusion models, thus allowing multiple approaches to forecasting. The modified algorithm is implemented in the Python programming language, which is fast in execution time, compact, and user friendly.

  11. Pre-emptive resource-constrained multimode project scheduling using genetic algorithm: A dynamic forward approach

    Directory of Open Access Journals (Sweden)

    Aidin Delgoshaei

    2016-09-01

    Full Text Available Purpose: The issue resource over-allocating is a big concern for project engineers in the process of scheduling project activities. Resource over-allocating drawback is frequently seen after scheduling of a project in practice which causes a schedule to be useless. Modifying an over-allocated schedule is very complicated and needs a lot of efforts and time. In this paper, a new and fast tracking method is proposed to schedule large scale projects which can help project engineers to schedule the project rapidly and with more confidence. Design/methodology/approach: In this article, a forward approach for maximizing net present value (NPV in multi-mode resource constrained project scheduling problem while assuming discounted positive cash flows (MRCPSP-DCF is proposed. The progress payment method is used and all resources are considered as pre-emptible. The proposed approach maximizes NPV using unscheduled resources through resource calendar in forward mode. For this purpose, a Genetic Algorithm is applied to solve. Findings: The findings show that the proposed method is an effective way to maximize NPV in MRCPSP-DCF problems while activity splitting is allowed. The proposed algorithm is very fast and can schedule experimental cases with 1000 variables and 100 resources in few seconds. The results are then compared with branch and bound method and simulated annealing algorithm and it is found the proposed genetic algorithm can provide results with better quality. Then algorithm is then applied for scheduling a hospital in practice. Originality/value: The method can be used alone or as a macro in Microsoft Office Project® Software to schedule MRCPSP-DCF problems or to modify resource over-allocated activities after scheduling a project. This can help project engineers to schedule project activities rapidly with more accuracy in practice.

  12. 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.

  13. 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.

  14. Discovery of Boolean metabolic networks: integer linear programming based approach.

    Science.gov (United States)

    Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing

    2018-04-11

    Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".

  15. Dynamic programming approach for partial decision rule optimization

    KAUST Repository

    Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

    2012-01-01

    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.

  16. 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.

  17. 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

  18. Molecular and Chemical Genetic Approaches to Developmental Origins of Aging and Disease in Zebrafish

    Science.gov (United States)

    Sasaki, Tomoyuki; Kishi, Shuji

    2013-01-01

    The incidence of diseases increases rapidly with age, accompanied by progressive deteriorations of physiological functions in organisms. Aging-associated diseases are sporadic but mostly inevitable complications arising from senescence. Senescence is often considered the antithesis of early development, but yet there may be factors and mechanisms in common between these two phenomena over the dynamic process of aging. The association between early development and late-onset disease with advancing age is thought to come from a consequence of developmental plasticity, the phenomenon by which one genotype can give rise to a range of physiologically and/or morphologically adaptive states in response to different environmental or genetic perturbations. On the one hand, we hypothesized that the future aging process can be predictive based on adaptivity during the early developmental period. Modulating the thresholds of adaptive plasticity by chemical genetic approaches, we have been investigating whether any relationship exists between the regulatory mechanisms that function in early development and in senescence using the zebrafish (Danio rerio), a small freshwater fish and a useful model animal for genetic studies. We have successfully conducted experiments to isolate zebrafish mutants expressing apparently altered senescence phenotypes during embryogenesis (“embryonic senescence”), subsequently showing shortened lifespan in adulthoods. We anticipate that previously uncharacterized developmental genes may mediate the aging process and play a pivotal role in senescence. On the other hand, unexpected senescence-related genes might also be involved in the early developmental process and regulation. The ease of manipulation using the zebrafish system allows us to conduct an exhaustive exploration of novel genes and small molecular compounds that can be linked to the senescence phenotype, and thereby facilitates searching for the evolutionary and developmental origins

  19. A genetic algorithm approach to optimization for the radiological worker allocation problem

    International Nuclear Information System (INIS)

    Yan Chen; Masakuni Narita; Masashi Tsuji; Sangduk Sa

    1996-01-01

    The worker allocation optimization problem in radiological facilities inevitably involves various types of requirements and constraints relevant to radiological protection and labor management. Some of these goals and constraints are not amenable to a rigorous mathematical formulation. Conventional methods for this problem rely heavily on sophisticated algebraic or numerical algorithms, which cause difficulties in the search for optimal solutions in the search space of worker allocation optimization problems. Genetic algorithms (GAB) are stochastic search algorithms introduced by J. Holland in the 1970s based on ideas and techniques from genetic and evolutionary theories. The most striking characteristic of GAs is the large flexibility allowed in the formulation of the optimal problem and the process of the search for the optimal solution. In the formulation, it is not necessary to define the optimal problem in rigorous mathematical terms, as required in the conventional methods. Furthermore, by designing a model of evolution for the optimal search problem, the optimal solution can be sought efficiently with computational simple manipulations without highly complex mathematical algorithms. We reported a GA approach to the worker allocation problem in radiological facilities in the previous study. In this study, two types of hard constraints were employed to reduce the huge search space, where the optimal solution is sought in such a way as to satisfy as many of soft constraints as possible. It was demonstrated that the proposed evolutionary method could provide the optimal solution efficiently compared with conventional methods. However, although the employed hard constraints could localize the search space into a very small region, it brought some complexities in the designed genetic operators and demanded additional computational burdens. In this paper, we propose a simplified evolutionary model with less restrictive hard constraints and make comparisons between

  20. A nuclear reload optimization approach using a real coded genetic algorithm with random keys

    International Nuclear Information System (INIS)

    Lima, Alan M.M. de; Schirru, Roberto; Medeiros, Jose A.C.C.

    2009-01-01

    The fuel reload of a Pressurized Water Reactor is made whenever the burn up of the fuel assemblies in the nucleus of the reactor reaches a certain value such that it is not more possible to maintain a critical reactor producing energy at nominal power. The problem of fuel reload optimization consists on determining the positioning of the fuel assemblies within the nucleus of the reactor in an optimized way to minimize the cost benefit relationship of fuel assemblies cost per maximum burn up, and also satisfying symmetry and safety restrictions. The fuel reload optimization problem difficulty grows exponentially with the number of fuel assemblies in the nucleus of the reactor. During decades the fuel reload optimization problem was solved manually by experts that used their knowledge and experience to build configurations of the reactor nucleus, and testing them to verify if safety restrictions of the plant are satisfied. To reduce this burden, several optimization techniques have been used, included the binary code genetic algorithm. In this work we show the use of a real valued coded approach of the genetic algorithm, with different recombination methods, together with a transformation mechanism called random keys, to transform the real values of the genes of each chromosome in a combination of discrete fuel assemblies for evaluation of the reload optimization. Four different recombination methods were tested: discrete recombination, intermediate recombination, linear recombination and extended linear recombination. For each of the 4 recombination methods 10 different tests using different seeds for the random number generator were conducted 10 generating, totaling 40 tests. The results of the application of the genetic algorithm are shown with formulation of real numbers for the problem of the nuclear reload of the plant Angra 1 type PWR. Since the best results in the literature for this problem were found by the parallel PSO we will it use for comparison

  1. Modelling of the Elasticity Modulus for Rock Using Genetic Expression Programming

    Directory of Open Access Journals (Sweden)

    Umit Atici

    2016-01-01

    Full Text Available In rock engineering projects, statically determined parameters are more reflective of actual load conditions than dynamic parameters. This study reports a new and efficient approach to the formulation of the static modulus of elasticity Es applying gene expression programming (GEP with nondestructive testing (NDT methods. The results obtained using GEP are compared with the results of multivariable linear regression analysis (MRA, univariate nonlinear regression analysis (URA, and the dynamic elasticity modulus (Ed. The GEP model was found to produce the most accurate calculation of Es. The proposed approach is a simple, nondestructive, and practical way to determine Es for anisotropic and heterogeneous rocks.

  2. A comparison of fitness-case sampling methods for genetic programming

    Science.gov (United States)

    Martínez, Yuliana; Naredo, Enrique; Trujillo, Leonardo; Legrand, Pierrick; López, Uriel

    2017-11-01

    Genetic programming (GP) is an evolutionary computation paradigm for automatic program induction. GP has produced impressive results but it still needs to overcome some practical limitations, particularly its high computational cost, overfitting and excessive code growth. Recently, many researchers have proposed fitness-case sampling methods to overcome some of these problems, with mixed results in several limited tests. This paper presents an extensive comparative study of four fitness-case sampling methods, namely: Interleaved Sampling, Random Interleaved Sampling, Lexicase Selection and Keep-Worst Interleaved Sampling. The algorithms are compared on 11 symbolic regression problems and 11 supervised classification problems, using 10 synthetic benchmarks and 12 real-world data-sets. They are evaluated based on test performance, overfitting and average program size, comparing them with a standard GP search. Comparisons are carried out using non-parametric multigroup tests and post hoc pairwise statistical tests. The experimental results suggest that fitness-case sampling methods are particularly useful for difficult real-world symbolic regression problems, improving performance, reducing overfitting and limiting code growth. On the other hand, it seems that fitness-case sampling cannot improve upon GP performance when considering supervised binary classification.

  3. Erlang Programming A Concurrent Approach to Software Development

    CERN Document Server

    Cesarini, Francesco

    2009-01-01

    This book offers you an in-depth explanation of Erlang, a programming language ideal for any situation where concurrency, fault-tolerance, and fast response is essential. You'll learn how to write complex concurrent programs in this language, regardless of your programming background or experience. Erlang Programming focuses on the language's syntax and semantics, and explains pattern matching, proper lists, recursion, debugging, networking, and concurrency, with exercises at the end of each chapter.

  4. Turtle Graphics implementation using a graphical dataflow programming approach

    OpenAIRE

    Lovejoy, Robert Steven

    1992-01-01

    Approved for public release; distribution is unlimited This thesis expands the concepts of object-oriented programming to implement a visual dataflow programming language. The main thrust of this research is to develop a functional prototype language, based upon the Turtle Graphics tool provided by LOGO programming language, for children to develop both their problem solving skills as well as their general programming skills. The language developed for this thesis was implemented in the...

  5. A bayesian approach to inferring the genetic population structure of sugarcane accessions from INTA (Argentina

    Directory of Open Access Journals (Sweden)

    Mariana Inés Pocovi

    2015-06-01

    Full Text Available Understanding the population structure and genetic diversity in sugarcane (Saccharum officinarum L. accessions from INTA germplasm bank (Argentina will be of great importance for germplasm collection and breeding improvement as it will identify diverse parental combinations to create segregating progenies with maximum genetic variability for further selection. A Bayesian approach, ordination methods (PCoA, Principal Coordinate Analysis and clustering analysis (UPGMA, Unweighted Pair Group Method with Arithmetic Mean were applied to this purpose. Sixty three INTA sugarcane hybrids were genotyped for 107 Simple Sequence Repeat (SSR and 136 Amplified Fragment Length Polymorphism (AFLP loci. Given the low probability values found with AFLP for individual assignment (4.7%, microsatellites seemed to perform better (54% for STRUCTURE analysis that revealed the germplasm to exist in five optimum groups with partly corresponding to their origin. However clusters shown high degree of admixture, F ST values confirmed the existence of differences among groups. Dissimilarity coefficients ranged from 0.079 to 0.651. PCoA separated sugarcane in groups that did not agree with those identified by STRUCTURE. The clustering including all genotypes neither showed resemblance to populations find by STRUCTURE, but clustering performed considering only individuals displaying a proportional membership > 0.6 in their primary population obtained with STRUCTURE showed close similarities. The Bayesian method indubitably brought more information on cultivar origins than classical PCoA and hierarchical clustering method.

  6. Intracellular, genetic or congenital immunisation--transgenic approaches to increase disease resistance of farm animals.

    Science.gov (United States)

    Müller, M; Brem, G

    1996-01-26

    Novel approaches to modify disease resistance or susceptibility in livestock are justified not only by economical reasons and with respect to animal welfare but also by recent advancements in molecular genetics. The control or elimination of infectious pathogens in farm animals is historically achieved by the use of vaccines and drugs and by quarantine safeguards and eradication. Currently, research on the improvement of disease resistance based on nucleic acid technology focuses on two main issues: additive gene transfer and the development of nucleic acid vaccines. The strategies aim at the stable or transient expression of components known to influence non-specific or specific host defence mechanisms against infectious pathogens. Thus, candidates for gene transfer experiments include all genes inducing or conferring innate and acquired immunity as well as specific disease resistance genes. Referring to the site and mode of action and the source of the effective agent the strategies are termed 'intracellular', 'genetic' and 'congenital' immunisation. The targeted disruption (deletive gene transfer) of disease susceptibility genes awaits the establishment of totipotential embryonic cell lineages in farm animals. The cytokine network regulates cellular viability, growth and differentiation in physiological and pathophysiological states. The identification of the JAK-STAT pathway used by many cytokines for their intracellular signal propagation has provided not only new target molecules for modulating the immune response but will also permit the further elucidation of host-pathogen interactions and resistance mechanisms.

  7. Pollen Sterility—A Promising Approach to Gene Confinement and Breeding for Genetically Modified Bioenergy Crops

    Directory of Open Access Journals (Sweden)

    Albert P. Kausch

    2012-10-01

    Full Text Available Advanced genetic and biotechnology tools will be required to realize the full potential of food and bioenergy crops. Given current regulatory concerns, many transgenic traits might never be deregulated for commercial release without a robust gene confinement strategy in place. The potential for transgene flow from genetically modified (GM crops is widely known. Pollen-mediated transfer is a major component of gene flow in flowering plants and therefore a potential avenue for the escape of transgenes from GM crops. One approach for preventing and/or mitigating transgene flow is the production of trait linked pollen sterility. To evaluate the feasibility of generating pollen sterility lines for gene confinement and breeding purposes we tested the utility of a promoter (Zm13Pro from a maize pollen-specific gene (Zm13 for driving expression of the reporter gene GUS and the cytotoxic gene barnase in transgenic rice (Oryza sativa ssp. Japonica cv. Nipponbare as a monocot proxy for bioenergy grasses. This study demonstrates that the Zm13 promoter can drive pollen-specific expression in stably transformed rice and may be useful for gametophytic transgene confinement and breeding strategies by pollen sterility in food and bioenergy crops.

  8. A Reverse Genetics Approach for the Design of Methyltransferase-Defective Live Attenuated Avian Metapneumovirus Vaccines.

    Science.gov (United States)

    Zhang, Yu; Sun, Jing; Wei, Yongwei; Li, Jianrong

    2016-01-01

    Avian metapneumovirus (aMPV), also known as avian pneumovirus or turkey rhinotracheitis virus, is the causative agent of turkey rhinotracheitis and is associated with swollen head syndrome in chickens. aMPV belongs to the family Paramyxoviridae which includes many important human pathogens such as human respiratory syncytial virus (RSV), human metapneumovirus (hMPV), and human parainfluenza virus type 3 (PIV3). The family also includes highly lethal emerging pathogens such as Nipah virus and Hendra virus, as well as agriculturally important viruses such as Newcastle disease virus (NDV). For many of these viruses, there is no effective vaccine. Here, we describe a reverse genetics approach to develop live attenuated aMPV vaccines by inhibiting the viral mRNA cap methyltransferase. The viral mRNA cap methyltransferase is an excellent target for the attenuation of paramyxoviruses because it plays essential roles in mRNA stability, efficient viral protein translation and innate immunity. We have described in detail the materials and methods used to generate recombinant aMPVs that lack viral mRNA cap methyltransferase activity. We have also provided methods to evaluate the genetic stability, pathogenesis, and immunogenicity of live aMPV vaccine candidates in turkeys.

  9. 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...

  10. A fixed recourse integer programming approach towards a ...

    African Journals Online (AJOL)

    Regardless of the success that linear programming and integer linear programming has had in applications in engineering, business and economics, one has to challenge the assumed reality that these optimization models represent. In this paper the certainty assumptions of an integer linear program application is ...

  11. Airline loyalty (programs) across borders : A geographic discontinuity approach

    NARCIS (Netherlands)

    de Jong, Gerben; Behrens, Christiaan; van Ommeren, Jos

    2018-01-01

    We analyze brand loyalty advantages of national airlines in their domestic countries using geocoded data from a major international frequent flier program. We employ a geographic discontinuity design that estimates discontinuities in program activity at the national borders of the program's

  12. A logic programming approach to medical errors in imaging.

    Science.gov (United States)

    Rodrigues, Susana; Brandão, Paulo; Nelas, Luís; Neves, José; Alves, Victor

    2011-09-01

    In 2000, the Institute of Medicine reported disturbing numbers on the scope it covers and the impact of medical error in the process of health delivery. Nevertheless, a solution to this problem may lie on the adoption of adverse event reporting and learning systems that can help to identify hazards and risks. It is crucial to apply models to identify the adverse events root causes, enhance the sharing of knowledge and experience. The efficiency of the efforts to improve patient safety has been frustratingly slow. Some of this insufficiency of progress may be assigned to the lack of systems that take into account the characteristic of the information about the real world. In our daily lives, we formulate most of our decisions normally based on incomplete, uncertain and even forbidden or contradictory information. One's knowledge is less based on exact facts and more on hypothesis, perceptions or indications. From the data collected on our adverse event treatment and learning system on medical imaging, and through the use of Extended Logic Programming to knowledge representation and reasoning, and the exploitation of new methodologies for problem solving, namely those based on the perception of what is an agent and/or multi-agent systems, we intend to generate reports that identify the most relevant causes of error and define improvement strategies, concluding about the impact, place of occurrence, form or type of event recorded in the healthcare institutions. The Eindhoven Classification Model was extended and adapted to the medical imaging field and used to classify adverse events root causes. Extended Logic Programming was used for knowledge representation with defective information, allowing for the modelling of the universe of discourse in terms of data and knowledge default. A systematization of the evolution of the body of knowledge about Quality of Information embedded in the Root Cause Analysis was accomplished. An adverse event reporting and learning system

  13. An Interval-Valued Approach to Business Process Simulation Based on Genetic Algorithms and the BPMN

    Directory of Open Access Journals (Sweden)

    Mario G.C.A. Cimino

    2014-05-01

    Full Text Available Simulating organizational processes characterized by interacting human activities, resources, business rules and constraints, is a challenging task, because of the inherent uncertainty, inaccuracy, variability and dynamicity. With regard to this problem, currently available business process simulation (BPS methods and tools are unable to efficiently capture the process behavior along its lifecycle. In this paper, a novel approach of BPS is presented. To build and manage simulation models according to the proposed approach, a simulation system is designed, developed and tested on pilot scenarios, as well as on real-world processes. The proposed approach exploits interval-valued data to represent model parameters, in place of conventional single-valued or probability-valued parameters. Indeed, an interval-valued parameter is comprehensive; it is the easiest to understand and express and the simplest to process, among multi-valued representations. In order to compute the interval-valued output of the system, a genetic algorithm is used. The resulting process model allows forming mappings at different levels of detail and, therefore, at different model resolutions. The system has been developed as an extension of a publicly available simulation engine, based on the Business Process Model and Notation (BPMN standard.

  14. A HYBRID GENETIC ALGORITHM-NEURAL NETWORK APPROACH FOR PRICING CORES AND REMANUFACTURED CORES

    Directory of Open Access Journals (Sweden)

    M. Seidi

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT:Sustainability has become a major issue in most economies, causing many leading companies to focus on product recovery and reverse logistics. Remanufacturing is an industrial process that makes used products reusable. One of the important aspects in both reverse logistics and remanufacturing is the pricing of returned and remanufactured products (called cores. In this paper, we focus on pricing the cores and remanufactured cores. First we present a mathematical model for this purpose. Since this model does not satisfy our requirements, we propose a simulation optimisation approach. This approach consists of a hybrid genetic algorithm based on a neural network employed as the fitness function. We use automata learning theory to obtain the learning rate required for training the neural network. Numerical results demonstrate that the optimal value of the acquisition price of cores and price of remanufactured cores is obtained by this approach.

    AFRIKAANSE OPSOMMING: Volhoubaarheid het ‘n belangrike saak geword in die meeste ekonomieë, wat verskeie maatskappye genoop het om produkherwinning en omgekeerde logistiek te onder oë te neem. Hervervaardiging is ‘n industriële proses wat gebruikte produkte weer bruikbaar maak. Een van die belangrike aspekte in beide omgekeerde logistiek en hervervaardiging is die prysbepaling van herwinne en hervervaardigde produkte. Hierdie artikel fokus op die prysbepalingsaspekte by wyse van ‘n wiskundige model.

  15. Plant responses to UV and blue light: biochemical and genetic approaches

    International Nuclear Information System (INIS)

    Jenkins, G.I.; Christie, J.M.; Fuglevand, G.; Long, J.C.; Jackson, J.A.

    1995-01-01

    UV and blue light control many aspects of plant growth and development. It is evident that several different photoreceptors mediate responses to UV and blue light, and there are reports of the functional and biochemical characterisation of a putative photoreceptor for phototropism and of the functional and molecular characterisation of the CRY1 photoreceptor, encoded by the Arabidopsis HY4 gene. The CRY1 photoreceptor mediates extension growth and gene expression responses to UV-A/blue light presumably through different or branching signal transduction pathways. Progress has been made in cell physiological and biochemical studies of UV/blue light signal transduction, but much remains to be done to relate candidate UV/blue signal transduction events to particular photoreceptors and responses. The application of a genetic approach in Arabidopsis has been responsible for many advances in understanding UV/blue responses, but further UV-B, UV-A and blue light response mutants need to be isolated. (author)

  16. Test interval optimization of safety systems of nuclear power plant using fuzzy-genetic approach

    International Nuclear Information System (INIS)

    Durga Rao, K.; Gopika, V.; Kushwaha, H.S.; Verma, A.K.; Srividya, A.

    2007-01-01

    Probabilistic safety assessment (PSA) is the most effective and efficient tool for safety and risk management in nuclear power plants (NPP). PSA studies not only evaluate risk/safety of systems but also their results are very useful in safe, economical and effective design and operation of NPPs. The latter application is popularly known as 'Risk-Informed Decision Making'. Evaluation of technical specifications is one such important application of Risk-Informed decision making. Deciding test interval (TI), one of the important technical specifications, with the given resources and risk effectiveness is an optimization problem. Uncertainty is inherently present in the availability parameters such as failure rate and repair time due to the limitation in assessing these parameters precisely. This paper presents a solution to test interval optimization problem with uncertain parameters in the model with fuzzy-genetic approach along with a case of application from a safety system of Indian pressurized heavy water reactor (PHWR)

  17. Optimization of Electrical System for Offshore Wind Farms via a Genetic Algorithm Approach

    DEFF Research Database (Denmark)

    Zhao, Menghua

    , and the LTC limitation of transformers, the power generation limits and the voltage operation range are considered as the constraints. The optimization method combined with probabilistic analysis is used to obtain the capacity of a given wind farm site. The OES-OWF is approached by Genetic Algorithm (GA...... to very different costs, system reliability, power quality, and power losses etc. Therefore, the optimization of electrical system design for offshore wind farms becomes more and more necessary. There are two tasks in this project: 1) the first one is to construct an algorithm for finding the capacity......). This platform is based on a knowledge database, and composed of several functional modules such as cost calculation, reliability evaluation, losses calculation, AC-DC integrated load flow algorithm etc. All these modules are based on a spreadsheet database which provides an interface for users to input...

  18. A NAÏVE APPROACH TO SPEED UP PORTFOLIO OPTIMIZATION PROBLEM USING A MULTIOBJECTIVE GENETIC ALGORITHM

    Directory of Open Access Journals (Sweden)

    Baixauli-Soler, J. Samuel

    2012-05-01

    Full Text Available Genetic algorithms (GAs are appropriate when investors have the objective of obtaining mean‑variance (VaR efficient frontier as minimising VaR leads to non‑convex and non‑differential risk‑return optimisation problems. However GAs are a time‑consuming optimisation technique. In this paper, we propose to use a naïve approach consisting of using samples split by quartile of risk to obtain complete efficient frontiers in a reasonable computation time. Our results show that using reduced problems which only consider a quartile of the assets allow us to explore the efficient frontier for a large range of risk values. In particular, the third quartile allows us to obtain efficient frontiers from the 1.8% to 2.5% level of VaR quickly, while that of the first quartile of assets is from 1% to 1.3% level of VaR.

  19. A New Approach to Commercialization of NASA's Human Research Program Technologies, Phase I

    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 program that...

  20. On the Reliability of Nonlinear Modeling using Enhanced Genetic Programming Techniques

    Science.gov (United States)

    Winkler, S. M.; Affenzeller, M.; Wagner, S.

    The use of genetic programming (GP) in nonlinear system identification enables the automated search for mathematical models that are evolved by an evolutionary process using the principles of selection, crossover and mutation. Due to the stochastic element that is intrinsic to any evolutionary process, GP cannot guarantee the generation of similar or even equal models in each GP process execution; still, if there is a physical model underlying to the data that are analyzed, then GP is expected to find these structures and produce somehow similar results. In this paper we define a function for measuring the syntactic similarity of mathematical models represented as structure trees; using this similarity function we compare the results produced by GP techniques for a data set representing measurement data of a BMW Diesel engine.

  1. Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions

    Science.gov (United States)

    Khoury, Mehdi; Liu, Honghai

    This research introduces and builds on the concept of Fuzzy Gaussian Inference (FGI) (Khoury and Liu in Proceedings of UKCI, 2008 and IEEE Workshop on Robotic Intelligence in Informationally Structured Space (RiiSS 2009), 2009) as a novel way to build Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions. This method is now combined with a Genetic Programming Fuzzy rule-based system in order to classify boxing moves from natural human Motion Capture data. In this experiment, FGI alone is able to recognise seven different boxing stances simultaneously with an accuracy superior to a GMM-based classifier. Results seem to indicate that adding an evolutionary Fuzzy Inference Engine on top of FGI improves the accuracy of the classifier in a consistent way.

  2. LPmerge: an R package for merging genetic maps by linear programming.

    Science.gov (United States)

    Endelman, Jeffrey B; Plomion, Christophe

    2014-06-01

    Consensus genetic maps constructed from multiple populations are an important resource for both basic and applied research, including genome-wide association analysis, genome sequence assembly and studies of evolution. The LPmerge software uses linear programming to efficiently minimize the mean absolute error between the consensus map and the linkage maps from each population. This minimization is performed subject to linear inequality constraints that ensure the ordering of the markers in the linkage maps is preserved. When marker order is inconsistent between linkage maps, a minimum set of ordinal constraints is deleted to resolve the conflicts. LPmerge is on CRAN at http://cran.r-project.org/web/packages/LPmerge. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  3. Prediction of Layer Thickness in Molten Borax Bath with Genetic Evolutionary Programming

    Science.gov (United States)

    Taylan, Fatih

    2011-04-01

    In this study, the vanadium carbide coating in molten borax bath process is modeled by evolutionary genetic programming (GEP) with bath composition (borax percentage, ferro vanadium (Fe-V) percentage, boric acid percentage), bath temperature, immersion time, and layer thickness data. Five inputs and one output data exist in the model. The percentage of borax, Fe-V, and boric acid, temperature, and immersion time parameters are used as input data and the layer thickness value is used as output data. For selected bath components, immersion time, and temperature variables, the layer thicknesses are derived from the mathematical expression. The results of the mathematical expressions are compared to that of experimental data; it is determined that the derived mathematical expression has an accuracy of 89%.

  4. Modelling the effect of structural QSAR parameters on skin penetration using genetic programming

    International Nuclear Information System (INIS)

    Chung, K K; Do, D Q

    2010-01-01

    In order to model relationships between chemical structures and biological effects in quantitative structure–activity relationship (QSAR) data, an alternative technique of artificial intelligence computing—genetic programming (GP)—was investigated and compared to the traditional method—statistical. GP, with the primary advantage of generating mathematical equations, was employed to model QSAR data and to define the most important molecular descriptions in QSAR data. The models predicted by GP agreed with the statistical results, and the most predictive models of GP were significantly improved when compared to the statistical models using ANOVA. Recently, artificial intelligence techniques have been applied widely to analyse QSAR data. With the capability of generating mathematical equations, GP can be considered as an effective and efficient method for modelling QSAR data

  5. An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach

    Directory of Open Access Journals (Sweden)

    Francis Oloo

    2017-01-01

    Full Text Available Conventionally, agent-based modelling approaches start from a conceptual model capturing the theoretical understanding of the systems of interest. Simulation outcomes are then used “at the end” to validate the conceptual understanding. In today’s data rich era, there are suggestions that models should be data-driven. Data-driven workflows are common in mathematical models. However, their application to agent-based models is still in its infancy. Integration of real-time sensor data into modelling workflows opens up the possibility of comparing simulations against real data during the model run. Calibration and validation procedures thus become automated processes that are iteratively executed during the simulation. We hypothesize that incorporation of real-time sensor data into agent-based models improves the predictive ability of such models. In particular, that such integration results in increasingly well calibrated model parameters and rule sets. In this contribution, we explore this question by implementing a flocking model that evolves in real-time. Specifically, we use genetic algorithms approach to simulate representative parameters to describe flight routes of homing pigeons. The navigation parameters of pigeons are simulated and dynamically evaluated against emulated GPS sensor data streams and optimised based on the fitness of candidate parameters. As a result, the model was able to accurately simulate the relative-turn angles and step-distance of homing pigeons. Further, the optimised parameters could replicate loops, which are common patterns in flight tracks of homing pigeons. Finally, the use of genetic algorithms in this study allowed for a simultaneous data-driven optimization and sensitivity analysis.

  6. Incorporating Functional Genomic Information in Genetic Association Studies Using an Empirical Bayes Approach.

    Science.gov (United States)

    Spencer, Amy V; Cox, Angela; Lin, Wei-Yu; Easton, Douglas F; Michailidou, Kyriaki; Walters, Kevin

    2016-04-01

    There is a large amount of functional genetic data available, which can be used to inform fine-mapping association studies (in diseases with well-characterised disease pathways). Single nucleotide polymorphism (SNP) prioritization via Bayes factors is attractive because prior information can inform the effect size or the prior probability of causal association. This approach requires the specification of the effect size. If the information needed to estimate a priori the probability density for the effect sizes for causal SNPs in a genomic region isn't consistent or isn't available, then specifying a prior variance for the effect sizes is challenging. We propose both an empirical method to estimate this prior variance, and a coherent approach to using SNP-level functional data, to inform the prior probability of causal association. Through simulation we show that when ranking SNPs by our empirical Bayes factor in a fine-mapping study, the causal SNP rank is generally as high or higher than the rank using Bayes factors with other plausible values of the prior variance. Importantly, we also show that assigning SNP-specific prior probabilities of association based on expert prior functional knowledge of the disease mechanism can lead to improved causal SNPs ranks compared to ranking with identical prior probabilities of association. We demonstrate the use of our methods by applying the methods to the fine mapping of the CASP8 region of chromosome 2 using genotype data from the Collaborative Oncological Gene-Environment Study (COGS) Consortium. The data we analysed included approximately 46,000 breast cancer case and 43,000 healthy control samples. © 2016 The Authors. *Genetic Epidemiology published by Wiley Periodicals, Inc.

  7. Genetics of PCOS: A systematic bioinformatics approach to unveil the proteins responsible for PCOS.

    Science.gov (United States)

    Panda, Pritam Kumar; Rane, Riya; Ravichandran, Rahul; Singh, Shrinkhla; Panchal, Hetalkumar

    2016-06-01

    Polycystic ovary syndrome (PCOS) is a hormonal imbalance in women, which causes problems during menstrual cycle and in pregnancy that sometimes results in fatality. Though the genetics of PCOS is not fully understood, early diagnosis and treatment can prevent long-term effects. In this study, we have studied the proteins involved in PCOS and the structural aspects of the proteins that are taken into consideration using computational tools. The proteins involved are modeled using Modeller 9v14 and Ab-initio programs. All the 43 proteins responsible for PCOS were subjected to phylogenetic analysis to identify the relatedness of the proteins. Further, microarray data analysis of PCOS datasets was analyzed that was downloaded from GEO datasets to find the significant protein-coding genes responsible for PCOS, which is an addition to the reported protein-coding genes. Various statistical analyses were done using R programming to get an insight into the structural aspects of PCOS that can be used as drug targets to treat PCOS and other related reproductive diseases.

  8. Genetic Modulation of Lipid Profiles following Lifestyle Modification or Metformin Treatment: The Diabetes Prevention Program

    Science.gov (United States)

    Jablonski, Kathleen A.; de Bakker, Paul I. W.; Taylor, Andrew; McAteer, Jarred; Pan, Qing; Horton, Edward S.; Delahanty, Linda M.; Altshuler, David; Shuldiner, Alan R.; Goldberg, Ronald B.; Florez, Jose C.; Bray, George A.; Culbert, Iris W.; Champagne, Catherine M.; Eberhardt, Barbara; Greenway, Frank; Guillory, Fonda G.; Herbert, April A.; Jeffirs, Michael L.; Kennedy, Betty M.; Lovejoy, Jennifer C.; Morris, Laura H.; Melancon, Lee E.; Ryan, Donna; Sanford, Deborah A.; Smith, Kenneth G.; Smith, Lisa L.; Amant, Julia A. St.; Tulley, Richard T.; Vicknair, Paula C.; Williamson, Donald; Zachwieja, Jeffery J.; Polonsky, Kenneth S.; Tobian, Janet; Ehrmann, David; Matulik, Margaret J.; Clark, Bart; Czech, Kirsten; DeSandre, Catherine; Hilbrich, Ruthanne; McNabb, Wylie; Semenske, Ann R.; Caro, Jose F.; Watson, Pamela G.; Goldstein, Barry J.; Smith, Kellie A.; Mendoza, Jewel; Liberoni, Renee; Pepe, Constance; Spandorfer, John; Donahue, Richard P.; Goldberg, Ronald B.; Prineas, Ronald; Rowe, Patricia; Calles, Jeanette; Cassanova-Romero, Paul; Florez, Hermes J.; Giannella, Anna; Kirby, Lascelles; Larreal, Carmen; McLymont, Valerie; Mendez, Jadell; Ojito, Juliet; Perry, Arlette; Saab, Patrice; Haffner, Steven M.; Montez, Maria G.; Lorenzo, Carlos; Martinez, Arlene; Hamman, Richard F.; Nash, Patricia V.; Testaverde, Lisa; Anderson, Denise R.; Ballonoff, Larry B.; Bouffard, Alexis; Calonge, B. Ned; Delve, Lynne; Farago, Martha; Hill, James O.; Hoyer, Shelley R.; Jortberg, Bonnie T.; Lenz, Dione; Miller, Marsha; Price, David W.; Regensteiner, Judith G.; Seagle, Helen; Smith, Carissa M.; Steinke, Sheila C.; VanDorsten, Brent; Horton, Edward S.; Lawton, Kathleen E.; Arky, Ronald A.; Bryant, Marybeth; Burke, Jacqueline P.; Caballero, Enrique; Callaphan, Karen M.; Ganda, Om P.; Franklin, Therese; Jackson, Sharon D.; Jacobsen, Alan M.; Jacobsen, Alan M.; Kula, Lyn M.; Kocal, Margaret; Malloy, Maureen A.; Nicosia, Maryanne; Oldmixon, Cathryn F.; Pan, Jocelyn; Quitingon, Marizel; Rubtchinsky, Stacy; Seely, Ellen W.; Schweizer, Dana; Simonson, Donald; Smith, Fannie; Solomon, Caren G.; Warram, James; Kahn, Steven E.; Montgomery, Brenda K.; Fujimoto, Wilfred; Knopp, Robert H.; Lipkin, Edward W.; Marr, Michelle; Trence, Dace; Kitabchi, Abbas E.; Murphy, Mary E.; Applegate, William B.; Bryer-Ash, Michael; Frieson, Sandra L.; Imseis, Raed; Lambeth, Helen; Lichtermann, Lynne C.; Oktaei, Hooman; Rutledge, Lily M.K.; Sherman, Amy R.; Smith, Clara M.; Soberman, Judith E.; Williams-Cleaves, Beverly; Metzger, Boyd E.; Johnson, Mariana K.; Behrends, Catherine; Cook, Michelle; Fitzgibbon, Marian; Giles, Mimi M.; Heard, Deloris; Johnson, Cheryl K.H.; Larsen, Diane; Lowe, Anne; Lyman, Megan; McPherson, David; Molitch, Mark E.; Pitts, Thomas; Reinhart, Renee; Roston, Susan; Schinleber, Pamela A.; Nathan, David M.; McKitrick, Charles; Turgeon, Heather; Abbott, Kathy; Anderson, Ellen; Bissett, Laurie; Cagliero, Enrico; Florez, Jose C.; Delahanty, Linda; Goldman, Valerie; Poulos, Alexandra; Olefsky, Jerrold M.; Carrion-Petersen, Mary Lou; Barrett-Connor, Elizabeth; Edelman, Steven V.; Henry, Robert R.; Horne, Javiva; Janesch, Simona Szerdi; Leos, Diana; Mudaliar, Sundar; Polonsky, William; Smith, Jean; Vejvoda, Karen; Pi-Sunyer, F. Xavier; Lee, Jane E.; Allison, David B.; Aronoff, Nancy J.; Crandall, Jill P.; Foo, Sandra T.; Pal, Carmen; Parkes, Kathy; Pena, Mary Beth; Rooney, Ellen S.; Wye, Gretchen E.H. Van; Viscovich, Kristine A.; Marrero, David G.; Prince, Melvin J.; Kelly, Susie M.; Dotson, Yolanda F.; Fineberg, Edwin S.; Guare, John C; Hadden, Angela M.; Ignaut, James M.; Jackson, Marcia L.; Kirkman, Marion S.; Mather, Kieren J.; Porter, Beverly D.; Roach, Paris J.; Rowland, Nancy D.; Wheeler, Madelyn L.; Ratner, Robert E.; Youssef, Gretchen; Shapiro, Sue; Bavido-Arrage, Catherine; Boggs, Geraldine; Bronsord, Marjorie; Brown, Ernestine; Cheatham, Wayman W.; Cola, Susan; Evans, Cindy; Gibbs, Peggy; Kellum, Tracy; Levatan, Claresa; Nair, Asha K.; Passaro, Maureen; Uwaifo, Gabriel; Saad, Mohammed F.; Budget, Maria; Jinagouda, Sujata; Akbar, Khan; Conzues, Claudia; Magpuri, Perpetua; Ngo, Kathy; Rassam, Amer; Waters, Debra; Xapthalamous, Kathy; Santiago, Julio V.; Dagogo-Jack, Samuel; White, Neil H.; Das, Samia; Santiago, Ana; Brown, Angela; Fisher, Edwin; Hurt, Emma; Jones, Tracy; Kerr, Michelle; Ryder, Lucy; Wernimont, Cormarie; Saudek, Christopher D.; Bradley, Vanessa; Sullivan, Emily; Whittington, Tracy; Abbas, Caroline; Brancati, Frederick L.; Clark, Jeanne M.; Charleston, Jeanne B.; Freel, Janice; Horak, Katherine; Jiggetts, Dawn; Johnson, Deloris; Joseph, Hope; Loman, Kimberly; Mosley, Henry; Rubin, Richard R.; Samuels, Alafia; Stewart, Kerry J.; Williamson, Paula; Schade, David S.; Adams, Karwyn S.; Johannes, Carolyn; Atler, Leslie F.; Boyle, Patrick J.; Burge, Mark R.; Canady, Janene L.; Chai, Lisa; Gonzales, Ysela; Hernandez-McGinnis, Doris A.; Katz, Patricia; King, Carolyn; Rassam, Amer; Rubinchik, Sofya; Senter, Willette; Waters, Debra; Shamoon, Harry; Brown, Janet O.; Adorno, Elsie; Cox, Liane; Crandall, Jill; Duffy, Helena; Engel, Samuel; Friedler, Allison; Howard-Century, Crystal J.; Kloiber, Stacey; Longchamp, Nadege; Martinez, Helen; Pompi, Dorothy; Scheindlin, Jonathan; Violino, Elissa; Walker, Elizabeth; Wylie-Rosett, Judith; Zimmerman, Elise; Zonszein, Joel; Orchard, Trevor; Wing, Rena R.; Koenning, Gaye; Kramer, M. Kaye; Barr, Susan; Boraz, Miriam; Clifford, Lisa; Culyba, Rebecca; Frazier, Marlene; Gilligan, Ryan; Harrier, Susan; Harris, Louann; Jeffries, Susan; Kriska, Andrea; Manjoo, Qurashia; Mullen, Monica; Noel, Alicia; Otto, Amy; Semler, Linda; Smith, Cheryl F.; Smith, Marie; Venditti, Elizabeth; Weinzierl, Valarie; Williams, Katherine V.; Wilson, Tara; Arakaki, Richard F.; Latimer, Renee W.; Baker-Ladao, Narleen K.; Beddow, Ralph; Dias, Lorna; Inouye, Jillian; Mau, Marjorie K.; Mikami, Kathy; Mohideen, Pharis; Odom, Sharon K.; Perry, Raynette U.; Knowler, William C.; Cooeyate, Norman; Hoskin, Mary A.; Percy, Carol A.; Acton, Kelly J.; Andre, Vickie L.; Barber, Rosalyn; Begay, Shandiin; Bennett, Peter H.; Benson, Mary Beth; Bird, Evelyn C.; Broussard, Brenda A.; Chavez, Marcella; Dacawyma, Tara; Doughty, Matthew S.; Duncan, Roberta; Edgerton, Cyndy; Ghahate, Jacqueline M.; Glass, Justin; Glass, Martia; Gohdes, Dorothy; Grant, Wendy; Hanson, Robert L.; Horse, Ellie; Ingraham, Louise E.; Jackson, Merry; Jay, Priscilla; Kaskalla, Roylen S.; Kessler, David; Kobus, Kathleen M.; Krakoff, Jonathan; Manus, Catherine; Michaels, Sara; Morgan, Tina; Nashboo, Yolanda; Nelson, Julie A.; Poirier, Steven; Polczynski, Evette; Reidy, Mike; Roumain, Jeanine; Rowse, Debra; Sangster, Sandra; Sewenemewa, Janet; Tonemah, Darryl; Wilson, Charlton; Yazzie, Michelle; Bain, Raymond; Fowler, Sarah; Brenneman, Tina; Abebe, Solome; Bamdad, Julie; Callaghan, Jackie; Edelstein, Sharon L.; Gao, Yuping; Grimes, Kristina L.; Grover, Nisha; Haffner, Lori; Jones, Steve; Jones, Tara L.; Katz, Richard; Lachin, John M.; Mucik, Pamela; Orlosky, Robert; Rochon, James; Sapozhnikova, Alla; Sherif, Hanna; Stimpson, Charlotte; Temprosa, Marinella; Walker-Murray, Fredricka; Marcovina, Santica; Strylewicz, Greg; Aldrich, F. Alan; O'Leary, Dan; Stamm, Elizabeth; Rautaharju, Pentti; Prineas, Ronald J.; Alexander, Teresa; Campbell, Charles; Hall, Sharon; Li, Yabing; Mills, Margaret; Pemberton, Nancy; Rautaharju, Farida; Zhang, Zhuming; Mayer-Davis, Elizabeth; Moran, Robert R.; Ganiats, Ted; David, Kristin; Sarkin, Andrew J.; Eastman, R.; Fradkin, Judith; Garfield, Sanford; Gregg, Edward; Zhang, Ping; Herman, William; Florez, Jose C.; Altshuler, David; de Bakker, Paul I.W.; Franks, Paul W.; Hanson, Robert L.; Jablonski, Kathleen; Knowler, William C.; McAteer, Jarred B.; Pollin, Toni I.; Shuldiner, Alan R.

    2012-01-01

    Weight-loss interventions generally improve lipid profiles and reduce cardiovascular disease risk, but effects are variable and may depend on genetic factors. We performed a genetic association analysis of data from 2,993 participants in the Diabetes Prevention Program to test the hypotheses that a genetic risk score (GRS) based on deleterious alleles at 32 lipid-associated single-nucleotide polymorphisms modifies the effects of lifestyle and/or metformin interventions on lipid levels and nuclear magnetic resonance (NMR) lipoprotein subfraction size and number. Twenty-three loci previously associated with fasting LDL-C, HDL-C, or triglycerides replicated (P = 0.04–1×10−17). Except for total HDL particles (r = −0.03, P = 0.26), all components of the lipid profile correlated with the GRS (partial |r| = 0.07–0.17, P = 5×10−5–1×10−19). The GRS was associated with higher baseline-adjusted 1-year LDL cholesterol levels (β = +0.87, SEE±0.22 mg/dl/allele, P = 8×10−5, P interaction = 0.02) in the lifestyle intervention group, but not in the placebo (β = +0.20, SEE±0.22 mg/dl/allele, P = 0.35) or metformin (β = −0.03, SEE±0.22 mg/dl/allele, P = 0.90; P interaction = 0.64) groups. Similarly, a higher GRS predicted a greater number of baseline-adjusted small LDL particles at 1 year in the lifestyle intervention arm (β = +0.30, SEE±0.012 ln nmol/L/allele, P = 0.01, P interaction = 0.01) but not in the placebo (β = −0.002, SEE±0.008 ln nmol/L/allele, P = 0.74) or metformin (β = +0.013, SEE±0.008 nmol/L/allele, P = 0.12; P interaction = 0.24) groups. Our findings suggest that a high genetic burden confers an adverse lipid profile and predicts attenuated response in LDL-C levels and small LDL particle number to dietary and physical activity interventions aimed at weight loss. PMID:22951888

  9. Genetic modulation of lipid profiles following lifestyle modification or metformin treatment: the Diabetes Prevention Program.

    Directory of Open Access Journals (Sweden)

    Toni I Pollin

    Full Text Available Weight-loss interventions generally improve lipid profiles and reduce cardiovascular disease risk, but effects are variable and may depend on genetic factors. We performed a genetic association analysis of data from 2,993 participants in the Diabetes Prevention Program to test the hypotheses that a genetic risk score (GRS based on deleterious alleles at 32 lipid-associated single-nucleotide polymorphisms modifies the effects of lifestyle and/or metformin interventions on lipid levels and nuclear magnetic resonance (NMR lipoprotein subfraction size and number. Twenty-three loci previously associated with fasting LDL-C, HDL-C, or triglycerides replicated (P = 0.04-1 × 10(-17. Except for total HDL particles (r = -0.03, P = 0.26, all components of the lipid profile correlated with the GRS (partial |r| = 0.07-0.17, P = 5 × 10(-5-1 10(-19. The GRS was associated with higher baseline-adjusted 1-year LDL cholesterol levels (β = +0.87, SEE ± 0.22 mg/dl/allele, P = 8 × 10(-5, P(interaction = 0.02 in the lifestyle intervention group, but not in the placebo (β = +0.20, SEE ± 0.22 mg/dl/allele, P = 0.35 or metformin (β = -0.03, SEE ± 0.22 mg/dl/allele, P = 0.90; P(interaction = 0.64 groups. Similarly, a higher GRS predicted a greater number of baseline-adjusted small LDL particles at 1 year in the lifestyle intervention arm (β = +0.30, SEE ± 0.012 ln nmol/L/allele, P = 0.01, P(interaction = 0.01 but not in the placebo (β = -0.002, SEE ± 0.008 ln nmol/L/allele, P = 0.74 or metformin (β = +0.013, SEE ± 0.008 nmol/L/allele, P = 0.12; P(interaction = 0.24 groups. Our findings suggest that a high genetic burden confers an adverse lipid profile and predicts attenuated response in LDL-C levels and small LDL particle number to dietary and physical activity interventions aimed at weight loss.

  10. An iterative method for tri-level quadratic fractional programming problems using fuzzy goal programming approach

    Science.gov (United States)

    Kassa, Semu Mitiku; Tsegay, Teklay Hailay

    2017-08-01

    Tri-level optimization problems are optimization problems with three nested hierarchical structures, where in most cases conflicting objectives are set at each level of hierarchy. Such problems are common in management, engineering designs and in decision making situations in general, and are known to be strongly NP-hard. Existing solution methods lack universality in solving these types of problems. In this paper, we investigate a tri-level programming problem with quadratic fractional objective functions at each of the three levels. A solution algorithm has been proposed by applying fuzzy goal programming approach and by reformulating the fractional constraints to equivalent but non-fractional non-linear constraints. Based on the transformed formulation, an iterative procedure is developed that can yield a satisfactory solution to the tri-level problem. The numerical results on various illustrative examples demonstrated that the proposed algorithm is very much promising and it can also be used to solve larger-sized as well as n-level problems of similar structure.

  11. Programming massively parallel processors a hands-on approach

    CERN Document Server

    Kirk, David B

    2010-01-01

    Programming Massively Parallel Processors discusses basic concepts about parallel programming and GPU architecture. ""Massively parallel"" refers to the use of a large number of processors to perform a set of computations in a coordinated parallel way. The book details various techniques for constructing parallel programs. It also discusses the development process, performance level, floating-point format, parallel patterns, and dynamic parallelism. The book serves as a teaching guide where parallel programming is the main topic of the course. It builds on the basics of C programming for CUDA, a parallel programming environment that is supported on NVI- DIA GPUs. Composed of 12 chapters, the book begins with basic information about the GPU as a parallel computer source. It also explains the main concepts of CUDA, data parallelism, and the importance of memory access efficiency using CUDA. The target audience of the book is graduate and undergraduate students from all science and engineering disciplines who ...

  12. Planning additional drilling campaign using two-space genetic algorithm: A game theoretical approach

    Science.gov (United States)

    Kumral, Mustafa; Ozer, Umit

    2013-03-01

    Grade and tonnage are the most important technical uncertainties in mining ventures because of the use of estimations/simulations, which are mostly generated from drill data. Open pit mines are planned and designed on the basis of the blocks representing the entire orebody. Each block has different estimation/simulation variance reflecting uncertainty to some extent. The estimation/simulation realizations are submitted to mine production scheduling process. However, the use of a block model with varying estimation/simulation variances will lead to serious risk in the scheduling. In the medium of multiple simulations, the dispersion variances of blocks can be thought to regard technical uncertainties. However, the dispersion variance cannot handle uncertainty associated with varying estimation/simulation variances of blocks. This paper proposes an approach that generates the configuration of the best additional drilling campaign to generate more homogenous estimation/simulation variances of blocks. In other words, the objective is to find the best drilling configuration in such a way as to minimize grade uncertainty under budget constraint. Uncertainty measure of the optimization process in this paper is interpolation variance, which considers data locations and grades. The problem is expressed as a minmax problem, which focuses on finding the best worst-case performance i.e., minimizing interpolation variance of the block generating maximum interpolation variance. Since the optimization model requires computing the interpolation variances of blocks being simulated/estimated in each iteration, the problem cannot be solved by standard optimization tools. This motivates to use two-space genetic algorithm (GA) approach to solve the problem. The technique has two spaces: feasible drill hole configuration with minimization of interpolation variance and drill hole simulations with maximization of interpolation variance. Two-space interacts to find a minmax solution

  13. Quadratic programming with fuzzy parameters: A membership function approach

    International Nuclear Information System (INIS)

    Liu, S.-T.

    2009-01-01

    Quadratic programming has been widely applied to solving real world problems. The conventional quadratic programming model requires the parameters to be known constants. In the real world, however, the parameters are seldom known exactly and have to be estimated. This paper discusses the fuzzy quadratic programming problems where the cost coefficients, constraint coefficients, and right-hand sides are represented by convex fuzzy numbers. Since the parameters in the program are fuzzy numbers, the derived objective value is a fuzzy number as well. Using Zadeh's extension principle, a pair of two-level mathematical programs is formulated to calculate the upper bound and lower bound of the objective values of the fuzzy quadratic program. Based on the duality theorem and by applying the variable transformation technique, the pair of two-level mathematical programs is transformed into a family of conventional one-level quadratic programs. Solving the pair of quadratic programs produces the fuzzy objective values of the problem. An example illustrates method proposed in this paper.

  14. A method of evolving novel feature extraction algorithms for detecting buried objects in FLIR imagery using genetic programming

    Science.gov (United States)

    Paino, A.; Keller, J.; Popescu, M.; Stone, K.

    2014-06-01

    In this paper we present an approach that uses Genetic Programming (GP) to evolve novel feature extraction algorithms for greyscale images. Our motivation is to create an automated method of building new feature extraction algorithms for images that are competitive with commonly used human-engineered features, such as Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). The evolved feature extraction algorithms are functions defined over the image space, and each produces a real-valued feature vector of variable length. Each evolved feature extractor breaks up the given image into a set of cells centered on every pixel, performs evolved operations on each cell, and then combines the results of those operations for every cell using an evolved operator. Using this method, the algorithm is flexible enough to reproduce both LBP and HOG features. The dataset we use to train and test our approach consists of a large number of pre-segmented image "chips" taken from a Forward Looking Infrared Imagery (FLIR) camera mounted on the hood of a moving vehicle. The goal is to classify each image chip as either containing or not containing a buried object. To this end, we define the fitness of a candidate solution as the cross-fold validation accuracy of the features generated by said candidate solution when used in conjunction with a Support Vector Machine (SVM) classifier. In order to validate our approach, we compare the classification accuracy of an SVM trained using our evolved features with the accuracy of an SVM trained using mainstream feature extraction algorithms, including LBP and HOG.

  15. An innovative approach for testing bioinformatics programs using metamorphic testing

    Directory of Open Access Journals (Sweden)

    Liu Huai

    2009-01-01

    Full Text Available Abstract Background Recent advances in experimental and computational technologies have fueled the development of many sophisticated bioinformatics programs. The correctness of such programs is crucial as incorrectly computed results may lead to wrong biological conclusion or misguide downstream experimentation. Common software testing procedures involve executing the target program with a set of test inputs and then verifying the correctness of the test outputs. However, due to the complexity of many bioinformatics programs, it is often difficult to verify the correctness of the test outputs. Therefore our ability to perform systematic software testing is greatly hindered. Results We propose to use a novel software testing technique, metamorphic testing (MT, to test a range of bioinformatics programs. Instead of requiring a mechanism to verify whether an individual test output is correct, the MT technique verifies whether a pair of test outputs conform to a set of domain specific properties, called metamorphic relations (MRs, thus greatly increases the number and variety of test cases that can be applied. To demonstrate how MT is used in practice, we applied MT to test two open-source bioinformatics programs, namely GNLab and SeqMap. In particular we show that MT is simple to implement, and is effective in detecting faults in a real-life program and some artificially fault-seeded programs. Further, we discuss how MT can be applied to test programs from various domains of bioinformatics. Conclusion This paper describes the application of a simple, effective and automated technique to systematically test a range of bioinformatics programs. We show how MT can be implemented in practice through two real-life case studies. Since many bioinformatics programs, particularly those for large scale simulation and data analysis, are hard to test systematically, their developers may benefit from using MT as part of the testing strategy. Therefore our work

  16. Searching for globally optimal functional forms for interatomic potentials using genetic programming with parallel tempering.

    Science.gov (United States)

    Slepoy, A; Peters, M D; Thompson, A P

    2007-11-30

    Molecular dynamics and other molecular simulation methods rely on a potential energy function, based only on the relative coordinates of the atomic nuclei. Such a function, called a force field, approximately represents the electronic structure interactions of a condensed matter system. Developing such approximate functions and fitting their parameters remains an arduous, time-consuming process, relying on expert physical intuition. To address this problem, a functional programming methodology was developed that may enable automated discovery of entirely new force-field functional forms, while simultaneously fitting parameter values. The method uses a combination of genetic programming, Metropolis Monte Carlo importance sampling and parallel tempering, to efficiently search a large space of candidate functional forms and parameters. The methodology was tested using a nontrivial problem with a well-defined globally optimal solution: a small set of atomic configurations was generated and the energy of each configuration was calculated using the Lennard-Jones pair potential. Starting with a population of random functions, our fully automated, massively parallel implementation of the method reproducibly discovered the original Lennard-Jones pair potential by searching for several hours on 100 processors, sampling only a minuscule portion of the total search space. This result indicates that, with further improvement, the method may be suitable for unsupervised development of more accurate force fields with completely new functional forms. Copyright (c) 2007 Wiley Periodicals, Inc.

  17. Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks.

    Science.gov (United States)

    Beal, Jacob; Lu, Ting; Weiss, Ron

    2011-01-01

    The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes (~ 50%) and latency of the optimized engineered gene networks. Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems.

  18. Systematic documentation and analysis of human genetic variation in hemoglobinopathies using the microattribution approach

    NARCIS (Netherlands)

    B. Giardine (Belinda); J. Borg (Joseph); D.R. Higgs (Douglas); K.R. Peterson (Kenneth R.); J.N.J. Philipsen (Sjaak); D. Maglott (Donna); B.K. Singleton (Belinda K.); D.J. Anstee (David J.); A.N. Basak (Nazli); B.H. Clark (Bruce); F.C. Costa (Flavia C.); P. Faustino (Paula); H. Fedosyuk (Halyna); A.E. Felice (Alex); A. Francina (Alain); R. Galanello (Renzo); M.V.E. Gallivan (Monica V. E.); M. Georgitsi (Marianthi); R.J. Gibbons (Richard J.); P.C. Giordano (Piero Carlo); C.L. Harteveld (Cornelis); J.D. Hoyer (James D.); M. Jarvis (Martin); P. Joly (Philippe); E. Kanavakis (Emmanuel); P. Kollia (Panagoula); S. Menzel (Stephan); W.G. Miller (William); K. Moradkhani (Kamran); J. Old (John); A. Papachatzpoulou (Adamantia); M.N. Papadakis (Manoussos); P. Papadopoulos (Petros); S. Pavlovic (Sonja); L. Perseu (Lucia); M. Radmilovic (Milena); C. Riemer (Cathy); S. Satta (Stefania); I.A. Schrijver (Ingrid); M. Stojiljkovic (Maja); S.L. Thein; J. Traeger-Synodinos (Joanne); R. Tully (Ray); T. Wada (Takahito); J.S. Waye (John); C. Wiemann (Claudia); B. Zukic (Branka); D.H.K. Chui (David H. K.); H. Wajcman (Henri); R. Hardison (Ross); G.P. Patrinos (George)

    2011-01-01

    textabstractWe developed a series of interrelated locus-specific databases to store all published and unpublished genetic variation related to hemoglobinopathies and thalassemia and implemented microattribution to encourage submission of unpublished observations of genetic variation to these public

  19. Biotechnological approaches for the genetic improvement of Jatropha curcas L.: A biodiesel plant

    KAUST Repository

    Kumar, Nitish; Singh, Amritpal S.; Kumari, Swati; Reddy, Muppala P.

    2015-01-01

    . In this review, an effort is made to project the current biotechnology and molecular biology tools employed in the direction of, evaluating the genetic diversity and phylogeny revelation of Jatropha spp., identification of genetic markers for desirable traits

  20. A chemical genetic approach to engineer phototropin kinases for substrate labeling.

    Science.gov (United States)

    Schnabel, Jonathan; Hombach, Peter; Waksman, Thomas; Giuriani, Giovanni; Petersen, Jan; Christie, John M

    2018-04-13

    Protein kinases (PKs) control many aspects of plant physiology by regulating signaling networks through protein phosphorylation. Phototropins (phots) are plasma membrane-associated serine/threonine PKs that control a range of physiological processes that collectively serve to optimize photosynthetic efficiency in plants. These include phototropism, leaf positioning and flattening, chloroplast movement, and stomatal opening. Despite their identification over two decades ago, only a handful of substrates have been identified for these PKs. Progress in this area has been hampered by the lack of a convenient means to confirm the identity of potential substrate candidates. Here we demonstrate that the kinase domain of Arabidopsis phot1 and phot2 can be successfully engineered to accommodate non-natural ATP analogues by substituting the bulky gatekeeper residue threonine for glycine. This approach circumvents the need for radioactivity to track phot kinase activity and follow light-induced receptor autophosphorylation in vitro by incorporating thiophosphate from N 6 -benzyl-ATPγS. Consequently, thiophosphorylation of phot substrate candidates can be readily monitored when added or co-expressed with phots in vitro Furthermore, gatekeeper-modified phot1 retained its functionality and its ability to accommodate N 6 -benzyl-ATPγS as a phosphodonor when expressed in Arabidopsis We therefore anticipate that this chemical genetic approach will provide new opportunities for labeling and identifying substrates for phots and other related AGC kinases under in vitro and near-native in vivo conditions. © 2018 Schnabel et al.

  1. Assuring the safety of genetically modified (GM) foods: the importance of an holistic, integrative approach.

    Science.gov (United States)

    Cockburn, Andrew

    2002-09-11

    Genes change continuously by natural mutation and recombination enabling man to select and breed crops having the most desirable traits such as yield or flavour. Genetic modification (GM) is a recent development which allows specific genes to be identified, isolated, copied and inserted into other plants with a high level of specificity. The food safety considerations for GM crops are basically the same as those arising from conventionally bred crops, very few of which have been subject to any testing yet are generally regarded as being safe to eat. In contrast a rigorous safety testing paradigm has been developed for GM crops, which utilises a systematic, stepwise and holistic approach. The resultant science based process, focuses on a classical evaluation of the toxic potential of the introduced novel trait and the wholesomeness of the transformed crop. In addition, detailed consideration is given to the history and safe use of the parent crop as well as that of the gene donor. The overall safety evaluation is conducted under the concept known as substantial equivalence which is enshrined in all international crop biotechnology guidelines. This provides the framework for a comparative approach to identify the similarities and differences between the GM product and its comparator which has a known history of safe use. By building a detailed profile on each step in the transformation process, from parent to new crop, and by thoroughly evaluating the significance from a safety perspective, of any differences that may be detected, a very comprehensive matrix of information is constructed which enables the conclusion as to whether the GM crop, derived food or feed is as safe as its traditional counterpart. Using this approach in the evaluation of more than 50 GM crops which have been approved worldwide, the conclusion has been that foods and feeds derived from genetically modified crops are as safe and nutritious as those derived from traditional crops. The lack of

  2. Estimating Typhoon Rainfall over Sea from SSM/I Satellite Data Using an Improved Genetic Programming

    Science.gov (United States)

    Yeh, K.; Wei, H.; Chen, L.; Liu, G.

    2010-12-01

    Estimating Typhoon Rainfall over Sea from SSM/I Satellite Data Using an Improved Genetic Programming Keh-Chia Yeha, Hsiao-Ping Weia,d, Li Chenb, and Gin-Rong Liuc a Department of Civil Engineering, National Chiao Tung University, Hsinchu, Taiwan, 300, R.O.C. b Department of Civil Engineering and Engineering Informatics, Chung Hua University, Hsinchu, Taiwan, 300, R.O.C. c Center for Space and Remote Sensing Research, National Central University, Tao-Yuan, Taiwan, 320, R.O.C. d National Science and Technology Center for Disaster Reduction, Taipei County, Taiwan, 231, R.O.C. Abstract This paper proposes an improved multi-run genetic programming (GP) and applies it to predict the rainfall using meteorological satellite data. GP is a well-known evolutionary programming and data mining method, used to automatically discover the complex relationships among nonlinear systems. The main advantage of GP is to optimize appropriate types of function and their associated coefficients simultaneously. This study makes an improvement to enhance escape ability from local optimums during the optimization procedure. The GP continuously runs several times by replacing the terminal nodes at the next run with the best solution at the current run. The current novel model improves GP, obtaining a highly nonlinear mathematical equation to estimate the rainfall. In the case study, this improved GP described above combining with SSM/I satellite data is employed to establish a suitable method for estimating rainfall at sea surface during typhoon periods. These estimated rainfalls are then verified with the data from four rainfall stations located at Peng-Jia-Yu, Don-Gji-Dao, Lan-Yu, and Green Island, which are four small islands around Taiwan. From the results, the improved GP can generate sophisticated and accurate nonlinear mathematical equation through two-run learning procedures which outperforms the traditional multiple linear regression, empirical equations and back-propagated network

  3. New Approaches for Very Large-Scale Integer Programming

    Science.gov (United States)

    2016-06-24

    DISTRIBUTION/ AVAILABILITY STATEMENT Approved for Public Release 13. SUPPLEMENTARY NOTES 14. ABSTRACT The focus of this project is new computational... heuristics for integer programs in order to rapidly improve dual bounds. 2. Choosing good branching variables in branch-and-bound algorithms for MIP. 3...programming, algorithms, parallel processing, machine learning, heuristics 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF

  4. Creating a foundation for a synergistic approach to program management

    Science.gov (United States)

    Knoll, Karyn T.

    1992-01-01

    In order to accelerate the movement of humans into space within reasonable budgetary constraints, NASA must develop an organizational structure that will allow the agency to efficiently use all the resources it has available for the development of any program the nation decides to undertake. This work considers the entire set of tasks involved in the successful development of any program. Areas that hold the greatest promise of accelerating programmatic development and/or increasing the efficiency of the use of available resources by being dealt with in a centralized manner rather than being handled by each program individually are identified. Using this information, an agency organizational structure is developed that will allow NASA to promote interprogram synergisms. In order for NASA to efficiently manage its programs in a manner that will allow programs to benefit from one another and thereby accelerate the movement of humans into space, several steps must be taken. First, NASA must develop an organizational structure that will allow potential interprogram synergisms to be identified and promoted. Key features of the organizational structure are recommended in this paper. Second, NASA must begin to develop the requirements for a program in a manner that will promote overall space program goals rather than achieving only the goals that apply to the program for which the requirements are being developed. Finally, NASA must consider organizing the agency around the functions required to support NASA's goals and objectives rather than around geographic locations.

  5. An Approach to Effortless Construction of Program Animations

    Science.gov (United States)

    Velazquez-Iturbide, J. Angel; Pareja-Flores, Cristobal; Urquiza-Fuentes, Jaime

    2008-01-01

    Program animation systems have not been as widely adopted by computer science educators as we might expect from the firm belief that they can help in enhancing computer science education. One of the most notable obstacles to their adoption is the considerable effort that the production of program animations represents for the instructor. We…

  6. Prevalent Approaches to Professional Development in State 4-H Programs

    Science.gov (United States)

    Smith, Martin H.; Worker, Steven M.; Schmitt-McQuitty, Lynn; Meehan, Cheryl L.; Lewis, Kendra M.; Schoenfelder, Emily; Brian, Kelley

    2017-01-01

    High-quality 4-H programming requires effective professional development of educators. Through a mixed methods study, we explored professional development offered through state 4-H programs. Survey results revealed that both in-person and online delivery modes were used commonly for 4-H staff and adult volunteers; for teen volunteers, in-person…

  7. Winning One Program at a Time: A Systemic Approach

    Science.gov (United States)

    Schultz, Adam; Zimmerman, Kay

    2016-01-01

    Many Universities are missing an opportunity to focus student recruitment marketing efforts and budget at the program level, which can offer lower priced advertising opportunities with higher conversion rates than traditional University level marketing initiatives. At NC State University, we have begun to deploy a scalable, low-cost, program level…

  8. Fusion material development program in the broader approach activities

    Energy Technology Data Exchange (ETDEWEB)

    Nishitani, T. [Directorates of Fusion Energy Research: Naka, Ibaraki, Japan Atomic Energy Agency, Naka, Ibaraki (Japan); Tanigawa, H.; Jitsukawa, S. [Japan Atomic Energy Agency, Tokai-mura, Naga-gun, Ibaraki-ken (Japan); Hayashi, K.; Takatsu, H. [Fusion Research and Development Directorate, Japan Momie Energy Agency, Ibaraki-ken (Japan); Yamanishi, T. [Tritium Process Laboratory, Japan Atomic Energy Research Institute, Tokai-mura, Ibaraki-ken (Japan); Tsuchiya, K. [Directorates of Fusion Energy Research, JAEA, Higashi-ibaraki-gun, Ibaraki-ken (Japan); MoIslang, A. [Forschungszentrum Karlsruhe GmbH, FZK, Karlsruhe (Germany); Baluc, N. [EPFL-Ecole Polytechnique Federale de Lausanne, Association Euratom-Confederation Suisse, UHD - CRPP, PPB, Lausanne (Switzerland); Pizzuto, A. [ENEA CR Frascat, Frascati (Italy); Hodgson, E.R. [CIEMAT-Centro de Investigaciones Energeticas Medioambientales y Tecnologicas, Association Euratom-CIEMAT, Madrid (Spain); Lasser, R.; Gasparotto, M. [EFDA CSU Garching (Germany)

    2007-07-01

    Full text of publication follows: The world fusion community is now launching construction of ITER, the first nuclear-grade fusion machine in the world. In parallel to the ITER program, Broader Approach (BA) activities are initiated by EU and Japan, mainly at Rokkasho BA site in Japan. The BA activities include the International Fusion Materials Irradiation Facility-Engineering Validation and Engineering Design Activities (IFMIF-EVEDA), the International Fusion Energy Research Center (IFERC), and the Satellite Tokamak. IFERC consists of three sub project; a DEMO Design and R and D coordination Center, a Computational Simulation Center, and an ITER Remote Experimentation Center. Technical R and Ds mainly on fusion materials will be implemented as a part of the DEMO Design and R and D coordination Center. Based on the common interest of each party toward DEMO, R and Ds on a) reduced activation ferritic martensitic (RAFM) steels as a DEMO blanket structural material, SiCf/SiC composites, advanced tritium breeders and neutron multiplier for DEMO blankets, and Tritium Technology were selected and assessed by European and Japanese experts. In the R and D on the RAFM steels, the fabrication technology, techniques to incorporate the fracture/rupture properties of the irradiated materials, and methods to predict the deformation and fracture behaviors of structures under irradiation will be investigated. For SiCf/SiC composites, standard methods to evaluate high-temperature and life-time properties will be developed. Not only for SiCf/SiC but also related ceramics, physical and chemical properties such as He and H permeability and absorption will be investigated under irradiation. As the advanced tritium breeder R and D, Japan and EU plan to establish the production technique for advanced breeder pebbles of Li{sub 2}TiO{sub 3} and Li{sub 4}SiO{sub 4}, respectively. Also physical, chemical, and mechanical properties will be investigated for produced breeder pebbles. For the

  9. Time series segmentation: a new approach based on Genetic Algorithm and Hidden Markov Model

    Science.gov (United States)

    Toreti, A.; Kuglitsch, F. G.; Xoplaki, E.; Luterbacher, J.

    2009-04-01

    The subdivision of a time series into homogeneous segments has been performed using various methods applied to different disciplines. In climatology, for example, it is accompanied by the well-known homogenization problem and the detection of artificial change points. In this context, we present a new method (GAMM) based on Hidden Markov Model (HMM) and Genetic Algorithm (GA), applicable to series of independent observations (and easily adaptable to autoregressive processes). A left-to-right hidden Markov model, estimating the parameters and the best-state sequence, respectively, with the Baum-Welch and Viterbi algorithms, was applied. In order to avoid the well-known dependence of the Baum-Welch algorithm on the initial condition, a Genetic Algorithm was developed. This algorithm is characterized by mutation, elitism and a crossover procedure implemented with some restrictive rules. Moreover the function to be minimized was derived following the approach of Kehagias (2004), i.e. it is the so-called complete log-likelihood. The number of states was determined applying a two-fold cross-validation procedure (Celeux and Durand, 2008). Being aware that the last issue is complex, and it influences all the analysis, a Multi Response Permutation Procedure (MRPP; Mielke et al., 1981) was inserted. It tests the model with K+1 states (where K is the state number of the best model) if its likelihood is close to K-state model. Finally, an evaluation of the GAMM performances, applied as a break detection method in the field of climate time series homogenization, is shown. 1. G. Celeux and J.B. Durand, Comput Stat 2008. 2. A. Kehagias, Stoch Envir Res 2004. 3. P.W. Mielke, K.J. Berry, G.W. Brier, Monthly Wea Rev 1981.

  10. Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach

    International Nuclear Information System (INIS)

    Kisi, Ozgur

    2014-01-01

    The study investigates the ability of FG (fuzzy genetic) approach in modeling solar radiation of seven cities from Mediterranean region of Anatolia, Turkey. Latitude, longitude, altitude and month of the year data from the Adana, K. Maras, Mersin, Antalya, Isparta, Burdur and Antakya cities are used as inputs to the FG model to estimate one month ahead solar radiation. FG model is compared with ANNs (artificial neural networks) and ANFIS (adaptive neruro fuzzzy inference system) models with respect to RMSE (root mean square errors), MAE (mean absolute errors) and determination coefficient (R 2 ) statistics. Comparison results indicate that the FG model performs better than the ANN and ANFIS models. It is found that the FG model can be successfully used for estimating solar radiation by using latitude, longitude, altitude and month of the year information. FG model with RMSE = 6.29 MJ/m 2 , MAE = 4.69 MJ/m 2 and R 2 = 0.905 in the test stage was found to be superior to the optimal ANN model with RMSE = 7.17 MJ/m 2 , MAE = 5.29 MJ/m 2 and R 2 = 0.876 and ANFIS model with RMSE = 6.75 MJ/m 2 , MAE = 5.10 MJ/m 2 and R 2 = 0.892 in estimating solar radiation. - Highlights: • SR (Solar radiation) of seven cities from Mediterranean region of Turkey is predicted. • FG (Fuzzy genetic) models are developed for accurately estimation of SR. • The ability of the FG models used in the study is found to be satisfactory. • FG models are compared with commonly used ANNs (artificial neural networks). • FG models are found to perform better than the ANNs models

  11. From animal models to human disease: a genetic approach for personalized medicine in ALS.

    Science.gov (United States)

    Picher-Martel, Vincent; Valdmanis, Paul N; Gould, Peter V; Julien, Jean-Pierre; Dupré, Nicolas

    2016-07-11

    Amyotrophic Lateral Sclerosis (ALS) is the most frequent motor neuron disease in adults. Classical ALS is characterized by the death of upper and lower motor neurons leading to progressive paralysis. Approximately 10 % of ALS patients have familial form of the disease. Numerous different gene mutations have been found in familial cases of ALS, such as mutations in superoxide dismutase 1 (SOD1), TAR DNA-binding protein 43 (TDP-43), fused in sarcoma (FUS), C9ORF72, ubiquilin-2 (UBQLN2), optineurin (OPTN) and others. Multiple animal models were generated to mimic the disease and to test future treatments. However, no animal model fully replicates the spectrum of phenotypes in the human disease and it is difficult to assess how a therapeutic effect in disease models can predict efficacy in humans. Importantly, the genetic and phenotypic heterogeneity of ALS leads to a variety of responses to similar treatment regimens. From this has emerged the concept of personalized medicine (PM), which is a medical scheme that combines study of genetic, environmental and clinical diagnostic testing, including biomarkers, to individualized patient care. In this perspective, we used subgroups of specific ALS-linked gene mutations to go through existing animal models and to provide a comprehensive profile of the differences and similarities between animal models of disease and human disease. Finally, we reviewed application of biomarkers and gene therapies relevant in personalized medicine approach. For instance, this includes viral delivering of antisense oligonucleotide and small interfering RNA in SOD1, TDP-43 and C9orf72 mice models. Promising gene therapies raised possibilities for treating differently the major mutations in familial ALS cases.

  12. Effects of genetic variants previously associated with fasting glucose and insulin in the Diabetes Prevention Program.

    Directory of Open Access Journals (Sweden)

    Jose C Florez

    Full Text Available Common genetic variants have been recently associated with fasting glucose and insulin levels in white populations. Whether these associations replicate in pre-diabetes is not known. We extended these findings to the Diabetes Prevention Program, a clinical trial in which participants at high risk for diabetes were randomized to placebo, lifestyle modification or metformin for diabetes prevention. We genotyped previously reported polymorphisms (or their proxies in/near G6PC2, MTNR1B, GCK, DGKB, GCKR, ADCY5, MADD, CRY2, ADRA2A, FADS1, PROX1, SLC2A2, GLIS3, C2CD4B, IGF1, and IRS1 in 3,548 Diabetes Prevention Program participants. We analyzed variants for association with baseline glycemic traits, incident diabetes and their interaction with response to metformin or lifestyle intervention. We replicated associations with fasting glucose at MTNR1B (P<0.001, G6PC2 (P = 0.002 and GCKR (P = 0.001. We noted impaired β-cell function in carriers of glucose-raising alleles at MTNR1B (P<0.001, and an increase in the insulinogenic index for the glucose-raising allele at G6PC2 (P<0.001. The association of MTNR1B with fasting glucose and impaired β-cell function persisted at 1 year despite adjustment for the baseline trait, indicating a sustained deleterious effect at this locus. We also replicated the association of MADD with fasting proinsulin levels (P<0.001. We detected no significant impact of these variants on diabetes incidence or interaction with preventive interventions. The association of several polymorphisms with quantitative glycemic traits is replicated in a cohort of high-risk persons. These variants do not have a detectable impact on diabetes incidence or response to metformin or lifestyle modification in the Diabetes Prevention Program.

  13. An integrated approach to fire penetration seal program management

    International Nuclear Information System (INIS)

    Rispoli, R.D.

    1996-01-01

    This paper discusses the utilization of a P.C. based program to facilitate the management of Entergy Operations Arkansas Nuclear One (ANO) fire barrier penetration seal program. The computer program was developed as part of a streamlining process to consolidate all aspects of the ANO Penetration Seal Program under one system. The program tracks historical information related to each seal such as maintenance activities, design modifications and evaluations. The program is integrated with approved penetration seal design details which have been substantiated by full scale fire tests. This control feature is intended to prevent the inadvertent utilization of an unacceptable penetration detail in a field application which may exceed the parameters tested. The system is also capable of controlling the scope of the periodic surveillance of penetration seals by randomly selecting the inspection population and generating associated inspection forms. Inputs to the data base are required throughout the modification and maintenance process to ensure configuration control and maintain accurate data base information. These inputs are verified and procedurally controlled by Fire Protection Engineering (FPE) personnel. The implementation of this system has resulted in significant cost savings and has minimized the allocation of resources necessary to ensure long term program viability

  14. A Strategic Approach to Implementation of Medical Mentorship Programs.

    Science.gov (United States)

    Caruso, Thomas J; Steinberg, Diane H; Piro, Nancy; Walker, Kimberly; Blankenburg, Rebecca; Rassbach, Caroline; Marquez, Juan L; Katznelson, Laurence; Dohn, Ann

    2016-02-01

    Mentors influence medical trainees' experiences through career enhancement and psychosocial support, yet some trainees never receive benefits from involved mentors. Our goals were to examine the effectiveness of 2 interventions aimed at increasing the number of mentors in training programs, and to assess group differences in mentor effectiveness, the relationship between trainees' satisfaction with their programs given the presence of mentors, and the relationship between the number of trainees with mentors and postgraduate year (PGY). In group 1, a physician adviser funded by the graduate medical education department implemented mentorships in 6 residency programs, while group 2 involved a training program with funded physician mentoring time. The remaining 89 training programs served as controls. Chi-square tests were used to determine differences. Survey responses from group 1, group 2, and controls were 47 of 84 (56%), 34 of 78 (44%), and 471 of 981 (48%, P = .38), respectively. The percentages of trainees reporting a mentor in group 1, group 2, and the control group were 89%, 97%, and 79%, respectively (P = .01). There were no differences in mentor effectiveness between groups. Mentored trainees were more likely to be satisfied with their programs (P = .01) and to report that faculty supported their professional aspirations (P = .001). Across all programs, fewer first-year trainees (59%) identified a mentor compared to PGY-2 through PGY-8 trainees (84%, P program is an effective way to create an educational environment that maximizes trainees' perceptions of mentorship and satisfaction with their training programs.

  15. Symbolic regression via genetic programming for data driven derivation of confinement scaling laws without any assumption on their mathematical form

    International Nuclear Information System (INIS)

    Murari, A; Peluso, E; Gelfusa, M; Lupelli, I; Lungaroni, M; Gaudio, P

    2015-01-01

    Many measurements are required to control thermonuclear plasmas and to fully exploit them scientifically. In the last years JET has shown the potential to generate about 50 GB of data per shot. These amounts of data require more sophisticated data analysis methodologies to perform correct inference and various techniques have been recently developed in this respect. The present paper covers a new methodology to extract mathematical models directly from the data without any a priori assumption about their expression. The approach, based on symbolic regression via genetic programming, is exemplified using the data of the International Tokamak Physics Activity database for the energy confinement time. The best obtained scaling laws are not in power law form and suggest a revisiting of the extrapolation to ITER. Indeed the best non-power law scalings predict confinement times in ITER approximately between 2 and 3 s. On the other hand, more comprehensive and better databases are required to fully profit from the power of these new methods and to discriminate between the hundreds of thousands of models that they can generate. (paper)

  16. Some useful structures for categorical approach for program behavior

    Directory of Open Access Journals (Sweden)

    Viliam Slodičák

    2011-06-01

    Full Text Available Using of category theory in computer science has extremely grown in the last decade. Categories allow us to express mathematical structures in unified way. Algebras are used for constructing basic structures used in computer programs. A program can be considered as an element of the initial algebra arising from the used programming language. In our contribution we formulate two ways of expressing algebras in categories. We also construct the codomain functor from the arrow category of algebras into the base category of sets which objects are also the carrier-sets of the algebras. This functor expresses the relation between algebras and carrier-sets.

  17. A review of genome-wide approaches to study the genetic basis for spermatogenic defects.

    Science.gov (United States)

    Aston, Kenneth I; Conrad, Donald F

    2013-01-01

    Rapidly advancing tools for genetic analysis on a genome-wide scale have been instrumental in identifying the genetic bases for many complex diseases. About half of male infertility cases are of unknown etiology in spite of tremendous efforts to characterize the genetic basis for the disorder. Advancing our understanding of the genetic basis for male infertility will require the application of established and emerging genomic tools. This chapter introduces many of the tools available for genetic studies on a genome-wide scale along with principles of study design and data analysis.

  18. Construction of the first compendium of chemical-genetic profiles in the fission yeast Schizosaccharomyces pombe and comparative compendium approach

    Energy Technology Data Exchange (ETDEWEB)

    Han, Sangjo [Bioinformatics Lab, Healthcare Group, SK Telecom, 9-1, Sunae-dong, Pundang-gu, Sungnam-si, Kyunggi-do 463-784 (Korea, Republic of); Lee, Minho [Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701 (Korea, Republic of); Chang, Hyeshik [Department of Biological Science, Seoul National University, 599 Gwanakro, Gwanak-gu, Seoul 151-747 (Korea, Republic of); Nam, Miyoung [Department of New Drug Discovery and Development, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 305-764 (Korea, Republic of); Park, Han-Oh [Bioneer Corp., 8-11 Munpyeongseo-ro, Daedeok-gu, Daejeon 306-220 (Korea, Republic of); Kwak, Youn-Sig [Department of Applied Biology, Gyeongsang National University, 501 Jinju-daero, Jinju, Gyeongnam 660-701 (Korea, Republic of); Ha, Hye-jeong [Aging Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-Gu, Daejeon 305-806 (Korea, Republic of); Kim, Dongsup [Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701 (Korea, Republic of); Hwang, Sung-Ook [Department of Obstetrics and Gynecology, Inha University Hospital, 7-206 Sinheung-dong, Jung-gu, Incheon 400-711 (Korea, Republic of); Hoe, Kwang-Lae [Department of New Drug Discovery and Development, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 305-764 (Korea, Republic of); Kim, Dong-Uk [Aging Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-Gu, Daejeon 305-806 (Korea, Republic of)

    2013-07-12

    Highlights: •The first compendium of chemical-genetic profiles form fission yeast was generated. •The first HTS of drug mode-of-action in fission yeast was performed. •The first comparative chemical genetic analysis between two yeasts was conducted. -- Abstract: Genome-wide chemical genetic profiles in Saccharomyces cerevisiae since the budding yeast deletion library construction have been successfully used to reveal unknown mode-of-actions of drugs. Here, we introduce comparative approach to infer drug target proteins more accurately using two compendiums of chemical-genetic profiles from the budding yeast S. cerevisiae and the fission yeast Schizosaccharomyces pombe. For the first time, we established DNA-chip based growth defect measurement of genome-wide deletion strains of S. pombe, and then applied 47 drugs to the pooled heterozygous deletion strains to generate chemical-genetic profiles in S. pombe. In our approach, putative drug targets were inferred from strains hypersensitive to given drugs by analyzing S. pombe and S. cerevisiae compendiums. Notably, many evidences in the literature revealed that the inferred target genes of fungicide and bactericide identified by such comparative approach are in fact the direct targets. Furthermore, by filtering out the genes with no essentiality, the multi-drug sensitivity genes, and the genes with less eukaryotic conservation, we created a set of drug target gene candidates that are expected to be directly affected by a given drug in human cells. Our study demonstrated that it is highly beneficial to construct the multiple compendiums of chemical genetic profiles using many different species. The fission yeast chemical-genetic compendium is available at (http://pombe.kaist.ac.kr/compendium)

  19. Automatic Generation of English-Japanese Translation Pattern Utilizing Genetic Programming Technique

    Science.gov (United States)

    Matsumura, Koki; Tamekuni, Yuji; Kimura, Shuhei

    There are a lot of constructional differences in an English-Japanese phrase template, and that often makes the act of translation difficult. Moreover, there exist various and tremendous phrase templates and sentence to be refered to. It is not easy to prepare the corpus that covers the all. Therefore, it is very significant to generate the translation pattern of the sentence pattern automatically from a viewpoint of the translation success rate and the capacity of the pattern dictionary. Then, for the purpose of realizing the automatic generation of the translation pattern, this paper proposed the new method for the generation of the translation pattern by using the genetic programming technique (GP). The technique tries to generate the translation pattern of various sentences which are not registered in the phrase template dictionary automatically by giving the genetic operation to the parsing tree of a basic pattern. The tree consists of the pair of the English-Japanese sentence generated as the first stage population. The analysis tree data base with 50,100,150,200 pairs was prepared as the first stage population. And this system was applied and executed for an English input of 1,555 sentences. As a result, the analysis tree increases from 200 to 517, and the accuracy rate of the translation pattern has improved from 42.57% to 70.10%. And, 86.71% of the generated translations was successfully done, whose meanings are enough acceptable and understandable. It seemed that this proposal technique became a clue to raise the translation success rate, and to find the possibility of the reduction of the analysis tree data base.

  20. System network planning expansion using mathematical programming, genetic algorithms and tabu search

    International Nuclear Information System (INIS)

    Sadegheih, A.; Drake, P.R.

    2008-01-01

    In this paper, system network planning expansion is formulated for mixed integer programming, a genetic algorithm (GA) and tabu search (TS). Compared with other optimization methods, GAs are suitable for traversing large search spaces, since they can do this relatively rapidly and because the use of mutation diverts the method away from local minima, which will tend to become more common as the search space increases in size. GA's give an excellent trade off between solution quality and computing time and flexibility for taking into account specific constraints in real situations. TS has emerged as a new, highly efficient, search paradigm for finding quality solutions to combinatorial problems. It is characterized by gathering knowledge during the search and subsequently profiting from this knowledge. The attractiveness of the technique comes from its ability to escape local optimality. The cost function of this problem consists of the capital investment cost in discrete form, the cost of transmission losses and the power generation costs. The DC load flow equations for the network are embedded in the constraints of the mathematical model to avoid sub-optimal solutions that can arise if the enforcement of such constraints is done in an indirect way. The solution of the model gives the best line additions and also provides information regarding the optimal generation at each generation point. This method of solution is demonstrated on the expansion of a 10 bus bar system to 18 bus bars. Finally, a steady-state genetic algorithm is employed rather than generational replacement, also uniform crossover is used

  1. Detecting high-order interactions of single nucleotide polymorphisms using genetic programming.

    Science.gov (United States)

    Nunkesser, Robin; Bernholt, Thorsten; Schwender, Holger; Ickstadt, Katja; Wegener, Ingo

    2007-12-15

    Not individual single nucleotide polymorphisms (SNPs), but high-order interactions of SNPs are assumed to be responsible for complex diseases such as cancer. Therefore, one of the major goals of genetic association studies concerned with such genotype data is the identification of these high-order interactions. This search is additionally impeded by the fact that these interactions often are only explanatory for a relatively small subgroup of patients. Most of the feature selection methods proposed in the literature, unfortunately, fail at this task, since they can either only identify individual variables or interactions of a low order, or try to find rules that are explanatory for a high percentage of the observations. In this article, we present a procedure based on genetic programming and multi-valued logic that enables the identification of high-order interactions of categorical variables such as SNPs. This method called GPAS cannot only be used for feature selection, but can also be employed for discrimination. In an application to the genotype data from the GENICA study, an association study concerned with sporadic breast cancer, GPAS is able to identify high-order interactions of SNPs leading to a considerably increased breast cancer risk for different subsets of patients that are not found by other feature selection methods. As an application to a subset of the HapMap data shows, GPAS is not restricted to association studies comprising several 10 SNPs, but can also be employed to analyze whole-genome data. Software can be downloaded from http://ls2-www.cs.uni-dortmund.de/~nunkesser/#Software

  2. Myth 8: The "Patch-On" Approach to Programming Is Effective

    Science.gov (United States)

    Tomlinson, Carol Ann

    2009-01-01

    It is not likely that any group of educators of the gifted ever sat around a table and came to the decision that a "patch-on" approach to programming for bright learners represented best practice. Nonetheless, it is as common today as 25 years ago that programming for students identified as gifted often represents such an approach. Patch-on…

  3. 76 FR 55673 - Vulnerability Assessments in Support of the Climate Ready Estuaries Program: A Novel Approach...

    Science.gov (United States)

    2011-09-08

    ... ENVIRONMENTAL PROTECTION AGENCY [FRL-9460-8; Docket ID No. EPA-HQ-ORD-2011-0485] Vulnerability... titled, Vulnerability Assessments in Support of the Climate Ready Estuaries Program: A Novel Approach...) and Vulnerability Assessments in Support of the Climate Ready Estuaries Program: A Novel Approach...

  4. A combined stochastic programming and optimal control approach to personal finance and pensions

    DEFF Research Database (Denmark)

    Konicz, Agnieszka Karolina; Pisinger, David; Rasmussen, Kourosh Marjani

    2015-01-01

    The paper presents a model that combines a dynamic programming (stochastic optimal control) approach and a multi-stage stochastic linear programming approach (SLP), integrated into one SLP formulation. Stochastic optimal control produces an optimal policy that is easy to understand and implement....

  5. Gap Analysis Approach for Construction Safety Program Improvement

    Directory of Open Access Journals (Sweden)

    Thanet Aksorn

    2007-06-01

    Full Text Available To improve construction site safety, emphasis has been placed on the implementation of safety programs. In order to successfully gain from safety programs, factors that affect their improvement need to be studied. Sixteen critical success factors of safety programs were identified from safety literature, and these were validated by safety experts. This study was undertaken by surveying 70 respondents from medium- and large-scale construction projects. It explored the importance and the actual status of critical success factors (CSFs. Gap analysis was used to examine the differences between the importance of these CSFs and their actual status. This study found that the most critical problems characterized by the largest gaps were management support, appropriate supervision, sufficient resource allocation, teamwork, and effective enforcement. Raising these priority factors to satisfactory levels would lead to successful safety programs, thereby minimizing accidents.

  6. Independent Evaluators of Federal Programs: Approaches, Devices, and Examples

    Science.gov (United States)

    2010-08-16

    rehabilitation of the disorder; the status of studies and clinical trials involving innovative treatments; a description of each treatment program and a...Transit Administration ( FTA ), a part of the Department of Transportation (DOT), oversees the safety and security of rail transit agencies which...an oversight body for each jurisdiction. The program is designed, according to a GAO summary, “as one in which FTA , other federal agencies, states

  7. A genetic algorithm approach for open-pit mine production scheduling

    Directory of Open Access Journals (Sweden)

    Aref Alipour

    2017-06-01

    Full Text Available In an Open-Pit Production Scheduling (OPPS problem, the goal is to determine the mining sequence of an orebody as a block model. In this article, linear programing formulation is used to aim this goal. OPPS problem is known as an NP-hard problem, so an exact mathematical model cannot be applied to solve in the real state. Genetic Algorithm (GA is a well-known member of evolutionary algorithms that widely are utilized to solve NP-hard problems. Herein, GA is implemented in a hypothetical Two-Dimensional (2D copper orebody model. The orebody is featured as two-dimensional (2D array of blocks. Likewise, counterpart 2D GA array was used to represent the OPPS problem’s solution space. Thereupon, the fitness function is defined according to the OPPS problem’s objective function to assess the solution domain. Also, new normalization method was used for the handling of block sequencing constraint. A numerical study is performed to compare the solutions of the exact and GA-based methods. It is shown that the gap between GA and the optimal solution by the exact method is less than % 5; hereupon GA is found to be efficiently in solving OPPS problem.

  8. Is it acceptable to approach colorectal cancer patients at diagnosis to discuss genetic testing? A pilot study

    OpenAIRE

    Porteous, M; Dunckley, M; Appleton, S; Catt, S; Dunlop, M; Campbell, H; Cull, A

    2003-01-01

    In this pilot study, the acceptability of approaching 111 newly diagnosed colorectal cancer patients with the offer of genetic testing for hereditary nonpolyposis colorectal cancer (HNPCC) was assessed. A total of 78% of participants found it highly acceptable to have the information about HNPCC brought to their attention at that time.

  9. An e-Learning Collaborative Filtering Approach to Suggest Problems to Solve in Programming Online Judges

    Science.gov (United States)

    Toledo, Raciel Yera; Mota, Yailé Caballero

    2014-01-01

    The paper proposes a recommender system approach to cover online judge's domains. Online judges are e-learning tools that support the automatic evaluation of programming tasks done by individual users, and for this reason they are usually used for training students in programming contest and for supporting basic programming teachings. The…

  10. Knowledge of Genetics and Attitudes toward Genetic Testing among College Students in Saudi Arabia.

    Science.gov (United States)

    Olwi, Duaa; Merdad, Leena; Ramadan, Eman

    2016-01-01

    Genetic testing has been gradually permeating the practice of medicine. Health-care providers may be confronted with new genetic approaches that require genetically informed decisions which will be influenced by patients' knowledge of genetics and their attitudes toward genetic testing. This study assesses the knowledge of genetics and attitudes toward genetic testing among college students. A cross-sectional study was conducted using a multistage stratified sample of 920 senior college students enrolled at King Abdulaziz University, Saudi Arabia. Information regarding knowledge of genetics, attitudes toward genetic testing, and sociodemographic data were collected using a self-administered questionnaire. In general, students had a good knowledge of genetics but lacked some fundamentals of genetics. The majority of students showed positive attitudes toward genetic testing, but some students showed negative attitudes toward certain aspects of genetic testing such as resorting to abortion in the case of an untreatable major genetic defect in an unborn fetus. The main significant predictors of knowledge were faculty, gender, academic year, and some prior awareness of 'genetic testing'. The main significant predictors of attitudes were gender, academic year, grade point average, and some prior awareness of 'genetic testing'. The knowledge of genetics among college students was higher than has been reported in other studies, and the attitudes toward genetic testing were fairly positive. Genetics educational programs that target youths may improve knowledge of genetics and create a public perception that further supports genetic testing. © 2016 S. Karger AG, Basel.

  11. Education and certification of genetic counselors.

    Science.gov (United States)

    Katsichti, L; Hadzipetros-Bardanis, M; Bartsocas, C S

    1999-01-01

    Genetic counseling is defined by the American Society of Human Genetics as a communication process which deals with the human problems associated with the occurrence, or risk of occurrence, of a genetic disorder in a family. The first graduate program (Master's degree) in genetic counseling started in 1969 at Sarah Lawrence College, NY, USA, while in 1979 the National Society of Genetic Counseling (NSGC) was established. Today, there are 29 programs in U.S.A. offering a Master's degree in Genetic Counseling, five programs in Canada, one in Mexico, one in England and one in S. Africa. Most of these graduate programs offer two year training, consisting of graduate courses, seminars, research and practical training. Emphasis is given in human physiology, biochemistry, clinical genetics, cytogenetics, molecular and biochemical genetics, population genetics and statistics, prenatal diagnosis, teratology and genetic counseling in relation to psychosocial and ethical issues. Certification for eligible candidates is available through the American Board of Medical Genetics (ABMG). Requirements for certification include a master's degree in human genetics, training at sites accredited by the ABMG, documentation of genetic counseling experience, evidence of continuing education and successful completion of a comprehensive ABMG certification examination. As professionals, genetic counselors should maintain expertise, should insure mechanisms for professional advancement and should always maintain the ability to approach their patients.

  12. The GEOTREF program, a new approach for geothermal investigation

    Science.gov (United States)

    Gérard, Frédéric; Viard, Simon; Garcia, Michel

    2017-04-01

    The GEOTREF is an R&D program supported by the ADEME, French environmental agency and by the «Investissement d'Avenir », a French government program to found innovative projects. The GEOTREF program aims to develop an integrated analysis of high temperature geothermal reservoir in volcanic context. It is a collaborative program between nine research laboratories and two industrial partners. This program is supported for four years and funds 12 PhDs and 5 post-doctoral grants in various fields: geology, petrography, petrophysics, geophysics, geochemistry, reservoir modelling. The first three years are dedicated to the exploration phases that will lead to the drilling implantation. The project has two main objectives. 1.- Developing innovative and interactive methods and workflows leading to develop prospection and exploration in per volcanic geothermal target. This objective implicates: Optimization of the targeting to mitigate financial risks Adapting oil and gas exploration methods to geothermal energy, especially in peri-volcanic context. 2.- Applying this concept to different prospects in the Caribbean and South America The first target zone is located in Guadeloupe, an island of the active arc of the subduction zone where the Atlantic plate subducts under the Caribbean one. The GEOTREF prospect zone is on the Basse Terre Island in its south part closed to the Soufriere volcano, the active volcanic system. On the same island a geothermal field is exploited in Bouillante, just northward from the GEOTREF targeting area.

  13. Estimation of genetic parameters for growth traits in a breeding program for rainbow trout (Oncorhynchus mykiss) in China.

    Science.gov (United States)

    Hu, G; Gu, W; Bai, Q L; Wang, B Q

    2013-04-26

    Genetic parameters and breeding values for growth traits were estimated in the first and, currently, the only family selective breeding program for rainbow trout (Oncorhynchus mykiss) in China. Genetic and phenotypic data were collected for growth traits from 75 full-sibling families with a 2-generation pedigree. Genetic parameters and breeding values for growth traits of rainbow trout were estimated using the derivative-free restricted maximum likelihood method. The goodness-of-fit of the models was tested using Akaike and Bayesian information criteria. Genetic parameters and breeding values were estimated using the best-fit model for each trait. The values for heritability estimating body weight and length ranged from 0.20 to 0.45 and from 0.27 to 0.60, respectively, and the heritability of condition factor was 0.34. Our results showed a moderate degree of heritability for growth traits in this breeding program and suggested that the genetic and phenotypic tendency of body length, body weight, and condition factor were similar. Therefore, the selection of phenotypic values based on pedigree information was also suitable in this research population.

  14. Review: fetal programming of polycystic ovary syndrome by androgen excess: evidence from experimental, clinical, and genetic association studies.

    Science.gov (United States)

    Xita, Nectaria; Tsatsoulis, Agathocles

    2006-05-01

    Polycystic ovary syndrome (PCOS) is a common endocrine disorder of premenopausal women, characterized by hyperandrogenism, polycystic ovaries, and chronic anovulation along with insulin resistance and abdominal obesity as frequent metabolic traits. Although PCOS manifests clinically during adolescence, emerging data suggest that the natural history of PCOS may originate in intrauterine life. Evidence from experimental, clinical, and genetic research supporting the hypothesis for the fetal origins of PCOS has been analyzed. Female primates, exposed in utero to androgen excess, exhibit the phenotypic features of PCOS during adult life. Clinical observations also support a potential fetal origin of PCOS. Women with fetal androgen excess disorders, including congenital 21-hydroxylase deficiency and congenital adrenal virilizing tumors, develop features characteristic of PCOS during adulthood despite the normalization of androgen excess after birth. The potential mechanisms of fetal androgen excess leading to a PCOS phenotype in humans are not clearly understood. However, maternal and/or fetal hyperandrogenism can provide a plausible mechanism for fetal programing of PCOS, and this, in part, may be genetically determined. Thus, genetic association studies have indicated that common polymorphic variants of genes determining androgen activity or genes that influence the availability of androgens to target tissues are associated with PCOS and increased androgen levels. These genomic variants may provide the genetic link to prenatal androgenization in human PCOS. Prenatal androgenization of the female fetus induced by genetic and environmental factors, or the interaction of both, may program differentiating target tissues toward the development of PCOS phenotype in adult life.

  15. Zero based programming: a viable security budgeting approach.

    Science.gov (United States)

    Roll, Frederick G

    2003-01-01

    To get additional dollars or avoid budget cuts or personnel reductions, healthcare security directors should consider a budget approach that best justifies the needs of the department or organization.

  16. Biosafety risk assessment approaches for insect-resistant genetically modified crops

    Directory of Open Access Journals (Sweden)

    Inaam Ullah

    2017-02-01

    Full Text Available Background: Environmental risk assessment (ERA is imperative for commercial release of insect resistant, genetically modified crops (IR-GMCs.An insect specific, spider venom peptideω-HXTX-Hv1a (Hvt was successfully expressed in cotton plants. The cotton plants producing Hvt protein have demonstrated resistance against economically important insect pest species. The study was performed to assess the effects of Hvt producing cotton plants on Honey bees (Apis mellifera. Methods: Three approaches were used to evaluate the effects of Hvt protein on adults of honeybees; whole plant assays in flight cages, in vitro assays with pollen of Hvt-cotton, and assays with elevated levels of purified Hvt protein. Pollens of Bt cotton or purified Bt proteins were used as control. Results: The field experiments did not yield any meaningful data due to high rate of mortality in all treatments including the control. However, the laboratory experiments provided conclusive results in which Hvt, purified or in pollens, did not affect the survival or longevity of the bees compared to the control. During the course of study we were able to compare the quality, effectiveness and economics of different experiments. Conclusions: We conclude that Hvt either purified or produced in cotton plants do not affect the survival or longevity of honey bees. We are also of the view that starting at laboratory level assays not only gives meaningful data but also saves a lot of time and money that can be spent on other important questions regarding safety of a particular transgenic crop. Hence, a purpose-based, tiered approach could be the best choice for pre-release ERA of IR-GMCs.

  17. Somatic cell and molecular genetics approach to DNA repair and mutagenesis

    International Nuclear Information System (INIS)

    Thompson, L.H.

    1985-01-01

    In the CHO cell line, UV-sensitive mutants representing five genetic complementation groups have been identified. Mutants from each of these groups were shown to be defective in performing the incision step of repair after exposure to UV. The large number of complementation groups of xeroderma pigmentosa mutations has raised the question whether these groups all correspond to single gene loci. The same issue applies to the 5 groups of UV-sensitive CHO mutants. One approach toward answering this question is to localize in the human karyotype the genes that complement the defects in the CHO mutants. Thus, by making CHO/human cell hybrids under the appropriate selective conditions, we have begun to map each of the complementing human genes. The mutation in strain UV20 (Group 2) was complemented by human chromosome 19. Preliminary evidence suggests that UV5 may also be complemented by human chromosome 19 while each of the other 3 groups involves a different human chromosome. Somewhat surprisingly, mutant EM9 is also complemented by a gene on chromosome 19

  18. Control of bovine spongiform encephalopathy by genetic engineering: possible approaches and regulatory considerations

    International Nuclear Information System (INIS)

    Gavora, J.S.; Kochhar, H.P.S.; Gifford, G.A.

    2005-01-01

    Transmissible spongiform encephalopathies (TSE) include bovine spongiform encephalopathy (BSE), scrapie in sheep and Creutzfeldt-Jakob disease (CJD) in humans. A new CJD variant (nvCJD) is believed to be related to consumption of meat from BSE cattle. In TSE individuals, prion proteins (PrP) with approximately 250 amino acids convert to the pathogenic prion PrP Sc , leading to a dysfunction of the central neural system. Research elsewhere with mice has indicated a possible genetic engineering approach to the introduction of BSE resistance: individuals with amino acid substitutions at positions 167 or 218, inoculated with a pathogenic prion protein, did not support PrP Sc replication. This raises the possibility of producing prion-resistant cattle with a single PrP amino acid substitution. Since prion-resistant animals might still harbour acquired prion infectivity, regulatory assessment of the engineered animals would need to ascertain that such possible 'carriers' do not result in a threat to animal and human health. (author)

  19. Molecular genetic approach to human meningioma: loss of genes on chromosome 22

    International Nuclear Information System (INIS)

    Seizinger, B.R.; De La Monte, S.; Atkins, L.; Gusella, J.F.; Martuza, R.L.

    1987-01-01

    A molecular genetic approach employing polymorphic DNA markers has been used to investigate the role of chromosomal aberrations in meningioma, one of the most common tumors of the human nervous system. Comparison of the alleles detected by DNA markers in tumor DNA versus DNA from normal tissue revealed chromosomal alterations present in primary surgical specimens. In agreement with cytogenetic studies of cultured meningiomas, the most frequent alteration detected was loss of heterozygosity on chromosome 22. Forty of 51 patients were constitutionally heterozygous for at least one chromosome 22 DNA marker. Seventeen of the 40 constitutionally heterozygotic patients (43%) displayed hemizygosity for the corresponding marker in their meningioma tumor tissues. Loss of heterozygosity was also detected at a significantly lower frequency for markers on several other autosomes. In view of the striking association between acoustic neuroma and meningioma in bilateral acoustic neurofibromatosis and the discovery that acoustic neuromas display specific loss of genes on chromosome 22, the authors propose that a common mechanism involving chromosome 22 is operative in the development of both tumor types. Fine-structure mapping to reveal partial deletions in meningiomas may provide the means to clone and characterize a gene (or genes) of importance for tumorigenesis in this and possibly other clinically associated tumors of the human nervous system

  20. BP neural network optimized by genetic algorithm approach for titanium and iron content prediction in EDXRF

    International Nuclear Information System (INIS)

    Wang Jun; Liu Mingzhe; Li Zhe; Li Lei; Shi Rui; Tuo Xianguo

    2015-01-01

    The quantitative elemental content analysis is difficult due to the uniform effect, particle effect and the element matrix effect, etc, when using energy dispersive X-ray fluorescence (EDXRF) technique. In this paper, a hybrid approach of genetic algorithm (GA) and back propagation (BP) neural network was proposed without considering the complex relationship between the concentration and intensity. The aim of GA optimized BP was to get better network initial weights and thresholds. The basic idea was that the reciprocal of the mean square error of the initialization BP neural network was set as the fitness value of the individual in GA, and the initial weights and thresholds were replaced by individuals, and then the optimal individual was sought by selection, crossover and mutation operations, finally a new BP neural network model was created with the optimal initial weights and thresholds. The calculation results of quantitative analysis of titanium and iron contents for five types of ore bodies in Panzhihua Mine show that the results of classification prediction are far better than that of overall forecasting, and relative errors of 76.7% samples are less than 2% compared with chemical analysis values, which demonstrates the effectiveness of the proposed method. (authors)

  1. Molecular Approaches for High Throughput Detection and Quantification of Genetically Modified Crops: A Review

    Directory of Open Access Journals (Sweden)

    Ibrahim B. Salisu

    2017-10-01

    Full Text Available As long as the genetically modified crops are gaining attention globally, their proper approval and commercialization need accurate and reliable diagnostic methods for the transgenic content. These diagnostic techniques are mainly divided into two major groups, i.e., identification of transgenic (1 DNA and (2 proteins from GMOs and their products. Conventional methods such as PCR (polymerase chain reaction and enzyme-linked immunosorbent assay (ELISA were routinely employed for DNA and protein based quantification respectively. Although, these Techniques (PCR and ELISA are considered as significantly convenient and productive, but there is need for more advance technologies that allow for high throughput detection and the quantification of GM event as the production of more complex GMO is increasing day by day. Therefore, recent approaches like microarray, capillary gel electrophoresis, digital PCR and next generation sequencing are more promising due to their accuracy and precise detection of transgenic contents. The present article is a brief comparative study of all such detection techniques on the basis of their advent, feasibility, accuracy, and cost effectiveness. However, these emerging technologies have a lot to do with detection of a specific event, contamination of different events and determination of fusion as well as stacked gene protein are the critical issues to be addressed in future.

  2. Combined Simulated Annealing and Genetic Algorithm Approach to Bus Network Design

    Science.gov (United States)

    Liu, Li; Olszewski, Piotr; Goh, Pong-Chai

    A new method - combined simulated annealing (SA) and genetic algorithm (GA) approach is proposed to solve the problem of bus route design and frequency setting for a given road network with fixed bus stop locations and fixed travel demand. The method involves two steps: a set of candidate routes is generated first and then the best subset of these routes is selected by the combined SA and GA procedure. SA is the main process to search for a better solution to minimize the total system cost, comprising user and operator costs. GA is used as a sub-process to generate new solutions. Bus demand assignment on two alternative paths is performed at the solution evaluation stage. The method was implemented on four theoretical grid networks of different size and a benchmark network. Several GA operators (crossover and mutation) were utilized and tested for their effectiveness. The results show that the proposed method can efficiently converge to the optimal solution on a small network but computation time increases significantly with network size. The method can also be used for other transport operation management problems.

  3. A group approach to genetic counselling of cardiomyopathy patients: satisfaction and psychological outcomes sufficient for further implementation.

    Science.gov (United States)

    Otten, Ellen; Birnie, Erwin; Ranchor, Adelita V; van Tintelen, J Peter; van Langen, Irene M

    2015-11-01

    The introduction of next-generation sequencing in everyday clinical genetics practise is increasing the number of genetic disorders that can be confirmed at DNA-level, and consequently increases the possibilities for cascade screening. This leads to a greater need for genetic counselling, whereas the number of professionals available to provide this is limited. We therefore piloted group genetic counselling for symptomatic cardiomyopathy patients at regional hospitals, to assess whether this could be an acceptable alternative to individual counselling. We performed a cohort study with pre- and post-counselling patient measurements using questionnaires, supplemented with evaluations of the group counselling format by the professionals involved. Patients from eight regional hospitals in the northern part of the Netherlands were included. Questionnaires comprised patient characteristics, psychological measures (personal perceived control (PPC), state and trait anxiety inventory (STAI)), and satisfaction with counsellors, counselling content and design. In total, 82 patients (mean age 57.5 year) attended one of 13 group sessions. Median PPC and STAI scores showed significantly higher control and lower anxiety after the counselling. Patients reported they were satisfied with the counsellors, and almost 75% of patients were satisfied with the group counselling. Regional professionals were also, overall, satisfied with the group sessions. The genetics professionals were less satisfied, mainly because of their perceived large time investment and less-than-expected group interaction. Hence, a group approach to cardiogenetic counselling is feasible, accessible, and psychologically effective, and could be one possible approach to counselling the increasing patient numbers in cardiogenetics.

  4. Geometric Programming Approach to an Interactive Fuzzy Inventory Problem

    Directory of Open Access Journals (Sweden)

    Nirmal Kumar Mandal

    2011-01-01

    Full Text Available An interactive multiobjective fuzzy inventory problem with two resource constraints is presented in this paper. The cost parameters and index parameters, the storage space, the budgetary cost, and the objective and constraint goals are imprecise in nature. These parameters and objective goals are quantified by linear/nonlinear membership functions. A compromise solution is obtained by geometric programming method. If the decision maker is not satisfied with this result, he/she may try to update the current solution to his/her satisfactory solution. In this way we implement man-machine interactive procedure to solve the problem through geometric programming method.

  5. Equilibrium and non-equilibrium concepts in forest genetic modelling: population- and individually-based approaches

    OpenAIRE

    Kramer, Koen; van der Werf, D. C.

    2010-01-01

    The environment is changing and so are forests, in their functioning, in species composition, and in the species’ genetic composition. Many empirical and process-based models exist to support forest management. However, most of these models do not consider the impact of environmental changes and forest management on genetic diversity nor on the rate of adaptation of critical plant processes. How genetic diversity and rates of adaptation depend on management actions is a crucial next step in m...

  6. Genetic Programming for the Downscaling of Extreme Rainfall Events on the East Coast of Peninsular Malaysia

    Directory of Open Access Journals (Sweden)

    Sahar Hadi Pour

    2014-11-01

    Full Text Available A genetic programming (GP-based logistic regression method is proposed in the present study for the downscaling of extreme rainfall indices on the east coast of Peninsular Malaysia, which is considered one of the zones in Malaysia most vulnerable to climate change. A National Centre for Environmental Prediction reanalysis dataset at 42 grid points surrounding the study area was used to select the predictors. GP models were developed for the downscaling of three extreme rainfall indices: days with larger than or equal to the 90th percentile of rainfall during the north-east monsoon; consecutive wet days; and consecutive dry days in a year. Daily rainfall data for the time periods 1961–1990 and 1991–2000 were used for the calibration and validation of models, respectively. The results are compared with those obtained using the multilayer perceptron neural network (ANN and linear regression-based statistical downscaling model (SDSM. It was found that models derived using GP can predict both annual and seasonal extreme rainfall indices more accurately compared to ANN and SDSM.

  7. Elevator Group Supervisory Control System Using Genetic Network Programming with Macro Nodes and Reinforcement Learning

    Science.gov (United States)

    Zhou, Jin; Yu, Lu; Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu; Markon, Sandor

    Elevator Group Supervisory Control System (EGSCS) is a very large scale stochastic dynamic optimization problem. Due to its vast state space, significant uncertainty and numerous resource constraints such as finite car capacities and registered hall/car calls, it is hard to manage EGSCS using conventional control methods. Recently, many solutions for EGSCS using Artificial Intelligence (AI) technologies have been reported. Genetic Network Programming (GNP), which is proposed as a new evolutionary computation method several years ago, is also proved to be efficient when applied to EGSCS problem. In this paper, we propose an extended algorithm for EGSCS by introducing Reinforcement Learning (RL) into GNP framework, and an improvement of the EGSCS' performances is expected since the efficiency of GNP with RL has been clarified in some other studies like tile-world problem. Simulation tests using traffic flows in a typical office building have been made, and the results show an actual improvement of the EGSCS' performances comparing to the algorithms using original GNP and conventional control methods. Furthermore, as a further study, an importance weight optimization algorithm is employed based on GNP with RL and its efficiency is also verified with the better performances.

  8. Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques

    Science.gov (United States)

    Kisi, Ozgur; Sanikhani, Hadi; Cobaner, Murat

    2017-08-01

    The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models' accuracy was also investigated. Including periodicity component in models' inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.

  9. Real Time Wave Forecasting Using Wind Time History and Genetic Programming

    Directory of Open Access Journals (Sweden)

    A.R. Kambekar

    2014-12-01

    Full Text Available The significant wave height and average wave period form an essential input for operational activities in ocean and coastal areas. Such information is important in issuing appropriate warnings to people planning any construction or instillation works in the oceanic environment. Many countries over the world routinely collect wave and wind data through a network of wave rider buoys. The data collecting agencies transmit the resulting information online to their registered users through an internet or a web-based system. Operational wave forecasts in addition to the measured data are also made and supplied online to the users. This paper discusses operational wave forecasting in real time mode at locations where wind rather than wave data are continuously recorded. It is based on the time series modeling and incorporates an artificial intelligence technique of genetic programming. The significant wave height and average wave period values are forecasted over a period of 96 hr in future from the observations of wind speed and directions extending to a similar time scale in the past. Wind measurements made by floating buoys at eight different locations around India over a period varying from 1.5 yr to 9.0 yr were considered. The platform of Matlab and C++ was used to develop a graphical user interface that will extend an internet based user-friendly access of the forecasts to any registered user of the data dissemination authority.

  10. Comparison between Decision Tree and Genetic Programming to distinguish healthy from stroke postural sway patterns.

    Science.gov (United States)

    Marrega, Luiz H G; Silva, Simone M; Manffra, Elisangela F; Nievola, Julio C

    2015-01-01

    Maintaining balance is a motor task of crucial importance for humans to perform their daily activities safely and independently. Studies in the field of Artificial Intelligence have considered different classification methods in order to distinguish healthy subjects from patients with certain motor disorders based on their postural strategies during the balance control. The main purpose of this paper is to compare the performance between Decision Tree (DT) and Genetic Programming (GP) - both classification methods of easy interpretation by health professionals - to distinguish postural sway patterns produced by healthy and stroke individuals based on 16 widely used posturographic variables. For this purpose, we used a posturographic dataset of time-series of center-of-pressure displacements derived from 19 stroke patients and 19 healthy matched subjects in three quiet standing tasks of balance control. Then, DT and GP models were trained and tested under two different experiments where accuracy, sensitivity and specificity were adopted as performance metrics. The DT method has performed statistically significant (P < 0.05) better in both cases, showing for example an accuracy of 72.8% against 69.2% from GP in the second experiment of this paper.

  11. Online Learning of Genetic Network Programming and its Application to Prisoner’s Dilemma Game

    Science.gov (United States)

    Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

    A new evolutionary model with the network structure named Genetic Network Programming (GNP) has been proposed recently. GNP, that is, an expansion of GA and GP, represents solutions as a network structure and evolves it by using “offline learning (selection, mutation, crossover)”. GNP can memorize the past action sequences in the network flow, so it can deal with Partially Observable Markov Decision Process (POMDP) well. In this paper, in order to improve the ability of GNP, Q learning (an off-policy TD control algorithm) that is one of the famous online methods is introduced for online learning of GNP. Q learning is suitable for GNP because (1) in reinforcement learning, the rewards an agent will get in the future can be estimated, (2) TD control doesn’t need much memory and can learn quickly, and (3) off-policy is suitable in order to search for an optimal solution independently of the policy. Finally, in the simulations, online learning of GNP is applied to a player for “Prisoner’s dilemma game” and its ability for online adaptation is confirmed.

  12. Comparison between dynamic programming and genetic algorithm for hydro unit economic load dispatch

    Directory of Open Access Journals (Sweden)

    Bin Xu

    2014-10-01

    Full Text Available The hydro unit economic load dispatch (ELD is of great importance in energy conservation and emission reduction. Dynamic programming (DP and genetic algorithm (GA are two representative algorithms for solving ELD problems. The goal of this study was to examine the performance of DP and GA while they were applied to ELD. We established numerical experiments to conduct performance comparisons between DP and GA with two given schemes. The schemes included comparing the CPU time of the algorithms when they had the same solution quality, and comparing the solution quality when they had the same CPU time. The numerical experiments were applied to the Three Gorges Reservoir in China, which is equipped with 26 hydro generation units. We found the relation between the performance of algorithms and the number of units through experiments. Results show that GA is adept at searching for optimal solutions in low-dimensional cases. In some cases, such as with a number of units of less than 10, GA's performance is superior to that of a coarse-grid DP. However, GA loses its superiority in high-dimensional cases. DP is powerful in obtaining stable and high-quality solutions. Its performance can be maintained even while searching over a large solution space. Nevertheless, due to its exhaustive enumerating nature, it costs excess time in low-dimensional cases.

  13. Generating and repairing genetically programmed DNA breaks during immunoglobulin class switch recombination

    Science.gov (United States)

    Nicolas, Laura; Cols, Montserrat; Choi, Jee Eun; Chaudhuri, Jayanta; Vuong, Bao

    2018-01-01

    Adaptive immune responses require the generation of a diverse repertoire of immunoglobulins (Igs) that can recognize and neutralize a seemingly infinite number of antigens. V(D)J recombination creates the primary Ig repertoire, which subsequently is modified by somatic hypermutation (SHM) and class switch recombination (CSR). SHM promotes Ig affinity maturation whereas CSR alters the effector function of the Ig. Both SHM and CSR require activation-induced cytidine deaminase (AID) to produce dU:dG mismatches in the Ig locus that are transformed into untemplated mutations in variable coding segments during SHM or DNA double-strand breaks (DSBs) in switch regions during CSR. Within the Ig locus, DNA repair pathways are diverted from their canonical role in maintaining genomic integrity to permit AID-directed mutation and deletion of gene coding segments. Recently identified proteins, genes, and regulatory networks have provided new insights into the temporally and spatially coordinated molecular interactions that control the formation and repair of DSBs within the Ig locus. Unravelling the genetic program that allows B cells to selectively alter the Ig coding regions while protecting non-Ig genes from DNA damage advances our understanding of the molecular processes that maintain genomic integrity as well as humoral immunity. PMID:29744038

  14. Predicting temperature drop rate of mass concrete during an initial cooling period using genetic programming

    Science.gov (United States)

    Bhattarai, Santosh; Zhou, Yihong; Zhao, Chunju; Zhou, Huawei

    2018-02-01

    Thermal cracking on concrete dams depends upon the rate at which the concrete is cooled (temperature drop rate per day) within an initial cooling period during the construction phase. Thus, in order to control the thermal cracking of such structure, temperature development due to heat of hydration of cement should be dropped at suitable rate. In this study, an attempt have been made to formulate the relation between cooling rate of mass concrete with passage of time (age of concrete) and water cooling parameters: flow rate and inlet temperature of cooling water. Data measured at summer season (April-August from 2009 to 2012) from recently constructed high concrete dam were used to derive a prediction model with the help of Genetic Programming (GP) software “Eureqa”. Coefficient of Determination (R) and Mean Square Error (MSE) were used to evaluate the performance of the model. The value of R and MSE is 0.8855 and 0.002961 respectively. Sensitivity analysis was performed to evaluate the relative impact on the target parameter due to input parameters. Further, testing the proposed model with an independent dataset those not included during analysis, results obtained from the proposed GP model are close enough to the real field data.

  15. Genetic Algorithm for Mixed Integer Nonlinear Bilevel Programming and Applications in Product Family Design

    Directory of Open Access Journals (Sweden)

    Chenlu Miao

    2016-01-01

    Full Text Available Many leader-follower relationships exist in product family design engineering problems. We use bilevel programming (BLP to reflect the leader-follower relationship and describe such problems. Product family design problems have unique characteristics; thus, mixed integer nonlinear BLP (MINLBLP, which has both continuous and discrete variables and multiple independent lower-level problems, is widely used in product family optimization. However, BLP is difficult in theory and is an NP-hard problem. Consequently, using traditional methods to solve such problems is difficult. Genetic algorithms (GAs have great value in solving BLP problems, and many studies have designed GAs to solve BLP problems; however, such GAs are typically designed for special cases that do not involve MINLBLP with one or multiple followers. Therefore, we propose a bilevel GA to solve these particular MINLBLP problems, which are widely used in product family problems. We give numerical examples to demonstrate the effectiveness of the proposed algorithm. In addition, a reducer family case study is examined to demonstrate practical applications of the proposed BLGA.

  16. A Systems Genetic Approach to Identify Low Dose Radiation-Induced Lymphoma Susceptibility/DOE2013FinalReport

    Energy Technology Data Exchange (ETDEWEB)

    Balmain, Allan [University of California, San Francisco; Song, Ihn Young [University of California, San Francisco

    2013-05-15

    The ultimate goal of this project is to identify the combinations of genetic variants that confer an individual's susceptibility to the effects of low dose (0.1 Gy) gamma-radiation, in particular with regard to tumor development. In contrast to the known effects of high dose radiation in cancer induction, the responses to low dose radiation (defined as 0.1 Gy or less) are much less well understood, and have been proposed to involve a protective anti-tumor effect in some in vivo scientific models. These conflicting results confound attempts to develop predictive models of the risk of exposure to low dose radiation, particularly when combined with the strong effects of inherited genetic variants on both radiation effects and cancer susceptibility. We have used a Systems Genetics approach in mice that combines genetic background analysis with responses to low and high dose radiation, in order to develop insights that will allow us to reconcile these disparate observations. Using this comprehensive approach we have analyzed normal tissue gene expression (in this case the skin and thymus), together with the changes that take place in this gene expression architecture a) in response to low or high- dose radiation and b) during tumor development. Additionally, we have demonstrated that using our expression analysis approach in our genetically heterogeneous/defined radiation-induced tumor mouse models can uniquely identify genes and pathways relevant to human T-ALL, and uncover interactions between common genetic variants of genes which may lead to tumor susceptibility.

  17. Quantitative genetics of Taura syndrome resistance in Pacific (Penaeus vannamei): A cure model approach

    DEFF Research Database (Denmark)

    Ødegård, Jørgen; Gitterle, Thomas; Madsen, Per

    2011-01-01

    cure survival model using Gibbs sampling, treating susceptibility and endurance as separate genetic traits. Results: Overall mortality at the end of test was 28%, while 38% of the population was considered susceptible to the disease. The estimated underlying heritability was high for susceptibility (0....... However, genetic evaluation of susceptibility based on the cure model showed clear associations with standard genetic evaluations that ignore the cure fraction for these data. Using the current testing design, genetic variation in observed survival time and absolute survival at the end of test were most...

  18. Safety of genetically engineered foods: approaches to assessing unintended health effects

    National Research Council Canada - National Science Library

    Committee on Identifying and Assessing Unintended Effects of Genetically Engineered Foods on Human Health, National Research Council

    2004-01-01

    Assists policymakers in evaluating the appropriate scientific methods for detecting unintended changes in food and assessing the potential for adverse health effects from genetically modified products...

  19. Nonverbatim Captioning in Dutch Television Programs: A Text Linguistic Approach

    Science.gov (United States)

    Schilperoord, Joost; de Groot, Vanja; van Son, Nic

    2005-01-01

    In the Netherlands, as in most other European countries, closed captions for the deaf summarize texts rather than render them verbatim. Caption editors argue that in this way television viewers have enough time to both read the text and watch the program. They also claim that the meaning of the original message is properly conveyed. However, many…

  20. Mindsets of Leadership Education Undergraduates: An Approach to Program Assessment

    Science.gov (United States)

    Ho, Sarah P.; Odom, Summer F.

    2015-01-01

    Students (N = 313) in undergraduate leadership degree programs at Texas A&M University were surveyed to determine their leadership mindset using hierarchical and systemic thinking preferences. Significant differences in thinking were found between gender and academic classification. Male leadership students scored greater in hierarchical…

  1. Using a Hybrid Approach for a Leadership Cohort Program

    Science.gov (United States)

    Norman, Maxine A.

    2013-01-01

    Because information technology continues to change rapidly, Extension is challenged with learning and using technology appropriately. We assert Extension cannot shy away from the challenges but must embrace technology because audiences and external forces demand it. A hybrid, or blended, format of a leadership cohort program was offered to public…

  2. Integer goal programming approach for finding a compromise ...

    African Journals Online (AJOL)

    In second model the cost and time spent on repairing the components are considered as two different objectives. Selective maintenance operation is used to select the repairable components and a multi-objective goal programming algorithm is proposed to obtain compromise selection of repairable components for the two ...

  3. A stochastic-programming approach to integrated asset and liability ...

    African Journals Online (AJOL)

    This increase in complexity has provided an impetus for the investigation into integrated asset- and liability-management frameworks that could realistically address dynamic portfolio allocation in a risk-controlled way. In this paper the authors propose a multi-stage dynamic stochastic-programming model for the integrated ...

  4. Optimum workforce-size model using dynamic programming approach

    African Journals Online (AJOL)

    This paper presents an optimum workforce-size model which determines the minimum number of excess workers (overstaffing) as well as the minimum total recruitment cost during a specified planning horizon. The model is an extension of other existing dynamic programming models for manpower planning in the sense ...

  5. An Introduction to Fortran Programming: An IPI Approach.

    Science.gov (United States)

    Fisher, D. D.; And Others

    This text is designed to give individually paced instruction in Fortran Programing. The text contains fifteen units. Unit titles include: Flowcharts, Input and Output, Loops, and Debugging. Also included is an extensive set of appendices. These were designed to contain a great deal of practical information necessary to the course. These appendices…

  6. Comparison of DOE and NIRMA approaches to configuration management programs

    International Nuclear Information System (INIS)

    Yang, E.Y.; Kulzick, K.C.

    1995-01-01

    One of the major management programs used for commercial, laboratory, and defense nuclear facilities is configuration management. The safe and efficient operation of a nuclear facility requires constant vigilance in maintaining the facility's design basis with its as-built condition. Numerous events have occurred that can be attributed to (either directly or indirectly) the extent to which configuration management principles have been applied. The nuclear industry, as a whole, has been addressing this management philosophy with efforts taken on by its constituent professional organizations. The purpose of this paper is to compare and contrast the implementation plans for enhancing a configuration management program as outlined in the U.S. Department of Energy's (DOE's) DOE-STD-1073-93, open-quotes Guide for Operational Configuration Management Program,close quotes with the following guidelines developed by the Nuclear Information and Records Management Association (NIRMA): 1. PP02-1994, open-quotes Position Paper on Configuration Managementclose quotes 2. PP03-1992, open-quotes Position Paper for Implementing a Configuration Management Enhancement Program for a Nuclear Facilityclose quotes 3. PP04-1994 open-quotes Position Paper for Configuration Management Information Systems.close quotes

  7. Genetic and Computational Approaches for Studying Plant Development and Abiotic Stress Responses Using Image-Based Phenotyping

    Science.gov (United States)

    Campbell, M. T.; Walia, H.; Grondin, A.; Knecht, A.

    2017-12-01

    The development of abiotic stress tolerant crops (i.e. drought, salinity, or heat stress) requires the discovery of DNA sequence variants associated with stress tolerance-related traits. However, many traits underlying adaptation to abiotic stress involve a suite of physiological pathways that may be induced at different times throughout the duration of stress. Conventional single-point phenotyping approaches fail to fully capture these temporal responses, and thus downstream genetic analysis may only identify a subset of the genetic variants that are important for adaptation to sub-optimal environments. Although genomic resources for crops have advanced tremendously, the collection of phenotypic data for morphological and physiological traits is laborious and remains a significant bottleneck in bridging the phenotype-genotype gap. In recent years, the availability of automated, image-based phenotyping platforms has provided researchers with an opportunity to collect morphological and physiological traits non-destructively in a highly controlled environment. Moreover, these platforms allow abiotic stress responses to be recorded throughout the duration of the experiment, and have facilitated the use of function-valued traits for genetic analyses in major crops. We will present our approaches for addressing abiotic stress tolerance in cereals. This talk will focus on novel open-source software to process and extract biological meaningful data from images generated from these phenomics platforms. In addition, we will discuss the statistical approaches to model longitudinal phenotypes and dissect the genetic basis of dynamic responses to these abiotic stresses throughout development.

  8. A bio-cultural approach to the study of food choice: The contribution of taste genetics, population and culture.

    Science.gov (United States)

    Risso, Davide S; Giuliani, Cristina; Antinucci, Marco; Morini, Gabriella; Garagnani, Paolo; Tofanelli, Sergio; Luiselli, Donata

    2017-07-01

    The study of food choice, one of the most complex human traits, requires an integrated approach that takes into account environmental, socio-cultural and biological diversity. We recruited 183 volunteers from four geo-linguistic groups and highly diversified in terms of both genetic background and food habits from whom we collected genotypes and phenotypes tightly linked to taste perception. We confirmed previous genetic associations, in particular with stevioside perception, and noted significant differences in food consumption: in particular, broccoli, mustard and beer consumption scores were significantly higher (Adjusted P = 0.02, Adjusted P diversity and cultural aspects in taste perception and food consumption. Published by Elsevier Ltd.

  9. Approaches in the risk assessment of genetically modified foods by the Hellenic Food Safety Authority.

    Science.gov (United States)

    Varzakas, Theodoros H; Chryssochoidis, G; Argyropoulos, D

    2007-04-01

    Risk analysis has become important to assess conditions and take decisions on control procedures. In this context it is considered a prerequisite in the evaluation of GM food. Many consumers worldwide worry that food derived from genetically modified organisms (GMOs) may be unhealthy and hence regulations on GMO authorisations and labelling have become more stringent. Nowadays there is a higher demand for non-GM products and these products could be differentiated from GM products using the identity preservation system (IP) that could apply throughout the grain processing system. IP is the creation of a transparent communication system that encompasses HACCP, traceability and related systems in the supply chain. This process guarantees that certain characteristics of the lots of food (non-GM origin) are maintained "from farm to fork". This article examines the steps taken by the Hellenic Food Safety Authority to examine the presence of GMOs in foods. The whole integrated European legislation framework currently in place still needs to be implemented in Greece. Penalties should be enforced to those who import, process GMOs without special licence and do not label those products. Similar penalties should be enforced to those companies that issue false certificates beyond the liabilities taken by the food enterprises for farmers' compensation. We argue that Greece has no serious reasons to choose the use of GMOs due to the fact that the structural and pedologic characteristics of the Greek agriculture favour the biological and integrated cultivation more. Greece is not in favour of the politics behind coexistence of conventional and GM plants and objects to the use of GMOs in the food and the environment because the processor has a big burden in terms of money, time and will suffer a great deal in order to prove that their products are GMO free or that any contamination is adventitious or technically unavoidable. Moreover, Greece owns a large variety of genetic

  10. The Nuclear program of Navy: an approach from the budget

    International Nuclear Information System (INIS)

    Alves, Marco Antonio

    2014-01-01

    After the initiatives arising in the 50s, to conduct scientific research in Brazil, in the nuclear sector, the Brazilian government decided to invest resources in the early '70s, to provide the country with full training in the nuclear fuel cycle, producing reactors research and power and reprocessing of spent nuclear fuel in the reactors. This course of action was intended to guarantee the necessary to make stronger our energy matrix, with the use of natural resources (uranium and thorium mines whose reserves are among the largest in the world) for the production of electricity, within view particularly development programs in effect at the time. Meanwhile, the Navy of Brazil, identifying the need to operate nuclear-powered submarines in the security and defense of the South Atlantic, institutionalized in 1979, a nuclear program which aimed to the complete domination independently of the nuclear cycle. Since then, what is realized is that the trajectory of the development of this program, in addition to external difficulties imposed by countries holders of this technology, budget internal issue became relevant. The irregularity of financial transfers caused the delay and the lack of guarantees of future budgets led to the unpredictability of strategic planning, leading the program to the 'vegetative' state for nearly a decade. However, from the 2000s, the possibility of economic exploitation by the expansion of the Brazilian Continental Shelf and the new discoveries of oil in the pre-salt tier shifted the vision balance then attributed to the use of nuclear technology as an energy source only, or intended for research and development in areas such as health and agriculture, reinforced the argument that Brazil must have this technology to 'discourage the concentration of hostile forces within the limits of Brazilian territorial waters'. In this context, the paper describes and analyzes how the Navy Nuclear Program (PNM) amounted to the

  11. Stochastic optimization in insurance a dynamic programming approach

    CERN Document Server

    Azcue, Pablo

    2014-01-01

    The main purpose of the book is to show how a viscosity approach can be used to tackle control problems in insurance. The problems covered are the maximization of survival probability as well as the maximization of dividends in the classical collective risk model. The authors consider the possibility of controlling the risk process by reinsurance as well as by investments. They show that optimal value functions are characterized as either the unique or the smallest viscosity solution of the associated Hamilton-Jacobi-Bellman equation; they also study the structure of the optimal strategies and show how to find them. The viscosity approach was widely used in control problems related to mathematical finance but until quite recently it was not used to solve control problems related to actuarial mathematical science. This book is designed to familiarize the reader on how to use this approach. The intended audience is graduate students as well as researchers in this area.

  12. A rigorous approach to facilitate and guarantee the correctness of the genetic testing management in human genome information systems.

    Science.gov (United States)

    Araújo, Luciano V; Malkowski, Simon; Braghetto, Kelly R; Passos-Bueno, Maria R; Zatz, Mayana; Pu, Calton; Ferreira, João E

    2011-12-22

    Recent medical and biological technology advances have stimulated the development of new testing systems that have been providing huge, varied amounts of molecular and clinical data. Growing data volumes pose significant challenges for information processing systems in research centers. Additionally, the routines of genomics laboratory are typically characterized by high parallelism in testing and constant procedure changes. This paper describes a formal approach to address this challenge through the implementation of a genetic testing management system applied to human genome laboratory. We introduced the Human Genome Research Center Information System (CEGH) in Brazil, a system that is able to support constant changes in human genome testing and can provide patients updated results based on the most recent and validated genetic knowledge. Our approach uses a common repository for process planning to ensure reusability, specification, instantiation, monitoring, and execution of processes, which are defined using a relational database and rigorous control flow specifications based on process algebra (ACP). The main difference between our approach and related works is that we were able to join two important aspects: 1) process scalability achieved through relational database implementation, and 2) correctness of processes using process algebra. Furthermore, the software allows end users to define genetic testing without requiring any knowledge about business process notation or process algebra. This paper presents the CEGH information system that is a Laboratory Information Management System (LIMS) based on a formal framework to support genetic testing management for Mendelian disorder studies. We have proved the feasibility and showed usability benefits of a rigorous approach that is able to specify, validate, and perform genetic testing using easy end user interfaces.

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

    DEFF Research Database (Denmark)

    Terepeta, Michal Tomasz

    -approximation of (or induced from) another flow algebra. Furthermore, we show how flow algebras can be used in communicating or weighted pushdown systems. To achieve that, we show that it is possible to relax some of the requirements imposed by original formulation of those techniques without compromising...... such a language. We also introduce an abstract domain that symbolically represents the messages sent between the concurrently executing processes. It stores prefixes or suffixes of communication traces including various constraints imposed on the messages. Since the problem has exponential complexity, we also...... 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...

  14. A new approach to nuclear reactor design optimization using genetic algorithms and regression analysis

    International Nuclear Information System (INIS)

    Kumar, Akansha; Tsvetkov, Pavel V.

    2015-01-01

    Highlights: • This paper presents a new method useful for the optimization of complex dynamic systems. • The method uses the strengths of; genetic algorithms (GA), and regression splines. • The method is applied to the design of a gas cooled fast breeder reactor design. • Tools like Java, R, and codes like MCNP, Matlab are used in this research. - Abstract: A module based optimization method using genetic algorithms (GA), and multivariate regression analysis has been developed to optimize a set of parameters in the design of a nuclear reactor. GA simulates natural evolution to perform optimization, and is widely used in recent times by the scientific community. The GA fits a population of random solutions to the optimized solution of a specific problem. In this work, we have developed a genetic algorithm to determine the values for a set of nuclear reactor parameters to design a gas cooled fast breeder reactor core including a basis thermal–hydraulics analysis, and energy transfer. Multivariate regression is implemented using regression splines (RS). Reactor designs are usually complex and a simulation needs a significantly large amount of time to execute, hence the implementation of GA or any other global optimization techniques is not feasible, therefore we present a new method of using RS in conjunction with GA. Due to using RS, we do not necessarily need to run the neutronics simulation for all the inputs generated from the GA module rather, run the simulations for a predefined set of inputs, build a multivariate regression fit to the input and the output parameters, and then use this fit to predict the output parameters for the inputs generated by GA. The reactor parameters are given by the, radius of a fuel pin cell, isotopic enrichment of the fissile material in the fuel, mass flow rate of the coolant, and temperature of the coolant at the core inlet. And, the optimization objectives for the reactor core are, high breeding of U-233 and Pu-239 in

  15. Loss of genetic diversity in Culex quinquefasciatus targeted by a lymphatic filariasis vector control program in Recife, Brazil.

    Science.gov (United States)

    Cartaxo, Marina F S; Ayres, Constância F J; Weetman, David

    2011-09-01

    Recife is one of the largest cities in north-eastern Brazil and is endemic for lymphatic filariasis transmitted by Culex quinquefasciatus. Since 2003 a control program has targeted mosquito larvae by elimination of breeding sites and bimonthly application of Bacillus sphaericus. To assess the impact of this program on the local vector population we monitored the genetic diversity and differentiation of Cx. quinquefasciatus using microsatellites and a B. sphaericus-resistance associated mutation (cqm1(REC)) over a 3-year period. We detected a significant but gradual decline in allelic diversity, which, coupled with subtle temporal genetic structure, suggests a major impact of the control program on the vector population. Selection on cqm1(REC) does not appear to be involved with loss of neutral diversity from the population, with no temporal trend in resistant allele frequency and no correlation with microsatellite differentiation. The evidence for short-term genetic drift we detected suggests a low ratio of effective population size: census population size for Cx. quinquefasciatus, perhaps coupled with strong geographically-restricted population structure. Spatial definition of populations will be an important step for success of an expanded vector control program. Copyright © 2011 Royal Society of Tropical Medicine and Hygiene. Published by Elsevier Ltd. All rights reserved.

  16. A Programming Language Approach to Safety in Home Networks

    DEFF Research Database (Denmark)

    Mortensen, Kjeld Høyer; Schougaard, Kari Rye; Schultz, Ulrik Pagh

    , even in a worst-case scenario where an unauthorized user gains remote control of the facilities. We address this safety issue at the programming language level by restricting the operations that can be performed on devices according to the physical location of the user initiating the request......-based restrictions on operations. This model has been implemented in a middleware for home AV devices written in Java, using infrared communication and a FireWire network to implement location awareness....

  17. A Programming Language Approach to Safety in Home Networks

    DEFF Research Database (Denmark)

    Mortensen, Kjeld Høyer; Schougaard, Kari Sofie Fogh; Schultz, Ulrik Pagh

    2003-01-01

    , even in a worst-case scenario where an unauthorized user gains remote control of the facilities. We address this safety issue at the programming language level by restricting the operations that can be performed on devices according to the physical location of the user initiating the request......-based restrictions on operations. This model has been implemented in a middleware for home AV devices written in Java, using infrared communication and a FireWire network to implement location awareness....

  18. Responsible genetic approach to stock restoration, sea ranching and stock enhancement of marine fishes and invertebrates

    DEFF Research Database (Denmark)

    Grant, W. Stewart; Jasper, James; Bekkevold, Dorte

    2017-01-01

    of marine fishes and invertebrates have been implemented with various outcomes. A review of the literature indicates that considerable effort has been directed toward culture technologies to maximize production, but scant attention has been given to genetic risks to wild populations. Genetic risks from...

  19. Minimal approaches to genetic improvement of growth rates in white spruce

    Science.gov (United States)

    D.T. Lester

    1973-01-01

    Several features of central importance to genetic improvement of white spruce have been demonstrated by tree breeders. First, white spruce is genetically a highly variable species and much of the existent variation can be readily incorporated in planting stock (Jeffers 1969, Holst and Teich 1969). Second, local seed often is not the best for rapid growth (Nienstaedt...

  20. A Top-down Approach to Genetic Circuit Synthesis and Optimized Technology Mapping

    DEFF Research Database (Denmark)

    Baig, Hasan; Madsen, Jan

    2017-01-01

    Genetic logic circuits are becoming popular as an emerging field of technology. They are composed of genetic parts of DNA and work inside a living cell to perform a dedicated boolean function triggered by the presence or absence of certain proteins or other species....