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Sample records for genetic programming model

  1. Automatic reactor model synthesis with genetic programming.

    PubMed

    Dürrenmatt, David J; Gujer, Willi

    2012-01-01

    Successful modeling of wastewater treatment plant (WWTP) processes requires an accurate description of the plant hydraulics. Common methods such as tracer experiments are difficult and costly and thus have limited applicability in practice; engineers are often forced to rely on their experience only. An implementation of grammar-based genetic programming with an encoding to represent hydraulic reactor models as program trees should fill this gap: The encoding enables the algorithm to construct arbitrary reactor models compatible with common software used for WWTP modeling by linking building blocks, such as continuous stirred-tank reactors. Discharge measurements and influent and effluent concentrations are the only required inputs. As shown in a synthetic example, the technique can be used to identify a set of reactor models that perform equally well. Instead of being guided by experience, the most suitable model can now be chosen by the engineer from the set. In a second example, temperature measurements at the influent and effluent of a primary clarifier are used to generate a reactor model. A virtual tracer experiment performed on the reactor model has good agreement with a tracer experiment performed on-site. PMID:22277238

  2. 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.

  3. Monthly pan evaporation modeling using linear genetic programming

    NASA Astrophysics Data System (ADS)

    Guven, Aytac; Kisi, Ozgur

    2013-10-01

    This study compares the accuracy of linear genetic programming (LGP), fuzzy genetic (FG), adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and Stephens-Stewart (SS) methods in modeling pan evaporations. Monthly climatic data including solar radiation, air temperature, relative humidity, wind speed and pan evaporation from Antalya and Mersin stations, in Turkey are used in the study. The study composed of two parts. First part of the study focuses the comparison of LGP models with those of the FG, ANFIS, ANN and SS models in estimating pan evaporations of Antalya and Mersin stations, separately. From the comparison results, the LGP models are found to be better than the other models. Comparison of LGP models with the other models in estimating pan evaporations of the Mersin Station by using both stations' inputs is focused in the second part of the study. The results indicate that the LGP models better accuracy than the FG, ANFIS, ANN and SS models. It is seen that the pan evaporations can be successfully estimated by the LGP method.

  4. Identification of response surface models using genetic programming

    NASA Astrophysics Data System (ADS)

    Lew, T. L.; Spencer, A. B.; Scarpa, F.; Worden, K.; Rutherford, A.; Hemez, F.

    2006-11-01

    There is a move in modern research in Structural Dynamics towards analysing the inherent uncertainty in a given problem. This may be quantifying or fusing uncertainty models, or can be propagation of uncertainty through a system or calculation. If the system of interest is represented by, e.g. a large Finite Element (FE) model the large number of computations involved can rule out many approaches due to the expense of carrying out many runs. One way of circumnavigating this problem is to replace the true system by an approximate surrogate/replacement model, which is fast-running compared to the original. In traditional approaches using response surfaces a simple least-squares multinomial model is often adopted. The objective of this paper is to extend the class of possible models considerably by carrying out a general symbolic regression using a Genetic Programming approach. The approach is demonstrated on both univariate and multivariate problems with both computational and experimental data.

  5. 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.

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

    NASA Astrophysics Data System (ADS)

    Theofilatos, Konstantinos; Georgopoulos, Efstratios; Likothanassis, Spiridon

    2009-09-01

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

  7. 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. PMID:21806098

  8. 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

  9. 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.

  10. 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.

  11. 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. PMID:23614773

  12. 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.

  13. Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches.

    PubMed

    Oksel, Ceyda; Winkler, David A; Ma, Cai Y; Wilkins, Terry; Wang, Xue Z

    2016-09-01

    The number of engineered nanomaterials (ENMs) being exploited commercially is growing rapidly, due to the novel properties they exhibit. Clearly, it is important to understand and minimize any risks to health or the environment posed by the presence of ENMs. Data-driven models that decode the relationships between the biological activities of ENMs and their physicochemical characteristics provide an attractive means of maximizing the value of scarce and expensive experimental data. Although such structure-activity relationship (SAR) methods have become very useful tools for modelling nanotoxicity endpoints (nanoSAR), they have limited robustness and predictivity and, most importantly, interpretation of the models they generate is often very difficult. New computational modelling tools or new ways of using existing tools are required to model the relatively sparse and sometimes lower quality data on the biological effects of ENMs. The most commonly used SAR modelling methods work best with large datasets, are not particularly good at feature selection, can be relatively opaque to interpretation, and may not account for nonlinearity in the structure-property relationships. To overcome these limitations, we describe the application of a novel algorithm, a genetic programming-based decision tree construction tool (GPTree) to nanoSAR modelling. We demonstrate the use of GPTree in the construction of accurate and interpretable nanoSAR models by applying it to four diverse literature datasets. We describe the algorithm and compare model results across the four studies. We show that GPTree generates models with accuracies equivalent to or superior to those of prior modelling studies on the same datasets. GPTree is a robust, automatic method for generation of accurate nanoSAR models with important advantages that it works with small datasets, automatically selects descriptors, and provides significantly improved interpretability of models. PMID:26956430

  14. Empirical predictive model for the vmax/ amax ratio of strong ground motions using genetic programming

    NASA Astrophysics Data System (ADS)

    Jafarian, Yaser; Kermani, Elnaz; Baziar, Mohammad H.

    2010-12-01

    Earthquake-induced deformation of structures is strongly influenced by the frequency content of input motion. Nevertheless, state-of-the-practice studies commonly use the intensity measures such as peak ground acceleration ( PGA), which are not frequency dependent. The vmax/ amax ratio of strong ground motions can be used in seismic hazard studies as a parameter that captures the influence of frequency content. In the present study, genetic programming (GP) is employed to develop a new empirical predictive equation for the vmax/ amax ratio of the shallow crustal strong ground motions recorded at free field sites. The proposed model is a function of earthquake magnitude, closest distance from source to site ( Rclstd), faulting mechanism, and average shear wave velocity over the top 30 m of site ( Vs30 ). A wide-ranging database of strong ground motion released by Pacific Earthquake Engineering Research Center (PEER) was utilized. It is demonstrated that residuals of the final equation show insignificant bias against the variations of the predictive parameters. The results indicate that vmax/ amax increases through increasing earthquake magnitude and source-to-site distance while magnitude dependency is considerably more than distance dependency. In addition, the proposed model predicts higher vmax/ amax ratio at softer sites that possess higher fundamental periods. Consequently, as an instance for the application of the proposed model, its reasonable performance in liquefaction potential assessment of sands and silty sands is presented.

  15. Establishment of the mathematical model for diagnosing the engine valve faults by genetic programming

    NASA Astrophysics Data System (ADS)

    Yang, Wen-Xian

    2006-05-01

    Available machine fault diagnostic methods show unsatisfactory performances on both on-line and intelligent analyses because their operations involve intensive calculations and are labour intensive. Aiming at improving this situation, this paper describes the development of an intelligent approach by using the Genetic Programming (abbreviated as GP) method. Attributed to the simple calculation of the mathematical model being constructed, different kinds of machine faults may be diagnosed correctly and quickly. Moreover, human input is significantly reduced in the process of fault diagnosis. The effectiveness of the proposed strategy is validated by an illustrative example, in which three kinds of valve states inherent in a six-cylinders/four-stroke cycle diesel engine, i.e. normal condition, valve-tappet clearance and gas leakage faults, are identified. In the example, 22 mathematical functions have been specially designed and 8 easily obtained signal features are used to construct the diagnostic model. Different from existing GPs, the diagnostic tree used in the algorithm is constructed in an intelligent way by applying a power-weight coefficient to each feature. The power-weight coefficients vary adaptively between 0 and 1 during the evolutionary process. Moreover, different evolutionary strategies are employed, respectively for selecting the diagnostic features and functions, so that the mathematical functions are sufficiently utilized and in the meantime, the repeated use of signal features may be fully avoided. The experimental results are illustrated diagrammatically in the following sections.

  16. 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...

  17. 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

  18. Toward improving the reliability of hydrologic prediction: Model structure uncertainty and its quantification using ensemble-based genetic programming framework

    NASA Astrophysics Data System (ADS)

    Parasuraman, Kamban; Elshorbagy, Amin

    2008-12-01

    Uncertainty analysis is starting to be widely acknowledged as an integral part of hydrological modeling. The conventional treatment of uncertainty analysis in hydrologic modeling is to assume a deterministic model structure, and treat its associated parameters as imperfectly known, thereby neglecting the uncertainty associated with the model structure. In this paper, a modeling framework that can explicitly account for the effect of model structure uncertainty has been proposed. The modeling framework is based on initially generating different realizations of the original data set using a non-parametric bootstrap method, and then exploiting the ability of the self-organizing algorithms, namely genetic programming, to evolve their own model structure for each of the resampled data sets. The resulting ensemble of models is then used to quantify the uncertainty associated with the model structure. The performance of the proposed modeling framework is analyzed with regards to its ability in characterizing the evapotranspiration process at the Southwest Sand Storage facility, located near Ft. McMurray, Alberta. Eddy-covariance-measured actual evapotranspiration is modeled as a function of net radiation, air temperature, ground temperature, relative humidity, and wind speed. Investigating the relation between model complexity, prediction accuracy, and uncertainty, two sets of experiments were carried out by varying the level of mathematical operators that can be used to define the predictand-predictor relationship. While the first set uses just the additive operators, the second set uses both the additive and the multiplicative operators to define the predictand-predictor relationship. The results suggest that increasing the model complexity may lead to better prediction accuracy but at an expense of increasing uncertainty. Compared to the model parameter uncertainty, the relative contribution of model structure uncertainty to the predictive uncertainty of a model is

  19. Atmospheric Downscaling using Genetic Programming

    NASA Astrophysics Data System (ADS)

    Zerenner, T.; Venema, V.; Simmer, C.

    2013-12-01

    The coupling of models for the different components of the soil-vegetation-atmosphere system is required to understand component interactions and feedback processes. The Transregional Collaborative Research Center 32 (TR 32) has developed a coupled modeling platform, TerrSysMP, consisting of the atmospheric model COSMO, the land-surface model CLM, and the hydrological model ParFlow. These component models are usually operated at different resolutions in space and time owing to the dominant processes. These different scales should also be considered in the coupling mode, because it is for instance unfeasible to run the computationally quite expensive atmospheric models at the usually much higher spatial resolution required by hydrological models. Thus up- and downscaling procedures are required at the interface between atmospheric model and land-surface/subsurface models. Here we present an advanced atmospheric downscaling scheme, that creates realistic fine-scale fields (e.g. 400 m resolution) of the atmospheric state variables from the coarse atmospheric model output (e.g. 2.8 km resolution). The mixed physical/statistical scheme is developed from a training data set of high-resolution atmospheric model runs covering a range different weather conditions using Genetic Programming (GP). GP originates from machine learning: From a set of functions (arithmetic expressions, IF-statements, etc.) and terminals (constants or variables) GP generates potential solutions to a given problem while minimizing a fitness or cost function. We use a multi-objective approach that aims at fitting spatial structures, spatially distributed variance and spatio-temporal correlation of the fields. We account for the spatio-temporal nature of the data in two ways. On the one hand we offer GP potential predictors, which are based on our physical understanding of the atmospheric processes involved (spatial and temporal gradients, etc.). On the other hand we include functions operating on

  20. 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

  1. Genetic risks and genetic model specification.

    PubMed

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

    2016-08-21

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

  2. 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.

  3. Variable Size Genetic Network Programming

    NASA Astrophysics Data System (ADS)

    Katagiri, Hironobu; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

    Genetic Network Programming (GNP) is a kind of volutionary methods, which evolves arbitrary directed graph programs. Previously, the program size of GNP was fixed. In the paper, a new method is proposed, where the program size is adaptively changed depending on the frequency of the use of nodes. To control and to decide a program size are important and difficult problems in Evolutionary Computation, especially, a well-known crossover operator tends to cause bloat. We introduce two additional operators, add operator and delete operator, that can change the number of each kind of nodes based on whether a node function is important in the environment or not. Simulation results shows that the proposed method brings about extremely better results compared with ordinary fixed size GNP.

  4. 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.

  5. Application of Genetic Programing to Develop a Modular Model for the Simulation of Stream Flow Time Series

    NASA Astrophysics Data System (ADS)

    Meshgi, A.; Babovic, V.; Chui, T. F. M.; Schmitter, P.

    2014-12-01

    Developing reliable methods to estimate stream flow has been a subject of interest due to its importance in planning, design and management of water resources within a basin. Machine learning tools such as Artificial Neural Network (ANN) and Genetic Programming (GP) have been widely applied for rainfall-runoff modeling as they require less computational time as compared to physically-based models. As GP is able to generate a function with understandable structure, it may offer advantages over other data driven techniques and therefore has been used in different studies to generate rainfall-runoff functions. However, to date, proposed formulations only contain rainfall and/or streamflow data and consequently are local and cannot be generalized and adopted in other catchments which have different physical characteristics. This study investigated the capability of GP in developing a physically interpretable model with understandable structure to simulate stream flow based on hydrological parameters (e.g. precipitation) and catchment conditions (e.g., initial groundwater table elevation and area of the catchment) by following a modular approach. The modular model resulted in two sub-models where the baseflow was first predicted and the direct runoff was then estimated for a semi-urban catchment in Singapore. The simulated results matched very well with observed data in both the training and the testing of data sets, giving NSEs of 0.97 and 0.96 respectively demonstrated the successful estimation of stream flow using the modular model derived in this study. The results of this study indicate that GP is an effective tool in developing a physically interpretable model with understandable structure to simulate stream flow that can be transferred to other catchments.

  6. 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.

  7. Coupled simulation-optimization model for coastal aquifer management using genetic programming-based ensemble surrogate models and multiple-realization optimization

    NASA Astrophysics Data System (ADS)

    Sreekanth, J.; Datta, Bithin

    2011-04-01

    Approximation surrogates are used to substitute the numerical simulation model within optimization algorithms in order to reduce the computational burden on the coupled simulation-optimization methodology. Practical utility of the surrogate-based simulation-optimization have been limited mainly due to the uncertainty in surrogate model simulations. We develop a surrogate-based coupled simulation-optimization methodology for deriving optimal extraction strategies for coastal aquifer management considering the predictive uncertainty of the surrogate model. Optimization models considering two conflicting objectives are solved using a multiobjective genetic algorithm. Objectives of maximizing the pumping from production wells and minimizing the barrier well pumping for hydraulic control of saltwater intrusion are considered. Density-dependent flow and transport simulation model FEMWATER is used to generate input-output patterns of groundwater extraction rates and resulting salinity levels. The nonparametric bootstrap method is used to generate different realizations of this data set. These realizations are used to train different surrogate models using genetic programming for predicting the salinity intrusion in coastal aquifers. The predictive uncertainty of these surrogate models is quantified and ensemble of surrogate models is used in the multiple-realization optimization model to derive the optimal extraction strategies. The multiple realizations refer to the salinity predictions using different surrogate models in the ensemble. Optimal solutions are obtained for different reliability levels of the surrogate models. The solutions are compared against the solutions obtained using a chance-constrained optimization formulation and single-surrogate-based model. The ensemble-based approach is found to provide reliable solutions for coastal aquifer management while retaining the advantage of surrogate models in reducing computational burden.

  8. 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)

  9. 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

  10. On Using Surrogates with Genetic Programming.

    PubMed

    Hildebrandt, Torsten; Branke, Jürgen

    2015-01-01

    One way to accelerate evolutionary algorithms with expensive fitness evaluations is to combine them with surrogate models. Surrogate models are efficiently computable approximations of the fitness function, derived by means of statistical or machine learning techniques from samples of fully evaluated solutions. But these models usually require a numerical representation, and therefore cannot be used with the tree representation of genetic programming (GP). In this paper, we present a new way to use surrogate models with GP. Rather than using the genotype directly as input to the surrogate model, we propose using a phenotypic characterization. This phenotypic characterization can be computed efficiently and allows us to define approximate measures of equivalence and similarity. Using a stochastic, dynamic job shop scenario as an example of simulation-based GP with an expensive fitness evaluation, we show how these ideas can be used to construct surrogate models and improve the convergence speed and solution quality of GP. PMID:24967694

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. Filter Circuit Design by Parallel Genetic Programming

    NASA Astrophysics Data System (ADS)

    Yano, Yuichi; Kato, Toshiji; Inoue, Kaoru; Miki, Mitsunori

    Genetic Programming (GP) is an extension of Genetic Algorithm(GA) to handle more structural problems. In this paper, an approach to filter circuit design by GP is proposed. By designing a gene which includes not only the parameters of consisting elements, but also the structural information of the circuit, it becomes possible to apply the proposed approach to various types of filter circuits. GP depends much on trial and error due to its probabilitic nature. To decrease this uncertainty and ensure the progress of the evolution, Parallel GP with multiple populations with the island model is also proposed. An MPI-based cluster system is used for realization of this parallel computing where each island correspondsd to each node. A lowpass and an asymmetric bandpass filters are designed. One hundred times of trials for multiple populations with and without migrations are tested in the design of lowpass filter to confirm the validity of the proposed method. In the asymmetric bandpass filter design, the results are compared with those of the circuit designed by hand to confirm the effectiveness of the proposed method. The proposed approach is applicable to various types of filter circuits. It can contribute to an automated design procedure, where it would require a expirenced designer if done by hand. It is also possible to obtain a new circuit design which would not be possible if done by hand.

  16. 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)

  17. 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.

  18. Feature extraction from multiple data sources using genetic programming

    NASA Astrophysics Data System (ADS)

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

    2002-08-01

    Feature extraction 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. We use the GENetic Imagery Exploitation (GENIE) software for this purpose, 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, wildfire burnscars, and forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.

  19. 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…

  20. Genetics in Non-Genetic Model Systems

    PubMed Central

    Lois, Carlos; Groves, James O

    2011-01-01

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

  1. Duplication of coding segments in genetic programming

    SciTech Connect

    Haynes, T.

    1996-12-31

    Research into the utility of non-coding segments, or introns, in genetic-based encodings has shown that they expedite the evolution of solutions in domains by protecting building blocks against destructive crossover. We consider a genetic programming system where non-coding segments can be removed, and the resultant chromosomes returned into the population. This parsimonious repair leads to premature convergence, since as we remove the naturally occurring non-coding segments, we strip away their protective backup feature. We then duplicate the coding segments in the repaired chromosomes, and place the modified chromosomes into the population. The duplication method significantly improves the learning rate in the domain we have considered. We also show that this method can be applied to other domains.

  2. 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.

  3. 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.

  4. Virus-Evolutionary Liner Genetic Programming

    NASA Astrophysics Data System (ADS)

    Tamura, Kenji; Mutoh, Atsuko; Nakamura, Tsuyoshi; Itoh, Hidenori

    Many kinds of evolutionary methods have been proposed. GA and GP in particular have been demonstrated its effectiveness in various problems these days, and many systems have been proposed. One is Virus-Evolutionary Genetic Algorithm (VE-GA), and the other is Linear Genetic Programming in C (LGPC). Each of systems is reported its performance. VE-GA is the coevolution system that host individual and virus individuals. That can spread schema effectively among the host individuals by using the virus infection and virus incorporation. LGPC implements the GP by representing the individuals to one dimension as if GA. LGPC can reduce a search cost of pointer and save the machine memory, and can reduce the time to implements GP programs. We proposed that a system introduce virus individuals in LGPC, and the analyzed performance of the system at two problems. Our system can spread schema among the population, and search solution effectively. The results of computer simulation show that this system can search for solution depending on LGPC applying problem's character compare with LGPC. A search cost of pointer

  5. Development of a modular streamflow model to quantify runoff contributions from different land uses in tropical urban environments using Genetic Programming

    NASA Astrophysics Data System (ADS)

    Meshgi, Ali; Schmitter, Petra; Chui, Ting Fong May; Babovic, Vladan

    2015-06-01

    The decrease of pervious areas during urbanization has severely altered the hydrological cycle, diminishing infiltration and therefore sub-surface flows during rainfall events, and further increasing peak discharges in urban drainage infrastructure. Designing appropriate waster sensitive infrastructure that reduces peak discharges requires a better understanding of land use specific contributions towards surface and sub-surface processes. However, to date, such understanding in tropical urban environments is still limited. On the other hand, the rainfall-runoff process in tropical urban systems experiences a high degree of non-linearity and heterogeneity. Therefore, this study used Genetic Programming to establish a physically interpretable modular model consisting of two sub-models: (i) a baseflow module and (ii) a quick flow module to simulate the two hydrograph flow components. The relationship between the input variables in the model (i.e. meteorological data and catchment initial conditions) and its overall structure can be explained in terms of catchment hydrological processes. Therefore, the model is a partial greying of what is often a black-box approach in catchment modelling. The model was further generalized to the sub-catchments of the main catchment, extending the potential for more widespread applications. Subsequently, this study used the modular model to predict both flow components of events as well as time series, and applied optimization techniques to estimate the contributions of various land uses (i.e. impervious, steep grassland, grassland on mild slope, mixed grasses and trees and relatively natural vegetation) towards baseflow and quickflow in tropical urban systems. The sub-catchment containing the highest portion of impervious surfaces (40% of the area) contributed the least towards the baseflow (6.3%) while the sub-catchment covered with 87% of relatively natural vegetation contributed the most (34.9%). The results from the quickflow

  6. Evaluation of a statewide program in genetic diseases.

    PubMed

    Mitchell, J A; Petroski, G

    1998-07-01

    We used the Genetics Office Automation System (GOAS), a database management system designed to facilitate collection and analysis of medical genetic data, to evaluate the Missouri Genetics Disease Program (MGDP). From 1985 through 1995, patient data were collected at four tertiary care genetic centers. The number of genetic visits per 100,000 people more than doubled from 1985 through 1995. The results of subpopulation analyses indicate that the MGDP has facilitated improvements in: (1) services for newborns and infants, (2) rural outreach programs, and (3) evaluation of the incidence and impact of genetic disorders. PMID:9677054

  7. Modeling Interference in Genetic Recombination

    PubMed Central

    McPeek, M. S.; Speed, T. P.

    1995-01-01

    In analyzing genetic linkage data it is common to assume that the locations of crossovers along a chromosome follow a Poisson process, whereas it has long been known that this assumption does not fit the data. In many organisms it appears that the presence of a crossover inhibits the formation of another nearby, a phenomenon known as ``interference.'' We discuss several point process models for recombination that incorporate position interference but assume no chromatid interference. Using stochastic simulation, we are able to fit the models to a multilocus Drosophila dataset by the method of maximum likelihood. We find that some biologically inspired point process models incorporating one or two additional parameters provide a dramatically better fit to the data than the usual ``no-interference'' Poisson model. PMID:7713406

  8. Multitask visual learning using genetic programming.

    PubMed

    Jaśkowski, Wojciech; Krawiec, Krzysztof; Wieloch, Bartosz

    2008-01-01

    We propose a multitask learning method of visual concepts within the genetic programming (GP) framework. Each GP individual is composed of several trees that process visual primitives derived from input images. Two trees solve two different visual tasks and are allowed to share knowledge with each other by commonly calling the remaining GP trees (subfunctions) included in the same individual. The performance of a particular tree is measured by its ability to reproduce the shapes contained in the training images. We apply this method to visual learning tasks of recognizing simple shapes and compare it to a reference method. The experimental verification demonstrates that such multitask learning often leads to performance improvements in one or both solved tasks, without extra computational effort. PMID:19053494

  9. 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.

  10. Evolving retrieval algorithms with a genetic programming scheme

    NASA Astrophysics Data System (ADS)

    Theiler, James P.; Harvey, Neal R.; Brumby, Steven P.; Szymanski, John J.; Alferink, Steve; Perkins, Simon J.; Porter, Reid B.; Bloch, Jeffrey J.

    1999-10-01

    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 reflected 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.

  11. 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.

  12. 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.

  13. Controlling Individuals Growth in Semantic Genetic Programming through Elitist Replacement.

    PubMed

    Castelli, Mauro; Vanneschi, Leonardo; Popovič, Aleš

    2016-01-01

    In 2012, Moraglio and coauthors introduced new genetic operators for Genetic Programming, called geometric semantic genetic operators. They have the very interesting advantage of inducing a unimodal error surface for any supervised learning problem. At the same time, they have the important drawback of generating very large data models that are usually very hard to understand and interpret. The objective of this work is to alleviate this drawback, still maintaining the advantage. More in particular, we propose an elitist version of geometric semantic operators, in which offspring are accepted in the new population only if they have better fitness than their parents. We present experimental evidence, on five complex real-life test problems, that this simple idea allows us to obtain results of a comparable quality (in terms of fitness), but with much smaller data models, compared to the standard geometric semantic operators. In the final part of the paper, we also explain the reason why we consider this a significant improvement, showing that the proposed elitist operators generate manageable models, while the models generated by the standard operators are so large in size that they can be considered unmanageable. PMID:27057158

  14. 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.

  15. 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.

  16. Evolutionary optimization of interatomic potentials using genetic programming.

    SciTech Connect

    Jayaraman, Saivenkataraman

    2010-06-01

    After more than 50 years of molecular simulations, accurate empirical models are still the bottleneck in the wide adoption of simulation techniques. Addressing this issue with a fresh paradigm is the need of the day. In this study, we outline a new genetic-programming based method to develop empirical models for a system purely from its energy and/or forces. While the approach was initially developed for the development of classical force-fields from ab-initio calculations, we also discuss its application to the molecular coarse-graining of methanol. Two models, one representing methanol by a single site and the other via two sites will be developed using this method. They will be validated against existing coarse-grained potentials for methanol by comparing thermophysical properties.

  17. 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.

  18. Model Cities Training Program.

    ERIC Educational Resources Information Center

    Tennessee Univ., Chattanooga.

    The Model Cities Training Program, the first in the country, is a 10-session course to be conducted in seminar form under the direction of the University of Tennessee at Chattanooga. The objective is to enable the 50 members of the Community Development Administration Board of Directors to: acquire knowledge of the structure of the Model Cities…

  19. 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.

  20. 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

  1. 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.

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

    PubMed

    Koza, J R

    1994-01-01

    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. PMID:7584397

  3. Programming Cells: Towardsan automated “Genetic Compiler”

    PubMed Central

    Clancy, Kevin; Voigt, Christopher A.

    2010-01-01

    I. Summary The increasing scale and sophistication of genetic engineering will necessitate a new generation of computer-aided design (CAD). For large genetic programs, keeping track of the DNA on the level of nucleotides becomes tedious and error prone. 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. PMID:20702081

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

    PubMed

    Peluffo, Alexandre E

    2015-07-01

    The metaphor of the "genetic program," indicating the genome as a set of instructions required to build a phenotype, has been very influential in biology despite various criticisms over the years. This metaphor, first published in 1961, is thought to have been invented independently in two different articles, one by Ernst Mayr and the other by François Jacob and Jacques Monod. Here, after a detailed analysis of what both parties meant by "genetic program," I show, using unpublished archives, the strong resemblance between the ideas of Mayr and Monod and suggest that their idea of genetic program probably shares a common origin. I explore the possibility that the two men met before 1961 and also exchanged their ideas through common friends and colleagues in the field of molecular biology. Based on unpublished correspondence of Jacob and Monod, I highlight the important events that influenced the preparation of their influential paper, which introduced the concept of the genetic program. Finally, I suggest that the genetic program metaphor may have preceded both papers and that it was probably used informally before 1961. PMID:26170444

  5. Joint Modeling of Imaging and Genetics

    PubMed Central

    Batmanghelich, Nematollah K.; Dalca, Adrian V.; Sabuncu, Mert R.; Golland, Polina

    2014-01-01

    We propose a unified Bayesian framework for detecting genetic variants associated with a disease while exploiting image-based features as an intermediate phenotype. Traditionally, imaging genetics methods comprise two separate steps. First, image features are selected based on their relevance to the disease phenotype. Second, a set of genetic variants are identified to explain the selected features. In contrast, our method performs these tasks simultaneously to ultimately assign probabilistic measures of relevance to both genetic and imaging markers. We derive an efficient approximate inference algorithm that handles high dimensionality of imaging genetic data. We evaluate the algorithm on synthetic data and show that it outperforms traditional models. We also illustrate the application of the method on ADNI data. PMID:24684016

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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. PMID:25197633

  11. Bloat free genetic programming: application to human oral bioavailability prediction.

    PubMed

    Silva, Sara; Vanneschi, Leonardo

    2012-01-01

    Being able to predict the human oral bioavailability for a potential new drug is extremely important for the drug discovery process. This problem has been addressed by several prediction tools, with Genetic Programming providing some of the best results ever achieved. In this paper we use the newest developments of Genetic Programming, in particular the latest bloat control method, Operator Equalisation, to find out how much improvement we can achieve on this problem. We show examples of some actual solutions and discuss their quality, comparing them with previously published results. We identify some unexpected behaviours related to overfitting, and discuss the way for further improving the practical usage of the Genetic Programming approach. PMID:23356009

  12. Genetic and environmental melanoma models in fish

    PubMed Central

    Patton, E Elizabeth; Mitchell, David L; Nairn, Rodney S

    2010-01-01

    Experimental animal models are extremely valuable for the study of human diseases, especially those with underlying genetic components. The exploitation of various animal models, from fruitflies to mice, has led to major advances in our understanding of the etiologies of many diseases, including cancer. Cutaneous malignant melanoma is a form of cancer for which both environmental insult (i.e., UV) and hereditary predisposition are major causative factors. Fish melanoma models have been used in studies of both spontaneous and induced melanoma formation. Genetic hybrids between platyfish and swordtails, different species of the genus Xiphophorus, have been studied since the 1920s to identify genetic determinants of pigmentation and melanoma formation. Recently, transgenesis has been used to develop zebrafish and medaka models for melanoma research. This review will provide a historical perspective on the use of fish models in melanoma research, and an updated summary of current and prospective studies using these unique experimental systems. PMID:20230482

  13. The Ising Model in Physics and Statistical Genetics

    PubMed Central

    Majewski, Jacek; Li, Hao; Ott, Jurg

    2001-01-01

    Interdisciplinary communication is becoming a crucial component of the present scientific environment. Theoretical models developed in diverse disciplines often may be successfully employed in solving seemingly unrelated problems that can be reduced to similar mathematical formulation. The Ising model has been proposed in statistical physics as a simplified model for analysis of magnetic interactions and structures of ferromagnetic substances. Here, we present an application of the one-dimensional, linear Ising model to affected-sib-pair (ASP) analysis in genetics. By analyzing simulated genetics data, we show that the simplified Ising model with only nearest-neighbor interactions between genetic markers has statistical properties comparable to much more complex algorithms from genetics analysis, such as those implemented in the Allegro and Mapmaker-Sibs programs. We also adapt the model to include epistatic interactions and to demonstrate its usefulness in detecting modifier loci with weak individual genetic contributions. A reanalysis of data on type 1 diabetes detects several susceptibility loci not previously found by other methods of analysis. PMID:11517425

  14. 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

  15. Algorithmic Trading with Developmental and Linear Genetic Programming

    NASA Astrophysics Data System (ADS)

    Wilson, Garnett; Banzhaf, Wolfgang

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

  16. 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.

  17. 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. PMID:26446984

  18. Reverse engineering of metabolic pathways from observed data using genetic programming.

    PubMed

    Koza, J R; Mydlowec, W; Lanza, G; Yu, J; Keane, M A

    2001-01-01

    Recent work has demonstrated that genetic programming is capable of automatically creating complex networks (such as analog electrical circuits and controllers) whose behavior is modeled by linear and non-linear continuous-time differential equations and whose behavior matches prespecified output values. The concentrations of substances participating in networks of chemical reactions are also modeled by non-linear continuous-time differential equations. This paper demonstrates that it is possible to automatically create (reverse engineer) a network of chemical reactions from observed time-domain data. Genetic programming starts with observed time-domain concentrations of input substances and automatically creates both the topology of the network of chemical reactions and the rates of each reaction within the network such that the concentration of the final product of the automatically created network matches the observed time-domain data. Specifically, genetic programming automatically created metabolic pathways involved in the phospholipid cycle and the synthesis and degradation of ketone bodies. PMID:11262962

  19. 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…

  20. 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

  1. Harnessing Automatic Data Collection to Enhance Genetic Improvement Programs

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Automatic data collection can improve data accuracy, reduce cost of obtaining data, and enable addition of other traits. In the United States, genetic improvement programs for dairy cattle have benefited from a long history of innovation related to data collection and processing: computerization in ...

  2. Composition of Music and Financial Strategies via Genetic Programming

    NASA Astrophysics Data System (ADS)

    Iba, Hitoshi; Aranha, Claus

    We present two applications of genetic programming to real world problems: musical composition and financial portfolio optimization. In each of these applications, a specialized genome representation is used in order to break the problem down into smaller instances and put them back together. Results showing the applicability of the approaches are presented.

  3. 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. PMID:26106410

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

    PubMed Central

    Vanneschi, Leonardo

    2015-01-01

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

  5. Co-evolution of Hetero Multiagent Systems using Genetic Network Programming

    NASA Astrophysics Data System (ADS)

    Hirasawa, Kotaro; Okubo, Masafumi; Hu, Jinglu; Murata, Junichi; Matsuya, Yuko

    Recently, many methods of evolutionary computation such as Genetic Algorithm(GA) and Genetic Programming(GP) have been developed as a basic tool for modeling and optimizing complex systems. GA has the genome of string structure, while the genome in GP is of tree structure. In this paper, a new evolutionary method named Genetic Network Programming(GNP), whose genome has network structure is applied to multiagent sysytems. Hetero Multiagent Sysytems with GNP are studied, where each agent of the same group has its own GNP program in order to build the adaptive agents against changing environments. Specifically, the comparison between Hetero Multiagent Systems and conventional Homo Multiagent Sysytems is carried out in simulations on ants behaviors.

  6. 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 genetic…

  7. 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

  8. Exponential family models and statistical genetics.

    PubMed

    Palmgren, J

    2000-02-01

    This article describes the evolution of applied exponential family models, starting at 1972, the year of publication of the seminal papers on generalized linear models and on Cox regression, and leading to multivariate (i) marginal models and inference based on estimating equations and (ii) random effects models and Bayesian simulation-based posterior inference. By referring to recent work in genetic epidemiology, on semiparametric methods for linkage analysis and on transmission/disequilibrium tests for haplotype transmission this paper illustrates the potential for the recent advances in applied probability and statistics to contribute to new and unified tools for statistical genetics. Finally, it is emphasized that there is a need for well-defined postgraduate education paths in medical statistics in the year 2000 and thereafter. PMID:10826159

  9. Modeling a magnetostrictive transducer using genetic algorithm

    NASA Astrophysics Data System (ADS)

    Almeida, L. A. L.; Deep, G. S.; Lima, A. M. N.; Neff, H.

    2001-05-01

    This work reports on the applicability of the genetic algorithm (GA) to the problem of parameter determination of magnetostrictive transducers. A combination of the Jiles-Atherton hysteresis model with a quadratic moment rotation model is simulated using known parameters of a sensor. The simulated sensor data are then used as input data for the GA parameter calculation method. Taking the previously known parameters, the accuracy of the GA parameter calculation method can be evaluated.

  10. 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

  11. 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

  12. 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. PMID:25312505

  13. Probabilistic Modeling of Imaging, Genetics and Diagnosis.

    PubMed

    Batmanghelich, Nematollah K; Dalca, Adrian; Quon, Gerald; Sabuncu, Mert; Golland, Polina

    2016-07-01

    We propose a unified Bayesian framework for detecting genetic variants associated with disease by exploiting image-based features as an intermediate phenotype. The use of imaging data for examining genetic associations promises new directions of analysis, but currently the most widely used methods make sub-optimal use of the richness that these data types can offer. Currently, image features are most commonly selected based on their relevance to the disease phenotype. Then, in a separate step, a set of genetic variants is identified to explain the selected features. In contrast, our method performs these tasks simultaneously in order to jointly exploit information in both data types. The analysis yields probabilistic measures of clinical relevance for both imaging and genetic markers. We derive an efficient approximate inference algorithm that handles the high dimensionality of image and genetic data. We evaluate the algorithm on synthetic data and demonstrate that it outperforms traditional models. We also illustrate our method on Alzheimer's Disease Neuroimaging Initiative data. PMID:26886973

  14. Modeling of Nonlinear Systems using Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Hayashi, Kayoko; Yamamoto, Toru; Kawada, Kazuo

    In this paper, a newly modeling system by using Genetic Algorithm (GA) is proposed. The GA is an evolutionary computational method that simulates the mechanisms of heredity or evolution of living things, and it is utilized in optimization and in searching for optimized solutions. Most process systems have nonlinearities, so it is necessary to anticipate exactly such systems. However, it is difficult to make a suitable model for nonlinear systems, because most nonlinear systems have a complex structure. Therefore the newly proposed method of modeling for nonlinear systems uses GA. Then, according to the newly proposed scheme, the optimal structure and parameters of the nonlinear model are automatically generated.

  15. 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

  16. 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. PMID:26004260

  17. 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

  18. Social Program Evaluation: Six Models.

    ERIC Educational Resources Information Center

    New Directions for Program Evaluation, 1980

    1980-01-01

    Representative models of program evaluation are described by their approach to values, and categorized by empirical style: positivism versus humanism. The models are: social process audit; experimental/quasi-experimental research design; goal-free evaluation; systems evaluation; cost-benefit analysis; and accountability program evaluation. (CP)

  19. 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. PMID:27288529

  20. Genetic programs constructed from layered logic gates in single cells

    PubMed Central

    Moon, Tae Seok; Lou, Chunbo; Tamsir, Alvin; Stanton, Brynne C.; Voigt, Christopher A.

    2014-01-01

    Genetic programs function to integrate environmental sensors, implement signal processing algorithms and control expression dynamics1. These programs consist of integrated genetic circuits that individually implement operations ranging from digital logic to dynamic circuits2–6, and they have been used in various cellular engineering applications, including the implementation of process control in metabolic networks and the coordination of spatial differentiation in artificial tissues. A key limitation is that the circuits are based on biochemical interactions occurring in the confined volume of the cell, so the size of programs has been limited to a few circuits1,7. Here we apply part mining and directed evolution to build a set of transcriptional AND gates in Escherichia coli. Each AND gate integrates two promoter inputs and controls one promoter output. This allows the gates to be layered by having the output promoter of an upstream circuit serve as the input promoter for a downstream circuit. Each gate consists of a transcription factor that requires a second chaperone protein to activate the output promoter. Multiple activator–chaperone pairs are identified from type III secretion pathways in different strains of bacteria. Directed evolution is applied to increase the dynamic range and orthogonality of the circuits. These gates are connected in different permutations to form programs, the largest of which is a 4-input AND gate that consists of 3 circuits that integrate 4 inducible systems, thus requiring 11 regulatory proteins. Measuring the performance of individual gates is sufficient to capture the behaviour of the complete program. Errors in the output due to delays (faults), a common problem for layered circuits, are not observed. This work demonstrates the successful layering of orthogonal logic gates, a design strategy that could enable the construction of large, integrated circuits in single cells. PMID:23041931

  1. 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.

  2. 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. PMID:25786796

  3. Mouse Genetic Models of Human Brain Disorders.

    PubMed

    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

  4. 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

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

    PubMed

    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

  6. 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

  7. Genetically engineered livestock for biomedical models.

    PubMed

    Rogers, Christopher S

    2016-06-01

    To commemorate Transgenic Animal Research Conference X, this review summarizes the recent progress in developing genetically engineered livestock species as biomedical models. The first of these conferences was held in 1997, which turned out to be a watershed year for the field, with two significant events occurring. One was the publication of the first transgenic livestock animal disease model, a pig with retinitis pigmentosa. Before that, the use of livestock species in biomedical research had been limited to wild-type animals or disease models that had been induced or were naturally occurring. The second event was the report of Dolly, a cloned sheep produced by somatic cell nuclear transfer. Cloning subsequently became an essential part of the process for most of the models developed in the last 18 years and is stilled used prominently today. This review is intended to highlight the biomedical modeling achievements that followed those key events, many of which were first reported at one of the previous nine Transgenic Animal Research Conferences. Also discussed are the practical challenges of utilizing livestock disease models now that the technical hurdles of model development have been largely overcome. PMID:26820410

  8. 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

    2015-01-01

    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. PMID:26329332

  9. The genetic diversity of triticale genotypes involved in Polish breeding programs.

    PubMed

    Niedziela, Agnieszka; Orłowska, Renata; Machczyńska, Joanna; Bednarek, Piotr T

    2016-01-01

    Genetic diversity analysis of triticale populations is useful for breeding programs, as it helps to select appropriate genetic material for classifying the parental lines, heterotic groups and predicting hybrid performance. In our study 232 breeding forms were analyzed using diversity arrays technology markers. Principal coordinate analysis followed by model-based Bayesian analysis of population structure revealed the presence of weak data structuring with three groups of data. In the first group, 17 spring and 17 winter forms were clustered. The second and the third groups were represented by 101 and 26 winter forms, respectively. Polymorphic information content values, as well as Shannon's Information Index, were higher for the first (0.319) and second (0.309) than for third (0.234) group. AMOVA analysis demonstrated a higher level of within variation (86 %) than among populations (14 %). This study provides the basic information on the presence of structure within a genetic pool of triticale breeding forms. PMID:27066368

  10. 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. PMID:23615969

  11. 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.

  12. 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.

  13. Perceptions of Latinas on the Traditional Prenatal Genetic Counseling Model.

    PubMed

    Thompson, Stephanie; Noblin, Sarah Jane; Lemons, Jennifer; Peterson, Susan K; Carreno, Carlos; Harbison, Andrea

    2015-08-01

    The traditional genetic counseling model encompasses an individualized counseling session that includes the presentation of information about genes, chromosomes, personalized risk assessment, and genetic testing and screening options. Counselors are challenged to balance the provision of enough basic genetic information to ensure clients' understanding of the genetic condition in question with a personalized discussion of what this information means to them. This study explored the perceptions Latinas have about prenatal genetic counseling sessions and aimed to determine if they had preferences about the delivery of care. Data were collected through focus groups and one-on-one, semi-structured interviews of 25 Spanish speaking Latinas who received genetic counseling during their current pregnancy. We implemented grounded theory to evaluate participant responses, and were able to identify common emergent themes. Several themes were identified including an overall satisfaction with their prenatal genetic counseling appointment, desire for a healthy baby, peace of mind following their appointment, lack of desire for invasive testing, and faith in God. Several participants stated a preference for group genetic counseling over the traditional individual genetic counseling model. Our data indicate that Latinas value the information presented at prenatal genetic counseling appointments despite disinterest in pursuing genetic testing or screening and suggest that group prenatal genetic counseling may be an effective alternative to the traditional genetic counseling model in the Latina population. PMID:25475921

  14. 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.

  15. 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.

  16. 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. PMID:20479505

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

    SciTech Connect

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

    1999-11-01

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

  18. Genetic programming approach to extracting features from remotely sensed imagery

    SciTech Connect

    Theiler, J. P.; Perkins, S. J.; Harvey, N. R.; Szymanski, J. J.; Brumby, Steven P.

    2001-01-01

    Multi-instrument data sets present an interesting challenge to feature extraction algorithm developers. Beyond the immediate problems of spatial co-registration, the remote sensing scientist must explore a complex algorithm space in which both spatial and spectral signatures may be required to identify a feature of interest. We describe a genetic programming/supervised classifier software system, called Genie, which evolves and combines spatio-spectral image processing tools for remotely sensed imagery. We describe our representation of candidate image processing pipelines, and discuss our set of primitive image operators. Our primary application has been in the field of geospatial feature extraction, including wildfire scars and general land-cover classes, using publicly available multi-spectral imagery (MSI) and hyper-spectral imagery (HSI). Here, we demonstrate our system on Landsat 7 Enhanced Thematic Mapper (ETM+) MSI. We exhibit an evolved pipeline, and discuss its operation and performance.

  19. 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.

  20. 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.

  1. Competitive speciation in quantitative genetic models.

    PubMed

    Drossel, B; Mckane, A

    2000-06-01

    We study sympatric speciation due to competition in an environment with a broad distribution of resources. We assume that the trait under selection is a quantitative trait, and that mating is assortative with respect to this trait. Our model alternates selection according to Lotka-Volterra-type competition equations, with reproduction using the ideas of quantitative genetics. The recurrence relations defined by these equations are studied numerically and analytically. We find that when a population enters a new environment, with a broad distribution of unexploited food sources, the population distribution broadens under a variety of conditions, with peaks at the edge of the distribution indicating the formation of subpopulations. After a long enough time period, the population can split into several subpopulations with little gene flow between them. PMID:10816369

  2. 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.

  3. 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.

  4. Aerothermal modeling program, phase 2

    NASA Technical Reports Server (NTRS)

    Mongia, H. C.; Patankar, S. V.; Murthy, S. N. B.; Sullivan, J. P.; Samuelsen, G. S.

    1985-01-01

    The main objectives of the Aerothermal Modeling Program, Phase 2 are: to develop an improved numerical scheme for incorporation in a 3-D combustor flow model; to conduct a benchmark quality experiment to study the interaction of a primary jet with a confined swirling crossflow and to assess current and advanced turbulence and scalar transport models; and to conduct experimental evaluation of the air swirler interaction with fuel injectors, assessments of current two-phase models, and verification the improved spray evaporation/dispersion models.

  5. 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

  6. Perinatal vs genetic programming of serotonin states associated with anxiety.

    PubMed

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

    2015-05-01

    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

  7. A genetic programming approach for time-series analysis and prediction in space physics.

    NASA Astrophysics Data System (ADS)

    Jorgensen, A. M.; Brumby, S. P.; Henderson, M. G.

    2004-12-01

    A central theme in space weather prediction is the ability to predict time-series of relevant quantities, both empirically, and from physics-based models. Empirical models are often based on educated guesses, or intuition. The task of finding an empirical relationship relating quantities can be tedious and time-consuming, especially when a large number of parameters are involved. Genetic Programming (GP) provides a method for automating the guesswork, and can in some instances automatically find functional relationships between data streams. GP is an evolutionary computation technique which is an extension of the Genetic Algorithm framework used for function optimization. In GP an evolutionary algorithm combines elementary function operators in an attempt to build a function which is able to reproduce a training example from a set of input data. We will illustrate how a GP algorithm can be used in space physics by addressing two relevant topics: The prediction of relativistic electron fluxes, and prediction of Dst.

  8. Genetically engineered mouse models for lung cancer.

    PubMed

    Kwak, I; Tsai, S Y; DeMayo, F J

    2004-01-01

    The lung is a complex organ consisting of numerous cell types that function to ensure sufficient gas exchange to oxygenate the blood. In order to accomplish this function, the lung must be exposed to the external environment and at the same time maintain a homeostatic balance between its function in gas exchange and the maintenance of inflammatory balance. During the past two decades, as molecular methodologies have evolved with the sequencing of entire genomes, the use of in vivo models to elucidate the molecular mechanisms involved in pulmonary physiology and disease have increased. The mouse has emerged as a potent model to investigate pulmonary physiology due to the explosion in molecular methods that now allow for the developmental and tissue-specific regulation of gene transcription. Initial efforts to manipulate gene expression in the mouse genome resulted in the generation of transgenic mice characterized by the constitutive expression of a specific gene and knockout mice characterized by the ablation of a specific gene. The utility of these original mouse models was limited, in many cases, by phenotypes resulting in embryonic or neonatal lethality that prevented analysis of the impact of the genetic manipulation on pulmonary biology. Second-generation transgenic mouse models employ multiple strategies that can either activate or silence gene expression thereby providing extensive temporal and spatial control of the experimental parameters of gene expression. These highly regulated mouse models are intended to serve as a foundation for further investigation of the molecular basis of human disease such as tumorigenesis. This review describes the principles, progress, and application of systems that are currently employed in the conditional regulation of gene expression in the investigation of lung cancer. PMID:14977417

  9. 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.

  10. 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.

  11. 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

  12. Recommendations for utilizing and reporting population genetic analyses: the reproducibility of genetic clustering using the program STRUCTURE.

    PubMed

    Gilbert, Kimberly J; Andrew, Rose L; Bock, Dan G; Franklin, Michelle T; Kane, Nolan C; Moore, Jean-Sébastien; Moyers, Brook T; Renaut, Sébastien; Rennison, Diana J; Veen, Thor; Vines, Timothy H

    2012-10-01

    Reproducibility is the benchmark for results and conclusions drawn from scientific studies, but systematic studies on the reproducibility of scientific results are surprisingly rare. Moreover, many modern statistical methods make use of 'random walk' model fitting procedures, and these are inherently stochastic in their output. Does the combination of these statistical procedures and current standards of data archiving and method reporting permit the reproduction of the authors' results? To test this, we reanalysed data sets gathered from papers using the software package STRUCTURE to identify genetically similar clusters of individuals. We find that reproducing structure results can be difficult despite the straightforward requirements of the program. Our results indicate that 30% of analyses were unable to reproduce the same number of population clusters. To improve this, we make recommendations for future use of the software and for reporting STRUCTURE analyses and results in published works. PMID:22998190

  13. A genetic programming approach to explore the crash severity on multi-lane roads.

    PubMed

    Das, Abhishek; Abdel-Aty, Mohamed

    2010-03-01

    The study aims at understanding the relationship of geometric and environmental factors with injury related crashes as well as with severe crashes through the development of classification models. The Linear Genetic Programming (LGP) method is used to achieve these objectives. LGP is based on the traditional genetic algorithm, except that it evolves computer programs. The methodology is different from traditional non-parametric methods like classification and regression trees which develop only one model, with fixed criteria, for any given dataset. The LGP on the other hand not only evolves numerous models through the concept of biological evolution, and using the evolutionary operators of crossover and mutation, but also allows the investigator to choose the best models, developed over various runs, based on classification rates. Discipulus software was used to evolve the models. The results included vision obstruction which was found to be a leading factor for severe crashes. Percentage of trucks, even if small, is more likely to make the crashes injury prone. The 'lawn and curb' median are found to be safe for angle/turning movement crashes. Dry surface conditions as well as good pavement conditions decrease the severity of crashes and so also wider shoulder and sidewalk widths. Interaction terms among variables like on-street parking with higher posted speed limit have been found to make injuries more probable. PMID:20159079

  14. 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...

  15. Overview of Responsive Model Program.

    ERIC Educational Resources Information Center

    Nimnicht, Glen P.

    The Responsive Model program assumes that the school environment should be designed to respond to the learner, and that school activities should be autotelic, or self-rewarding, not dependent upon rewards or punishment unrelated to the activity. Developmental theory, certain ideas of operant conditioning, and flexible learning sequences are used…

  16. Genetic and Environmental Bases of Reading and Spelling: A Unified Genetic Dual Route Model

    ERIC Educational Resources Information Center

    Bates, Timothy C.; Castles, Anne; Luciano, Michelle; Wright, Margaret J.; Coltheart, Max; Martin, Nicholas G.

    2007-01-01

    We develop and test a dual-route model of genetic effects on reading aloud and spelling, based on irregular and non-word reading and spelling performance assessed in 1382 monozygotic and dizygotic twins. As in earlier research, most of the variance in reading was due to genetic effects. However, there were three more specific conclusions: the…

  17. 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…

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

    PubMed

    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

  19. 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

  20. Runway configuration improvement programming model.

    NASA Technical Reports Server (NTRS)

    Yu, J. C.; Gibson, D. R.

    1973-01-01

    The basic objectives of the study were to subject a set of runway configurations to cost analysis and to develop a dynamic programming model which would enable an airport to economically match the ground capacity to its air traffic demand. Quantitative differences in the capacity of runway configurations result from the various aircraft/aircraft and aircraft/air-system interactions. A problem formulation and solution procedure is presented which is intended to be a meaningful technique for the long-range planning of runway expansion programs.

  1. 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.

  2. 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…

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

    SciTech Connect

    Russell, Liane B

    2013-01-01

    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

  4. Genetic programming based ensemble system for microarray data classification.

    PubMed

    Liu, Kun-Hong; Tong, Muchenxuan; Xie, Shu-Tong; Yee Ng, Vincent To

    2015-01-01

    Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved. PMID:25810748

  5. 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. PMID:21609273

  6. Genetic programming as an analytical tool for non-linear dielectric spectroscopy.

    PubMed

    Woodward, A M; Gilbert, R J; Kell, D B

    1999-05-01

    By modelling the non-linear effects of membranous enzymes on an applied oscillating electromagnetic field using supervised multivariate analysis methods, Non-Linear Dielectric Spectroscopy (NLDS) has previously been shown to produce quantitative information that is indicative of the metabolic state of various organisms. The use of Genetic Programming (GP) for the multivariate analysis of NLDS data recorded from yeast fermentations is discussed, and GPs are compared with previous results using Partial Least Squares (PLS) and Artificial Neural Nets (NN). GP considerably outperforms these methods, both in terms of the precision of the predictions and their interpretability. PMID:10379559

  7. 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.

  8. Practical implications for genetic modeling in the genomics era.

    PubMed

    VanRaden, P M

    2016-03-01

    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, including genetic by environmental interactions and correlations among traits, and accounting for nonadditive inheritance or nonnormal distributions. Data include phenotypes and pedigrees during the last century and genotypes within the last decade. The genomic data can include single nucleotide polymorphisms, quantitative trait loci, insertions, deletions, and haplotypes. Subsets must be selected to reduce computation because total numbers of variants that can be imputed have increased rapidly from thousands to millions. Current computation using 60,671 markers takes just a few days. Nonlinear models can account for the nonnormal distribution of genomic effects, but reliability is usually better than that of linear models only for traits influenced by major genes. Numbers of genotyped animals have also increased rapidly in the joint North American database from a few thousand in 2009 to over 1 million in 2015. Most are young females and will contribute to estimating allele effects in the future, but only about 150,000 have phenotypes so far. Genomic preselection can bias traditional animal models because Mendelian sampling of phenotyped progeny and mates is no longer expected to average zero; however, estimates of bias are small in current US data. Single-step models that combine pedigree and genomic relationships can account for preselection, but approximations are required for affordable computation. Traditional animal models may include all breeds and crossbreds, but most genomic evaluations are still computed within breed. Models that include inbreeding, heterosis, dominance, and interactions can improve predictions for individual matings. Multitrait genomic models may

  9. 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.

  10. 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…

  11. 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

  12. Genetic drift in an infinite population. The pseudohitchhiking model.

    PubMed Central

    Gillespie, J H

    2000-01-01

    Selected substitutions at one locus can induce stochastic dynamics that resemble genetic drift at a closely linked neutral locus. The pseudohitchhiking model is a one-locus model that approximates these effects and can be used to describe the major consequences of linked selection. As the changes in neutral allele frequencies when hitchhiking are rapid, diffusion theory is not appropriate for studying neutral dynamics. A stationary distribution and some results on substitution processes are presented that use the theory of continuous-time Markov processes with discontinuous sample paths. The coalescent of the pseudohitchhiking model is shown to have a random number of branches at each node, which leads to a frequency spectrum that is different from that of the equilibrium neutral model. If genetic draft, the name given to these induced stochastic effects, is a more important stochastic force than genetic drift, then a number of paradoxes that have plagued population genetics disappear. PMID:10835409

  13. 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

  14. 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)

  15. Multiple Comparisons in Genetic Association Studies: A Hierarchical Modeling Approach

    PubMed Central

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

    2016-01-01

    Multiple comparisons or multiple testing has been viewed as a thorny issue in genetic association studies aiming to detect disease-associated genetic variants from a large number of genotyped variants. We alleviate the problem of multiple comparisons by proposing a hierarchical modeling approach that is fundamentally different from the existing methods. The proposed hierarchical models simultaneously fit as many variables as possible and shrink unimportant effects towards zero. Thus, the hierarchical models yield more efficient estimates of parameters than the traditional methods that analyze genetic variants separately, and also coherently address the multiple comparisons problem due to largely reducing the effective number of genetic effects and the number of statistically ‘significant’ effects. We develop a method for computing the effective number of genetic effects in hierarchical generalized linear models, and propose a new adjustment for multiple comparisons, the hierarchical Bonferroni correction, based on the effective number of genetic effects. Our approach not only increases the power to detect disease-associated variants but also controls the Type I error. We illustrate and evaluate our method with real and simulated data sets from genetic association studies. The method has been implemented in our freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). PMID:24259248

  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. Models to explore genetics of human aging.

    PubMed

    Karasik, David; Newman, Anne

    2015-01-01

    Genetic studies have bestowed insight into the biological mechanisms underlying inter-individual differences in susceptibility to (or resistance to) organisms’ aging. Recent advances in molecular and genetic epidemiology provide tools to explore the genetic sources of the variability in biological aging in humans. To be successful, the genetic study of a complex condition such as aging requires the clear definition of essential traits that can characterize the aging process phenotypically. Phenotypes of human aging have long relied on mortality rate or exceptional longevity. Genome-wide association studies (GWAS) have been shown to present an unbiased approach to the identification of new candidate genes for human diseases. The GWAS approach can also be used for positive health phenotypes such as longevity or a delay in age-related chronic disease, as well as for other age related changes such as loss of telomere length or lens transparency. Sequencing, either in targeted regions or across the whole genome can further identify rare variation that may contribute to the biological aging mechanisms. To date, the results of the GWAS for longevity are rather disappointing, possibly in part due to the small number of individuals with GWAS data who have reached advanced old age.Human aging phenotypes are needed that can be assessed prior to death, and should be both heritable and validated as predictors of longevity. Potentially, phenotypes that focus on “successful” or “healthy” aging will be more powerful as they can be measured in large numbers of people and also are clinically relevant.We postulate that construction of an integrated phenotype of aging can be achieved capitalizing on multiple traits that may have weak correlations, but a shared underlying genetic architecture. This is based on a hypothesis that convergent results from multiple individual aging-related traits will point out the pleiotropic signals responsible for the overall rate of aging of

  18. 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

  19. 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…

  20. 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…

  1. 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%.

  2. A genetic program promotes C. elegans longevity at cold temperatures via a thermosensitive TRP channel.

    PubMed

    Xiao, Rui; Zhang, Bi; Dong, Yongming; Gong, Jianke; Xu, Tao; Liu, Jianfeng; Xu, X Z Shawn

    2013-02-14

    Both poikilotherms and homeotherms live longer at lower body temperatures, highlighting a general role of temperature reduction in lifespan extension. However, the underlying mechanisms remain unclear. One prominent model is that cold temperatures reduce the rate of chemical reactions, thereby slowing the rate of aging. This view suggests that cold-dependent lifespan extension is simply a passive thermodynamic process. Here, we challenge this view in C. elegans by showing that genetic programs actively promote longevity at cold temperatures. We find that TRPA-1, a cold-sensitive TRP channel, detects temperature drop in the environment to extend lifespan. This effect requires cold-induced, TRPA-1-mediated calcium influx and a calcium-sensitive PKC that signals to the transcription factor DAF-16/FOXO. Human TRPA1 can functionally substitute for worm TRPA-1 in promoting longevity. Our results reveal a previously unrecognized function for TRP channels, link calcium signaling to longevity, and, importantly, demonstrate that genetic programs contribute to lifespan extension at cold temperatures. PMID:23415228

  3. 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. PMID:27270699

  4. 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.

  5. Identification of student misconceptions in genetics problem solving via computer program

    NASA Astrophysics Data System (ADS)

    Browning, Mark E.; Lehman, James D.

    A genetics problem practice program and tutor on microcomputer was used by 135 undergraduate education majors enrolled in an introductory biology course at Purdue University. The program presented four genetics problems, two monohybrid and two dihybrid, and required the users to predict the number and type of each class of offspring. Student responses were recorded on diskette and analyzed for evidence of misconceptions and difficulties in the genetics problem-solving process. Three main areas of difficulty were identified: difficulties with computational skills, difficulties in the determination of gametes, and inappropriate application of previous learning to new problem situations.

  6. Genetic and non-genetic animal models for autism spectrum disorders (ASD).

    PubMed

    Ergaz, Zivanit; Weinstein-Fudim, Liza; Ornoy, Asher

    2016-09-01

    Autism spectrum disorder (ASD) is associated, in addition to complex genetic factors, with a variety of prenatal, perinatal and postnatal etiologies. We discuss the known animal models, mostly in mice and rats, of ASD that helps us to understand the etiology, pathogenesis and treatment of human ASD. We describe only models where behavioral testing has shown autistic like behaviors. Some genetic models mimic known human syndromes like fragile X where ASD is part of the clinical picture, and others are without defined human syndromes. Among the environmentally induced ASD models in rodents, the most common model is the one induced by valproic acid (VPA) either prenatally or early postnatally. VPA induces autism-like behaviors following single exposure during different phases of brain development, implying that the mechanism of action is via a general biological mechanism like epigenetic changes. Maternal infection and inflammation are also associated with ASD in man and animal models. PMID:27142188

  7. Program Models with Exemplary Components.

    ERIC Educational Resources Information Center

    North Carolina State Dept. of Public Instruction, Raleigh. Div. for Exceptional Children.

    The document describes 17 programs of interagency transitional programming and planning for exceptional students exiting public schools in North Carolina and elsewhere. It is intended to disseminate information on successful programs in the state and encourage visitation between local school administrative units. Programs were selected according…

  8. Generalized population models and the nature of genetic drift.

    PubMed

    Der, Ricky; Epstein, Charles L; Plotkin, Joshua B

    2011-09-01

    The Wright-Fisher model of allele dynamics forms the basis for most theoretical and applied research in population genetics. Our understanding of genetic drift, and its role in suppressing the deterministic forces of Darwinian selection has relied on the specific form of sampling inherent to the Wright-Fisher model and its diffusion limit. Here we introduce and analyze a broad class of forward-time population models that share the same mean and variance as the Wright-Fisher model, but may otherwise differ. The proposed class unifies and further generalizes a number of population-genetic processes of recent interest, including the Λ and Cannings processes. Even though these models all have the same variance effective population size, they encode a rich diversity of alternative forms of genetic drift, with significant consequences for allele dynamics. We characterize in detail the behavior of standard population-genetic quantities across this family of generalized models. Some quantities, such as heterozygosity, remain unchanged; but others, such as neutral absorption times and fixation probabilities under selection, deviate by orders of magnitude from the Wright-Fisher model. We show that generalized population models can produce startling phenomena that differ qualitatively from classical behavior - such as assured fixation of a new mutant despite the presence of genetic drift. We derive the forward-time continuum limits of the generalized processes, analogous to Kimura's diffusion limit of the Wright-Fisher process, and we discuss their relationships to the Kingman and non-Kingman coalescents. Finally, we demonstrate that some non-diffusive, generalized models are more likely, in certain respects, than the Wright-Fisher model itself, given empirical data from Drosophila populations. PMID:21718713

  9. 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. PMID:25700148

  10. 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.

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

    PubMed

    Huang, Pu; Feldman, Maximilian; Schroder, Stephan; Bahri, Bochra A; Diao, Xianmin; Zhi, Hui; Estep, Matt; Baxter, Ivan; Devos, Katrien M; Kellogg, Elizabeth A

    2014-10-01

    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 model system for C4 grasses and bioenergy crops, due to its rapid life cycle, large amount of seed production and small diploid genome, among other characters. However, remarkably little is known about the genetic diversity in natural populations of this species. In this study, we survey the genetic diversity of a worldwide sample of more than 200 S. viridis accessions, using the genotyping-by-sequencing technique. Two distinct genetic groups in S. viridis and a third group resembling S. italica were identified, with considerable admixture among the three groups. We find the genetic variation of North American S. viridis correlates with both geography and climate and is representative of the total genetic diversity in this species. This pattern may reflect several introduction/dispersal events of S. viridis into North America. We also modelled demographic history and show signal of recent population decline in one subgroup. Finally, we show linkage disequilibrium decay is rapid (<45 kb) in our total sample and slow in genetic subgroups. These results together provide an in-depth understanding of the pattern of genetic diversity of this new model species on a broad geographic scale. They also provide key guidelines for on-going and future work including germplasm preservation, local adaptation, crossing designs and genomewide association studies. PMID:25185718

  12. 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. PMID:25860438

  13. 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...

  14. Multi-locus models of genetic risk of disease

    PubMed Central

    2010-01-01

    Background Evidence for genetic contribution to complex diseases is described by recurrence risks to relatives of diseased individuals. Genome-wide association studies allow a description of the genetics of the same diseases in terms of risk loci, their effects and allele frequencies. To reconcile the two descriptions requires a model of how risks from individual loci combine to determine an individual's overall risk. Methods We derive predictions of risk to relatives from risks at individual loci under a number of models and compare them with published data on disease risk. Results The model in which risks are multiplicative on the risk scale implies equality between the recurrence risk to monozygotic twins and the square of the recurrence risk to sibs, a relationship often not observed, especially for low prevalence diseases. We show that this theoretical equality is achieved by allowing impossible probabilities of disease. Other models, in which probabilities of disease are constrained to a maximum of one, generate results more consistent with empirical estimates for a range of diseases. Conclusions The unconstrained multiplicative model, often used in theoretical studies because of its mathematical tractability, is not a realistic model. We find three models, the constrained multiplicative, Odds (or Logit) and Probit (or liability threshold) models, all fit the data on risk to relatives. Currently, in practice it would be difficult to differentiate between these models, but this may become possible if genetic variants that explain the majority of the genetic variance are identified. PMID:20181060

  15. 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

  16. Genetics

    MedlinePlus

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

  17. Genetics

    MedlinePlus

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

  18. The Spiral-Interactive Program Evaluation Model.

    ERIC Educational Resources Information Center

    Khaleel, Ibrahim Adamu

    1988-01-01

    Describes the spiral interactive program evaluation model, which is designed to evaluate vocational-technical education programs in secondary schools in Nigeria. Program evaluation is defined; utility oriented and process oriented models for evaluation are described; and internal and external evaluative factors and variables that define each…

  19. Cucumber as a Model for Organellar Genetics

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Mitochondria are found in the cells of all eukaryotes, are imperative for energy production, and play important roles in programmed cell death, ageing, and disease development. Mitochondria possess their own DNA and encode for approximately 20 proteins, as well as their own ribosomal and transfer R...

  20. 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

  1. LISREL Modeling: Genetic and Environmental Influences on IQ Revisited.

    ERIC Educational Resources Information Center

    Chipuer, Heather M.; And Others

    1990-01-01

    A model-fitting analysis of the covariance structure of an intelligence quotient (IQ) data set is reported using a model that considers additive and nonadditive genetic parameters and shared and nonshared environment parameters that permit different estimates for different types of relatives. The use of LISREL for such purposes is reviewed. (SLD)

  2. 'NEANTHES ARENACEODENTATA', A CYTOGENETIC MODEL FOR MARINE GENETIC TOXICOLOGY

    EPA Science Inventory

    Genetic toxicants are present in polluted marine environments and may represent a long-term threat to populations of marine organisms. A cytogenetic model is useful to study the effects of these toxicants. The polychaeta, Neanthes arenaceodentata, was chosen as such a model becau...

  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. Population programs for the detection of couples at risk for severe monogenic genetic diseases.

    PubMed

    Zlotogora, Joël

    2009-08-01

    Population genetic screening programs for carrier detection of severe genetic disorders exist worldwide, mainly for beta-thalassemia. These screening programs are either mandatory or voluntary. In several Arab countries and Iran, the state has made thalassemia carrier detection mandatory, while tests for detecting carriers are required by the religious authorities in Cyprus. In all the existing mandatory genetic screening programs, the couples have to get the information about the tests before marriage, but the decision whether or not to marry is left to them. Voluntary programs exist for instance in several Mediterranean countries for the prevention of thalassemia and for several genetic diseases among Jews. While voluntary programs leave the decision to be screened or not to the individual, a major problem is that in many cases awareness about the existence of screening tests is very sparse. Some programs, for instance in Canada or Australia, therefore provide education about genetic tests and screening at school in order to allow the individuals to be able to make an informed decision about their reproductive choices. PMID:19390864

  5. 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. PMID:23512010

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

    PubMed

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

    2006-01-01

    We show how Genetic Programming (GP) can be used to evolve useful quantum computing artefacts of increasing sophistication and usefulness: firstly specific quantum circuits, then quantum programs, and finally system-independent quantum algorithms. We conclude the paper by presenting a human-competitive Quantum Fourier Transform (QFT) algorithm evolved by GP. PMID:16536889

  7. Management Internship Program: A Model.

    ERIC Educational Resources Information Center

    Zabezensky, Ferne; And Others

    1986-01-01

    Examines the Maricopa Community College District's management internship program, detailing the history and operation of the program. Describes program eligibility criteria, the intern's role as Vice Chancellor for Human Services, the provision of a graduate course in management, the rotation of assignments, intern projects, and evaluation.…

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

    NASA Astrophysics Data System (ADS)

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

    2004-01-01

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

  9. 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.

  10. 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.

  11. 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. PMID:25958956

  12. 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

  13. Genetic Animal Models of Parkinson’s Disease

    PubMed Central

    Dawson, Ted M.; Ko, Han Seok; Dawson, Valina L.

    2010-01-01

    Parkinson’s disease (PD) is a progressive neurodegenerative disorder that is characterized by the degeneration of dopamine (DA) and non-DA neurons, the almost uniform presence of Lewy bodies, and motor deficits. Although the majority of PD is sporadic, specific genetic defects in rare familial cases have provided unique insights into the pathogenesis of PD. Through the creation of animal and cellular models of mutations in LRRK2 and α-synuclein, which are linked to autosomal dominant PD, and mutations in parkin, DJ-1, and PINK1, which are responsible for autosomal recessive PD, insight into the molecular mechanisms of this disorder are leading to new ideas about the pathogenesis of PD. In this review, we discuss the animal models for these genetic causes of PD, their limitations and value. Moreover, we discuss future directions and potential strategies for optimization of the genetic models. PMID:20547124

  14. 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

  15. 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

  16. 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

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

    PubMed

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

    2015-11-01

    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

  18. 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

  19. Genetics of bovine respiratory disease in cattle: can breeding programs reduce the problem?

    PubMed

    Berry, Donagh P

    2014-12-01

    Genetics is responsible for approximately half the observed change in performance internationally in well-structured cattle breeding programs. Almost all, if not all, individual characteristics, including animal health, have a genetic basis. Once genetic variation exists then breeding for improvement is possible. Although the heritability of most health traits is low to moderate, considerable exploitable genetic variation does exist. From the limited studies undertaken, and mostly from limited datasets, the direct heritability of susceptibility to BRD varied from 0.07 to 0.22 and the maternal heritability (where estimated) varied from 0.05 to 0.07. Nonetheless, considerable genetic variation clearly exists; the genetic standard deviation for the direct component (binary trait), although differing across populations, varied from 0.08 to 0.20 while the genetic standard deviation for the maternal component varied from 0.04 to 0.07. Little is known about the genetic correlation between genetic predisposition to BRD and animal performance; the estimation of these correlations should be prioritized. (Long-term) Breeding strategies to reduce the incidence of BRD in cattle should be incorporated into national BRD eradication or control strategies. PMID:25434407

  20. 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.

  1. 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.

  2. 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. PMID:25940384

  3. 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.

  4. 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…

  5. Idaho Comprehensive Guidance and Counseling Program Model.

    ERIC Educational Resources Information Center

    Idaho State Dept. of Education, Boise.

    This handbook describes a series of procedures for establishing school guidance and counseling programs in Idaho based on the Missouri Comprehensive Guidance Program model developed by Dr. Norman Gysbers. It begins with a conceptual framework and moves through the development, evaluation, and refinement steps of setting guidance programs in Idaho…

  6. Disentangling the relationship between tumor genetic programs and immune responsiveness.

    PubMed

    Bedognetti, Davide; Hendrickx, Wouter; Ceccarelli, Michele; Miller, Lance D; Seliger, Barbara

    2016-04-01

    Correlative studies in humans have demonstrated that an active immune microenvironment characterized by the presence of a T-helper 1 immune response typifies a tumor phenotype associated with better outcome and increased responsiveness to immune manipulation. This phenotype also signifies the counter activation of immune-regulatory mechanisms. Variables modulating the development of an effective anti-tumor immune response are increasingly scrutinized as potential therapeutic targets. Genetic alterations of cancer cells that functionally influence intratumoral immune response include mutational load, specific mutations of genes involved in oncogenic pathways and copy number aberrations involving chemokine and cytokine genes. Inhibiting oncogenic pathways that prevent the development of the immune-favorable cancer phenotype may complement modern immunotherapeutic approaches. PMID:26967649

  7. 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...

  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. 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

  10. Genetically engineered mouse models to study prostate cancer.

    PubMed

    Brzezinska, Elspeth A; Nixon, Colin; Patel, Rachana; Leung, Hing Y

    2015-01-01

    Genetically engineered mouse models have become fundamental tools in the basic and translational research of prostate cancer. There is a plethora of models available to dissect the genetic alterations and aberrant signaling events associated with human prostate cancer and, furthermore, to investigate new and "personalized" therapies to treat the disease. In this chapter, we discuss some of the models recently and currently used to study prostate cancer in vivo, and some considerations when selecting an appropriate model to investigate particular aspects of the disease. We describe the methods required to isolate prostate tumors and conduct basic characterization of the tumor to determine tumor load and histopathology. We also discuss important aspects to be considered when processing samples for further analysis. PMID:25636465

  11. Modeling dietary influences on offspring metabolic programming in Drosophila melanogaster.

    PubMed

    Brookheart, Rita T; Duncan, Jennifer G

    2016-09-01

    The influence of nutrition on offspring metabolism has become a hot topic in recent years owing to the growing prevalence of maternal and childhood obesity. Studies in mammals have identified several factors correlating with parental and early offspring dietary influences on progeny health; however, the molecular mechanisms that underlie these factors remain undiscovered. Mammalian metabolic tissues and pathways are heavily conserved in Drosophila melanogaster, making the fly an invaluable genetic model organism for studying metabolism. In this review, we discuss the metabolic similarities between mammals and Drosophila and present evidence supporting its use as an emerging model of metabolic programming. PMID:27450801

  12. Covariant Tarpeian Method for Bloat Control in Genetic Programming

    NASA Astrophysics Data System (ADS)

    Poli, Riccardo

    In this paper a simple modification of the Tarpeian bloat-control method is presented which allows one to dynamically set the parameters of the method in such a way to guarantee that the mean program size will either keep a particular value (e.g., its initial value) or will follow a schedule chosen by the user. The mathematical derivation of the technique as well as its numerical and empirical corroboration are presented.

  13. 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…

  14. 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

  15. Mapping and Cracking Sensorimotor Circuits in Genetic Model Organisms

    PubMed Central

    Clark, Damon A.; Freifeld, Limor; Clandinin, Thomas R.

    2013-01-01

    One central goal of systems neuroscience is to understand how neural circuits implement the computations that link sensory inputs to behavior. Work combining electrophysiological and imaging-based approaches to measure neural activity with pharmacological and electrophysiological manipulations has provided fundamental insights. More recently, genetic approaches have been used to monitor and manipulate neural activity, opening up new experimental opportunities and challenges. Here, we discuss issues associated with applying genetic approaches to circuit dissection in sensorimotor transformations, outlining important considerations for experimental design and considering how modeling can complement experimental approaches. PMID:23719159

  16. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-09-01

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

  18. Genetic animal models of dystonia: common features and diversities.

    PubMed

    Richter, Franziska; Richter, Angelika

    2014-10-01

    Animal models are pivotal for studies of pathogenesis and treatment of disorders of the central nervous system which in its complexity cannot yet be modeled in vitro or using computer simulations. The choice of a specific model to test novel therapeutic strategies for a human disease should be based on validity of the model for the approach: does the model reflect symptoms, pathogenesis and treatment response present in human patients? In the movement disorder dystonia, prior to the availability of genetically engineered mice, spontaneous mutants were chosen based on expression of dystonic features, including abnormal muscle contraction, movements and postures. Recent discovery of a number of genes and gene products involved in dystonia initiated research on pathogenesis of the disorder, and the creation of novel models based on gene mutations. Here we present a review of current models of dystonia, with a focus on genetic rodent models, which will likely be first choice in the future either for pathophysiological or for preclinical drug testing or both. In order to help selection of a model depending on expression of a specific feature of dystonia, this review is organized by symptoms and current knowledge of pathogenesis of dystonia. We conclude that albeit there is increasing need for research on pathogenesis of the disease and development of improved models, current models do replicate features of dystonia and are useful tools to develop urgently demanded treatment for this debilitating disorder. PMID:25034123

  19. 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.

  20. GENETICS AND GENOMICS - INTEGRATION OF MOLECULAR GENETICS INTO A BREEDING PROGRAM FOR RAINBOW TROUT

    Technology Transfer Automated Retrieval System (TEKTRAN)

    At the National Center for Cool and Cold Water Aquaculture (US Department of Agriculture, Ag. Research Service) in Leetown, WV, we have a broodstock development program now entering the 2nd generation of family based selective breeding using expected breeding values (EBVs). Our major breeding objec...

  1. Identifying genetically driven clinical phenotypes using linear mixed models.

    PubMed

    Mosley, Jonathan D; Witte, John S; Larkin, Emma K; Bastarache, Lisa; Shaffer, Christian M; Karnes, Jason H; Stein, C Michael; Phillips, Elizabeth; Hebbring, Scott J; Brilliant, Murray H; Mayer, John; Ye, Zhan; Roden, Dan M; Denny, Joshua C

    2016-01-01

    We hypothesized that generalized linear mixed models (GLMMs), which estimate the additive genetic variance underlying phenotype variability, would facilitate rapid characterization of clinical phenotypes from an electronic health record. We evaluated 1,288 phenotypes in 29,349 subjects of European ancestry with single-nucleotide polymorphism (SNP) genotyping on the Illumina Exome Beadchip. We show that genetic liability estimates are primarily driven by SNPs identified by prior genome-wide association studies and SNPs within the human leukocyte antigen (HLA) region. We identify 44 (false discovery rate q<0.05) phenotypes associated with HLA SNP variation and show that hypothyroidism is genetically correlated with Type I diabetes (rG=0.31, s.e. 0.12, P=0.003). We also report novel SNP associations for hypothyroidism near HLA-DQA1/HLA-DQB1 at rs6906021 (combined odds ratio (OR)=1.2 (95% confidence interval (CI): 1.1-1.2), P=9.8 × 10(-11)) and for polymyalgia rheumatica near C6orf10 at rs6910071 (OR=1.5 (95% CI: 1.3-1.6), P=1.3 × 10(-10)). Phenome-wide application of GLMMs identifies phenotypes with important genetic drivers, and focusing on these phenotypes can identify novel genetic associations. PMID:27109359

  2. Identifying genetically driven clinical phenotypes using linear mixed models

    PubMed Central

    Mosley, Jonathan D.; Witte, John S.; Larkin, Emma K.; Bastarache, Lisa; Shaffer, Christian M.; Karnes, Jason H.; Stein, C. Michael; Phillips, Elizabeth; Hebbring, Scott J.; Brilliant, Murray H.; Mayer, John; Ye, Zhan; Roden, Dan M.; Denny, Joshua C.

    2016-01-01

    We hypothesized that generalized linear mixed models (GLMMs), which estimate the additive genetic variance underlying phenotype variability, would facilitate rapid characterization of clinical phenotypes from an electronic health record. We evaluated 1,288 phenotypes in 29,349 subjects of European ancestry with single-nucleotide polymorphism (SNP) genotyping on the Illumina Exome Beadchip. We show that genetic liability estimates are primarily driven by SNPs identified by prior genome-wide association studies and SNPs within the human leukocyte antigen (HLA) region. We identify 44 (false discovery rate q<0.05) phenotypes associated with HLA SNP variation and show that hypothyroidism is genetically correlated with Type I diabetes (rG=0.31, s.e. 0.12, P=0.003). We also report novel SNP associations for hypothyroidism near HLA-DQA1/HLA-DQB1 at rs6906021 (combined odds ratio (OR)=1.2 (95% confidence interval (CI): 1.1–1.2), P=9.8 × 10−11) and for polymyalgia rheumatica near C6orf10 at rs6910071 (OR=1.5 (95% CI: 1.3–1.6), P=1.3 × 10−10). Phenome-wide application of GLMMs identifies phenotypes with important genetic drivers, and focusing on these phenotypes can identify novel genetic associations. PMID:27109359

  3. 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

  4. 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.

  5. Stress models of depression: forming genetically vulnerable strains.

    PubMed

    Henn, Fritz A; Vollmayr, Barbara

    2005-01-01

    Among the most useful models for depressive disorders are those, which involve a stress induced change in behaviour. Learned helplessness is one such model and is induced through exposure to uncontrollable and unpredictable aversive events. Learned helplessness as induced in rats using foot shock is well characterized and has good face validity and predictive validity as a model of depression, including alterations in HPA axis activity and REM sleep characteristic of depression. The data concerning the validity will be briefly reviewed. The model can also be used to look at the role of genetics through selective breeding. These studies will be reviewed and the utility of the genetic strains for understanding the interaction of stress and affect will be examined. A second model of depression using exposure to chronic stress also has high face and predictive validity. A new form of this approach, recently described, also is suitable for the examination of genetic factors leading to depressive like behaviour and this will be presented. PMID:15925700

  6. Genetic implanted fuzzy model for water saturation determination

    NASA Astrophysics Data System (ADS)

    Bagheripour, Parisa; Asoodeh, Mojtaba

    2014-04-01

    The portion of rock pore volume occupied with non-hydrocarbon fluids is called water saturation, which plays a significant role in reservoir description and management. Accurate water saturation, directly measured from special core analysis is highly expensive and time consuming. Furthermore, indirect measurements of water saturation from well log interpretation such as empirical correlations or statistical methods do not provide satisfying results. Recent works showed that fuzzy logic is a robust tool for handling geosciences problems which provide more reliable results compared with empirical correlations or statistical methods. This study goes further to improve fuzzy logic for enhancing accuracy of final prediction. It employs hybrid genetic algorithm-pattern search technique instead of widely held subtractive clustering approach for setting up fuzzy rules and for extracting optimal parameters involved in computational structure of fuzzy model. The proposed strategy, called genetic implanted fuzzy model, was used to formulate conventional well log data, including sonic transit time, neutron porosity, formation bulk density, true resistivity, and gamma ray into water saturation, obtained from subtractive clustering approach. Results indicated genetic implanted fuzzy model performed more satisfyingly compared with traditional fuzzy logic model. The propounded model was successfully applied to one of Iranian carbonate reservoir rocks.

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

    NASA Astrophysics Data System (ADS)

    Semenkin, E.; Semenkina, M.

    2015-01-01

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

  8. 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. 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…

  10. Current Progress of Genetically Engineered Pig Models for Biomedical Research

    PubMed Central

    Gün, Gökhan

    2014-01-01

    Abstract The first transgenic pigs were generated for agricultural purposes about three decades ago. Since then, the micromanipulation techniques of pig oocytes and embryos expanded from pronuclear injection of foreign DNA to somatic cell nuclear transfer, intracytoplasmic sperm injection-mediated gene transfer, lentiviral transduction, and cytoplasmic injection. Mechanistically, the passive transgenesis approach based on random integration of foreign DNA was developed to active genetic engineering techniques based on the transient activity of ectopic enzymes, such as transposases, recombinases, and programmable nucleases. Whole-genome sequencing and annotation of advanced genome maps of the pig complemented these developments. The full implementation of these tools promises to immensely increase the efficiency and, in parallel, to reduce the costs for the generation of genetically engineered pigs. Today, the major application of genetically engineered pigs is found in the field of biomedical disease modeling. It is anticipated that genetically engineered pigs will increasingly be used in biomedical research, since this model shows several similarities to humans with regard to physiology, metabolism, genome organization, pathology, and aging. PMID:25469311

  11. 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)

  12. 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…

  13. New directions and changing faces for the USDA sunflower genetics programs

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This document is a summary of a slideshow presented at the National Sunflower Association’s annual Research Forum in Fargo, ND, in January 2008. Background information was provided about new staff in the Sunflower Genetics programs as well as progress during this, the transitional year. Studies to b...

  14. 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

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

    PubMed

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

    2013-02-01

    Biochemical systems are characterised by cyclic/reversible reciprocal actions, non-linear interactions and a mixed relationship structures (linear and non-linear; static and dynamic). Deciphering the architecture of such systems using measured data to provide quantitative information regarding the nature of relationships that exist between the measured variables is a challenging proposition. Causality detection is one of the methodologies that are applied to elucidate biochemical networks from such data. Autoregressive-based modelling approach such as granger causality, partial directed coherence, directed transfer function and canonical variate analysis have been applied on different systems for deciphering such interactions, but with limited success. In this study, the authors propose a genetic programming-based causality detection (GPCD) methodology which blends evolutionary computation-based procedures along with parameter estimation methods to derive a mathematical model of the system. Application of the GPCD methodology on five data sets that contained the different challenges mentioned above indicated that GPCD performs better than the other methods in uncovering the exact structure with less false positives. On a glycolysis data set, GPCD was able to fill the 'interaction gaps' which were missed by other methods. PMID:23848052

  16. 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.

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

    PubMed

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

    2016-01-01

    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. PMID:26919851

  18. Genetic "code": representations and dynamical models of genetic components and networks.

    PubMed

    Gilman, Alex; Arkin, Adam P

    2002-01-01

    Dynamical modeling of biological systems is becoming increasingly widespread as people attempt to grasp biological phenomena in their full complexity and make sense of an accelerating stream of experimental data. We review a number of recent modeling studies that focus on systems specifically involving gene expression and regulation. These systems include bacterial metabolic operons and phase-variable piliation, bacteriophages T7 and lambda, and interacting networks of eukaryotic developmental genes. A wide range of conceptual and mathematical representations of genetic components and phenomena appears in these works. We discuss these representations in depth and give an overview of the tools currently available for creating and exploring dynamical models. We argue that for modeling to realize its full potential as a mainstream biological research technique the tools must become more general and flexible, and formal, standardized representations of biological knowledge and data must be developed. PMID:12142360

  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. 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. PMID:27183564

  1. Genetic animal models of malformations of cortical development and epilepsy.

    PubMed

    Wong, Michael; Roper, Steven N

    2016-02-15

    Malformations of cortical development constitute a variety of pathological brain abnormalities that commonly cause severe, medically-refractory epilepsy, including focal lesions, such as focal cortical dysplasia, heterotopias, and tubers of tuberous sclerosis complex, and diffuse malformations, such as lissencephaly. Although some cortical malformations result from environmental insults during cortical development in utero, genetic factors are increasingly recognized as primary pathogenic factors across the entire spectrum of malformations. Genes implicated in causing different cortical malformations are involved in a variety of physiological functions, but many are focused on regulation of cell proliferation, differentiation, and neuronal migration. Advances in molecular genetic methods have allowed the engineering of increasingly sophisticated animal models of cortical malformations and associated epilepsy. These animal models have identified some common mechanistic themes shared by a number of different cortical malformations, but also revealed the diversity and complexity of cellular and molecular mechanisms that lead to the development of the pathological lesions and resulting epileptogenesis. PMID:25911067

  2. Genetic analysis of carcass traits in beef cattle using random regression models.

    PubMed

    Englishby, T M; Banos, G; Moore, K L; Coffey, M P; Evans, R D; Berry, D P

    2016-04-01

    Livestock mature at different rates depending, in part, on their genetic merit; therefore, the optimal age at slaughter for progeny of certain sires may differ. The objective of the present study was to examine sire-level genetic profiles for carcass weight, carcass conformation, and carcass fat in cattle of multiple beef and dairy breeds, including crossbreeds. Slaughter records from 126,214 heifers and 124,641 steers aged between 360 and 1,200 d and from 86,089 young bulls aged between 360 and 720 d were used in the analysis; animals were from 15,127 sires. Variance components for each trait across age at slaughter were generated using sire random regression models that included quadratic polynomials for fixed and random effects; heterogeneous residual variances were assumed across ages. Heritability estimates across genders ranged from 0.08 (±0.02) to 0.34 (±0.02) for carcass weight, from 0.24 (±0.02) to 0.42 (±0.01) for conformation, and from 0.16 (±0.03) to 0.40 (±0.02) for fat score. Genetic correlations within each trait across ages weakened as the interval between ages compared lengthened but were all >0.64, suggesting a similar genetic background for each trait across different ages. Eigenvalues and eigenfunctions of the additive genetic covariance matrix revealed genetic variability among animals in their growth profiles for carcass traits, although most of the genetic variability was associated with the height of the growth profile. At the same age, a positive genetic correlation (0.60 to 0.78; SE ranged from 0.01 to 0.04) existed between carcass weight and conformation, whereas negative genetic correlations existed between fatness and both conformation (-0.46 to 0.08; SE ranged from 0.02 to 0.09) and carcass weight (-0.48 to -0.16; SE ranged from 0.02 to 0.14) at the same age. The estimated genetic parameters in the present study indicate genetic variability in the growth trajectory in cattle, which can be exploited through breeding programs and

  3. A Family-Centered Model for Sharing Genetic Risk.

    PubMed

    Daly, Mary B

    2015-01-01

    The successes of the Human Genome Project have ushered in a new era of genomic science. To effectively translate these discoveries, it will be critical to improve the communication of genetic risk within families. This will require a systematic approach that accounts for the nature of family relationships and sociocultural beliefs. This paper proposes the application of the Family Systems Illness Model, used in the setting of cancer care, to the evolving field of genomics. PMID:26479564

  4. 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…

  5. Public Service Employment: CETA Program Models.

    ERIC Educational Resources Information Center

    Corpuz, Ray E., Jr.

    This monograph on Comprehensive Employment and Training Act (CETA) program models for practitioners who are implementing or redesigning public service employment programs explores three major approaches to public service employment: countercyclical, employability development, and structural targeting. The content is presented in six sections. The…

  6. 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…

  7. The Interdependent Learning Model. Program Summary.

    ERIC Educational Resources Information Center

    Far West Lab. for Educational Research and Development, Berkeley, CA.

    This document is the sixth in a series of 12 early childhood program descriptions compiled by the Far West Laboratory for Educational Research and Development. The program described here is the Interdependent Learning Model located at the Institute for Developmental Studies at New York University in New York City. The Interdependent Learning Model…

  8. 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

  9. A model agreement for genetic research in socially identifiable populations.

    PubMed

    Foster, M W; Bernsten, D; Carter, T H

    1998-09-01

    Genetic research increasingly focuses on population-specific human genetic diversity. However, the naming of a human population in public databases and scientific publications entails collective risks for its members. Those collective risks can be evaluated and protections can be put in place by the establishment of a dialogue with the subject population, before a research study is initiated. Here we describe an agreement to undertake genetic research with a Native American tribe. We identified the culturally appropriate public and private social units within which community members are accustomed to make decisions about health. We then engaged those units in a process of communal discourse. In their discourses about our proposed study, community members expressed most concern about culturally specific implications. We also found that, in this population, private social units were more influential in communal decision making than were public authorities. An agreement was reached that defined the scope of research, provided options for naming the population in publications (including anonymity), and addressed the distribution of royalties from intellectual property, the future use of archival samples, and specific cultural concerns. We found that informed consent by individuals could not fully address these collective issues. This approach may serve as a general model for the undertaking of population-specific genetic studies. PMID:9718343

  10. 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. PMID:26545937