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Sample records for genetic programming model

  1. Developing robotic behavior using a genetic programming model

    SciTech Connect

    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.

  2. A Model Program for Translational Medicine in Epilepsy Genetics.

    PubMed

    Smith, Lacey A; Ullmann, Jeremy F P; Olson, Heather E; Achkar, Christelle M El; Truglio, Gessica; Kelly, McKenna; Rosen-Sheidley, Beth; Poduri, Annapurna

    2017-03-01

    Recent technological advances in gene sequencing have led to a rapid increase in gene discovery in epilepsy. However, the ability to assess pathogenicity of variants, provide functional analysis, and develop targeted therapies has not kept pace with rapid advances in sequencing technology. Thus, although clinical genetic testing may lead to a specific molecular diagnosis for some patients, test results often lead to more questions than answers. As the field begins to focus on therapeutic applications of genetic diagnoses using precision medicine, developing processes that offer more than equivocal test results is essential. The success of precision medicine in epilepsy relies on establishing a correct genetic diagnosis, analyzing functional consequences of genetic variants, screening potential therapeutics in the preclinical laboratory setting, and initiating targeted therapy trials for patients. The authors describe the structure of a comprehensive, pediatric Epilepsy Genetics Program that can serve as a model for translational medicine in epilepsy.

  3. Regionalization of runoff models derived by genetic programming

    NASA Astrophysics Data System (ADS)

    Heřmanovský, M.; Havlíček, V.; Hanel, M.; Pech, P.

    2017-04-01

    The aim of this study is to assess the potential of hydrological models derived by genetic programming (GP) to estimate runoff at ungauged catchments by regionalization. A set of 176 catchments from the MOPEX (Model Parameter Estimation Experiment) project was used for our analysis. Runoff models for each catchment were derived by genetic programming (hereafter GP models). A comparison of efficiency was made between GP models and three conceptual models (SAC-SMA, BTOPMC, GR4J). The efficiency of the GP models was in general comparable with that of the SAC-SMA and BTOPMC models but slightly lower (up to 10% for calibration and 15% in validation) than for the GR4J model. The relationship between the efficiency of the GP models and catchment descriptors (CDs) was investigated. From 13 available CDs the aridity index and mean catchment elevation explained most of the variation in the efficiency of the GP models. The runoff for each catchment was then estimated considering GP models from single or multiple physically similar catchments (donors). Better results were obtained with multiple donor catchments. Increasing the number of CDs used for quantification of physical similarity improves the efficiency of the GP models in runoff simulation. The best regionalization results were obtained with 6 CDs together with 6 donors. Our results show that transfer of the GP models is possible and leads to satisfactory results when applied at physically similar catchments. The GP models can be therefore used as an alternative for runoff modelling at ungauged catchments if similar gauged catchments can be identified and successfully simulated.

  4. Suspended sediment modeling using genetic programming and soft computing techniques

    NASA Astrophysics Data System (ADS)

    Kisi, Ozgur; Dailr, Ali Hosseinzadeh; Cimen, Mesut; Shiri, Jalal

    2012-07-01

    SummaryModeling suspended sediment load is an important factor in water resources engineering as it crucially affects the design and management of water resources structures. In this study the genetic programming (GP) technique was applied for estimating the daily suspended sediment load in two stations in Cumberland River in U.S. Daily flow and sediment data from 1972 to 1989 were used to train and test the applied genetic programming models. The effect of various GP operators on sediment load estimation was investigated. The optimal fitness function, operator functions, linking function and learning algorithm were obtained for modeling daily suspended sediment. The GP estimates were compared with those of the Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANNs) and Support Vector Machine (SVM) results, in term of coefficient of determination, mean absolute error, coefficient of residual mass and variance accounted for. The comparison results indicated that the GP is superior to the ANFIS, ANN and SVM models in estimating daily suspended sediment load.

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

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

  6. Mathematical Modeling of Intestinal Iron Absorption Using Genetic Programming

    PubMed Central

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

    2017-01-01

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

  7. Genetic programming model for forecast of short and noisy data

    NASA Astrophysics Data System (ADS)

    Sivapragasam, C.; Vincent, P.; Vasudevan, G.

    2007-01-01

    Though forecasting of river flow has received a great deal of attention from engineers and researchers throughout the world, this still continues to be a challenging task owing to the complexity of the process. In the last decade or so, artificial neural networks (ANNs) have been widely applied, and their ability to model complex phenomena has been clearly demonstrated. However, the success of ANNs depends very crucially on having representative records of sufficient length. Further, the forecast accuracy decreases rapidly with an increase in the forecast horizon. In this study, the use of the Darwinian theory-based recent evolutionary technique of genetic programming (GP) is suggested to forecast fortnightly flow up to 4-lead. It is demonstrated that short lead predictions can be significantly improved from a short and noisy time series if the stochastic (noise) component is appropriately filtered out. The deterministic component can then be easily modelled. Further, only the immediate antecedent exogenous and/or non-exogenous inputs can be assumed to control the process. With an increase in the forecast horizon, the stochastic components also play an important role in the forecast, besides the inherent difficulty in ascertaining the appropriate input variables which can be assumed to govern the underlying process. GP is found to be an efficient tool to identify the most appropriate input variables to achieve reasonable prediction accuracy for higher lead-period forecasts. A comparison with ANNs suggests that though there is no significant difference in the prediction accuracy, GP does offer some unique advantages. Copyright

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

    PubMed

    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.

  9. Avionics equipment failure prediction based on genetic programming and grey model

    NASA Astrophysics Data System (ADS)

    Deng, Xiujian; Luo, Qiang; Zhao, Yiyang; Feng, Qi

    2017-01-01

    Avionics equipment failure prediction by conventional GM (Grey Model) may yield large forecasting errors. Combining GM (1, 1) model with genetic programming algorithm, a kind of GP-GM (1, 1) forecast model was established to minimize such errors. Forecasting sequence was calculated by means of GM (1, 1) model, then genetic programming algorithm was used to modify them further, and the degradation trend prediction of characteristic parameters of avionics equipment was realized. The validity of GP-GM (1, 1) prediction model was testified by tracking and forecasting the experiment data of avionics equipment in real environment.

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

    PubMed

    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.

  11. A wavelet-linear genetic programming model for sodium (Na+) concentration forecasting in rivers

    NASA Astrophysics Data System (ADS)

    Ravansalar, Masoud; Rajaee, Taher; Zounemat-Kermani, Mohammad

    2016-06-01

    The prediction of water quality parameters in water resources such as rivers is of importance issue that needs to be considered in better management of irrigation systems and water supplies. In this respect, this study proposes a new hybrid wavelet-linear genetic programming (WLGP) model for prediction of monthly sodium (Na+) concentration. The 23-year monthly data used in this study, were measured from the Asi River at the Demirköprü gauging station located in Antakya, Turkey. At first, the measured discharge (Q) and Na+ datasets are initially decomposed into several sub-series using discrete wavelet transform (DWT). Then, these new sub-series are imposed to the ad hoc linear genetic programming (LGP) model as input patterns to predict monthly Na+ one month ahead. The results of the new proposed WLGP model are compared with LGP, WANN and ANN models. Comparison of the models represents the superiority of the WLGP model over the LGP, WANN and ANN models such that the Nash-Sutcliffe efficiencies (NSE) for WLGP, WANN, LGP and ANN models were 0.984, 0.904, 0.484 and 0.351, respectively. The achieved results even points to the superiority of the single LGP model than the ANN model. Continuously, the capability of the proposed WLGP model in terms of prediction of the Na+ peak values is also presented in this study.

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

    NASA Astrophysics Data System (ADS)

    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

  13. Constraints in Genetic Programming

    NASA Technical Reports Server (NTRS)

    Janikow, Cezary Z.

    1996-01-01

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

  14. Genetic Programming Based Approach for Modeling Time Series Data of Real Systems

    NASA Astrophysics Data System (ADS)

    Ahalpara, Dilip P.; Parikh, Jitendra C.

    Analytic models of a computer generated time series (logistic map) and three real time series (ion saturation current in Aditya Tokamak plasma, NASDAQ composite index and Nifty index) are constructed using Genetic Programming (GP) framework. In each case, the optimal map that results from fitting part of the data set also provides a very good description of the rest of the data. Predictions made using the map iteratively are very good for computer generated time series but not for the data of real systems. For such cases, an extended GP model is proposed and illustrated. A comparison of these results with those obtained using Artificial Neural Network (ANN) is also carried out.

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

    PubMed

    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.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    NASA Astrophysics Data System (ADS)

    Kisi, Ozgur; Sanikhani, Hadi; Cobaner, Murat

    2016-05-01

    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.

  18. Nonlinear model identification of an experimental ball-and-tube system using a genetic programming approach

    NASA Astrophysics Data System (ADS)

    Coelho, Leandro dos Santos; Pessôa, Marcelo Wicthoff

    2009-07-01

    Most processes in industry are characterized by nonlinear and time-varying behavior. The identification of mathematical models typically nonlinear systems is vital in many fields of engineering. The developed mathematical models can be used to study the behavior of the underlying system as well as for supervision, fault detection, prediction, estimation of unmeasurable variables, optimization and model-based control purposes. A variety of system identification techniques are applied to the modeling of process dynamics. Recently, the identification of nonlinear systems by genetic programming (GP) approaches has been successfully applied in many applications. GP is a paradigm of evolutionary computation field based on a structure description method that applies the principles of natural evolution to optimization problems and its nature is a generalized hierarchy computer program description. GP adopts a tree structure code to describe an identification problem. Unlike the traditional approximation methods where the structure of an approximate model is fixed, the structure of the GP tree itself is modified and optimized and, thus, there is a possibility that GP trees could be more appropriate or accurate approximate models. This paper focuses the GP method for structure selection in a system identification applications. The proposed GP method combines different techniques for tuning of crossover and mutation probabilities with an orthogonal least-squares (OLS) algorithm to estimate the contribution of the branches of the tree to the accuracy of the discrete polynomial Nonlinear AutoRegressive with eXogenous inputs (NARX) model. The nonlinear system identification procedure, based on a NARX representation and GP, is applied to empirical case study of an experimental ball-and-tube system. The results demonstrate that the GP with OLS is a promising technique for NARX modeling.

  19. The Stock Price Prediction and Sell-buy Strategy Model by Genetic Network Programming

    NASA Astrophysics Data System (ADS)

    Mori, Shigeo; Hirasawa, Kotaro; Hu, Jinglu

    Various stock prices predicting and sell-buy strategy models have been so far proposed. They are classified as the fundamental analysis using the achievements of the companies and the trend of business, etc., and the technical analysis which carries out the numerical analysis of the movement of stock prices. On the other hand, as one of the methods for data mining which finds out the regularity from a vast quantity of stock price data, Genetic Algorithm (GA) has been so far applied widely. As a concrete example, the optimal values of parameters of stock indices like various moving averages and rates of deviation, etc. is computed by GA, and there have been developed various methods for predicting stock prices and determinig sell-buy strategy based on it. However, it is hard to determine which is the most effective index by the conventional GA. Moreover, the most effective one depends on the brands. So in this paper, a stock price prediction and sell-buy strategy model which searches for the optimal combination of various indices in the technical analysis has been proposed using Genetic Network programming and its effectiveness is confirmed by simulations.

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

    PubMed

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

    2013-01-01

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

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

    PubMed

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

    2009-01-01

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

  2. Optimal Modeling of Urban Ambient Air Ozone Concentration Based on Its Precursors' Concentrations and Temperature, Employing Genetic Programming and Genetic Algorithm.

    PubMed

    Mousavi, Seyed Mahmoud; Husseinzadeh, Danial; Alikhani, Sadegh

    2014-04-01

    Efficient models are required to predict the optimum values of ozone concentration in different levels of its precursors' concentrations and temperatures. A novel model based on the application of a genetic programming (GP) optimization is presented in this article. Ozone precursors' concentrations and run time average temperature have been chosen as model's parameters. Generalization performances of two different homemade models based on genetic programming and genetic algorithm (GA), which can be used for calculating theoretical ozone concentration, are compared with conventional semi-empirical model performance. Experimental data of Mashhad city ambient air have been employed to investigate the prediction ability of properly trained GP, GA, and conventional semi-empirical models. It is clearly demonstrated that the in-house algorithm which is used for the model based on GP, provides better generalization performance over the model optimized with GA and the conventional semi-empirical ones. The proposed model is found accurate enough and can be used for urban air ozone concentration prediction.

  3. Atmospheric Downscaling using Genetic Programming

    NASA Astrophysics Data System (ADS)

    Zerenner, Tanja; Venema, Victor; Simmer, Clemens

    2013-04-01

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

  4. National Dairy Genetic Evaluation Program

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The National Dairy Genetic Evaluation Program is a continuation of ongoing USDA collaboration with the U.S. dairy industry on genetic evaluation of dairy cattle since 1908. Data are provided by dairy records processing centers (yield, health, pedigree, and reproduction traits), breed registry societ...

  5. A synthetic genetic edge detection program.

    PubMed

    Tabor, Jeffrey J; Salis, Howard M; Simpson, Zachary Booth; Chevalier, Aaron A; Levskaya, Anselm; Marcotte, Edward M; Voigt, Christopher A; Ellington, Andrew D

    2009-06-26

    Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

  6. A Synthetic Genetic Edge Detection Program

    PubMed Central

    Tabor, Jeffrey J.; Salis, Howard; Simpson, Zachary B.; Chevalier, Aaron A.; Levskaya, Anselm; Marcotte, Edward M.; Voigt, Christopher A.; Ellington, Andrew D.

    2009-01-01

    Summary Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E.coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks. PMID:19563759

  7. Texture segmentation by genetic programming.

    PubMed

    Song, Andy; Ciesielski, Vic

    2008-01-01

    This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features. Due to the absence of time-consuming feature extraction, the evolved classifiers enable the development of the proposed texture segmentation algorithm. This GP based method can achieve a segmentation speed that is significantly higher than that of conventional methods. This method does not require a human expert to manually construct models for texture feature extraction. In an analysis of the evolved classifiers, it can be seen that these GP classifiers are not arbitrary. Certain textural regularities are captured by these classifiers to discriminate different textures. GP has been shown in this study as a feasible and a powerful approach for texture classification and segmentation, which are generally considered as complex vision tasks.

  8. Programming models

    SciTech Connect

    Daniel, David J; Mc Pherson, Allen; Thorp, John R; Barrett, Richard; Clay, Robert; De Supinski, Bronis; Dube, Evi; Heroux, Mike; Janssen, Curtis; Langer, Steve; Laros, Jim

    2011-01-14

    A programming model is a set of software technologies that support the expression of algorithms and provide applications with an abstract representation of the capabilities of the underlying hardware architecture. The primary goals are productivity, portability and performance.

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

    PubMed Central

    Mendyk, Aleksander; Güres, Sinan; Szlęk, Jakub; Wiśniowska, Barbara; Kleinebudde, Peter

    2015-01-01

    The purpose of this work was to develop a mathematical model of the drug dissolution (Q) from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modeling of Q versus extrudate diameter (d) and the time variable (t) and (2) indirect modeling through Weibull equation. ANNs provided also information about minimum achievable generalization error and the way to enhance the original dataset used for adjustment of the equations' parameters. Two inputs were found important for the drug dissolution: d and t. The extrudates length (L) was found not important. Both GP modeling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modeling of Q versus d and t resulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs' black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modeling strategies. PMID:26101544

  10. Modelling and prediction of complex non-linear processes by using Pareto multi-objective genetic programming

    NASA Astrophysics Data System (ADS)

    Jamali, A.; Khaleghi, E.; Gholaminezhad, I.; Nariman-zadeh, N.

    2016-05-01

    In this paper, a new multi-objective genetic programming (GP) with a diversity preserving mechanism and a real number alteration operator is presented and successfully used for Pareto optimal modelling of some complex non-linear systems using some input-output data. In this study, two different input-output data-sets of a non-linear mathematical model and of an explosive cutting process are considered separately in three-objective optimisation processes. The pertinent conflicting objective functions that have been considered for such Pareto optimisations are namely, training error (TE), prediction error (PE), and the length of tree (complexity of the network) (TL) of the GP models. Such three-objective optimisation implementations leads to some non-dominated choices of GP-type models for both cases representing the trade-offs among those objective functions. Therefore, optimal Pareto fronts of such GP models exhibit the trade-off among the corresponding conflicting objectives and, thus, provide different non-dominated optimal choices of GP-type models. Moreover, the results show that no significant optimality in TE and PE may occur when the TL of the corresponding GP model exceeds some values.

  11. Evolving evolutionary algorithms using linear genetic programming.

    PubMed

    Oltean, Mihai

    2005-01-01

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

  12. Application of Genetic Programming (GP) Formalism for Building Disease Predictive Models from Protein-Protein Interactions (PPI) Data.

    PubMed

    Vyas, Renu; Bapat, Sanket; Goel, Purva; Karthikeyan, Muthukumarasamy; Tambe, Sanjeev S; Kulkarni, Bhaskar D

    2016-10-26

    Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approach for predicting PPIs related to a disease. In a case study, a dataset consisting of one hundred and thirty five PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalization ability. A high correlation coefficient(CC) of 0.893, low root mean square error (RMSE) and mean absolute percentage error (MAPE) values of 478.221 and 0.239, respectively were achieved for both the training and test set outputs. To validate the discriminatory nature of the model, it was applied on a dataset of diabetes complexes where it yielded significantly low CC values. Thus, the GP model developed here serves a dual purpose: (a)a predictor of the binding energy of cancer related PPI complexes, and (b)a classifier for discriminating PPI complexes related to cancer from those of other diseases.

  13. Dynamical genetic programming in XCSF.

    PubMed

    Preen, Richard J; Bull, Larry

    2013-01-01

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

  14. Fault detection using genetic programming

    NASA Astrophysics Data System (ADS)

    Zhang, Liang; B. Jack, Lindsay; Nandi, Asoke K.

    2005-03-01

    Genetic programming (GP) is a stochastic process for automatically generating computer programs. GP has been applied to a variety of problems which are too wide to reasonably enumerate. As far as the authors are aware, it has rarely been used in condition monitoring (CM). In this paper, GP is used to detect faults in rotating machinery. Featuresets from two different machines are used to examine the performance of two-class normal/fault recognition. The results are compared with a few other methods for fault detection: Artificial neural networks (ANNs) have been used in this field for many years, while support vector machines (SVMs) also offer successful solutions. For ANNs and SVMs, genetic algorithms have been used to do feature selection, which is an inherent function of GP. In all cases, the GP demonstrates performance which equals or betters that of the previous best performing approaches on these data sets. The training times are also found to be considerably shorter than the other approaches, whilst the generated classification rules are easy to understand and independently validate.

  15. The Genetic Programming of Industrial Microorganisms.

    ERIC Educational Resources Information Center

    Hopwood, David A.

    1981-01-01

    Traces the development of the field of industrial microbial genetics, describing a range of techniques for genetic programing. Includes a discussion of site-directed mutagenesis, protoplast fusion, and recombinant DNA manipulations. (CS)

  16. Applications of Genetic Programming in Cancer Research

    PubMed Central

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

    2012-01-01

    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 allows 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. PMID:18929677

  17. Applications of genetic programming in cancer research.

    PubMed

    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. LIGO detector characterization with genetic programming

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

  19. Genetics and the unity of biology. Program

    SciTech Connect

    Not Available

    1988-12-31

    International Congresses of Genetics, convened just once every five years, provide a rare opportunity for overview in the field of genetic engineering. The Congress, held August 20-27, 1988 in Toronto, Canada focused on the theme Genetics and the Unity of Biology, which was chosen because the concepts of modern genetics have provided biology with a unifying theoretical structure. This program guide contains a schedule of all Congress activities and a listing of all Symposia, Workshops and Poster Sessions held.

  20. Functional Localization of Genetic Network Programming

    NASA Astrophysics Data System (ADS)

    Eto, Shinji; Hirasawa, Kotaro; Hu, Jinglu

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

  1. Genetic algorithms as discovery programs

    SciTech Connect

    Hilliard, M.R.; Liepins, G.

    1986-01-01

    Genetic algorithms are mathematical counterparts to natural selection and gene recombination. As such, they have provided one of the few significant breakthroughs in machine learning. Used with appropriate reward functions and apportionment of credit, they have been successfully applied to gas pipeline operation, x-ray registration and mathematical optimization problems. This paper discusses the basics of genetic algorithms, describes a few successes, and reports on current progress at Oak Ridge National Laboratory in applications to set covering and simulated robots.

  2. Genetic Parallel Programming: design and implementation.

    PubMed

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

    2006-01-01

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

  3. Improving Search Properties in Genetic Programming

    NASA Technical Reports Server (NTRS)

    Janikow, Cezary Z.; DeWeese, Scott

    1997-01-01

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

  4. Successful technical trading agents using genetic programming.

    SciTech Connect

    Othling, Andrew S.; Kelly, John A.; Pryor, Richard J.; Farnsworth, Grant V.

    2004-10-01

    Genetic programming (GP) has proved to be a highly versatile and useful tool for identifying relationships in data for which a more precise theoretical construct is unavailable. In this project, we use a GP search to develop trading strategies for agent based economic models. These strategies use stock prices and technical indicators, such as the moving average convergence/divergence and various exponentially weighted moving averages, to generate buy and sell signals. We analyze the effect of complexity constraints on the strategies as well as the relative performance of various indicators. We also present innovations in the classical genetic programming algorithm that appear to improve convergence for this problem. Technical strategies developed by our GP algorithm can be used to control the behavior of agents in economic simulation packages, such as ASPEN-D, adding variety to the current market fundamentals approach. The exploitation of arbitrage opportunities by technical analysts may help increase the efficiency of the simulated stock market, as it does in the real world. By improving the behavior of simulated stock markets, we can better estimate the effects of shocks to the economy due to terrorism or natural disasters.

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

    PubMed

    Clancy, Kevin; Voigt, Christopher A

    2010-08-01

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

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

    PubMed

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

    2012-10-01

    Recent advances in research and biotechnology are making genetics and genomics increasingly relevant to the lives and health of the general public. For the public to make informed healthcare and public policy decisions relating to genetic information, there is a need for increased genetic literacy. Biotech 101 is a free, short-course for the local community introducing participants to topics in genetics, genomics, and biotechnology, created at the HudsonAlpha Institute for Biotechnology. This study evaluated the effectiveness of Biotech 101 in increasing the genetic literacy of program participants through pre-and-post surveys. Genetic literacy was measured through increases in self-perceived knowledge for each content area covered through the course and the self-reported impact the course had on various aspects of participants' lives. Three hundred ninety-two individuals attended Biotech 101 during the first three course offerings. Participants reported a significant increase in self-perceived knowledge for each content area (p < 0.01). Participants also reported the program had high levels of impact on their lives and decision-making, a high likelihood for continued self-learning, and overwhelming satisfaction with course content and logistics. Biotech 101 is an effective mechanism for impacting participants' lives and genetic literacy and serves as a model for other similar programs, adding to the currently limited evidence base regarding public educational strategies in genetics and biotechnology.

  7. Genetic Network Programming with Reconstructed Individuals

    NASA Astrophysics Data System (ADS)

    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.

  8. Programming Models in HPC

    SciTech Connect

    Shipman, Galen M.

    2016-06-13

    These are the slides for a presentation on programming models in HPC, at the Los Alamos National Laboratory's Parallel Computing Summer School. The following topics are covered: Flynn's Taxonomy of computer architectures; single instruction single data; single instruction multiple data; multiple instruction multiple data; address space organization; definition of Trinity (Intel Xeon-Phi is a MIMD architecture); single program multiple data; multiple program multiple data; ExMatEx workflow overview; definition of a programming model, programming languages, runtime systems; programming model and environments; MPI (Message Passing Interface); OpenMP; Kokkos (Performance Portable Thread-Parallel Programming Model); Kokkos abstractions, patterns, policies, and spaces; RAJA, a systematic approach to node-level portability and tuning; overview of the Legion Programming Model; mapping tasks and data to hardware resources; interoperability: supporting task-level models; Legion S3D execution and performance details; workflow, integration of external resources into the programming model.

  9. Modeling EERE deployment programs

    SciTech Connect

    Cort, K. A.; Hostick, D. J.; Belzer, D. B.; Livingston, O. V.

    2007-11-01

    The purpose of the project was to identify and characterize the modeling of deployment programs within the EERE Technology Development (TD) programs, address possible improvements to the modeling process, and note gaps in knowledge for future research.

  10. Improved model reduction and tuning of fractional-order PI(λ)D(μ) controllers for analytical rule extraction with genetic programming.

    PubMed

    Das, Saptarshi; Pan, Indranil; Das, Shantanu; Gupta, Amitava

    2012-03-01

    Genetic algorithm (GA) has been used in this study for a new approach of suboptimal model reduction in the Nyquist plane and optimal time domain tuning of proportional-integral-derivative (PID) and fractional-order (FO) PI(λ)D(μ) controllers. Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H(2)-norm-based reduced parameter modeling technique. With the tuned controller parameters and reduced-order model parameter dataset, optimum tuning rules have been developed with a test-bench of higher-order processes via genetic programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by a weighted sum of error index and controller effort. From the reported Pareto optimal front of the GP-based optimal rule extraction technique, a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP-based tuning rules has been compared with the original GA-based control performance for the PID and PI(λ)D(μ) controllers, handling four different classes of representative higher-order processes. These rules are very useful for process control engineers, as they inherit the power of the GA-based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three-dimensional plots of the required variation in PID/fractional-order PID (FOPID) controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP-based tuning rules has also been shown with credible simulation examples.

  11. Computer Center: BASIC String Models of Genetic Information Transfer.

    ERIC Educational Resources Information Center

    Spain, James D., Ed.

    1984-01-01

    Discusses some of the major genetic information processes which may be modeled by computer program string manipulation, focusing on replication and transcription. Also discusses instructional applications of using string models. (JN)

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

    ERIC Educational Resources Information Center

    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…

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

    SciTech Connect

    Szymanski, J. J.; Brumby, Steven P.; Pope, P. A.; Eads, D. R.; Galassi, M. C.; Harvey, N. R.; Perkins, S. J.; Porter, R. B.; Theiler, J. P.; Young, A. C.; Bloch, J. J.; David, N. A.; Esch-Mosher, D. 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.

  14. Mendelian genetics: Paradigm, conjecture, or research program

    NASA Astrophysics Data System (ADS)

    Oldham, V.; Brouwer, W.

    Kuhn's model of the structure of scientific revolutions, Popper's hypothetic-deductive model of science, and Lakatos's methodology of competing research programs are applied to a historical episode in biology. Each of these three models offers a different explanatory system for the development, neglect, and eventual acceptance of Mendel's paradigm of inheritance. The authors conclude that both rational and nonrational criteria play an important role during times of crisis in science, when different research programs compete for acceptance. It is suggested that Kuhn's model, emphasizing the nonrational basis of science, and Popper's model, emphasizing the rational basis of science, can be used fruitfully in high school science courses.

  15. Genetically programmed superparamagnetic behavior of mammalian cells.

    PubMed

    Kim, Taeuk; Moore, David; Fussenegger, Martin

    2012-12-31

    Although magnetic fields and paramagnetic inorganic materials were abundant on planet earth during the entire evolution of living species the interaction of organisms with these physical forces remains a little-understood phenomenon. Interestingly, rather than being genetically encoded, organisms seem to accumulate and take advantage of inorganic nanoparticles to sense or react to magnetic fields. Using a synthetic biology-inspired approach we have genetically programmed mammalian cells to show superparamagnetic behavior. The combination of ectopic production of the human ferritin heavy chain 1 (hFTH1), engineering the cells for expression of an iron importer, the divalent metal ion transferase 1 (DMT1) and the design of an iron-loading culture medium to maximize cellular iron uptake enabled efficient iron mineralization in intracellular ferritin particles and conferred superparamagnetic behavior to the entire cell. When captured by a magnetic field the superparamagnetic cells reached attraction velocities of up to 30 μm/s and could be efficiently separated from complex cell mixtures using standard magnetic cell separation equipment. Technology that enables magnetic separation of genetically programmed superparamagnetic cells in the absence of inorganic particles could foster novel opportunities in diagnostics and cell-based therapies.

  16. Modeling EERE Deployment Programs

    SciTech Connect

    Cort, K. A.; Hostick, D. J.; Belzer, D. B.; Livingston, O. V.

    2007-11-01

    This report compiles information and conclusions gathered as part of the “Modeling EERE Deployment Programs” project. The purpose of the project was to identify and characterize the modeling of deployment programs within the EERE Technology Development (TD) programs, address possible improvements to the modeling process, and note gaps in knowledge in which future research is needed.

  17. Los Alamos Programming Models

    SciTech Connect

    Bergen, Benjamin Karl

    2016-07-07

    This is the PDF of a powerpoint presentation from a teleconference on Los Alamos programming models. It starts by listing their assumptions for the programming models and then details a hierarchical programming model at the System Level and Node Level. Then it details how to map this to their internal nomenclature. Finally, a list is given of what they are currently doing in this regard.

  18. Restoration of degraded images using genetic programming

    NASA Astrophysics Data System (ADS)

    Hernández-Beltrán, José Enrique; Díaz-Ramírez, Víctor H.; Trujillo, Leonardo; Legrand, Pierrick

    2016-09-01

    In image restoration problems it is commonly assumed that image degradations are linear. In real-life this assumption is not always satisfied causing linear restoration methods fail. In this work, we present the design of an image restoration filtering based on genetic programming. The proposed filtering is given by a secuence of basic mathematical operators that allows to retrieve an undegraded image from an image degraded with noise. Computer simulations results obtained with the proposed algorithm in terms of objective metrics are analyzed and discussed by processing images degraded with noise. The obtained results are compared with those obtained with existing linear filters.

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

    PubMed

    Chavoya, Arturo; Lopez-Martin, Cuauhtemoc; Andalon-Garcia, Irma R; Meda-Campaña, M E

    2012-01-01

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

  20. Discovering Knowledge from Noisy Databases Using Genetic Programming.

    ERIC Educational Resources Information Center

    Wong, Man Leung; Leung, Kwong Sak; Cheng, Jack C. Y.

    2000-01-01

    Presents a framework that combines Genetic Programming and Inductive Logic Programming, two approaches in data mining, to induce knowledge from noisy databases. The framework is based on a formalism of logic grammars and is implemented as a data mining system called LOGENPRO (Logic Grammar-based Genetic Programming System). (Contains 34…

  1. Modeling EERE Deployment Programs

    SciTech Connect

    Cort, Katherine A.; Hostick, Donna J.; Belzer, David B.; Livingston, Olga V.

    2007-11-08

    The purpose of this report is to compile information and conclusions gathered as part of three separate tasks undertaken as part of the overall project, “Modeling EERE Deployment Programs,” sponsored by the Planning, Analysis, and Evaluation office within the Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE). The purpose of the project was to identify and characterize the modeling of deployment programs within the EERE Technology Development (TD) programs, address improvements to modeling in the near term, and note gaps in knowledge where future research is needed.

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

    PubMed

    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. EVOLVING RETRIEVAL ALGORITHMS WITH A GENETIC PROGRAMMING SCHEME

    SciTech Connect

    J. THEILER; ET AL

    1999-06-01

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

  4. Stream Flow Prediction by Remote Sensing and Genetic Programming

    NASA Technical Reports Server (NTRS)

    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.

  5. Genetic models of focal epilepsies.

    PubMed

    Boillot, Morgane; Baulac, Stéphanie

    2016-02-15

    Focal epilepsies were for a long time thought to be acquired disorders secondary to cerebral lesions. However, the important role of genetic factors in focal epilepsies is now well established. Several focal epilepsy syndromes are now proven to be monogenic disorders. While earlier genetic studies suggested a strong contribution of ion channel and neurotransmitter receptor genes, later work has revealed alternative pathways, among which the mammalian target of rapamycin (mTOR) signal transduction pathway with DEPDC5. In this article, we provide an update on the mutational spectrum of neuronal nicotinic acetylcholine receptor genes (CHRNA4, CHRNB2, CHRNA2) and KCNT1 causing autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE), and of LGI1 in autosomal dominant epilepsy with auditory features (ADEAF). We also emphasize, through a review of the current literature, the contribution of in vitro and in vivo models developed to unveil the pathogenic mechanisms underlying these two epileptic syndromes.

  6. Climate system modeling program

    SciTech Connect

    1995-12-31

    The Climate System Modeling Project is a component activity of NSF's Climate Modeling, Analysis and Prediction Program, supported by the Atmospheric Sciences Program, Geosciences Directorate. Its objective is to accelerate progress toward reliable prediction of global and regional climate changes in the decades ahead. CSMP operates through workshops, support for post-docs and graduate students and other collaborative activities designed to promote interdisciplinary and strategic work in support of the overall objective (above) and specifically in three areas, (1) Causes of interdecadal variability in the climate system, (2) Interactions of regional climate forcing with global processes, and (3) Scientific needs of climate assessment.

  7. Genetic mouse models of depression.

    PubMed

    Barkus, Christopher

    2013-01-01

    This chapter focuses on the use of genetically modified mice in investigating the neurobiology of depressive behaviour. First, the behavioural tests commonly used as a model of depressive-like behaviour in rodents are described. These tests include those sensitive to antidepressant treatment such as the forced swim test and the tail suspension test, as well as other tests that encompass the wider symptomatology of a depressive episode. A selection of example mutant mouse lines is then presented to illustrate the use of these tests. As our understanding of depression increases, an expanding list of candidate genes is being investigated using mutant mice. Here, mice relevant to the monoamine and corticotrophin-releasing factor hypotheses of depression are covered as well as those relating to the more recent candidate, brain-derived neurotrophic factor. This selection provides interesting examples of the use of complimentary lines, such as those that have genetic removal or overexpression, and also opposing behavioural changes seen following manipulation of closely related genes. Finally, factors such as the issue of background strain and influence of environmental factors are reflected upon, before considering what can realistically be expected of a mouse model of this complex psychiatric disorder.

  8. Adaptable Constrained Genetic Programming: Extensions and Applications

    NASA Technical Reports Server (NTRS)

    Janikow, Cezary Z.

    2005-01-01

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

  9. Alternative Living Kidney Donation Programs Boost Genetically Unrelated Donation

    PubMed Central

    Poldervaart, Rosalie A.; Laging, Mirjam; Royaards, Tessa; Kal-van Gestel, Judith A.; van Agteren, Madelon; de Klerk, Marry; Zuidema, Willij; Betjes, Michiel G. H.; Roodnat, Joke I.

    2015-01-01

    Donor-recipient ABO and/or HLA incompatibility used to lead to donor decline. Development of alternative transplantation programs enabled transplantation of incompatible couples. How did that influence couple characteristics? Between 2000 and 2014, 1232 living donor transplantations have been performed. In conventional and ABO-incompatible transplantation the willing donor becomes an actual donor for the intended recipient. In kidney-exchange and domino-donation the donor donates indirectly to the intended recipient. The relationship between the donor and intended recipient was studied. There were 935 conventional and 297 alternative program transplantations. There were 66 ABO-incompatible, 68 domino-paired, 62 kidney-exchange, and 104 altruistic donor transplantations. Waiting list recipients (n = 101) were excluded as they did not bring a living donor. 1131 couples remained of whom 196 participated in alternative programs. Genetically unrelated donors (486) were primarily partners. Genetically related donors (645) were siblings, parents, children, and others. Compared to genetically related couples, almost three times as many genetically unrelated couples were incompatible and participated in alternative programs (P < 0.001). 62% of couples were genetically related in the conventional donation program versus 32% in alternative programs (P < 0.001). Patient and graft survival were not significantly different between recipient programs. Alternative donation programs increase the number of transplantations by enabling genetically unrelated donors to donate. PMID:26421181

  10. Genetic Network Programming with Intron-Like Nodes

    NASA Astrophysics Data System (ADS)

    Mabu, Shingo; Chen, Yan; Eto, Shinji; Shimada, Kaoru; Hirasawa, Kotaro

    Recently, Genetic Network Programming (GNP) has been proposed, which is an extension of Genetic Algorithm(GA) and Genetic Programming(GP). GNP can make compact programs and can memorize the past history in it implicitly, because it expresses the solution by directed graphs and therefore, it can reuse the nodes. In this research, intron-like nodes are introduced for improving the performance of GNP. The aim of introducing intron-like nodes is to use every node as much as possible. It is found from simulations that the intron-like nodes are useful for improving the training speed and generalization ability.

  11. Evolution of a computer program for classifying protein segments as transmembrane domains using genetic programming

    SciTech Connect

    Koza, J.R.

    1994-12-31

    The recently-developed genetic programming paradigm is used to evolve a computer program to classify a given protein segment as being a transmembrane domain or non-transmembrane area of the protein. Genetic programming starts with a primordial ooze of randomly generated computer programs composed of available programmatic ingredients and then genetically breeds the population of programs using the Darwinian principle of survival of the fittest and an analog of the naturally occurring genetic operation of crossover (sexual recombination). Automatic function definition enables genetic programming to dynamically create subroutines dynamically during the run. Genetic programming is given a training set of differently-sized protein segments and their correct classification (but no biochemical knowledge, such as hydrophobicity values). Correlation is used as the fitness measure to drive the evolutionary process. The best genetically-evolved program achieves an out-of-sample correlation of 0.968 and an out-of-sample error rate of 1.6%. This error rate is better than that reported for four other algorithms reported at the First International Conference on Intelligent Systems for Molecular Biology. Our genetically evolved program is an instance of an algorithm discovered by an automated learning paradigm that is superior to that written by human investigators.

  12. Genetically modified pig models for human diseases.

    PubMed

    Fan, Nana; Lai, Liangxue

    2013-02-20

    Genetically modified animal models are important for understanding the pathogenesis of human disease and developing therapeutic strategies. Although genetically modified mice have been widely used to model human diseases, some of these mouse models do not replicate important disease symptoms or pathology. Pigs are more similar to humans than mice in anatomy, physiology, and genome. Thus, pigs are considered to be better animal models to mimic some human diseases. This review describes genetically modified pigs that have been used to model various diseases including neurological, cardiovascular, and diabetic disorders. We also discuss the development in gene modification technology that can facilitate the generation of transgenic pig models for human diseases.

  13. Model Program Evaluations. Fact Sheet

    ERIC Educational Resources Information Center

    Arkansas Safe Schools Initiative Division, 2002

    2002-01-01

    There are probably thousands of programs and courses intended to prevent or reduce violence in this nation's schools. Evaluating these many programs has become a problem or goal in itself. There are now many evaluation programs, with many levels of designations, such as model, promising, best practice, exemplary and noteworthy. "Model program" is…

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

    PubMed

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

    2014-01-01

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

  15. Solving deterministic non-linear programming problem using Hopfield artificial neural network and genetic programming techniques

    NASA Astrophysics Data System (ADS)

    Vasant, P.; Ganesan, T.; Elamvazuthi, I.

    2012-11-01

    A fairly reasonable result was obtained for non-linear engineering problems using the optimization techniques such as neural network, genetic algorithms, and fuzzy logic independently in the past. Increasingly, hybrid techniques are being used to solve the non-linear problems to obtain better output. This paper discusses the use of neuro-genetic hybrid technique to optimize the geological structure mapping which is known as seismic survey. It involves the minimization of objective function subject to the requirement of geophysical and operational constraints. In this work, the optimization was initially performed using genetic programming, and followed by hybrid neuro-genetic programming approaches. Comparative studies and analysis were then carried out on the optimized results. The results indicate that the hybrid neuro-genetic hybrid technique produced better results compared to the stand-alone genetic programming method.

  16. Genetic coding and gene expression - new Quadruplet genetic coding model

    NASA Astrophysics Data System (ADS)

    Shankar Singh, Rama

    2012-07-01

    Successful demonstration of human genome project has opened the door not only for developing personalized medicine and cure for genetic diseases, but it may also answer the complex and difficult question of the origin of life. It may lead to making 21st century, a century of Biological Sciences as well. Based on the central dogma of Biology, genetic codons in conjunction with tRNA play a key role in translating the RNA bases forming sequence of amino acids leading to a synthesized protein. This is the most critical step in synthesizing the right protein needed for personalized medicine and curing genetic diseases. So far, only triplet codons involving three bases of RNA, transcribed from DNA bases, have been used. Since this approach has several inconsistencies and limitations, even the promise of personalized medicine has not been realized. The new Quadruplet genetic coding model proposed and developed here involves all four RNA bases which in conjunction with tRNA will synthesize the right protein. The transcription and translation process used will be the same, but the Quadruplet codons will help overcome most of the inconsistencies and limitations of the triplet codes. Details of this new Quadruplet genetic coding model and its subsequent potential applications including relevance to the origin of life will be presented.

  17. Genetically Engineered Pig Models for Human Diseases

    PubMed Central

    Prather, Randall S.; Lorson, Monique; Ross, Jason W.; Whyte, Jeffrey J.; Walters, Eric

    2015-01-01

    Although pigs are used widely as models of human disease, their utility as models has been enhanced by genetic engineering. Initially, transgenes were added randomly to the genome, but with the application of homologous recombination, zinc finger nucleases, and transcription activator-like effector nuclease (TALEN) technologies, now most any genetic change that can be envisioned can be completed. To date these genetic modifications have resulted in animals that have the potential to provide new insights into human diseases for which a good animal model did not exist previously. These new animal models should provide the preclinical data for treatments that are developed for diseases such as Alzheimer's disease, cystic fibrosis, retinitis pigmentosa, spinal muscular atrophy, diabetes, and organ failure. These new models will help to uncover aspects and treatments of these diseases that were otherwise unattainable. The focus of this review is to describe genetically engineered pigs that have resulted in models of human diseases. PMID:25387017

  18. Primer on molecular genetics. DOE Human Genome Program

    SciTech Connect

    Not Available

    1992-04-01

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

  19. Primer on Molecular Genetics; DOE Human Genome Program

    DOE R&D Accomplishments Database

    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.

  20. Catchcopy Creation Support System Using Electronic Dictionary and Genetic Programming

    NASA Astrophysics Data System (ADS)

    Matsudaira, Tomomi; Hagiwara, Masafumi

    In this paper, we propose a catchcopy creation support system. In respect of a vocabulary and getting an idea, it is difficult for inexperienced people to make a catch copy. This system will support users from these points. In the system, EDR electronic dictionary and Genetic Programming are employed. EDR electronic dictionary which has large-scale knowledge is used as a knowledge base. Genetic programing is used to make catchcopy using some words chosen by user. Proposed system requires arbitrary number of words as an input, and shows words relevant to the input. User chooses words from displayed words. Candidates of catchcopy are made by genetic programing algorithm using chosen words. We implemented a catchcopy creation support system from a viewpoint of a way-of-thinking support tool.

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

    PubMed

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

    2016-02-01

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

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

    PubMed

    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.

  3. Polyglot Programming in Applications Used for Genetic Data Analysis

    PubMed Central

    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. PMID:25197633

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

    NASA Technical Reports Server (NTRS)

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

    2002-01-01

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

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

    PubMed

    Catania, Francesco; Schmitz, Jürgen

    2015-01-01

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

  6. Responsible integration of biological and psychosocial models: comments on "Genetic associations with intimate partner violence in a sample of hazardous drinking men in batterer intervention programs".

    PubMed

    Abbey, Antonia

    2014-04-01

    Despite research demonstrating that gene expression differs in response to social environmental circumstances, deterministic views of biology are common. Stuart and colleagues (2014) encourage readers to think about genetic factors in the same dynamic and probabilistic manner that they consider other causes of intimate partner violence. Given that participants had co-occurring alcohol problems, future studies should evaluate how different genetic polymorphisms uniquely and synergistically contribute to heavy drinking and aggression under different socio-environmental conditions. Psychological expectancies have a powerful impact on behavior, thus extreme caution is required before labeling people as genetically predisposed to violence.

  7. Obesity-programmed mice are rescued by early genetic intervention

    PubMed Central

    Bumaschny, Viviana F.; Yamashita, Miho; Casas-Cordero, Rodrigo; Otero-Corchón, Verónica; de Souza, Flávio S.J.; Rubinstein, Marcelo; Low, Malcolm J.

    2012-01-01

    Obesity is a chronic metabolic disorder affecting half a billion people worldwide. Major difficulties in managing obesity are the cessation of continued weight loss in patients after an initial period of responsiveness and rebound to pretreatment weight. It is conceivable that chronic weight gain unrelated to physiological needs induces an allostatic regulatory state that defends a supranormal adipose mass despite its maladaptive consequences. To challenge this hypothesis, we generated a reversible genetic mouse model of early-onset hyperphagia and severe obesity by selectively blocking the expression of the proopiomelanocortin gene (Pomc) in hypothalamic neurons. Eutopic reactivation of central POMC transmission at different stages of overweight progression normalized or greatly reduced food intake in these obesity-programmed mice. Hypothalamic Pomc rescue also attenuated comorbidities such as hyperglycemia, hyperinsulinemia, and hepatic steatosis and normalized locomotor activity. However, effectiveness of treatment to normalize body weight and adiposity declined progressively as the level of obesity at the time of Pomc induction increased. Thus, our study using a novel reversible monogenic obesity model reveals the critical importance of early intervention for the prevention of subsequent allostatic overload that auto-perpetuates obesity. PMID:23093774

  8. Obesity-programmed mice are rescued by early genetic intervention.

    PubMed

    Bumaschny, Viviana F; Yamashita, Miho; Casas-Cordero, Rodrigo; Otero-Corchón, Verónica; de Souza, Flávio S J; Rubinstein, Marcelo; Low, Malcolm J

    2012-11-01

    Obesity is a chronic metabolic disorder affecting half a billion people worldwide. Major difficulties in managing obesity are the cessation of continued weight loss in patients after an initial period of responsiveness and rebound to pretreatment weight. It is conceivable that chronic weight gain unrelated to physiological needs induces an allostatic regulatory state that defends a supranormal adipose mass despite its maladaptive consequences. To challenge this hypothesis, we generated a reversible genetic mouse model of early-onset hyperphagia and severe obesity by selectively blocking the expression of the proopiomelanocortin gene (Pomc) in hypothalamic neurons. Eutopic reactivation of central POMC transmission at different stages of overweight progression normalized or greatly reduced food intake in these obesity-programmed mice. Hypothalamic Pomc rescue also attenuated comorbidities such as hyperglycemia, hyperinsulinemia, and hepatic steatosis and normalized locomotor activity. However, effectiveness of treatment to normalize body weight and adiposity declined progressively as the level of obesity at the time of Pomc induction increased. Thus, our study using a novel reversible monogenic obesity model reveals the critical importance of early intervention for the prevention of subsequent allostatic overload that auto-perpetuates obesity.

  9. Considering genetic characteristics in German Holstein breeding programs.

    PubMed

    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

  10. Genetic model compensation: Theory and applications

    NASA Astrophysics Data System (ADS)

    Cruickshank, David Raymond

    1998-12-01

    The adaptive filtering algorithm known as Genetic Model Compensation (GMC) was originally presented in the author's Master's Thesis. The current work extends this earlier work. GMC uses a genetic algorithm to optimize filter process noise parameters in parallel with the estimation of the state and based only on the observational information available to the filter. The original stochastic state model underlying GMC was inherited from the antecedent, non-adaptive Dynamic Model Compensation (DMC) algorithm. The current work develops the stochastic state model from a linear system viewpoint, avoiding the simplifications and approximations of the earlier development, and establishes Riemann sums as unbiased estimators of the stochastic integrals which describe the evolution of the random state components. These are significant developments which provide GMC with a solid theoretical foundation. Orbit determination is the area of application in this work, and two types of problems are studied: real-time autonomous filtering using absolute GPS measurements and precise post-processed filtering using differential GPS measurements. The first type is studied in a satellite navigation simulation in which pseudorange and pseudorange rate measurements are processed by an Extended Kalman Filter which incorporates both DMC and GMC. Both estimators are initialized by a geometric point solution algorithm. Using measurements corrupted by simulated Selective Availability errors, GMC reduces mean RSS position error by 6.4 percent, reduces mean clock bias error by 46 percent, and displays a marked improvement in covariance consistency relative to DMC. To study the second type of problem, GMC is integrated with NASA Jet Propulsion Laboratory's Gipsy/Oasis-II (GOA-II) precision orbit determination program creating an adaptive version of GOA-II's Reduced Dynamic Tracking (RDT) process noise formulation. When run as a sequential estimator with GPS measurements from the TOPEX satellite and

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

    PubMed

    Castelli, Mauro; Trujillo, Leonardo; Vanneschi, Leonardo

    2015-01-01

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

  12. Genetic evaluation of a Great Lakes lake trout hatchery program

    USGS Publications Warehouse

    Page, K.S.; Scribner, K.T.; Bast, D.; Holey, M.E.; Burnham-Curtis, M. K.

    2005-01-01

    Efforts over several decades to restore lake trout Salvelinus namaycush in U.S. waters of the upper Great Lakes have emphasized the stocking of juveniles from each of six hatchery broodstocks. Retention of genetic diversity across all offspring life history stages throughout the hatchery system has been an important component of the restoration hatchery and stocking program. Different stages of the lake trout hatchery program were examined to determine how effective hatchery practices have been in minimizing the loss of genetic diversity in broodstock adults and in progeny stocked. Microsatellite loci were used to estimate allele frequencies, measures of genetic diversity, and relatedness for wild source populations, hatchery broodstocks, and juveniles. We also estimated the effective number of breeders for each broodstock. Hatchery records were used to track destinations of fertilized eggs from all spawning dates to determine whether adult contributions to stocking programs were proportional to reproductive effort. Overall, management goals of maintaining genetic diversity were met across all stages of the hatchery program; however, we identified key areas where changes in mating regimes and in the distribution of fertilized gametes and juveniles could be improved. Estimates of effective breeding population size (Nb) were 9-41% of the total number of adults spawned. Low estimates of Nb were primarily attributed to spawning practices, including the pooling of gametes from multiple males and females and the reuse of males. Nonrandom selection and distribution of fertilized eggs before stocking accentuated declines in effective breeding population size and increased levels of relatedness of juveniles distributed to different rearing facilities and stocking locales. Adoption of guidelines that decrease adult reproductive variance and promote more equitable reproductive contributions of broodstock adults to juveniles would further enhance management goals of

  13. Probabilistic graphical models for genetic association studies.

    PubMed

    Mourad, Raphaël; Sinoquet, Christine; Leray, Philippe

    2012-01-01

    Probabilistic graphical models have been widely recognized as a powerful formalism in the bioinformatics field, especially in gene expression studies and linkage analysis. Although less well known in association genetics, many successful methods have recently emerged to dissect the genetic architecture of complex diseases. In this review article, we cover the applications of these models to the population association studies' context, such as linkage disequilibrium modeling, fine mapping and candidate gene studies, and genome-scale association studies. Significant breakthroughs of the corresponding methods are highlighted, but emphasis is also given to their current limitations, in particular, to the issue of scalability. Finally, we give promising directions for future research in this field.

  14. Genetically modified pig models for neurodegenerative disorders.

    PubMed

    Holm, Ida E; Alstrup, Aage Kristian Olsen; Luo, Yonglun

    2016-01-01

    Increasing incidence of neurodegenerative disorders such as Alzheimer's disease and Parkinson's disease has become one of the most challenging health issues in ageing humans. One approach to combat this is to generate genetically modified animal models of neurodegenerative disorders for studying pathogenesis, prognosis, diagnosis, treatment, and prevention. Owing to the genetic, anatomic, physiologic, pathologic, and neurologic similarities between pigs and humans, genetically modified pig models of neurodegenerative disorders have been attractive large animal models to bridge the gap of preclinical investigations between rodents and humans. In this review, we provide a neuroanatomical overview in pigs and summarize and discuss the generation of genetically modified pig models of neurodegenerative disorders including Alzheimer's diseases, Huntington's disease, Parkinson's disease, amyotrophic lateral sclerosis, spinal muscular atrophy, and ataxia-telangiectasia. We also highlight how non-invasive bioimaging technologies such as positron emission tomography (PET), computer tomography (CT), and magnetic resonance imaging (MRI), and behavioural testing have been applied to characterize neurodegenerative pig models. We further propose a multiplex genome editing and preterm recloning (MAP) approach by using the rapid growth of the ground-breaking precision genome editing technology CRISPR/Cas9 and somatic cell nuclear transfer (SCNT). With this approach, we hope to shorten the temporal requirement in generating multiple transgenic pigs, increase the survival rate of founder pigs, and generate genetically modified pigs that will more closely resemble the disease-causing mutations and recapitulate pathological features of human conditions.

  15. Genetic models of homosexuality: generating testable predictions.

    PubMed

    Gavrilets, Sergey; Rice, William R

    2006-12-22

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism.

  16. Genetic models of homosexuality: generating testable predictions

    PubMed Central

    Gavrilets, Sergey; Rice, William R

    2006-01-01

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism. PMID:17015344

  17. A Spatial Statistical Model for Landscape Genetics

    PubMed Central

    Guillot, Gilles; Estoup, Arnaud; Mortier, Frédéric; Cosson, Jean François

    2005-01-01

    Landscape genetics is a new discipline that aims to provide information on how landscape and environmental features influence population genetic structure. The first key step of landscape genetics is the spatial detection and location of genetic discontinuities between populations. However, efficient methods for achieving this task are lacking. In this article, we first clarify what is conceptually involved in the spatial modeling of genetic data. Then we describe a Bayesian model implemented in a Markov chain Monte Carlo scheme that allows inference of the location of such genetic discontinuities from individual geo-referenced multilocus genotypes, without a priori knowledge on populational units and limits. In this method, the global set of sampled individuals is modeled as a spatial mixture of panmictic populations, and the spatial organization of populations is modeled through the colored Voronoi tessellation. In addition to spatially locating genetic discontinuities, the method quantifies the amount of spatial dependence in the data set, estimates the number of populations in the studied area, assigns individuals to their population of origin, and detects individual migrants between populations, while taking into account uncertainty on the location of sampled individuals. The performance of the method is evaluated through the analysis of simulated data sets. Results show good performances for standard data sets (e.g., 100 individuals genotyped at 10 loci with 10 alleles per locus), with high but also low levels of population differentiation (e.g., FST < 0.05). The method is then applied to a set of 88 individuals of wolverines (Gulo gulo) sampled in the northwestern United States and genotyped at 10 microsatellites. PMID:15520263

  18. Initialization Method for Grammar-Guided Genetic Programming

    NASA Astrophysics Data System (ADS)

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

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

  19. Achieving World-Class Schools: Mastering School Improvement Using a Genetic Model.

    ERIC Educational Resources Information Center

    Kimmelman, Paul L.; Kroeze, David J.

    In providing its program for education reform, this book uses, as an analogy, the genetic model taken from the Human Genome project. In the first part, "Theoretical Underpinnings," the book explains why a genetic model can be used to improve school systems; describes the critical components of a world-class school system; and details the…

  20. 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.

    PubMed

    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.

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

    PubMed

    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

  2. Genetic demographic networks: Mathematical model and applications.

    PubMed

    Kimmel, Marek; Wojdyła, Tomasz

    2016-10-01

    Recent improvement in the quality of genetic data obtained from extinct human populations and their ancestors encourages searching for answers to basic questions regarding human population history. The most common and successful are model-based approaches, in which genetic data are compared to the data obtained from the assumed demography model. Using such approach, it is possible to either validate or adjust assumed demography. Model fit to data can be obtained based on reverse-time coalescent simulations or forward-time simulations. In this paper we introduce a computational method based on mathematical equation that allows obtaining joint distributions of pairs of individuals under a specified demography model, each of them characterized by a genetic variant at a chosen locus. The two individuals are randomly sampled from either the same or two different populations. The model assumes three types of demographic events (split, merge and migration). Populations evolve according to the time-continuous Moran model with drift and Markov-process mutation. This latter process is described by the Lyapunov-type equation introduced by O'Brien and generalized in our previous works. Application of this equation constitutes an original contribution. In the result section of the paper we present sample applications of our model to both simulated and literature-based demographies. Among other we include a study of the Slavs-Balts-Finns genetic relationship, in which we model split and migrations between the Balts and Slavs. We also include another example that involves the migration rates between farmers and hunters-gatherers, based on modern and ancient DNA samples. This latter process was previously studied using coalescent simulations. Our results are in general agreement with the previous method, which provides validation of our approach. Although our model is not an alternative to simulation methods in the practical sense, it provides an algorithm to compute pairwise

  3. The Alpha-1 Association Genetic Counseling Program: an innovative approach to service.

    PubMed

    McGee, Dawn; Strange, Charlie; McClure, Rebecca; Schwarz, Laura; Erven, Marlene

    2011-08-01

    In an era of specialty medicine, genetic counselors are becoming increasingly focused in their service provision. The Alpha-1 Association Genetic Counseling Program, established in September 2007, specializes in confidential toll-free genetic counseling provided by a certified genetic counselor for Alpha-1 Antitrypsin deficiency, a co-dominant condition associated with lung and/or liver disease. The program received more than 600 callers in its first 2 years. Sixty-seven percent of new callers were family members, carriers, or health professionals. The number of callers increased between the first 2 years, with the greatest increases being family members and health professionals. Testing options and explanation of results encompassed 60% of initial reasons for calls. Seventy-two percent of referrals came from family and friends, test result letters, and the Alpha-1 Association. Between year 1 and 2 family member referrals showed the largest increase. This disease-specific genetic counseling program provides a model that may be useful for other rare disease communities.

  4. A genetic programming approach to oral cancer prognosis

    PubMed Central

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

    2016-01-01

    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. PMID:27688975

  5. Automatic programming of simulation models

    NASA Technical Reports Server (NTRS)

    Schroer, Bernard J.; Tseng, Fan T.; Zhang, Shou X.; Dwan, Wen S.

    1988-01-01

    The objective of automatic programming is to improve the overall environment for describing the program. This improved environment is realized by a reduction in the amount of detail that the programmer needs to know and is exposed to. Furthermore, this improved environment is achieved by a specification language that is more natural to the user's problem domain and to the user's way of thinking and looking at the problem. The goal of this research is to apply the concepts of automatic programming (AP) to modeling discrete event simulation system. Specific emphasis is on the design and development of simulation tools to assist the modeler define or construct a model of the system and to then automatically write the corresponding simulation code in the target simulation language, GPSS/PC. A related goal is to evaluate the feasibility of various languages for constructing automatic programming simulation tools.

  6. Advances on genetic rat models of epilepsy.

    PubMed

    Serikawa, Tadao; Mashimo, Tomoji; Kuramoro, Takashi; Voigt, Birger; Ohno, Yukihiro; Sasa, Masashi

    2015-01-01

    Considering the suitability of laboratory rats in epilepsy research, we and other groups have been developing genetic models of epilepsy in this species. After epileptic rats or seizure-susceptible rats were sporadically found in outbred stocks, the epileptic traits were usually genetically-fixed by selective breeding. So far, the absence seizure models GAERS and WAG/Rij, audiogenic seizure models GEPR-3 and GEPR-9, generalized tonic-clonic seizure models IER, NER and WER, and Canavan-disease related epileptic models TRM and SER have been established. Dissection of the genetic bases including causative genes in these epileptic rat models would be a significant step toward understanding epileptogenesis. N-ethyl-N-nitrosourea (ENU) mutagenesis provides a systematic approach which allowed us to develop two novel epileptic rat models: heat-induced seizure susceptible (Hiss) rats with an Scn1a missense mutation and autosomal dominant lateral temporal epilepsy (ADLTE) model rats with an Lgi1 missense mutation. In addition, we have established episodic ataxia type 1 (EA1) model rats with a Kcna1 missense mutation derived from the ENU-induced rat mutant stock, and identified a Cacna1a missense mutation in a N-Methyl-N-nitrosourea (MNU)-induced mutant rat strain GRY, resulting in the discovery of episodic ataxia type 2 (EA2) model rats. Thus, epileptic rat models have been established on the two paths: 'phenotype to gene' and 'gene to phenotype'. In the near future, development of novel epileptic rat models will be extensively promoted by the use of sophisticated genome editing technologies.

  7. Advances on genetic rat models of epilepsy

    PubMed Central

    Serikawa, Tadao; Mashimo, Tomoji; Kuramoto, Takashi; Voigt, Birger; Ohno, Yukihiro; Sasa, Masashi

    2014-01-01

    Considering the suitability of laboratory rats in epilepsy research, we and other groups have been developing genetic models of epilepsy in this species. After epileptic rats or seizure-susceptible rats were sporadically found in outbred stocks, the epileptic traits were usually genetically-fixed by selective breeding. So far, the absence seizure models GAERS and WAG/Rij, audiogenic seizure models GEPR-3 and GEPR-9, generalized tonic-clonic seizure models IER, NER and WER, and Canavan-disease related epileptic models TRM and SER have been established. Dissection of the genetic bases including causative genes in these epileptic rat models would be a significant step toward understanding epileptogenesis. N-ethyl-N-nitrosourea (ENU) mutagenesis provides a systematic approach which allowed us to develop two novel epileptic rat models: heat-induced seizure susceptible (Hiss) rats with an Scn1a missense mutation and autosomal dominant lateral temporal epilepsy (ADLTE) model rats with an Lgi1 missense mutation. In addition, we have established episodic ataxia type 1 (EA1) model rats with a Kcna1 missense mutation derived from the ENU-induced rat mutant stock, and identified a Cacna1a missense mutation in a N-Methyl-N-nitrosourea (MNU)-induced mutant rat strain GRY, resulting in the discovery of episodic ataxia type 2 (EA2) model rats. Thus, epileptic rat models have been established on the two paths: ‘phenotype to gene’ and ‘gene to phenotype’. In the near future, development of novel epileptic rat models will be extensively promoted by the use of sophisticated genome editing technologies. PMID:25312505

  8. Animal Models of Parkinson's Disease: Vertebrate Genetics

    PubMed Central

    Lee, Yunjong; Dawson, Valina L.; Dawson, Ted M.

    2012-01-01

    Parkinson's disease (PD) is a complex genetic disorder that is associated with environmental risk factors and aging. Vertebrate genetic models, especially mice, have aided the study of autosomal-dominant and autosomal-recessive PD. Mice are capable of showing a broad range of phenotypes and, coupled with their conserved genetic and anatomical structures, provide unparalleled molecular and pathological tools to model human disease. These models used in combination with aging and PD-associated toxins have expanded our understanding of PD pathogenesis. Attempts to refine PD animal models using conditional approaches have yielded in vivo nigrostriatal degeneration that is instructive in ordering pathogenic signaling and in developing therapeutic strategies to cure or halt the disease. Here, we provide an overview of the generation and characterization of transgenic and knockout mice used to study PD followed by a review of the molecular insights that have been gleaned from current PD mouse models. Finally, potential approaches to refine and improve current models are discussed. PMID:22960626

  9. Aerothermal modeling program, phase 1

    NASA Technical Reports Server (NTRS)

    Sturgess, G. J.

    1983-01-01

    The physical modeling embodied in the computational fluid dynamics codes is discussed. The objectives were to identify shortcomings in the models and to provide a program plan to improve the quantitative accuracy. The physical models studied were for: turbulent mass and momentum transport, heat release, liquid fuel spray, and gaseous radiation. The approach adopted was to test the models against appropriate benchmark-quality test cases from experiments in the literature for the constituent flows that together make up the combustor real flow.

  10. Genetical ESS-models. I. Concepts and basic model.

    PubMed

    Thomas, B

    1985-08-01

    Evolutionarily Stable Strategies (ESS) in phenotypic models are used to explain the evolution of animal interactive behaviour. As the behavioural features under consideration are assumed to be genetically determined, the question arises how underlying a genetical system might affect the results of phenotypic ESS-models. This question can be fully treated in terms of ESS-theory. A method of designing Genetical ESS-Models is proposed, which transfers the question of evolutionary stability to a "lower" level, the genetical basis. Genetical ESS-models - although nonlinear even in the simplest cases - can be analysed in a way that is familiar to ESS-theorists and yield immediate results on gene pool ESSs, which then may or may not maintain ESSs on the phenotypic level. Moreover, general results can be obtained to characterize evolutionarily stable gene pool states and their interrelation with commonsense, phenotypic ESSs. This part of the article presents the basic concepts and an outline of the method of genetical ESS-models. It gives, as a demonstration, a complete analysis for phenotypic two-strategy models (linear or nonlinear) based on a diploid, diallelic single-locus system under random mating. The results in this case suggest that a phenotypic ESS should indeed be expected to evolve but, maybe, only after passing through a succession of temporarily stable states.

  11. Animal models for genetic neuromuscular diseases.

    PubMed

    Vainzof, Mariz; Ayub-Guerrieri, Danielle; Onofre, Paula C G; Martins, Poliana C M; Lopes, Vanessa F; Zilberztajn, Dinorah; Maia, Lucas S; Sell, Karen; Yamamoto, Lydia U

    2008-03-01

    The neuromuscular disorders are a heterogeneous group of genetic diseases, caused by mutations in genes coding sarcolemmal, sarcomeric, and citosolic muscle proteins. Deficiencies or loss of function of these proteins leads to variable degree of progressive loss of motor ability. Several animal models, manifesting phenotypes observed in neuromuscular diseases, have been identified in nature or generated in laboratory. These models generally present physiological alterations observed in human patients and can be used as important tools for genetic, clinic, and histopathological studies. The mdx mouse is the most widely used animal model for Duchenne muscular dystrophy (DMD). Although it is a good genetic and biochemical model, presenting total deficiency of the protein dystrophin in the muscle, this mouse is not useful for clinical trials because of its very mild phenotype. The canine golden retriever MD model represents a more clinically similar model of DMD due to its larger size and significant muscle weakness. Autosomal recessive limb-girdle MD forms models include the SJL/J mice, which develop a spontaneous myopathy resulting from a mutation in the Dysferlin gene, being a model for LGMD2B. For the human sarcoglycanopahties (SG), the BIO14.6 hamster is the spontaneous animal model for delta-SG deficiency, whereas some canine models with deficiency of SG proteins have also been identified. More recently, using the homologous recombination technique in embryonic stem cell, several mouse models have been developed with null mutations in each one of the four SG genes. All sarcoglycan-null animals display a progressive muscular dystrophy of variable severity and share the property of a significant secondary reduction in the expression of the other members of the sarcoglycan subcomplex and other components of the Dystrophin-glycoprotein complex. Mouse models for congenital MD include the dy/dy (dystrophia-muscularis) mouse and the allelic mutant dy(2J)/dy(2J) mouse

  12. Computational Modeling Program

    NASA Technical Reports Server (NTRS)

    Govindan, T. R.; Davis, Robert J.

    1998-01-01

    An Integrated Product Team (IPT) has been formed at NASA Ames Research Center which has set objectives to investigate devices and processes suitable for meeting NASA requirements on ultrahigh performance computers, fast and low power devices, and high temperature wide bandgap materials. These devices may ultimately be sub-100nm feature-size. Processes and equipment must meet the stringent demands posed by the fabrication of such small devices. Until now, the reactors for Chemical Vapor Deposition (CVD) and plasma processes have been designed by trial and error procedures. Further, once the reactor is in place, optimum processing parameters are found through expensive and time-consuming experimentation. If reliable models are available that describe processes and the operation of the reactors, that chore would be reduced to a routine task while being a cost-effective option. The goal is to develop such a design tool, validate that tool using available data from current generation processes and reactors, and then use that tool to explore avenues for meeting NASA needs for ultrasmall device fabrication. Under the present grant, ARL/Penn State along with other IPT members has been developing models and computer code to meet IPT goals. Some of the accomplishments achieved during the first year of the grant are described in this report

  13. A ``model`` geophysics program

    SciTech Connect

    Nyquist, J.E.

    1994-03-01

    In 1993, I tested a radio-controlled airplane designed by Jim Walker of Brigham Young University for low-elevation aerial photography. Model-air photography retains most of the advantages of standard aerial photography --- the photographs can be used to detect lineaments, to map roads and buildings, and to construct stereo pairs to measure topography --- and it is far less expensive. Proven applications on the Oak Ridge Reservation include: updating older aerial records to document new construction; using repeated overflights of the same area to capture seasonal changes in vegetation and the effects of major storms; and detecting waste trench boundaries from the color and character of the overlying grass. Aerial photography is only one of many possible applications of radio-controlled aircraft. Currently, I am funded by the Department of Energy`s Office of Technology Development to review the state of the art in microavionics, both military and civilian, to determine ways this emerging technology can be used for environmental site characterization. Being particularly interested in geophysical applications, I am also collaborating with electrical engineers at Oak Ridge National Laboratory to design a model plane that will carry a 3-component flux-gate magnetometer and a global positioning system, which I hope to test in the spring of 1994.

  14. Mating System and Genetic Composition of the Macaw Palm (Acrocomia aculeata): Implications for Breeding and Genetic Conservation Programs.

    PubMed

    Lanes, Éder C M; Motoike, Sérgio Y; Kuki, Kacilda N; Resende, Marcos D V; Caixeta, Eveline T

    2016-11-01

    Acrocomia aculeata (Arecaceae), a palm endemic to South and Central America, is a potential oil crop. Knowledge of the mating system of this species is limited to its reproductive biology and to studies using molecular markers. The present study analyzed genetic diversity between its developmental stages and determined its prevailing mating system in order to support genetic conservation and breeding programs. We tested 9 microsatellite markers in 27 mother trees (adult plants) and 157 offspring (juvenile plants) from the southeastern region of Brazil. Heterozygosity levels differed between the 2 studied life stages, as indicated by the fixation index of adult and juvenile trees, suggesting that selection against homozygotes occurs during the plant life cycle. The mating system parameters analyzed indicate that A. aculeata is predominantly outcrossing (allogamous). However, its low levels of selfing suggest that there is individual variation with regard to self-incompatibility, which can be a survival strategy in isolated or fragmented habitats. Deviations in variance effective size were detected because of high mating rates among relatives and correlated matings. These findings indicate that the main source of inbreeding results from biparental inbreeding in the population and that the progenies are predominantly composed of full-sibs. The information provided by this study on the ecology and reproduction dynamics of A. aculeata should be useful to both breeding and genetic conservation programs, allowing the development of more precise mathematical models and the estimation of the appropriate number of mother trees for seed collection.

  15. [Genetic programming used for the measurement of CO concentration based on nondispersive infrared absorption spectroscopy].

    PubMed

    Chen, Jin; Duan, Fa-jie; Tong, Ying; Gao, Qiang

    2011-07-01

    Nondispersive infrared absorption spectroscopy(NDIR) is an important method to measure CO concentration in the air. In the present study, an open-path measurement system and continuous measuring device was developed, and genetic programming was used to establish the calibration model of subjects' light intensity sampling values. Continuous measurements were carried out in 10 different concentration of CO, and 40 sampled data were acquired and analyzed. For validation set, the correlation coefficient was 0.9997. The biggest relative error of validation was 4.00%, and the average relative error was 1.11%. Results show that genetic programming can be a good method for the modeling of gas concentration measurements equipped with NDIR systems.

  16. Genetically modified pigs to model human diseases.

    PubMed

    Flisikowska, Tatiana; Kind, Alexander; Schnieke, Angelika

    2014-02-01

    Genetically modified mice are powerful tools to investigate the molecular basis of many human diseases. Mice are, however, of limited value for preclinical studies, because they differ significantly from humans in size, general physiology, anatomy and lifespan. Considerable efforts are, thus, being made to develop alternative animal models for a range of human diseases. These promise powerful new resources that will aid the development of new diagnostics, medicines and medical procedures. Here, we provide a comprehensive review of genetically modified porcine models described in the scientific literature: various cancers, cystic fibrosis, Duchenne muscular dystrophy, autosomal polycystic kidney disease, Huntington’s disease, spinal muscular atrophy, haemophilia A, X-linked severe combined immunodeficiency, retinitis pigmentosa, Stargardt disease, Alzheimer’s disease, various forms of diabetes mellitus and cardiovascular diseases.

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

    PubMed

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

    2013-08-01

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

  18. A Genetic Porcine Model of Cancer

    PubMed Central

    Schook, Lawrence B.; Collares, Tiago V.; Hu, Wenping; Liang, Ying; Rodrigues, Fernanda M.; Rund, Laurie A.; Schachtschneider, Kyle M.; Seixas, Fabiana K.; Singh, Kuldeep; Wells, Kevin D.; Walters, Eric M.; Prather, Randall S.; Counter, Christopher M.

    2015-01-01

    The large size of the pig and its similarity in anatomy, physiology, metabolism, and genetics to humans make it an ideal platform to develop a genetically defined, large animal model of cancer. To this end, we created a transgenic “oncopig” line encoding Cre recombinase inducible porcine transgenes encoding KRASG12D and TP53R167H, which represent a commonly mutated oncogene and tumor suppressor in human cancers, respectively. Treatment of cells derived from these oncopigs with the adenovirus encoding Cre (AdCre) led to KRASG12D and TP53R167H expression, which rendered the cells transformed in culture and tumorigenic when engrafted into immunocompromised mice. Finally, injection of AdCre directly into these oncopigs led to the rapid and reproducible tumor development of mesenchymal origin. Transgenic animals receiving AdGFP (green fluorescent protein) did not have any tumor mass formation or altered histopathology. This oncopig line could thus serve as a genetically malleable model for potentially a wide spectrum of cancers, while controlling for temporal or spatial genesis, which should prove invaluable to studies previously hampered by the lack of a large animal model of cancer. PMID:26132737

  19. Invited commentary: Evaluating vaccination programs using genetic sequence data.

    PubMed

    Halloran, M Elizabeth; Holmes, Edward C

    2009-12-15

    Genomic data will become an increasingly important component of epidemiologic studies in coming years. The authors of the accompanying Journal article, van Ballegooijen et al. (Am J Epidemiol. 2009;170(12):1455-1463), are to be commended for attempting to use the coalescent analysis of viral sequence data to evaluate a hepatitis B vaccination program. Coalescent theory attempts to link the phylogenetic history of populations with rates of population growth and decline. In particular, under certain assumptions, a reduction in genetic diversity can be interpreted as a reduction in disease incidence. However, the authors of this commentary contend that van Ballegooijen et al.'s interpretation of changes in viral genetic diversity as a measure of hepatitis B vaccine effectiveness has major limitations. Because of the potential use of these methods in future vaccination studies, the authors discuss the utility of these methods and the data requirements needed for them to be convincing. First, data sets should be large enough to provide sufficient epidemiologic-scale resolution. Second, data need to reflect sufficiently fine-grained temporal sampling. Third, other processes that can potentially influence genetic diversity and confuse demographic inferences should be considered.

  20. Model Checker for Java Programs

    NASA Technical Reports Server (NTRS)

    Visser, Willem

    2007-01-01

    Java Pathfinder (JPF) is a verification and testing environment for Java that integrates model checking, program analysis, and testing. JPF consists of a custom-made Java Virtual Machine (JVM) that interprets bytecode, combined with a search interface to allow the complete behavior of a Java program to be analyzed, including interleavings of concurrent programs. JPF is implemented in Java, and its architecture is highly modular to support rapid prototyping of new features. JPF is an explicit-state model checker, because it enumerates all visited states and, therefore, suffers from the state-explosion problem inherent in analyzing large programs. It is suited to analyzing programs less than 10kLOC, but has been successfully applied to finding errors in concurrent programs up to 100kLOC. When an error is found, a trace from the initial state to the error is produced to guide the debugging. JPF works at the bytecode level, meaning that all of Java can be model-checked. By default, the software checks for all runtime errors (uncaught exceptions), assertions violations (supports Java s assert), and deadlocks. JPF uses garbage collection and symmetry reductions of the heap during model checking to reduce state-explosion, as well as dynamic partial order reductions to lower the number of interleavings analyzed. JPF is capable of symbolic execution of Java programs, including symbolic execution of complex data such as linked lists and trees. JPF is extensible as it allows for the creation of listeners that can subscribe to events during searches. The creation of dedicated code to be executed in place of regular classes is supported and allows users to easily handle native calls and to improve the efficiency of the analysis.

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

    PubMed

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

    2015-01-01

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

  2. Canalization, genetic assimilation and preadaptation. A quantitative genetic model.

    PubMed Central

    Eshel, I; Matessi, C

    1998-01-01

    We propose a mathematical model to analyze the evolution of canalization for a trait under stabilizing selection, where each individual in the population is randomly exposed to different environmental conditions, independently of its genotype. Without canalization, our trait (primary phenotype) is affected by both genetic variation and environmental perturbations (morphogenic environment). Selection of the trait depends on individually varying environmental conditions (selecting environment). Assuming no plasticity initially, morphogenic effects are not correlated with the direction of selection in individual environments. Under quite plausible assumptions we show that natural selection favors a system of canalization that tends to repress deviations from the phenotype that is optimal in the most common selecting environment. However, many experimental results, dating back to Waddington and others, indicate that natural canalization systems may fail under extreme environments. While this can be explained as an impossibility of the system to cope with extreme morphogenic pressure, we show that a canalization system that tends to be inactivated in extreme environments is even more advantageous than rigid canalization. Moreover, once this adaptive canalization is established, the resulting evolution of primary phenotype enables substantial preadaptation to permanent environmental changes resembling extreme niches of the previous environment. PMID:9691063

  3. Population genetics models of local ancestry.

    PubMed

    Gravel, Simon

    2012-06-01

    Migrations have played an important role in shaping the genetic diversity of human populations. Understanding genomic data thus requires careful modeling of historical gene flow. Here we consider the effect of relatively recent population structure and gene flow and interpret genomes of individuals that have ancestry from multiple source populations as mosaics of segments originating from each population. This article describes general and tractable models for local ancestry patterns with a focus on the length distribution of continuous ancestry tracts and the variance in total ancestry proportions among individuals. The models offer improved agreement with Wright-Fisher simulation data when compared to the state-of-the art and can be used to infer time-dependent migration rates from multiple populations. Considering HapMap African-American (ASW) data, we find that a model with two distinct phases of "European" gene flow significantly improves the modeling of both tract lengths and ancestry variances.

  4. Brachypodium distachyon as a Genetic Model System.

    PubMed

    Kellogg, Elizabeth A

    2015-01-01

    Brachypodium distachyon has emerged as a powerful model system for studying the genetics of flowering plants. Originally chosen for its phylogenetic proximity to the large-genome cereal crops wheat and barley, it is proving to be useful for more than simply providing markers for comparative mapping. Studies in B. distachyon have provided new insight into the structure and physiology of plant cell walls, the development and chemical composition of endosperm, and the genetic basis for cold tolerance. Recent work on auxin transport has uncovered mechanisms that apply to all angiosperms other than Arabidopsis. In addition to the areas in which it is currently used, B. distachyon is uniquely suited for studies of floral development, vein patterning, the controls of the perennial versus annual habit, and genome organization.

  5. wisepair: a computer program for individual matching in genetic tracking studies.

    PubMed

    Rothstein, Andrew P; McLaughlin, Ryan; Acevedo-Gutiérrez, Alejandro; Schwarz, Dietmar

    2017-03-01

    Individual-based data sets tracking organisms over space and time are fundamental to answering broad questions in ecology and evolution. A 'permanent' genetic tag circumvents a need to invasively mark or tag animals, especially if there are little phenotypic differences among individuals. However, genetic tracking of individuals does not come without its limits; correctly matching genotypes and error rates associated with laboratory work can make it difficult to parse out matched individuals. In addition, defining a sampling design that effectively matches individuals in the wild can be a challenge for researchers. Here, we combine the two objectives of defining sampling design and reducing genotyping error through an efficient Python-based computer-modelling program, wisepair. We describe the methods used to develop the computer program and assess its effectiveness through three empirical data sets, with and without reference genotypes. Our results show that wisepair outperformed similar genotype matching programs using previously published from reference genotype data of diurnal poison frogs (Allobates femoralis) and without-reference (faecal) genotype sample data sets of harbour seals (Phoca vitulina) and Eurasian otters (Lutra lutra). In addition, due to limited sampling effort in the harbour seal data, we present optimal sampling designs for future projects. wisepair allows for minimal sacrifice in the available methods as it incorporates sample rerun error data, allelic pairwise comparisons and probabilistic simulations to determine matching thresholds. Our program is the lone tool available to researchers to define parameters a priori for genetic tracking studies.

  6. Rapid Target Modeling Through Genetic Inheritance Mechanism Genetically Evolved Target Prototypmg (GETP). Phase I

    DTIC Science & Technology

    1996-12-10

    Phase I Final Report Rapid Target Modeling Through Genetic Inheritance Mechanism Genetically Evolved Target Prototyping (GETP) Pbiai Dat December 10...COVERED 12/10/96 Final Report 5/7/96-12/10/96 A. TITE AND SUBTITU S. FUNDING NUMBERS Rapid Target Modeling Through Genetic Inheritance Mechanism... Genetically Evolved Target Prototyping (GETP) 6. AUTHOR(S) Dr. Jerzy Bala (P1) Dr. Peter Pachowicz (Co-P1) B.K. Gogia (PM) 7. PERFORMING ORGANIZATION

  7. Evolution of cartesian genetic programs for development of learning neural architecture.

    PubMed

    Khan, Gul Muhammad; Miller, Julian F; Halliday, David M

    2011-01-01

    Although artificial neural networks have taken their inspiration from natural neurological systems, they have largely ignored the genetic basis of neural functions. Indeed, evolutionary approaches have mainly assumed that neural learning is associated with the adjustment of synaptic weights. The goal of this paper is to use evolutionary approaches to find suitable computational functions that are analogous to natural sub-components of biological neurons and demonstrate that intelligent behavior can be produced as a result of this additional biological plausibility. Our model allows neurons, dendrites, and axon branches to grow or die so that synaptic morphology can change and affect information processing while solving a computational problem. The compartmental model of a neuron consists of a collection of seven chromosomes encoding distinct computational functions inside the neuron. Since the equivalent computational functions of neural components are very complex and in some cases unknown, we have used a form of genetic programming known as Cartesian genetic programming (CGP) to obtain these functions. We start with a small random network of soma, dendrites, and neurites that develops during problem solving by repeatedly executing the seven chromosomal programs that have been found by evolution. We have evaluated the learning potential of this system in the context of a well-known single agent learning problem, known as Wumpus World. We also examined the harder problem of learning in a competitive environment for two antagonistic agents, in which both agents are controlled by independent CGP computational networks (CGPCN). Our results show that the agents exhibit interesting learning capabilities.

  8. Latent spatial models and sampling design for landscape genetics

    USGS Publications Warehouse

    Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.

    2016-01-01

    We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.

  9. Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

    Zhang, Mengjie; Gao, Xiaoying; Lou, Weijun

    2007-10-01

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

  11. Parallel processor engine model program

    NASA Technical Reports Server (NTRS)

    Mclaughlin, P.

    1984-01-01

    The Parallel Processor Engine Model Program is a generalized engineering tool intended to aid in the design of parallel processing real-time simulations of turbofan engines. It is written in the FORTRAN programming language and executes as a subset of the SOAPP simulation system. Input/output and execution control are provided by SOAPP; however, the analysis, emulation and simulation functions are completely self-contained. A framework in which a wide variety of parallel processing architectures could be evaluated and tools with which the parallel implementation of a real-time simulation technique could be assessed are provided.

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

    PubMed

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

    2013-12-01

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

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

    PubMed

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

    1996-09-01

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

  14. Large genetic animal models of Huntington's Disease.

    PubMed

    Morton, A Jennifer; Howland, David S

    2013-01-01

    The dominant nature of the Huntington's disease gene mutation has allowed genetic models to be developed in multiple species, with the mutation causing an abnormal neurological phenotype in all animals in which it is expressed. Many different rodent models have been generated. The most widely used of these, the transgenic R6/2 mouse, carries the mutation in a fragment of the human huntingtin gene and has a rapidly progressive and fatal neurological phenotype with many relevant pathological changes. Nevertheless, their rapid decline has been frequently questioned in the context of a disease that takes years to manifest in humans, and strenuous efforts have been made to make rodent models that are genetically more 'relevant' to the human condition, including full length huntingtin gene transgenic and knock-in mice. While there is no doubt that we have learned, and continue to learn much from rodent models, their usefulness is limited by two species constraints. First, the brains of rodents differ significantly from humans in both their small size and their neuroanatomical organization. Second, rodents have much shorter lifespans than humans. Here, we review new approaches taken to these challenges in the development of models of Huntington's disease in large brained, long-lived animals. We discuss the need for such models, and how they might be used to fill specific niches in preclinical Huntington's disease research, particularly in testing gene-based therapeutics. We discuss the advantages and disadvantages of animals in which the prodromal period of disease extends over a long time span. We suggest that there is considerable 'value added' for large animal models in preclinical Huntington's disease research.

  15. Evolutionary model with genetics, aging, and knowledge

    NASA Astrophysics Data System (ADS)

    Bustillos, Armando Ticona; de Oliveira, Paulo Murilo

    2004-02-01

    We represent a process of learning by using bit strings, where 1 bits represent the knowledge acquired by individuals. Two ways of learning are considered: individual learning by trial and error, and social learning by copying knowledge from other individuals or from parents in the case of species with parental care. The age-structured bit string allows us to study how knowledge is accumulated during life and its influence over the genetic pool of a population after many generations. We use the Penna model to represent the genetic inheritance of each individual. In order to study how the accumulated knowledge influences the survival process, we include it to help individuals to avoid the various death situations. Modifications in the Verhulst factor do not show any special feature due to its random nature. However, by adding years to life as a function of the accumulated knowledge, we observe an improvement of the survival rates while the genetic fitness of the population becomes worse. In this latter case, knowledge becomes more important in the last years of life where individuals are threatened by diseases. Effects of offspring overprotection and differences between individual and social learning can also be observed. Sexual selection as a function of knowledge shows some effects when fidelity is imposed.

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

    SciTech Connect

    Harris, Sean

    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.

  17. Mouse Genetic Models of Human Brain Disorders

    PubMed Central

    Leung, Celeste; Jia, Zhengping

    2016-01-01

    Over the past three decades, genetic manipulations in mice have been used in neuroscience as a major approach to investigate the in vivo function of genes and their alterations. In particular, gene targeting techniques using embryonic stem cells have revolutionized the field of mammalian genetics and have been at the forefront in the generation of numerous mouse models of human brain disorders. In this review, we will first examine childhood developmental disorders such as autism, intellectual disability, Fragile X syndrome, and Williams-Beuren syndrome. We will then explore psychiatric disorders such as schizophrenia and lastly, neurodegenerative disorders including Alzheimer’s disease and Parkinson’s disease. We will outline the creation of these mouse models that range from single gene deletions, subtle point mutations to multi-gene manipulations, and discuss the key behavioral phenotypes of these mice. Ultimately, the analysis of the models outlined in this review will enhance our understanding of the in vivo role and underlying mechanisms of disease-related genes in both normal brain function and brain disorders, and provide potential therapeutic targets and strategies to prevent and treat these diseases. PMID:27047540

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

    PubMed

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

    2011-01-01

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

  19. Models of genetic counseling and their effects on multicultural genetic counseling.

    PubMed

    Lewis, Linwood J

    2002-06-01

    This theoretical paper examines challenges to multicultural genetic counseling, counseling between culturally different clients and counselors, in the context of Kessler's typology of models of genetic counseling (Kessler S (1997) J Genet Counsel 6:287-295). It is suggested that challenges such as resistance to multicultural genetic counseling education may be due to conceptions about genetic counseling as a biomedical field that transcends questions of culture as well as lack of multicultural training or prejudice. Directions for future research and recommendations for multicultural genetic counseling education are briefly explored.

  20. Automatic programming of simulation models

    NASA Technical Reports Server (NTRS)

    Schroer, Bernard J.; Tseng, Fan T.; Zhang, Shou X.; Dwan, Wen S.

    1990-01-01

    The concepts of software engineering were used to improve the simulation modeling environment. Emphasis was placed on the application of an element of rapid prototyping, or automatic programming, to assist the modeler define the problem specification. Then, once the problem specification has been defined, an automatic code generator is used to write the simulation code. The following two domains were selected for evaluating the concepts of software engineering for discrete event simulation: manufacturing domain and a spacecraft countdown network sequence. The specific tasks were to: (1) define the software requirements for a graphical user interface to the Automatic Manufacturing Programming System (AMPS) system; (2) develop a graphical user interface for AMPS; and (3) compare the AMPS graphical interface with the AMPS interactive user interface.

  1. Outcomes of a Genetics Education Program for Nursing Faculty.

    ERIC Educational Resources Information Center

    Prows, Cynthia A.; Hetteberg, Carol; Johnson, Nancy; Latta, Kathy; Lovell, Anne; Saal, Howard M.; Warren, Nancy Steinberg

    2003-01-01

    Summer institutes with follow-up continuing education sought to increase nursing faculty's knowledge and teaching of genetics. Outcome measures following four summer institutes (n=126) revealed significant improvements in genetics knowledge, increased genetics content in curricula, and development of new elective genetics courses. (Contains 22…

  2. Large animal models of rare genetic disorders: sheep as phenotypically relevant models of human genetic disease.

    PubMed

    Pinnapureddy, Ashish R; Stayner, Cherie; McEwan, John; Baddeley, Olivia; Forman, John; Eccles, Michael R

    2015-09-02

    Animals that accurately model human disease are invaluable in medical research, allowing a critical understanding of disease mechanisms, and the opportunity to evaluate the effect of therapeutic compounds in pre-clinical studies. Many types of animal models are used world-wide, with the most common being small laboratory animals, such as mice. However, rodents often do not faithfully replicate human disease, despite their predominant use in research. This discordancy is due in part to physiological differences, such as body size and longevity. In contrast, large animal models, including sheep, provide an alternative to mice for biomedical research due to their greater physiological parallels with humans. Completion of the full genome sequences of many species, and the advent of Next Generation Sequencing (NGS) technologies, means it is now feasible to screen large populations of domesticated animals for genetic variants that resemble human genetic diseases, and generate models that more accurately model rare human pathologies. In this review, we discuss the notion of using sheep as large animal models, and their advantages in modelling human genetic disease. We exemplify several existing naturally occurring ovine variants in genes that are orthologous to human disease genes, such as the Cln6 sheep model for Batten disease. These, and other sheep models, have contributed significantly to our understanding of the relevant human disease process, in addition to providing opportunities to trial new therapies in animals with similar body and organ size to humans. Therefore sheep are a significant species with respect to the modelling of rare genetic human disease, which we summarize in this review.

  3. Adolescent Perpetrator Treatment Programs: Program Models.

    ERIC Educational Resources Information Center

    Abbey, Joan M.

    It has become increasingly evident that juveniles are the perpetrators of a substantial nunber of sexual assaults. Programs designed to treat these adolescent perpetrators usually have similar goals. They attempt to reduce the youth's risk of recidivism by helping him to recognize his problem, take responsibility for his actions, learn how to…

  4. Structural health monitoring feature design by genetic programming

    NASA Astrophysics Data System (ADS)

    Harvey, Dustin Y.; Todd, Michael D.

    2014-09-01

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

  5. SMP: A solid modeling program

    NASA Technical Reports Server (NTRS)

    Randall, D. P.; Jones, K. H.; Vonofenheim, W. H.; Gates, R. L.

    1984-01-01

    A prototype solid modeling program, SMP, developed by CSC for Langley Research Center (LaRC) is documented in this paper. The SMP software is employed by the System and Experiments Branch (SEB) of the Space Systems Division (SSD) for preliminary space station design, but is intended as a general purpose tool. The SMP document provides details concerning: the basic geometric modeling primitives and associated operators, the data representation scheme utilized to structure the geometric model, the available commands for both editing and displaying the solid model, the interactive user interface and the input/output interfaces to external software, and the utility of the package in the LaRC computing environment. The document is sufficiently detailed to serve both as a user's guide and reference manual.

  6. Classifying nucleic acid sub-sequences as introns or exons using genetic programming

    SciTech Connect

    Handley, S.

    1995-12-31

    An evolutionary computation technique, genetic programming, created programs that classify messenger RNA sequences into one of two classes: (1) the sequence is expressed as (part of) a protein (an exon), or (2) not expressed as protein (an intron).

  7. Perinatal vs Genetic Programming of Serotonin States Associated with Anxiety

    PubMed Central

    Altieri, Stefanie C; Yang, Hongyan; O'Brien, Hannah J; Redwine, Hannah M; Senturk, Damla; Hensler, Julie G; Andrews, Anne M

    2015-01-01

    Large numbers of women undergo antidepressant treatment during pregnancy; however, long-term consequences for their offspring remain largely unknown. Rodents exposed to serotonin transporter (SERT)-inhibiting antidepressants during development show changes in adult emotion-like behavior. These changes have been equated with behavioral alterations arising from genetic reductions in SERT. Both models are highly relevant to humans yet they vary in their time frames of SERT disruption. We find that anxiety-related behavior and, importantly, underlying serotonin neurotransmission diverge between the two models. In mice, constitutive loss of SERT causes life-long increases in anxiety-related behavior and hyperserotonemia. Conversely, early exposure to the antidepressant escitalopram (ESC; Lexapro) results in decreased anxiety-related behavior beginning in adolescence, which is associated with adult serotonin system hypofunction in the ventral hippocampus. Adult behavioral changes resulting from early fluoxetine (Prozac) exposure were different from those of ESC and, although somewhat similar to SERT deficiency, were not associated with changes in hippocampal serotonin transmission in late adulthood. These findings reveal dissimilarities in adult behavior and neurotransmission arising from developmental exposure to different widely prescribed antidepressants that are not recapitulated by genetic SERT insufficiency. Moreover, they support a pivotal role for serotonergic modulation of anxiety-related behavior. PMID:25523893

  8. Explanatory Models of Genetics and Genetic Risk among a Selected Group of Students

    PubMed Central

    Goltz, Heather Honoré; Bergman, Margo; Goodson, Patricia

    2016-01-01

    This exploratory qualitative study focuses on how college students conceptualize genetics and genetic risk, concepts essential for genetic literacy (GL) and genetic numeracy (GN), components of overall health literacy (HL). HL is dependent on both the background knowledge and culture of a patient, and lower HL is linked to increased morbidity and mortality for a number of chronic health conditions (e.g., diabetes and cancer). A purposive sample of 86 students from three Southwestern universities participated in eight focus groups. The sample ranged in age from 18 to 54 years, and comprised primarily of female (67.4%), single (74.4%), and non-White (57%) participants, none of whom were genetics/biology majors. A holistic-content approach revealed broad categories concerning participants’ explanatory models (EMs) of genetics and genetic risk. Participants’ EMs were grounded in highly contextualized narratives that only partially overlapped with biomedical models. While higher education levels should be associated with predominately knowledge-based EM of genetic risk, this study shows that even in well-educated populations cultural factors can dominate. Study findings reveal gaps in how this sample of young adults obtains, processes, and understands genetic/genomic concepts. Future studies should assess how individuals with low GL and GN obtain and process genetics and genetic risk information and incorporate this information into health decision making. Future work should also address the interaction of communication between health educators, providers, and genetic counselors, to increase patient understanding of genetic risk. PMID:27376052

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

    PubMed

    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.

  10. 76 FR 72424 - Submission for OMB Review; Comment Request Information Program on the Genetic Testing Registry

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-11-23

    ... Submission for OMB Review; Comment Request Information Program on the Genetic Testing Registry AGENCY... Genetic Testing Registry; Type of Information Collection Request: New collection; Need and Use of Information Collection: Laboratory tests for more than 2,000 genetic conditions are available; however,...

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

    PubMed Central

    MAXIMINO, Luciana Paula; PICOLINI-PEREIRA, Mirela Machado; CARVALHO, José Luiz Brito

    2014-01-01

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

  12. EPA'S GENETIC DIVERSITY RESEARCH PROGRAM: ECOLOGICAL INDICATOR DEVELOPMENT

    EPA Science Inventory

    Genetic diversity is a fundamental component of biodiversity that is affected by environmental stressors in predictable ways and limits potential responses of a population to future stressors. Understanding patterns of genetic diversity enhances the value and interpretation of o...

  13. Genetic Algorithm Approaches to Prebiobiotic Chemistry Modeling

    NASA Technical Reports Server (NTRS)

    Lohn, Jason; Colombano, Silvano

    1997-01-01

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

  14. Network growth models and genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Foster, D. V.; Kauffman, S. A.; Socolar, J. E. S.

    2006-03-01

    We study a class of growth algorithms for directed graphs that are candidate models for the evolution of genetic regulatory networks. The algorithms involve partial duplication of nodes and their links, together with the innovation of new links, allowing for the possibility that input and output links from a newly created node may have different probabilities of survival. We find some counterintuitive trends as the parameters are varied, including the broadening of the in-degree distribution when the probability for retaining input links is decreased. We also find that both the scaling of transcription factors with genome size and the measured degree distributions for genes in yeast can be reproduced by the growth algorithm if and only if a special seed is used to initiate the process.

  15. Network growth models and genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Socolar, Joshua; Foster, David; Kauffman, Stuart

    2006-03-01

    We study a class of growth algorithms for directed graphs that are candidate models for the evolution of genetic regulatory networks. The algorithms involve partial duplication of nodes and their links, together with innovation of new links, allowing for the possibility that input and output links from a newly created node may have different probabilities of survival. We find some counterintuitive trends as parameters are varied, including the broadening of indegree distribution when the probability for retaining input links is decreased. We also find that both the scaling of transcription factors with genome size and the measured degree distributions for genes in yeast can be reproduced by the growth algorithm if and only if a special seed is used to initiate the process.

  16. Genetic models of poljes in Sicily

    NASA Astrophysics Data System (ADS)

    Di Maggio, Cipriano; Madonia, Giuliana; Vattano, Marco; De Waele, Jo

    2016-04-01

    Geomorphological and geological studies have been carried out to contribute to the recognition of controlling causes and to the definition of genetic models for poljes of Sicily. A polje is a kilometric closed depression developed mainly on karst rocks, with a conspicuously flat and alluviated bottom affected by intermittent flooding. A polje is usually characterised by relatively steep slopes enclosing an almost perfectly horizontal floor, caused by lateral solution planation related to flooding events. The origin of a polje is due to dissolution of the land surface, although geological structure generally influences its genesis. These large depressions are often elongated according to the direction of main faults, in consequence of a control due to tectonics or to differential erosion. The performed researches have shown the existence of at least seven poljes located along the north-western (chain zone) and the southern (deformed foredeep zone) areas of Sicily. These large karst depressions are developed on Mesozoic limestone/dolomitic rocks within the chain zone and on Messinian gypsum rocks within the deformed foredeep zone. They are up to 4 km in length, can reach surfaces of 3-8 km2 and are around hundred metres deep, with steep slopes and a flat bottom. Generally, they are open, occasionally active depressions and their genesis seems to be strongly controlled by structure. In particular, the studied poljes occur in two different geological/geomorphological settings: a) in graben-like tectonic depressions, where important fault slopes/scarps border the flat bottom; b) in complex depressions controlled by structure, where wide fault line slopes/scarps or large inclined degraded structural surfaces mark the poljes. Finally, landscape analysis leads to the proposition of two main genetic models in which the development of poljes is primarily due to tectonics or differential erosion followed by dissolution.

  17. Model Programs Compensatory Education: Preschool Program, Fresno, California.

    ERIC Educational Resources Information Center

    American Institutes for Research in the Behavioral Sciences, Palo Alto, CA.

    Part of a series of various Model Programs which informs educators about successful ongoing programs, the report describes the Fresno, California, preschool program that began as a pilot project serving 45 preschool, disadvantaged children during the 1964-65 academic year, and which during the 1969-70 academic year served 750 students at 19…

  18. Information Business: Applying Infometry (Informational Geometry) in Cognitive Coordination and Genetic Programming for Electronic Information Packaging and Marketing.

    ERIC Educational Resources Information Center

    Tsai, Bor-sheng

    1994-01-01

    Describes the use of infometry, or informational geometry, to meet the challenges of information service businesses. Highlights include theoretical models for cognitive coordination and genetic programming; electronic information packaging; marketing electronic information products, including cost-benefit analyses; and recapitalization, including…

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

    PubMed

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

    2012-01-01

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

  20. Genetic programming approach to evaluate complexity of texture images

    NASA Astrophysics Data System (ADS)

    Ciocca, Gianluigi; Corchs, Silvia; Gasparini, Francesca

    2016-11-01

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

  1. A Tri-part Model for Genetics Literacy: Exploring Undergraduate Student Reasoning About Authentic Genetics Dilemmas

    NASA Astrophysics Data System (ADS)

    Shea, Nicole A.; Duncan, Ravit Golan; Stephenson, Celeste

    2015-08-01

    Genetics literacy is becoming increasingly important as advancements in our application of genetic technologies such as stem cell research, cloning, and genetic screening become more prevalent. Very few studies examine how genetics literacy is applied when reasoning about authentic genetic dilemmas. However, there is evidence that situational features of a reasoning task may influence how students apply content knowledge as they generate and support arguments. Understanding how students apply content knowledge to reason about authentic and complex issues is important for considering instructional practices that best support student thinking and reasoning. In this conceptual report, we present a tri-part model for genetics literacy that embodies the relationships between content knowledge use, argumentation quality, and the role of situational features in reasoning to support genetics literacy. Using illustrative examples from an interview study with early career undergraduate students majoring in the biological sciences and late career undergraduate students majoring in genetics, we provide insights into undergraduate student reasoning about complex genetics issues and discuss implications for teaching and learning. We further discuss the need for research about how the tri-part model of genetics literacy can be used to explore students' thinking and reasoning abilities in genetics.

  2. Genetic adaptation to captivity in species conservation programs.

    PubMed

    Frankham, Richard

    2008-01-01

    As wild environments are often inhospitable, many species have to be captive-bred to save them from extinction. In captivity, species adapt genetically to the captive environment and these genetic adaptations are overwhelmingly deleterious when populations are returned to wild environments. I review empirical evidence on (i) the genetic basis of adaptive changes in captivity, (ii) factors affecting the extent of genetic adaptation to captivity, and (iii) means for minimizing its deleterious impacts. Genetic adaptation to captivity is primarily due to rare alleles that in the wild were deleterious and partially recessive. The extent of adaptation to captivity depends upon selection intensity, genetic diversity, effective population size and number of generation in captivity, as predicted by quantitative genetic theory. Minimizing generations in captivity provides a highly effective means for minimizing genetic adaptation to captivity, but is not a practical option for most animal species. Population fragmentation and crossing replicate captive populations provide practical means for minimizing the deleterious effects of genetic adaptation to captivity upon populations reintroduced into the wild. Surprisingly, equalization of family sizes reduces the rate of genetic adaptation, but not the deleterious impacts upon reintroduced populations. Genetic adaptation to captivity is expected to have major effects on reintroduction success for species that have spent many generations in captivity. This issue deserves a much higher priority than it is currently receiving.

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

    PubMed

    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

  4. The Mouse House: A brief history of the ORNL mouse-genetics program, 1947–2009

    SciTech Connect

    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

  5. The ASC Sequoia Programming Model

    SciTech Connect

    Seager, M

    2008-08-06

    In the late 1980's and early 1990's, Lawrence Livermore National Laboratory was deeply engrossed in determining the next generation programming model for the Integrated Design Codes (IDC) beyond vectorization for the Cray 1s series of computers. The vector model, developed in mid 1970's first for the CDC 7600 and later extended from stack based vector operation to memory to memory operations for the Cray 1s, lasted approximately 20 years (See Slide 5). The Cray vector era was deemed an extremely long lived era as it allowed vector codes to be developed over time (the Cray 1s were faster in scalar mode than the CDC 7600) with vector unit utilization increasing incrementally over time. The other attributes of the Cray vector era at LLNL were that we developed, supported and maintained the Operating System (LTSS and later NLTSS), communications protocols (LINCS), Compilers (Civic Fortran77 and Model), operating system tools (e.g., batch system, job control scripting, loaders, debuggers, editors, graphics utilities, you name it) and math and highly machine optimized libraries (e.g., SLATEC, and STACKLIB). Although LTSS was adopted by Cray for early system generations, they later developed COS and UNICOS operating systems and environment on their own. In the late 1970s and early 1980s two trends appeared that made the Cray vector programming model (described above including both the hardware and system software aspects) seem potentially dated and slated for major revision. These trends were the appearance of low cost CMOS microprocessors and their attendant, departmental and mini-computers and later workstations and personal computers. With the wide spread adoption of Unix in the early 1980s, it appeared that LLNL (and the other DOE Labs) would be left out of the mainstream of computing without a rapid transition to these 'Killer Micros' and modern OS and tools environments. The other interesting advance in the period is that systems were being developed with multiple

  6. Comprehensive Neurocognitive Endophenotyping Strategies for Mouse Models of Genetic Disorders

    PubMed Central

    Hunsaker, Michael R.

    2012-01-01

    There is a need for refinement of the current behavioral phenotyping methods for mouse models of genetic disorders. The current approach is to perform a behavioral screen using standardized tasks to define a broad phenotype of the model. This phenotype is then compared to what is known concerning the disorder being modeled. The weakness inherent in this approach is twofold: First, the tasks that make up these standard behavioral screens do not model specific behaviors associated with a given genetic mutation but rather phenotypes affected in various genetic disorders; secondly, these behavioral tasks are insufficiently sensitive to identify subtle phenotypes. An alternate phenotyping strategy is to determine the core behavioral phenotypes of the genetic disorder being studied and develop behavioral tasks to evaluate specific hypotheses concerning the behavioral consequences of the genetic mutation. This approach emphasizes direct comparisons between the mouse and human that facilitate the development of neurobehavioral biomarkers or quantitative outcome measures for studies of genetic disorders across species. PMID:22266125

  7. Wave equation modelling using Julia programming language

    NASA Astrophysics Data System (ADS)

    Kim, Ahreum; Ryu, Donghyun; Ha, Wansoo

    2016-04-01

    Julia is a young high-performance dynamic programming language for scientific computations. It provides an extensive mathematical function library, a clean syntax and its own parallel execution model. We developed 2d wave equation modeling programs using Julia and C programming languages and compared their performance. We used the same modeling algorithm for the two modeling programs. We used Julia version 0.3.9 in this comparison. We declared data type of function arguments and used inbounds macro in the Julia program. Numerical results showed that the C programs compiled with Intel and GNU compilers were faster than Julia program, about 18% and 7%, respectively. Taking the simplicity of dynamic programming language into consideration, Julia can be a novel alternative of existing statically typed programming languages.

  8. Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery.

    PubMed

    Engoren, Milo; Habib, Robert H; Dooner, John J; Schwann, Thomas A

    2013-08-01

    As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction group and 216 (8.0 %) of the 2,711 patient Validation group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p < .001). Artificial neural nets were less accurate with AU ROC = 0.597 ± .001 in the Construction group. Predictive accuracy of all three techniques fell in the Validation group. However, the accuracy of genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate.

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

    ERIC Educational Resources Information Center

    Bachmann, B. J.; And Others

    1973-01-01

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

  10. A Developmental-Genetic Model of Alcoholism: Implications for Genetic Research.

    ERIC Educational Resources Information Center

    Devor, Eric J.

    1994-01-01

    Research for biological-genetic markers of alcoholism is discussed in context of a multifactorial, heterogeneous, developmental model. Suggested that strategies used in linkage and association studies will require modification. Also suggested several extant associations of genetic markers represent true secondary interactive phenomena that alter…

  11. Functional-mixed effects models for candidate genetic mapping in imaging genetic studies.

    PubMed

    Lin, Ja-An; Zhu, Hongtu; Mihye, Ahn; Sun, Wei; Ibrahim, Joseph G

    2014-12-01

    The aim of this paper is to develop a functional-mixed effects modeling (FMEM) framework for the joint analysis of high-dimensional imaging data in a large number of locations (called voxels) of a three-dimensional volume with a set of genetic markers and clinical covariates. Our FMEM is extremely useful for efficiently carrying out the candidate gene approaches in imaging genetic studies. FMEM consists of two novel components including a mixed effects model for modeling nonlinear genetic effects on imaging phenotypes by introducing the genetic random effects at each voxel and a jumping surface model for modeling the variance components of the genetic random effects and fixed effects as piecewise smooth functions of the voxels. Moreover, FMEM naturally accommodates the correlation structure of the genetic markers at each voxel, while the jumping surface model explicitly incorporates the intrinsically spatial smoothness of the imaging data. We propose a novel two-stage adaptive smoothing procedure to spatially estimate the piecewise smooth functions, particularly the irregular functional genetic variance components, while preserving their edges among different piecewise-smooth regions. We develop weighted likelihood ratio tests and derive their exact approximations to test the effect of the genetic markers across voxels. Simulation studies show that FMEM significantly outperforms voxel-wise approaches in terms of higher sensitivity and specificity to identify regions of interest for carrying out candidate genetic mapping in imaging genetic studies. Finally, FMEM is used to identify brain regions affected by three candidate genes including CR1, CD2AP, and PICALM, thereby hoping to shed light on the pathological interactions between these candidate genes and brain structure and function.

  12. Functional Mixed Effects Models for Candidate Genetic Mapping in Imaging Genetic Studies

    PubMed Central

    Lin, Ja-An; Zhu, Hongtu; Mihye, Ahn; Sun, Wei; Ibrahim, Joseph G

    2014-01-01

    The aim of this paper is to develop a functional mixed effects modeling (FMEM) framework for the joint analysis of high-dimensional imaging data in a large number of locations (called voxels) of a three-dimensional volume with a set of genetic markers and clinical covariates. Our FMEM is extremely useful for effciently carrying out the candidate gene approaches in imaging genetic studies. FMEM consists of two novel components including a mixed effects model for modeling nonlinear genetic effects on imaging phenotypes by introducing the genetic random effects at each voxel and a jumping surface model for modeling the variance components of the genetic random effects and fixed effects as piecewise smooth functions of the voxels. Moreover, FMEM naturally accommodates the correlation structure of genetic markers at each voxel, while the jumping surface model explicitly incorporates the intrinsically spatial smoothness of the imaging data. We propose a novel two-stage adaptive smoothing procedure to spatially estimate the piecewise smooth functions, particularly the irregular functional genetic variance components, while preserving their edges among different piecewise-smooth regions. We develop weighted likelihood ratio tests and derive their exact approximations to test the effect of the genetic markers across voxels. Simulation studies show that FMEM significantly outperforms voxel-wise approaches in terms of higher sensitivity and specificity to identify regions of interest for carrying out candidate genetic mapping in imaging genetic studies. Finally, FMEM is used to identify brain regions affected by three candidate genes including CR1, CD2AP, and PICALM, thereby hoping to shed light on the pathological interactions between these candidate genes and brain structure and function. PMID:25270690

  13. Teaching Genetic Counseling Skills: Incorporating a Genetic Counseling Adaptation Continuum Model to Address Psychosocial Complexity.

    PubMed

    Shugar, Andrea

    2016-11-28

    Genetic counselors are trained health care professionals who effectively integrate both psychosocial counseling and information-giving into their practice. Preparing genetic counseling students for clinical practice is a challenging task, particularly when helping them develop effective and active counseling skills. Resistance to incorporating these skills may stem from decreased confidence, fear of causing harm or a lack of clarity of psycho-social goals. The author reflects on the personal challenges experienced in teaching genetic counselling students to work with psychological and social complexity, and proposes a Genetic Counseling Adaptation Continuum model and methodology to guide students in the use of advanced counseling skills.

  14. Conceptual Modeling via Logic Programming

    DTIC Science & Technology

    1990-01-01

    31 2.7 Approaches Other Than Logic Programming ............................. 33 2.7.1 L isp...Development Environment Needs ................................ 84 5.1.4 Alternative Logic Programming Implementation Approaches ......... 85 5.1.5 User... APPROACH and logic programming techniques. Section 2 The CMLP project consisted of three describes the task outputs. interrelated investi ations: 3

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

    NASA Astrophysics Data System (ADS)

    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

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

    SciTech Connect

    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.

  17. A Mutation Model from First Principles of the Genetic Code.

    PubMed

    Thorvaldsen, Steinar

    2016-01-01

    The paper presents a neutral Codons Probability Mutations (CPM) model of molecular evolution and genetic decay of an organism. The CPM model uses a Markov process with a 20-dimensional state space of probability distributions over amino acids. The transition matrix of the Markov process includes the mutation rate and those single point mutations compatible with the genetic code. This is an alternative to the standard Point Accepted Mutation (PAM) and BLOcks of amino acid SUbstitution Matrix (BLOSUM). Genetic decay is quantified as a similarity between the amino acid distribution of proteins from a (group of) species on one hand, and the equilibrium distribution of the Markov chain on the other. Amino acid data for the eukaryote, bacterium, and archaea families are used to illustrate how both the CPM and PAM models predict their genetic decay towards the equilibrium value of 1. A family of bacteria is studied in more detail. It is found that warm environment organisms on average have a higher degree of genetic decay compared to those species that live in cold environments. The paper addresses a new codon-based approach to quantify genetic decay due to single point mutations compatible with the genetic code. The present work may be seen as a first approach to use codon-based Markov models to study how genetic entropy increases with time in an effectively neutral biological regime. Various extensions of the model are also discussed.

  18. Genetic Counseling Assistants: an Integral Piece of the Evolving Genetic Counseling Service Delivery Model.

    PubMed

    Pirzadeh-Miller, Sara; Robinson, Linda S; Read, Parker; Ross, Theodora S

    2016-11-10

    This study explores the potential impact of the genetic counseling assistant (GCA) position on the efficiency of the genetic counseling field, evaluates attitudes regarding expansion of the genetic counseling field to include the GCA, and presents data on GCA endeavors and GCA job tasks as reported by GCAs, certified genetic counselors (CGCs), and program directors (PDs). Data on GCA roles and attitudes toward different aspects of the GCA position were collected via surveys of CGCs who have worked with GCAs, PDs who have and have not had experience with GCAs in their programs, and GCAs. We analyzed responses from 63 individuals: 27 PDs, 22 CGCs, and 14 GCAs. GCAs' impact on efficiency was calculated via internal analysis of genetic patient volume per genetic counselor within the University of Texas Southwestern (UTSW) patient database prior to, and since the addition of, a GCA to the practice. The response rates for PDs, CGCs, and GCAs were 27 %, 79 %, and 61 %, respectively. Every CGC stated the GCA increased their efficiency. CGCs with a GCA reported a 60 % average increase in patient volume. This figure was congruent with internal data from the UTSW cancer genetics program (58.5 % increase). Appropriate responsibilities for GCAs as reported by CGCs and PDs (>90 %) include: data entry, shipping tests, administrative tasks, research, and ordering supplies. Regarding GCAs delivering test results, there was response variation whether this should be a job duty: 42 % of CGCs agreed to GCAs delivering negative results to patients, compared to 22 % of program directors. Twenty-two percent of PDs expressed concern about the job title "Genetic Counseling Assistant." Ninety percent of CGCs felt that GCA was a career path to becoming a CGC, compared to 42 % of PDs. Eighty-three percent of GCAs who decided to apply to CGC graduate programs were accepted. We conclude the addition of a GCA to a genetic counseling practice contributes to increased efficiency and is one

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

    DTIC Science & Technology

    2004-03-01

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

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

    NASA Astrophysics Data System (ADS)

    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%.

  1. Differential program evaluation model in child protection.

    PubMed

    Lalayants, Marina

    2012-01-01

    Increasingly attention has been focused to the degree to which social programs have effectively and efficiently delivered services. Using the differential program evaluation model by Tripodi, Fellin, and Epstein (1978) and by Bielawski and Epstein (1984), this paper described the application of this model to evaluating a multidisciplinary clinical consultation practice in child protection. This paper discussed the uses of the model by demonstrating them through the four stages of program initiation, contact, implementation, and stabilization. This organizational case study made a contribution to the model by introducing essential and interrelated elements of a "practical evaluation" methodology in evaluating social programs, such as a participatory evaluation approach; learning, empowerment and sustainability; and a flexible individualized approach to evaluation. The study results demonstrated that by applying the program development model, child-protective administrators and practitioners were able to evaluate the existing practices and recognize areas for program improvement.

  2. Genetically Engineered Humanized Mouse Models for Preclinical Antibody Studies

    PubMed Central

    Proetzel, Gabriele; Wiles, Michael V.; Roopenian, Derry C.

    2015-01-01

    The use of genetic engineering has vastly improved our capabilities to create animal models relevant in preclinical research. With the recent advances in gene-editing technologies, it is now possible to very rapidly create highly tunable mouse models as needs arise. Here, we provide an overview of genetic engineering methods, as well as the development of humanized neonatal Fc receptor (FcRn) models and their use for monoclonal antibody in vivo studies. PMID:24150980

  3. Comprehensive Physical Education Program Model

    ERIC Educational Resources Information Center

    Kamiya, Artie

    2005-01-01

    In 2004, the Wake County Public School System (North Carolina) received $1.3 million as one of 237 national winners of the $70 million federal Carol M. White Physical Education Program (PEP) Grant competition. The PEP Grant program is funded by the U.S. Department of Education and provides monies to school districts able to demonstrate the…

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

    PubMed

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

    2016-01-01

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

  5. Assessing the integration of genomic medicine in genetic counseling training programs.

    PubMed

    Profato, Jessica; Gordon, Erynn S; Dixon, Shannan; Kwan, Andrea

    2014-08-01

    Medical genetics has entered a period of transition from genetics to genomics. Genetic counselors (GCs) may take on roles in the clinical implementation of genomics. This study explores the perspectives of program directors (PDs) on including genomic medicine in GC training programs, as well as the status of this integration. Study methods included an online survey, an optional one-on-one telephone interview, and an optional curricula content analysis. The majority of respondents (15/16) reported that it is important to include genomic medicine in program curricula. Most topics of genomic medicine are either "currently taught" or "under development" in all participating programs. Interview data from five PDs and one faculty member supported the survey data. Integrating genomics in training programs is challenging, and it is essential to develop genomics resources for curricula.

  6. Genetically Engineered Mouse Models for Studying Inflammatory Bowel Disease

    PubMed Central

    Mizoguchi, Atsushi; Takeuchi, Takahito; Himuro, Hidetomo; Okada, Toshiyuki; Mizoguchi, Emiko

    2015-01-01

    Inflammatory bowel disease (IBD) is a chronic intestinal inflammatory condition that is mediated by very complex mechanisms controlled by genetic, immune, and environmental factors. More than 74 kinds of genetically engineered mouse strains have been established since 1993 for studying IBD. Although mouse models cannot fully reflect human IBD, they have provided significant contributions for not only understanding the mechanism, but also developing new therapeutic means for IBD. Indeed, 20 kinds of genetically engineered mouse models carry the susceptibility genes identified in human IBD, and the functions of some other IBD susceptibility genes have also been dissected out using mouse models. Cutting-edge technologies such as cell-specific and inducible knockout systems, which were recently employed to mouse IBD models, have further enhanced the ability of investigators to provide important and unexpected rationales for developing new therapeutic strategies for IBD. In this review article, we briefly introduce 74 kinds of genetically engineered mouse models that spontaneously develop intestinal inflammation. PMID:26387641

  7. Technical Note: Calculation of standard errors of estimates of genetic parameters with the multiple-trait derivative-free restricted maximal likelihood programs

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The MTDFREML (Boldman et al., 1995) set of programs was written to handle partially missing data in an expedient manner. When estimating (co)variance components and genetic parameters for multiple trait models, the programs have not been able to estimate standard errors of those estimates for multi...

  8. Petrogenetic Modeling with a Spreadsheet Program.

    ERIC Educational Resources Information Center

    Holm, Paul Eric

    1988-01-01

    Describes how interactive programs for scientific modeling may be created by using spreadsheet software such as LOTUS 1-2-3. Lists the advantages of using this method. Discusses fractional distillation, batch partial melting, and combination models as examples. (CW)

  9. Comparing estimates of genetic variance across different relationship models.

    PubMed

    Legarra, Andres

    2016-02-01

    Use of relationships between individuals to estimate genetic variances and heritabilities via mixed models is standard practice in human, plant and livestock genetics. Different models or information for relationships may give different estimates of genetic variances. However, comparing these estimates across different relationship models is not straightforward as the implied base populations differ between relationship models. In this work, I present a method to compare estimates of variance components across different relationship models. I suggest referring genetic variances obtained using different relationship models to the same reference population, usually a set of individuals in the population. Expected genetic variance of this population is the estimated variance component from the mixed model times a statistic, Dk, which is the average self-relationship minus the average (self- and across-) relationship. For most typical models of relationships, Dk is close to 1. However, this is not true for very deep pedigrees, for identity-by-state relationships, or for non-parametric kernels, which tend to overestimate the genetic variance and the heritability. Using mice data, I show that heritabilities from identity-by-state and kernel-based relationships are overestimated. Weighting these estimates by Dk scales them to a base comparable to genomic or pedigree relationships, avoiding wrong comparisons, for instance, "missing heritabilities".

  10. Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

    NASA Astrophysics Data System (ADS)

    Mediero, L.; Garrote, L.; Chavez-Jimenez, A.

    2012-12-01

    Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.

  11. Compare and Contrast Program Planning Models

    ERIC Educational Resources Information Center

    Baskas, Richard S.

    2011-01-01

    This paper will examine the differences and similarities between two program planning models, Tyler and Caffarella, to reveal their strengths and weaknesses. When adults are involved in training sessions, there are various program planning models that can be used, depending on the goal of the training session. Researchers developed these models…

  12. Program Development and Evaluation: A Modeling Process.

    ERIC Educational Resources Information Center

    Green, Donald W.; Corgiat, RayLene

    A model of program development and evaluation was developed at Genesee Community College, utilizing a system theory/process of deductive and inductive reasoning to ensure coherence and continuity within the program. The model links activities to specific measurable outcomes. Evaluation checks and feedback are built in at various levels so that…

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

    PubMed

    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.

  14. Model Checking Abstract PLEXIL Programs with SMART

    NASA Technical Reports Server (NTRS)

    Siminiceanu, Radu I.

    2007-01-01

    We describe a method to automatically generate discrete-state models of abstract Plan Execution Interchange Language (PLEXIL) programs that can be analyzed using model checking tools. Starting from a high-level description of a PLEXIL program or a family of programs with common characteristics, the generator lays the framework that models the principles of program execution. The concrete parts of the program are not automatically generated, but require the modeler to introduce them by hand. As a case study, we generate models to verify properties of the PLEXIL macro constructs that are introduced as shorthand notation. After an exhaustive analysis, we conclude that the macro definitions obey the intended semantics and behave as expected, but contingently on a few specific requirements on the timing semantics of micro-steps in the concrete executive implementation.

  15. Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices.

    PubMed

    Goel, Purva; Bapat, Sanket; Vyas, Renu; Tambe, Amruta; Tambe, Sanjeev S

    2015-11-13

    The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (>0.9) values of the coefficient of determination (R(2)) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science.

  16. Model Checking JAVA Programs Using Java Pathfinder

    NASA Technical Reports Server (NTRS)

    Havelund, Klaus; Pressburger, Thomas

    2000-01-01

    This paper describes a translator called JAVA PATHFINDER from JAVA to PROMELA, the "programming language" of the SPIN model checker. The purpose is to establish a framework for verification and debugging of JAVA programs based on model checking. This work should be seen in a broader attempt to make formal methods applicable "in the loop" of programming within NASA's areas such as space, aviation, and robotics. Our main goal is to create automated formal methods such that programmers themselves can apply these in their daily work (in the loop) without the need for specialists to manually reformulate a program into a different notation in order to analyze the program. This work is a continuation of an effort to formally verify, using SPIN, a multi-threaded operating system programmed in Lisp for the Deep-Space 1 spacecraft, and of previous work in applying existing model checkers and theorem provers to real applications.

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

    PubMed

    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.

  18. Inductive time series modeling program

    SciTech Connect

    Kirk, B.L.; Rust, B.W.

    1985-10-01

    A number of features that comprise environmental time series share a common mathematical behavior. Analysis of the Mauna Loa carbon dioxide record and other time series is aimed at constructing mathematical functions which describe as many major features of the data as possible. A trend function is fit to the data, removed, and the resulting residuals analyzed for any significant behavior. This is repeated until the residuals are driven to white noise. In the following discussion, the concept of trend will include cyclic components. The mathematical tools and program packages used are VARPRO (Golub and Pereyra 1973), for the least squares fit, and a modified version of our spectral analysis program (Kirk et al. 1979), for spectrum and noise analysis. The program is written in FORTRAN. All computations are done in double precision, except for the plotting calls where the DISSPLA package is used. The core requirement varies between 600 K and 700 K. The program is implemented on the IBM 360/370. Currently, the program can analyze up to five different time series where each series contains no more than 300 points. 12 refs.

  19. Employing multi-objective Genetic Programming to the downscaling of near-surface atmospheric fields

    NASA Astrophysics Data System (ADS)

    Zerenner, Tanja; Venema, Victor; Friederichs, Petra; Simmer, Clemens

    2015-04-01

    The coupling of models for the different components of the Soil-Vegetation-Atmosphere-System is required to investigate component interactions and feedback processes. However, the component models for atmosphere, land-surface and subsurface are usually operated at different resolutions in space and time owing to the dominant processes. The computationally expensive atmospheric models are typically employed at a coarser resolution than land-surface and subsurface models. Thus up- and downscaling procedures are required at the interface between the atmospheric model and the land-surface/subsurface models. We apply multi-objective Genetic Programming (GP) to a training data set of high-resolution atmospheric model runs to learn downscaling rules, i. e., equations or short programs that reconstruct the fine-scale fields of the near-surface atmospheric state variables from the coarse atmospheric model output. Like artificial neural networks, GP can flexibly incorporate multivariate and nonlinear relations, but offers the advantage that the solutions are human readable and thus can be checked for physical consistency. Further, the Strength Pareto Approach for multi-objective fitness assignment allows to consider multiple characteristics of the fine-scale fields during the learning procedure. We have applied the described machine learning methodology to a training data set of 400 m resolution COSMO model runs to learn downscaling rules which recover realistic fine-scale structures from the coarsened fields at 2.8 km resolution. Hence we are currently downscaling by a factor of 7. The COSMO model is the weather forecast model developed and maintained by the German Weather Service and is contained in the Terrestrial Systems Modeling Platform (TerrSysMP), which couples the atmospheric COSMO model to land-surface model CLM and subsurface hydrological model ParFlow. Finally we aim at implementing the learned downscaling rules in the TerrSysMP to achieve scale

  20. Towards improving the knowledge of underlying mechanisms of Rainfall-Runoff process using Genetic Programming

    NASA Astrophysics Data System (ADS)

    Chadalawada, Jayashree; Babovic, Vladan

    2016-04-01

    Genetic Programming (GP) is a valuable tool for modelling nonlinear dynamic systems. GP implements the trial and error process to successfully discover the governing model structure that best fits the data, via, testing many random permutations of model components and structures, retaining the best parts of the structures and recombining them to form complete mathematical models. The potential of GP has not been exploited to the fullest extent in the field of hydrology to understand the complex dynamics involved. The state of the art applications of GP in hydrological modelling involve the use of GP as a short-term prediction and forecast tool rather than as a framework for the development of a better model. In today's scenario with the increasing monitoring programmes and computational power, the techniques like GP can be employed for the development and evaluation of hydrological models, balancing prior information, model complexity, parameter and output uncertainty. In this contribution, as a preliminary step to the overall motive stated above, the GP is trained to capture the dynamics of the rainfall- runoff process using tank system, where each tank is a storage unit in a watershed that corresponds to varying depths below the surface. The tank model considers rainfall minus losses as the input and generates flows at different levels as the output thereby capturing the phenomenon of storage, infiltration and percolation. The meteorological data employed in this study belongs to the Kent Ridge catchment of National University Singapore, a small urban catchment (8.5 hectares) that receives a mean annual rainfall of 2500mm and consists of all the major landuses of Singapore. The algorithm so designed can capture the response of the model employed for simulation, returning the exact number of tanks and appropriate parameters present in the model structure, thereby providing useful physical insight of the catchment.

  1. How Pupils Use a Model for Abstract Concepts in Genetics

    ERIC Educational Resources Information Center

    Venville, Grady; Donovan, Jenny

    2008-01-01

    The purpose of this research was to explore the way pupils of different age groups use a model to understand abstract concepts in genetics. Pupils from early childhood to late adolescence were taught about genes and DNA using an analogical model (the wool model) during their regular biology classes. Changing conceptual understandings of the…

  2. The prediction of the degree of exposure to solvent of amino acid residues via genetic programming

    SciTech Connect

    Handley, S.

    1994-12-31

    In this paper I evolve programs that predict the degree of exposure to solvent (the buriedness) of amino acid residues given only the primary structure. I use genetic programming to evolve programs that take as input the primary structure and that output the buriedness of each residue. I trained these programs on a set of 82 proteins from the Brookhaven Protein Data Bank (PDB) and cross-validated them on a separate testing set of 40 proteins, also from the PDB. The best program evolved had a correlation of 0.434 between the predicted and observed buriednesses on the testing set.

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

    PubMed

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

    2016-10-01

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

  4. Application of genetic programming and Landsat multi-date imagery for urban growth monitoring

    NASA Astrophysics Data System (ADS)

    Djerriri, Khelifa; Malki, Mimoun

    2013-10-01

    Monitoring of earth surface changes from space by using multi-date satellite imagery was always a main concern to researchers in the field of remotely sensed image processing. Thus, several techniques have been proposed to saving technicians from interpreting and digitizing hundreds of areas by hand. The exploiting of simple, easy to memorize and often comprehensible mathematical models such band-ratios and indices are one of the widely used techniques in remote sensing for the extraction of particular land-cover/land-use like urban and vegetation areas. The results of these models generally only need the definition of adequate threshold or using simple unsupervised classification algorithms to discriminate between the class of interest and the background. In our work a genetic programming based approach has been adopted to evolve simple mathematical expression to extract urban areas from image series. The model is built from a single image by using a basic set of operators between spectral bands and maximizing a fitness function, which is based on the using of the M-statistic criterion. The model was constructed from the Landsat 5 TM image acquired in 2006 by using training samples extracted with the help of a Quick-bird high spatial resolution satellite image acquired the same day as the Landsat image over the city of Oran, Algeria. The model has been tested to extract urban areas from multi-date series of Landsat TM imagery

  5. Geometric Modeling Application Interface Program

    DTIC Science & Technology

    1990-11-01

    Manual IDEF-Extended ( IDEFIX ) Integrated Information Support System (IISS), ICAM Project 6201, Contract F33615-80-C-5155, December 1985. Interim...Differential Geometry of Curves and Surfaces, M. P. de Carmo, Prentice-Hall, Inc., 1976. IDEFIX Readers Reference, D. Appleton Company, December 1985...Modeling. IDEFI -- IDEF Information Modeling. IDEFIX -- IDEF Extended Information Modeling. IDEF2 -- IDEF Dynamics Modeling. IDSS -- Integrated Decision

  6. Analysis of the Multi Strategy Goal Programming for Micro-Grid Based on Dynamic ant Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Qiu, J. P.; Niu, D. X.

    Micro-grid is one of the key technologies of the future energy supplies. Take economic planning. reliability, and environmental protection of micro grid as a basis for the analysis of multi-strategy objective programming problems for micro grid which contains wind power, solar power, and battery and micro gas turbine. Establish the mathematical model of each power generation characteristics and energy dissipation. and change micro grid planning multi-objective function under different operating strategies to a single objective model based on AHP method. Example analysis shows that in combination with dynamic ant mixed genetic algorithm can get the optimal power output of this model.

  7. Tetrahymena as a Unicellular Model Eukaryote: Genetic and Genomic Tools.

    PubMed

    Ruehle, Marisa D; Orias, Eduardo; Pearson, Chad G

    2016-06-01

    Tetrahymena thermophila is a ciliate model organism whose study has led to important discoveries and insights into both conserved and divergent biological processes. In this review, we describe the tools for the use of Tetrahymena as a model eukaryote, including an overview of its life cycle, orientation to its evolutionary roots, and methodological approaches to forward and reverse genetics. Recent genomic tools have expanded Tetrahymena's utility as a genetic model system. With the unique advantages that Tetrahymena provide, we argue that it will continue to be a model organism of choice.

  8. Genetic management of infectious diseases: a heterogeneous epidemio-genetic model illustrated with S. aureus mastitis.

    PubMed

    Detilleux, Johann C

    2005-01-01

    Given that individuals are genetically heterogeneous in their degree of resistance to infection, a model is proposed to formulate appropriate choices that will limit the spread of an infectious disease. The model is illustrated with data on S. aureus mastitis and is based on parameters characterizing the spread of the disease (contact rate, probability of infection after contact, and rate of recovery after infection), the demography (replacement and culling rates) and the genetic composition (degree of relationship and heritability of the disease trait) of the animal population. To decrease infection pressure, it is possible to apply non-genetic procedures that increase the culling (e.g., culling of chronically infected cows) and recovery (e.g., antibiotic therapy) rates of infected cows. But the contribution of the paper is to show that genetic management of infectious disease is also theoretically possible as a control measure complementary to non-genetic actions. Indeed, the probability for an uninfected individual to become infected after contact with an infected one is partially related to their degree of kinship: the more closely they are related, the more likely they are to share identical genes like those associated to the non-resistance to infection. Different prospective genetic management procedures are proposed to decrease the contact rate between infected and uninfected relatives and keep the number of secondary cases generated by one infected animal below 1.

  9. Program Model Checking: A Practitioner's Guide

    NASA Technical Reports Server (NTRS)

    Pressburger, Thomas T.; Mansouri-Samani, Masoud; Mehlitz, Peter C.; Pasareanu, Corina S.; Markosian, Lawrence Z.; Penix, John J.; Brat, Guillaume P.; Visser, Willem C.

    2008-01-01

    Program model checking is a verification technology that uses state-space exploration to evaluate large numbers of potential program executions. Program model checking provides improved coverage over testing by systematically evaluating all possible test inputs and all possible interleavings of threads in a multithreaded system. Model-checking algorithms use several classes of optimizations to reduce the time and memory requirements for analysis, as well as heuristics for meaningful analysis of partial areas of the state space Our goal in this guidebook is to assemble, distill, and demonstrate emerging best practices for applying program model checking. We offer it as a starting point and introduction for those who want to apply model checking to software verification and validation. The guidebook will not discuss any specific tool in great detail, but we provide references for specific tools.

  10. Genetic algorithms for modelling and optimisation

    NASA Astrophysics Data System (ADS)

    McCall, John

    2005-12-01

    Genetic algorithms (GAs) are a heuristic search and optimisation technique inspired by natural evolution. They have been successfully applied to a wide range of real-world problems of significant complexity. This paper is intended as an introduction to GAs aimed at immunologists and mathematicians interested in immunology. We describe how to construct a GA and the main strands of GA theory before speculatively identifying possible applications of GAs to the study of immunology. An illustrative example of using a GA for a medical optimal control problem is provided. The paper also includes a brief account of the related area of artificial immune systems.

  11. Model Programs: Childhood Education. Interdependent Learner Model of a Follow Through Program.

    ERIC Educational Resources Information Center

    American Institutes for Research in the Behavioral Sciences, Silver Spring, MD.

    In order to insure maximum success of the school's Head Start program, parents and teachers associated with a public school in Harlem chose a program based on the Interdependent Learner Model Follow Through Program originated at New York University. The federally funded program was introduced into kindergarten and first-grade classrooms and…

  12. Learning genetic inquiry through the use, revision, and justification of explanatory models

    NASA Astrophysics Data System (ADS)

    Cartier, Jennifer Lorraine

    Central to the process of inquiry in science is the construction and assessment of models that can be used to explain (and in some cases, predict) natural phenomena. This dissertation is a qualitative study of student learning in a high school biology course that was designed to give students opportunities to learn about genetic inquiry in part by providing them with authentic experiences doing inquiry in the discipline. With the aid of a computer program that generates populations of "fruit flies", the students in this class worked in groups structured like scientific communities to build, revise, and defend explanatory models for various inheritance phenomena. Analysis of the ways in which the first cohort of students assessed their inheritance models revealed that all students assessed models based upon empirical fit (data/model match). However, in contrast to the practice of scientists and despite explicit instruction, students did not consistently apply conceptual assessment criteria to their models. That is, they didn't seek consistency between underlying concepts or processes in their models and those of other important genetic models, such as meiosis. This is perhaps in part because they lacked an understanding of models as conceptual rather than physical entities. Subsequently, the genetics curriculum was altered in order to create more opportunities for students to address epistemological issues associated with model assessment throughout the course. The second cohort of students' understanding of models changed over the nine-week period: initially the majority of students equated scientific models with "proof" (generally physical) of "theories"; at the end of the course, most students demonstrated understanding of the conceptual nature of scientific models and the need to justify such knowledge according to both its empirical utility and conceptual consistency. Through model construction and assessment (i.e. scientific inquiry), students were able to

  13. New probabilistic graphical models for genetic regulatory networks studies.

    PubMed

    Wang, Junbai; Cheung, Leo Wang-Kit; Delabie, Jan

    2005-12-01

    This paper introduces two new probabilistic graphical models for reconstruction of genetic regulatory networks using DNA microarray data. One is an independence graph (IG) model with either a forward or a backward search algorithm and the other one is a Gaussian network (GN) model with a novel greedy search method. The performances of both models were evaluated on four MAPK pathways in yeast and three simulated data sets. Generally, an IG model provides a sparse graph but a GN model produces a dense graph where more information about gene-gene interactions may be preserved. The results of our proposed models were compared with several other commonly used models, and our models have shown to give superior performance. Additionally, we found the same common limitations in the prediction of genetic regulatory networks when using only DNA microarray data.

  14. Requirements Modeling with Agent Programming

    NASA Astrophysics Data System (ADS)

    Dasgupta, Aniruddha; Krishna, Aneesh; Ghose, Aditya K.

    Agent-oriented conceptual modeling notations are highly effective in representing requirements from an intentional stance and answering questions such as what goals exist, how key actors depend on each other, and what alternatives must be considered. In this chapter, we review an approach to executing i* models by translating these into set of interacting agents implemented in the CASO language and suggest how we can perform reasoning with requirements modeled (both functional and non-functional) using i* models. In this chapter we particularly incorporate deliberation into the agent design. This allows us to benefit from the complementary representational capabilities of the two frameworks.

  15. Diversifying the Health Professions: A Model Program

    ERIC Educational Resources Information Center

    Ralston, Penny A.

    2003-01-01

    Objective: To describe a university-based mentoring program in the food and nutritional sciences that addresses the need for multicultural professionals in allied health fields. Methods: The conceptual model for the program includes inputs (goals, resources), transformational process (professional development, social support and recognition) and…

  16. Bridge Program: An Alternative Curricular Model

    ERIC Educational Resources Information Center

    George, Deborah A.

    2012-01-01

    With the motivation for career advancement, many adult learners have chosen to return to graduate education or professional programs. The bridge program is one relatively new alternative curricular model available for adult learners who wish to build on their education within their chosen profession. Evidence on the effectiveness of such programs…

  17. Sexual dimorphic evolution of metabolic programming in non-genetic non-alimentary mild metabolic syndrome model in mice depends on feed-back mechanisms integrity for pro-opiomelanocortin-derived endogenous substances.

    PubMed

    Loizzo, Stefano; Vella, Stefano; Loizzo, Alberto; Fortuna, Andrea; Di Biase, Antonella; Salvati, Serafina; Frajese, Giovanni V; Agrapart, Vincent; Ramirez Morales, Rafael; Spampinato, Santi; Campana, Gabriele; Capasso, Anna; Galietta, Gabriella; Guarino, Irene; Carta, Stefania; Carru, Ciriaco; Zinellu, Angelo; Ghirlanda, Giovanni; Seghieri, Giuseppe; Renzi, Paolo; Franconi, Flavia

    2010-08-01

    Previously, we showed that our post-natal handling model induces pro-opiomelanocortin-derived (POMC) endogenous systems alterations in male mice at weaning. These alterations last up to adult age, and are at the basis of adult hormonal and metabolic conditions similar to mild metabolic syndrome/type-2 diabetes. Here, we evaluate how sex influences post-natal programming in these metabolic conditions. Subjects are adult control (non-handled) female (NHF) and male (NHM) CD-1 mice; adult post-natal handled female (HF) and male (HM) mice. Handling consists of daily maternal separation (10 min) plus sham injection, from birth to weaning (21 days). In adult handled males (90-days old) we find not only POMC-derived hormones alterations (enhanced basal plasma corticosterone (+91%) and ACTH (+109%)) but also overweight (+5.4%), fasting hyperglycemia (+40%), hypertriglyceridemia (+21%), enhanced brain mRNA expression of hydroxysteroid(11-beta)dehydrogenase type-1 (HSD11B1) (+49%), and decreased mRNA-HSD11B2 (-39%). Conversely, uric acid, creatinine, HDL(C), total cholesterol, glucose and insulin incremental area under-the-curve are not affected. In females, post-natal handling does not produce both hormonal and dysmetabolic diabetes-like changes; but handling enhances n3- and n6-poly-unsaturated, and decreases saturated fatty acids content in erythrocyte membrane composition in HF versus NHF. In conclusion, for the first time we show that female sex in mice exerts effective protection against the hypothalamus-pituitary-adrenal homeostasis disruption induced by our post-natal handling model on POMC cleavage products; endocrine disruption is in turn responsible for altered metabolic programming in male mice. The role of sex hormones is still to be elucidated.

  18. Identification of spatial genetic boundaries using a multifractal model in human population genetics.

    PubMed

    Xue, Fuzhong; Wang, Jiezhen; Hu, Ping; Ma, Daoxin; Liu, Jing; Li, Guifu; Zhang, Li; Wu, Min; Sun, Guoqing; Hou, Haifeng

    2005-10-01

    There are two purposes in displaying spatial genetic structure. One is that a visual representation of the variation of the genetic variable should be provided in the contour map. The other is that spatial genetic structure should be reflected by the patterns or the gradients with genetic boundaries in the map. Nevertheless, most conventional interpolation methods, such as Cavalli-Sforza's method in genography, inverse distance-weighted methods, and the Kriging technique, focus only on the first primary purpose because of their arbitrary thresholds marked on the maps. In this paper we present an application of the contour area multifractal model (CAMM) to human population genetics. The method enables the analysis of the geographic distribution of a genetic marker and provides an insight into the spatial and geometric properties of obtained patterns. Furthermore, the CAMM may overcome some of the limitations of other interpolation techniques because no arbitrary thresholds are necessary in the computation of genetic boundaries. The CAMM is built by establishing power law relationships between the area A (> or =rho) in the contour map and the value p itself after plotting these values on a log-log graph. A series of straight-line segments can be fitted to the points on the log-log graph, each representing a power law relationship between the area A (> or =rho) and the cutoff genetic variable value for rho in a particular range. These straight-line segments can yield a group of cutoff values, which can be identified as the genetic boundaries that can classify the map of genetic variable into discrete genetic zones. These genetic zones usually correspond to spatial genetic structure on the landscape. To provide a better understanding of the interest in the CAMM approach, we analyze the spatial genetic structures of three loci (ABO, HLA-A, and TPOX) in China using the CAMM. Each synthetic principal component (SPC) contour map of the three loci is created by using both

  19. Aerothermal modeling program, phase 1

    NASA Technical Reports Server (NTRS)

    Srinivasan, R.; Reynolds, R.; Ball, I.; Berry, R.; Johnson, K.; Mongia, H.

    1983-01-01

    Aerothermal submodels used in analytical combustor models are analyzed. The models described include turbulence and scalar transport, gaseous full combustion, spray evaporation/combustion, soot formation and oxidation, and radiation. The computational scheme is discussed in relation to boundary conditions and convergence criteria. Also presented is the data base for benchmark quality test cases and an analysis of simple flows.

  20. Constraining compartmental models using multiple voltage recordings and genetic algorithms.

    PubMed

    Keren, Naomi; Peled, Noam; Korngreen, Alon

    2005-12-01

    Compartmental models with many nonlinearly and nonhomogeneous distributions of voltage-gated conductances are routinely used to investigate the physiology of complex neurons. However, the number of loosely constrained parameters makes manually constructing the desired model a daunting if not impossible task. Recently, progress has been made using automated parameter search methods, such as genetic algorithms (GAs). However, these methods have been applied to somatically recorded action potentials using relatively simple target functions. Using a genetic minimization algorithm and a reduced compartmental model based on a previously published model of layer 5 neocortical pyramidal neurons we compared the efficacy of five cost functions (based on the waveform of the membrane potential, the interspike interval, trajectory density, and their combinations) to constrain the model. When the model was constrained using somatic recordings only, a combined cost function was found to be the most effective. This combined cost function was then applied to investigate the contribution of dendritic and axonal recordings to the ability of the GA to constrain the model. The more recording locations from the dendrite and the axon that were added to the data set the better was the genetic minimization algorithm able to constrain the compartmental model. Based on these simulations we propose an experimental scheme that, in combination with a genetic minimization algorithm, may be used to constrain compartmental models of neurons.

  1. Programming model for distributed intelligent systems

    NASA Technical Reports Server (NTRS)

    Sztipanovits, J.; Biegl, C.; Karsai, G.; Bogunovic, N.; Purves, B.; Williams, R.; Christiansen, T.

    1988-01-01

    A programming model and architecture which was developed for the design and implementation of complex, heterogeneous measurement and control systems is described. The Multigraph Architecture integrates artificial intelligence techniques with conventional software technologies, offers a unified framework for distributed and shared memory based parallel computational models and supports multiple programming paradigms. The system can be implemented on different hardware architectures and can be adapted to strongly different applications.

  2. Data-Generating Program for ASKA Modeling

    NASA Technical Reports Server (NTRS)

    Karimi-Dechesh, A.; Cheng, T. K.

    1985-01-01

    Carrier plate assemblies of NASA Space Shuttle thermal protection system provided for easy access to protected vital parts of Shuttle. Each assembly mounted on substructure with fasteners through holes in protective tiles. Automatic System of Kinematic Analysis (ASKA) finite-element program evaluates these assemblies. PLATEFORT computer program developed as data generator for ASKA modeling. PLATEFORT greatly reduces amount of time and data required for building ASKA model of these assemblies.

  3. Practical implications for genetic modeling in the genomics era

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Genetic models convert data into estimated breeding values and other information useful to breeders. The goal is to provide accurate and timely predictions of the future performance for each animal (or embryo). Modeling involves defining traits, editing raw data, removing environmental effects, incl...

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

    PubMed

    Khan, Mohammad Wahab; Alam, Mansaf

    2012-08-01

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

  5. Genetics on the Fly: A Primer on the Drosophila Model System

    PubMed Central

    Hales, Karen G.; Korey, Christopher A.; Larracuente, Amanda M.; Roberts, David M.

    2015-01-01

    Fruit flies of the genus Drosophila have been an attractive and effective genetic model organism since Thomas Hunt Morgan and colleagues made seminal discoveries with them a century ago. Work with Drosophila has enabled dramatic advances in cell and developmental biology, neurobiology and behavior, molecular biology, evolutionary and population genetics, and other fields. With more tissue types and observable behaviors than in other short-generation model organisms, and with vast genome data available for many species within the genus, the fly’s tractable complexity will continue to enable exciting opportunities to explore mechanisms of complex developmental programs, behaviors, and broader evolutionary questions. This primer describes the organism’s natural history, the features of sequenced genomes within the genus, the wide range of available genetic tools and online resources, the types of biological questions Drosophila can help address, and historical milestones. PMID:26564900

  6. The quantitative genetics of indirect genetic effects: a selective review of modelling issues.

    PubMed

    Bijma, P

    2014-01-01

    Indirect genetic effects (IGE) occur when the genotype of an individual affects the phenotypic trait value of another conspecific individual. IGEs can have profound effects on both the magnitude and the direction of response to selection. Models of inheritance and response to selection in traits subject to IGEs have been developed within two frameworks; a trait-based framework in which IGEs are specified as a direct consequence of individual trait values, and a variance-component framework in which phenotypic variance is decomposed into a direct and an indirect additive genetic component. This work is a selective review of the quantitative genetics of traits affected by IGEs, with a focus on modelling, estimation and interpretation issues. It includes a discussion on variance-component vs trait-based models of IGEs, a review of issues related to the estimation of IGEs from field data, including the estimation of the interaction coefficient Ψ (psi), and a discussion on the relevance of IGEs for response to selection in cases where the strength of interaction varies among pairs of individuals. An investigation of the trait-based model shows that the interaction coefficient Ψ may deviate considerably from the corresponding regression coefficient when feedback occurs. The increasing research effort devoted to IGEs suggests that they are a widespread phenomenon, probably particularly in natural populations and plants. Further work in this field should considerably broaden our understanding of the quantitative genetics of inheritance and response to selection in relation to the social organisation of populations.

  7. Predicting genetic interactions from Boolean models of biological networks.

    PubMed

    Calzone, Laurence; Barillot, Emmanuel; Zinovyev, Andrei

    2015-08-01

    Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing us to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and the phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed.

  8. [Improvement of genetics teaching using literature-based learning model].

    PubMed

    Liang, Liang; Shiqian, Liang; Hongyan, Qin; Yong, Ji; Hua, Han

    2015-06-01

    Genetics is one of the most important courses for undergraduate students majoring in life science. In recent years, new knowledge and technologies are continually updated with deeper understanding of life science. However, the teaching model of genetics is still based on theoretical instruction, which makes the abstract principles hard to understand by students and directly affects the teaching effect. Thus, exploring a new teaching model is necessary. We have carried out a new teaching model, literature-based learning, in the course on Microbial Genetics for undergraduate students majoring in biotechnology since 2010. Here we comprehensively analyzed the implementation and application value of this model including pre-course knowledge, how to choose professional literature, how to organize teaching process and the significance of developing this new teaching model for students and teachers. Our literature-based learning model reflects the combination of "cutting-edge" and "classic" and makes book knowledge easy to understand, which improves students' learning effect, stimulates their interests, expands their perspectives and develops their ability. This practice provides novel insight into exploring new teaching model of genetics and cultivating medical talents capable of doing both basic and clinical research in the "precision medicine" era.

  9. Modeling the Volcanic Source at Long Valley, CA, Using a Genetic Algorithm Technique

    NASA Technical Reports Server (NTRS)

    Tiampo, Kristy F.

    1999-01-01

    In this project, we attempted to model the deformation pattern due to the magmatic source at Long Valley caldera using a real-value coded genetic algorithm (GA) inversion similar to that found in Michalewicz, 1992. The project has been both successful and rewarding. The genetic algorithm, coded in the C programming language, performs stable inversions over repeated trials, with varying initial and boundary conditions. The original model used a GA in which the geophysical information was coded into the fitness function through the computation of surface displacements for a Mogi point source in an elastic half-space. The program was designed to invert for a spherical magmatic source - its depth, horizontal location and volume - using the known surface deformations. It also included the capability of inverting for multiple sources.

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

    PubMed

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

    1999-01-01

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

  11. Modeling genetic inheritance of copy number variations

    PubMed Central

    Wang, Kai; Chen, Zhen; Tadesse, Mahlet G.; Glessner, Joseph; Grant, Struan F. A.; Hakonarson, Hakon; Bucan, Maja

    2008-01-01

    Copy number variations (CNVs) are being used as genetic markers or functional candidates in gene-mapping studies. However, unlike single nucleotide polymorphism or microsatellite genotyping techniques, most CNV detection methods are limited to detecting total copy numbers, rather than copy number in each of the two homologous chromosomes. To address this issue, we developed a statistical framework for intensity-based CNV detection platforms using family data. Our algorithm identifies CNVs for a family simultaneously, thus avoiding the generation of calls with Mendelian inconsistency while maintaining the ability to detect de novo CNVs. Applications to simulated data and real data indicate that our method significantly improves both call rates and accuracy of boundary inference, compared to existing approaches. We further illustrate the use of Mendelian inheritance to infer SNP allele compositions in each of the two homologous chromosomes in CNV regions using real data. Finally, we applied our method to a set of families genotyped using both the Illumina HumanHap550 and Affymetrix genome-wide 5.0 arrays to demonstrate its performance on both inherited and de novo CNVs. In conclusion, our method produces accurate CNV calls, gives probabilistic estimates of CNV transmission and builds a solid foundation for the development of linkage and association tests utilizing CNVs. PMID:18832372

  12. Model Accounting Program. Adopters Guide.

    ERIC Educational Resources Information Center

    Beaverton School District 48, OR.

    The accounting cluster demonstration project conducted at Aloha High School in the Beaverton, Oregon, school district developed a model curriculum for high school accounting. The curriculum is based on interviews with professionals in the accounting field and emphasizes the use of computers. It is suitable for use with special needs students as…

  13. A model for monitoring of Hsp90-buffered genetic variations

    NASA Astrophysics Data System (ADS)

    Kozeko, Liudmyla

    Genetic material of terrestrial organisms can be considerably injured by cosmic rays and UV-radiation in the space environment. Organisms onboard are also exposed to the entire complex of negative physical factors which can generate genetic variations and affect morphogenesis. However, species phenotypes must be robust to genetic variation, requiring "buffering" systems to ensure normal development. The molecular chaperone Hsp90 can serve as such "a buffer". It is important in the maturation and conformational regulation of a diverse set of signal transducers. The requirement of many principal regulatory proteins for Hsp90 renders entire metabolic pathways sensitive to impairment of its function. So inhibition of Hsp90 function can open cryptic genetic variations and produce morphological changes. In this paper, we present a model for monitoring of cryptic Hsp90-buffered genetic variations arising during exposure to space and spaceflight factors. This model has been developed with Arabidopsis thaliana seeds gathered in natural habitats with high anthropogenic pressure and wild type (Col-0) seeds subjected to negative influences (UV, heavy metals) experimentally. The phenotypic traits of early seedlings grown under reduction of Hsp90 activity were characterized to estimate Hsp90-buffered genetic variations. Geldanamycin was used as an inhibitor of Hsp90 function.

  14. Oxidative Stress in Genetic Mouse Models of Parkinson's Disease

    PubMed Central

    Varçin, Mustafa; Bentea, Eduard; Michotte, Yvette; Sarre, Sophie

    2012-01-01

    There is extensive evidence in Parkinson's disease of a link between oxidative stress and some of the monogenically inherited Parkinson's disease-associated genes. This paper focuses on the importance of this link and potential impact on neuronal function. Basic mechanisms of oxidative stress, the cellular antioxidant machinery, and the main sources of cellular oxidative stress are reviewed. Moreover, attention is given to the complex interaction between oxidative stress and other prominent pathogenic pathways in Parkinson's disease, such as mitochondrial dysfunction and neuroinflammation. Furthermore, an overview of the existing genetic mouse models of Parkinson's disease is given and the evidence of oxidative stress in these models highlighted. Taken into consideration the importance of ageing and environmental factors as a risk for developing Parkinson's disease, gene-environment interactions in genetically engineered mouse models of Parkinson's disease are also discussed, highlighting the role of oxidative damage in the interplay between genetic makeup, environmental stress, and ageing in Parkinson's disease. PMID:22829959

  15. Zebrafish: A Model for the Study of Addiction Genetics

    PubMed Central

    Klee, Eric W; Schneider, Henning; Clark, Karl; Cousin, Margot; Ebbert, Jon; Hooten, Michael; Karpyak, Victor; Warner, David; Ekker, Stephen

    2013-01-01

    Drug abuse and dependence are multifaceted disorders with complex genetic underpinnings. Identifying specific genetic correlates is challenging and may be more readily accomplished by defining endophenotypes specific for addictive disorders. Symptoms and syndromes, including acute drug response, consumption, preference, and withdrawal, are potential endophenotypes characterizing addiction that have been investigated using model organisms. We present a review of major genes involved in serotonergic, dopaminergic, GABAergic, and adrenoreceptor signaling that are considered to be directly involved in nicotine, opioid, cannabinoid, and ethanol use and dependence. The zebrafish genome encodes likely homologs of the vast majority of these loci. We also review the known expression patterns of these genes in zebrafish. The information presented in this review provides support for the use of zebrafish as a viable model for studying genetic factors related to drug addiction. Expansion of investigations into drug response using model organisms holds the potential to advance our understanding of drug response and addiction in humans. PMID:22207143

  16. Considerations when choosing a genetic model organism for metabolomics studies.

    PubMed

    Reed, Laura K; Baer, Charles F; Edison, Arthur S

    2017-02-01

    Model organisms are important in many areas of chemical biology. In metabolomics, model organisms can provide excellent samples for methods development as well as the foundation of comparative phylometabolomics, which will become possible as metabolomics applications expand. Comparative studies of conserved and unique metabolic pathways will help in the annotation of metabolites as well as provide important new targets of investigation in biology and biomedicine. However, most chemical biologists are not familiar with genetics, which needs to be considered when choosing a model organism. In this review we summarize the strengths and weaknesses of several genetic systems, including natural isolates, recombinant inbred lines, and genetic mutations. We also discuss methods to detect targets of selection on the metabolome.

  17. Predicting Diabetic Nephropathy Using a Multifactorial Genetic Model

    PubMed Central

    Blech, Ilana; Wainstein, Julio; Rubinstein, Ardon; Harman-Boehm, Ilana; Cohen, Joseph; Pollin, Toni I.; Glaser, Benjamin

    2011-01-01

    Aims The tendency to develop diabetic nephropathy is, in part, genetically determined, however this genetic risk is largely undefined. In this proof-of-concept study, we tested the hypothesis that combined analysis of multiple genetic variants can improve prediction. Methods Based on previous reports, we selected 27 SNPs in 15 genes from metabolic pathways involved in the pathogenesis of diabetic nephropathy and genotyped them in 1274 Ashkenazi or Sephardic Jewish patients with Type 1 or Type 2 diabetes of >10 years duration. A logistic regression model was built using a backward selection algorithm and SNPs nominally associated with nephropathy in our population. The model was validated by using random “training” (75%) and “test” (25%) subgroups of the original population and by applying the model to an independent dataset of 848 Ashkenazi patients. Results The logistic model based on 5 SNPs in 5 genes (HSPG2, NOS3, ADIPOR2, AGER, and CCL5) and 5 conventional variables (age, sex, ethnicity, diabetes type and duration), and allowing for all possible two-way interactions, predicted nephropathy in our initial population (C-statistic = 0.672) better than a model based on conventional variables only (C = 0.569). In the independent replication dataset, although the C-statistic of the genetic model decreased (0.576), it remained highly associated with diabetic nephropathy (χ2 = 17.79, p<0.0001). In the replication dataset, the model based on conventional variables only was not associated with nephropathy (χ2 = 3.2673, p = 0.07). Conclusion In this proof-of-concept study, we developed and validated a genetic model in the Ashkenazi/Sephardic population predicting nephropathy more effectively than a similarly constructed non-genetic model. Further testing is required to determine if this modeling approach, using an optimally selected panel of genetic markers, can provide clinically useful prediction and if generic models can be developed for

  18. Model-free Estimation of Recent Genetic Relatedness

    PubMed Central

    Conomos, Matthew P.; Reiner, Alexander P.; Weir, Bruce S.; Thornton, Timothy A.

    2016-01-01

    Genealogical inference from genetic data is essential for a variety of applications in human genetics. In genome-wide and sequencing association studies, for example, accurate inference on both recent genetic relatedness, such as family structure, and more distant genetic relatedness, such as population structure, is necessary for protection against spurious associations. Distinguishing familial relatedness from population structure with genotype data, however, is difficult because both manifest as genetic similarity through the sharing of alleles. Existing approaches for inference on recent genetic relatedness have limitations in the presence of population structure, where they either (1) make strong and simplifying assumptions about population structure, which are often untenable, or (2) require correct specification of and appropriate reference population panels for the ancestries in the sample, which might be unknown or not well defined. Here, we propose PC-Relate, a model-free approach for estimating commonly used measures of recent genetic relatedness, such as kinship coefficients and IBD sharing probabilities, in the presence of unspecified structure. PC-Relate uses principal components calculated from genome-screen data to partition genetic correlations among sampled individuals due to the sharing of recent ancestors and more distant common ancestry into two separate components, without requiring specification of the ancestral populations or reference population panels. In simulation studies with population structure, including admixture, we demonstrate that PC-Relate provides accurate estimates of genetic relatedness and improved relationship classification over widely used approaches. We further demonstrate the utility of PC-Relate in applications to three ancestrally diverse samples that vary in both size and genealogical complexity. PMID:26748516

  19. Genetics

    MedlinePlus

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

  20. A multilingual programming model for coupled systems.

    SciTech Connect

    Ong, E. T.; Larson, J. W.; Norris, B.; Tobis, M.; Steder, M.; Jacob, R. L.; Mathematics and Computer Science; Univ. of Wisconsin; Univ. of Chicago; The Australian National Univ.

    2008-01-01

    Multiphysics and multiscale simulation systems share a common software requirement-infrastructure to implement data exchanges between their constituent parts-often called the coupling problem. On distributed-memory parallel platforms, the coupling problem is complicated by the need to describe, transfer, and transform distributed data, known as the parallel coupling problem. Parallel coupling is emerging as a new grand challenge in computational science as scientists attempt to build multiscale and multiphysics systems on parallel platforms. An additional coupling problem in these systems is language interoperability between their constituent codes. We have created a multilingual parallel coupling programming model based on a successful open-source parallel coupling library, the Model Coupling Toolkit (MCT). This programming model's capabilities reach beyond MCT's native Fortran implementation to include bindings for the C++ and Python programming languages. We describe the method used to generate the interlanguage bindings. This approach enables an object-based programming model for implementing parallel couplings in non-Fortran coupled systems and in systems with language heterogeneity. We describe the C++ and Python versions of the MCT programming model and provide short examples. We report preliminary performance results for the MCT interpolation benchmark. We describe a major Python application that uses the MCT Python bindings, a Python implementation of the control and coupling infrastructure for the community climate system model. We conclude with a discussion of the significance of this work to productivity computing in multidisciplinary computational science.

  1. ENU mutagenesis to generate genetically modified rat models.

    PubMed

    van Boxtel, Ruben; Gould, Michael N; Cuppen, Edwin; Smits, Bart M G

    2010-01-01

    The rat is one of the most preferred model organisms in biomedical research and has been extremely useful for linking physiology and pathology to the genome. However, approaches to genetically modify specific genes in the rat germ line remain relatively scarce. To date, the most efficient approach for generating genetically modified rats has been the target-selected N-ethyl-N-nitrosourea (ENU) mutagenesis-based technology. Here, we describe the detailed protocols for ENU mutagenesis and mutant retrieval in the rat model organism.

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

    ERIC Educational Resources Information Center

    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)

  3. Programming Models for Heterogeneous Multicore Systems

    DTIC Science & Technology

    2011-08-08

    Badia, F.D. Igual, J. Labarta, R. Mayo and E.S. Quintana- Orti . “An extension of the StarSs Programming Model for Platforms with Multiple GPUs...R. Mayo, J.M. Perez, J. Planas, E.S. Quintana- Orti . “A Proposal to Extend the OpenMP Tasking Model for Heterogeneous Architectures ” LNCS Vol. 5568

  4. Educational outcomes of a workplace screening program for genetic susceptibility to hemochromatosis.

    PubMed

    Nisselle, A E; Collins, V R; Gason, A A; Flouris, A; Delatycki, M B; Allen, K J; Aitken, M A; Metcalfe, S A

    2006-02-01

    Education is an essential component of a genetic screening program. Knowledge outcomes were measured after large-scale workplace education and screening for genetic susceptibility to hereditary hemochromatosis. The aim was to assess knowledge of concepts presented, impact of mode of delivery, and knowledge retention. Education in a group setting was delivered via oral or video presentation and knowledge assessed using self-administered questionnaires at baseline, 1 month, and 12 months. Over 60% of 11 679 participants correctly answered all questions at baseline, scoring higher with clinical concepts (disease etiology and treatment) than genetic concepts (penetrance and genetic heterogeneity). Revising the education program significantly increased correct responses for etiology (p < 0.002), whilst modifying the knowledge assessment tool significantly increased correct responses for etiology (p < 0.001) and gene penetrance (p < 0.001). For three of the four concepts assessed, use of video was as effective as oral presentation for knowledge outcomes. A significantly higher proportion of those at increased risk of disease (n = 44) responded correctly at 12 months than did controls (n = 82; p = 0.011 for etiology, p = 0.002 for treatment and p = 0.003 for penetrance). Hence, genetic screening can be successfully offered in a group workplace setting, with participants remembering clinical concepts better than genetic concepts up to 1 year later.

  5. Genetically informed ecological niche models improve climate change predictions.

    PubMed

    Ikeda, Dana H; Max, Tamara L; Allan, Gerard J; Lau, Matthew K; Shuster, Stephen M; Whitham, Thomas G

    2017-01-01

    We examined the hypothesis that ecological niche models (ENMs) more accurately predict species distributions when they incorporate information on population genetic structure, and concomitantly, local adaptation. Local adaptation is common in species that span a range of environmental gradients (e.g., soils and climate). Moreover, common garden studies have demonstrated a covariance between neutral markers and functional traits associated with a species' ability to adapt to environmental change. We therefore predicted that genetically distinct populations would respond differently to climate change, resulting in predicted distributions with little overlap. To test whether genetic information improves our ability to predict a species' niche space, we created genetically informed ecological niche models (gENMs) using Populus fremontii (Salicaceae), a widespread tree species in which prior common garden experiments demonstrate strong evidence for local adaptation. Four major findings emerged: (i) gENMs predicted population occurrences with up to 12-fold greater accuracy than models without genetic information; (ii) tests of niche similarity revealed that three ecotypes, identified on the basis of neutral genetic markers and locally adapted populations, are associated with differences in climate; (iii) our forecasts indicate that ongoing climate change will likely shift these ecotypes further apart in geographic space, resulting in greater niche divergence; (iv) ecotypes that currently exhibit the largest geographic distribution and niche breadth appear to be buffered the most from climate change. As diverse agents of selection shape genetic variability and structure within species, we argue that gENMs will lead to more accurate predictions of species distributions under climate change.

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

    PubMed

    Bikard, David; Marraffini, Luciano A

    2012-02-01

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

  7. Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming

    ERIC Educational Resources Information Center

    Zafra, Amelia; Ventura, Sebastian

    2009-01-01

    The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a…

  8. Rapid SAR target modeling through genetic inheritance mechanism

    NASA Astrophysics Data System (ADS)

    Bala, Jerzy; Pachowicz, Peter W.; Vafaie, Halleh

    1997-07-01

    The paper presents a methodology and GETP experimental system for rapid SAR target signature generation from limited initial sensory data. The methodology exploits and integrates the following four processes: (1) analysis of initial SAR image signatures and their transformation into higher-level blob representation, (2) blob modeling, (3) genetic inheritance modeling to generate new instances of a target model in blob representation, and (4) synthesis of new SAR signatures from genetically evolved blob data. The GETP system takes several SAR signatures of the target and transforms each signature into more general scattered blob graphs, where each blob represents local energy cluster. A single graph node is describe by blob relative position, confidence, and iconic data. Graph data is forwarded to the genetic modeling process while blob image is stored in a catalog. Genetic inheritance is applied to the initial population of graph data. New graph models of the target are generated and evaluated. Selected graph variations are forwarded to the synthesis process. The synthesis process restores target signature from a given graph and a catalog of blobs. The background is synthesized to complement the signature. Initial experimental results are illustrated with 64 X 32 image sections of a tank.

  9. Developmental genetics in emerging rodent models: case studies and perspectives.

    PubMed

    Mallarino, Ricardo; Hoekstra, Hopi E; Manceau, Marie

    2016-08-01

    For decades, mammalian developmental genetic studies have focused almost entirely on two laboratory models: Mus and Rattus, species that breed readily in the laboratory and for which a wealth of molecular and genetic resources exist. These species alone, however, do not capture the remarkable diversity of morphological, behavioural and physiological traits seen across rodents, a group that represents >40% of all mammal species. Due to new advances in molecular tools and genomic technologies, studying the developmental events underlying natural variation in a wide range of species for a wide range of traits has become increasingly feasible. Here we review several recent studies and discuss how they not only provided technical resources for newly emerging rodent models in developmental genetics but also are instrumental in further encouraging scientists, from a wide range of research fields, to capitalize on the great diversity in development that has evolved among rodents.

  10. Dissecting genetic and environmental mutation signatures with model organisms.

    PubMed

    Segovia, Romulo; Tam, Annie S; Stirling, Peter C

    2015-08-01

    Deep sequencing has impacted on cancer research by enabling routine sequencing of genomes and exomes to identify genetic changes associated with carcinogenesis. Researchers can now use the frequency, type, and context of all mutations in tumor genomes to extract mutation signatures that reflect the driving mutational processes. Identifying mutation signatures, however, may not immediately suggest a mechanism. Consequently, several recent studies have employed deep sequencing of model organisms exposed to discrete genetic or environmental perturbations. These studies exploit the simpler genomes and availability of powerful genetic tools in model organisms to analyze mutation signatures under controlled conditions, forging mechanistic links between mutational processes and signatures. We discuss the power of this approach and suggest that many such studies may be on the horizon.

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

    NASA Astrophysics Data System (ADS)

    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

  12. Intrasystem Analysis Program (IAP) Model Improvement.

    DTIC Science & Technology

    1982-02-01

    is designed for use by an EMC systems engineer with a minimum of computer experience. The input data requirements, program control, and output formats...implemented within IEMCAP. It was recommended that the proposed models be coded into a stand-alone computer program which can be exercised in a...in all cases this computation is transferred to the receptor input port where actual measurements are more readily obtained. An EMI margin of value

  13. Authorized Course of Instruction for the Quinmester Program. Science: Genetics; Continuity of Life; and Perpetuating the Species.

    ERIC Educational Resources Information Center

    Dade County Public Schools, Miami, FL.

    Each of the three secondary school science units, prepared for the Dade County Florida Quinmester Program, concerns some aspects of genetics. "Genetics" requires previous study of biology and concentrates on in-depth study of the nature, transmission, and function of the genetic material. There are no formal prerequisites for the units…

  14. Evolving complex dynamics in electronic models of genetic networks

    NASA Astrophysics Data System (ADS)

    Mason, Jonathan; Linsay, Paul S.; Collins, J. J.; Glass, Leon

    2004-09-01

    Ordinary differential equations are often used to model the dynamics and interactions in genetic networks. In one particularly simple class of models, the model genes control the production rates of products of other genes by a logical function, resulting in piecewise linear differential equations. In this article, we construct and analyze an electronic circuit that models this class of piecewise linear equations. This circuit combines CMOS logic and RC circuits to model the logical control of the increase and decay of protein concentrations in genetic networks. We use these electronic networks to study the evolution of limit cycle dynamics. By mutating the truth tables giving the logical functions for these networks, we evolve the networks to obtain limit cycle oscillations of desired period. We also investigate the fitness landscapes of our networks to determine the optimal mutation rate for evolution.

  15. Corn Storage Protein - A Molecular Genetic Model

    SciTech Connect

    Messing, Joachim

    2013-05-31

    Corn is the highest yielding crop on earth and probably the most valuable agricultural product of the United States. Because it converts sun energy through photosynthesis into starch and proteins, we addressed energy savings by focusing on protein quality. People and animals require essential amino acids derived from the digestion of proteins. If proteins are relatively low in certain essential amino acids, the crop becomes nutritionally defective and has to be supplemented. Such deficiency affects meat and fish production and countries where corn is a staple. Because corn seed proteins have relatively low levels of lysine and methionine, a diet has to be supplemented with soybeans for the missing lysine and with chemically synthesized methionine. We therefore have studied genes expressed during maize seed development and their chromosomal organization. A critical technical requirement for the understanding of the molecular structure of genes and their positional information was DNA sequencing. Because of the length of sequences, DNA sequencing methods themselves were insufficient for this type of analysis. We therefore developed the so-called “DNA shotgun sequencing” strategy, where overlapping DNA fragments were sequenced in parallel and used to reconstruct large DNA molecules via overlaps. Our publications became the most frequently cited ones during the decade of 1981-1990 and former Associate Director of Science for the Office of Basic Energy Sciences Patricia M. Dehmer presented our work as one of the great successes of this program. A major component of the sequencing strategy was the development of bacterial strains and vectors, which were also used to develop the first biotechnology crops. These crops possessed new traits thanks to the expression of foreign genes in plants. To enable such expression, chimeric genes had to be constructed using our materials and methods by the industry. Because we made our materials and methods freely available to

  16. An approximate multitrait model for genetic evaluation in dairy cattle with a robust estimation of genetic trends.

    PubMed

    Lassen, Jan; Sørensen, Morten Kargo; Madsen, Per; Ducrocq, Vincent

    2007-01-01

    In a stochastic simulation study of a dairy cattle population three multitrait models for estimation of genetic parameters and prediction of breeding values were compared. The first model was an approximate multitrait model using a two-step procedure. The first step was a single trait model for all traits. The solutions for fixed effects from these analyses were subtracted from the phenotypes. A multitrait model only containing an overall mean, an additive genetic and a residual term was applied on these preadjusted data. The second model was similar to the first model, but the multitrait model also contained a year effect. The third model was a full multitrait model. Genetic trends for total merit and for the individual traits in the breeding goal were compared for the three scenarios to rank the models. The full multitrait model gave the highest genetic response, but was not significantly better than the approximate multitrait model including a year effect. The inclusion of a year effect into the second step of the approximate multitrait model significantly improved the genetic trend for total merit. In this study, estimation of genetic parameters for breeding value estimation using models corresponding to the ones used for prediction of breeding values increased the accuracy on the breeding values and thereby the genetic progress.

  17. Mapping genetic determinants of kidney damage in rat models.

    PubMed

    Schulz, Angela; Kreutz, Reinhold

    2012-07-01

    During the last two decades, significant progress in our understanding of the development of kidney diseases has been achieved by unravelling the mechanisms underlying rare familial forms of human kidney diseases. Due to the genetic heterogeneity in human populations and the complex multifactorial pathogenesis of the disease phenotypes, the dissection of the genetic basis of common chronic kidney diseases (CKD) remains a difficult task. In this regard, several inbred rat models provide valuable complementary tools to uncover the genetic basis of complex renal disease phenotypes that are related to common forms of CKD. In this review, data obtained in nine experimental rat models, including the Buffalo (BUF), Dahl salt-sensitive (SS), Fawn-hooded hypertensive (FHH), Goto-Kakizaki (GK), Lyon hypertensive (LH), Munich Wistar Frömter (MWF), Sabra hypertension-prone (SBH), spontaneously hypertensive rat (SHR) and stroke-prone spontaneously hypertensive rat (SHRSP) inbred strains, that contributed to the genetic dissection of renal disease phenotypes are presented. In this panel of inbred strains, a large number of quantitative trait loci (QTL) linked to albuminuria/proteinuria and other functional or structural kidney abnormalities could be identified by QTL mapping analysis and follow-up studies including consomic and congenic rat lines. The comprehensive exploitation of the genotype-renal phenotype associations that are inherited in this panel of rat strains is suitable for making a significant contribution to the development of an integrated approach to the systems genetics of common CKD.

  18. Genetic signatures of natural selection in a model invasive ascidian

    NASA Astrophysics Data System (ADS)

    Lin, Yaping; Chen, Yiyong; Yi, Changho; Fong, Jonathan J.; Kim, Won; Rius, Marc; Zhan, Aibin

    2017-03-01

    Invasive species represent promising models to study species’ responses to rapidly changing environments. Although local adaptation frequently occurs during contemporary range expansion, the associated genetic signatures at both population and genomic levels remain largely unknown. Here, we use genome-wide gene-associated microsatellites to investigate genetic signatures of natural selection in a model invasive ascidian, Ciona robusta. Population genetic analyses of 150 individuals sampled in Korea, New Zealand, South Africa and Spain showed significant genetic differentiation among populations. Based on outlier tests, we found high incidence of signatures of directional selection at 19 loci. Hitchhiking mapping analyses identified 12 directional selective sweep regions, and all selective sweep windows on chromosomes were narrow (~8.9 kb). Further analyses indentified 132 candidate genes under selection. When we compared our genetic data and six crucial environmental variables, 16 putatively selected loci showed significant correlation with these environmental variables. This suggests that the local environmental conditions have left significant signatures of selection at both population and genomic levels. Finally, we identified “plastic” genomic regions and genes that are promising regions to investigate evolutionary responses to rapid environmental change in C. robusta.

  19. Genetic signatures of natural selection in a model invasive ascidian

    PubMed Central

    Lin, Yaping; Chen, Yiyong; Yi, Changho; Fong, Jonathan J.; Kim, Won; Rius, Marc; Zhan, Aibin

    2017-01-01

    Invasive species represent promising models to study species’ responses to rapidly changing environments. Although local adaptation frequently occurs during contemporary range expansion, the associated genetic signatures at both population and genomic levels remain largely unknown. Here, we use genome-wide gene-associated microsatellites to investigate genetic signatures of natural selection in a model invasive ascidian, Ciona robusta. Population genetic analyses of 150 individuals sampled in Korea, New Zealand, South Africa and Spain showed significant genetic differentiation among populations. Based on outlier tests, we found high incidence of signatures of directional selection at 19 loci. Hitchhiking mapping analyses identified 12 directional selective sweep regions, and all selective sweep windows on chromosomes were narrow (~8.9 kb). Further analyses indentified 132 candidate genes under selection. When we compared our genetic data and six crucial environmental variables, 16 putatively selected loci showed significant correlation with these environmental variables. This suggests that the local environmental conditions have left significant signatures of selection at both population and genomic levels. Finally, we identified “plastic” genomic regions and genes that are promising regions to investigate evolutionary responses to rapid environmental change in C. robusta. PMID:28266616

  20. Population genetics of Setaria viridis, a new model system

    Technology Transfer Automated Retrieval System (TEKTRAN)

    An extensive survey of the standing genetic variation in natural populations is among the priority steps in developing a species into a model system. In recent years, green foxtail (Setaria viridis), along with its domesticated form foxtail millet (S. italica), has rapidly become a promising new mod...

  1. Comparing GWAS Results of Complex Traits Using Full Genetic Model and Additive Models for Revealing Genetic Architecture

    PubMed Central

    Monir, Md. Mamun; Zhu, Jun

    2017-01-01

    Most of the genome-wide association studies (GWASs) for human complex diseases have ignored dominance, epistasis and ethnic interactions. We conducted comparative GWASs for total cholesterol using full model and additive models, which illustrate the impacts of the ignoring genetic variants on analysis results and demonstrate how genetic effects of multiple loci could differ across different ethnic groups. There were 15 quantitative trait loci with 13 individual loci and 3 pairs of epistasis loci identified by full model, whereas only 14 loci (9 common loci and 5 different loci) identified by multi-loci additive model. Again, 4 full model detected loci were not detected using multi-loci additive model. PLINK-analysis identified two loci and GCTA-analysis detected only one locus with genome-wide significance. Full model identified three previously reported genes as well as several new genes. Bioinformatics analysis showed some new genes are related with cholesterol related chemicals and/or diseases. Analyses of cholesterol data and simulation studies revealed that the full model performs were better than the additive-model performs in terms of detecting power and unbiased estimations of genetic variants of complex traits. PMID:28079101

  2. Cooperative global security programs modeling & simulation.

    SciTech Connect

    Briand, Daniel

    2010-05-01

    The national laboratories global security programs implement sustainable technical solutions for cooperative nonproliferation, arms control, and physical security systems worldwide. To help in the development and execution of these programs, a wide range of analytical tools are used to model, for example, synthetic tactical environments for assessing infrastructure protection initiatives and tactics, systematic approaches for prioritizing nuclear and biological threat reduction opportunities worldwide, and nuclear fuel cycle enrichment and spent fuel management for nuclear power countries. This presentation will describe how these models are used in analyses to support the Obama Administration's agenda and bilateral/multinational treaties, and ultimately, to reduce weapons of mass destruction and terrorism threats through international technical cooperation.

  3. Academic program models for undergraduate biomedical engineering.

    PubMed

    Krishnan, Shankar M

    2014-01-01

    There is a proliferation of medical devices across the globe for the diagnosis and therapy of diseases. Biomedical engineering (BME) plays a significant role in healthcare and advancing medical technologies thus creating a substantial demand for biomedical engineers at undergraduate and graduate levels. There has been a surge in undergraduate programs due to increasing demands from the biomedical industries to cover many of their segments from bench to bedside. With the requirement of multidisciplinary training within allottable duration, it is indeed a challenge to design a comprehensive standardized undergraduate BME program to suit the needs of educators across the globe. This paper's objective is to describe three major models of undergraduate BME programs and their curricular requirements, with relevant recommendations to be applicable in institutions of higher education located in varied resource settings. Model 1 is based on programs to be offered in large research-intensive universities with multiple focus areas. The focus areas depend on the institution's research expertise and training mission. Model 2 has basic segments similar to those of Model 1, but the focus areas are limited due to resource constraints. In this model, co-op/internship in hospitals or medical companies is included which prepares the graduates for the work place. In Model 3, students are trained to earn an Associate Degree in the initial two years and they are trained for two more years to be BME's or BME Technologists. This model is well suited for the resource-poor countries. All three models must be designed to meet applicable accreditation requirements. The challenges in designing undergraduate BME programs include manpower, facility and funding resource requirements and time constraints. Each academic institution has to carefully analyze its short term and long term requirements. In conclusion, three models for BME programs are described based on large universities, colleges, and

  4. Rodent Models of Genetic Contributions to Motivation to Abuse Alcohol

    PubMed Central

    Crabbe, John C.

    2016-01-01

    The distinction between alcohol use (normative) and abuse (unfortunately common) implies dysregulation of motivation directed toward the drug. Genetic contributions to abuse risk are mediated through personality differences, other predispositions to drink excessively, and differences in sensitivity to the acute and chronic consequences of the drug. How to assess motivation in laboratory animals is not straightforward but risk factors for and consequences of alcohol abuse can be modeled with reasonable fidelity in laboratory rodents. Remarkably few rodent studies focus on the genetic contributions to alcohol’s reinforcing value: almost all examine preferential drinking of unflavored alcohol over water. Such studies will likely never avoid the confounding role of taste preferences and most often yield intake levels insufficient to yield a pharmacologically significant blood alcohol level. Genotypes that avoid alcohol probably do so based on pre-ingestive sensory cues; however, post-ingestive consequences are also important. Thus, the quest for improved measures of reinforcing value continues. We have genetic differences aplenty, but still lack evidence that any genotype will readily self-administer alcohol to the devastating extent that many alcoholics will. Encouraging results that are emerging include improved behavioral methods for elevating alcohol intake and inferring alcohol reinforcement, as well as new genetic animal models. Several ingenious assays to index alcohol’s motivational effects have been used extensively. Alcoholic drinking that attempts to prevent or to alleviate withdrawal symptoms has been modeled. Another characteristic of alcoholic drinking is its persistence despite abundant evidence to the drinker of the damaging effects of the excessive drinking on work, relationships, and/or health. Modeling such persistence in rodents has been uncommon to date. New genetic animal models include lines of mice selectively bred for chronic high drinking

  5. Declarative simulation of dynamicals systems: the 812 programming language and its application to the simulation of genetic networks.

    PubMed

    Giavitto, Jean-Louis; Michel, Olivier; Delaplace, Franck

    2003-01-01

    A major part of biological processes can be modeled as dynamical systems (DS), that is, as a time-varying state. In this article, we advocate a declarative approach for prototyping the simulation of DS. We introduce the concepts of collection, stream and fabric. A fabric is a multi-dimensional object that represents the successive values of a structured set of variables. A declarative programming language, called 8 1/2 has been developed to support the concept of fabrics. Several examples of working 8 1/2 programs are given to illustrate the relevance of the fabric data structure for simulation applications and to show how recursive fabric definitions can be easily used to model various biological phenomena in a natural way (a resolution of PDE, a simulation in artificial life, the Turing diffusion-reaction process and various examples of genetic networks). In the conclusion, we recapitulate several lessons we have learned from the 8 1/2 project.

  6. Simplified process model discovery based on role-oriented genetic mining.

    PubMed

    Zhao, Weidong; Liu, Xi; Dai, Weihui

    2014-01-01

    Process mining is automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them are based on control flow. Meanwhile, the existing role-oriented process mining methods focus on correctness and integrity of roles while ignoring role complexity of the process model, which directly impacts understandability and quality of the model. To address these problems, we propose a genetic programming approach to mine the simplified process model. Using a new metric of process complexity in terms of roles as the fitness function, we can find simpler process models. The new role complexity metric of process models is designed from role cohesion and coupling, and applied to discover roles in process models. Moreover, the higher fitness derived from role complexity metric also provides a guideline for redesigning process models. Finally, we conduct case study and experiments to show that the proposed method is more effective for streamlining the process by comparing with related studies.

  7. Supermultiplicative Speedups of Probabilistic Model-Building Genetic Algorithms

    DTIC Science & Technology

    2009-02-01

    simulations. We (Todd Martinez (2005 MacArthur fellow), Duanc Johnson, Kumara Sastry and David E. Goldberg) have applied inultiobjcctive GAs and model...AUTHOR(S) David E. Goldberg. Kumara Sastry. Martin Pelikan 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S...Speedups of Probabilistic Model-Building Genetic Algorithms AFOSR Grant No. FA9550-06-1-0096 February 1, 2006 to November 30, 2008 David E. Goldberg

  8. A genetic algorithm for solving supply chain network design model

    NASA Astrophysics Data System (ADS)

    Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.

    2013-09-01

    Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.

  9. Genetic code: an alternative model of translation.

    PubMed

    Damjanović, Zvonimir M; Rakocević, Miloje M

    2005-06-01

    Our earlier studies of translation have led us to a specific numeric coding of nucleotides (A = 0, C = 1, G = 2, and U = 3)--that is, a quaternary numeric system; to ordering of digrams and codons (read right to left: .yx and Z.yx) as ordinal numbers from 000 to 111; and to seek hypothetic transformation of mRNA to 20 canonic amino acids. In this work, we show that amino acids match the ordinal number--that is, follow as transforms of their respective digrams and/or mRNA-codons. Sixteen digrams and their respective amino acids appear as a parallel (discrete) array. A first approximation of translation in this view is demonstrated by a "twisted" spiral on the side of "phantom" codons and by ordering amino acids in the form of a cross on the other side, whereby the transformation of digrams and/or phantom codons to amino acids appears to be one-to-one! Classification of canonical amino acids derived from our dynamic model clarifies physicochemical criteria, such as purinity, pyrimidinity, and particularly codon rules. The system implies both the rules of Siemion and Siemion and of Davidov, as well as balances of atomic and nucleon numbers within groups of amino acids. Formalization in this system offers the possibility of extrapolating backward to the initial organization of heredity.

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

    PubMed

    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 < 0.05). Analysis of this 2-year program indicated that people who are ready to have children or expand their families are more likely to get screened and encourage others to be screened. The most effective outreach efforts targeted influencers who then encouraged screening in the target population. Educating influencers and increasing overall awareness were the most effective outreach strategies.

  11. Genetic counseling graduate student debt: impact on program, career and life choices.

    PubMed

    Kuhl, Ashley; Reiser, Catherine; Eickhoff, Jens; Petty, Elizabeth M

    2014-10-01

    The cost of education is rising, increasing student financial aid and debt for students pursuing higher education. A few studies have assessed the impact of student debt in medicine, physical therapy and social work, but little is known about the impact of student debt on genetic counseling students and graduates. To address this gap in knowledge, a web-based study of 408 recent alumni of genetic counseling programs in North America was conducted to assess the impact of student debt on program, career and life choices. Over half (63 %; n = 256/408) of the participants reported that loans were extremely important in their ability to attend their training program, with most using subsidized loans no longer available to current graduate students. While participants were generally satisfied with their genetic counseling education, 83 % (n = 282/342) of participants with student debt reported feeling burdened by their debt, which had a median of $40,000-$50,000. This debt is relatively close to the median starting salary reported by survey participants ($45,000-$50,000), breaching the "20-10 rule" that states student debt should not exceed 20 % of annual net income. In response to this critical issue, we propose recommendations for the genetic counseling field that may help alleviate student debt impact and burden.

  12. Mining and modeling human genetics for autism therapeutics.

    PubMed

    Smith, Daniel G; Ehlers, Michael D

    2012-10-01

    A growing understanding of the genetic origins of autism spectrum disorders (ASDs) and the impact of ASD risk genes on synaptic function presents new opportunities for drug discovery. Large-scale human genetics studies have begun to reveal molecular pathways and potential therapeutic drug targets. Subsequent validation and characterization of ASD risk genes in mouse models holds promise for defining relevant cellular mechanisms and brain circuits associated with the core behavioral symptoms of autism. Here we review recent advances in the molecular therapeutics in ASDs and discuss opportunities and obstacles for converting emerging biology into new medicines. We present emerging concepts on the impact of risk genes during development and adulthood that define points of intervention. We further highlight ongoing clinical trials in patients with syndromic forms of autism. These clinical studies will be an important test of the utility of human genetics as a starting point for drug discovery in ASDs.

  13. Program Model Checking as a New Trend

    NASA Technical Reports Server (NTRS)

    Havelund, Klaus; Visser, Willem; Clancy, Daniel (Technical Monitor)

    2002-01-01

    This paper introduces a special section of STTT (International Journal on Software Tools for Technology Transfer) containing a selection of papers that were presented at the 7th International SPIN workshop, Stanford, August 30 - September 1, 2000. The workshop was named SPIN Model Checking and Software Verification, with an emphasis on model checking of programs. The paper outlines the motivation for stressing software verification, rather than only design and model verification, by presenting the work done in the Automated Software Engineering group at NASA Ames Research Center within the last 5 years. This includes work in software model checking, testing like technologies and static analysis.

  14. Genetic demixing and evolution in linear stepping stone models

    PubMed Central

    Korolev, K. S.; Avlund, Mikkel; Hallatschek, Oskar; Nelson, David R.

    2010-01-01

    Results for mutation, selection, genetic drift, and migration in a one-dimensional continuous population are reviewed and extended. The population is described by a continuous limit of the stepping stone model, which leads to the stochastic Fisher-Kolmogorov-Petrovsky-Piscounov equation with additional terms describing mutations. Although the stepping stone model was first proposed for population genetics, it is closely related to “voter models” of interest in nonequilibrium statistical mechanics. The stepping stone model can also be regarded as an approximation to the dynamics of a thin layer of actively growing pioneers at the frontier of a colony of micro-organisms undergoing a range expansion on a Petri dish. The population tends to segregate into monoallelic domains. This segregation slows down genetic drift and selection because these two evolutionary forces can only act at the boundaries between the domains; the effects of mutation, however, are not significantly affected by the segregation. Although fixation in the neutral well-mixed (or “zero-dimensional”) model occurs exponentially in time, it occurs only algebraically fast in the one-dimensional model. An unusual sublinear increase is also found in the variance of the spatially averaged allele frequency with time. If selection is weak, selective sweeps occur exponentially fast in both well-mixed and one-dimensional populations, but the time constants are different. The relatively unexplored problem of evolutionary dynamics at the edge of an expanding circular colony is studied as well. Also reviewed are how the observed patterns of genetic diversity can be used for statistical inference and the differences are highlighted between the well-mixed and one-dimensional models. Although the focus is on two alleles or variants, q-allele Potts-like models of gene segregation are considered as well. Most of the analytical results are checked with simulations and could be tested against recent spatial

  15. Genetic demixing and evolution in linear stepping stone models

    NASA Astrophysics Data System (ADS)

    Korolev, K. S.; Avlund, Mikkel; Hallatschek, Oskar; Nelson, David R.

    2010-04-01

    Results for mutation, selection, genetic drift, and migration in a one-dimensional continuous population are reviewed and extended. The population is described by a continuous limit of the stepping stone model, which leads to the stochastic Fisher-Kolmogorov-Petrovsky-Piscounov equation with additional terms describing mutations. Although the stepping stone model was first proposed for population genetics, it is closely related to “voter models” of interest in nonequilibrium statistical mechanics. The stepping stone model can also be regarded as an approximation to the dynamics of a thin layer of actively growing pioneers at the frontier of a colony of micro-organisms undergoing a range expansion on a Petri dish. The population tends to segregate into monoallelic domains. This segregation slows down genetic drift and selection because these two evolutionary forces can only act at the boundaries between the domains; the effects of mutation, however, are not significantly affected by the segregation. Although fixation in the neutral well-mixed (or “zero-dimensional”) model occurs exponentially in time, it occurs only algebraically fast in the one-dimensional model. An unusual sublinear increase is also found in the variance of the spatially averaged allele frequency with time. If selection is weak, selective sweeps occur exponentially fast in both well-mixed and one-dimensional populations, but the time constants are different. The relatively unexplored problem of evolutionary dynamics at the edge of an expanding circular colony is studied as well. Also reviewed are how the observed patterns of genetic diversity can be used for statistical inference and the differences are highlighted between the well-mixed and one-dimensional models. Although the focus is on two alleles or variants, q -allele Potts-like models of gene segregation are considered as well. Most of the analytical results are checked with simulations and could be tested against recent spatial

  16. A genetic animal model of differential sensitivity to methamphetamine reinforcement.

    PubMed

    Shabani, Shkelzen; Dobbs, Lauren K; Ford, Matthew M; Mark, Gregory P; Finn, Deborah A; Phillips, Tamara J

    2012-06-01

    Sensitivity to reinforcement from methamphetamine (MA) likely influences risk for MA addiction, and genetic differences are one source of individual variation. Generation of two sets of selectively bred mouse lines for high and low MA drinking has shown that genetic factors influence MA intake, and pronounced differences in sensitivity to rewarding and aversive effects of MA play a significant role. Further validation of these lines as a unique genetic model relevant to MA addiction was obtained using operant methods to study MA reinforcement. High and low MA drinking line mice were used to test the hypotheses that: 1) oral and intracerebroventricular (ICV) MA serve as behavioral reinforcers, and 2) MA exhibits greater reinforcing efficacy in high than low MA drinking mice. Operant responses resulted in access to an MA or non-MA drinking tube or intracranial delivery of MA. Behavioral activation consequent to orally consumed MA was determined. MA available for consumption maintained higher levels of reinforced instrumental responding in high than low MA drinking line mice, and MA intake in the oral operant procedure was greater in high than low MA drinking line mice. Behavioral activation was associated with amount of MA consumed during operant sessions. High line mice delivered more MA via ICV infusion than did low line mice across a range of doses. Thus, genetic risk factors play a critical role in the reinforcing efficacy of MA and the oral self-administration procedure is suitable for delineating genetic contributions to MA reinforcement.

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

    USGS Publications Warehouse

    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 brood stock 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 brood stock 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.

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

    USGS Publications Warehouse

    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.

  19. Illustration of the maternal animal model used for genetic evaluation of beef cattle.

    PubMed

    Crews, D H; Wang, Z

    2007-07-01

    National cattle evaluation programs for weaning weight in most beef breed associations involve implementation of the maternal animal model to predict direct and maternal EPD. With this model, direct breeding values are predicted for all animals with records or pedigree ties to animals with records, or both. Even though maternal genetic value is expressed only in animals that become dams, these effects are transmitted by all parents and inherited from parents by all animals, leading to maternal breeding values being predicted for all animals as well. A small example data set was simulated involving 12 parents, 8 nonparents, and 13 animals with weaning weight records. The pedigree was developed to include paternal and maternal half-sib families, full-sibs, and some inbreeding, similar to field populations of beef cattle. Assembly of the mixed model equations and solutions for the maternal animal model are illustrated explicitly to assist animal breeding students in their understanding of the properties of the maternal animal model and to explicitly implement the model. Model parameters and moments, fixed contemporary group solutions, adjustment of breeding values for merit of mates, interpretation of maternal permanent environmental effect solutions, and alternatives for the assembly of the equations are shown. This example should lead to increased student and producer understanding of genetic improvement programs for weaning weight in beef cattle.

  20. A Brush Seals Program Modeling and Developments

    NASA Technical Reports Server (NTRS)

    Hendricks, Robert C.; Flower, Ralph; Howe, Harold

    1996-01-01

    Some events of a U.S. Army/NASA Lewis Research Center brush seals program are reviewed, and the development of ceramic brush seals is described. Some preliminary room-temperature flow data are modeled and compare favorably to the results of Ergun.

  1. Evaluation Model for Career Programs. Final Report.

    ERIC Educational Resources Information Center

    Byerly, Richard L.; And Others

    A study was conducted to provide and test an evaluative model that could be utilized in providing curricular evaluation of the various career programs. Two career fields, dental assistant and auto mechanic, were chosen for study. A questionnaire based upon the actual job performance was completed by six groups connected with the auto mechanics and…

  2. Math and Science Model Programs Manual.

    ERIC Educational Resources Information Center

    Sawyer, Donna, Comp.; And Others

    This implementation manual has been developed to describe four model mathematics and science programs designed to increase African-American students' interest in mathematics and science. The manual will help affiliates of the Urban League to mobilize existing community resources to achieve the goals of the national education initiative. The four…

  3. Locking distributed feedback laser diode frequency to gas absorption lines based on genetic programming

    NASA Astrophysics Data System (ADS)

    Quan, Wei; Li, Guanghui; Fang, Zishan; Zhai, Yueyang; Li, Xinyi; Liu, Feng

    2017-01-01

    Distributed feedback laser is widely used as the pump beam and probe beam in atomic physical and quantum experiments. As the frequency stability is a vital characteristic to the laser diode in these experiments, a saturated absorption frequency stabilization method assisted with the function of current and frequency is proposed. The relationship between the current and frequency is acquired based on the genetic programming (GP) algorithm. To verify the feasibility of the method, the frequency stabilization system is comprised of two parts that are modeling the relation between the current and frequency by GP and processing the saturated absorption signal. The results of the frequency stabilization experiment proved that this method can not only narrow the frequency searching range near the atomic line center but also compensate for the phase delay between the saturated absorption peak and the zero crossing point of the differential error signal. The reduced phase delay increases the locking probability and makes the wavelength drift only 0.015 pm/h, which converted to frequency drift is 7 MHz/h after frequency locking on the Rb absorption line.

  4. Closed-loop separation control over a sharp edge ramp using genetic programming

    NASA Astrophysics Data System (ADS)

    Debien, Antoine; von Krbek, Kai A. F. F.; Mazellier, Nicolas; Duriez, Thomas; Cordier, Laurent; Noack, Bernd R.; Abel, Markus W.; Kourta, Azeddine

    2016-03-01

    We experimentally perform open and closed-loop control of a separating turbulent boundary layer downstream from a sharp edge ramp. The turbulent boundary layer just above the separation point has a Reynolds number Re_{θ }≈ 3500 based on momentum thickness. The goal of the control is to mitigate separation and early re-attachment. The forcing employs a spanwise array of active vortex generators. The flow state is monitored with skin-friction sensors downstream of the actuators. The feedback control law is obtained using model-free genetic programming control (GPC) (Gautier et al. in J Fluid Mech 770:442-457, 2015). The resulting flow is assessed using the momentum coefficient, pressure distribution and skin friction over the ramp and stereo PIV. The PIV yields vector field statistics, e.g. shear layer growth, the back-flow area and vortex region. GPC is benchmarked against the best periodic forcing. While open-loop control achieves separation reduction by locking-on the shedding mode, GPC gives rise to similar benefits by accelerating the shear layer growth. Moreover, GPC uses less actuation energy.

  5. Negotiation Support Agent Based on Fuzzy Decision Making by Genetic Programming with the Coupled Chaos System

    NASA Astrophysics Data System (ADS)

    Matsumura, Koki; Goto, Michihiko; Hamamatsu, Yoshio

    This paper describes a negotiation agent system based on the fuzzy decision making. The method of seeking appropriate membership functions and a reasonable agreement point was examined by means of the genetic programming technique with the coupled chaos system, which is an intelligent principle. The negotiation rule is based on the negotiation model expressed by the utility theory in the process of decision making. And the concession process was modified with the opponent’s movement and the persistence of each negotiator. In order to search for a membership function more efficiently, the dynamic state of symbiosis between individuals, which was caused by the coupled chaos system, was taken advantage of. Then the effectiveness of the technique was examined by applying it to a practical negotiation case which needs cooperative decision making. As a result, the following findings were obtained. This technique helps discover practicable membership functions in a vast search area, and achieve the solution search with high efficiency. This technique is also considered to be applied to the negotiation support easily.

  6. Evolutionary conservation and modulation of a juvenile growth-regulating genetic program

    PubMed Central

    Delaney, Angela; Padmanabhan, Vasantha; Rezvani, Geoffrey; Chen, Weiping; Forcinito, Patricia; Cheung, Crystal S.F.; Baron, Jeffrey; Lui, Julian C.K.

    2014-01-01

    Body size varies enormously among mammalian species. In small mammals, body growth is typically suppressed rapidly, within weeks, whereas in large mammals, growth is suppressed slowly, over years, allowing for a greater adult size. We recently reported evidence that body growth suppression in rodents is caused in part by a juvenile genetic program that occurs in multiple tissues simultaneously and involves the downregulation of a large set of growth-promoting genes. We hypothesized that this genetic program is conserved in large mammals but that its time course is evolutionarily modulated such that it plays out more slowly, allowing for more prolonged growth. Consistent with this hypothesis, using expression microarray analysis, we identified a set of genes that are downregulated with age in both juvenile sheep kidney and lung. This overlapping gene set was enriched for genes involved in cell proliferation and growth and showed striking similarity to a set of genes downregulated with age in multiple organs of the juvenile mouse and rat, indicating that the multiorgan juvenile genetic program previously described in rodents has been conserved in the 80 million years since sheep and rodents diverged in evolution. Using microarray and real-time PCR, we found that the pace of this program was most rapid in mice, more gradual in rats, and most gradual in sheep. The findings support the hypothesis that a growth-regulating genetic program is conserved among mammalian species but that its pace is modulated to allow more prolonged growth and therefore greater adult body size in larger mammals. PMID:24776848

  7. [The emphases and basic procedures of genetic counseling in psychotherapeutic model].

    PubMed

    Zhang, Yuan-Zhi; Zhong, Nanbert

    2006-11-01

    The emphases and basic procedures of genetic counseling are all different with those in old models. In the psychotherapeutic model, genetic counseling will not only focus on counselees' genetic disorders and birth defects, but also their psychological problems. "Client-centered therapy" termed by Carl Rogers plays an important role in genetic counseling process. The basic procedures of psychotherapeutic model of genetic counseling include 7 steps: initial contact, introduction, agendas, inquiry of family history, presenting information, closing the session and follow-up.

  8. An animal model of differential genetic risk for methamphetamine intake

    PubMed Central

    Phillips, Tamara J.; Shabani, Shkelzen

    2015-01-01

    The question of whether genetic factors contribute to risk for methamphetamine (MA) use and dependence has not been intensively investigated. Compared to human populations, genetic animal models offer the advantages of control over genetic family history and drug exposure. Using selective breeding, we created lines of mice that differ in genetic risk for voluntary MA intake and identified the chromosomal addresses of contributory genes. A quantitative trait locus was identified on chromosome 10 that accounts for more than 50% of the genetic variance in MA intake in the selected mouse lines. In addition, behavioral and physiological screening identified differences corresponding with risk for MA intake that have generated hypotheses that are testable in humans. Heightened sensitivity to aversive and certain physiological effects of MA, such as MA-induced reduction in body temperature, are hallmarks of mice bred for low MA intake. Furthermore, unlike MA-avoiding mice, MA-preferring mice are sensitive to rewarding and reinforcing MA effects, and to MA-induced increases in brain extracellular dopamine levels. Gene expression analyses implicate the importance of a network enriched in transcription factor genes, some of which regulate the mu opioid receptor gene, Oprm1, in risk for MA use. Neuroimmune factors appear to play a role in differential response to MA between the mice bred for high and low intake. In addition, chromosome 10 candidate gene studies provide strong support for a trace amine-associated receptor 1 gene, Taar1, polymorphism in risk for MA intake. MA is a trace amine-associated receptor 1 (TAAR1) agonist, and a non-functional Taar1 allele segregates with high MA consumption. Thus, reduced TAAR1 function has the potential to increase risk for MA use. Overall, existing findings support the MA drinking lines as a powerful model for identifying genetic factors involved in determining risk for harmful MA use. Future directions include the development of a

  9. Integrated Modeling Program, Applied Chemical Theory (IMPACT)

    PubMed Central

    BANKS, JAY L.; BEARD, HEGE S.; CAO, YIXIANG; CHO, ART E.; DAMM, WOLFGANG; FARID, RAMY; FELTS, ANTHONY K.; HALGREN, THOMAS A.; MAINZ, DANIEL T.; MAPLE, JON R.; MURPHY, ROBERT; PHILIPP, DEAN M.; REPASKY, MATTHEW P.; ZHANG, LINDA Y.; BERNE, BRUCE J.; FRIESNER, RICHARD A.; GALLICCHIO, EMILIO; LEVY, RONALD M.

    2009-01-01

    We provide an overview of the IMPACT molecular mechanics program with an emphasis on recent developments and a description of its current functionality. With respect to core molecular mechanics technologies we include a status report for the fixed charge and polarizable force fields that can be used with the program and illustrate how the force fields, when used together with new atom typing and parameter assignment modules, have greatly expanded the coverage of organic compounds and medicinally relevant ligands. As we discuss in this review, explicit solvent simulations have been used to guide our design of implicit solvent models based on the generalized Born framework and a novel nonpolar estimator that have recently been incorporated into the program. With IMPACT it is possible to use several different advanced conformational sampling algorithms based on combining features of molecular dynamics and Monte Carlo simulations. The program includes two specialized molecular mechanics modules: Glide, a high-throughput docking program, and QSite, a mixed quantum mechanics/molecular mechanics module. These modules employ the IMPACT infrastructure as a starting point for the construction of the protein model and assignment of molecular mechanics parameters, but have then been developed to meet specialized objectives with respect to sampling and the energy function. PMID:16211539

  10. Integrated Modeling Program, Applied Chemical Theory (IMPACT).

    PubMed

    Banks, Jay L; Beard, Hege S; Cao, Yixiang; Cho, Art E; Damm, Wolfgang; Farid, Ramy; Felts, Anthony K; Halgren, Thomas A; Mainz, Daniel T; Maple, Jon R; Murphy, Robert; Philipp, Dean M; Repasky, Matthew P; Zhang, Linda Y; Berne, Bruce J; Friesner, Richard A; Gallicchio, Emilio; Levy, Ronald M

    2005-12-01

    We provide an overview of the IMPACT molecular mechanics program with an emphasis on recent developments and a description of its current functionality. With respect to core molecular mechanics technologies we include a status report for the fixed charge and polarizable force fields that can be used with the program and illustrate how the force fields, when used together with new atom typing and parameter assignment modules, have greatly expanded the coverage of organic compounds and medicinally relevant ligands. As we discuss in this review, explicit solvent simulations have been used to guide our design of implicit solvent models based on the generalized Born framework and a novel nonpolar estimator that have recently been incorporated into the program. With IMPACT it is possible to use several different advanced conformational sampling algorithms based on combining features of molecular dynamics and Monte Carlo simulations. The program includes two specialized molecular mechanics modules: Glide, a high-throughput docking program, and QSite, a mixed quantum mechanics/molecular mechanics module. These modules employ the IMPACT infrastructure as a starting point for the construction of the protein model and assignment of molecular mechanics parameters, but have then been developed to meet specialized objectives with respect to sampling and the energy function.

  11. Evolving a Nelder-Mead Algorithm for Optimization with Genetic Programming.

    PubMed

    Fajfar, Iztok; Puhan, Janez; Bűrmen, Árpád

    2016-01-25

    We used genetic programming to evolve a direct search optimization algorithm, similar to that of the standard downhill simplex optimization method proposed by Nelder and Mead (1965). In the training process, we used several ten-dimensional quadratic functions with randomly displaced parameters and different randomly generated starting simplices. The genetically obtained optimization algorithm showed overall better performance than the original Nelder-Mead method on a standard set of test functions. We observed that many parts of the genetically produced algorithm were seldom or never executed, which allowed us to greatly simplify the algorithm by removing the redundant parts. The resulting algorithm turns out to be considerably simpler than the original Nelder-Mead method while still performing better than the original method.

  12. ZATPAC: a model consortium evaluates teen programs.

    PubMed

    Owen, Kathryn; Murphy, Dana; Parsons, Chris

    2009-09-01

    How do we advance the environmental literacy of young people, support the next generation of environmental stewards and increase the diversity of the leadership of zoos and aquariums? We believe it is through ongoing evaluation of zoo and aquarium teen programming and have founded a consortium to pursue those goals. The Zoo and Aquarium Teen Program Assessment Consortium (ZATPAC) is an initiative by six of the nation's leading zoos and aquariums to strengthen institutional evaluation capacity, model a collaborative approach toward assessing the impact of youth programs, and bring additional rigor to evaluation efforts within the field of informal science education. Since its beginning in 2004, ZATPAC has researched, developed, pilot-tested and implemented a pre-post program survey instrument designed to assess teens' knowledge of environmental issues, skills and abilities to take conservation actions, self-efficacy in environmental actions, and engagement in environmentally responsible behaviors. Findings from this survey indicate that teens who join zoo/aquarium programs are already actively engaged in many conservation behaviors. After participating in the programs, teens showed a statistically significant increase in their reported knowledge of conservation and environmental issues and their abilities to research, explain, and find resources to take action on conservation issues of personal concern. Teens also showed statistically significant increases pre-program to post-program for various conservation behaviors, including "I talk with my family and/or friends about things they can do to help the animals or the environment," "I save water...," "I save energy...," "When I am shopping I look for recycled products," and "I help with projects that restore wildlife habitat."

  13. A Tri-Part Model for Genetics Literacy: Exploring Undergraduate Student Reasoning about Authentic Genetics Dilemmas

    ERIC Educational Resources Information Center

    Shea, Nicole A.; Duncan, Ravit Golan; Stephenson, Celeste

    2015-01-01

    Genetics literacy is becoming increasingly important as advancements in our application of genetic technologies such as stem cell research, cloning, and genetic screening become more prevalent. Very few studies examine how genetics literacy is applied when reasoning about authentic genetic dilemmas. However, there is evidence that situational…

  14. A unified model of the standard genetic code

    PubMed Central

    Morgado, Eberto R.

    2017-01-01

    The Rodin–Ohno (RO) and the Delarue models divide the table of the genetic code into two classes of aminoacyl-tRNA synthetases (aaRSs I and II) with recognition from the minor or major groove sides of the tRNA acceptor stem, respectively. These models are asymmetric but they are biologically meaningful. On the other hand, the standard genetic code (SGC) can be derived from the primeval RNY code (R stands for purines, Y for pyrimidines and N any of them). In this work, the RO-model is derived by means of group actions, namely, symmetries represented by automorphisms, assuming that the SGC originated from a primeval RNY code. It turns out that the RO-model is symmetric in a six-dimensional (6D) hypercube. Conversely, using the same automorphisms, we show that the RO-model can lead to the SGC. In addition, the asymmetric Delarue model becomes symmetric by means of quotient group operations. We formulate isometric functions that convert the class aaRS I into the class aaRS II and vice versa. We show that the four polar requirement categories display a symmetrical arrangement in our 6D hypercube. Altogether these results cannot be attained, neither in two nor in three dimensions. We discuss the present unified 6D algebraic model, which is compatible with both the SGC (based upon the primeval RNY code) and the RO-model.

  15. A unified model of the standard genetic code.

    PubMed

    José, Marco V; Zamudio, Gabriel S; Morgado, Eberto R

    2017-03-01

    The Rodin-Ohno (RO) and the Delarue models divide the table of the genetic code into two classes of aminoacyl-tRNA synthetases (aaRSs I and II) with recognition from the minor or major groove sides of the tRNA acceptor stem, respectively. These models are asymmetric but they are biologically meaningful. On the other hand, the standard genetic code (SGC) can be derived from the primeval RNY code (R stands for purines, Y for pyrimidines and N any of them). In this work, the RO-model is derived by means of group actions, namely, symmetries represented by automorphisms, assuming that the SGC originated from a primeval RNY code. It turns out that the RO-model is symmetric in a six-dimensional (6D) hypercube. Conversely, using the same automorphisms, we show that the RO-model can lead to the SGC. In addition, the asymmetric Delarue model becomes symmetric by means of quotient group operations. We formulate isometric functions that convert the class aaRS I into the class aaRS II and vice versa. We show that the four polar requirement categories display a symmetrical arrangement in our 6D hypercube. Altogether these results cannot be attained, neither in two nor in three dimensions. We discuss the present unified 6D algebraic model, which is compatible with both the SGC (based upon the primeval RNY code) and the RO-model.

  16. Genetic variants associated with neurodegenerative Alzheimer disease in natural models.

    PubMed

    Salazar, Claudia; Valdivia, Gonzalo; Ardiles, Álvaro O; Ewer, John; Palacios, Adrián G

    2016-02-26

    The use of transgenic models for the study of neurodegenerative diseases has made valuable contributions to the field. However, some important limitations, including protein overexpression and general systemic compensation for the missing genes, has caused researchers to seek natural models that show the main biomarkers of neurodegenerative diseases during aging. Here we review some of these models-most of them rodents, focusing especially on the genetic variations in biomarkers for Alzheimer diseases, in order to explain their relationships with variants associated with the occurrence of the disease in humans.

  17. A Linguistic Model in Component Oriented Programming

    NASA Astrophysics Data System (ADS)

    Crăciunean, Daniel Cristian; Crăciunean, Vasile

    2016-12-01

    It is a fact that the component-oriented programming, well organized, can bring a large increase in efficiency in the development of large software systems. This paper proposes a model for building software systems by assembling components that can operate independently of each other. The model is based on a computing environment that runs parallel and distributed applications. This paper introduces concepts as: abstract aggregation scheme and aggregation application. Basically, an aggregation application is an application that is obtained by combining corresponding components. In our model an aggregation application is a word in a language.

  18. Using Workflow Modeling to Identify Areas to Improve Genetic Test Processes in the University of Maryland Translational Pharmacogenomics Project

    PubMed Central

    Cutting, Elizabeth M.; Overby, Casey L.; Banchero, Meghan; Pollin, Toni; Kelemen, Mark; Shuldiner, Alan R.; Beitelshees, Amber L.

    2015-01-01

    Delivering genetic test results to clinicians is a complex process. It involves many actors and multiple steps, requiring all of these to work together in order to create an optimal course of treatment for the patient. We used information gained from focus groups in order to illustrate the current process of delivering genetic test results to clinicians. We propose a business process model and notation (BPMN) representation of this process for a Translational Pharmacogenomics Project being implemented at the University of Maryland Medical Center, so that personalized medicine program implementers can identify areas to improve genetic testing processes. We found that the current process could be improved to reduce input errors, better inform and notify clinicians about the implications of certain genetic tests, and make results more easily understood. We demonstrate our use of BPMN to improve this important clinical process for CYP2C19 genetic testing in patients undergoing invasive treatment of coronary heart disease. PMID:26958179

  19. Using Workflow Modeling to Identify Areas to Improve Genetic Test Processes in the University of Maryland Translational Pharmacogenomics Project.

    PubMed

    Cutting, Elizabeth M; Overby, Casey L; Banchero, Meghan; Pollin, Toni; Kelemen, Mark; Shuldiner, Alan R; Beitelshees, Amber L

    Delivering genetic test results to clinicians is a complex process. It involves many actors and multiple steps, requiring all of these to work together in order to create an optimal course of treatment for the patient. We used information gained from focus groups in order to illustrate the current process of delivering genetic test results to clinicians. We propose a business process model and notation (BPMN) representation of this process for a Translational Pharmacogenomics Project being implemented at the University of Maryland Medical Center, so that personalized medicine program implementers can identify areas to improve genetic testing processes. We found that the current process could be improved to reduce input errors, better inform and notify clinicians about the implications of certain genetic tests, and make results more easily understood. We demonstrate our use of BPMN to improve this important clinical process for CYP2C19 genetic testing in patients undergoing invasive treatment of coronary heart disease.

  20. An Integrated Biochemistry and Genetics Outreach Program Designed for Elementary School Students

    PubMed Central

    Ross, Eric D.; Lee, Sarah K.; Radebaugh, Catherine A.; Stargell, Laurie A.

    2012-01-01

    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. PMID:22135354

  1. An integrated biochemistry and genetics outreach program designed for elementary school students.

    PubMed

    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. Zebrafish: A Model System for the Study of Eye Genetics

    PubMed Central

    Fadool, James M.; Dowling, John E.

    2008-01-01

    Over the last decade, the use of the zebrafish as a genetic model has moved beyond the proof-of-concept for the analysis of vertebrate embryonic development to demonstrated utility as a mainstream model organism for the understanding of human disease. The initial identification of a variety of zebrafish mutations affecting the eye and retina, and the subsequent cloning of mutated genes have revealed cellular, molecular and physiological processes fundamental to visual system development. With the increasing development of genetic manipulations, sophisticated techniques for phenotypic characterization, behavioral approaches and screening strategies, the identification of novel genes or novel gene functions will have important implications for our understanding of human eye diseases, pathogenesis, and treatment. PMID:17962065

  3. Sleep and Development in Genetically Tractable Model Organisms.

    PubMed

    Kayser, Matthew S; Biron, David

    2016-05-01

    Sleep is widely recognized as essential, but without a clear singular function. Inadequate sleep impairs cognition, metabolism, immune function, and many other processes. Work in genetic model systems has greatly expanded our understanding of basic sleep neurobiology as well as introduced new concepts for why we sleep. Among these is an idea with its roots in human work nearly 50 years old: sleep in early life is crucial for normal brain maturation. Nearly all known species that sleep do so more while immature, and this increased sleep coincides with a period of exuberant synaptogenesis and massive neural circuit remodeling. Adequate sleep also appears critical for normal neurodevelopmental progression. This article describes recent findings regarding molecular and circuit mechanisms of sleep, with a focus on development and the insights garnered from models amenable to detailed genetic analyses.

  4. Lasing from Escherichia coli bacteria genetically programmed to express green fluorescent protein

    NASA Astrophysics Data System (ADS)

    Gather, Malte C.; Yun, Seok Hyun

    2011-08-01

    We report on lasing action from colonies of Escherichia coli bacteria that are genetically programmed to synthesize the green fluorescent protein (GFP). When embedded in a Fabry--Perot type cavity and excited by ns-pulses of blue light (465nm), the bacteria generate green laser emission (˜520nm). Broad illumination of pump light yields simultaneous lasing over a large area in bacterial colonies.

  5. Lasing from Escherichia coli bacteria genetically programmed to express green fluorescent protein.

    PubMed

    Gather, Malte C; Yun, Seok Hyun

    2011-08-15

    We report on lasing action from colonies of Escherichia coli bacteria that are genetically programmed to synthesize the green fluorescent protein (GFP). When embedded in a Fabry-Perot type cavity and excited by ns-pulses of blue light (465 nm), the bacteria generate green laser emission (∼520 nm). Broad illumination of pump light yields simultaneous lasing over a large area in bacterial colonies.

  6. Behavior Principles Structural Model of a Follow Through Program, Dayton, Ohio: Model Programs. Childhood Education.

    ERIC Educational Resources Information Center

    American Institutes for Research in the Behavioral Sciences, Palo Alto, CA.

    Prepared for a White House Conference on Children (December 1970), this report describes a program in which first- through third-graders in three schools in Dayton, Ohio, participate in a model of a Follow Through program sponsored by Siegfried Engelmann and Wesley Becker of the University of Oregon at Eugene. All teachers chose to participate,…

  7. Genetic variability and population structure of Salvia lachnostachys: implications for breeding and conservation programs.

    PubMed

    Erbano, Marianna; Schühli, Guilherme Schnell E; Santos, Élide Pereira Dos

    2015-04-08

    The genetic diversity and population structure of Salvia lachnostachys Benth were assessed. Inter Simple Sequence Repeat (ISSR) molecular markers were used to investigate the restricted distribution of S. lachnostachys in Parana State, Brazil. Leaves of 73 individuals representing three populations were collected. DNA was extracted and submitted to PCR-ISSR amplification with nine tested primers. Genetic diversity parameters were evaluated. Our analysis indicated 95.6% polymorphic loci (stress value 0.02) with a 0.79 average Simpson's index. The Nei-Li distance dendrogram and principal component analysis largely recovered the geographical origin of each sample. Four major clusters were recognized representing each collected population. Nei's gene diversity and Shannon's information index were 0.25 and 0.40 respectively. As is typical for outcrossing herbs, the majority of genetic variation occurred at the population level (81.76%). A high gene flow (Nm = 2.48) was observed with a correspondingly low fixation index. These values were generally similar to previous studies on congeneric species. The results of principal coordinate analysis (PCA) and of arithmetic average (UPGMA) were consistent and all three populations appear distinct as in STRUCTURE analysis. In addition, this analysis indicated a majority intrapopulation genetic variation. Despite the human pressure on natural populations our study found high levels of genetic diversity for S. lachnostachys. This was the first molecular assessment for this endemic species with medicinal proprieties and the results can guide for subsequent bioprospection, breeding programs or conservation actions.

  8. Generating Effective Models and Parameters for RNA Genetic Circuits.

    PubMed

    Hu, Chelsea Y; Varner, Jeffrey D; Lucks, Julius B

    2015-08-21

    RNA genetic circuitry is emerging as a powerful tool to control gene expression. However, little work has been done to create a theoretical foundation for RNA circuit design. A prerequisite to this is a quantitative modeling framework that accurately describes the dynamics of RNA circuits. In this work, we develop an ordinary differential equation model of transcriptional RNA genetic circuitry, using an RNA cascade as a test case. We show that parameter sensitivity analysis can be used to design a set of four simple experiments that can be performed in parallel using rapid cell-free transcription-translation (TX-TL) reactions to determine the 13 parameters of the model. The resulting model accurately recapitulates the dynamic behavior of the cascade, and can be easily extended to predict the function of new cascade variants that utilize new elements with limited additional characterization experiments. Interestingly, we show that inconsistencies between model predictions and experiments led to the model-guided discovery of a previously unknown maturation step required for RNA regulator function. We also determine circuit parameters in two different batches of TX-TL, and show that batch-to-batch variation can be attributed to differences in parameters that are directly related to the concentrations of core gene expression machinery. We anticipate the RNA circuit models developed here will inform the creation of computer aided genetic circuit design tools that can incorporate the growing number of RNA regulators, and that the parametrization method will find use in determining functional parameters of a broad array of natural and synthetic regulatory systems.

  9. The system-resonance approach in modeling genetic structures.

    PubMed

    Petoukhov, Sergey V

    2016-01-01

    The founder of the theory of resonance in structural chemistry Linus Pauling established the importance of resonance patterns in organization of living systems. Any living organism is a great chorus of coordinated oscillatory processes. From the formal point of view, biological organism is an oscillatory system with a great number of degrees of freedom. Such systems are studied in the theory of oscillations using matrix mathematics of their resonance characteristics. This study is devoted to a new approach for modeling genetically inherited structures and processes in living organisms using mathematical tools of the theory of resonances. This approach reveals hidden relationships in a number of genetic phenomena and gives rise to a new class of bio-mathematical models, which contribute to a convergence of biology with physics and informatics. In addition some relationships of molecular-genetic ensembles with mathematics of noise-immunity coding of information in modern communications technology are shown. Perspectives of applications of the phenomena of vibrational mechanics for modeling in biology are discussed.

  10. Implementing a predictive modeling program, part II: Use of motivational interviewing in a predictive modeling program.

    PubMed

    Calhoun, Jean; Admire, Kaye S

    2005-01-01

    This is the second article of a two-part series about issues encountered in implementing a predictive modeling program. Part I looked at how to effectively implement a program and discussed helpful hints and lessons learned for case managers who are required to change their approach to patients. In Part II, we discuss the readiness to change model, examine the spirit of motivational interviewing and related techniques, and explore how motivational interviewing is different from more traditional interviewing and assessment methods.

  11. Human Factors Engineering Program Review Model

    DTIC Science & Technology

    2004-02-01

    AA NUREG -0711,Rev. 2 Human Factors Engineering Program Review Model 20081009191 I i m To] Bi U.S. Nuclear Regulatory Commission Office of...Material As of November 1999, you may electronically access NUREG -series publications and other NRC records at NRC’s Public Electronic Reading Room at...http://www.nrc.qov/readinq-rm.html. Publicly released records include, to name a few, NUREG -series publications; Federal Register notices; applicant

  12. Vascular Anomalies: From Genetics toward Models for Therapeutic Trials

    PubMed Central

    Uebelhoer, Melanie; Boon, Laurence M.; Vikkula, Miikka

    2012-01-01

    Vascular anomalies are localized abnormalities that occur during vascular development. Several causative genes have been identified not only for inherited but also for some sporadic forms, and the molecular pathways involved are becoming understood. This gives us the opportunity to generate animals carrying the causative genetic defects, which we hope model the phenotype seen in human patients. These models would enable us not only to test known antiangiogenic drugs, but also to develop novel approaches for treatment, directly targeting the mutated protein or molecules implicated in the pathophysiological signaling pathways. PMID:22908197

  13. Multiple Magnetic Dipole Modeling Coupled with a Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Lientschnig, G.

    2012-05-01

    Magnetic field measurements of scientific spacecraft can be modelled successfully with the multiple magnetic dipole method. The existing GANEW software [1] uses a modified Gauss-Newton algorithm to find good magnetic dipole models. However, this deterministic approach relies on suitable guesses of the initial parameters which require a lot of expertise and time-consuming interaction of the user. Here, the use of probabilistic methods employing genetic algorithms is put forward. Stochastic methods like these are well- suited for providing good initial starting points for GANEW. Furthermore a computer software is reported upon that was successfully tested and used for a Cluster II satellite.

  14. Genetic Diversity of Brazilian Aedes aegypti: Patterns following an Eradication Program

    PubMed Central

    Monteiro, Fernando A.; Shama, Renata; Martins, Ademir J.; Gloria-Soria, Andrea; Brown, Julia E.; Powell, Jeffrey R.

    2014-01-01

    Background Aedes aegypti is the most important vector of dengue fever in Brazil, where severe epidemics have recently taken place. Ae. aegypti in Brazil was the subject of an intense eradication program in the 1940s and 50s to control yellow fever. Brazil was the largest country declared free of this mosquito by the Pan-American Health Organization in 1958. Soon after relaxation of this program, Ae. aegypti reappeared in this country, and by the early 1980s dengue fever had been reported. The aim of this study is to analyze the present-day genetic patterns of Ae. aegypti populations in Brazil. Methodology/Principal Findings We studied the genetic variation in samples of 11 widely spread populations of Ae. aegypti in Brazil based on 12 well-established microsatellite loci. Our principal finding is that present-day Brazilian Ae. aegypti populations form two distinct groups, one in the northwest and one in the southeast of the country. These two groups have genetic affinities to northern South American countries and the Caribbean, respectively. This is consistent with what has been reported for other genetic markers such as mitochondrial DNA and allele frequencies at the insecticide resistance gene, kdr. Conclusions/Significance We conclude that the genetic patterns in present day populations of Ae. aegypti in Brazil are more consistent with a complete eradication of the species in the recent past followed by re-colonization, rather than the alternative possibility of expansion from residual pockets of refugia. At least two colonizations are likely to have taken place, one from northern South American countries (e.g., Venezuela) that founded the northwestern group, and one from the Caribbean that founded the southeastern group. The proposed source areas were never declared free of Ae. aegypti. PMID:25233218

  15. Genetically engineered livestock: ethical use for food and medical models.

    PubMed

    Garas, Lydia C; Murray, James D; Maga, Elizabeth A

    2015-01-01

    Recent advances in the production of genetically engineered (GE) livestock have resulted in a variety of new transgenic animals with desirable production and composition changes. GE animals have been generated to improve growth efficiency, food composition, and disease resistance in domesticated livestock species. GE animals are also used to produce pharmaceuticals and as medical models for human diseases. The potential use of these food animals for human consumption has prompted an intense debate about food safety and animal welfare concerns with the GE approach. Additionally, public perception and ethical concerns about their use have caused delays in establishing a clear and efficient regulatory approval process. Ethically, there are far-reaching implications of not using genetically engineered livestock, at a detriment to both producers and consumers, as use of this technology can improve both human and animal health and welfare.

  16. Founder effects, inbreeding, and loss of genetic diversity in four avian reintroduction programs.

    PubMed

    Jamieson, Ian G

    2011-02-01

    The number of individuals translocated and released as part of a reintroduction is often small, as is the final established population, because the reintroduction site is typically small. Small founder and small resulting populations can result in population bottlenecks, which are associated with increased rates of inbreeding and loss of genetic diversity, both of which can affect the long-term viability of reintroduced populations. I used information derived from pedigrees of four monogamous bird species reintroduced onto two different islands (220 and 259 ha) in New Zealand to compare the pattern of inbreeding and loss of genetic diversity among the reintroduced populations. Although reintroduced populations founded with few individuals had higher levels of inbreeding, as predicted, other factors, including biased sex ratio and skewed breeding success, contributed to high levels of inbreeding and loss of genetic diversity. Of the 10-58 individuals released, 4-25 genetic founders contributed at least one living descendent and yielded approximately 3-11 founder-genome equivalents (number of genetic founders assuming an equal contribution of offspring and no random loss of alleles across generations) after seven breeding seasons. This range is much lower than the 20 founder-genome equivalents recommended for captive-bred populations. Although the level of inbreeding in one reintroduced population initially reached three times that of a closely related species, the long-term estimated rate of inbreeding of this one population was approximately one-third that of the other species due to differences in carrying capacities of the respective reintroduction sites. The increasing number of reintroductions to suitable areas that are smaller than those I examined here suggests that it might be useful to develop long-term strategies and guidelines for reintroduction programs, which would minimize inbreeding and maintain genetic diversity.

  17. Resistance to genetic insect control: Modelling the effects of space.

    PubMed

    Watkinson-Powell, Benjamin; Alphey, Nina

    2017-01-21

    Genetic insect control, such as self-limiting RIDL(2) (Release of Insects Carrying a Dominant Lethal) technology, is a development of the sterile insect technique which is proposed to suppress wild populations of a number of major agricultural and public health insect pests. This is achieved by mass rearing and releasing male insects that are homozygous for a repressible dominant lethal genetic construct, which causes death in progeny when inherited. The released genetically engineered ('GE') insects compete for mates with wild individuals, resulting in population suppression. A previous study modelled the evolution of a hypothetical resistance to the lethal construct using a frequency-dependent population genetic and population dynamic approach. This found that proliferation of resistance is possible but can be diluted by the introgression of susceptible alleles from the released homozygous-susceptible GE males. We develop this approach within a spatial context by modelling the spread of a lethal construct and resistance trait, and the effect on population control, in a two deme metapopulation, with GE release in one deme. Results show that spatial effects can drive an increased or decreased evolution of resistance in both the target and non-target demes, depending on the effectiveness and associated costs of the resistant trait, and on the rate of dispersal. A recurrent theme is the potential for the non-target deme to act as a source of resistant or susceptible alleles for the target deme through dispersal. This can in turn have a major impact on the effectiveness of insect population control.

  18. Genetic mouse models of brain ageing and Alzheimer's disease.

    PubMed

    Bilkei-Gorzo, Andras

    2014-05-01

    Progression of brain ageing is influenced by a complex interaction of genetic and environmental factors. Analysis of genetically modified animals with uniform genetic backgrounds in a standardised, controlled environment enables the dissection of critical determinants of brain ageing on a molecular level. Human and animal studies suggest that increased load of damaged macromolecules, efficacy of DNA maintenance, mitochondrial activity, and cellular stress defences are critical determinants of brain ageing. Surprisingly, mouse lines with genetic impairment of anti-oxidative capacity generally did not show enhanced cognitive ageing but rather an increased sensitivity to oxidative challenge. Mouse lines with impaired mitochondrial activity had critically short life spans or severe and rapidly progressing neurodegeneration. Strains with impaired clearance in damaged macromolecules or defects in the regulation of cellular stress defences showed alterations in the onset and progression of cognitive decline. Importantly, reduced insulin/insulin-like growth factor signalling generally increased life span but impaired cognitive functions revealing a complex interaction between ageing of the brain and of the body. Brain ageing is accompanied by an increased risk of developing Alzheimer's disease. Transgenic mouse models expressing high levels of mutant human amyloid precursor protein showed a number of symptoms and pathophysiological processes typical for early phase of Alzheimer's disease. Generally, therapeutic strategies effective against Alzheimer's disease in humans were also active in the Tg2576, APP23, APP/PS1 and 5xFAD lines, but a large number of false positive findings were also reported. The 3xtg AD model likely has the highest face and construct validity but further studies are needed.

  19. Transgenic animal models of neurodegeneration based on human genetic studies

    PubMed Central

    Richie, Christopher T.; Hoffer, Barry J.; Airavaara, Mikko

    2011-01-01

    The identification of genes linked to neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), Huntington's disease (HD) and Parkinson's disease (PD) has led to the development of animal models for studying mechanism and evaluating potential therapies. None of the transgenic models developed based on disease-associated genes have been able to fully recapitulate the behavioral and pathological features of the corresponding disease. However, there has been enormous progress made in identifying potential therapeutic targets and understanding some of the common mechanisms of neurodegeneration. In this review, we will discuss transgenic animal models for AD, ALS, HD and PD that are based on human genetic studies. All of the diseases discussed have active or complete clinical trials for experimental treatments that benefited from transgenic models of the disease. PMID:20931247

  20. Genetic diversity in the SIR model of pathogen evolution.

    PubMed

    Gordo, Isabel; Gomes, M Gabriela M; Reis, Daniel G; Campos, Paulo R A

    2009-01-01

    We introduce a model for assessing the levels and patterns of genetic diversity in pathogen populations, whose epidemiology follows a susceptible-infected-recovered model (SIR). We model the population of pathogens as a metapopulation composed of subpopulations (infected hosts), where pathogens replicate and mutate. Hosts transmit pathogens to uninfected hosts. We show that the level of pathogen variation is well predicted by analytical expressions, such that pathogen neutral molecular variation is bounded by the level of infection and increases with the duration of infection. We then introduce selection in the model and study the invasion probability of a new pathogenic strain whose fitness (R(0)(1+s)) is higher than the fitness of the resident strain (R(0)). We show that this invasion probability is given by the relative increment in R(0) of the new pathogen (s). By analyzing the patterns of genetic diversity in this framework, we identify the molecular signatures during the replacement and compare these with those observed in sequences of influenza A.

  1. Effective Genetic-Risk Prediction Using Mixed Models

    PubMed Central

    Golan, David; Rosset, Saharon

    2014-01-01

    For predicting genetic risk, we propose a statistical approach that is specifically adapted to dealing with the challenges imposed by disease phenotypes and case-control sampling. Our approach (termed Genetic Risk Scores Inference [GeRSI]), combines the power of fixed-effects models (which estimate and aggregate the effects of single SNPs) and random-effects models (which rely primarily on whole-genome similarities between individuals) within the framework of the widely used liability-threshold model. We demonstrate in extensive simulation that GeRSI produces predictions that are consistently superior to current state-of-the-art approaches. When applying GeRSI to seven phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) study, we confirm that the use of random effects is most beneficial for diseases that are known to be highly polygenic: hypertension (HT) and bipolar disorder (BD). For HT, there are no significant associations in the WTCCC data. The fixed-effects model yields an area under the ROC curve (AUC) of 54%, whereas GeRSI improves it to 59%. For BD, using GeRSI improves the AUC from 55% to 62%. For individuals ranked at the top 10% of BD risk predictions, using GeRSI substantially increases the BD relative risk from 1.4 to 2.5. PMID:25279982

  2. Random amplified polymorphic markers as indicator for genetic conservation program in Iranian pheasant (Phasianus colchicus).

    PubMed

    Elyasi Zarringhabaie, Ghorban; Javanmard, Arash; Pirahary, Ommolbanin

    2012-01-01

    The objective of present study was identification of genetic similarity between wild Iran and captive Azerbaijan Pheasant using PCR-RAPD markers. For this purpose, in overall, 28 birds were taken for DNA extraction and subsequently 15 arbitrary primers were applied for PCR-RAPD technique. After electrophoresis, five primers exhibited sufficient variability which yielded overall 65 distinct bands, 59 polymorphic bands, for detalis, range of number of bands per primer was 10 to 14, and produced size varied between 200 to 1500 bp. Highest and lowest polymorphic primers were OPC5, OPC16 (100%) and OPC15 (81%), respectively. Result of genetic variation between two groups was accounted as nonsignificant (8.12%) of the overall variation. According to our expectation the wild Iranian birds showed higher genetic diversity value than the Azerbaijan captive birds. As general conclusion, two pheasant populations have almost same genetic origin and probably are subpopulations of one population. The data reported herein could open the opportunity to search for suitable conservation strategy to improve richness of Iran biodiversity and present study here was the first report that might have significant impact on the breeding and conservation program of Iranian pheasant gene pool. Analyses using more regions, more birds, and more DNA markers will be useful to confirm or to reject these findings.

  3. A Program Budgeting Cost Model for School District Planning.

    ERIC Educational Resources Information Center

    Dougharty, Laurence A.; And Others

    This report provides a detailed description of an education program cost model designed to accept descriptions of the size and composition of resources used in a particular program and translate them into an estimate of program cost, for convenient comparison of alternatives. The model also translates ("crosswalks") the program budget into…

  4. Testing of a Program Evaluation Model: Final Report.

    ERIC Educational Resources Information Center

    Nagler, Phyllis J.; Marson, Arthur A.

    A program evaluation model developed by Moraine Park Technical Institute (MPTI) is described in this report. Following background material, the four main evaluation criteria employed in the model are identified as program quality, program relevance to community needs, program impact on MPTI, and the transition and growth of MPTI graduates in the…

  5. Center for Programming Models for Scalable Parallel Computing: Future Programming Models

    SciTech Connect

    Gao, Guang, R.

    2008-07-24

    The mission of the pmodel center project is to develop software technology to support scalable parallel programming models for terascale systems. The goal of the specific UD subproject is in the context developing an efficient and robust methodology and tools for HPC programming. More specifically, the focus is on developing new programming models which facilitate programmers in porting their application onto parallel high performance computing systems. During the course of the research in the past 5 years, the landscape of microprocessor chip architecture has witnessed a fundamental change – the emergence of multi-core/many-core chip architecture appear to become the mainstream technology and will have a major impact to for future generation parallel machines. The programming model for shared-address space machines is becoming critical to such multi-core architectures. Our research highlight is the in-depth study of proposed fine-grain parallelism/multithreading support on such future generation multi-core architectures. Our research has demonstrated the significant impact such fine-grain multithreading model can have on the productivity of parallel programming models and their efficient implementation.

  6. A genetic algorithm-based job scheduling model for big data analytics.

    PubMed

    Lu, Qinghua; Li, Shanshan; Zhang, Weishan; Zhang, Lei

    Big data analytics (BDA) applications are a new category of software applications that process large amounts of data using scalable parallel processing infrastructure to obtain hidden value. Hadoop is the most mature open-source big data analytics framework, which implements the MapReduce programming model to process big data with MapReduce jobs. Big data analytics jobs are often continuous and not mutually separated. The existing work mainly focuses on executing jobs in sequence, which are often inefficient and consume high energy. In this paper, we propose a genetic algorithm-based job scheduling model for big data analytics applications to improve the efficiency of big data analytics. To implement the job scheduling model, we leverage an estimation module to predict the performance of clusters when executing analytics jobs. We have evaluated the proposed job scheduling model in terms of feasibility and accuracy.

  7. Using genetic algorithm to solve a new multi-period stochastic optimization model

    NASA Astrophysics Data System (ADS)

    Zhang, Xin-Li; Zhang, Ke-Cun

    2009-09-01

    This paper presents a new asset allocation model based on the CVaR risk measure and transaction costs. Institutional investors manage their strategic asset mix over time to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. One may use a multi-period portfolio optimization model in order to determine an optimal asset mix. Recently, an alternative stochastic programming model with simulated paths was proposed by Hibiki [N. Hibiki, A hybrid simulation/tree multi-period stochastic programming model for optimal asset allocation, in: H. Takahashi, (Ed.) The Japanese Association of Financial Econometrics and Engineering, JAFFE Journal (2001) 89-119 (in Japanese); N. Hibiki A hybrid simulation/tree stochastic optimization model for dynamic asset allocation, in: B. Scherer (Ed.), Asset and Liability Management Tools: A Handbook for Best Practice, Risk Books, 2003, pp. 269-294], which was called a hybrid model. However, the transaction costs weren't considered in that paper. In this paper, we improve Hibiki's model in the following aspects: (1) The risk measure CVaR is introduced to control the wealth loss risk while maximizing the expected utility; (2) Typical market imperfections such as short sale constraints, proportional transaction costs are considered simultaneously. (3) Applying a genetic algorithm to solve the resulting model is discussed in detail. Numerical results show the suitability and feasibility of our methodology.

  8. Modeling AEC-New approaches to study rare genetic disorders.

    PubMed

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

    2014-10-01

    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.

  9. In vivo Drosophilia genetic model for calcium oxalate nephrolithiasis

    PubMed Central

    Hirata, Taku; Cabrero, Pablo; Berkholz, Donald S.; Bondeson, Daniel P.; Ritman, Erik L.; Thompson, James R.; Dow, Julian A. T.

    2012-01-01

    Nephrolithiasis is a major public health problem with a complex and varied etiology. Most stones are composed of calcium oxalate (CaOx), with dietary excess a risk factor. Because of complexity of mammalian system, the details of stone formation remain to be understood. Here we have developed a nephrolithiasis model using the genetic model Drosophila melanogaster, which has a simple, transparent kidney tubule. Drosophilia reliably develops CaOx stones upon dietary oxalate supplementation, and the nucleation and growth of microliths can be viewed in real time. The Slc26 anion transporter dPrestin (Slc26a5/6) is strongly expressed in Drosophilia kidney, and biophysical analysis shows that it is a potent oxalate transporter. When dPrestin is knocked down by RNAi in fly kidney, formation of microliths is reduced, identifying dPrestin as a key player in oxalate excretion. CaOx stone formation is an ancient conserved process across >400 My of divergent evolution (fly and human), and from this study we can conclude that the fly is a good genetic model of nephrolithiasis. PMID:22993075

  10. In vivo Drosophilia genetic model for calcium oxalate nephrolithiasis.

    PubMed

    Hirata, Taku; Cabrero, Pablo; Berkholz, Donald S; Bondeson, Daniel P; Ritman, Erik L; Thompson, James R; Dow, Julian A T; Romero, Michael F

    2012-12-01

    Nephrolithiasis is a major public health problem with a complex and varied etiology. Most stones are composed of calcium oxalate (CaOx), with dietary excess a risk factor. Because of complexity of mammalian system, the details of stone formation remain to be understood. Here we have developed a nephrolithiasis model using the genetic model Drosophila melanogaster, which has a simple, transparent kidney tubule. Drosophilia reliably develops CaOx stones upon dietary oxalate supplementation, and the nucleation and growth of microliths can be viewed in real time. The Slc26 anion transporter dPrestin (Slc26a5/6) is strongly expressed in Drosophilia kidney, and biophysical analysis shows that it is a potent oxalate transporter. When dPrestin is knocked down by RNAi in fly kidney, formation of microliths is reduced, identifying dPrestin as a key player in oxalate excretion. CaOx stone formation is an ancient conserved process across >400 My of divergent evolution (fly and human), and from this study we can conclude that the fly is a good genetic model of nephrolithiasis.

  11. Annual fish as a genetic model for aging.

    PubMed

    Herrera, Michael; Jagadeeswaran, Pudur

    2004-02-01

    Advancement in the genetics of aging and identification of longevity genes has been largely due to the model organisms such as Caenorhabditis elegans and Drosophila melanogaster. However, knowledge gained from these invertebrates will not be able to identify vertebrate-specific longevity genes. The mouse has a relatively long life span of about 3 years, which limits its utility for screening of longevity genes. Fish have been used in aging studies. However, systematic comparison of survivorship curves for fish is lacking. In this study, we compared the survivorship curves of zebrafish and 2 different annual fish, namely, Cynolebias nigripinnis and Nothobranchius rachovii. These studies established that Nothobranchius rachovii has the shortest life span (8.5 months, at which time 10% of population remains). We also established that it is possible to breed Nothobranchius rachovii under laboratory conditions, and showed that their embryos can be stored for several months and hatched at any time by adding water. In addition, we have isolated 31 cDNA markers out of 71 attempted amplifications based on corresponding homologous genomic sequences in zebrafish and Fugu available from public databases, suggesting that approximately 40% of the genes from Nothobranchius rachovii could be easily isolated. Thus, the ability to be bred under laboratory conditions and the availability of cDNA markers for mapping, along with the major advantage of a relatively short life span, make Nothobranchius rachovii an attractive vertebrate genetic model for aging over other available vertebrate models.

  12. Mining functional modules in genetic networks with decomposable graphical models.

    PubMed

    Dejori, Mathäus; Schwaighofer, Anton; Tresp, Volker; Stetter, Martin

    2004-01-01

    In recent years, graphical models have become an increasingly important tool for the structural analysis of genome-wide expression profiles at the systems level. Here we present a new graphical modelling technique, which is based on decomposable graphical models, and apply it to a set of gene expression profiles from acute lymphoblastic leukemia (ALL). The new method explains probabilistic dependencies of expression levels in terms of the concerted action of underlying genetic functional modules, which are represented as so-called "cliques" in the graph. In addition, the method uses continuous-valued (instead of discretized) expression levels, and makes no particular assumption about their probability distribution. We show that the method successfully groups members of known functional modules to cliques. Our method allows the evaluation of the importance of genes for global cellular functions based on both link count and the clique membership count.

  13. Modelling of Genetically Engineered Microorganisms Introduction in Closed Artificial Microcosms

    NASA Astrophysics Data System (ADS)

    Pechurkin, N. S.; Brilkov, A. V.; Ganusov, V. V.; Kargatova, T. V.; Maksimova, E. E.; Popova, L. Yu.

    1999-01-01

    The possibility of introducing genetically engineered microorganisms (GEM) into simple biotic cycles of laboratory water microcosms was investigated. The survival of the recombinant strain Escherichia coli Z905 (Apr, Lux+) in microcosms depends on the type of model ecosystems. During the absence of algae blooming in the model ecosystem, the part of plasmid-containing cells E. coli decreased fast, and the structure of the plasmid was also modified. In conditions of algae blooming (Ankistrodesmus sp.) an almost total maintenance of plasmid-containing cells was observed in E.coli population. A mathematics model of GEM's behavior in water ecosystems with different level of complexity has been formulated. Mechanisms causing the difference in luminescent exhibition of different species are discussed, and attempts are made to forecast the GEM's behavior in water ecosystems.

  14. Landscape models for nuclear genetic diversity and genetic structure in white-footed mice (Peromyscus leucopus)

    PubMed Central

    Taylor, Z S; Hoffman, S M G

    2014-01-01

    Dramatic changes in the North American landscape over the last 12 000 years have shaped the genomes of the small mammals, such as the white-footed mouse (Peromyscus leucopus), which currently inhabit the region. However, very recent interactions of populations with each other and the environment are expected to leave the most pronounced signature on rapidly evolving nuclear microsatellite loci. We analyzed landscape characteristics and microsatellite markers of P. leucopus populations along a transect from southern Ohio to northern Michigan, in order to evaluate hypotheses about the spatial distribution of genetic heterogeneity. Genetic diversity increased to the north and was best approximated by a single-variable model based on habitat availability within a 0.5-km radius of trapping sites. Interpopulation differentiation measured by clustering analysis was highly variable and not significantly related to latitude or habitat availability. Interpopulation differentiation measured as FST values and chord distance was correlated with the proportion of habitat intervening, but was best explained by agricultural distance and by latitude. The observed gradients in diversity and interpopulation differentiation were consistent with recent habitat availability being the major constraint on effective population size in this system, and contradicted the predictions of both the postglacial expansion and core-periphery hypotheses. PMID:24448564

  15. The ontology of genetic susceptibility factors (OGSF) and its application in modeling genetic susceptibility to vaccine adverse events

    PubMed Central

    2014-01-01

    Background Due to human variations in genetic susceptibility, vaccination often triggers adverse events in a small population of vaccinees. Based on our previous work on ontological modeling of genetic susceptibility to disease, we developed an Ontology of Genetic Susceptibility Factors (OGSF), a biomedical ontology in the domain of genetic susceptibility and genetic susceptibility factors. The OGSF framework was then applied in the area of vaccine adverse events (VAEs). Results OGSF aligns with the Basic Formal Ontology (BFO). OGSF defines ‘genetic susceptibility’ as a subclass of BFO:disposition and has a material basis ‘genetic susceptibility factor’. The ‘genetic susceptibility to pathological bodily process’ is a subclasses of ‘genetic susceptibility’. A VAE is a type of pathological bodily process. OGSF represents different types of genetic susceptibility factors including various susceptibility alleles (e.g., SNP and gene). A general OGSF design pattern was developed to represent genetic susceptibility to VAE and associated genetic susceptibility factors using experimental results in genetic association studies. To test and validate the design pattern, two case studies were populated in OGSF. In the first case study, human gene allele DBR*15:01 is susceptible to influenza vaccine Pandemrix-induced Multiple Sclerosis. The second case study reports genetic susceptibility polymorphisms associated with systemic smallpox VAEs. After the data of the Case Study 2 were represented using OGSF-based axioms, SPARQL was successfully developed to retrieve the susceptibility factors stored in the populated OGSF. A network of data from the Case Study 2 was constructed by using ontology terms and individuals as nodes and ontology relations as edges. Different social network analys is (SNA) methods were then applied to verify core OGSF terms. Interestingly, a SNA hub analysis verified all susceptibility alleles of SNPs and a SNA closeness analysis verified

  16. A Linear Programming Model to Optimize Various Objective Functions of a Foundation Type State Support Program.

    ERIC Educational Resources Information Center

    Matzke, Orville R.

    The purpose of this study was to formulate a linear programming model to simulate a foundation type support program and to apply this model to a state support program for the public elementary and secondary school districts in the State of Iowa. The model was successful in producing optimal solutions to five objective functions proposed for…

  17. Program listing for the REEDM (Rocket Exhaust Effluent Diffusion Model) computer program

    NASA Technical Reports Server (NTRS)

    Bjorklund, J. R.; Dumbauld, R. K.; Cheney, C. S.; Geary, H. V.

    1982-01-01

    The program listing for the REEDM Computer Program is provided. A mathematical description of the atmospheric dispersion models, cloud-rise models, and other formulas used in the REEDM model; vehicle and source parameters, other pertinent physical properties of the rocket exhaust cloud and meteorological layering techniques; user's instructions for the REEDM computer program; and worked example problems are contained in NASA CR-3646.

  18. Model Programs: Reading. Remedial Reading Program, Pojoaque, New Mexico.

    ERIC Educational Resources Information Center

    American Institutes for Research in the Behavioral Sciences, Palo Alto, CA.

    The elementary school in Pojoaque, New Mexico, has recently developed a remedial reading program for children in grades 2 to 4. Eighty-three children participated in 1969-70. As the population of the area is 76 percent Spanish-American, 12 percent Indian, 12 percent white, and less than 1 percent black, work in the program focuses on language and…

  19. Model Programs--Childhood Education; Foster Grandparent Program, Nashville, Tennessee.

    ERIC Educational Resources Information Center

    American Institutes for Research in the Behavioral Sciences, Silver Spring, MD.

    The Foster Grandparent Program was started in Nashville, Tennessee, as a demonstration program under the Office of Economic Opportunity; it was designed to help senior citizens support themselves by acting as grandparents to children who do not have their own. At Clover Bottom Hospital and School for the Retarded Child, 13 foster grandmothers work…

  20. Developing a Professional Development Program Model Based on Teachers' Needs

    ERIC Educational Resources Information Center

    Lee, Hea-Jin

    2005-01-01

    This paper presents a model of a teacher needs-based (TNB) professional development program. The TNB model formed the foundation of three externally funded professional development programs. The objectives of this model are to maximize the effects of a professional development program, and to help participants sustain their learning over the long…

  1. High-Significance Averages of Event-Related Potential Via Genetic Programming

    NASA Astrophysics Data System (ADS)

    Citi, Luca; Poli, Riccardo; Cinel, Caterina

    In this paper we use register-based genetic programming with memory-with memory to discover probabilistic membership functions that are used to divide up data-sets of event-related potentials recorded via EEG in psycho-physiological experiments based on the corresponding response times. The objective is to evolve membership functions which lead to maximising the statistical significance with which true brain waves can be reconstructed when averaging the trials in each bin. Results show that GP can significantly improve the fidelity with which ERP components can be recovered.

  2. Buying and Selling Stocks of Multi Brands Using Genetic Network Programming with Control Nodes

    NASA Astrophysics Data System (ADS)

    Ohkawa, Etsushi; Chen, Yan; Bao, Zhiguo; Mabu, Shingo; Shimada, Kaoru; Hirasawa, Kotaro

    A new evolutionary method named “Genetic Network Programming with control nodes, GNPcn” has been applied to determine the timing of buying or selling stocks. GNPcn represents its solutions as directed graph structures which has some useful features inherently. For example, GNPcn has an implicit memory function which memorizes the past action sequences of agents and GNPcn can re-use nodes repeatedly in the network flow, so very compact graph structures can be made. GNPcn can determine the strategy of buying and selling stocks of multi issues. The effectiveness of the proposed method is confirmed by simulations.

  3. Genetic Diseases and Genetic Determinism Models in French Secondary School Biology Textbooks

    ERIC Educational Resources Information Center

    Castera, Jeremy; Bruguiere, Catherine; Clement, Pierre

    2008-01-01

    The presentation of genetic diseases in French secondary school biology textbooks is analysed to determine the major conceptions taught in the field of human genetics. References to genetic diseases, and the processes by which they are explained (monogeny, polygeny, chromosomal anomaly and environmental influence) are studied in recent French…

  4. Calibration of Uncertainty Analysis of the SWAT Model Using Genetic Algorithms and Bayesian Model Averaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In this paper, the Genetic Algorithms (GA) and Bayesian model averaging (BMA) were combined to simultaneously conduct calibration and uncertainty analysis for the Soil and Water Assessment Tool (SWAT). In this hybrid method, several SWAT models with different structures are first selected; next GA i...

  5. Genetic-based EM algorithm for learning Gaussian mixture models.

    PubMed

    Pernkopf, Franz; Bouchaffra, Djamel

    2005-08-01

    We propose a genetic-based expectation-maximization (GA-EM) algorithm for learning Gaussian mixture models from multivariate data. This algorithm is capable of selecting the number of components of the model using the minimum description length (MDL) criterion. Our approach benefits from the properties of Genetic algorithms (GA) and the EM algorithm by combination of both into a single procedure. The population-based stochastic search of the GA explores the search space more thoroughly than the EM method. Therefore, our algorithm enables escaping from local optimal solutions since the algorithm becomes less sensitive to its initialization. The GA-EM algorithm is elitist which maintains the monotonic convergence property of the EM algorithm. The experiments on simulated and real data show that the GA-EM outperforms the EM method since: 1) We have obtained a better MDL score while using exactly the same termination condition for both algorithms. 2) Our approach identifies the number of components which were used to generate the underlying data more often than the EM algorithm.

  6. Improved sea level anomaly prediction through combination of data relationship analysis and genetic programming in Singapore Regional Waters

    NASA Astrophysics Data System (ADS)

    Kurniawan, Alamsyah; Ooi, Seng Keat; Babovic, Vladan

    2014-11-01

    With recent advances in measurement and information technology, there is an abundance of data available for analysis and modelling of hydrodynamic systems. Spatial and temporal data coverage, better quality and reliability of data modelling and data driven techniques have resulted in more favourable acceptance by the hydrodynamic community. The data mining tools and techniques are being applied in variety of hydro-informatics applications ranging from data mining for pattern discovery to data driven models and numerical model error correction. The present study explores the feasibility of applying mutual information theory by evaluating the amount of information contained in observed and prediction errors of non-tidal barotropic numerical modelling (i.e. assuming that the hydrodynamic model, available at this point, is best representation of the physics in the domain of interest) by relating them to variables that reflect the state at which the predictions are made such as input data, state variables and model output. In addition, the present study explores the possibility of employing ‘genetic programming' (GP) as an offline data driven modelling tool to capture the sea level anomaly (SLA) dynamics and then using them for updating the numerical model prediction in real time applications. These results suggest that combination of data relationship analysis and GP models helps to improve the forecasting ability by providing information of significant predicative parameters. It is found that GP based SLA prediction error forecast model can provide significant improvement when applied as data assimilation schemes for updating the SLA prediction obtained from primary hydrodynamic models.

  7. Tackling intraspecific genetic structure in distribution models better reflects species geographical range.

    PubMed

    Marcer, Arnald; Méndez-Vigo, Belén; Alonso-Blanco, Carlos; Picó, F Xavier

    2016-04-01

    Genetic diversity provides insight into heterogeneous demographic and adaptive history across organisms' distribution ranges. For this reason, decomposing single species into genetic units may represent a powerful tool to better understand biogeographical patterns as well as improve predictions of the effects of GCC (global climate change) on biodiversity loss. Using 279 georeferenced Iberian accessions, we used classes of three intraspecific genetic units of the annual plant Arabidopsis thaliana obtained from the genetic analyses of nuclear SNPs (single nucleotide polymorphisms), chloroplast SNPs, and the vernalization requirement for flowering. We used SDM (species distribution models), including climate, vegetation, and soil data, at the whole-species and genetic-unit levels. We compared model outputs for present environmental conditions and with a particularly severe GCC scenario. SDM accuracy was high for genetic units with smaller distribution ranges. Kernel density plots identified the environmental variables underpinning potential distribution ranges of genetic units. Combinations of environmental variables accounted for potential distribution ranges of genetic units, which shrank dramatically with GCC at almost all levels. Only two genetic clusters increased their potential distribution ranges with GCC. The application of SDM to intraspecific genetic units provides a detailed picture on the biogeographical patterns of distinct genetic groups based on different genetic criteria. Our approach also allowed us to pinpoint the genetic changes, in terms of genetic background and physiological requirements for flowering, that Iberian A. thaliana may experience with a GCC scenario applying SDM to intraspecific genetic units.

  8. An Intelligent Model for Pairs Trading Using Genetic Algorithms.

    PubMed

    Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An

    2015-01-01

    Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.

  9. Controversies about the genetic model of colorectal tumorigenesis.

    PubMed

    Waliszewski, P

    1995-01-01

    According to the genetic model, intestinal tumorigenesis is a result of the ordered in time inactivation of tumor suppressor genes and the activation of oncogenes. A tacit assumption is that both genes involved in the regulation of proliferation and growth factor-inducible genes, although inactivated, would not be changed during that process. The model requires that cancer cell population is homogenous, exists in a deterministic environment, and develops in a teleological manner. Meanwhile, tumorigenesis is rather a combination of both deterministic and stochastic molecular phenomena. Therefore, a novel notion of bifurcating point genes is defined as a generalization of the idea of tumor suppressor genes and oncogenes. Alternative stochastic mechanisms of tumorigenesis are discussed such as a decreased expression of intestinal-specific genes in cancer cells, most likely reflecting adaptation to survival within a heterogeneous, and non-equilibrated cellular population.

  10. An Intelligent Model for Pairs Trading Using Genetic Algorithms

    PubMed Central

    Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An

    2015-01-01

    Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice. PMID:26339236

  11. Multilevel modeling for inference of genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Ng, Shu-Kay; Wang, Kui; McLachlan, Geoffrey J.

    2005-12-01

    Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.

  12. Exploring Middle School Students' Understanding of Three Conceptual Models in Genetics

    NASA Astrophysics Data System (ADS)

    Bresler Freidenreich, Hava; Golan Duncan, Ravit; Shea, Nicole

    2011-11-01

    Genetics is the cornerstone of modern biology and a critical aspect of scientific literacy. Research has shown, however, that many high school graduates lack fundamental understandings in genetics necessary to make informed decisions about issues and emerging technologies in this domain, such as genetic screening, genetically modified foods, etc. Genetic literacy entails understanding three interrelated models: a genetic model that describes patterns of genetic inheritance, a meiotic model that describes the process by which genes are segregated into sex cells, and a molecular model that describes the mechanisms that link genotypes to phenotypes within an individual. Currently, much of genetics instruction, especially in terms of the molecular model, occurs at the high school level, and we know little about the ways in which middle school students can reason about these models. Furthermore, we do not know the extent to which carefully designed instruction can help younger students develop coherent and interrelated understandings in genetics. In this paper, we discuss a research study aimed at elucidating middle school students' abilities to reason about the three genetic models. As part of our research, we designed an eight-week inquiry unit that was implemented in a combined sixth- to eighth-grade science classroom. We describe our instructional design and report results based on an analysis of written assessments, clinical interviews, and artifacts of the unit. Our findings suggest that middle school students are able to successfully reason about all three genetic models.

  13. Coregulation of genetic programs by the transcription factors NFIB and STAT5.

    PubMed

    Robinson, Gertraud W; Kang, Keunsoo; Yoo, Kyung Hyun; Tang, Yong; Zhu, Bing-Mei; Yamaji, Daisuke; Colditz, Vera; Jang, Seung Jian; Gronostajski, Richard M; Hennighausen, Lothar

    2014-05-01

    Mammary-specific genetic programs are activated during pregnancy by the common transcription factor signal transducer and activator of transcription (STAT) 5. More than one third of these genes carry nuclear factor I/B (NFIB) binding motifs that coincide with STAT5 in vivo binding, suggesting functional synergy between these two transcription factors. The role of NFIB in this governance was investigated in mice from which Nfib had been inactivated in mammary stem cells or in differentiating alveolar epithelium. Although NFIB was not required for alveolar expansion, the combined absence of NFIB and STAT5 prevented the formation of functional alveoli. NFIB controlled the expression of mammary-specific and STAT5-regulated genes and chromatin immunoprecipitation-sequencing established STAT5 and NFIB binding at composite regulatory elements containing histone H3 lysine dimethylation enhancer marks and progesterone receptor binding. By integrating previously published chromatin immunoprecipitation-sequencing data sets, the presence of NFIB-STAT5 modules in other cell types was investigated. Notably, genomic sites bound by NFIB in hair follicle stem cells were also occupied by STAT5 in mammary epithelium and coincided with enhancer marks. Many of these genes were under NFIB control in both hair follicle stem cells and mammary alveolar epithelium. We propose that NFIB-STAT5 modules, possibly in conjunction with other transcription factors, control cell-specific genetic programs.

  14. Coregulation of Genetic Programs by the Transcription Factors NFIB and STAT5

    PubMed Central

    Robinson, Gertraud W.; Kang, Keunsoo; Yoo, Kyung Hyun; Tang, Yong; Zhu, Bing-Mei; Yamaji, Daisuke; Colditz, Vera; Jang, Seung Jian; Gronostajski, Richard M.

    2014-01-01

    Mammary-specific genetic programs are activated during pregnancy by the common transcription factor signal transducer and activator of transcription (STAT) 5. More than one third of these genes carry nuclear factor I/B (NFIB) binding motifs that coincide with STAT5 in vivo binding, suggesting functional synergy between these two transcription factors. The role of NFIB in this governance was investigated in mice from which Nfib had been inactivated in mammary stem cells or in differentiating alveolar epithelium. Although NFIB was not required for alveolar expansion, the combined absence of NFIB and STAT5 prevented the formation of functional alveoli. NFIB controlled the expression of mammary-specific and STAT5-regulated genes and chromatin immunoprecipitation-sequencing established STAT5 and NFIB binding at composite regulatory elements containing histone H3 lysine dimethylation enhancer marks and progesterone receptor binding. By integrating previously published chromatin immunoprecipitation-sequencing data sets, the presence of NFIB-STAT5 modules in other cell types was investigated. Notably, genomic sites bound by NFIB in hair follicle stem cells were also occupied by STAT5 in mammary epithelium and coincided with enhancer marks. Many of these genes were under NFIB control in both hair follicle stem cells and mammary alveolar epithelium. We propose that NFIB-STAT5 modules, possibly in conjunction with other transcription factors, control cell-specific genetic programs. PMID:24678731

  15. Exploration of Genetic Programming Optimal Parameters for Feature Extraction from Remote Sensed Imagery

    NASA Astrophysics Data System (ADS)

    Gao, P.; Shetty, S.; Momm, H. G.

    2014-11-01

    Evolutionary computation is used for improved information extraction from high-resolution satellite imagery. The utilization of evolutionary computation is based on stochastic selection of input parameters often defined in a trial-and-error approach. However, exploration of optimal input parameters can yield improved candidate solutions while requiring reduced computation resources. In this study, the design and implementation of a system that investigates the optimal input parameters was researched in the problem of feature extraction from remotely sensed imagery. The two primary assessment criteria were the highest fitness value and the overall computational time. The parameters explored include the population size and the percentage and order of mutation and crossover. The proposed system has two major subsystems; (i) data preparation: the generation of random candidate solutions; and (ii) data processing: evolutionary process based on genetic programming, which is used to spectrally distinguish the features of interest from the remaining image background of remote sensed imagery. The results demonstrate that the optimal generation number is around 1500, the optimal percentage of mutation and crossover ranges from 35% to 40% and 5% to 0%, respectively. Based on our findings the sequence that yielded better results was mutation over crossover. These findings are conducive to improving the efficacy of utilizing genetic programming for feature extraction from remotely sensed imagery.

  16. An integrated genetic-demographic model to unravel the origin of genetic structure in European eel (Anguilla anguilla L.)

    PubMed Central

    Andrello, Marco; Bevacqua, Daniele; Maes, Gregory E; De Leo, Giulio A

    2011-01-01

    The evolutionary enlightened management of species with complex life cycles often requires the development of mathematical models integrating demographic and genetic data. The genetic structure of the endangered European eel (Anguilla anguilla L.) has been thoroughly analyzed in several studies in the past years. However, the interpretation of the key demographic and biologic processes that determine the observed spatio-temporal genetic structure has been very challenging owing to the complex life cycle of this catadromous species. Here, we present the first integrated demographic-genetic model applied to the European eel that explicitly accounts for different levels of larval and adult mixing during oceanic migrations and allows us to explore alternative hypotheses on genetic differentiation. Our analyses show that (i) very low levels of mixing occurring during larval dispersal or adult migration are sufficient to erase entirely any genetic differences among sub-populations; (ii) small-scale temporal differentiation in recruitment can arise if the spawning stock is subdivided in distinct reproductive groups; and (iii) the geographic differentiation component might be overestimated if a limited number of temporal recruits are analyzed. Our study can inspire the scientific debate on the interpretation of genetic structure in other species characterized by complex life cycle and long-range migrations. PMID:25568002

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

    NASA Astrophysics Data System (ADS)

    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.

  18. Applying Genetic Programming with Substructure Discovery to a Traffic Signal Control Problem

    NASA Astrophysics Data System (ADS)

    Kumagai, Juncichi; Ojima, Yasuo; Takashige, Souichi; Kameya, Yoshitaka; Sato, Taisuke

    Nowadays the increase of traffic causes numerous serious traffic jams, and traffic signals are desired to work adaptively for dynamic traffic flows. In this paper, we view such a problem of traffic signal control as a multi-agent problem where each signal has a controlling agent, and aim to make the agents work cooperatively depending on the traffic status. To build such an agent program automatically, we introduce genetic programming (GP), an evolutionary method for program construction. In GP, it is known as important to encapsulate the substructures of a program which leads to higher fitness to the environment, and we propose a new encapsulation method using an efficient technique for discovering frequent substructures, which has been recently proposed in the data mining field. We also conducted a simulation with a real traffic data, and confirmed that GP with our encapsulation method outperforms the normal GP. It is also observed that the best individual has a communication part that chooses an appropriate communication area and adapts to the traffic status.

  19. Effect of Keishibukuryogan on Genetic and Dietary Obesity Models

    PubMed Central

    Gao, Fengying; Fujimoto, Makoto; Saiki, Ikuo; Hayakawa, Yoshihiro

    2015-01-01

    Obesity has been recognized as one of the most important risk factors for a variety of chronic diseases, such as diabetes, hypertension/cardiovascular diseases, steatosis/hepatitis, and cancer. Keishibukuryogan (KBG, Gui Zhi Fu Ling Wan in Chinese) is a traditional Chinese/Japanese (Kampo) medicine that has been known to improve blood circulation and is also known for its anti-inflammatory or scavenging effect. In this study, we evaluated the effect of KBG in two distinct rodent models of obesity driven by either a genetic (SHR/NDmcr-cp rat model) or dietary (high-fat diet-induced mouse obesity model) mechanism. Although there was no significant effect on the body composition in either the SHR rat or the DIO mouse models, KBG treatment significantly decreased the serum level of leptin and liver TG level in the DIO mouse, but not in the SHR rat model. Furthermore, a lower fat deposition in liver and a smaller size of adipocytes in white adipose tissue were observed in the DIO mice treated with KBG. Importantly, we further found downregulation of genes involved in lipid metabolism in the KBG-treated liver, along with decreased liver TG and cholesterol level. Our present data experimentally support in fact that KBG can be an attractive Kampo medicine to improve obese status through a regulation of systemic leptin level and/or lipid metabolism. PMID:25793003

  20. An enhanced nonparametric streamflow disaggregation model with genetic algorithm

    NASA Astrophysics Data System (ADS)

    Lee, T.; Salas, J. D.; Prairie, J.

    2010-08-01

    Stochastic streamflow generation is generally utilized for planning and management of water resources systems. For this purpose, a number of parametric and nonparametric models have been suggested in literature. Among them, temporal and spatial disaggregation approaches play an important role particularly to make sure that historical variance-covariance properties are preserved at various temporal and spatial scales. In this paper, we review the underlying features of existing nonparametric disaggregation methods, identify some of their pros and cons, and propose a disaggregation algorithm that is capable of surmounting some of the shortcomings of the current models. The proposed models hinge on k-nearest neighbor resampling, the accurate adjusting procedure, and a genetic algorithm. The models have been tested and compared to an existing nonparametric disaggregation approach using data of the Colorado River system. It has been shown that the model is capable of (1) reproducing the season-to-season correlations including the correlation between the last season of the previous year and the first season of the current year, (2) minimizing or avoiding the generation of flow patterns across the year that are literally the same as those of the historical records, and (3) minimizing or avoiding the generation of negative flows. In addition, it is applicable to intermittent river regimes.

  1. Modeling Student Participation in School Nutrition Programs.

    ERIC Educational Resources Information Center

    Barnes, Roberta Ott

    This report describes the analyses of student participation in two school nutrition programs, the School Breakfast Program (SBP) and the National School Lunch Program (NSLP). Data were collected from students and their families during the 1983-84 school year as part of the National Evaluation of the School Nutrition Programs (NESNP). Each program…

  2. Accessible Genetics Research Ethics Education (AGREE): A Web-Based Program for IRBs and Investigators

    SciTech Connect

    Sugarman, Jeremy; Lee, Linda

    2006-03-31

    The primary objective of this project was to design and evaluate a series of web-based educational modules on genetics research ethics for members of Institutional Review Boards and investigators to facilitate the development and oversight of important research that is sensitive to the relevant ethical, legal and social issues. After a needs assessment was completed in March of 2003, five online educational modules on the ethics of research in genetics were developed, tested, and made available through a host website for AGREE: http://agree.mc.duke.edu/index.html. The 5 modules are: (1) Ethics and Genetics Research in Populations; (2) Ethics in Behavioral Genetics Research; (3) Ethical Issues in Research on Gene-Environment Interactions; (4) Ethical Issues in Reproductive Genetics Research; and (5) Ethical Issues in Diagnostic and Therapeutic Research. The development process adopted a tested approach used at Duke University School of Medicine in providing education for researchers and IRB members, supplementing it with expert input and a rigorous evaluation. The host website also included a description of the AGREE; short bios on the AGREE Investigators and Expert Advisory Panel; streaming media of selected presentations from a conference, Working at the Frontiers of Law and Science: Applications of the Human Genome held October 2-3, 2003, at the University of North Carolina at Chapel Hill; and links to online resources in genomics, research ethics, ethics in genomics research, and related organizations. The web site was active beginning with the posting of the first module and was maintained throughout the project period. We have also secured agreement to keep the site active an additional year beyond the project period. AGREE met its primary objective of creating web-based educational modules related to the ethical issues in genetics research. The modules have been disseminated widely. While it is clearly easier to judge the quality of the educational experience

  3. The Use of Molecular Modeling Programs in Medicinal Chemistry Instruction.

    ERIC Educational Resources Information Center

    Harrold, Marc W.

    1992-01-01

    This paper describes and evaluates the use of a molecular modeling computer program (Alchemy II) in a pharmaceutical education program. Provided are the hardware requirements and basic program features as well as several examples of how this program and its features have been applied in the classroom. (GLR)

  4. Genetics

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  5. Genetics

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  6. DNA Commission of the International Society for Forensic Genetics: Recommendations on the validation of software programs performing biostatistical calculations for forensic genetics applications.

    PubMed

    Coble, M D; Buckleton, J; Butler, J M; Egeland, T; Fimmers, R; Gill, P; Gusmão, L; Guttman, B; Krawczak, M; Morling, N; Parson, W; Pinto, N; Schneider, P M; Sherry, S T; Willuweit, S; Prinz, M

    2016-11-01

    The use of biostatistical software programs to assist in data interpretation and calculate likelihood ratios is essential to forensic geneticists and part of the daily case work flow for both kinship and DNA identification laboratories. Previous recommendations issued by the DNA Commission of the International Society for Forensic Genetics (ISFG) covered the application of bio-statistical evaluations for STR typing results in identification and kinship cases, and this is now being expanded to provide best practices regarding validation and verification of the software required for these calculations. With larger multiplexes, more complex mixtures, and increasing requests for extended family testing, laboratories are relying more than ever on specific software solutions and sufficient validation, training and extensive documentation are of upmost importance. Here, we present recommendations for the minimum requirements to validate bio-statistical software to be used in forensic genetics. We distinguish between developmental validation and the responsibilities of the software developer or provider, and the internal validation studies to be performed by the end user. Recommendations for the software provider address, for example, the documentation of the underlying models used by the software, validation data expectations, version control, implementation and training support, as well as continuity and user notifications. For the internal validations the recommendations include: creating a validation plan, requirements for the range of samples to be tested, Standard Operating Procedure development, and internal laboratory training and education. To ensure that all laboratories have access to a wide range of samples for validation and training purposes the ISFG DNA commission encourages collaborative studies and public repositories of STR typing results.

  7. Human factors engineering program review model

    SciTech Connect

    Not Available

    1994-07-01

    The staff of the Nuclear Regulatory Commission is performing nuclear power plant design certification reviews based on a design process plan that describes the human factors engineering (HFE) program elements that are necessary and sufficient to develop an acceptable detailed design specification and an acceptable implemented design. There are two principal reasons for this approach. First, the initial design certification applications submitted for staff review did not include detailed design information. Second, since human performance literature and industry experiences have shown that many significant human factors issues arise early in the design process, review of the design process activities and results is important to the evaluation of an overall design. However, current regulations and guidance documents do not address the criteria for design process review. Therefore, the HFE Program Review Model (HFE PRM) was developed as a basis for performing design certification reviews that include design process evaluations as well as review of the final design. A central tenet of the HFE PRM is that the HFE aspects of the plant should be developed, designed, and evaluated on the basis of a structured top-down system analysis using accepted HFE principles. The HFE PRM consists of ten component elements. Each element in divided into four sections: Background, Objective, Applicant Submittals, and Review Criteria. This report describes the development of the HFE PRM and gives a detailed description of each HFE review element.

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

    PubMed

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

    2015-01-01

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

  9. [Genetically engineered mice: mouse models for cancer research].

    PubMed

    Szymańska, Hanna

    2007-10-26

    Genetically engineered mice (GEM) have been extensively used to model human cancer. Mouse models mimic the morphology, histopathology, phenotype, and genotype of the corresponding cancer in humans. GEM mice are created by random integration of a transgene into the genome, which results in gene overexpression (transgenic mice); gene deletion (knock-out mice); or targeted insertion of the transgene in a selected locus (knock-in mice). Knock-out may be constitutive, i.e. total inactivation of the gene of interest in any cell, or conditional, i.e. tissue-specific inactivation of the gene. Gene knock-down (RNAi) and humanization of the mouse are more sophisticated models of GEM mice. RNA interference (RNAi) is a mechanism in which double-stranded RNAs inhibits the respective gene expression by inducing degradation of its mRNA. Humanization is based on replacing a mouse gene by its human counterpart. The alterations in genes in GEM have to be heritable. The opportunities provided by employing GEM cancer models are: analysis of the role of specific cancer genes and modifier genes, evaluation of conventional cancer therapies and new drugs, identification of cancer markers of tumor growth, analysis of the influence of the tumor's microenvironment on tumor formation, and the definition of the pre-clinical, discrete steps of tumorigenesis. The validation of mouse models of human cancer is the task of the MMHCC (Mouse Models of Human Cancer Consortium). The GEM models of breast, pancreatic, intestinal and colon, and prostate cancer are the most actively explored. In contrast, the models of brain tumors and ovary, cervical, and skin cancer are in the early stage of investigation.

  10. A new perspective on dark energy modeling via genetic algorithms

    NASA Astrophysics Data System (ADS)

    Nesseris, Savvas; García-Bellido, Juan

    2012-11-01

    We use Genetic Algorithms to extract information from several cosmological probes, such as the type Ia supernovae (SnIa), the Baryon Acoustic Oscillations (BAO) and the growth rate of matter perturbations. This is done by implementing a model independent and bias-free reconstruction of the various scales and distances that characterize the data, like the luminosity dL(z) and the angular diameter distance dA(z) in the SnIa and BAO data, respectively, or the dependence with redshift of the matter density Ωm(a) in the growth rate data, fσ8(z). These quantities can then be used to reconstruct the expansion history of the Universe, and the resulting Dark Energy (DE) equation of state w(z) in the context of FRW models, or the mass radial function ΩM(r) in LTB models. In this way, the reconstruction is completely independent of our prior bias. Furthermore, we use this method to test the Etherington relation, ie the well-known relation between the luminosity and the angular diameter distance, η≡dL(z)/(1+z)2dA(z), which is equal to 1 in metric theories of gravity. We find that the present data seem to suggest a 3-σ deviation from one at redshifts z ~ 0.5. Finally, we present a novel way, within the Genetic Algorithm paradigm, to analytically estimate the errors on the reconstructed quantities by calculating a Path Integral over all possible functions that may contribute to the likelihood. We show that this can be done regardless of the data being correlated or uncorrelated with each other and we also explicitly demonstrate that our approach is in good agreement with other error estimation techniques like the Fisher Matrix approach and the Bootstrap Monte Carlo.

  11. 75 FR 29555 - Medicare Program; Medicare Coverage Gap Discount Program Model Manufacturer Agreement and...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-05-26

    ... Services [CMS-4151-NC] RIN 0938-AQ04 Medicare Program; Medicare Coverage Gap Discount Program Model... contains a draft model agreement for use by the Secretary and manufacturers under the Medicare Coverage Gap Discount Program established by section 3301 of the Patient Protection and Affordable Care Act, as...

  12. Linear programming models for cost reimbursement.

    PubMed Central

    Diehr, G; Tamura, H

    1989-01-01

    Tamura, Lauer, and Sanborn (1985) reported a multiple regression approach to the problem of determining a cost reimbursement (rate-setting) formula for facilities providing long-term care (nursing homes). In this article we propose an alternative approach to this problem, using an absolute-error criterion instead of the least-squares criterion used in regression, with a variety of side constraints incorporated in the derivation of the formula. The mathematical tool for implementation of this approach is linear programming (LP). The article begins with a discussion of the desirable characteristics of a rate-setting formula. The development of a formula with these properties can be easily achieved, in terms of modeling as well as computation, using LP. Specifically, LP provides an efficient computational algorithm to minimize absolute error deviation, thus protecting rates from the effects of unusual observations in the data base. LP also offers modeling flexibility to impose a variety of policy controls. These features are not readily available if a least-squares criterion is used. Examples based on actual data are used to illustrate alternative LP models for rate setting. PMID:2759871

  13. New Genetics

    MedlinePlus

    ... Home > Science Education > The New Genetics The New Genetics Living Laboratories Classroom Poster Order a Free Copy ... Piece to a Century-Old Evolutionary Puzzle Computing Genetics Model Organisms RNA Interference The New Genetics is ...

  14. Modeling Weather Impact on Ground Delay Programs

    NASA Technical Reports Server (NTRS)

    Wang, Yao; Kulkarni, Deepak

    2011-01-01

    Scheduled arriving aircraft demand may exceed airport arrival capacity when there is abnormal weather at an airport. In such situations, Federal Aviation Administration (FAA) institutes ground-delay programs (GDP) to delay flights before they depart from their originating airports. Efficient GDP planning depends on the accuracy of prediction of airport capacity and demand in the presence of uncertainties in weather forecast. This paper presents a study of the impact of dynamic airport surface weather on GDPs. Using the National Traffic Management Log, effect of weather conditions on the characteristics of GDP events at selected busy airports is investigated. Two machine learning methods are used to generate models that map the airport operational conditions and weather information to issued GDP parameters and results of validation tests are described.

  15. Recent Enhancements to the Genetic Risk Prediction Model BRCAPRO

    PubMed Central

    Mazzola, Emanuele; Blackford, Amanda; Parmigiani, Giovanni; Biswas, Swati

    2015-01-01

    BRCAPRO is a widely used model for genetic risk prediction of breast cancer. It is a function within the R package BayesMendel and is used to calculate the probabilities of being a carrier of a deleterious mutation in one or both of the BRCA genes, as well as the probability of being affected with breast and ovarian cancer within a defined time window. Both predictions are based on information contained in the counselee’s family history of cancer. During the last decade, BRCAPRO has undergone several rounds of successive refinements: the current version is part of release 2.1 of BayesMendel. In this review, we showcase some of the most notable features of the software resulting from these recent changes. We provide examples highlighting each feature, using artificial pedigrees motivated by complex clinical examples. We illustrate how BRCAPRO is a comprehensive software for genetic risk prediction with many useful features that allow users the flexibility to incorporate varying amounts of available information. PMID:25983549

  16. 19 Gene × Environment Interaction Models in Psychiatric Genetics

    PubMed Central

    Karg, Katja; Sen, Srijan

    2013-01-01

    Gene-environment (G×E) interaction research is an emerging area in psychiatry, with the number of G×E studies growing rapidly in the past two decades. This article aims to give a comprehensive introduction to the field, with an emphasis on central theoretical and practical problems that are worth considering before conducting a G×E interaction study. On the theoretical side, we discuss two fundamental, but controversial questions about (1) the validity of statistical models for biological interaction and (2) the utility of G×E research for psychiatric genetics. On the practical side, we focus on study characteristics that potentially influence the outcome of G×E interaction studies and discuss strengths and pitfalls of different study designs, including recent approaches like Genome-Environment Wide Interaction Studies (GEWIS). Finally, we discuss recent developments in G×E interaction research on the most heavily investigated example in psychiatric genetics, the interaction between a serotonin transporter gene promoter variant (5-HTTLPR) and stress on depression. PMID:22241248

  17. A Genetic Model of Substrate Reduction Therapy for Mucopolysaccharidosis*

    PubMed Central

    Lamanna, William C.; Lawrence, Roger; Sarrazin, Stéphane; Lameda-Diaz, Carlos; Gordts, Philip L. S. M.; Moremen, Kelley W.; Esko, Jeffrey D.

    2012-01-01

    Inherited defects in the ability to catabolize glycosaminoglycans result in lysosomal storage disorders known as mucopolysaccharidoses (MPS), causing severe pathology, particularly in the brain. Enzyme replacement therapy has been used to treat mucopolysaccharidoses; however, neuropathology has remained refractory to this approach. To test directly whether substrate reduction might be feasible for treating MPS disease, we developed a genetic model for substrate reduction therapy by crossing MPS IIIa mice with animals partially deficient in heparan sulfate biosynthesis due to heterozygosity in Ext1 and Ext2, genes that encode the copolymerase required for heparan sulfate chain assembly. Reduction of heparan sulfate by 30–50% using this genetic strategy ameliorated the amount of disease-specific biomarker and pathology in multiple tissues, including the brain. In addition, we were able to demonstrate that substrate reduction therapy can improve the efficacy of enzyme replacement therapy in cell culture and in mice. These results provide proof of principle that targeted inhibition of heparan sulfate biosynthetic enzymes together with enzyme replacement might prove beneficial for treating mucopolysaccharidoses. PMID:22952226

  18. Y genetic data support the Neolithic demic diffusion model.

    PubMed

    Chikhi, Lounes; Nichols, Richard A; Barbujani, Guido; Beaumont, Mark A

    2002-08-20

    There still is no general agreement on the origins of the European gene pool, even though Europe has been more thoroughly investigated than any other continent. In particular, there is continuing controversy about the relative contributions of European Palaeolithic hunter-gatherers and of migrant Near Eastern Neolithic farmers, who brought agriculture to Europe. Here, we apply a statistical framework that we have developed to obtain direct estimates of the contribution of these two groups at the time they met. We analyze a large dataset of 22 binary markers from the non-recombining region of the Y chromosome (NRY), by using a genealogical likelihood-based approach. The results reveal a significantly larger genetic contribution from Neolithic farmers than did previous indirect approaches based on the distribution of haplotypes selected by using post hoc criteria. We detect a significant decrease in admixture across the entire range between the Near East and Western Europe. We also argue that local hunter-gatherers contributed less than 30% in the original settlements. This finding leads us to reject a predominantly cultural transmission of agriculture. Instead, we argue that the demic diffusion model introduced by Ammerman and Cavalli-Sforza [Ammerman, A. J. & Cavalli-Sforza, L. L. (1984) The Neolithic Transition and the Genetics of Populations in Europe (Princeton Univ. Press, Princeton)] captures the major features of this dramatic episode in European prehistory.

  19. Comparison between genetic programming and an ensemble Kalman filter as data assimilation techniques for probabilistic flood forecasting

    NASA Astrophysics Data System (ADS)

    Mediero, L.; Garrote, L.; Requena, A.; Chávez, A.

    2012-04-01

    Flood events are among the natural disasters that cause most economic and social damages in Europe. Information and Communication Technology (ICT) developments in last years have enabled hydrometeorological observations available in real-time. High performance computing promises the improvement of real-time flood forecasting systems and makes the use of post processing techniques easier. This is the case of data assimilation techniques, which are used to develop an adaptive forecast model. In this paper, a real-time framework for probabilistic flood forecasting is presented and two data assimilation techniques are compared. The first data assimilation technique uses genetic programming to adapt the model to the observations as new information is available, updating the estimation of the probability distribution of the model parameters. The second data assimilation technique uses an ensemble Kalman filter to quantify errors in both hydrologic model and observations, updating estimates of system states. Both forecast models take the result of the hydrologic model calibration as a starting point and adapts the individuals of this first population to the new observations in each operation time step. Data assimilation techniques have great potential when are used in hydrological distributed models. The distributed RIBS (Real-time Interactive Basin Simulator) rainfall-runoff model was selected to simulate the hydrological process in the basin. The RIBS model is deterministic, but it is run in a probabilistic way through Monte Carlo simulations over the probability distribution functions that best characterise the most relevant model parameters, which were identified by a probabilistic multi-objective calibration developed in a previous work. The Manzanares River basin was selected as a case study. Data assimilation processes are computationally intensive. Therefore, they are well suited to test the applicability of the potential of the Grid technology to

  20. Human operator identification model and related computer programs

    NASA Technical Reports Server (NTRS)

    Kessler, K. M.; Mohr, J. N.

    1978-01-01

    Four computer programs which provide computational assistance in the analysis of man/machine systems are reported. The programs are: (1) Modified Transfer Function Program (TF); (2) Time Varying Response Program (TVSR); (3) Optimal Simulation Program (TVOPT); and (4) Linear Identification Program (SCIDNT). The TV program converts the time domain state variable system representative to frequency domain transfer function system representation. The TVSR program computes time histories of the input/output responses of the human operator model. The TVOPT program is an optimal simulation program and is similar to TVSR in that it produces time histories of system states associated with an operator in the loop system. The differences between the two programs are presented. The SCIDNT program is an open loop identification code which operates on the simulated data from TVOPT (or TVSR) or real operator data from motion simulators.

  1. 45 CFR 2532.30 - Other innovative and model programs.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... 45 Public Welfare 4 2010-10-01 2010-10-01 false Other innovative and model programs. 2532.30 Section 2532.30 Public Welfare Regulations Relating to Public Welfare (Continued) CORPORATION FOR NATIONAL AND COMMUNITY SERVICE INNOVATIVE AND SPECIAL DEMONSTRATION PROGRAMS § 2532.30 Other innovative and model programs. (a) The Corporation may...

  2. The "Plant Drosophila": E.B. Babcock, the genus "Crepis," and the evolution of a genetics research program at Berkeley, 1915-1947.

    PubMed

    Smocovitis, Vassiliki Betty

    2009-01-01

    This paper explores the research and administrative efforts of Ernest Brown Babcock, head of the Division of Genetics in the College of Agriculture at the University of California, Berkeley, the first academic unit so named in the United States. It explores the rationale for his choice of "model organism," the development--and transformation--of his ambitious genetics research program centering on the weedy plant genus named "Crepis" (commonly known as the hawkbeard), along with examining in detail the historical development of the understanding of genetic mechanisms of evolutionary change in plants leading to the period of the evolutionary synthesis. Chosen initially as the plant counterpart of Thomas Hunt Morgan's "Drosophila melanogaster," the genus "Crepis" instead came to serve as the counterpart of Theodosius Dobzhansky's "Drosophila pseudoobscura," leading the way in plant evolutionary genetics, and eventually providing the first comprehensive systematic treatise of any genus that was part of the movement known as biosystematics, or the "new" systematics. The paper also suggests a historical rethinking of the application of the terms model organism, research program, and experimental system in the history of biology.

  3. Behavioral phenotypes of genetic mouse models of autism.

    PubMed

    Kazdoba, T M; Leach, P T; Crawley, J N

    2016-01-01

    More than a hundred de novo single gene mutations and copy-number variants have been implicated in autism, each occurring in a small subset of cases. Mutant mouse models with syntenic mutations offer research tools to gain an understanding of the role of each gene in modulating biological and behavioral phenotypes relevant to autism. Knockout, knockin and transgenic mice incorporating risk gene mutations detected in autism spectrum disorder and comorbid neurodevelopmental disorders are now widely available. At present, autism spectrum disorder is diagnosed solely by behavioral criteria. We developed a constellation of mouse behavioral assays designed to maximize face validity to the types of social deficits and repetitive behaviors that are central to an autism diagnosis. Mouse behavioral assays for associated symptoms of autism, which include cognitive inflexibility, anxiety, hyperactivity, and unusual reactivity to sensory stimuli, are frequently included in the phenotypic analyses. Over the past 10 years, we and many other laboratories around the world have employed these and additional behavioral tests to phenotype a large number of mutant mouse models of autism. In this review, we highlight mouse models with mutations in genes that have been identified as risk genes for autism, which work through synaptic mechanisms and through the mTOR signaling pathway. Robust, replicated autism-relevant behavioral outcomes in a genetic mouse model lend credence to a causal role for specific gene contributions and downstream biological mechanisms in the etiology of autism.

  4. Toward Developing Genetic Algorithms to Aid in Critical Infrastructure Modeling

    SciTech Connect

    Not Available

    2007-05-01

    Today’s society relies upon an array of complex national and international infrastructure networks such as transportation, telecommunication, financial and energy. Understanding these interdependencies is necessary in order to protect our critical infrastructure. The Critical Infrastructure Modeling System, CIMS©, examines the interrelationships between infrastructure networks. CIMS© development is sponsored by the National Security Division at the Idaho National Laboratory (INL) in its ongoing mission for providing critical infrastructure protection and preparedness. A genetic algorithm (GA) is an optimization technique based on Darwin’s theory of evolution. A GA can be coupled with CIMS© to search for optimum ways to protect infrastructure assets. This includes identifying optimum assets to enforce or protect, testing the addition of or change to infrastructure before implementation, or finding the optimum response to an emergency for response planning. This paper describes the addition of a GA to infrastructure modeling for infrastructure planning. It first introduces the CIMS© infrastructure modeling software used as the modeling engine to support the GA. Next, the GA techniques and parameters are defined. Then a test scenario illustrates the integration with CIMS© and the preliminary results.

  5. Behavioral phenotypes of genetic mouse models of autism

    PubMed Central

    Kazdoba, T. M.; Leach, P. T.; Crawley, J. N.

    2016-01-01

    More than a hundred de novo single gene mutations and copy-number variants have been implicated in autism, each occurring in a small subset of cases. Mutant mouse models with syntenic mutations offer research tools to gain an understanding of the role of each gene in modulating biological and behavioral phenotypes relevant to autism. Knockout, knockin and transgenic mice incorporating risk gene mutations detected in autism spectrum disorder and comorbid neurodevelopmental disorders are now widely available. At present, autism spectrum disorder is diagnosed solely by behavioral criteria. We developed a constellation of mouse behavioral assays designed to maximize face validity to the types of social deficits and repetitive behaviors that are central to an autism diagnosis. Mouse behavioral assays for associated symptoms of autism, which include cognitive inflexibility, anxiety, hyperactivity, and unusual reactivity to sensory stimuli, are frequently included in the phenotypic analyses. Over the past 10 years, we and many other laboratories around the world have employed these and additional behavioral tests to phenotype a large number of mutant mouse models of autism. In this review, we highlight mouse models with mutations in genes that have been identified as risk genes for autism, which work through synaptic mechanisms and through the mTOR signaling pathway. Robust, replicated autism-relevant behavioral outcomes in a genetic mouse model lend credence to a causal role for specific gene contributions and downstream biological mechanisms in the etiology of autism. PMID:26403076

  6. Modelling and genetic algorithm based optimisation of inverse supply chain

    NASA Astrophysics Data System (ADS)

    Bányai, T.

    2009-04-01

    (Recycling of household appliances with emphasis on reuse options). The purpose of this paper is the presentation of a possible method for avoiding the unnecessary environmental risk and landscape use through unprovoked large supply chain of collection systems of recycling processes. In the first part of the paper the author presents the mathematical model of recycling related collection systems (applied especially for wastes of electric and electronic products) and in the second part of the work a genetic algorithm based optimisation method will be demonstrated, by the aid of which it is possible to determine the optimal structure of the inverse supply chain from the point of view economical, ecological and logistic objective functions. The model of the inverse supply chain is based on a multi-level, hierarchical collection system. In case of this static model it is assumed that technical conditions are permanent. The total costs consist of three parts: total infrastructure costs, total material handling costs and environmental risk costs. The infrastructure-related costs are dependent only on the specific fixed costs and the specific unit costs of the operation points (collection, pre-treatment, treatment, recycling and reuse plants). The costs of warehousing and transportation are represented by the material handling related costs. The most important factors determining the level of environmental risk cost are the number of out of time recycled (treated or reused) products, the number of supply chain objects and the length of transportation routes. The objective function is the minimization of the total cost taking into consideration the constraints. However a lot of research work discussed the design of supply chain [8], but most of them concentrate on linear cost functions. In the case of this model non-linear cost functions were used. The non-linear cost functions and the possible high number of objects of the inverse supply chain leaded to the problem of choosing a

  7. Fuzzy multi-objective chance-constrained programming model for hazardous materials transportation

    NASA Astrophysics Data System (ADS)

    Du, Jiaoman; Yu, Lean; Li, Xiang

    2016-04-01

    Hazardous materials transportation is an important and hot issue of public safety. Based on the shortest path model, this paper presents a fuzzy multi-objective programming model that minimizes the transportation risk to life, travel time and fuel consumption. First, we present the risk model, travel time model and fuel consumption model. Furthermore, we formulate a chance-constrained programming model within the framework of credibility theory, in which the lengths of arcs in the transportation network are assumed to be fuzzy variables. A hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is designed for finding a satisfactory solution. Finally, some numerical examples are given to demonstrate the efficiency of the proposed model and algorithm.

  8. A Model Training Program: NJASBO's State Certification Program.

    ERIC Educational Resources Information Center

    Rodabaugh, Karl

    1997-01-01

    In 1991, the New Jersey Association of School Business Officials was selected as a nontraditional provider and asked to develop and implement a new state-approved certification program. The idea was to produce administrators who are adept at strategic planning, financial management and accounting, school law, personnel management, facility…

  9. Genetic variants and animal models in SNCA and Parkinson disease.

    PubMed

    Deng, Hao; Yuan, Lamei

    2014-05-01

    Parkinson disease (PD; MIM 168600) is the second most common progressive neurodegenerative disorder characterized by a variety of motor and non-motor features. To date, at least 20 loci and 15 disease-causing genes for parkinsonism have been identified. Among them, the α-synuclein (SNCA) gene was associated with PARK1/PARK4. Point mutations, duplications and triplications in the SNCA gene cause a rare dominant form of PD in familial and sporadic PD cases. The α-synuclein protein, a member of the synuclein family, is abundantly expressed in the brain. The protein is the major component of Lewy bodies and Lewy neurites in dopaminergic neurons in PD. Further understanding of its role in the pathogenesis of PD through various genetic techniques and animal models will likely provide new insights into our understanding, therapy and prevention of PD.

  10. Genetically manipulated mouse models of lung disease: potential and pitfalls

    PubMed Central

    Choi, Alexander J. S.; Owen, Caroline A.; Choi, Augustine M. K.

    2012-01-01

    Gene targeting in mice (transgenic and knockout) has provided investigators with an unparalleled armamentarium in recent decades to dissect the cellular and molecular basis of critical pathophysiological states. Fruitful information has been derived from studies using these genetically engineered mice with significant impact on our understanding, not only of specific biological processes spanning cell proliferation to cell death, but also of critical molecular events involved in the pathogenesis of human disease. This review will focus on the use of gene-targeted mice to study various models of lung disease including airways diseases such as asthma and chronic obstructive pulmonary disease, and parenchymal lung diseases including idiopathic pulmonary fibrosis, pulmonary hypertension, pneumonia, and acute lung injury. We will attempt to review the current technological approaches of generating gene-targeted mice and the enormous dataset derived from these studies, providing a template for lung investigators. PMID:22198907

  11. Landscape genetics of raccoons (Procyon lotor) associated with ridges and valleys of Pennsylvania: implications for oral rabies vaccination programs.

    PubMed

    Root, J Jeffrey; Puskas, Robert B; Fischer, Justin W; Swope, Craig B; Neubaum, Melissa A; Reeder, Serena A; Piaggio, Antoinette J

    2009-12-01

    Raccoons are the reservoir for the raccoon rabies virus variant in the United States. To combat this threat, oral rabies vaccination (ORV) programs are conducted in many eastern states. To aid in these efforts, the genetic structure of raccoons (Procyon lotor) was assessed in southwestern Pennsylvania to determine if select geographic features (i.e., ridges and valleys) serve as corridors or hindrances to raccoon gene flow (e.g., movement) and, therefore, rabies virus trafficking in this physiographic region. Raccoon DNA samples (n = 185) were collected from one ridge site and two adjacent valleys in southwestern Pennsylvania (Westmoreland, Cambria, Fayette, and Somerset counties). Raccoon genetic structure within and among these study sites was characterized at nine microsatellite loci. Results indicated that there was little population subdivision among any sites sampled. Furthermore, analyses using a model-based clustering approach indicated one essentially panmictic population was present among all the raccoons sampled over a reasonably broad geographic area (e.g., sites up to 36 km apart). However, a signature of isolation by distance was detected, suggesting that widths of ORV zones are critical for success. Combined, these data indicate that geographic features within this landscape influence raccoon gene flow only to a limited extent, suggesting that ridges of this physiographic system will not provide substantial long-term natural barriers to rabies virus trafficking. These results may be of value for future ORV efforts in Pennsylvania and other eastern states with similar landscapes.

  12. A Component-based Programming Model for Composite, Distributed Applications

    NASA Technical Reports Server (NTRS)

    Eidson, Thomas M.; Bushnell, Dennis M. (Technical Monitor)

    2001-01-01

    The nature of scientific programming is evolving to larger, composite applications that are composed of smaller element applications. These composite applications are more frequently being targeted for distributed, heterogeneous networks of computers. They are most likely programmed by a group of developers. Software component technology and computational frameworks are being proposed and developed to meet the programming requirements of these new applications. Historically, programming systems have had a hard time being accepted by the scientific programming community. In this paper, a programming model is outlined that attempts to organize the software component concepts and fundamental programming entities into programming abstractions that will be better understood by the application developers. The programming model is designed to support computational frameworks that manage many of the tedious programming details, but also that allow sufficient programmer control to design an accurate, high-performance application.

  13. Experimental Population Genetics in the Introductory Genetics Laboratory Using "Drosophila" as a Model Organism

    ERIC Educational Resources Information Center

    Johnson, Ronald; Kennon, Tillman

    2009-01-01

    Hypotheses of population genetics are derived and tested by students in the introductory genetics laboratory classroom as they explore the effects of biotic variables (physical traits of fruit flies) and abiotic variables (island size and distance) on fruit fly populations. In addition to this hypothesis-driven experiment, the development of…

  14. Competent Geometric Semantic Genetic Programming for Symbolic Regression and Boolean Function Synthesis.

    PubMed

    Pawlak, Tomasz P; Krawiec, Krzysztof

    2017-02-16

    Program semantics is a promising recent research thread in Genetic Programming (GP). Over a dozen of semantic-aware search, selection, and initialization operators for GP have been proposed to date. Some of those operators are designed to exploit the geometric properties of semantic space, while some others focus on making offspring effective, i.e., semantically different from their parents. Only a small fraction of previous works aimed at addressing both these features simultaneously. In this paper, we propose a suite of competent operators that combine effectiveness with geometry for population initialization, mate selection, mutation and crossover. We present a theoretical rationale behind these operators and compare them experimentally to operators known from literature on symbolic regression and Boolean function synthesis benchmarks. We analyze each operator in isolation as well as verify how they fare together in an evolutionary run, concluding that the competent operators are superior on a wide range of performance indicators, including best-of-run fitness, test-set fitness, and program size.

  15. Genetic-program-based data mining for hybrid decision-theoretic algorithms and theories

    NASA Astrophysics Data System (ADS)

    Smith, James F., III

    2005-03-01

    A genetic program (GP) based data mining (DM) procedure has been developed that automatically creates decision theoretic algorithms. A GP is an algorithm that uses the theory of evolution to automatically evolve other computer programs or mathematical expressions. The output of the GP is a computer program or mathematical expression that is optimal in the sense that it maximizes a fitness function. The decision theoretic algorithms created by the DM algorithm are typically designed for making real-time decisions about the behavior of systems. The database that is mined by the DM typically consists of many scenarios characterized by sensor output and labeled by experts as to the status of the scenario. The DM procedure will call a GP as a data mining function. The GP incorporates the database and expert"s rules into its fitness function to evolve an optimal decision theoretic algorithm. A decision theoretic algorithm created through this process will be discussed as well as validation efforts showing the utility of the decision theoretic algorithm created by the DM process. GP based data mining to determine equations related to scientific theories and automatic simplification methods based on computer algebra will also be discussed.

  16. Evaluation of aerothermal modeling computer programs

    NASA Technical Reports Server (NTRS)

    Hsieh, K. C.; Yu, S. T.

    1987-01-01

    Various computer programs based upon the SIMPLE or SIMPLER algorithm were studied and compared for numerical accuracy, efficiency, and grid dependency. Four two-dimensional and one three-dimensional code originally developed by a number of research groups were considered. In general, the accuracy and computational efficieny of these TEACH type programs were improved by modifying the differencing schemes and their solvers. A brief description of each program is given. Error reduction, spline flux and second upwind differencing programs are covered.

  17. Genetic counselor perceptions of genetic counseling session goals: a validation study of the reciprocal-engagement model.

    PubMed

    Hartmann, Julianne E; Veach, Patricia McCarthy; MacFarlane, Ian M; LeRoy, Bonnie S

    2015-04-01

    Although some researchers have attempted to define genetic counseling practice goals, no study has obtained consensus about the goals from a large sample of genetic counselors. The Reciprocal-Engagement Model (REM; McCarthy Veach, Bartels & LeRoy, 2007) articulates 17 goals of genetic counseling practice. The present study investigated whether these goals could be generalized as a model of practice, as determined by a larger group of clinical genetic counselors. Accordingly, 194 genetic counselors were surveyed regarding their opinions about the importance of each goal and their perceptions of how frequently they achieve each goal. Mean importance ratings suggest they viewed every goal as important. Factor analysis of the 17 goals yielded four factors: Understanding and Appreciation, Support and Guidance, Facilitative Decision-Making, and Patient-Centered Education. Patient-Centered Education and Facilitative Decision-Making goals received the highest mean importance ratings. Mean frequency ratings were consistently lower than importance ratings, suggesting genetic counseling goals may be difficult to achieve and/or not applicable in all situations. A number of respondents provided comments about the REM goals that offer insight into factors related to implementing the goals in clinical practice. This study presents preliminary evidence concerning the validity of the goals component of the REM.

  18. Simplified Process Model Discovery Based on Role-Oriented Genetic Mining

    PubMed Central

    Liu, Xi

    2014-01-01

    Process mining is automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them are based on control flow. Meanwhile, the existing role-oriented process mining methods focus on correctness and integrity of roles while ignoring role complexity of the process model, which directly impacts understandability and quality of the model. To address these problems, we propose a genetic programming approach to mine the simplified process model. Using a new metric of process complexity in terms of roles as the fitness function, we can find simpler process models. The new role complexity metric of process models is designed from role cohesion and coupling, and applied to discover roles in process models. Moreover, the higher fitness derived from role complexity metric also provides a guideline for redesigning process models. Finally, we conduct case study and experiments to show that the proposed method is more effective for streamlining the process by comparing with related studies. PMID:24616618

  19. Prospects for genetically modified non-human primate models, including the common marmoset.

    PubMed

    Sasaki, Erika

    2015-04-01

    Genetically modified mice have contributed much to studies in the life sciences. In some research fields, however, mouse models are insufficient for analyzing the molecular mechanisms of pathology or as disease models. Often, genetically modified non-human primate (NHP) models are desired, as they are more similar to human physiology, morphology, and anatomy. Recent progress in studies of the reproductive biology in NHPs has enabled the introduction of exogenous genes into NHP genomes or the alteration of endogenous NHP genes. This review summarizes recent progress in the production of genetically modified NHPs, including the common marmoset, and future perspectives for realizing genetically modified NHP models for use in life sciences research.

  20. Exploring the possibility of modeling a genetic counseling guideline using agile methodology.

    PubMed

    Choi, Jeeyae

    2013-01-01

    Increased demand of genetic counseling services heightened the necessity of a computerized genetic counseling decision support system. In order to develop an effective and efficient computerized system, modeling of genetic counseling guideline is an essential step. Throughout this pilot study, Agile methodology with United Modeling Language (UML) was utilized to model a guideline. 13 tasks and 14 associated elements were extracted. Successfully constructed conceptual class and activity diagrams revealed that Agile methodology with UML was a suitable tool to modeling a genetic counseling guideline.

  1. Geometric Modeling Applications Interface Program (GMAP). Volume 2. Program Description

    DTIC Science & Technology

    1989-09-01

    Retirement for Cause ..................................................... 3- 41 3-21 Interrelationship of GMAP Documents...M a 9 2a. ~* .E 0 4) -------- U- 00 004-4 a___ cam 0 0 Z CL cw; 3- 41 CI FTR560240001U September 1989 Initially, GMAP looked at several programs...oteInpcinPanGnrto Sub System Intgratof IBIs.tfaiithPats hinecto AMofa Syservic Rene aoressr bade fo sufaenoaiesuin fluo.ResCInteetrface inspetin. ee eainhp 3.3.21

  2. Velocity inversion in cross-hole seismic tomography bycounter-propagation neural network, genetic algorithmand evolutionary programming techniques

    NASA Astrophysics Data System (ADS)

    Nath, Sankar Kumar; Chakraborty, Subrata; Singh, Sanjiv Kumar; Ganguly, Nilanjan

    1999-07-01

    The disadvantages of conventional seismic tomographic ray tracing and inversion by calculus-based techniques include the assumption of a single ray path for each source-receiver pair, the non-inclusion of head waves, long computation times, and the difficulty in finding ray paths in a complicated velocity distribution. A ray-tracing algorithm is therefore developed using the reciprocity principle and dynamic programming approach. This robust forward calculation routine is subsequently used for the cross-hole seismic velocity inversion. Seismic transmission tomography can be considered to be a function approximation problem; that is, of mapping the traveltime vector to the velocity vector. This falls under the purview of pattern classification problems, so we propose a forward-only counter-propagation neural network (CPNN) technique for the tomographic imaging of the subsurface. The limitation of neural networks, however, lies in the requirement of exhaustive training for its use in routine interpretation. Since finding the optimal solution, sometimes from poor initial models, is the ultimate goal, global optimization and search techniques such as simulated evolution are also implemented in the cross-well traveltime tomography. Genetic algorithms (GA), evolution strategies and evolutionary programming (EP) are the main avenues of research in simulated evolution. Part of this investigation therefore deals with GA and EP schemes for tomographic applications. In the present work on simulated evolution, a new genetic operator called `region-growing mutation' is introduced to speed up the search process. The potential of the forward-only CPNN, GA and EP methods is demonstrated in three synthetic examples. Velocity tomograms of the first model present plausible images of a diagonally orientated velocity contrast bounding two constant-velocity areas by both the CPNN and GA schemes, but the EP scheme could not image the model completely. In the second case, while GA and EP

  3. Teaching Human Genetics with Mustard: Rapid Cycling "Brassica rapa" (Fast Plants Type) as a Model for Human Genetics in the Classroom Laboratory

    ERIC Educational Resources Information Center

    Wendell, Douglas L.; Pickard, Dawn

    2007-01-01

    We have developed experiments and materials to model human genetics using rapid cycling "Brassica rapa", also known as Fast Plants. Because of their self-incompatibility for pollination and the genetic diversity within strains, "B. rapa" can serve as a relevant model for human genetics in teaching laboratory experiments. The experiment presented…

  4. GRAVITATIONAL LENS MODELING WITH GENETIC ALGORITHMS AND PARTICLE SWARM OPTIMIZERS

    SciTech Connect

    Rogers, Adam; Fiege, Jason D.

    2011-02-01

    Strong gravitational lensing of an extended object is described by a mapping from source to image coordinates that is nonlinear and cannot generally be inverted analytically. Determining the structure of the source intensity distribution also requires a description of the blurring effect due to a point-spread function. This initial study uses an iterative gravitational lens modeling scheme based on the semilinear method to determine the linear parameters (source intensity profile) of a strongly lensed system. Our 'matrix-free' approach avoids construction of the lens and blurring operators while retaining the least-squares formulation of the problem. The parameters of an analytical lens model are found through nonlinear optimization by an advanced genetic algorithm (GA) and particle swarm optimizer (PSO). These global optimization routines are designed to explore the parameter space thoroughly, mapping model degeneracies in detail. We develop a novel method that determines the L-curve for each solution automatically, which represents the trade-off between the image {chi}{sup 2} and regularization effects, and allows an estimate of the optimally regularized solution for each lens parameter set. In the final step of the optimization procedure, the lens model with the lowest {chi}{sup 2} is used while the global optimizer solves for the source intensity distribution directly. This allows us to accurately determine the number of degrees of freedom in the problem to facilitate comparison between lens models and enforce positivity on the source profile. In practice, we find that the GA conducts a more thorough search of the parameter space than the PSO.

  5. Automated synthesis of both the topology and numerical parameters for seven patented optical lens systems using genetic programming

    NASA Astrophysics Data System (ADS)

    Jones, Lee W.; Al-Sakran, Sameer H.; Koza, John R.

    2005-08-01

    This paper describes how genetic programming was used as an automated invention machine to synthesize both the topology and numerical parameters for seven previously patented optical lens systems, including one aspherical system and one issued in the 21st-century. Two of the evolved optical lens systems infringe the claims of the patents and the others are novel solutions that satisfy the design goals stated in the patent. The automatic synthesis was done "from scratch"--that is, without starting from a pre-existing good design and without pre-specifying the number of lenses, the topological layout of the lenses, or the numerical parameters of the lenses. Genetic programming is a form of evolutionary computation used to automatically solve problems. It starts from a high-level statement of what needs to be done and progressively breeds a population of candidate individuals over many generations using the principle of Darwinian natural selection and genetic recombination. The paper describes how genetic programming created eyepieces that duplicated the functionality of seven previously patented lens systems. The seven designs were created in a substantially similar and routine way, suggesting that the use of genetic programming in the automated design of both the topology and numerical parameters for optical lens systems may have widespread utility.

  6. Marine biomass program: plant breeding and genetics. Annual report, September 1984-December 1985

    SciTech Connect

    Neushul, M.; Harger, B.W.W.; Lewis, R.J.

    1986-03-01

    By building on past efforts and adding to the data base that has been assembled, and through collaborative research with others, progress has been made toward the long-term goal of growing macroalgae in the sea as a future source of substitute natural gas. It is encouraging that the authors program is being emulated in Japan and Sweden, and that there is growing interest in using the unique GRI kelp seedstock collection by workers in Germany, Japan, Alaska, Oregon, California, and elsewhere. This annual report discusses progress made in propagating kelps, and the floating gulf-weed, Sargassum. Work on kelp genetics has revealed high levels of compatability between species and genera, based on 166 hybridization tests.

  7. Applying genetic programming to the prediction of alternative mRNA splice variants.

    PubMed

    Vukusic, Ivana; Grellscheid, Sushma Nagaraja; Wiehe, Thomas

    2007-04-01

    Genetic programming (GP) can be used to classify a given gene sequence as either constitutively or alternatively spliced. We describe the principles of GP and apply it to a well-defined data set of alternatively spliced genes. A feature matrix of sequence properties, such as nucleotide composition or exon length, was passed to the GP system "Discipulus." To test its performance we concentrated on cassette exons (SCE) and retained introns (SIR). We analyzed 27,519 constitutively spliced and 9641 cassette exons including their neighboring introns; in addition we analyzed 33,316 constitutively spliced introns compared to 2712 retained introns. We find that the classifier yields highly accurate predictions on the SIR data with a sensitivity of 92.1% and a specificity of 79.2%. Prediction accuracies on the SCE data are lower, 47.3% (sensitivity) and 70.9% (specificity), indicating that alternative splicing of introns can be better captured by sequence properties than that of exons.

  8. Genetic programming:  a novel method for the quantitative analysis of pyrolysis mass spectral data.

    PubMed

    Gilbert, R J; Goodacre, R; Woodward, A M; Kell, D B

    1997-11-01

    A technique for the analysis of multivariate data by genetic programming (GP) is described, with particular reference to the quantitative analysis of orange juice adulteration data collected by pyrolysis mass spectrometry (PyMS). The dimensionality of the input space was reduced by ranking variables according to product moment correlation or mutual information with the outputs. The GP technique as described gives predictive errors equivalent to, if not better than, more widespread methods such as partial least squares and artificial neural networks but additionally can provide a means for easing the interpretation of the correlation between input and output variables. The described application demonstrates that by using the GP method for analyzing PyMS data the adulteration of orange juice with 10% sucrose solution can be quantified reliably over a 0-20% range with an RMS error in the estimate of ∼1%.

  9. Hybrid mice as genetic models of high alcohol consumption.

    PubMed

    Blednov, Y A; Ozburn, A R; Walker, D; Ahmed, S; Belknap, J K; Harris, R A

    2010-01-01

    We showed that F1 hybrid genotypes may provide a broader variety of ethanol drinking phenotypes than the inbred progenitor strains used to create the hybrids (Blednov et al. in Alcohol Clin Exp Res 29:1949-1958, 2005). To extend this work, we characterized alcohol consumption as well as intake of other tastants (saccharin, quinine and sodium chloride) in five inbred strains of mice (FVB, SJL, B6, BUB, NZB) and in their reciprocal F1 hybrids with B6 (FVBxB6; B6xFVB; NZBxB6; B6xNZB; BUBxB6; B6xBUB; SJLxB6; B6xSJL). We also compared ethanol intake in these mice for several concentrations before and after two periods of abstinence. F1 hybrid mice derived from the crosses of B6 and FVB and also B6 and SJL drank higher levels of ethanol than their progenitor strains, demonstrating overdominance for two-bottle choice drinking test. The B6 and NZB hybrid showed additivity in two-bottle choice drinking, whereas the hybrid of B6 and BUB demonstrated full or complete dominance. Genealogical origin, as well as non-alcohol taste preferences (sodium chloride), predicted ethanol consumption. Mice derived from the crosses of B6 and FVB showed high sustained alcohol preference and the B6 and NZB hybrids showed reduced alcohol preference after periods of abstinence. These new genetic models offer some advantages over inbred strains because they provide high, sustained, alcohol intake, and should allow mapping of loci important for the genetic architecture of these traits.

  10. Toward a genetically-informed model of borderline personality disorder.

    PubMed

    Livesley, John

    2008-02-01

    This article describes a conceptual framework for describing borderline personality disorder (BPD) based on empirical studies of the phenotypic structure and genetic architecture of personality. The proposed phenotype has 2 components: (1) a description of core self and interpersonal pathology-the defining features of personality disorder-as these features are expressed in the disorder; and (2) a set of traits based on the anxious-dependent or emotional dysregulation factor of the four-factor model of PD. Four kinds of traits are described: emotional (anxiousness, emotional reactivity, emotional intensity, and pessimistic-anhedonia), interpersonal (submissiveness, insecure attachment, social apprehensiveness, and need for approval), cognitive (cognitive dysregulation), and self-harm (behaviors and ideas). Formulation of the phenotype was guided by the conceptualization of personality as a system of interrelated sub-systems. The psychopathology associated with BPD involves most components of the system. The trait structure of the disorder is assumed to reflect the genetic architecture of personality and individual traits are assumed to be based on adaptive mechanisms. It is suggested that borderline traits are organized around the trait of anxiousness and that an important feature of BPD is dysregulation of the threat management system leading to pervasive fearfulness and unstable emotions. The interpersonal traits are assumed to be heritable characteristics that evolved to deal with interpersonal threats that arose as a result of social living. The potential for unstable and conflicted interpersonal relationships that is inherent to the disorder is assumed to result from the interplay between the adaptive structure of personality and psychosocial adversity. The etiology of the disorder is discussed in terms of biological and environmental factors associated with each component of the phenotype.

  11. Matricellular protein CCN1 activates a proinflammatory genetic program in murine macrophages.

    PubMed

    Bai, Tao; Chen, Chih-Chiun; Lau, Lester F

    2010-03-15

    CCN1 (CYR61) is a matricellular protein that is highly expressed at sites of inflammation and wound repair. In these contexts, CCN1 can modify the activities of specific cytokines, enabling TNF-alpha to be cytotoxic without blocking NF-kappaB activity and enhancing the apoptotic activity of Fas ligand and TRAIL. In this paper, we show that CCN1 supports the adhesion of macrophages through integrin alpha(M)beta(2) and syndecan-4, activates NFkappaB-mediated transcription, and induces a proinflammatory genetic program characteristic of classically activated M1 macrophages that participates in Th1 responses. The effects of CCN1 include upregulation of cytokines (TNF-alpha, IL-1alpha, IL-1beta, IL-6, and IL-12b), chemokines (MIP-1alpha; MCP-3; growth-related oncogenes 1 and 2; and inflammatory protein 10), and regulators of oxidative stress and complement (inducible NO synthase and C3) and downregulation of specific receptors (TLR4 and IL-10Rbeta) and anti-inflammatory factors (TGF-beta1). CCN1 regulates this genetic program through at least two distinct mechanisms: an immediate-early response resulting from direct activation of NF-kappaB by CCN1, leading to the synthesis of cytokines including TNF-alpha and inflammatory protein 10; and a delayed response resulting from CCN1-induced TNF-alpha, which acts as an autocrine/paracrine mediator to activate the expression of other cytokines including IL-1beta and IL-6. These results identify CCN1 as a novel component of the extracellular matrix that activates proinflammatory genes in macrophages, implicating its role in regulating macrophage function during inflammation.

  12. Model Program Generator: System and Programming Documentation, Spring 1982 Version.

    DTIC Science & Technology

    1982-05-01

    cCMND_EXP> I BOOLEAN-TER14 /SVOEPl/ c OR. /SVNXOP/ cBOOLERU_TEK /Svtacop/]I * /STALW. ( cCNP -EXP)t:.I /SVCOMD/ /E(3 )/ DOO0LEAN_EXPRESSIOtN OSVaHPn /EO7...as follows: ALLOCATE ERROR, ACCERROR j ACCERROR - 10’B ALLCATE $ERR_- LAB p SERRLAB - EDPROGRAM The declarations written to the PLIDCL file are as...7.3.1 PROGRAM HEADING If the node is a module name (type MODL) we produce the code: name: PROCEDURE OPTIONS(MAIN) j This code is routed to the file

  13. Molecular genetics and animal models in autistic disorder.

    PubMed

    Andres, Christian

    2002-01-01

    Autistic disorder is a behavioural syndrome beginning before the age of 3 years and lasting over the whole lifetime. It is characterised by impaired communication, impaired social interactions, and repetitive interests and behaviour. The prevalence is about 7/10,000 taking a restrictive definition and more than 1/500 with a broader definition, including all the pervasive developmental disorders. The importance of genetic factors has been highlighted by epidemiological studies showing that autistic disorder is one of the most genetic neuropsychiatric diseases. The relative risk of first relatives is about 100-fold higher than the risk in the normal population and the concordance in monozygotic twin is about 60%. Different strategies have been applied on the track of susceptibility genes. The systematic search of linked loci led to contradictory results, in part due to the heterogeneity of the clinical definitions, to the differences in the DNA markers, and to the different methods of analysis used. An oversimplification of the inferred model is probably also cause of our disappointment. More work is necessary to give a clearer picture. One region emerges more frequently: the long arm of chromosome 7. Several candidate genes have been studied and some gave indications of association: the Reelin gene and the Wnt2 gene. Cytogenetical abnormalities are frequent at 15q11-13, the region of the Angelman and Prader-Willi syndrome. Imprinting plays an important role in this region, no candidate gene has been identified in autism. Biochemical abnormalities have been found in the serotonin system. Association and linkage studies gave no consistent results with some serotonin receptors and in the transporter, although it seems interesting to go further in the biochemical characterisation of the serotonin transporter activity, particularly in platelets, easily accessible. Two monogenic diseases have been associated with autistic disorder: tuberous sclerosis and fragile X. A

  14. Genetic evaluation of egg production curve in Thai native chickens by random regression and spline models.

    PubMed

    Mookprom, S; Boonkum, W; Kunhareang, S; Siripanya, S; Duangjinda, M

    2017-02-01

    The objective of this research is to investigate appropriate random regression models with various covariance functions, for the genetic evaluation of test-day egg production. Data included 7,884 monthly egg production records from 657 Thai native chickens (Pradu Hang Dam) that were obtained during the first to sixth generation and were born during 2007 to 2014 at the Research and Development Network Center for Animal Breeding (Native Chickens), Khon Kaen University. Average annual and monthly egg productions were 117 ± 41 and 10.20 ± 6.40 eggs, respectively. Nine random regression models were analyzed using the Wilmink function (WM), Koops and Grossman function (KG), Legendre polynomials functions with second, third, and fourth orders (LG2, LG3, LG4), and spline functions with 4, 5, 6, and 8 knots (SP4, SP5, SP6, and SP8). All covariance functions were nested within the same additive genetic and permanent environmental random effects, and the variance components were estimated by Restricted Maximum Likelihood (REML). In model comparisons, mean square error (MSE) and the coefficient of detemination (R(2)) calculated the goodness of fit; and the correlation between observed and predicted values [Formula: see text] was used to calculate the cross-validated predictive abilities. We found that the covariance functions of SP5, SP6, and SP8 proved appropriate for the genetic evaluation of the egg production curves for Thai native chickens. The estimated heritability of monthly egg production ranged from 0.07 to 0.39, and the highest heritability was found during the first to third months of egg production. In conclusion, the spline functions within monthly egg production can be applied to breeding programs for the improvement of both egg number and persistence of egg production.

  15. Program Documentation of the Plant Job Scheduling Model System

    DTIC Science & Technology

    1987-11-30

    Model is used to make initial computer runs and the Post Processor Model for subsequent "what if" evaluations . Accessing the programs for model runs and...changes to existing conventional ammunition programs and budoets based on varied options, and quickly evaluate efi,.cts of the changes. 2.2 System...up by FY, the screen would organize the data for that block in ascending order by ypar . 5.16.3 Verification Procedures. Verification of the program

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

    PubMed

    Yoshikawa, Takeo

    2004-01-01

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

  17. Genetic regulatory network models of biological clocks: evolutionary history matters.

    PubMed

    Knabe, Johannes F; Nehaniv, Chrystopher L; Schilstra, Maria J

    2008-01-01

    We study the evolvability and dynamics of artificial genetic regulatory networks (GRNs), as active control systems, realizing simple models of biological clocks that have evolved to respond to periodic environmental stimuli of various kinds with appropriate periodic behaviors. GRN models may differ in the evolvability of expressive regulatory dynamics. A new class of artificial GRNs with an evolvable number of complex cis-regulatory control sites--each involving a finite number of inhibitory and activatory binding factors--is introduced, allowing realization of complex regulatory logic. Previous work on biological clocks in nature has noted the capacity of clocks to oscillate in the absence of environmental stimuli, putting forth several candidate explanations for their observed behavior, related to anticipation of environmental conditions, compartmentation of activities in time, and robustness to perturbations of various kinds or to unselected accidents of neutral selection. Several of these hypotheses are explored by evolving GRNs with and without (Gaussian) noise and blackout periods for environmental stimulation. Robustness to certain types of perturbation appears to account for some, but not all, dynamical properties of the evolved networks. Unselected abilities, also observed for biological clocks, include the capacity to adapt to change in wavelength of environmental stimulus and to clock resetting.

  18. Developing a Model of Advanced Training to Promote Career Advancement for Certified Genetic Counselors: An Investigation of Expanded Skills, Advanced Training Paths, and Professional Opportunities.

    PubMed

    Baty, Bonnie J; Trepanier, Angela; Bennett, Robin L; Davis, Claire; Erby, Lori; Hippman, Catriona; Lerner, Barbara; Matthews, Anne; Myers, Melanie F; Robbins, Carol B; Singletary, Claire N

    2016-08-01

    There are currently multiple paths through which genetic counselors can acquire advanced knowledge and skills. However, outside of continuing education opportunities, there are few formal training programs designed specifically for the advanced training of genetic counselors. In the genetic counseling profession, there is currently considerable debate about the paths that should be available to attain advanced skills, as well as the skills that might be needed for practice in the future. The Association of Genetic Counseling Program Directors (AGCPD) convened a national committee, the Committee on Advanced Training for Certified Genetic Counselors (CATCGC), to investigate varied paths to post-master's training and career development. The committee began its work by developing three related grids that view career advancement from the viewpoints of the skills needed to advance (skills), ways to obtain these skills (paths), and existing genetic counselor positions that offer career change or advancement (positions). Here we describe previous work related to genetic counselor career advancement, the charge of the CATCGC, our preliminary work in developing a model through which to view genetic counselor advanced training and career advancement opportunities, and our next steps in further developing and disseminating the model.

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

    NASA Astrophysics Data System (ADS)

    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.

  20. Hierarchical linear modeling of longitudinal pedigree data for genetic association analysis.

    PubMed

    Tan, Qihua; B Hjelmborg, Jacob V; Thomassen, Mads; Jensen, Andreas Kryger; Christiansen, Lene; Christensen, Kaare; Zhao, Jing Hua; Kruse, Torben A

    2014-01-01

    Genetic association analysis on complex phenotypes under a longitudinal design involving pedigrees encounters the problem of correlation within pedigrees, which could affect statistical assessment of the genetic effects. Approaches have been proposed to integrate kinship correlation into the mixed-effect models to explicitly model the genetic relationship. These have proved to be an efficient way of dealing with sample clustering in pedigree data. Although current algorithms implemented in popular statistical packages are useful for adjusting relatedness in the mixed modeling of genetic effects on the mean level of a phenotype, they are not sufficiently straightforward to handle the kinship correlation on the time-dependent trajectories of a phenotype. We introduce a 2-level hierarchical linear model to separately assess the genetic associations with the mean level and the rate of change of a phenotype, integrating kinship correlation in the analysis. We apply our method to the Genetic Analysis Workshop 18 genome-wide association studies data on chromosome 3 to estimate the genetic effects on systolic blood pressure measured over time in large pedigrees. Our method identifies genetic variants associated with blood pressure with estimated inflation factors of 0.99, suggesting that our modeling of random effects efficiently handles the genetic relatedness in pedigrees. Application to simulated data captures important variants specified in the simulation. Our results show that the method is useful for genetic association studies in related samples using longitudinal design.

  1. Estimates of phenotypic and genetic parameters for birth weight of Brown Swiss calves in Turkey using an animal model.

    PubMed

    Sahin, A; Ulutas, Z; Yilmaz Adkinson, A; Adkinson, R W

    2012-06-01

    A study was conducted to assess the influence of genetic and environmental factors on Brown Swiss calf birth weight, and to estimate variance components, genetic parameters, and breeding values. Data were collected on 1,761 Brown Swiss calves born from 1990 to 2005 in the Konuklar State Farm in Turkey. Mean birth weight for all calves was 39.3 ± 0.09 kg. Least squares mean birth weights for male and female Brown Swiss calves were 40.3 ± 0.02 and 39.0 ± 0.02 kg, respectively. Variance components, genetic parameters, and breeding values for birth weight in Brown Swiss calves were estimated by restricted error maximum likelihood (REML)-best linear unbiased prediction(BLUP) procedures using an MTDFREML (multiple trait derivative free restricted maximum likelihood) program employing an animal model. Direct heritability (h(d)(2)), maternal heritability (h(m)(2)), total heritability (h(T)(2)), r(am) and c(am) estimates were 0.12, 0.09, 0.23, -0.58, and -0.06, respectively. The estimated maternal permanent environmental variance expressed as a proportion of the phenotypic variance (c(2)) was 0.05. Breeding values were estimated for the trait and used to evaluate genetic trends across the time period investigated. The genetic trend linear regression was not different from zero. No genetic trend for birth weight was expected, since there had been no direct selection pressure on the trait. Absence of a trend confirms that there was no change due to selection pressure on correlated traits. Genetic and environmental parameter estimates were similar to literature values indicating that effective selection methods used in more developed improvement programs would be effective in Turkey as well.

  2. Form Follows Function: A Model for Clinical Supervision of Genetic Counseling Students.

    PubMed

    Wherley, Colleen; Veach, Patricia McCarthy; Martyr, Meredith A; LeRoy, Bonnie S

    2015-10-01

    Supervision plays a vital role in genetic counselor training, yet models describing genetic counseling supervision processes and outcomes are lacking. This paper describes a proposed supervision model intended to provide a framework to promote comprehensive and consistent clinical supervision training for genetic counseling students. Based on the principle "form follows function," the model reflects and reinforces McCarthy Veach et al.'s empirically derived model of genetic counseling practice - the "Reciprocal Engagement Model" (REM). The REM consists of mutually interactive educational, relational, and psychosocial components. The Reciprocal Engagement Model of Supervision (REM-S) has similar components and corresponding tenets, goals, and outcomes. The 5 REM-S tenets are: Learning and applying genetic information are key; Relationship is integral to genetic counseling supervision; Student autonomy must be supported; Students are capable; and Student emotions matter. The REM-S outcomes are: Student understands and applies information to independently provide effective services, develop professionally, and engage in self-reflective practice. The 16 REM-S goals are informed by the REM of genetic counseling practice and supported by prior literature. A review of models in medicine and psychology confirms the REM-S contains supervision elements common in healthcare fields, while remaining unique to genetic counseling. The REM-S shows promise for enhancing genetic counselor supervision training and practice and for promoting research on clinical supervision. The REM-S is presented in detail along with specific examples and training and research suggestions.

  3. Specific Genetic Disorders

    MedlinePlus

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

  4. Combining hydrodynamic modelling with genetics: Can passive larval drift shape the genetic structure of Baltic Mytilus populations?

    PubMed

    Stuckas, Heiko; Knöbel, Loreen; Schade, Hanna; Breusing, Corinna; Hinrichsen, Hans-Harald; Bartel, Manuela; Langguth, Klaudia; Melzner, Frank

    2017-02-26

    While secondary contact between Mytilus edulis and M. trossulus in North America results in mosaic hybrid zone formation, both species form a hybrid swarm in the Baltic. Despite pervasive gene flow, Baltic Mytilus species maintain substantial genetic and phenotypic differentiation. Exploring mechanisms underlying the contrasting genetic composition in Baltic Mytilus species will allow insights into processes such as speciation or adaptation to extremely low salinity. Previous studies in the Baltic indicated that only weak interspecific reproductive barriers exist and discussed the putative role of adaptation to environmental conditions. Using a combination of hydrodynamic modelling and multilocus genotyping we investigate how oceanographic conditions influence passive larval dispersal and hybrid swarm formation in the Baltic. By combining our analyses with previous knowledge we show a genetic transition of Baltic Mytilus species along longitude 12°-13°E, i.e. a virtual line between Malmö (Sweden) and Stralsund (Germany). Although larval transport only occurs over short distances (10-30 km), limited larval dispersal could not explain the position of this genetic transition zone. Instead, the genetic transition zone is located at the area of maximum salinity change (15 to 10 psu). Thus, we argue that selection results in weak reproductive barriers and local adaptation. This scenario could maintain genetic and phenotypic differences between Baltic Mytilus species despite pervasive introgressive hybridization. This article is protected by copyright. All rights reserved.

  5. Are mouse models of human mycobacterial diseases relevant? Genetics says: ‘yes!’

    PubMed Central

    Apt, Alexander S

    2011-01-01

    Relevance and accuracy of experimental mouse models of tuberculosis (TB) are the subject of constant debate. This article briefly reviews genetic aspects of this problem and provides a few examples of mycobacterial diseases with similar or identical genetic control in mice and humans. The two species display more similarities than differences regarding both genetics of susceptibility/severity of mycobacterial diseases and the networks of protective and pathological immune reactions. In the opinion of the author, refined mouse models of mycobacterial diseases are extremely useful for modelling the corresponding human conditions, if genetic diversity is taken into account. PMID:21896006

  6. The dc modeling program (DCMP): Version 2. 0

    SciTech Connect

    Chapman, D.G. )

    1990-08-01

    In this project one of the main objectives was the refinement of tools for the study of HVDC systems. The original software was prepared in project RP1964-2 (EL-4365) as power flow and stability program models for HVDC systems. In this project new modeling capabilities were added to both the power flow and stability models. Additionally, the HVDC specific model capabilities were integrated into a new program, termed the Standalone program, for use in the development and testing of HVDC models. This manual provides technical background for programmers and those interested in understanding, augmenting or transporting the dc models.

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

    PubMed Central

    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

    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

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

    PubMed

    Pollin, Toni I; Isakova, Tamara; 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; Franks, Paul W

    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.

  9. A new explained-variance based genetic risk score for predictive modeling of disease risk.

    PubMed

    Che, Ronglin; Motsinger-Reif, Alison A

    2012-09-25

    The goal of association mapping is to identify genetic variants that predict disease, and as the field of human genetics matures, the number of successful association studies is increasing. Many such studies have shown that for many diseases, risk is explained by a reasonably large number of variants that each explains a very small amount of disease risk. This is prompting the use of genetic risk scores in building predictive models, where information across several variants is combined for predictive modeling. In the current study, we compare the performance of four previously proposed genetic risk score methods and present a new method for constructing genetic risk score that incorporates explained variance information. The methods compared include: a simple count Genetic Risk Score, an odds ratio weighted Genetic Risk Score, a direct logistic regression Genetic Risk Score, a polygenic Genetic Risk Score, and the new explained variance weighted Genetic Risk Score. We compare the methods using a wide range of simulations in two steps, with a range of the number of deleterious single nucleotide polymorphisms (SNPs) explaining disease risk, genetic modes, baseline penetrances, sample sizes, relative risks (RR) and minor allele frequencies (MAF). Several measures of model performance were compared including overall power, C-statistic and Akaike's Information Criterion. Our results show the relative performance of methods differs significantly, with the new explained variance weighted GRS (EV-GRS) generally performing favorably to the other methods.

  10. A Genetically Engineered Mouse Model of Neuroblastoma Driven by Mutated ALK and MYCN

    DTIC Science & Technology

    2015-09-01

    AWARD NUMBER: W81XWH-13-1-0220 TITLE: A Genetically Engineered Mouse Model of Neuroblastoma Driven by Mutated ALK and MYCN PRINCIPAL...4. TITLE AND SUBTITLE A Genetically Engineered Mouse Model of Neuroblastoma Driven by Mutated ALK and MYCN 5a. CONTRACT NUMBER 5b. GRANT NUMBER... genetic and epigenetic changes that occur during tumorigenesis. 15. SUBJECT TERMS Anaplastic lymphoma kinase, neuroblastoma, ALK, ALKF1174L, MYCN, CDK7

  11. Mitochondrial polymorphisms in rat genetic models of hypertension.

    PubMed

    Kumarasamy, Sivarajan; Gopalakrishnan, Kathirvel; Shafton, Asher; Nixon, Jeremy; Thangavel, Jayakumar; Farms, Phyllis; Joe, Bina

    2010-06-01

    Hypertension is a complex trait that has been studied extensively for genetic contributions of the nuclear genome. We examined mitochondrial genomes of the hypertensive strains: the Dahl Salt-Sensitive (S) rat, the Spontaneously Hypertensive Rat (SHR), and the Albino Surgery (AS) rat, and the relatively normotensive strains: the Dahl Salt-Resistant (R) rat, the Milan Normotensive Strain (MNS), and the Lewis rat (LEW). These strains were used previously for linkage analysis for blood pressure (BP) in our laboratory. The results provide evidence to suggest that variations in the mitochondrial genome do not account for observed differences in blood pressure between the S and R rats. However, variants were detected among the mitochondrial genomes of the various hypertensive strains, S, SHR, and AS, and also among the normotensive strains R, MNS, and LEW. A total of 115, 114, 106, 106, and 16 variations in mtDNA were observed between the comparisons S versus LEW, S versus MNS, S versus SHR, S versus AS, and SHR versus AS, respectively. Among the 13 genes coding for proteins of the electron transport chain, 8 genes had nonsynonymous variations between S, LEW, MNS, SHR, and AS. The lack of any sequence variants between the mitochondrial genomes of S and R rats provides conclusive evidence that divergence in blood pressure between these two inbred strains is exclusively programmed through their nuclear genomes. The variations detected among the various hypertensive strains provides the basis to construct conplastic strains and further evaluate the effects of these variants on hypertension and associated phenotypes.

  12. Which BRCA genetic testing programs are ready for implementation in health care? A systematic review of economic evaluations

    PubMed Central

    D'Andrea, Elvira; Marzuillo, Carolina; De Vito, Corrado; Di Marco, Marco; Pitini, Erica; Vacchio, Maria Rosaria; Villari, Paolo

    2016-01-01

    Purpose: There is considerable evidence regarding the efficacy and effectiveness of BRCA genetic testing programs, but whether they represent good use of financial resources is not clear. Therefore, we aimed to identify the main health-care programs for BRCA testing and to evaluate their cost-effectiveness. Methods: We performed a systematic review of full economic evaluations of health-care programs involving BRCA testing. Results: Nine economic evaluations were included, and four main categories of BRCA testing programs were identified: (i) population-based genetic screening of individuals without cancer, either comprehensive or targeted based on ancestry; (ii) family history (FH)-based genetic screening, i.e., testing individuals without cancer but with FH suggestive of BRCA mutation; (iii) familial mutation (FM)-based genetic screening, i.e., testing individuals without cancer but with known familial BRCA mutation; and (iv) cancer-based genetic screening, i.e., testing individuals with BRCA-related cancers. Conclusions: Currently BRCA1/2 population-based screening represents good value for the money among Ashkenazi Jews only. FH-based screening is potentially very cost-effective, although further studies that include costs of identifying high-risk women are needed. There is no evidence of cost-effectiveness for BRCA screening of all newly diagnosed cases of breast/ovarian cancers followed by cascade testing of relatives, but programs that include tools for identifying affected women at higher risk for inherited forms are promising. Cost-effectiveness is highly sensitive to the cost of BRCA1/2 testing. Genet Med 18 12, 1171–1180. PMID:27906166

  13. Genetic analysis of survival and fitness in turkeys with multiple-trait animal models.

    PubMed

    Quinton, C D; Wood, B J; Miller, S P

    2011-11-01

    Genetic parameters for production, survival, and structural fitness traits recorded in pedigreed turkey sire and dam parental lines from a nucleus breeding program were estimated with multiple-trait animal models. Survival and conformation traits were scored in binary terms of health, where 0 = died or affected, and 1 = survived or healthy. Walking ability at 20 wk was subjectively scored from 1 (poor) to 6 (excellent). Body weights and egg production displayed moderate heritability (h(2) = 0.18 to 0.35). Early survival (to 3 wk) displayed low heritability (h(2) = 0.02 and 0.04 for the dam and sire lines, respectively). Late survival (3 to 23 wk) and longevity (age at death or cull) had low to moderate heritability (h(2) = 0.12 to 0.14). Walking ability had moderate heritability (h(2) = 0.26, 0.25). Leg structure health displayed low heritability (h(2) = 0.08), as did hip structure, foot, and skin health (h(2) ≤ 0.02). Crop health displayed moderate heritability (h(2) = 0.12). Walking ability, hip and leg structures, footpad, and breast skin health had negative genetic correlations with BW (r(G) = -0.50 to -0.23). Egg production had moderate positive genetic correlation with late survival (r(G) = 0.61). Genetic correlations between early and late survival were close to zero (r(G) = 0.10 and 0.03 for the dam and sire lines, respectively). Walking ability had high positive genetic correlations with late survival, longevity, hip structure, and leg structure in both lines (r(G) = 0.51 to 0.91). These genetic parameters indicate that unchecked selection for growth could decrease survival, walking ability, and hip, leg, footpad, and skin health in turkeys. However, index selection should be effective at improving fitness, survival, and growth simultaneously in commercial turkey lines. Walking ability should be a good indicator trait for selection to improve overall late survival and hip and leg health in turkeys.

  14. Building a genetic risk model for bipolar disorder from genome-wide association data with random forest algorithm

    PubMed Central

    Chuang, Li-Chung; Kuo, Po-Hsiu

    2017-01-01

    A genetic risk score could be beneficial in assisting clinical diagnosis for complex diseases with high heritability. With large-scale genome-wide association (GWA) data, the current study constructed a genetic risk model with a machine learning approach for bipolar disorder (BPD). The GWA dataset of BPD from the Genetic Association Information Network was used as the training data for model construction, and the Systematic Treatment Enhancement Program (STEP) GWA data were used as the validation dataset. A random forest algorithm was applied for pre-filtered markers, and variable importance indices were assessed. 289 candidate markers were selected by random forest procedures with good discriminability; the area under the receiver operating characteristic curve was 0.944 (0.935–0.953) in the training set and 0.702 (0.681–0.723) in the STEP dataset. Using a score with the cutoff of 184, the sensitivity and specificity for BPD was 0.777 and 0.854, respectively. Pathway analyses revealed important biological pathways for identified genes. In conclusion, the present study identified informative genetic markers to differentiate BPD from healthy controls with acceptable discriminability in the validation dataset. In the future, diagnosis classification can be further improved by assessing more comprehensive clinical risk factors and jointly analysing them with genetic data in large samples. PMID:28045094

  15. A Model Program for Dental Assisting Education in California.

    ERIC Educational Resources Information Center

    California State Dept. of Education, Sacramento. Bureau of Industrial Education.

    Intended to provide assistance for developing new programs and improving existing ones, the guide was constructed by dental assisting instructors and other professional participants in a 196 5 workshop conference. Elements of the model program were derived from a statistical analysis of California junior colleg e programs in dental assisting and…

  16. Testing Assumptions: The Impact of Two Study Abroad Program Models

    ERIC Educational Resources Information Center

    Norris, Emily Mohajeri; Dwyer, Mary M.

    2005-01-01

    There are many untested, long-held assumptions within the field of study abroad concerning the impact of program elements such as study duration, language of instruction, program models, and student housing choices. One assumption embraced within the field is that direct enrollment (or full immersion) programs are more effective at achieving a…

  17. Support for Career Development in Youth: Program Models and Evaluations

    ERIC Educational Resources Information Center

    Mekinda, Megan A.

    2012-01-01

    This article examines four influential programs--Citizen Schools, After School Matters, career academies, and Job Corps--to demonstrate the diversity of approaches to career programming for youth. It compares the specific program models and draws from the evaluation literature to discuss strengths and weaknesses of each. The article highlights…

  18. Subscale Beryllium Mirrors Demonstrator (SBMD) Program Summary and Ball Modeling

    NASA Technical Reports Server (NTRS)

    Kendrick, Stephen; Brown, Robert; Stahl, Philip (Technical Monitor)

    2001-01-01

    The SBMD Program was to design, fabricate, and test a 0.5-m beryllium lightweighted mirror applicable to space deployable systems with demanding optical and areal density requirements. This presentation summarizes the program's objectives and the mirror's tested technical performance along with lessons learned. In addition, test results are compared to modeling predictions. The SBMD Program was funded by NASA MSFC.

  19. An Implementation Model for a Communication across the Curriculum Program.

    ERIC Educational Resources Information Center

    Powell, Karen Sterkel; Jankovich, Jackie L.

    1997-01-01

    Presents a model outlining 10 steps for developing and implementing a Communication across the Curriculum (CAC) program, written from the authors' experience as coordinators of the CAC program at their university. Notes also inputs and resources needed to implement such a program, which offers an integrated approach for developing students'…

  20. Energy efficiency in nonprofit agencies: Creating effective program models

    SciTech Connect

    Brown, M.A.; Prindle, B.; Scherr, M.I.; White, D.L.

    1990-08-01

    Nonprofit agencies are a critical component of the health and human services system in the US. It has been clearly demonstrated by programs that offer energy efficiency services to nonprofits that, with minimal investment, they can educe their energy consumption by ten to thirty percent. This energy conservation potential motivated the Department of Energy and Oak Ridge National Laboratory to conceive a project to help states develop energy efficiency programs for nonprofits. The purpose of the project was two-fold: (1) to analyze existing programs to determine which design and delivery mechanisms are particularly effective, and (2) to create model programs for states to follow in tailoring their own plans for helping nonprofits with energy efficiency programs. Twelve existing programs were reviewed, and three model programs were devised and put into operation. The model programs provide various forms of financial assistance to nonprofits and serve as a source of information on energy efficiency as well. After examining the results from the model programs (which are still on-going) and from the existing programs, several replicability factors'' were developed for use in the implementation of programs by other states. These factors -- some concrete and practical, others more generalized -- serve as guidelines for states devising program based on their own particular needs and resources.

  1. Melanocortin MC₁ receptor in human genetics and model systems.

    PubMed

    Beaumont, Kimberley A; Wong, Shu S; Ainger, Stephen A; Liu, Yan Yan; Patel, Mira P; Millhauser, Glenn L; Smith, Jennifer J; Alewood, Paul F; Leonard, J Helen; Sturm, Richard A

    2011-06-11

    The melanocortin MC(1) receptor is a G-protein coupled receptor expressed in the melanocytes of the skin and hair and is known for its key role in the regulation of human pigmentation. Melanocortin MC(1) receptor activation after ultraviolet radiation exposure results in a switch from the red/yellow pheomelanin to the brown/black eumelanin pigment synthesis within cutaneous melanocytes; this pigment is then transferred to the surrounding keratinocytes of the skin. The increase in melanin maturation and uptake results in tanning of the skin, providing a physical protection of skin cells from ultraviolet radiation induced DNA damage. Melanocortin MC(1) receptor polymorphism is widespread within the Caucasian population and some variant alleles are associated with red hair colour, fair skin, poor tanning and increased risk of skin cancer. Here we will discuss the use of mouse coat colour models, human genetic association studies, and in vitro cell culture studies to determine the complex functions of the melanocortin MC(1) receptor and the molecular mechanisms underlying the association between melanocortin MC(1) receptor variant alleles and the red hair colour phenotype. Recent research indicates that melanocortin MC(1) receptor has many non-pigmentary functions, and that the increased risk of skin cancer conferred by melanocortin MC(1) receptor variant alleles is to some extent independent of pigmentation phenotypes. The use of new transgenic mouse models, the study of novel melanocortin MC(1) receptor response genes and the use of more advanced human skin models such as 3D skin reconstruction may provide key elements in understanding the pharmacogenetics of human melanocortin MC(1) receptor polymorphism.

  2. A graph-based evolutionary algorithm: Genetic Network Programming (GNP) and its extension using reinforcement learning.

    PubMed

    Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu

    2007-01-01

    This paper proposes a graph-based evolutionary algorithm called Genetic Network Programming (GNP). Our goal is to develop GNP, which can deal with dynamic environments efficiently and effectively, based on the distinguished expression ability of the graph (network) structure. The characteristics of GNP are as follows. 1) GNP programs are composed of a number of nodes which execute simple judgment/processing, and these nodes are connected by directed links to each other. 2) The graph structure enables GNP to re-use nodes, thus the structure can be very compact. 3) The node transition of GNP is executed according to its node connections without any terminal nodes, thus the past history of the node transition affects the current node to be used and this characteristic works as an implicit memory function. These structural characteristics are useful for dealing with dynamic environments. Furthermore, we propose an extended algorithm, "GNP with Reinforcement Learning (GNPRL)" which combines evolution and reinforcement learning in order to create effective graph structures and obtain better results in dynamic environments. In this paper, we applied GNP to the problem of determining agents' behavior to evaluate its effectiveness. Tileworld was used as the simulation environment. The results show some advantages for GNP over conventional methods.

  3. Communication Arts Curriculum: A Model Program. Revised.

    ERIC Educational Resources Information Center

    Tamaqua Area School District, PA.

    This publication describes, in three sections, a high school Communication Arts Curriculum (CAC) program designed to further students' communication skills as they participate in student-centered learning activities in the fine arts, the practical arts, and the performing arts. "Program Operation" includes a course outline and inventories for…

  4. Visual Teaching Model for Introducing Programming Languages

    ERIC Educational Resources Information Center

    Shehane, Ronald; Sherman, Steven

    2014-01-01

    This study examines detailed usage of online training videos that were designed to address specific course problems that were encountered in an online computer programming course. The study presents the specifics of a programming course where training videos were used to provide students with a quick start path to learning a new programming…

  5. Model Teacher - School Dental Hygiene Program.

    ERIC Educational Resources Information Center

    Smith, Lowell W.

    The purpose of this study, which was carried out during the 1972-73 school year at three parochial schools in the Houston area, was to determine the effectiveness of the Toothkeeper Program, a multimedia program of oral hygiene training carefully developed and packaged to establish effective long-term dental hygiene practice. The study population…

  6. The TEMPO Model: Outreach Program for Educators.

    ERIC Educational Resources Information Center

    Abouzeid, Mary P.; Scott, Virginia A.

    1995-01-01

    Describes TEMPO (Teaching Educators McGuffey Practicums Off-Grounds), a program at the University of Virginia that combines satellite broadcasts with two-way audio and live onsite instruction. The program delivers graduate reading courses to 50 different sites. Highlights include instructional design challenges, extra support for faculty, and…

  7. Within a smoking-cessation program, what impact does genetic information on lung cancer need to have to demonstrate cost-effectiveness?

    PubMed Central

    2010-01-01

    Background Many smoking-cessation programs and pharmaceutical aids demonstrate substantial health gains for a relatively low allocation of resources. Genetic information represents a type of individualized or personal feedback regarding the risk of developing lung cancer, and hence the potential benefits from stopping smoking, may motivate the person to remain smoke-free. The purpose of this study was to explore what the impact of a genetic test needs to have within a typical smoking-cessation program aimed at heavy smokers in order to be cost-effective. Methods Two strategies were modelled for a hypothetical cohort of heavy smokers aged 50 years; individuals either received or did not receive a genetic test within the course of a usual smoking-cessation intervention comprising nicotine replacement therapy (NRT) and counselling. A Markov model was constructed using evidence from published randomized controlled trials and meta-analyses for estimates on 12-month quit rates and long-term relapse rates. Epidemiological data were used for estimates on lung cancer risk stratified by time since quitting and smoking patterns. Extensive sensitivity analyses were used to explore parameter uncertainty. Results The discounted incremental cost per QALY was AU$34,687 (95% CI $12,483, $87,734) over 35 years. At a willingness-to-pay of AU$20,000 per QALY gained, the genetic testing strategy needs to produce a 12-month quit rate of at least 12.4% or a relapse rate 12% lower than NRT and counselling alone for it to be equally cost-effective. The likelihood that adding a genetic test to the usual smoking-cessation intervention is cost-effective was 20.6% however cost-effectiveness ratios were favourable in certain situations (e.g., applied to men only, a 60 year old cohort). Conclusions The findings were sensitive to small changes in critical variables such as the 12-month quit rates and relapse rates. As such, the cost-effectiveness of the genetic testing smoking cessation program

  8. Genetic Model Fitting in IQ, Assortative Mating & Components of IQ Variance.

    ERIC Educational Resources Information Center

    Capron, Christiane; Vetta, Adrian R.; Vetta, Atam

    1998-01-01

    The biometrical school of scientists who fit models to IQ data traces their intellectual ancestry to R. Fisher (1918), but their genetic models have no predictive value. Fisher himself was critical of the concept of heritability, because assortative mating, such as for IQ, introduces complexities into the study of a genetic trait. (SLD)

  9. Building an advanced climate model: Program plan for the CHAMMP (Computer Hardware, Advanced Mathematics, and Model Physics) Climate Modeling Program

    SciTech Connect

    Not Available

    1990-12-01

    The issue of global warming and related climatic changes from increasing concentrations of greenhouse gases in the atmosphere has received prominent attention during the past few years. The Computer Hardware, Advanced Mathematics, and Model Physics (CHAMMP) Climate Modeling Program is designed to contribute directly to this rapid improvement. The goal of the CHAMMP Climate Modeling Program is to develop, verify, and apply a new generation of climate models within a coordinated framework that incorporates the best available scientific and numerical approaches to represent physical, biogeochemical, and ecological processes, that fully utilizes the hardware and software capabilities of new computer architectures, that probes the limits of climate predictability, and finally that can be used to address the challenging problem of understanding the greenhouse climate issue through the ability of the models to simulate time-dependent climatic changes over extended times and with regional resolution.

  10. Functional Genetic Screen to Identify Interneurons Governing Behaviorally Distinct Aspects of Drosophila Larval Motor Programs

    PubMed Central

    Clark, Matt Q.; McCumsey, Stephanie J.; Lopez-Darwin, Sereno; Heckscher, Ellie S.; Doe, Chris Q.

    2016-01-01

    Drosophila larval crawling is an attractive system to study rhythmic motor output at the level of animal behavior. Larval crawling consists of waves of muscle contractions generating forward or reverse locomotion. In addition, larvae undergo additional behaviors, including head casts, turning, and feeding. It is likely that some neurons (e.g., motor neurons) are used in all these behaviors, but the identity (or even existence) of neurons dedicated to specific aspects of behavior is unclear. To identify neurons that regulate specific aspects of larval locomotion, we performed a genetic screen to identify neurons that, when activated, could elicit distinct motor programs. We used 165 Janelia CRM-Gal4 lines—chosen for sparse neuronal expression—to ectopically express the warmth-inducible neuronal activator TrpA1, and screened for locomotor defects. The primary screen measured forward locomotion velocity, and we identified 63 lines that had locomotion velocities significantly slower than controls following TrpA1 activation (28°). A secondary screen was performed on these lines, revealing multiple discrete behavioral phenotypes, including slow forward locomotion, excessive reverse locomotion, excessive turning, excessive feeding, immobile, rigid paralysis, and delayed paralysis. While many of the Gal4 lines had motor, sensory, or muscle expression that may account for some or all of the phenotype, some lines showed specific expression in a sparse pattern of interneurons. Our results show that distinct motor programs utilize distinct subsets of interneurons, and provide an entry point for characterizing interneurons governing different elements of the larval motor program. PMID:27172197

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

    PubMed

    Florez, Jose C; Jablonski, Kathleen A; McAteer, Jarred B; Franks, Paul W; Mason, Clinton C; Mather, Kieren; Horton, Edward; Goldberg, Ronald; Dabelea, Dana; Kahn, Steven E; Arakaki, Richard F; Shuldiner, Alan R; Knowler, William C

    2012-01-01

    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.

  12. Assessment of Genetic Heterogeneity in Structured Plant Populations Using Multivariate Whole-Genome Regression Models

    PubMed Central

    Lehermeier, Christina; Schön, Chris-Carolin; de los Campos, Gustavo

    2015-01-01

    Plant breeding populations exhibit varying levels of structure and admixture; these features are likely to induce heterogeneity of marker effects across subpopulations. Traditionally, structure has been dealt with as a potential confounder, and various methods exist to “correct” for population stratification. However, these methods induce a mean correction that does not account for heterogeneity of marker effects. The animal breeding literature offers a few recent studies that consider modeling genetic heterogeneity in multibreed data, using multivariate models. However, these methods have received little attention in plant breeding where population structure can have different forms. In this article we address the problem of analyzing data from heterogeneous plant breeding populations, using three approaches: (a) a model that ignores population structure [A-genome-based best linear unbiased prediction (A-GBLUP)], (b) a stratified (i.e., within-group) analysis (W-GBLUP), and (c) a multivariate approach that uses multigroup data and accounts for heterogeneity (MG-GBLUP). The performance of the three models was assessed on three different data sets: a diversity panel of rice (Oryza sativa), a maize (Zea mays L.) half-sib panel, and a wheat (Triticum aestivum L.) data set that originated from plant breeding programs. The estimated genomic correlations between subpopulations varied from null to moderate, depending on the genetic distance between subpopulations and traits. Our assessment of prediction accuracy features cases where ignoring population structure leads to a parsimonious more powerful model as well as others where the multivariate and stratified approaches have higher predictive power. In general, the multivariate approach appeared slightly more robust than either the A- or the W-GBLUP. PMID:26122758

  13. Program evaluation models and related theories: AMEE guide no. 67.

    PubMed

    Frye, Ann W; Hemmer, Paul A

    2012-01-01

    This Guide reviews theories of science that have influenced the development of common educational evaluation models. Educators can be more confident when choosing an appropriate evaluation model if they first consider the model's theoretical basis against their program's complexity and their own evaluation needs. Reductionism, system theory, and (most recently) complexity theory have inspired the development of models commonly applied in evaluation studies today. This Guide describes experimental and quasi-experimental models, Kirkpatrick's four-level model, the Logic Model, and the CIPP (Context/Input/Process/Product) model in the context of the theories that influenced their development and that limit or support their ability to do what educators need. The goal of this Guide is for educators to become more competent and confident in being able to design educational program evaluations that support intentional program improvement while adequately documenting or describing the changes and outcomes-intended and unintended-associated with their programs.

  14. The dc modeling program (DCMP): Version 2. 0

    SciTech Connect

    Chapman, D.G. )

    1990-08-01

    In this project one of the main objectives was the refinement of tools for the study of HVDC systems. The original software was prepared in project RP1964-2 (EL-4365) as power flow and stability program models for HVDC systems. In this project new modeling capabilities were added to both the power flow and stability models. Additionally, the HVDC specific model capabilities were integrated into a new program, termed the Standalone program, for use in the development and testing of HVDC models.

  15. The dc modeling program (DCMP): Version 2. 0

    SciTech Connect

    Chapman, D.G. )

    1990-08-01

    In this project one of the main objectives was the refinement of tools for the study of HVDC systems. The original software was prepared in project RP1964-2 (EL-4365) as power flow and stability program models for HVDC systems. In this project new modeling capabilities were added to both the power flow and stability models. Additionally, the HVDC specific model capabilities were integrated into a new program, termed the Standalone program, for use in the development and testing of HVDC models. This volume provides information on the application of the software in the form of a User's Manual.

  16. SMP: A solid modeling program version 2.0

    NASA Technical Reports Server (NTRS)

    Randall, D. P.; Jones, K. H.; Vonofenheim, W. H.; Gates, R. L.; Matthews, C. G.

    1986-01-01

    The Solid Modeling Program (SMP) provides the capability to model complex solid objects through the composition of primitive geometric entities. In addition to the construction of solid models, SMP has extensive facilities for model editing, display, and analysis. The geometric model produced by the software system can be output in a format compatible with existing analysis programs such as PATRAN-G. The present version of the SMP software supports six primitives: boxes, cones, spheres, paraboloids, tori, and trusses. The details for creating each of the major primitive types is presented. The analysis capabilities of SMP, including interfaces to existing analysis programs, are discussed.

  17. A Comparison of Three Programming Models for Adaptive Applications

    NASA Technical Reports Server (NTRS)

    Shan, Hong-Zhang; Singh, Jaswinder Pal; Oliker, Leonid; Biswa, Rupak; Kwak, Dochan (Technical Monitor)

    2000-01-01

    We study the performance and programming effort for two major classes of adaptive applications under three leading parallel programming models. We find that all three models can achieve scalable performance on the state-of-the-art multiprocessor machines. The basic parallel algorithms needed for different programming models to deliver their best performance are similar, but the implementations differ greatly, far beyond the fact of using explicit messages versus implicit loads/stores. Compared with MPI and SHMEM, CC-SAS (cache-coherent shared address space) provides substantial ease of programming at the conceptual and program orchestration level, which often leads to the performance gain. However it may also suffer from the poor spatial locality of physically distributed shared data on large number of processors. Our CC-SAS implementation of the PARMETIS partitioner itself runs faster than in the other two programming models, and generates more balanced result for our application.

  18. Using microsatellite DNA markers to determine the genetic identity of parental clones used in the Louisiana sugarcane breeding program

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Sugarcane propagates asexually through vegetative cuttings. To validate the genetic identity of sugarcane clones during shipping and handling, we produced molecular fingerprints based on 21 microsatellite (SSR) DNA markers for 116 Louisiana parental clones that were included in the crossing program...

  19. Genetic structure and diversity of parental cultivars involved in China mainland sugarcane breeding programs as inferred from DNA microsatellites

    Technology Transfer Automated Retrieval System (TEKTRAN)

    More than 1,400 Saccharum accessions of worldwide origin were available in the Chinese sugarcane breeding program, but the genetic diversity and population structure of these accessions has not been fully investigated. In this study, 96 proven important parental accessions of various geographical or...

  20. Development of Genetic Occurrence Models for Geothermal Prospecting

    NASA Astrophysics Data System (ADS)

    Walker, J. D.; Sabin, A.; Unruh, J.; Monastero, F. C.; Combs, J.

    2007-12-01

    , including high heat flow, anomalous temperature water wells, high-temperature indications from aqueous geothermometry and geochemistry, Pliocene or younger ages from low-temperature thermochronometers, as well as more obvious factors such as geysers and fumaroles (which by definition will be missing for blind resources). Our occurrence-model strategy inverts the current approach that relies first on obvious evidence of geothermal activity. We evaluated our approach by retrospectively applying the protocol to the characteristics of producing geothermal fields, and in all cases, known resource areas fit the parameters identified from a genetic perspective.

  1. Rodent models of genetic contributions to motivation to abuse alcohol.

    PubMed

    Crabbe, John C

    2014-01-01

    In summary, there are remarkably few studies focused on the genetic contributions to alcohol's reinforcing values. Almost all such studies examine the two-bottle preference test. Despite the deficiencies I have raised in its interpretation, a rodent genotype's willingness to drink ethanol when water is freely available offers a reasonable aggregate estimate of alcohol's reinforcing value relative to other genotypes (Green and Grahame 2008). As indicated above, however, preference drinking studies will likely never avoid the confounding role of taste preferences and most often yield intake levels not sufficient to yield a pharmacologically significant BAL. Thus, the quest for improved measures of reinforcing value continues. Of the potential motivational factors considered by McClearn in his seminal review in this series, we can safely conclude that rodent alcohol drinking is not primarily directed at obtaining calories. The role of taste (and odor) remains a challenge. McClearn appears to have been correct that especially those genotypes that avoid alcohol are probably doing so based on preingestive sensory cues; however, postingestive consequences are also important. Cunningham's intragastric model shows the role of both preingestional and postingestional modulating factors for the best known examples, the usually nearly absolutely alcohol-avoiding DBA/2J and HAP-2 mice. Much subsequent data reinforce McClearn's earlier conclusion that C57BL/6J mice, at least, do not regulate their intake around a given self-administered dose of alcohol by adjusting their intake. This leaves us with the puzzle of why nearly all genotypes, even those directionally selectively bred for high voluntary intake for many generations, fail to self-administer intoxicating amounts of alcohol. Since McClearn's review, many ingenious assays to index alcohol's motivational effects have been used extensively, and new methods for inducing dependence have supplanted the older ones prevalent in

  2. Model Preservation Program for a Small University Library.

    ERIC Educational Resources Information Center

    Robbins, Louise S.

    This report proposes a preservation program assuming a model of a university library serving 5,000 or fewer students and 350 or fewer faculty members. The model program is not for a comprehensive university or research institution, and the library's collection is one developed and used as a curriculum-support collection. The goal of the…

  3. Model Self-Improvement Program for Inmates (SIPI). Final Report.

    ERIC Educational Resources Information Center

    Northeast Texas Community Coll., Mount Pleasant.

    The Model Self-Improvement Program for Inmates (SIPI) was a joint effort of Northeast Texas Community College, the Lone Star Steel Company, and the sheriff's department of Morris County, Texas, to provide a model life skills program for incarcerated individuals. A curriculum that included life skills and vocational and academic training was…

  4. A Generalized Evaluation Model for Primary Prevention Programs.

    ERIC Educational Resources Information Center

    Barling, Phillip W.; Cramer, Kathryn D.

    A generalized evaluation model (GEM) has been developed to evaluate primary prevention program impact. The GEM model views primary prevention dynamically; delineating four structural components (program, organization, target population, system) and four developmental stages (initiation, establishment, integration, continuation). The interaction of…

  5. A Model for Integrating Program Development and Evaluation.

    ERIC Educational Resources Information Center

    Brown, J. Lynne; Kiernan, Nancy Ellen

    1998-01-01

    A communication model consisting of input from target audience, program delivery, and outcomes (receivers' perception of message) was applied to an osteoporosis-prevention program for working mothers ages 21 to 45. Due to poor completion rate on evaluation instruments and failure of participants to learn key concepts, the model was used to improve…

  6. A Model for Evaluating Development Programs. Miscellaneous Report.

    ERIC Educational Resources Information Center

    Burton, John E., Jr.; Rogers, David L.

    Taking the position that the Classical Experimental Evaluation (CEE) Model does not do justice to the process of acquiring information necessary for decision making re planning, programming, implementing, and recycling program activities, this paper presents the Inductive, System-Process (ISP) evaluation model as an alternative to be used in…

  7. A simulation model for wind energy storage systems. Volume 3: Program descriptions

    NASA Technical Reports Server (NTRS)

    Warren, A. W.; Edsinger, R. W.; Burroughs, J. D.

    1977-01-01

    Program descriptions, flow charts, and program listings for the SIMWEST model generation program, the simulation program, the file maintenance program, and the printer plotter program are given. For Vol 2, see .

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

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed Central

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

    2014-01-01

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

  10. Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS)

    NASA Astrophysics Data System (ADS)

    Emamgolizadeh, S.; Bateni, S. M.; Shahsavani, D.; Ashrafi, T.; Ghorbani, H.

    2015-10-01

    The soil cation exchange capacity (CEC) is one of the main soil chemical properties, which is required in various fields such as environmental and agricultural engineering as well as soil science. In situ measurement of CEC is time consuming and costly. Hence, numerous studies have used traditional regression-based techniques to estimate CEC from more easily measurable soil parameters (e.g., soil texture, organic matter (OM), and pH). However, these models may not be able to adequately capture the complex and highly nonlinear relationship between CEC and its influential soil variables. In this study, Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) were employed to estimate CEC from more readily measurable soil physical and chemical variables (e.g., OM, clay, and pH) by developing functional relations. The GEP- and MARS-based functional relations were tested at two field sites in Iran. Results showed that GEP and MARS can provide reliable estimates of CEC. Also, it was found that the MARS model (with root-mean-square-error (RMSE) of 0.318 Cmol+ kg-1 and correlation coefficient (R2) of 0.864) generated slightly better results than the GEP model (with RMSE of 0.270 Cmol+ kg-1 and R2 of 0.807). The performance of GEP and MARS models was compared with two existing approaches, namely artificial neural network (ANN) and multiple linear regression (MLR). The comparison indicated that MARS and GEP outperformed the MLP model, but they did not perform as good as ANN. Finally, a sensitivity analysis was conducted to determine the most and the least influential variables affecting CEC. It was found that OM and pH have the most and least significant effect on CEC, respectively.

  11. [The discussion of the infiltrative model of mathematical knowledge to genetics teaching].

    PubMed

    Liu, Jun; Luo, Pei-Gao

    2011-11-01

    Genetics, the core course of biological field, is an importance major-basic course in curriculum of many majors related with biology. Due to strong theoretical and practical as well as abstract of genetics, it is too difficult to study on genetics for many students. At the same time, mathematics is one of the basic courses in curriculum of the major related natural science, which has close relationship with the establishment, development and modification of genetics. In this paper, to establish the intrinsic logistic relationship and construct the integral knowledge network and to help students improving the analytic, comprehensive and logistic abilities, we applied some mathematical infiltrative model genetic knowledge in genetics teaching, which could help students more deeply learn and understand genetic knowledge.

  12. Evaluation of an Individual Placement and Support model (IPS) program.

    PubMed

    Lucca, Anna M; Henry, Alexis D; Banks, Steven; Simon, Lorna; Page, Stephanie

    2004-01-01

    While randomized clinical trials (RCTs) have helped to establish Individual Placement and Support (IPS) programs as an evidence-based practice, it is important to evaluate whether "real world" IPS programs can be implemented with fidelity and achieve outcomes comparable to programs evaluated in RCTs. The current evaluation examined retrospectively employment outcomes for go participants from an IPS-model Services for Employment and Education (SEE) program in Massachusetts over a 4.5-year period. Evaluators accessed demographic, functioning, and employment data from three sources--SEE program records/database, clinical records, and the Massachusetts Department of Mental Health Client Tracking system. Results indicate that the SEE program maintained high IPS fidelity and achieved employment outcomes comparable or superior to other SE and IPS model programs described in the literature.

  13. Heterozygosities and genetic relationship of tea cultivars revealed by simple sequence repeat markers and implications for breeding and genetic mapping programs.

    PubMed

    Tan, L Q; Zhang, C C; Qi, G N; Wang, L Y; Wei, K; Chen, S X; Zou, Y; Wu, L Y; Cheng, H

    2015-03-06

    Genetic maps are essential tools for quantitative trait locus analysis and marker-assisted selection breeding. In order to select parents that are highly heterozygous for genetic mapping, the heterozygosity (HS) of 24 tea cultivars (Camellia sinensis) was analyzed with 72 simple sequence repeat markers. In total, 359 alleles were obtained with an average of 4.99 per marker. The HS varied greatly from 37.5 to 71.0% with an average of 51.3%. On average, tea cultivars from Fujian Province showed a higher level of heterozygosity (59.8%) than those from Zhejiang (48.5%) and Yunnan (44.5%), and the 12 national tea cultivars were generally more heterozygous than the 12 provincial cultivars. Unweighted pair-group analysis using the arithmetic average grouping divided the 24 cultivars into 2 groups that are consistent with the morphological classification. All dual combinations of the 24 cultivars were studied to calculate the percentage of mappable markers when using pseudo-testcross mapping strategy, and results showed that this value also varied greatly from 51.4 to 90.3%. The genetic relationships and HS differences among different cultivars were discussed, and tea cultivars with high HS were recommended as cross parents for genetic mapping programs.

  14. Plasmodium falciparum genetic crosses in a humanized mouse model

    PubMed Central

    Vaughan, Ashley M.; Pinapati, Richard S.; Cheeseman, Ian H.; Camargo, Nelly; Fishbaugher, Matthew; Checkley, Lisa A.; Nair, Shalini; Hutyra, Carolyn A.; Nosten, François H.; Anderson, Timothy J. C.; Ferdig, Michael T.; Kappe, Stefan H. I.

    2015-01-01

    Genetic crosses of phenotypically distinct strains of the human malaria parasite Plasmodium falciparum are a powerful tool for identifying genes controlling drug resistance and other key phenotypes. Previous studies relied on the isolation of recombinant parasites from splenectomized chimpanzees, a research avenue that is no longer available. Here, we demonstrate that human-liver chimeric mice support recovery of recombinant progeny for the identification of genetic determinants of parasite traits and adaptations. PMID:26030447

  15. Review of Pathological Hallmarks of Schizophrenia: Comparison of Genetic Models With Patients and Nongenetic Models

    PubMed Central

    Jaaro-Peled, Hanna; Ayhan, Yavuz; Pletnikov, Mikhail V.; Sawa, Akira

    2010-01-01

    Schizophrenia is a condition that impairs higher brain functions, some of which are specific to humans. After identification of susceptibility genes for schizophrenia, many efforts have been made to generate genetics-based models for the disease. It is under debate whether behavioral deficits observed in rodents are sufficient to characterize these models. Alternatively, anatomical and neuropathological changes identified in brains of patients with schizophrenia may be utilized as translatable characteristics between humans and rodents, which are important for validation of the models. Here, we overview such anatomical and neuropathological changes in humans: enlarged ventricles, dendritic changes in the pyramidal neurons, and alteration of specific subtypes of interneurons. In this review, we will overview such morphological changes in brains from patients with schizophrenia. Then, we will describe that some of these alterations are already recapitulated even in classic nongenetic models for schizophrenia. Finally, in comparison with the changes in patients and nongenetic models, we will discuss the anatomical and neuropathological manifestation in genetic models for schizophrenia. PMID:19903746

  16. Technical note: Calculation of standard errors of estimates of genetic parameters with the multiple-trait derivative-free restricted maximal likelihood programs.

    PubMed

    Kachman, S D; Van Vleck, L D

    2007-10-01

    The multiple-trait derivative-free REML set of programs was written to handle partially missing data for multiple-trait analyses as well as single-trait models. Standard errors of genetic parameters were reported for univariate models and for multiple-trait analyses only when all traits were measured on animals with records. In addition to estimating (co)variance components for multiple-trait models with partially missing data, this paper shows how the multiple-trait derivative-free REML set of programs can also estimate SE by augmenting the data file when not all animals have all traits measured. Although the standard practice has been to eliminate records with partially missing data, that practice uses only a subset of the available data. In some situations, the elimination of partial records can result in elimination of all the records, such as one trait measured in one environment and a second trait measured in a different environment. An alternative approach requiring minor modifications of the original data and model was developed that provides estimates of the SE using an augmented data set that gives the same residual log likelihood as the original data for multiple-trait analyses when not all traits are measured. Because the same residual vector is used for the original data and the augmented data, the resulting REML estimators along with their sampling properties are identical for the original and augmented data, so that SE for estimates of genetic parameters can be calculated.

  17. Genetic and genomic analysis of RNases in model cyanobacteria.

    PubMed

    Cameron, Jeffrey C; Gordon, Gina C; Pfleger, Brian F

    2015-10-01

    Cyanobacteria are diverse photosynthetic microbes with the ability to convert CO2 into useful products. However, metabolic engineering of cyanobacteria remains challenging because of the limited resources for modifying the expression of endogenous and exogenous biochemical pathways. Fine-tuned control of protein production will be critical to optimize the biological conversion of CO2 into desirable molecules. Messenger RNAs (mRNAs) are labile intermediates that play critical roles in determining the translation rate and steady-state protein concentrations in the cell. The majority of studies on mRNA turnover have focused on the model heterotrophic bacteria Escherichia coli and Bacillus subtilis. These studies have elucidated many RNA modifying and processing enzymes and have highlighted the differences between these Gram-negative and Gram-positive bacteria, respectively. In contrast, much less is known about mRNA turnover in cyanobacteria. We generated a compendium of the major ribonucleases (RNases) and provide an in-depth analysis of RNase III-like enzymes in commonly studied and diverse cyanobacteria. Furthermore, using targeted gene deletion, we genetically dissected the RNases in Synechococcus sp. PCC 7002, one of the fastest growing and industrially attractive cyanobacterial strains. We found that all three cyanobacterial homologs of RNase III and a member of the RNase II/R family are not essential under standard laboratory conditions, while homologs of RNase E/G, RNase J1/J2, PNPase, and a different member of the RNase II/R family appear to be essential for growth. This work will enhance our understanding of native control of gene expression and will facilitate the development of an RNA-based toolkit for metabolic engineering in cyanobacteria.

  18. Education of nurses in genetics.

    PubMed Central

    Forsman, I

    1988-01-01

    The need for education of nurses in genetics was articulated more than 25 years ago. This article reviews the knowledge of practicing nurses about genetics as well as the content of genetics in nursing curricula. Implementation of federal legislation that mandated increased availability of genetic services and genetics education provided support for the examination of genetics content in curricula for health professionals, including nurses, and for the development of model programs to expand this content. Recent efforts to begin to develop a pool of nurse faculty who are well prepared in genetics will be described, as well as programs to provide the necessary content through continuing-education efforts. These efforts are expected to substantially improve the capability of nurses to contribute more effectively in the delivery of genetic services. PMID:3177390

  19. Motivational Interviewing in the Reciprocal Engagement Model of Genetic Counseling: a Method Overview and Case Illustration.

    PubMed

    Ash, Erin

    2016-12-28

    Motivational Interviewing is a well-described counseling method that has been applied to a broad range of health behavior encounters. Genetic counseling is an emerging area of utilization for the method of Motivational Interviewing. The relational and technical elements of the MI method are described within the context of genetic counseling encounters. Case excerpts will be used to illustrate incorporation of MI methods into the Reciprocal Engagement Model of the genetic counseling encounter.

  20. [Biochemical genetics in St. Petersburg university: from the gene-enzyme model to medical biotechnology].

    PubMed

    Padkina, M V; Sambuk, E V

    2007-10-01

    The history of biochemical genetic research in St. Petersburg (Leningrad) State University is described. The main research projects and achievements of the Laboratory of Biochemical Genetics in studies on the mechanisms of gene expression control, coordinated regulation of metabolism, and the relationship of the physiological state of yeast cells with the maintenance of genetic stability are discussed. The fundamental importance of studies on the acid phosphatase model for the formation and development of medical biotechnology in St. Petersburg University is demonstrated.

  1. Development and Implementation of a Program Management Maturity Model

    SciTech Connect

    Hartwig, Laura; Smith, Matt

    2008-12-15

    In 2006, Honeywell Federal Manufacturing & Technologies (FM&T) announced an updatedvision statement for the organization. The vision is “To be the most admired team within the NNSA [National Nuclear Security Administration] for our relentless drive to convert ideas into the highest quality products and services for National Security by applying the right technology, outstanding program management and best commercial practices.” The challenge to provide outstanding program management was taken up by the Program Management division and the Program Integration Office (PIO) of the company. This article describes how Honeywell developed and deployed a program management maturity model to drive toward excellence.

  2. Assessing a landscape barrier using genetic simulation modelling: implications for raccoon rabies management.

    PubMed

    Rees, Erin E; Pond, Bruce A; Cullingham, Catherine I; Tinline, Rowland; Ball, David; Kyle, Christopher J; White, Bradley N

    2008-08-15

    Landscape barriers influence movement patterns of animals, which in turn, affect spatio-temporal spread of infectious wildlife disease. We compare genetic data from computer simulations to those acquired from field samples to measure the effect of a landscape barrier on raccoon (Procyon lotor) movement, enabling risk assessment of raccoon rabies disease spread across the Niagara River from New York State into Ontario, an area currently uninfected by rabies. An individual-based spatially explicit model is used to simulate the expansion of a raccoon population to cross the Niagara River, for different permeabilities of the river to raccoon crossings. Since the model records individual raccoon genetics, the genetic population structure of neutral mitochondrial DNA haplotypes are characterised in the expanding population, every 25 years, using a genetic distance measure, phi ST, Mantel tests and a gene diversity measure. The river barrier effect is assessed by comparing genetic measures computed from model outputs to those calculated from 166 raccoons recently sampled from the same landscape. The "best fit" between modelled scenarios and field data indicate the river prevents 50% of attempts to cross the river. Founder effects dominated the colonizing genetic population structure, and, as the river barrier effect increased, its genetic diversity decreased. Using gene flow to calibrate the effect of the river as a barrier to movement provides an estimate of the effect of a river in reducing the likelihood of cross-river infection. Including individual genetic markers in simulation modelling benefits investigations of disease spread and control.

  3. Organizational analysis of three community support program models.

    PubMed

    Reinke, B; Greenley, J R

    1986-06-01

    Little attention has been paid to the organizational and administrative characteristics of effective community support programs for the chronic mentally ill. The authors analyzed three successful support programs in Wisconsin that employ three different models of service delivery: one provides services through caseworkers who carry specialized caseloads, another through local nonprofessionals who work with a centrally located program coordinator, and the third through a team of various mental health workers. Each program has tailored its organizational process to suit the types of clients it sees, the size of its catchment area, and the availability of other professional resources. The interrelated strengths and weaknesses of each model are discussed.

  4. Optimization of Water Distribution and Water Quality by Genetic Algorithm and Nonlinear Programming

    NASA Astrophysics Data System (ADS)

    Tu, M.; Tsai, F. T.; Yeh, W. W.

    2001-12-01

    When managing a regional water distribution system, it is not only important to optimize water allocation but also to meet the desired water quality requirements. This paper develops a multicommodity flow model that can be used to optimize water distribution and water quality in a regional water supply system. Waters from different sources with different quality are considered as distinct commodities, which concurrently share a single water distribution system. Volumetric water blend is used to represent water quality in the proposed model. The multicommodity model is capable of handling two-way flow pipes, as represented undirectional arcs, and the perfect mixing condition. Additionally, blending requirements are specified at certain control nodes within the water distribution system to ensure that downstream users receive the desired water quality. The developed multicommodity flow model is imbedded in a nonlinear optimization model. To reduce nonlinearity and to improve convergence, GA is combined with a gradient-based-algorithm to solve the nonlinearly constrained optimization model in that GA is used to search for the optimal direction for all undirectional arcs in the system and iteratively linked with a nonlinear programming solver. The proposed methodology was first tested and verified on a simplified hypothetical system and then applied to the regional water distribution system of the Metropolitan Water District of Southern California. The results obtained indicate that the optimization model can efficiently allocate waters from different sources with different quality to satisfy the blending requirements, the perfect mixing and two-way flow conditions.

  5. Genetic and epigenetic catalysts in early-life programming of adult cardiometabolic disorders.

    PubMed

    Estampador, Angela C; Franks, Paul W

    2014-01-01

    Evidence has emerged across the past few decades that the lifetime risk of developing morbidities like type 2 diabetes, obesity, and cardiovascular disease may be influenced by exposures that occur in utero and in childhood. Developmental abnormalities are known to occur at various stages in fetal growth. Epidemiological and mechanistic studies have sought to delineate developmental processes and plausible risk factors influencing pregnancy outcomes and later health. Whether these observations reflect causal processes or are confounded by genetic and social factors remains unclear, although animal (and some human) studies suggest that epigenetic programming events may be involved. Regardless of the causal basis to observations of early-life risk factors and later disease risk, the fact that such associations exist and that they are of a fairly large magnitude justifies further research around this topic. Furthermore, additional information is needed to substantiate public health guidelines on lifestyle behaviors during pregnancy to improve infant health outcomes. Indeed, lifestyle intervention clinical trials in pregnancy are now coming online, where materials and data are being collected that should facilitate understanding of the causal nature of intrauterine exposures related with gestational weight gain, such as elevated maternal blood glucose concentrations. In this review, we provide an overview of these concepts.

  6. Low-level feature extraction for edge detection using genetic programming.

    PubMed

    Fu, Wenlong; Johnston, Mark; Zhang, Mengjie

    2014-08-01

    Edge detection is a subjective task. Traditionally, a moving window approach is used, but the window size in edge detection is a tradeoff between localization accuracy and noise rejection. An automatic technique for searching a discriminated pixel's neighbors to construct new edge detectors is appealing to satisfy different tasks. In this paper, we propose a genetic programming (GP) system to automatically search pixels (a discriminated pixel and its neighbors) to construct new low-level subjective edge detectors for detecting edges in natural images, and analyze the pixels selected by the GP edge detectors. Automatically searching pixels avoids the problem of blurring edges from a large window and noise influence from a small window. Linear and second-order filters are constructed from the pixels with high occurrences in these GP edge detectors. The experiment results show that the proposed GP system has good performance. A comparison between the filters with the pixels selected by GP and all pixels in a fixed window indicates that the set of pixels selected by GP is compact but sufficiently rich to construct good edge detectors.

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

    PubMed

    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.

  8. Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem

    PubMed Central

    Lu, Qiang; Ren, Jun; Wang, Zhiguang

    2016-01-01

    A researcher can infer mathematical expressions of functions quickly by using his professional knowledge (called Prior Knowledge). But the results he finds may be biased and restricted to his research field due to limitation of his knowledge. In contrast, Genetic Programming method can discover fitted mathematical expressions from the huge search space through running evolutionary algorithms. And its results can be generalized to accommodate different fields of knowledge. However, since GP has to search a huge space, its speed of finding the results is rather slow. Therefore, in this paper, a framework of connection between Prior Formula Knowledge and GP (PFK-GP) is proposed to reduce the space of GP searching. The PFK is built based on the Deep Belief Network (DBN) which can identify candidate formulas that are consistent with the features of experimental data. By using these candidate formulas as the seed of a randomly generated population, PFK-GP finds the right formulas quickly by exploring the search space of data features. We have compared PFK-GP with Pareto GP on regression of eight benchmark problems. The experimental results confirm that the PFK-GP can reduce the search space and obtain the significant improvement in the quality of SR. PMID:26819577

  9. The genetic program for cartilage development has deep homology within Bilateria.

    PubMed

    Tarazona, Oscar A; Slota, Leslie A; Lopez, Davys H; Zhang, GuangJun; Cohn, Martin J

    2016-05-05

    The evolution of novel cell types led to the emergence of new tissues and organs during the diversification of animals. The origin of the chondrocyte, the cell type that synthesizes cartilage matrix, was central to the evolution of the vertebrate endoskeleton. Cartilage-like tissues also exist outside the vertebrates, although their relationship to vertebrate cartilage is enigmatic. Here we show that protostome and deuterostome cartilage share structural and chemical properties, and that the mechanisms of cartilage development are extensively conserved--from induction of chondroprogenitor cells by Hedgehog and β-catenin signalling, to chondrocyte differentiation and matrix synthesis by SoxE and SoxD regulation of clade A fibrillar collagen (ColA) genes--suggesting that the chondrogenic gene regulatory network evolved in the common ancestor of Bilateria. These results reveal deep homology of the genetic program for cartilage development in Bilateria and suggest that activation of this ancient core chondrogenic network underlies the parallel evolution of cartilage tissues in Ecdysozoa, Lophotrochozoa and Deuterostomia.

  10. Genetic characterization of physical activity behaviours in university students enrolled in kinesiology degree programs.

    PubMed

    Many, Gina M; Kendrick, Zachary; Deschamps, Chelsea L; Sprouse, Courtney; Tosi, Laura L; Devaney, Joseph M; Gordish-Dressman, Heather; Barfield, Whitney; Hoffman, Eric P; Houmard, Joseph A; Pescatello, Linda S; Vogel, Hans J; Shearer, Jane; Hittel, Dustin S

    2017-03-01

    Studies of physical activity behaviours have increasingly shown the importance of heritable factors such as genetic variation. Nonsynonymous polymorphisms of alpha-actinin 3 (ACTN3) and the β-adrenergic receptors 1 and 3 (ADRB1 and ADRB3) have been previously associated with exercise capacity and cardiometabolic health. We thus hypothesized that these polymorphisms are also related to physical activity behaviours in young adults. To test this hypothesis we examined relationships between ACTN3 (R577X), ARDB1 (Arg389Gly), ADRB3 (Trp64Arg), and physical activity behaviours in university students. We stratified for student enrollment in kinesiology degree programs compared with nonmajors as we previously found this to be a predictor of physical activity. We did not identify novel associations between physical activity and ACTN3. However, the minor alleles of ADRB1 and ADRB3 were significantly underrepresented in kinesiology students compared with nonmajors. Furthermore, carriers of the ADRB1 minor allele reported reduced participation in moderate physical activity and increased afternoon fatigue compared with ancestral allele homozygotes. Together, these findings suggest that the heritability of physical activity behaviours in young adults may be linked to nonsynonymous polymorphisms within β-adrenergic receptors.

  11. Semantic Search-Based Genetic Programming and the Effect of Intron Deletion.

    PubMed

    Castelli, Mauro; Vanneschi, Leonardo; Silva, Sara

    2014-01-01

    The concept of semantics (in the sense of input-output behavior of solutions on training data) has been the subject of a noteworthy interest in the genetic programming (GP) research community over the past few years. In this paper, we present a new GP system that uses the concept of semantics to improve search effectiveness. It maintains a distribution of different semantic behaviors and biases the search toward solutions that have similar semantics to the best solutions that have been found so far. We present experimental evidence of the fact that the new semantics-based GP system outperforms the standard GP and the well-known bacterial GP on a set of test functions, showing particularly interesting results for noncontinuous (i.e., generally harder to optimize) test functions. We also observe that the solutions generated by the proposed GP system often have a larger size than the ones returned by standard GP and bacterial GP and contain an elevated number of introns, i.e., parts of code that do not have any effect on the semantics. Nevertheless, we show that the deletion of introns during the evolution does not affect the performance of the proposed method.

  12. Object detection via feature synthesis using MDL-based genetic programming.

    PubMed

    Lin, Yingqiang; Bhanu, Bir

    2005-06-01

    In this paper, we use genetic programming (GP) to synthesize composite operators and composite features from combinations of primitive operations and primitive features for object detection. The motivation for using GP is to overcome the human experts' limitations of focusing only on conventional combinations of primitive image processing operations in the feature synthesis. GP attempts many unconventional combinations that in some cases yield exceptionally good results. To improve the efficiency of GP and prevent its well-known code bloat problem without imposing severe restriction on the GP search, we design a new fitness function based on minimum description length principle to incorporate both the pixel labeling error and the size of a composite operator into the fitness evaluation process. To further improve the efficiency of GP, smart crossover, smart mutation and a public library ideas are incorporated to identify and keep the effective components of composite operators. Our experiments, which are performed on selected training regions of a training image to reduce the training time, show that compared to normal GP, our GP algorithm finds effective composite operators more quickly and the learned composite operators can be applied to the whole training image and other similar testing images. Also, compared to a traditional region-of-interest extraction algorithm, the composite operators learned by GP are more effective and efficient for object detection.

  13. Manual for Dissemination of Promising Program Models.

    ERIC Educational Resources Information Center

    Martin, Susan C.

    The Massachusetts Board of Education recently adopted a 5-year plan to assist Local Education Agencies (LEAs) in the curriculum areas of reading, writing, arithmetic, science, and computer literacy. However, reduction in funds and staff at the LEA level has decreased the ability of local programs to implement marketing and assistance. This manual,…

  14. A Model for Evaluating Title 1 Programs.

    ERIC Educational Resources Information Center

    Rost, Paul; And Others

    Albuquerque's Title I evaluation staff is in the process of generating a comprehensive local evaluation design because it considers the federally required product evaluation unsatisfactory. The required mean-gain comparisons were extended beyond the dimension of program to the dimensions of school, grade, and Title I instructor. This evaluation…

  15. Linear Programming and Genetic Algorithm Based Optimization for the Weighting Scheme of a Value Focused Thinking Hierarchy

    DTIC Science & Technology

    2007-11-02

    scarce resources ( Bazaraa vii). The modeling capabilities linear programming provides has made it a success in many fields of study. Since the...Planning and Programming of Facility Construction Projects. 12 May 1994. Bazaraa , Mokhtar S., John J Jarvis and Hanif D. Sherali. Linear Programming

  16. Factors influencing QTL mapping accuracy under complicated genetic models by computer simulation.

    PubMed

    Su, C F; Wang, W; Gong, S L; Zuo, J H; Li, S J

    2016-12-19

    The accuracy of quantitative trait loci (QTLs) identified using different sample sizes and marker densities was evaluated in different genetic models. Model I assumed one additive QTL; Model II assumed three additive QTLs plus one pair of epistatic QTLs; and Model III assumed two additive QTLs with opposite genetic effects plus two pairs of epistatic QTLs. Recombinant inbred lines (RILs) (50-1500 samples) were simulated according to the Models to study the influence of different sample sizes under different genetic models on QTL mapping accuracy. RILs with 10-100 target chromosome markers were simulated according to Models I and II to evaluate the influence of marker density on QTL mapping accuracy. Different marker densities did not significantly influence accurate estimation of genetic effects with simple additive models, but influenced QTL mapping accuracy in the additive and epistatic models. The optimum marker density was approximately 20 markers when the recombination fraction between two adjacent markers was 0.056 in the additive and epistatic models. A sample size of 150 was sufficient for detecting simple additive QTLs. Thus, a sample size of approximately 450 is needed to detect QTLs with additive and epistatic models. Sample size must be approximately 750 to detect QTLs with additive, epistatic, and combined effects between QTLs. The sample size should be increased to >750 if the genetic models of the data set become more complicated than Model III. Our results provide a theoretical basis for marker-assisted selection breeding and molecular design breeding.

  17. Genetic hotels for the standard genetic code: evolutionary analysis based upon novel three-dimensional algebraic models.

    PubMed

    José, Marco V; Morgado, Eberto R; Govezensky, Tzipe

    2011-07-01

    Herein, we rigorously develop novel 3-dimensional algebraic models called Genetic Hotels of the Standard Genetic Code (SGC). We start by considering the primeval RNA genetic code which consists of the 16 codons of type RNY (purine-any base-pyrimidine). Using simple algebraic operations, we show how the RNA code could have evolved toward the current SGC via two different intermediate evolutionary stages called Extended RNA code type I and II. By rotations or translations of the subset RNY, we arrive at the SGC via the former (type I) or via the latter (type II), respectively. Biologically, the Extended RNA code type I, consists of all codons of the type RNY plus codons obtained by considering the RNA code but in the second (NYR type) and third (YRN type) reading frames. The Extended RNA code type II, comprises all codons of the type RNY plus codons that arise from transversions of the RNA code in the first (YNY type) and third (RNR) nucleotide bases. Since the dimensions of remarkable subsets of the Genetic Hotels are not necessarily integer numbers, we also introduce the concept of algebraic fractal dimension. A general decoding function which maps each codon to its corresponding amino acid or the stop signals is also derived. The Phenotypic Hotel of amino acids is also illustrated. The proposed evolutionary paths are discussed in terms of the existing theories of the evolution of the SGC. The adoption of 3-dimensional models of the Genetic and Phenotypic Hotels will facilitate the understanding of the biological properties of the SGC.

  18. From the genetic to the computer program: the historicity of 'data' and 'computation' in the investigations on the nematode worm C. elegans (1963-1998).

    PubMed

    García-Sancho, Miguel

    2012-03-01

    This paper argues that the history of the computer, of the practice of computation and of the notions of 'data' and 'programme' are essential for a critical account of the emergence and implications of data-driven research. In order to show this, I focus on the transition that the investigations on the worm C. elegans experienced in the Laboratory of Molecular Biology of Cambridge (UK). Throughout the 1980s, this research programme evolved from a study of the genetic basis of the worm's development and behaviour to a DNA mapping and sequencing initiative. By examining the changing computing technologies which were used at the Laboratory, I demonstrate that by the time of this transition researchers shifted from modelling the worm's genetic programme on a mainframe apparatus to writing minicomputer programs aimed at providing map and sequence data which was then circulated to other groups working on the genetics of C. elegans. The shift in the worm research should thus not be simply explained in the application of computers which transformed the project from hypothesis-driven to a data-intensive endeavour. The key factor was rather a historically specific technology-in-house and easy programmable minicomputers-which redefined the way of achieving the project's long-standing goal, leading the genetic programme to co-evolve with the practices of data production and distribution.

  19. Consideration of an applied model of public health program infrastructure.

    PubMed

    Lavinghouze, René; Snyder, Kimberly; Rieker, Patricia; Ottoson, Judith

    2013-01-01

    Systemic infrastructure is key to public health achievements. Individual public health program infrastructure feeds into this larger system. Although program infrastructure is rarely defined, it needs to be operationalized for effective implementation and evaluation. The Ecological Model of Infrastructure (EMI) is one approach to defining program infrastructure. The EMI consists of 5 core (Leadership, Partnerships, State Plans, Engaged Data, and Managed Resources) and 2 supporting (Strategic Understanding and Tactical Action) elements that are enveloped in a program's context. We conducted a literature search across public health programs to determine support for the EMI. Four of the core elements were consistently addressed, and the other EMI elements were intermittently addressed. The EMI provides an initial and partial model for understanding program infrastructure, but additional work is needed to identify evidence-based indicators of infrastructure elements that can be used to measure success and link infrastructure to public health outcomes, capacity, and sustainability.

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

    PubMed

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

    2013-02-01

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