Science.gov

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

    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.

  18. 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.

  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. 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

  8. 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

  9. 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.

  10. 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.

  11. 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.

  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. 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.

  1. 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

  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. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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

  10. 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

  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. 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

  13. 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

  14. 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

  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. 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

  18. 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

  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. 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.

  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. 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

  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. 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

  7. 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…

  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. 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

  2. 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.

  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. 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

  9. 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

  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. A ``model`` geophysics program

    SciTech Connect

    Nyquist, J.E.

    1994-03-01

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

  14. 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. 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

  16. 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.

  17. 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

  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. 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.

  9. 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.

  10. 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

  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. 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…

  17. 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…

  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. 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.

  9. 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

  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. 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)

  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. 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

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

    ERIC Educational Resources Information Center

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

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

  18. 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. 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%.

  20. 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

  1. 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

  2. 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…

  3. 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…

  4. 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

  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 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.

  7. Wave equation modelling using Julia programming language

    NASA Astrophysics Data System (ADS)

    Kim, Ahreum; Ryu, Donghyun; Ha, Wansoo

    2016-04-01

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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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...

  13. 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…

  14. 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

  15. 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

  16. 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

  17. Genetics

    MedlinePlus

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

  18. Genetics

    MedlinePlus

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

  19. 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. 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

  2. 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

  3. 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.…

  4. 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)

  5. '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...

  6. 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...

  7. 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

  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. 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.

  10. 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

  11. 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.

  12. 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

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

    PubMed Central

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

    2012-01-01

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

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

    PubMed Central

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

    2013-01-01

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

  16. 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. 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

  18. 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

  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. 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…

  2. 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.

  3. 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…

  4. 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.

  5. 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

  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. 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)

  8. 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...

  9. 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…

  10. 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

  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. 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.

  14. 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

  15. 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

  16. 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

  17. 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.

  18. 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.

  19. 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...

  20. 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.

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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.

  6. 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)

  7. 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

  8. 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.

  9. 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...

  10. 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…

  11. 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

  12. 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…

  13. 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

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. 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

  19. 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

  20. 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…

  1. 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…

  2. 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…

  3. 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…

  4. 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

  5. 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

  6. 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

  7. 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

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

    PubMed

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

    2014-10-01

    The cost of education is rising, increasing student financial aid and debt for students pursuing higher education. A few studies have assessed the impact of student debt in medicine, physical therapy and social work, but little is known about the impact of student debt on genetic counseling students and graduates. To address this gap in knowledge, a web-based study of 408 recent alumni of genetic counseling programs in North America was conducted to assess the impact of student debt on program, career and life choices. Over half (63 %; n = 256/408) of the participants reported that loans were extremely important in their ability to attend their training program, with most using subsidized loans no longer available to current graduate students. While participants were generally satisfied with their genetic counseling education, 83 % (n = 282/342) of participants with student debt reported feeling burdened by their debt, which had a median of $40,000-$50,000. This debt is relatively close to the median starting salary reported by survey participants ($45,000-$50,000), breaching the "20-10 rule" that states student debt should not exceed 20 % of annual net income. In response to this critical issue, we propose recommendations for the genetic counseling field that may help alleviate student debt impact and burden. PMID:24578121

  10. Genetic Counseling Graduate Student Debt: Impact on Program, Career and Life Choices

    PubMed Central

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

    2015-01-01

    The cost of education is rising, increasing student financial aid and debt for students pursuing higher education. A few studies have assessed the impact of student debt in medicine, physical therapy and social work, but little is known about the impact of student debt on genetic counseling students and graduates. To address this gap in knowledge, a web-based study of 408 recent alumni of genetic counseling programs in North America was conducted to assess the impact of student debt on program, career and life choices. Over half (63%; n=256/408) of the participants reported that loans were extremely important in their ability to attend their training program, with most using subsidized loans no longer available to current graduate students. While participants were generally satisfied with their genetic counseling education, 83% (n=282/342) of participants with student debt reported feeling burdened by their debt, which had a median of $40,000-$50,000. This debt is relatively close to the median starting salary reported by survey participants ($45,000-$50,000), breaching the “20-10 rule” that states student debt should not exceed 20% of annual net income. In response to this critical issue, we propose recommendations for the genetic counseling field that may help alleviate student debt impact and burden. PMID:24578121

  11. 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

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

    USGS Publications Warehouse

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

    2010-01-01

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

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

    USGS Publications Warehouse

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

    2010-01-01

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

  14. 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

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

    PubMed

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

    2015-08-21

    RNA genetic circuitry is emerging as a powerful tool to control gene expression. However, little work has been done to create a theoretical foundation for RNA circuit design. A prerequisite to this is a quantitative modeling framework that accurately describes the dynamics of RNA circuits. In this work, we develop an ordinary differential equation model of transcriptional RNA genetic circuitry, using an RNA cascade as a test case. We show that parameter sensitivity analysis can be used to design a set of four simple experiments that can be performed in parallel using rapid cell-free transcription-translation (TX-TL) reactions to determine the 13 parameters of the model. The resulting model accurately recapitulates the dynamic behavior of the cascade, and can be easily extended to predict the function of new cascade variants that utilize new elements with limited additional characterization experiments. Interestingly, we show that inconsistencies between model predictions and experiments led to the model-guided discovery of a previously unknown maturation step required for RNA regulator function. We also determine circuit parameters in two different batches of TX-TL, and show that batch-to-batch variation can be attributed to differences in parameters that are directly related to the concentrations of core gene expression machinery. We anticipate the RNA circuit models developed here will inform the creation of computer aided genetic circuit design tools that can incorporate the growing number of RNA regulators, and that the parametrization method will find use in determining functional parameters of a broad array of natural and synthetic regulatory systems. PMID:26046393

  16. Model Accounting Program. Adopters Guide.

    ERIC Educational Resources Information Center

    Beaverton School District 48, OR.

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

  17. The genetics of speciation: Insights from Fisher's geometric model.

    PubMed

    Fraïsse, Christelle; Gunnarsson, P Alexander; Roze, Denis; Bierne, Nicolas; Welch, John J

    2016-07-01

    Research in speciation genetics has uncovered many robust patterns in intrinsic reproductive isolation, and fitness landscape models have been useful in interpreting these patterns. Here, we examine fitness landscapes based on Fisher's geometric model. Such landscapes are analogous to models of optimizing selection acting on quantitative traits, and have been widely used to study adaptation and the distribution of mutational effects. We show that, with a few modifications, Fisher's model can generate all of the major findings of introgression studies (including "speciation genes" with strong deleterious effects, complex epistasis and asymmetry), and the major patterns in overall hybrid fitnesses (including Haldane's Rule, the speciation clock, heterosis, hybrid breakdown, and male-female asymmetry in the F1). We compare our approach to alternative modeling frameworks that assign fitnesses to genotypes by identifying combinations of incompatible alleles. In some cases, the predictions are importantly different. For example, Fisher's model can explain conflicting empirical results about the rate at which incompatibilities accumulate with genetic divergence. In other cases, the predictions are identical. For example, the quality of reproductive isolation is little affected by the manner in which populations diverge. PMID:27252049

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

    NASA Astrophysics Data System (ADS)

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

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

  19. Self similarity in a model of genetic microevolution

    NASA Astrophysics Data System (ADS)

    Strier, Damián E.; Zanette, Damián H.

    A mathematical model of genetic microevolution is presented. The model stands for a population of genotypes evolving in the genotypic space. Its dynamics is governed by a master evolution equation which takes into account both the presence of a fluctuating fitness landscape and genotypic variations of the offspring with respect to the parents. We found that, under rather general conditions, the population growth rate exhibits self-similarity. This result provides a clue to universal scaling features of evolution in the large-time scale, as observed from paleobiological evidence.

  20. Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Ali Ghorbani, Mohammad; Khatibi, Rahman; Aytek, Ali; Makarynskyy, Oleg; Shiri, Jalal

    2010-05-01

    Water level forecasting at various time intervals using records of past time series is of importance in water resources engineering and management. In the last 20 years, emerging approaches over the conventional harmonic analysis techniques are based on using Genetic Programming (GP) and Artificial Neural Networks (ANNs). In the present study, the GP is used to forecast sea level variations, three time steps ahead, for a set of time intervals comprising 12 h, 24 h, 5 day and 10 day time intervals using observed sea levels. The measurements from a single tide gauge at Hillarys Boat Harbor, Western Australia, were used to train and validate the employed GP for the period from December 1991 to December 2002. Statistical parameters, namely, the root mean square error, correlation coefficient and scatter index, are used to measure their performances. These were compared with a corresponding set of published results using an Artificial Neural Network model. The results show that both these artificial intelligence methodologies perform satisfactorily and may be considered as alternatives to the harmonic analysis.

  1. A Fuzzy Goal Programming Procedure for Solving Multiobjective Load Flow Problems via Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Biswas, Papun; Chakraborti, Debjani

    2010-10-01

    This paper describes how the genetic algorithms (GAs) can be efficiently used to fuzzy goal programming (FGP) formulation of optimal power flow problems having multiple objectives. In the proposed approach, the different constraints, various relationships of optimal power flow calculations are fuzzily described. In the model formulation of the problem, the membership functions of the defined fuzzy goals are characterized first for measuring the degree of achievement of the aspiration levels of the goals specified in the decision making context. Then, the achievement function for minimizing the regret for under-deviations from the highest membership value (unity) of the defined membership goals to the extent possible on the basis of priorities is constructed for optimal power flow problems. In the solution process, the GA method is employed to the FGP formulation of the problem for achievement of the highest membership value (unity) of the defined membership functions to the extent possible in the decision making environment. In the GA based solution search process, the conventional Roulette wheel selection scheme, arithmetic crossover and random mutation are taken into consideration to reach a satisfactory decision. The developed method has been tested on IEEE 6-generator 30-bus System. Numerical results show that this method is promising for handling uncertain constraints in practical power systems.

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  3. The evolution of menstruation: A new model for genetic assimilation

    PubMed Central

    Emera, D.; Romero, R.; Wagner, G.

    2012-01-01

    Why do humans menstruate while most mammals do not? Here, we present our answer to this long-debated question, arguing that (i) menstruation occurs as a mechanistic consequence of hormone-induced differentiation of the endometrium (referred to as spontaneous decidualization, or SD); (ii) SD evolved because of maternal-fetal conflict; and (iii) SD evolved by genetic assimilation of the decidualization reaction, which is induced by the fetus in non-menstruating species. The idea that menstruation occurs as a consequence of SD has been proposed in the past, but here we present a novel hypothesis on how SD evolved. We argue that decidualization became genetically stabilized in menstruating lineages, allowing females to prepare for pregnancy without any signal from the fetus. We present three models for the evolution of SD by genetic assimilation, based on recent advances in our understanding of the mechanisms of endometrial differentiation and implantation. Testing these models will ultimately shed light on the evolutionary significance of menstruation, as well as on the etiology of human reproductive disorders like endometriosis and recurrent pregnancy loss. PMID:22057551

  4. Using Dynamic Programming and Genetic Algorithms to Reduce Erosion Risks From Forest Roads

    NASA Astrophysics Data System (ADS)

    Madej, M.; Eschenbach, E.; Teasley, R.; Diaz, C.; Wartella, J.; Simi, J.

    2002-12-01

    Many anadromous fisheries streams in the Pacific Northwest have been damaged by various land use activities, including timber harvest and road construction. Unpaved forest roads can cause erosion and downstream sedimentation damage in anadromous fish-bearing streams. Although road decommissioning and road upgrading activities have been conducted on many of these roads, these activities have usually been implemented and evaluated on a site-specific basis without the benefit of a watershed perspective. Land managers still struggle with designing the most effective road treatment plan to minimize erosion while keeping costs reasonable across a large land base. Trade-offs between costs of different levels of treatment and the net effect on reducing sediment risks to streams need to be quantified. For example, which problems should be treated first, and by what treatment method? Is it better to fix one large problem or 100 small problems? If sediment reduction to anadromous fish-bearing streams is the desired outcome of road treatment activities, a more rigorous evaluation of risks and optimization of treatments is needed. Two approaches, Dynamic Programming (DP) and Genetic Algorithms (GA), were successfully used to determine the most effective treatment levels for roads and stream crossings in a pilot study basin with approximately 200 road segments and stream crossings and in an actual watershed with approximately 600 road segments and crossings. The optimization models determine the treatment levels for roads and crossings that maximize the total sediment saved within a watershed while maintaining the total treatment cost within the specified budget. The optimization models import GIS data on roads and crossings and export the optimal treatment level for each road and crossing to the GIS watershed model.

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

    PubMed

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

    2015-01-01

    Recent advances in the production of genetically engineered (GE) livestock have resulted in a variety of new transgenic animals with desirable production and composition changes. GE animals have been generated to improve growth efficiency, food composition, and disease resistance in domesticated livestock species. GE animals are also used to produce pharmaceuticals and as medical models for human diseases. The potential use of these food animals for human consumption has prompted an intense debate about food safety and animal welfare concerns with the GE approach. Additionally, public perception and ethical concerns about their use have caused delays in establishing a clear and efficient regulatory approval process. Ethically, there are far-reaching implications of not using genetically engineered livestock, at a detriment to both producers and consumers, as use of this technology can improve both human and animal health and welfare. PMID:25387117

  6. A multilingual programming model for coupled systems.

    SciTech Connect

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

    2008-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Bányai, T.

    2009-04-01

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

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

    PubMed Central

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

    2012-01-01

    Exposure to genetic and biochemical experiments typically occurs late in one’s academic career. By the time students have the opportunity to select specialized courses in these areas, many have already developed negative attitudes toward the sciences. Given little or no direct experience with the fields of genetics and biochemistry, it is likely that many young people rule these out as potential areas of study or career path. To address this problem, we developed a 7-week (∼1 hr/week) hands-on course to introduce fifth grade students to basic concepts in genetics and biochemistry. These young students performed a series of investigations (ranging from examining phenotypic variation, in vitro enzymatic assays, and yeast genetic experiments) to explore scientific reasoning through direct experimentation. Despite the challenging material, the vast majority of students successfully completed each experiment, and most students reported that the experience increased their interest in science. Additionally, the experiments within the 7-week program are easily performed by instructors with basic skills in biological sciences. As such, this program can be implemented by others motivated to achieve a broader impact by increasing the accessibility of their university and communicating to a young audience a positive impression of the sciences and the potential for science as a career. PMID:22135354

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

    PubMed Central

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

    2011-01-01

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

  10. Modeling monthly pan evaporations using fuzzy genetic approach

    NASA Astrophysics Data System (ADS)

    Kişi, Özgür; Tombul, Mustafa

    2013-01-01

    SummaryThis study investigates the ability of fuzzy genetic (FG) approach in estimation of monthly pan evaporations. Various monthly climatic data, that are, solar radiation, air temperature, relative humidity and wind speed from two stations, Antalya and Mersin, in Mediterranean Region of Turkey, were used as inputs to the FG technique so as to estimate monthly pan evaporations. In the first part of the study, FG models were compared with neuro-fuzzy (ANFIS), artificial neural networks (ANNs) and Stephens-Stewart (SS) methods in estimating pan evaporations of Antalya and Mersin stations, separately. Comparison of the models revealed that the FG models generally performed better than the ANFIS, ANN and SS models. In the second part of the study, models were compared to each other in two different applications. In the first application the input data of Antalya Station were used as inputs to the models to estimate pan evaporation data of Mersin Station. The pan evaporation data of Mersin Station were estimated using the input data of Antalya and Mersin stations in the second application. Comparison results indicated that the FG models performed better than the ANFIS and ANN models. Comparison of the accuracy of the applied models in estimating total pan evaporations showed that the FG model provided the closest estimate. It was concluded that monthly pan evaporations could be successfully estimated by the FG approach.

  11. Two mathematical programming models of cheese manufacture.

    PubMed

    Burke, J A

    2006-02-01

    The standardization problem faced by cheese makers is formulated as a nonlinear programming problem using the assumptions of the Van Slyke cheese yield formula. The objective function of the model is to minimize the net cost of producing a given quantity of cheese subject to a set of production constraints. An approximation of the standardization problem formulated as a linear programming problem is also presented. Two different approaches to finding a solution are provided. The model is implemented in Microsoft Excel and solved with the standard add-in solver available in that program. An example is provided to contrast the difference between the nonlinear programming and its linear approximation, and a second example is used to illustrate the yield implications of ultrafiltered milk protein products in Cheddar cheese production. Additionally, a method for pricing inputs using the sensitivity analysis generated by the solver is demonstrated. PMID:16428648

  12. Programs Help Create And Evaluate Markov Models

    NASA Technical Reports Server (NTRS)

    Butler, Ricky W.; Boerschlein, David P.

    1993-01-01

    Pade Approximation With Scaling (PAWS) and Scaled Taylor Exponential Matrix (STEM) computer programs provide flexible, user-friendly, language-based interface for creation and evaluation of Markov models describing behaviors of fault-tolerant reconfigurable computer systems. Produce exact solution for probabilities of system failures and provide conservative estimates of numbers of significant digits in solutions. Also offer as part of bundled package with SURE and ASSIST, two other reliable analysis programs developed by Systems Validation Methods group at Langley Research Center.

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

    PubMed Central

    2014-01-01

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

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

    PubMed Central

    Taylor, Z S; Hoffman, S M G

    2014-01-01

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

  15. Simplifying clinical use of the genetic risk prediction model BRCAPRO.

    PubMed

    Biswas, Swati; Atienza, Philamer; Chipman, Jonathan; Hughes, Kevin; Barrera, Angelica M Gutierrez; Amos, Christopher I; Arun, Banu; Parmigiani, Giovanni

    2013-06-01

    Health care providers need simple tools to identify patients at genetic risk of breast and ovarian cancers. Genetic risk prediction models such as BRCAPRO could fill this gap if incorporated into Electronic Medical Records or other Health Information Technology solutions. However, BRCAPRO requires potentially extensive information on the counselee and her family history. Thus, it may be useful to provide simplified version(s) of BRCAPRO for use in settings that do not require exhaustive genetic counseling. We explore four simplified versions of BRCAPRO, each using less complete information than the original model. BRCAPROLYTE uses information on affected relatives only up to second degree. It is in clinical use but has not been evaluated. BRCAPROLYTE-Plus extends BRCAPROLYTE by imputing the ages of unaffected relatives. BRCAPROLYTE-Simple reduces the data collection burden associated with BRCAPROLYTE and BRCAPROLYTE-Plus by not collecting the family structure. BRCAPRO-1Degree only uses first-degree affected relatives. We use data on 2,713 individuals from seven sites of the Cancer Genetics Network and MD Anderson Cancer Center to compare these simplified tools with the Family History Assessment Tool (FHAT) and BRCAPRO, with the latter serving as the benchmark. BRCAPROLYTE retains high discrimination; however, because it ignores information on unaffected relatives, it overestimates carrier probabilities. BRCAPROLYTE-Plus and BRCAPROLYTE-Simple provide better calibration than BRCAPROLYTE, so they have higher specificity for similar values of sensitivity. BRCAPROLYTE-Plus performs slightly better than BRCAPROLYTE-Simple. The Areas Under the ROC curve are 0.783 (BRCAPRO), 0.763 (BRCAPROLYTE), 0.772 (BRCAPROLYTE-Plus), 0.773 (BRCAPROLYTE-Simple), 0.728 (BRCAPRO-1Degree), and 0.745 (FHAT). The simpler versions, especially BRCAPROLYTE-Plus and BRCAPROLYTE-Simple, lead to only modest loss in overall discrimination compared to BRCAPRO in this dataset. Thus, we conclude that

  16. Accelerating Cancer Modeling with RNAi and Nongermline Genetically Engineered Mouse Models

    PubMed Central

    Livshits, Geulah; Lowe, Scott W.

    2014-01-01

    For more than two decades, genetically engineered mouse models have been key to our mechanistic understanding of tumorigenesis and cancer progression. Recently, the massive quantity of data emerging from cancer genomics studies has demanded a corresponding increase in the efficiency and throughput of in vivo models for functional testing of putative cancer genes. Already a mainstay of cancer research, recent innovations in RNA interference (RNAi) technology have extended its utility for studying gene function and genetic interactions, enabling tissue-specific, inducible and reversible gene silencing in vivo. Concurrent advances in embryonic stem cell (ESC) culture and genome engineering have accelerated several steps of genetically engineered mouse model production and have facilitated the incorporation of RNAi technology into these models. Here, we review the current state of these technologies and examine how their integration has the potential to dramatically enhance the throughput and capabilities of animal models for cancer. PMID:24184755

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

    PubMed

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

    2014-10-01

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

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

    PubMed

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

    2004-01-01

    In recent years, graphical models have become an increasingly important tool for the structural analysis of genome-wide expression profiles at the systems level. Here we present a new graphical modelling technique, which is based on decomposable graphical models, and apply it to a set of gene expression profiles from acute lymphoblastic leukemia (ALL). The new method explains probabilistic dependencies of expression levels in terms of the concerted action of underlying genetic functional modules, which are represented as so-called "cliques" in the graph. In addition, the method uses continuous-valued (instead of discretized) expression levels, and makes no particular assumption about their probability distribution. We show that the method successfully groups members of known functional modules to cliques. Our method allows the evaluation of the importance of genes for global cellular functions based on both link count and the clique membership count. PMID:15268775

  19. A Review of Modeling Techniques for Genetic Regulatory Networks

    PubMed Central

    Yaghoobi, Hanif; Haghipour, Siyamak; Hamzeiy, Hossein; Asadi-Khiavi, Masoud

    2012-01-01

    Understanding the genetic regulatory networks, the discovery of interactions between genes and understanding regulatory processes in a cell at the gene level are the major goals of system biology and computational biology. Modeling gene regulatory networks and describing the actions of the cells at the molecular level are used in medicine and molecular biology applications such as metabolic pathways and drug discovery. Modeling these networks is also one of the important issues in genomic signal processing. After the advent of microarray technology, it is possible to model these networks using time–series data. In this paper, we provide an extensive review of methods that have been used on time–series data and represent the features, advantages and disadvantages of each. Also, we classify these methods according to their nature. A parallel study of these methods can lead to the discovery of new synthetic methods or improve previous methods. PMID:23493097

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

    NASA Astrophysics Data System (ADS)

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

    1999-01-01

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

  1. Cooperative global security programs modeling & simulation.

    SciTech Connect

    Briand, Daniel

    2010-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-11-01

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

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

    ERIC Educational Resources Information Center

    Castera, Jeremy; Bruguiere, Catherine; Clement, Pierre

    2008-01-01

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

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

    PubMed Central

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

    2014-01-01

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

  5. The economic and environmental value of genetic improvements in fattening pigs: An integrated dynamic model approach.

    PubMed

    Niemi, J K; Sevón-Aimonen, M-L; Stygar, A H; Partanen, K

    2015-08-01

    The selection of animals for improved performance affects the profitability of pig fattening and has environmental consequences. The goal of this paper was to examine how changes in genetic and market parameters impact the biophysical (feeding patterns, timing of slaughter, nitrogen excretion) and economic (return per pig space unit) results describing pig fattening in a Finnish farm. The analysis can be viewed as focusing on terminal line breeding goals. An integrated model using recursive stochastic dynamic programming and a biological pig growth model was used to estimate biophysical results and economic values. Combining these models allowed us to provide more accurate estimates for the value of genetic improvement and, thus, provide better feedback to animal breeding programs than the traditional approach, which is based on fixed management patterns. Besides the benchmark scenario, the results were simulated for 5 other scenarios. In each scenario, genotype was improved regarding daily growth potential, carcass lean meat content, or the parameters of the Gompertz growth curve (maturing rate [], adult weight of protein [α], and adult weight of lipid mass []). The change in each parameter was equal to approximately 1 SD genetic improvement (ceteris paribus). Increasing , , daily growth potential, or carcass lean meat content increased the return on pig space unit by €12.60, €7.60, €4.10, or €2.90 per year, respectively, whereas an increase in decreased the return by €3.10. The genetic improvement in and resulted in the highest decrease in nitrogen excretion calculated in total or per kilogram of carcass gain but only under the optimal feeding pattern. Simulated changes in the Gompertz growth function parameters imply greater changes in ADG and lean meat content than changes in scenarios focusing on improving ADG and lean meat content directly. The economic value of genetic improvements as well as the quantity of nitrogen excreted during the fattening

  6. Using Epidemiological Models and Genetic Selection to Identify Theoretical Opportunities to Reduce Disease Impact

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Selection for disease resistance is a contemporary topic with developing approaches for genetic improvement. Merging the sciences of genetic selection and epidemiology is essential to identify selection schemes to enhance disease resistance. Epidemiological models can identify theoretical opportuni...

  7. Genetics

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  8. Genetics

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  9. Program Model Checking as a New Trend

    NASA Technical Reports Server (NTRS)

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

    2002-01-01

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

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

    PubMed

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

    2016-04-01

    Genetic diversity provides insight into heterogeneous demographic and adaptive history across organisms' distribution ranges. For this reason, decomposing single species into genetic units may represent a powerful tool to better understand biogeographical patterns as well as improve predictions of the effects of GCC (global climate change) on biodiversity loss. Using 279 georeferenced Iberian accessions, we used classes of three intraspecific genetic units of the annual plant Arabidopsis thaliana obtained from the genetic analyses of nuclear SNPs (single nucleotide polymorphisms), chloroplast SNPs, and the vernalization requirement for flowering. We used SDM (species distribution models), including climate, vegetation, and soil data, at the whole-species and genetic-unit levels. We compared model outputs for present environmental conditions and with a particularly severe GCC scenario. SDM accuracy was high for genetic units with smaller distribution ranges. Kernel density plots identified the environmental variables underpinning potential distribution ranges of genetic units. Combinations of environmental variables accounted for potential distribution ranges of genetic units, which shrank dramatically with GCC at almost all levels. Only two genetic clusters increased their potential distribution ranges with GCC. The application of SDM to intraspecific genetic units provides a detailed picture on the biogeographical patterns of distinct genetic groups based on different genetic criteria. Our approach also allowed us to pinpoint the genetic changes, in terms of genetic background and physiological requirements for flowering, that Iberian A. thaliana may experience with a GCC scenario applying SDM to intraspecific genetic units. PMID:27066224

  11. An interactive program for software reliability modeling

    NASA Technical Reports Server (NTRS)

    Farr, W. H.; Smith, O. D.

    1984-01-01

    With the tremendous growth in computer software, the demand has arisen for producing cost effective reliable software. Over the last 10 years an area of research has developed which attempts to address this problem by estimating a program's current reliability by modeling either the times between error detections or the error counts in past testing periods. A new tool for interactive software reliability analysis using the computer is described. This computer program allows the user to perform a complete reliability analysis using any of eight well-known models appearing in the literature. Some of the capabilities of the program are illustrated by means of an analysis of a set of simulated error data.

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

    PubMed

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

    2012-01-01

    The objective of present study was identification of genetic similarity between wild Iran and captive Azerbaijan Pheasant using PCR-RAPD markers. For this purpose, in overall, 28 birds were taken for DNA extraction and subsequently 15 arbitrary primers were applied for PCR-RAPD technique. After electrophoresis, five primers exhibited sufficient variability which yielded overall 65 distinct bands, 59 polymorphic bands, for detalis, range of number of bands per primer was 10 to 14, and produced size varied between 200 to 1500 bp. Highest and lowest polymorphic primers were OPC5, OPC16 (100%) and OPC15 (81%), respectively. Result of genetic variation between two groups was accounted as nonsignificant (8.12%) of the overall variation. According to our expectation the wild Iranian birds showed higher genetic diversity value than the Azerbaijan captive birds. As general conclusion, two pheasant populations have almost same genetic origin and probably are subpopulations of one population. The data reported herein could open the opportunity to search for suitable conservation strategy to improve richness of Iran biodiversity and present study here was the first report that might have significant impact on the breeding and conservation program of Iranian pheasant gene pool. Analyses using more regions, more birds, and more DNA markers will be useful to confirm or to reject these findings. PMID:23002388

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

    NASA Astrophysics Data System (ADS)

    Zhang, Xin-Li; Zhang, Ke-Cun

    2009-09-01

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

  14. Non-Categorical Preschool Model Program.

    ERIC Educational Resources Information Center

    Bolen, Jacqueline M.; And Others

    Special education teachers at the graduate level developed a model noncategorical preschool program for five normal or severely handicapped children which incorporated parent training and behavioral research. The staff assumed such tasks as designing classroom/clinic/observation areas, arranging for materials, training parents, and attending…

  15. Evaluation Model for Career Programs. Final Report.

    ERIC Educational Resources Information Center

    Byerly, Richard L.; And Others

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

  16. Model Professional Development Programs Win Recognition.

    ERIC Educational Resources Information Center

    Price, Kathleen C., Ed.; Quinn, Peggy, Ed.

    1999-01-01

    This bulletin is designed to illustrate the broad range of research and improvement activities supported by the Office of Educational Research and Improvement. Contents include: "Model Professional Development Programs Win Recognition,""Are Our Schools Safe?,""Charter Schools on the Rise,""What to Expect Your First Year of Teaching,""Evaluating…

  17. A Brush Seals Program Modeling and Developments

    NASA Technical Reports Server (NTRS)

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

    1996-01-01

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    NASA Astrophysics Data System (ADS)

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

    2011-11-01

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

  20. An Intelligent Model for Pairs Trading Using Genetic Algorithms

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2009-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Kashid, Satishkumar S.; Maity, Rajib

    2012-08-01

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

  4. Effect of keishibukuryogan on genetic and dietary obesity models.

    PubMed

    Gao, Fengying; Yokoyama, Satoru; Fujimoto, Makoto; Tsuneyama, Koichi; Saiki, Ikuo; Shimada, Yutaka; Hayakawa, Yoshihiro

    2015-01-01

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

  5. Genetic Variability and Population Structure of Salvia lachnostachys: Implications for Breeding and Conservation Programs

    PubMed Central

    Erbano, Marianna; Schühli, Guilherme Schnell e; dos Santos, Élide Pereira

    2015-01-01

    The genetic diversity and population structure of Salvia lachnostachys Benth were assessed. Inter Simple Sequence Repeat (ISSR) molecular markers were used to investigate the restricted distribution of S. lachnostachys in Parana State, Brazil. Leaves of 73 individuals representing three populations were collected. DNA was extracted and submitted to PCR-ISSR amplification with nine tested primers. Genetic diversity parameters were evaluated. Our analysis indicated 95.6% polymorphic loci (stress value 0.02) with a 0.79 average Simpson’s index. The Nei-Li distance dendrogram and principal component analysis largely recovered the geographical origin of each sample. Four major clusters were recognized representing each collected population. Nei’s gene diversity and Shannon’s information index were 0.25 and 0.40 respectively. As is typical for outcrossing herbs, the majority of genetic variation occurred at the population level (81.76%). A high gene flow (Nm = 2.48) was observed with a correspondingly low fixation index. These values were generally similar to previous studies on congeneric species. The results of principal coordinate analysis (PCA) and of arithmetic average (UPGMA) were consistent and all three populations appear distinct as in STRUCTURE analysis. In addition, this analysis indicated a majority intrapopulation genetic variation. Despite the human pressure on natural populations our study found high levels of genetic diversity for S. lachnostachys. This was the first molecular assessment for this endemic species with medicinal proprieties and the results can guide for subsequent bioprospection, breeding programs or conservation actions. PMID:25856679

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

    SciTech Connect

    Nesseris, Savvas; García-Bellido, Juan E-mail: juan.garciabellido@uam.es

    2012-11-01

    We use Genetic Algorithms to extract information from several cosmological probes, such as the type Ia supernovae (SnIa), the Baryon Acoustic Oscillations (BAO) and the growth rate of matter perturbations. This is done by implementing a model independent and bias-free reconstruction of the various scales and distances that characterize the data, like the luminosity d{sub L}(z) and the angular diameter distance d{sub A}(z) in the SnIa and BAO data, respectively, or the dependence with redshift of the matter density Ω{sub m}(a) in the growth rate data, fσ{sub 8}(z). These quantities can then be used to reconstruct the expansion history of the Universe, and the resulting Dark Energy (DE) equation of state w(z) in the context of FRW models, or the mass radial function Ω{sub M}(r) in LTB models. In this way, the reconstruction is completely independent of our prior bias. Furthermore, we use this method to test the Etherington relation, ie the well-known relation between the luminosity and the angular diameter distance, η≡d{sub L}(z)/(1+z){sup 2}d{sub A}(z), which is equal to 1 in metric theories of gravity. We find that the present data seem to suggest a 3-σ deviation from one at redshifts z ∼ 0.5. Finally, we present a novel way, within the Genetic Algorithm paradigm, to analytically estimate the errors on the reconstructed quantities by calculating a Path Integral over all possible functions that may contribute to the likelihood. We show that this can be done regardless of the data being correlated or uncorrelated with each other and we also explicitly demonstrate that our approach is in good agreement with other error estimation techniques like the Fisher Matrix approach and the Bootstrap Monte Carlo.

  7. Lifestyle and Metformin Ameliorate Insulin Sensitivity Independently of the Genetic Burden of Established Insulin Resistance Variants in Diabetes Prevention Program Participants.

    PubMed

    Hivert, Marie-France; Christophi, Costas A; Franks, Paul W; Jablonski, Kathleen A; Ehrmann, David A; Kahn, Steven E; Horton, Edward S; Pollin, Toni I; Mather, Kieren J; Perreault, Leigh; Barrett-Connor, Elizabeth; Knowler, William C; Florez, Jose C

    2016-02-01

    Large genome-wide association studies of glycemic traits have identified genetics variants that are associated with insulin resistance (IR) in the general population. It is unknown whether people with genetic enrichment for these IR variants respond differently to interventions that aim to improve insulin sensitivity. We built a genetic risk score (GRS) based on 17 established IR variants and effect sizes (weighted IR-GRS) in 2,713 participants of the Diabetes Prevention Program (DPP) with genetic consent. We tested associations between the weighted IR-GRS and insulin sensitivity index (ISI) at baseline in all participants, and with change in ISI over 1 year of follow-up in the DPP intervention (metformin and lifestyle) and control (placebo) arms. All models were adjusted for age, sex, ethnicity, and waist circumference at baseline (plus baseline ISI for 1-year ISI change models). A higher IR-GRS was associated with lower baseline ISI (β = -0.754 [SE = 0.229] log-ISI per unit, P = 0.001 in fully adjusted models). There was no differential effect of treatment for the association between the IR-GRS on the change in ISI; higher IR-GRS was associated with an attenuation in ISI improvement over 1 year (β = -0.520 [SE = 0.233], P = 0.03 in fully adjusted models; all treatment arms). Lifestyle intervention and metformin treatment improved the ISI, regardless of the genetic burden of IR variants. PMID:26525880

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

    PubMed Central

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

    2012-01-01

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

  9. Recent Enhancements to the Genetic Risk Prediction Model BRCAPRO

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  12. The NIAID Radiation Countermeasures Program business model.

    PubMed

    Hafer, Nathaniel; Maidment, Bert W; Hatchett, Richard J

    2010-12-01

    The National Institute of Allergy and Infectious Diseases (NIAID) Radiation/Nuclear Medical Countermeasures Development Program has developed an integrated approach to providing the resources and expertise required for the research, discovery, and development of radiation/nuclear medical countermeasures (MCMs). These resources and services lower the opportunity costs and reduce the barriers to entry for companies interested in working in this area and accelerate translational progress by providing goal-oriented stewardship of promising projects. In many ways, the radiation countermeasures program functions as a "virtual pharmaceutical firm," coordinating the early and mid-stage development of a wide array of radiation/nuclear MCMs. This commentary describes the radiation countermeasures program and discusses a novel business model that has facilitated product development partnerships between the federal government and academic investigators and biopharmaceutical companies. PMID:21142762

  13. The NIAID Radiation Countermeasures Program Business Model

    PubMed Central

    Hafer, Nathaniel; Maidment, Bert W.

    2010-01-01

    The National Institute of Allergy and Infectious Diseases (NIAID) Radiation/Nuclear Medical Countermeasures Development Program has developed an integrated approach to providing the resources and expertise required for the research, discovery, and development of radiation/nuclear medical countermeasures (MCMs). These resources and services lower the opportunity costs and reduce the barriers to entry for companies interested in working in this area and accelerate translational progress by providing goal-oriented stewardship of promising projects. In many ways, the radiation countermeasures program functions as a “virtual pharmaceutical firm,” coordinating the early and mid-stage development of a wide array of radiation/nuclear MCMs. This commentary describes the radiation countermeasures program and discusses a novel business model that has facilitated product development partnerships between the federal government and academic investigators and biopharmaceutical companies. PMID:21142762

  14. Transnational nursing programs: models, advantages and challenges.

    PubMed

    Wilson, Michael

    2002-07-01

    Conducting transnational programs can be a very rewarding activity for a School, Faculty or University. Apart from increasing the profile of the university, the conduct of transnational programs can also provide the university with openings for business opportunities, consultative activities, and collaborative research. It can also be a costly exercise placing an enormous strain on limited resources with little reward for the provider. Transnational ventures can become nonviable entities in a very short period of time due to unanticipated global economic trends. Transnational courses offered by Faculties of Business and Computing are commonplace, however, there is a growing number of health science programs, particularly nursing that are being offered transnational. This paper plans an overview of several models employed for the delivery of transnational nursing courses and discusses several key issues pertaining to conducting courses outside the host university's country. PMID:12383742

  15. Behavioral phenotypes of genetic mouse models of autism

    PubMed Central

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

    2016-01-01

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

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

    SciTech Connect

    Not Available

    2007-05-01

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

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

    ERIC Educational Resources Information Center

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

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

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

    NASA Astrophysics Data System (ADS)

    Citi, Luca; Poli, Riccardo; Cinel, Caterina

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

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

    NASA Astrophysics Data System (ADS)

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

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

  20. Sphingolipids and Membrane Biology as Determined from Genetic Models

    PubMed Central

    Rao, Raghavendra Pralhada; Acharya, Jairaj K

    2008-01-01

    The importance of sphingolipids in membrane biology was appreciated early in the twentieth century when several human inborn errors of metabolism were linked to defects in sphingolipid degradation. The past two decades have seen an explosion of information linking sphingolipids with cellular processes. Studies have unraveled mechanistic details of the sphingolipid metabolic pathways, and these findings are being exploited in the development of novel therapies, some now in clinical trials. Pioneering work in yeast has laid the foundation for identifying genes encoding the enzymes of the pathways. The advent of the era of genomics and bioinformatics has led to the identification of homologous genes in other species and the subsequent creation of animal knock-out lines for these genes. Discoveries from these efforts have re-kindled interest in the role of sphingolipids in membrane biology. This review highlights some of the recent advances in understanding sphingolipids’ roles in membrane biology as determined from genetic models. PMID:18035569

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

    ERIC Educational Resources Information Center

    Johnson, Ronald; Kennon, Tillman

    2009-01-01

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

  2. Programming Sensor Networks Using Remora Component Model

    NASA Astrophysics Data System (ADS)

    Taherkordi, Amirhosein; Loiret, Frédéric; Abdolrazaghi, Azadeh; Rouvoy, Romain; Le-Trung, Quan; Eliassen, Frank

    The success of high-level programming models in Wireless Sensor Networks (WSNs) is heavily dependent on factors such as ease of programming, code well-structuring, degree of code reusability, and required software development effort. Component-based programming has been recognized as an effective approach to meet such requirements. Most of componentization efforts in WSNs were ineffective due to various reasons, such as high resource demand or limited scope of use. In this paper, we present Remora, a new approach to practical and efficient component-based programming in WSNs. Remora offers a well-structured programming paradigm that fits very well with resource limitations of embedded systems, including WSNs. Furthermore, the special attention to event handling in Remora makes our proposal more practical for WSN applications, which are inherently event-driven. More importantly, the mutualism between Remora and underlying system software promises a new direction towards separation of concerns in WSNs. Our evaluation results show that a well-configured Remora application has an acceptable memory overhead and a negligible CPU cost.

  3. Model suicide vector for containment of genetically engineered microorganisms.

    PubMed

    Bej, A K; Perlin, M H; Atlas, R M

    1988-10-01

    A model suicide vector (pBAP19h), designed for the potential containment of genetically engineered microorganisms, was made by constructing a plasmid with the hok gene, which codes for a lethal polypeptide, under the control of the lac promoter. The vector plasmid also codes for carbenicillin resistance. In the absence of carbenicillin, induction of the hok gene in vitro caused elimination of all detectable cells containing the suicide vector; pBAP19h-free cells of the culture survived and grew exponentially. In the presence of carbenicillin, however, the number of cells containing pBAP19h initially declined after induction of hok but then multiplied exponentially. The surviving cells still had a fully functional hok gene and had apparently developed resistance to the action of the Hok polypeptide. Thus, high selective pressure against the loss of the suicide vector led to a failure of the system. Soil microcosm experiments confirmed the ability of a suicide vector to restrict the growth of a genetically engineered microorganism in the absence of selective pressure against the loss of the plasmid, with 90 to 99% elimination of hok-bearing cells within 24 h of hok induction. However, some pBAP19h-bearing cells survived in the soil microcosms after hok induction. The surviving cells contained an active hok gene but were not capable of normal growth even after elimination of the hok gene; it appears that a mutation that made them Hok resistant also reduced their capacity for membrane functions needed for energy generation and exponential cell growth. Thus, the model suicide vector was shown to be functional in soil as well as in vitro.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:3060017

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

    NASA Astrophysics Data System (ADS)

    Bányai, T.

    2009-04-01

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

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

    ERIC Educational Resources Information Center

    Wendell, Douglas L.; Pickard, Dawn

    2007-01-01

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

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

    PubMed

    Aler, R; Borrajo, D; Isasi, P

    2001-01-01

    Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming (GP). There have been recent attempts to apply GP to planning that fit two approaches: (a) using GP to search in plan space or (b) to evolve a planner. In this article, we propose to evolve only the heuristics to make a particular planner more efficient. This approach is more feasible than (b) because it does not have to build a planner from scratch but can take advantage of already existing planning systems. It is also more efficient than (a) because once the heuristics have been evolved, they can be used to solve a whole class of different planning problems in a planning domain, instead of running GP for every new planning problem. Empirical results show that our approach (EvoCK) is able to evolve heuristics in two planning domains (the blocks world and the logistics domain) that improve PRODIGY4.0 performance. Additionally, we experiment with a new genetic operator --Instance-Based Crossover--that is able to use traces of the base planner as raw genetic material to be injected into the evolving population. PMID:11709102

  7. Genetic Models for the Study of Luteinizing Hormone Receptor Function

    PubMed Central

    Narayan, Prema

    2015-01-01

    The luteinizing hormone/chorionic gonadotropin receptor (LHCGR) is essential for fertility in men and women. LHCGR binds luteinizing hormone (LH) as well as the highly homologous chorionic gonadotropin. Signaling from LHCGR is required for steroidogenesis and gametogenesis in males and females and for sexual differentiation in the male. The importance of LHCGR in reproductive physiology is underscored by the large number of naturally occurring inactivating and activating mutations in the receptor that result in reproductive disorders. Consequently, several genetically modified mouse models have been developed for the study of LHCGR function. They include targeted deletion of LH and LHCGR that mimic inactivating mutations in hormone and receptor, expression of a constitutively active mutant in LHCGR that mimics activating mutations associated with familial male-limited precocious puberty and transgenic models of LH and hCG overexpression. This review summarizes the salient findings from these models and their utility in understanding the physiological and pathological consequences of loss and gain of function in LHCGR signaling. PMID:26483755

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

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

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

    SciTech Connect

    Sugarman, Jeremy; Lee, Linda

    2006-03-31

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

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

    SciTech Connect

    Rogers, Adam; Fiege, Jason D.

    2011-02-01

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

  11. The use of genetic algorithms to model protoplanetary discs

    NASA Astrophysics Data System (ADS)

    Hetem, Annibal; Gregorio-Hetem, Jane

    2007-12-01

    The protoplanetary discs of T Tauri and Herbig Ae/Be stars have previously been studied using geometric disc models to fit their spectral energy distribution (SED). The simulations provide a means to reproduce the signatures of various circumstellar structures, which are related to different levels of infrared excess. With the aim of improving our previous model, which assumed a simple flat-disc configuration, we adopt here a reprocessing flared-disc model that assumes hydrostatic, radiative equilibrium. We have developed a method to optimize the parameter estimation based on genetic algorithms (GAs). This paper describes the implementation of the new code, which has been applied to Herbig stars from the Pico dos Dias Survey catalogue, in order to illustrate the quality of the fitting for a variety of SED shapes. The star AB Aur was used as a test of the GA parameter estimation, and demonstrates that the new code reproduces successfully a canonical example of the flared-disc model. The GA method gives a good quality of fit, but the range of input parameters must be chosen with caution, as unrealistic disc parameters can be derived. It is confirmed that the flared-disc model fits the flattened SEDs typical of Herbig stars; however, embedded objects (increasing SED slope) and debris discs (steeply decreasing SED slope) are not well fitted with this configuration. Even considering the limitation of the derived parameters, the automatic process of SED fitting provides an interesting tool for the statistical analysis of the circumstellar luminosity of large samples of young stars.

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

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

  13. Prediction of cancer class with majority voting genetic programming classifier using gene expression data.

    PubMed

    Paul, Topon Kumar; Iba, Hitoshi

    2009-01-01

    In order to get a better understanding of different types of cancers and to find the possible biomarkers for diseases, recently, many researchers are analyzing the gene expression data using various machine learning techniques. However, due to a very small number of training samples compared to the huge number of genes and class imbalance, most of these methods suffer from overfitting. In this paper, we present a majority voting genetic programming classifier (MVGPC) for the classification of microarray data. Instead of a single rule or a single set of rules, we evolve multiple rules with genetic programming (GP) and then apply those rules to test samples to determine their labels with majority voting technique. By performing experiments on four different public cancer data sets, including multiclass data sets, we have found that the test accuracies of MVGPC are better than those of other methods, including AdaBoost with GP. Moreover, some of the more frequently occurring genes in the classification rules are known to be associated with the types of cancers being studied in this paper. PMID:19407358

  14. An Approach to Extract Informative Rules for Web Page Recommendation by Genetic Programming

    NASA Astrophysics Data System (ADS)

    Kim, Jaekwang; Yoon, Kwangho; Lee, Jee-Hyong

    Clickstreams in users' navigation logs have various data which are related to users' web surfing. Those are visit counts, stay times, product types, etc. When we observe these data, we can divide clickstreams into sub-clickstreams so that the pages in a sub-clickstream share more contexts with each other than with the pages in other sub-clickstreams. In this paper, we propose a method which extracts more informative rules from clickstreams for web page recommendation based on genetic programming and association rules. First, we split clickstreams into sub-clickstreams by contexts for generating more informative rules. In order to split clickstreams in consideration of context, we extract six features from users' navigation logs. A set of split rules is generated by combining those features through genetic programming, and then informative rules for recommendation are extracted with the association rule mining algorithm. Through experiments, we verify that the proposed method is more effective than the other methods in various conditions.

  15. Toward a genetically-informed model of borderline personality disorder.

    PubMed

    Livesley, John

    2008-02-01

    This article describes a conceptual framework for describing borderline personality disorder (BPD) based on empirical studies of the phenotypic structure and genetic architecture of personality. The proposed phenotype has 2 components: (1) a description of core self and interpersonal pathology-the defining features of personality disorder-as these features are expressed in the disorder; and (2) a set of traits based on the anxious-dependent or emotional dysregulation factor of the four-factor model of PD. Four kinds of traits are described: emotional (anxiousness, emotional reactivity, emotional intensity, and pessimistic-anhedonia), interpersonal (submissiveness, insecure attachment, social apprehensiveness, and need for approval), cognitive (cognitive dysregulation), and self-harm (behaviors and ideas). Formulation of the phenotype was guided by the conceptualization of personality as a system of interrelated sub-systems. The psychopathology associated with BPD involves most components of the system. The trait structure of the disorder is assumed to reflect the genetic architecture of personality and individual traits are assumed to be based on adaptive mechanisms. It is suggested that borderline traits are organized around the trait of anxiousness and that an important feature of BPD is dysregulation of the threat management system leading to pervasive fearfulness and unstable emotions. The interpersonal traits are assumed to be heritable characteristics that evolved to deal with interpersonal threats that arose as a result of social living. The potential for unstable and conflicted interpersonal relationships that is inherent to the disorder is assumed to result from the interplay between the adaptive structure of personality and psychosocial adversity. The etiology of the disorder is discussed in terms of biological and environmental factors associated with each component of the phenotype. PMID:18312122

  16. Smooth-Threshold Multivariate Genetic Prediction with Unbiased Model Selection.

    PubMed

    Ueki, Masao; Tamiya, Gen

    2016-04-01

    We develop a new genetic prediction method, smooth-threshold multivariate genetic prediction, using single nucleotide polymorphisms (SNPs) data in genome-wide association studies (GWASs). Our method consists of two stages. At the first stage, unlike the usual discontinuous SNP screening as used in the gene score method, our method continuously screens SNPs based on the output from standard univariate analysis for marginal association of each SNP. At the second stage, the predictive model is built by a generalized ridge regression simultaneously using the screened SNPs with SNP weight determined by the strength of marginal association. Continuous SNP screening by the smooth thresholding not only makes prediction stable but also leads to a closed form expression of generalized degrees of freedom (GDF). The GDF leads to the Stein's unbiased risk estimation (SURE), which enables data-dependent choice of optimal SNP screening cutoff without using cross-validation. Our method is very rapid because computationally expensive genome-wide scan is required only once in contrast to the penalized regression methods including lasso and elastic net. Simulation studies that mimic real GWAS data with quantitative and binary traits demonstrate that the proposed method outperforms the gene score method and genomic best linear unbiased prediction (GBLUP), and also shows comparable or sometimes improved performance with the lasso and elastic net being known to have good predictive ability but with heavy computational cost. Application to whole-genome sequencing (WGS) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) exhibits that the proposed method shows higher predictive power than the gene score and GBLUP methods. PMID:26947266

  17. Genetic education to diverse communities employing a community empowerment model.

    PubMed

    Mittman, I S

    1998-01-01

    Lack of equity in access to health care, in general, and genetic services in particular, places communities of color at a distinct disadvantage when considering the rapidly evolving genetic technology. Much of this disparity is owed to lack of trust and credibility in the genetic care system as well as multiple ethnocultural barriers to services. This paper presents a 3-year community outreach demonstration project in genetic education. The project employed the premise that the empowerment of the target communities to take active part in their genetic education, with attention to a wide array of the community's health care needs, is the most efficacious manner in which to provide genetic education to underserved communities. PMID:15178975

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

    NASA Astrophysics Data System (ADS)

    Mabu, Shingo; Hirasawa, Kotaro; Furuzuki, Takayuki

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

  19. Form Follows Function: A Model for Clinical Supervision of Genetic Counseling Students.

    PubMed

    Wherley, Colleen; Veach, Patricia McCarthy; Martyr, Meredith A; LeRoy, Bonnie S

    2015-10-01

    Supervision plays a vital role in genetic counselor training, yet models describing genetic counseling supervision processes and outcomes are lacking. This paper describes a proposed supervision model intended to provide a framework to promote comprehensive and consistent clinical supervision training for genetic counseling students. Based on the principle "form follows function," the model reflects and reinforces McCarthy Veach et al.'s empirically derived model of genetic counseling practice - the "Reciprocal Engagement Model" (REM). The REM consists of mutually interactive educational, relational, and psychosocial components. The Reciprocal Engagement Model of Supervision (REM-S) has similar components and corresponding tenets, goals, and outcomes. The 5 REM-S tenets are: Learning and applying genetic information are key; Relationship is integral to genetic counseling supervision; Student autonomy must be supported; Students are capable; and Student emotions matter. The REM-S outcomes are: Student understands and applies information to independently provide effective services, develop professionally, and engage in self-reflective practice. The 16 REM-S goals are informed by the REM of genetic counseling practice and supported by prior literature. A review of models in medicine and psychology confirms the REM-S contains supervision elements common in healthcare fields, while remaining unique to genetic counseling. The REM-S shows promise for enhancing genetic counselor supervision training and practice and for promoting research on clinical supervision. The REM-S is presented in detail along with specific examples and training and research suggestions. PMID:25956951

  20. Specific Genetic Disorders

    MedlinePlus

    ... of Genetic Terms Definitions for genetic terms Specific Genetic Disorders Many human diseases have a genetic component. ... Condition in an Adult The Undiagnosed Diseases Program Genetic Disorders Achondroplasia Alpha-1 Antitrypsin Deficiency Antiphospholipid Syndrome ...

  1. Model-Free Dual Heuristic Dynamic Programming.

    PubMed

    Ni, Zhen; He, Haibo; Zhong, Xiangnan; Prokhorov, Danil V

    2015-08-01

    Model-based dual heuristic dynamic programming (MB-DHP) is a popular approach in approximating optimal solutions in control problems. Yet, it usually requires offline training for the model network, and thus resulting in extra computational cost. In this brief, we propose a model-free DHP (MF-DHP) design based on finite-difference technique. In particular, we adopt multilayer perceptron with one hidden layer for both the action and the critic networks design, and use delayed objective functions to train both the action and the critic networks online over time. We test both the MF-DHP and MB-DHP approaches with a discrete time example and a continuous time example under the same parameter settings. Our simulation results demonstrate that the MF-DHP approach can obtain a control performance competitive with that of the traditional MB-DHP approach while requiring less computational resources. PMID:25955997

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

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

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

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

    PubMed

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

    2009-12-01

    Raccoons are the reservoir for the raccoon rabies virus variant in the United States. To combat this threat, oral rabies vaccination (ORV) programs are conducted in many eastern states. To aid in these efforts, the genetic structure of raccoons (Procyon lotor) was assessed in southwestern Pennsylvania to determine if select geographic features (i.e., ridges and valleys) serve as corridors or hindrances to raccoon gene flow (e.g., movement) and, therefore, rabies virus trafficking in this physiographic region. Raccoon DNA samples (n = 185) were collected from one ridge site and two adjacent valleys in southwestern Pennsylvania (Westmoreland, Cambria, Fayette, and Somerset counties). Raccoon genetic structure within and among these study sites was characterized at nine microsatellite loci. Results indicated that there was little population subdivision among any sites sampled. Furthermore, analyses using a model-based clustering approach indicated one essentially panmictic population was present among all the raccoons sampled over a reasonably broad geographic area (e.g., sites up to 36 km apart). However, a signature of isolation by distance was detected, suggesting that widths of ORV zones are critical for success. Combined, these data indicate that geographic features within this landscape influence raccoon gene flow only to a limited extent, suggesting that ridges of this physiographic system will not provide substantial long-term natural barriers to rabies virus trafficking. These results may be of value for future ORV efforts in Pennsylvania and other eastern states with similar landscapes. PMID:19125658

  4. Python Program to Select HII Region Models

    NASA Astrophysics Data System (ADS)

    Miller, Clare; Lamarche, Cody; Vishwas, Amit; Stacey, Gordon J.

    2016-01-01

    HII regions are areas of singly ionized Hydrogen formed by the ionizing radiaiton of upper main sequence stars. The infrared fine-structure line emissions, particularly Oxygen, Nitrogen, and Neon, can give important information about HII regions including gas temperature and density, elemental abundances, and the effective temperature of the stars that form them. The processes involved in calculating this information from observational data are complex. Models, such as those provided in Rubin 1984 and those produced by Cloudy (Ferland et al, 2013) enable one to extract physical parameters from observational data. However, the multitude of search parameters can make sifting through models tedious. I digitized Rubin's models and wrote a Python program that is able to take observed line ratios and their uncertainties and find the Rubin or Cloudy model that best matches the observational data. By creating a Python script that is user friendly and able to quickly sort through models with a high level of accuracy, this work increases efficiency and reduces human error in matching HII region models to observational data.

  5. Developing a Model of Advanced Training to Promote Career Advancement for Certified Genetic Counselors: An Investigation of Expanded Skills, Advanced Training Paths, and Professional Opportunities.

    PubMed

    Baty, Bonnie J; Trepanier, Angela; Bennett, Robin L; Davis, Claire; Erby, Lori; Hippman, Catriona; Lerner, Barbara; Matthews, Anne; Myers, Melanie F; Robbins, Carol B; Singletary, Claire N

    2016-08-01

    There are currently multiple paths through which genetic counselors can acquire advanced knowledge and skills. However, outside of continuing education opportunities, there are few formal training programs designed specifically for the advanced training of genetic counselors. In the genetic counseling profession, there is currently considerable debate about the paths that should be available to attain advanced skills, as well as the skills that might be needed for practice in the future. The Association of Genetic Counseling Program Directors (AGCPD) convened a national committee, the Committee on Advanced Training for Certified Genetic Counselors (CATCGC), to investigate varied paths to post-master's training and career development. The committee began its work by developing three related grids that view career advancement from the viewpoints of the skills needed to advance (skills), ways to obtain these skills (paths), and existing genetic counselor positions that offer career change or advancement (positions). Here we describe previous work related to genetic counselor career advancement, the charge of the CATCGC, our preliminary work in developing a model through which to view genetic counselor advanced training and career advancement opportunities, and our next steps in further developing and disseminating the model. PMID:26739839

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

    SciTech Connect

    Gao, Guang, R.

    2008-07-24

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

  7. Estimates of phenotypic and genetic parameters for birth weight of Brown Swiss calves in Turkey using an animal model.

    PubMed

    Sahin, A; Ulutas, Z; Yilmaz Adkinson, A; Adkinson, R W

    2012-06-01

    A study was conducted to assess the influence of genetic and environmental factors on Brown Swiss calf birth weight, and to estimate variance components, genetic parameters, and breeding values. Data were collected on 1,761 Brown Swiss calves born from 1990 to 2005 in the Konuklar State Farm in Turkey. Mean birth weight for all calves was 39.3 ± 0.09 kg. Least squares mean birth weights for male and female Brown Swiss calves were 40.3 ± 0.02 and 39.0 ± 0.02 kg, respectively. Variance components, genetic parameters, and breeding values for birth weight in Brown Swiss calves were estimated by restricted error maximum likelihood (REML)-best linear unbiased prediction(BLUP) procedures using an MTDFREML (multiple trait derivative free restricted maximum likelihood) program employing an animal model. Direct heritability (h(d)(2)), maternal heritability (h(m)(2)), total heritability (h(T)(2)), r(am) and c(am) estimates were 0.12, 0.09, 0.23, -0.58, and -0.06, respectively. The estimated maternal permanent environmental variance expressed as a proportion of the phenotypic variance (c(2)) was 0.05. Breeding values were estimated for the trait and used to evaluate genetic trends across the time period investigated. The genetic trend linear regression was not different from zero. No genetic trend for birth weight was expected, since there had been no direct selection pressure on the trait. Absence of a trend confirms that there was no change due to selection pressure on correlated traits. Genetic and environmental parameter estimates were similar to literature values indicating that effective selection methods used in more developed improvement programs would be effective in Turkey as well. PMID:22203217

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

    ERIC Educational Resources Information Center

    Dougharty, Laurence A.; And Others

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

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

    ERIC Educational Resources Information Center

    Nagler, Phyllis J.; Marson, Arthur A.

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

  10. An SSR-Based Genetic Linkage Map of the Model Grass Brachypodium distachyon

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The grass species Brachypodium distachyon (Brachypodium) has been adopted as a model system for grasses. While many genome resources are being developed, genetic resources will be essential to make full use of this model. Here, we describe the first molecular map of diploid Brachypodium. The genetic...

  11. Mouse models for induced genetic instability at endogenous loci.

    PubMed

    Reliene, Ramune; Schiestl, Robert H

    2003-10-13

    Exposure to environmental factors and genetic predisposition of an individual may lead individually or in combination to various genetic diseases including cancer. These diseases may be a consequence of genetic instability resulting in large-scale genomic rearrangements, such as DNA deletions, duplications, and translocations. This review focuses on mouse assays detecting genetic instability at endogenous loci. The frequency of DNA deletions by homologous recombination at the pink-eyed unstable (p(un)) locus is elevated in mice with mutations in ATM, Trp53, Gadd45, and WRN genes and after exposure to carcinogens. Other quantitative in vivo assays detecting loss of heterozygosity events, such as the mammalian spot assay, Dlb-1 mouse and Aprt mouse assays, are also reviewed. These in vivo test systems may predict hazardous effects of an environmental agent and/or genetic predisposition to cancer. PMID:14557804

  12. FEASIBILITY STUDY ON EXECUTIVE PROGRAM DEVELOPMENT FOR BASIN ECOSYSTEMS MODELING

    EPA Science Inventory

    The project was undertaken in order to provide a feasibility study in developing and implementing a complete executive program to interface automatically various basin-wide water quality models for use by relatively inexperienced modelers. This executive program should ultimately...

  13. Genetic variation, climate models and the ecological genetics of Larix occidentalis

    SciTech Connect

    Rehfeldt, G.E.

    1995-12-31

    Provenance tests of 138 populations of Larix occidentalis revealed genetic differentiation for eight variables describing growth, phenology, tolerance to spring frosts, effects of Meria laricis needle cast, and survival. Geographic variables accounted for as much as 34% of the variance among Rocky Mountain populations. Patterns of genetic variation were dominated by the effects of latitude and elevation, with populations from the north and from high elevations having the lowest growth potential, the least tolerance to the needle cast, and the lowest survival. However, the slope of the geographic clines was relatively flat. Populations in the same geographic area, for instance, need to be separated by about 500 m in elevation before genetic differentiation can be expected.

  14. Estimation and interpretation of genetic effects with epistasis using the NOIA model.

    PubMed

    Alvarez-Castro, José M; Carlborg, Orjan; Rönnegård, Lars

    2012-01-01

    We introduce this communication with a brief outline of the historical landmarks in genetic modeling, especially concerning epistasis. Then, we present methods for the use of genetic modeling in QTL analyses. In particular, we summarize the essential expressions of the natural and orthogonal interactions (NOIA) model of genetic effects. Our motivation for reviewing that theory here is twofold. First, this review presents a digest of the expressions for the application of the NOIA model, which are often mixed with intermediate and additional formulae in the original articles. Second, we make the required theory handy for the reader to relate the genetic concepts to the particular mathematical expressions underlying them. We illustrate those relations by providing graphical interpretations and a diagram summarizing the key features for applying genetic modeling with epistasis in comprehensive QTL analyses. Finally, we briefly review some examples of the application of NOIA to real data and the way it improves the interpretability of the results. PMID:22565838

  15. Modeling Hypercalciuria in the Genetic Hypercalciuric Stone-Forming Rat

    PubMed Central

    Frick, Kevin K.; Krieger, Nancy S.; Bushinsky, David A.

    2015-01-01

    Purpose of Review In this review we discuss how the Genetic Hypercalciuric Stone-Forming (GHS) rats, which closely model idiopathic hypercalciuria and stone formation in humans, provide insights into the pathophysiology and consequences of clinical hypercalciuria. Recent Findings Hypercalciuria in the GHS rats is due to a systemic dysregulation of calcium transport, as manifest by increased intestinal calcium absorption, increased bone resorption and decreased renal tubule calcium reabsorption. Increased levels of vitamin D receptor in intestine, bone and kidney appear to mediate these changes. The excess receptors are biologically active and increase tissue sensitivity to exogenous vitamin D. Bones of GHS rats have decreased bone mineral density (BMD) as compared with Sprague Dawley rats, and exogenous 1,25(OH)2D3 exacerbates the loss of BMD. Thiazide diuretics improve the BMD in GHS rats. Summary Studying GHS rats allows direct investigation of the effects of alterations in diet and utilization of pharmacologic therapy on hypercalciuria, urine supersaturation, stone formation and bone quality in ways that are not possible in humans. PMID:26050120

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

    PubMed Central

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

    2014-01-01

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

  17. Models, algorithms and programs for phylogeny reconciliation.

    PubMed

    Doyon, Jean-Philippe; Ranwez, Vincent; Daubin, Vincent; Berry, Vincent

    2011-09-01

    Gene sequences contain a gold mine of phylogenetic information. But unfortunately for taxonomists this information does not only tell the story of the species from which it was collected. Genes have their own complex histories which record speciation events, of course, but also many other events. Among them, gene duplications, transfers and losses are especially important to identify. These events are crucial to account for when reconstructing the history of species, and they play a fundamental role in the evolution of genomes, the diversification of organisms and the emergence of new cellular functions. We review reconciliations between gene and species trees, which are rigorous approaches for identifying duplications, transfers and losses that mark the evolution of a gene family. Existing reconciliation models and algorithms are reviewed and difficulties in modeling gene transfers are discussed. We also compare different reconciliation programs along with their advantages and disadvantages. PMID:21949266

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

    ERIC Educational Resources Information Center

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

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

  19. QUEST: A Model Transfer Program and Evaluation of QUEST Program.

    ERIC Educational Resources Information Center

    Holohan, Ronald J.

    These reports describe and evaluate Illinois Central College's Quality Undergraduate Education for Student Transfers (QUEST) program. The core of the program is to develop in students 15 competencies identified as essential to an educated person: communication skills, mathematics skills, appreciation of physical well-being, understanding of the…

  20. Corrective Reading Programs, Wichita, Kansas: Model Programs. Childhood Education.

    ERIC Educational Resources Information Center

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

    Prepared for a White House Conference on Children (December 1970), this report describes the Corrective Reading Program of Wichita, supported in target areas by funds from ESEA Title I and considered an effective large-scale remedial program which operates in the public schools. Special classes are held in each school building in which the program…

  1. Factor analysis models for structuring covariance matrices of additive genetic effects: a Bayesian implementation

    PubMed Central

    de los Campos, Gustavo; Gianola, Daniel

    2007-01-01

    Multivariate linear models are increasingly important in quantitative genetics. In high dimensional specifications, factor analysis (FA) may provide an avenue for structuring (co)variance matrices, thus reducing the number of parameters needed for describing (co)dispersion. We describe how FA can be used to model genetic effects in the context of a multivariate linear mixed model. An orthogonal common factor structure is used to model genetic effects under Gaussian assumption, so that the marginal likelihood is multivariate normal with a structured genetic (co)variance matrix. Under standard prior assumptions, all fully conditional distributions have closed form, and samples from the joint posterior distribution can be obtained via Gibbs sampling. The model and the algorithm developed for its Bayesian implementation were used to describe five repeated records of milk yield in dairy cattle, and a one common FA model was compared with a standard multiple trait model. The Bayesian Information Criterion favored the FA model. PMID:17897592

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

    NASA Astrophysics Data System (ADS)

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

    2005-08-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1982-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Ahmed, Soha; Zhang, Mengjie; Peng, Lifeng

    2014-07-01

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

  5. Genetic Network Programming with Automatically Generated Macro Nodes of Variable Size

    NASA Astrophysics Data System (ADS)

    Mabu, Shingo; Hatakeyama, Hiroyuki; Nakagoe, Hiroshi; Hirasawa, Kotaro; Furuzuki, Takayuki

    Recently, Genetic Network Programming (GNP) has been proposed as one of the evolutionary algorithms. It represents its solutions as directed graph structures and the distinguished abilities have been shown. However, when we apply GNP to complex problems like the real world one, GNP must have robustness against the changes of environments and evolve quickly. Therefore, we introduced Automatically Generated Macro Nodes (AGMs) to GNP (GNP with AGMs). Actually GNP with AGMs has shown higher performances than the conventional GNP in terms of the fitness and the speed of evolution. In this paper, a new mechanism, AGMs with variable size, is introduced to GNP. Conventional AGMs have the fixed number of nodes and they evolve using only genetic operations, while a new method allows AGM to add nodes by necessity and delete nodes which do not contribute to the evolution of the AGM. The proposed GNP with AGMs of variable size is expected to evolve effectively and efficiently when it is applied to agent systems and also expected to make better behavior sequences of agents more easily than the conventional GNP algorithm. In the simulations, the proposed and conventional methods are applied to a tileworld problem and they are compared with each other. From the results, GNP with AGMs of variable size shows better fitness than GNP with AGMs of fixed size and the conventional GNP when adapting ten different environments.

  6. Genetic programming of catalytic Pseudomonas putida biofilms for boosting biodegradation of haloalkanes.

    PubMed

    Benedetti, Ilaria; de Lorenzo, Víctor; Nikel, Pablo I

    2016-01-01

    Bacterial biofilms outperform planktonic counterparts in whole-cell biocatalysis. The transition between planktonic and biofilm lifestyles of the platform strain Pseudomonas putida KT2440 is ruled by a regulatory network controlling the levels of the trigger signal cyclic di-GMP (c-di-GMP). This circumstance was exploited for designing a genetic device that over-runs the synthesis or degradation of c-di-GMP--thus making P. putida to form biofilms at user's will. For this purpose, the transcription of either yedQ (diguanylate cyclase) or yhjH (c-di-GMP phoshodiesterase) from Escherichia coli was artificially placed under the tight control of a cyclohexanone-responsive expression system. The resulting strain was subsequently endowed with a synthetic operon and tested for 1-chlorobutane biodegradation. Upon addition of cyclohexanone to the culture medium, the thereby designed P. putida cells formed biofilms displaying high dehalogenase activity. These results show that the morphologies and physical forms of whole-cell biocatalysts can be genetically programmed while purposely designing their biochemical activity. PMID:26620533

  7. Phenotype profile of a genetic mouse model for Muenke syndrome

    PubMed Central

    Koyama, Eiki; Agochukwu, Nneamaka B.; Bartlett, Scott P.; Muenke, Maximilian

    2014-01-01

    Purpose The Muenke syndrome mutation (FGFR3P250R), which was discovered 15 years ago, represents the single most common craniosynostosis mutation. Muenke syndrome is characterized by coronal suture synostosis, mid-face hypoplasia, subtle limb anomalies, and hearing loss. However, the spectrum of clinical presentation continues to expand. To better understand the pathophysiology of the Muenke syndrome, we present collective findings from several recent studies that have characterized a genetically equivalent mouse model for Muenke syndrome (FgfR3P244R) and compare them with human phenotypes. Conclusions FgfR3P244R mutant mice show premature fusion of facial sutures, premaxillary and/or zygomatic sutures, but rarely the coronal suture. The mice also lack the typical limb phenotype. On the other hand, the mutant mice display maxillary retrusion in association with a shortening of the anterior cranial base and a premature closure of intersphenoidal and spheno-occipital synchondroses, resembling human midface hypoplasia. In addition, sensorineural hearing loss is detected in all FgfR3P244R mutant mice as in the majority of Muenke syndrome patients. It is caused by a defect in the mechanism of cell fate determination in the organ of Corti. The mice also express phenotypes that have not been previously described in humans, such as reduced cortical bone thickness, hypoplastic trabecular bone, and defective temporomandibular joint structure. Therefore, the FgfR3P244R mouse provides an excellent opportunity to study disease mechanisms of some classical phenotypes of Muenke syndrome and to test novel therapeutic strategies. The mouse model can also be further explored to discover previously unreported yet potentially significant phenotypes of Muenke syndrome. PMID:22872265

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

    NASA Astrophysics Data System (ADS)

    Khoury, Mehdi; Liu, Honghai

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

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

    PubMed

    Wagner, Markus; Neumann, Frank; Urli, Tommaso

    2015-01-01

    In genetic programming, the size of a solution is typically not specified in advance, and solutions of larger size may have a larger benefit. The flexibility often comes at the cost of the so-called bloat problem: individuals grow without providing additional benefit to the quality of solutions, and the additional elements can block the optimization process. Consequently, problems that are relatively easy to optimize cannot be handled by variable-length evolutionary algorithms. In this article, we analyze different single- and multiobjective algorithms on the sorting problem, a problem that typically lacks independent and additive fitness structures. We complement the theoretical results with comprehensive experiments to indicate the tightness of existing bounds, and to indicate bounds where theoretical results are missing. PMID:25870929

  10. Marine biomass program: plant breeding and genetics. Annual report, September 1984-December 1985

    SciTech Connect

    Neushul, M.; Harger, B.W.W.; Lewis, R.J.

    1986-03-01

    By building on past efforts and adding to the data base that has been assembled, and through collaborative research with others, progress has been made toward the long-term goal of growing macroalgae in the sea as a future source of substitute natural gas. It is encouraging that the authors program is being emulated in Japan and Sweden, and that there is growing interest in using the unique GRI kelp seedstock collection by workers in Germany, Japan, Alaska, Oregon, California, and elsewhere. This annual report discusses progress made in propagating kelps, and the floating gulf-weed, Sargassum. Work on kelp genetics has revealed high levels of compatability between species and genera, based on 166 hybridization tests.

  11. An Approach to Self-Assembling Swarm Robots Using Multitree Genetic Programming

    PubMed Central

    An, Jinung

    2013-01-01

    In recent days, self-assembling swarm robots have been studied by a number of researchers due to their advantages such as high efficiency, stability, and scalability. However, there are still critical issues in applying them to practical problems in the real world. The main objective of this study is to develop a novel self-assembling swarm robot algorithm that overcomes the limitations of existing approaches. To this end, multitree genetic programming is newly designed to efficiently discover a set of patterns necessary to carry out the mission of the self-assembling swarm robots. The obtained patterns are then incorporated into their corresponding robot modules. The computational experiments prove the effectiveness of the proposed approach. PMID:23861655

  12. An approach to self-assembling swarm robots using multitree genetic programming.

    PubMed

    Lee, Jong-Hyun; Ahn, Chang Wook; An, Jinung

    2013-01-01

    In recent days, self-assembling swarm robots have been studied by a number of researchers due to their advantages such as high efficiency, stability, and scalability. However, there are still critical issues in applying them to practical problems in the real world. The main objective of this study is to develop a novel self-assembling swarm robot algorithm that overcomes the limitations of existing approaches. To this end, multitree genetic programming is newly designed to efficiently discover a set of patterns necessary to carry out the mission of the self-assembling swarm robots. The obtained patterns are then incorporated into their corresponding robot modules. The computational experiments prove the effectiveness of the proposed approach. PMID:23861655

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

    NASA Astrophysics Data System (ADS)

    Pan, Indranil; Das, Saptarshi

    2015-07-01

    In this paper, we propose a novel methodology for automatically finding new chaotic attractors through a computational intelligence technique known as multi-gene genetic programming (MGGP). We apply this technique to the case of the Lorenz attractor and evolve several new chaotic attractors based on the basic Lorenz template. The MGGP algorithm automatically finds new nonlinear expressions for the different state variables starting from the original Lorenz system. The Lyapunov exponents of each of the attractors are calculated numerically based on the time series of the state variables using time delay embedding techniques. The MGGP algorithm tries to search the functional space of the attractors by aiming to maximise the largest Lyapunov exponent (LLE) of the evolved attractors. To demonstrate the potential of the proposed methodology, we report over one hundred new chaotic attractor structures along with their parameters, which are evolved from just the Lorenz system alone.

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

    ERIC Educational Resources Information Center

    Lee, Hea-Jin

    2005-01-01

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

  15. 45 CFR 2532.30 - Other innovative and model programs.

    Code of Federal Regulations, 2013 CFR

    2013-10-01

    ... 45 Public Welfare 4 2013-10-01 2013-10-01 false Other innovative and model programs. 2532.30 Section 2532.30 Public Welfare Regulations Relating to Public Welfare (Continued) CORPORATION FOR NATIONAL... model programs. (a) The Corporation may support other innovative and model programs such as...

  16. 45 CFR 2532.30 - Other innovative and model programs.

    Code of Federal Regulations, 2012 CFR

    2012-10-01

    ... 45 Public Welfare 4 2012-10-01 2012-10-01 false Other innovative and model programs. 2532.30 Section 2532.30 Public Welfare Regulations Relating to Public Welfare (Continued) CORPORATION FOR NATIONAL... model programs. (a) The Corporation may support other innovative and model programs such as...

  17. 45 CFR 2532.30 - Other innovative and model programs.

    Code of Federal Regulations, 2011 CFR

    2011-10-01

    ... 45 Public Welfare 4 2011-10-01 2011-10-01 false Other innovative and model programs. 2532.30 Section 2532.30 Public Welfare Regulations Relating to Public Welfare (Continued) CORPORATION FOR NATIONAL... model programs. (a) The Corporation may support other innovative and model programs such as...

  18. 45 CFR 2532.30 - Other innovative and model programs.

    Code of Federal Regulations, 2014 CFR

    2014-10-01

    ... 45 Public Welfare 4 2014-10-01 2014-10-01 false Other innovative and model programs. 2532.30 Section 2532.30 Public Welfare Regulations Relating to Public Welfare (Continued) CORPORATION FOR NATIONAL... model programs. (a) The Corporation may support other innovative and model programs such as...

  19. Mortality modeling of early detection programs.

    PubMed

    Lee, Sandra J; Zelen, Marvin

    2008-06-01

    Consider a group of subjects who are offered an opportunity to receive a sequence of periodic special examinations for the purpose of diagnosing a chronic disease earlier relative to usual care. The mortality for the early detection group is to be compared with a group receiving usual care. Benefit is reflected in a potential reduction in mortality. This article develops a general probability model that can be used to predict cumulative mortality for each of these groups. The elements of the model assume (i) a four-state progressive disease model in which a subject may be in a disease-free state (or a disease state that cannot be detected), preclinical disease state (capable of being diagnosed by a special exam), clinical state (diagnosis by usual care), and a death state; (ii) age-dependent transitions into the states; (iii) age-dependent examination sensitivity; (iv) age-dependent sojourn time in each state; and (v) the distribution of disease stages on diagnosis conditional on modality of detection. The model may be used to (i) compare mortality rates for different screening schedules; (ii) explore potential benefit of subpopulations; and (iii) compare relative reductions in disease-specific mortality due to advances and dissemination of both treatment and early detection screening programs. PMID:17725809

  20. Experimental design for estimating unknown groundwater pumping using genetic algorithm and reduced order model

    NASA Astrophysics Data System (ADS)

    Ushijima, Timothy T.; Yeh, William W.-G.

    2013-10-01

    An optimal experimental design algorithm is developed to select locations for a network of observation wells that provide maximum information about unknown groundwater pumping in a confined, anisotropic aquifer. The design uses a maximal information criterion that chooses, among competing designs, the design that maximizes the sum of squared sensitivities while conforming to specified design constraints. The formulated optimization problem is non-convex and contains integer variables necessitating a combinatorial search. Given a realistic large-scale model, the size of the combinatorial search required can make the problem difficult, if not impossible, to solve using traditional mathematical programming techniques. Genetic algorithms (GAs) can be used to perform the global search; however, because a GA requires a large number of calls to a groundwater model, the formulated optimization problem still may be infeasible to solve. As a result, proper orthogonal decomposition (POD) is applied to the groundwater model to reduce its dimensionality. Then, the information matrix in the full model space can be searched without solving the full model. Results from a small-scale test case show identical optimal solutions among the GA, integer programming, and exhaustive search methods. This demonstrates the GA's ability to determine the optimal solution. In addition, the results show that a GA with POD model reduction is several orders of magnitude faster in finding the optimal solution than a GA using the full model. The proposed experimental design algorithm is applied to a realistic, two-dimensional, large-scale groundwater problem. The GA converged to a solution for this large-scale problem.

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

    PubMed

    Pollin, Toni I; Isakova, Tamara; Jablonski, Kathleen A; de Bakker, Paul I W; Taylor, Andrew; McAteer, Jarred; Pan, Qing; Horton, Edward S; Delahanty, Linda M; Altshuler, David; Shuldiner, Alan R; Goldberg, Ronald B; Florez, Jose C; Franks, Paul W

    2012-01-01

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

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

    PubMed Central

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

    2012-01-01

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

  3. New Genetics

    MedlinePlus

    ... human genome, behavioral genetics, pharmacogenetics, drug resistance, biofilms, computer modeling. » more Chapter 5: 21st-Century Genetics Covers systems biology, GFP, genetic testing, privacy concerns, DNA forensics, ...

  4. Plasmodium falciparum genetic crosses in a humanized mouse model

    PubMed Central

    Vaughan, Ashley M.; Pinapati, Richard S.; Cheeseman, Ian H.; Camargo, Nelly; Fishbaugher, Matthew; Checkley, Lisa A.; Nair, Shalini; Hutyra, Carolyn A.; Nosten, François H.; Anderson, Timothy J. C.; Ferdig, Michael T.; Kappe, Stefan H. I.

    2015-01-01

    Genetic crosses of phenotypically distinct strains of the human malaria parasite Plasmodium falciparum are a powerful tool for identifying genes controlling drug resistance and other key phenotypes. Previous studies relied on the isolation of recombinant parasites from splenectomized chimpanzees, a research avenue that is no longer available. Here, we demonstrate that human-liver chimeric mice support recovery of recombinant progeny for the identification of genetic determinants of parasite traits and adaptations. PMID:26030447

  5. Community College Programs for Older Adults: A Resource Directory of Guidelines, Comprehensive Programming Models, and Selected Programs.

    ERIC Educational Resources Information Center

    Beckman, Brenda Marshall; Ventura-Merkel, Catherine

    In an effort to more effectively disseminate information about community college programs for older adults, this directory was developed for three purposes: to make guidelines available for establishing, expanding, or revising programs; to offer a selection of successful programming models; and to provide a compendium of existing programs. Part I…

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

    NASA Astrophysics Data System (ADS)

    Du, Jiaoman; Yu, Lean; Li, Xiang

    2016-04-01

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

  7. Ballooning dispersal using silk: world fauna, phylogenies, genetics and models.

    PubMed

    Bell, J R; Bohan, D A; Shaw, E M; Weyman, G S

    2005-04-01

    Aerial dispersal using silk ('ballooning') has evolved in spiders (Araneae), spider mites (Acari) and in the larvae of moths (Lepidoptera). Since the 17th century, over 500 observations of ballooning behaviours have been published, yet there is an absence of any evolutionary synthesis of these data. In this paper the literature is reviewed, extensively documenting the known world fauna that balloon and the principal behaviours involved. This knowledge is then incorporated into the current evolutionary phylogenies to examine how ballooning might have arisen. Whilst it is possible that ballooning co-evolved with silk and emerged as early as the Devonian (410-355 mya), it is arguably more likely that ballooning evolved in parallel with deciduous trees, herbaceous annuals and grasses in the Cretaceous (135-65 mya). During this period, temporal (e.g. bud burst, chlorophyll thresholds) and spatial (e.g. herbivory, trampling) heterogeneities in habitat structuring predominated and intensified into the Cenozoic (65 mya to the present). It is hypothesized that from the ancestral launch mechanism known as 'suspended ballooning', widely used by individuals in plant canopies, 'tip-toe' and 'rearing' take-off behaviours were strongly selected for as habitats changed. It is contended that ballooning behaviour in all three orders can be described as a mixed Evolutionary Stable Strategy. This comprises individual bet-hedging due to habitat unpredictability, giving an underlying randomness to individual ballooning, with adjustments to the individual ballooning probability being conferred by more predictable habitat changes or colonization strategies. Finally, current methods used to study ballooning, including modelling and genetic research, are illustrated and an indication of future prospects given. PMID:15877859

  8. Different concepts and models of information for family-relevant genetic findings: comparison and ethical analysis.

    PubMed

    Lenk, Christian; Frommeld, Debora

    2015-08-01

    Genetic predispositions often concern not only individual persons, but also other family members. Advances in the development of genetic tests lead to a growing number of genetic diagnoses in medical practice and to an increasing importance of genetic counseling. In the present article, a number of ethical foundations and preconditions for this issue are discussed. Four different models for the handling of genetic information are presented and analyzed including a discussion of practical implications. The different models' ranges of content reach from a strictly autonomous position over self-governed arrangements in the practice of genetic counseling up to the involvement of official bodies and committees. The different models show a number of elements which seem to be very useful for the handling of genetic data in families from an ethical perspective. In contrast, the limitations of the standard medical attempt regarding confidentiality and personal autonomy in the context of genetic information in the family are described. Finally, recommendations for further ethical research and the development of genetic counseling in families are given. PMID:25894235

  9. An Evaluation Model for Innovative Individualized Programs.

    ERIC Educational Resources Information Center

    Weber, Margaret B.

    1977-01-01

    Program evaluation is a tri-level process: evaluation of the learners, of the program against its own objectives, and as compared against a criterion program. Evaluation of innovative programs is primarily an issue of definition, and they should be judged in terms of the needs they were designed to satisfy. (Author/CTM)

  10. Human factors engineering program review model

    SciTech Connect

    Not Available

    1994-07-01

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

  11. Education of nurses in genetics.

    PubMed Central

    Forsman, I

    1988-01-01

    The need for education of nurses in genetics was articulated more than 25 years ago. This article reviews the knowledge of practicing nurses about genetics as well as the content of genetics in nursing curricula. Implementation of federal legislation that mandated increased availability of genetic services and genetics education provided support for the examination of genetics content in curricula for health professionals, including nurses, and for the development of model programs to expand this content. Recent efforts to begin to develop a pool of nurse faculty who are well prepared in genetics will be described, as well as programs to provide the necessary content through continuing-education efforts. These efforts are expected to substantially improve the capability of nurses to contribute more effectively in the delivery of genetic services. PMID:3177390

  12. Modeling distributed systems with logic programming languages

    SciTech Connect

    Lenders, P.M.

    1985-01-01

    This thesis proposes new concepts for an ideal integrated specification and simulation workstation. The transition model approach to distributed systems specification is improved by the introduction of communicating finite state automata (CFSA), and a Prolog implementation of CFSA. Liveness and safety properties are proved with Prolog. Bidirectional input-output (bi-io), a new input-output mechanism is introduced, which eases distributed systems programming. It generalizes regular input-output mechanisms, replacing two concepts with one single concept. Moreover, it is concise and powerful, and for some applications suppresses deadlock problems. Bi-io is proposed as an extension of Communicating Sequential Processes (CSP). An axiomatic semantics of the extended CSP language is given, which follows the weakest precondition approach. The similarities between CFSA and CSP (with its weakest precondition semantics) suggest that the two descriptive methods should be used together with the ideal specification and simulation workstation.

  13. Modeling Weather Impact on Ground Delay Programs

    NASA Technical Reports Server (NTRS)

    Wang, Yao; Kulkarni, Deepak

    2011-01-01

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

  14. The Arizona Telemedicine Program business model.

    PubMed

    Barker, Gail P; Krupinski, Elizabeth A; McNeely, Richard A; Holcomb, Michael J; Lopez, Ana Maria; Weinstein, Ronald S

    2005-01-01

    The Arizona Telemedicine Program (ATP) was established in 1996 when state funding was provided to implement eight telemedicine sites. Since then the ATP has expanded to connect 55 health-care organizations through a membership programme formalized through legal contracts. The ATP's membership model is based on an application service provider (ASP) concept, whereby organizations can share services at lower cost; that is, the ATP acts as a broker for services. The membership fee schedule is flexible, allowing clients to purchase only those services desired. An annual membership fee is paid by every user, based on the services requested. The membership programme income has provided a steady revenue stream for the ATP. The membership-derived revenue represented 30% of the ATP's 2.6 million dollars total income during fiscal year 2003/04. PMID:16356313

  15. Population genetics of autopolyploids under a mixed mating model and the estimation of selfing rate.

    PubMed

    Hardy, Olivier J

    2016-01-01

    Nowadays, the population genetics analysis of autopolyploid species faces many difficulties due to (i) limited development of population genetics tools under polysomic inheritance, (ii) difficulties to assess allelic dosage when genotyping individuals and (iii) a form of inbreeding resulting from the mechanism of 'double reduction'. Consequently, few data analysis computer programs are applicable to autopolyploids. To contribute bridging this gap, this article first derives theoretical expectations for the inbreeding and identity disequilibrium coefficients under polysomic inheritance in a mixed mating model. Moment estimators of these coefficients are proposed when exact genotypes or just markers phenotypes (i.e. allelic dosage unknown) are available. This led to the development of estimators of the selfing rate based on adult genotypes or phenotypes and applicable to any even-ploidy level. Their statistical performances and robustness were assessed by numerical simulations. Contrary to inbreeding-based estimators, the identity disequilibrium-based estimator using phenotypes is robust (absolute bias generally < 0.05), even in the presence of double reduction, null alleles or biparental inbreeding due to isolation by distance. A fairly good precision of the selfing rate estimates (root mean squared error < 0.1) is already achievable using a sample of 30-50 individuals phenotyped at 10 loci bearing 5-10 alleles each, conditions reachable using microsatellite markers. Diallelic markers (e.g. SNP) can also perform satisfactorily in diploids and tetraploids but more polymorphic markers are necessary for higher ploidy levels. The method is implemented in the software SPAGeDi and should contribute to reduce the lack of population genetics tools applicable to autopolyploids. PMID:25981126

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

    ERIC Educational Resources Information Center

    Harrold, Marc W.

    1992-01-01

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

  17. Effect of Bead and Illustrations Models on High School Students' Achievement in Molecular Genetics

    ERIC Educational Resources Information Center

    Rotbain, Yosi; Marbach-Ad, Gili; Stavy, Ruth

    2006-01-01

    Our main goal in this study was to explore whether the use of models in molecular genetics instruction in high school can contribute to students' understanding of concepts and processes in genetics. Three comparable groups of 11th and 12th graders participated: The control group (116 students) was taught in the traditional lecture format, while…

  18. Functional Genetic Screen to Identify Interneurons Governing Behaviorally Distinct Aspects of Drosophila Larval Motor Programs.

    PubMed

    Clark, Matt Q; McCumsey, Stephanie J; Lopez-Darwin, Sereno; Heckscher, Ellie S; Doe, Chris Q

    2016-01-01

    Drosophila larval crawling is an attractive system to study rhythmic motor output at the level of animal behavior. Larval crawling consists of waves of muscle contractions generating forward or reverse locomotion. In addition, larvae undergo additional behaviors, including head casts, turning, and feeding. It is likely that some neurons (e.g., motor neurons) are used in all these behaviors, but the identity (or even existence) of neurons dedicated to specific aspects of behavior is unclear. To identify neurons that regulate specific aspects of larval locomotion, we performed a genetic screen to identify neurons that, when activated, could elicit distinct motor programs. We used 165 Janelia CRM-Gal4 lines-chosen for sparse neuronal expression-to ectopically express the warmth-inducible neuronal activator TrpA1, and screened for locomotor defects. The primary screen measured forward locomotion velocity, and we identified 63 lines that had locomotion velocities significantly slower than controls following TrpA1 activation (28°). A secondary screen was performed on these lines, revealing multiple discrete behavioral phenotypes, including slow forward locomotion, excessive reverse locomotion, excessive turning, excessive feeding, immobile, rigid paralysis, and delayed paralysis. While many of the Gal4 lines had motor, sensory, or muscle expression that may account for some or all of the phenotype, some lines showed specific expression in a sparse pattern of interneurons. Our results show that distinct motor programs utilize distinct subsets of interneurons, and provide an entry point for characterizing interneurons governing different elements of the larval motor program. PMID:27172197

  19. Functional Genetic Screen to Identify Interneurons Governing Behaviorally Distinct Aspects of Drosophila Larval Motor Programs

    PubMed Central

    Clark, Matt Q.; McCumsey, Stephanie J.; Lopez-Darwin, Sereno; Heckscher, Ellie S.; Doe, Chris Q.

    2016-01-01

    Drosophila larval crawling is an attractive system to study rhythmic motor output at the level of animal behavior. Larval crawling consists of waves of muscle contractions generating forward or reverse locomotion. In addition, larvae undergo additional behaviors, including head casts, turning, and feeding. It is likely that some neurons (e.g., motor neurons) are used in all these behaviors, but the identity (or even existence) of neurons dedicated to specific aspects of behavior is unclear. To identify neurons that regulate specific aspects of larval locomotion, we performed a genetic screen to identify neurons that, when activated, could elicit distinct motor programs. We used 165 Janelia CRM-Gal4 lines—chosen for sparse neuronal expression—to ectopically express the warmth-inducible neuronal activator TrpA1, and screened for locomotor defects. The primary screen measured forward locomotion velocity, and we identified 63 lines that had locomotion velocities significantly slower than controls following TrpA1 activation (28°). A secondary screen was performed on these lines, revealing multiple discrete behavioral phenotypes, including slow forward locomotion, excessive reverse locomotion, excessive turning, excessive feeding, immobile, rigid paralysis, and delayed paralysis. While many of the Gal4 lines had motor, sensory, or muscle expression that may account for some or all of the phenotype, some lines showed specific expression in a sparse pattern of interneurons. Our results show that distinct motor programs utilize distinct subsets of interneurons, and provide an entry point for characterizing interneurons governing different elements of the larval motor program. PMID:27172197

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

    PubMed

    Florez, Jose C; Jablonski, Kathleen A; McAteer, Jarred B; Franks, Paul W; Mason, Clinton C; Mather, Kieren; Horton, Edward; Goldberg, Ronald; Dabelea, Dana; Kahn, Steven E; Arakaki, Richard F; Shuldiner, Alan R; Knowler, William C

    2012-01-01

    Common genetic variants have been recently associated with fasting glucose and insulin levels in white populations. Whether these associations replicate in pre-diabetes is not known. We extended these findings to the Diabetes Prevention Program, a clinical trial in which participants at high risk for diabetes were randomized to placebo, lifestyle modification or metformin for diabetes prevention. We genotyped previously reported polymorphisms (or their proxies) in/near G6PC2, MTNR1B, GCK, DGKB, GCKR, ADCY5, MADD, CRY2, ADRA2A, FADS1, PROX1, SLC2A2, GLIS3, C2CD4B, IGF1, and IRS1 in 3,548 Diabetes Prevention Program participants. We analyzed variants for association with baseline glycemic traits, incident diabetes and their interaction with response to metformin or lifestyle intervention. We replicated associations with fasting glucose at MTNR1B (P<0.001), G6PC2 (P = 0.002) and GCKR (P = 0.001). We noted impaired β-cell function in carriers of glucose-raising alleles at MTNR1B (P<0.001), and an increase in the insulinogenic index for the glucose-raising allele at G6PC2 (P<0.001). The association of MTNR1B with fasting glucose and impaired β-cell function persisted at 1 year despite adjustment for the baseline trait, indicating a sustained deleterious effect at this locus. We also replicated the association of MADD with fasting proinsulin levels (P<0.001). We detected no significant impact of these variants on diabetes incidence or interaction with preventive interventions. The association of several polymorphisms with quantitative glycemic traits is replicated in a cohort of high-risk persons. These variants do not have a detectable impact on diabetes incidence or response to metformin or lifestyle modification in the Diabetes Prevention Program. PMID:22984506

  1. An implementation of continuous genetic algorithm in parameter estimation of predator-prey model

    NASA Astrophysics Data System (ADS)

    Windarto

    2016-03-01

    Genetic algorithm is an optimization method based on the principles of genetics and natural selection in life organisms. The main components of this algorithm are chromosomes population (individuals population), parent selection, crossover to produce new offspring, and random mutation. In this paper, continuous genetic algorithm was implemented to estimate parameters in a predator-prey model of Lotka-Volterra type. For simplicity, all genetic algorithm parameters (selection rate and mutation rate) are set to be constant along implementation of the algorithm. It was found that by selecting suitable mutation rate, the algorithms can estimate these parameters well.

  2. Using microsatellite DNA markers to determine the genetic identity of parental clones used in the Louisiana sugarcane breeding program

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Sugarcane propagates asexually through vegetative cuttings. To validate the genetic identity of sugarcane clones during shipping and handling, we produced molecular fingerprints based on 21 microsatellite (SSR) DNA markers for 116 Louisiana parental clones that were included in the crossing program...

  3. Genetic structure and diversity of parental cultivars involved in China mainland sugarcane breeding programs as inferred from DNA microsatellites

    Technology Transfer Automated Retrieval System (TEKTRAN)

    More than 1,400 Saccharum accessions of worldwide origin were available in the Chinese sugarcane breeding program, but the genetic diversity and population structure of these accessions has not been fully investigated. In this study, 96 proven important parental accessions of various geographical or...

  4. 45 CFR 2532.30 - Other innovative and model programs.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

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

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

    ERIC Educational Resources Information Center

    Rodabaugh, Karl

    1997-01-01

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

  6. Genetics of PTSD: Fear Conditioning as a Model for Future Research.

    PubMed

    Amstadter, Ananda B; Nugent, Nicole R; Koenen, Karestan C

    2009-06-01

    In the last decade, the number of publications in psychiatric genetics has nearly tripled but little attention has been paid to the role of genetic factors in the etiology of posttraumatic stress disorder (PTSD). The present review summarizes the current state of genetic research on PTSD. First, we outline information regarding genetic influences provided by family investigations and by twin studies. Second, we propose the fear-conditioning model of PTSD as a framework for the nomination of candidate genes that may be related to the disorder. Third, we review lines of evidence from three neurobiological systems involved in fear conditioning, and we summarize published investigations of genetic variants studied in association with PTSD in these three systems. Finally, we review gene-by-environment interaction research, a promising novel approach to genetic research in PTSD. PMID:19779593

  7. Final Report: Programming Models for Shared Memory Clusters

    SciTech Connect

    May, J.; de Supinski, B.; Pudliner, B.; Taylor, S.; Baden, S.

    2000-01-04

    Most large parallel computers now built use a hybrid architecture called a shared memory cluster. In this design, a computer consists of several nodes connected by an interconnection network. Each node contains a pool of memory and multiple processors that share direct access to it. Because shared memory clusters combine architectural features of shared memory computers and distributed memory computers, they support several different styles of parallel programming or programming models. (Further information on the design of these systems and their programming models appears in Section 2.) The purpose of this project was to investigate the programming models available on these systems and to answer three questions: (1) How easy to use are the different programming models in real applications? (2) How do the hardware and system software on different computers affect the performance of these programming models? (3) What are the performance characteristics of different programming models for typical LLNL applications on various shared memory clusters?

  8. Genetically altered mouse models: the good, the bad, and the ugly.

    PubMed

    Thyagarajan, Tamizchelvi; Totey, Satish; Danton, Mary Jo S; Kulkarni, Ashok B

    2003-01-01

    Targeted gene disruption in mice is a powerful tool for generating murine models for human development and disease. While the human genome program has helped to generate numerous candidate genes, few genes have been characterized for their precise in vivo functions. Gene targeting has had an enormous impact on our ability to delineate the functional roles of these genes. Many gene knockout mouse models faithfully mimic the phenotypes of the human diseases. Because some models display an unexpected or no phenotype, controversy has arisen about the value of gene-targeting strategies. We argue in favor of gene-targeting strategies, provided they are used with caution, particularly in interpreting phenotypes in craniofacial and oral biology, where many genes have pleiotropic roles. The potential pitfalls are outweighed by the unique opportunities for developing and testing different therapeutic strategies before they are introduced into the clinic. In the future, we believe that genetically engineered animal models will be indispensable for gaining important insights into the molecular mechanisms underlying development, as well as disease pathogenesis, diagnosis, prevention, and treatment. PMID:12799320

  9. Morphogenesis timing of genetically programmed brain malformations in relation to epilepsy.

    PubMed

    Sarnat, Harvey B; Flores-Sarnat, Laura

    2014-01-01

    Cerebral malformations are best understood as abnormal tissue morphogenesis in the context of disorders of ontogenesis. In neuroembryology, the timing of onset and duration of abnormal genetic expression and neurodevelopmental processes are primordial and must always be assessed, regardless whether the dysgenesis is primarily genetic in origin or acquired in utero due to ischemia, fetal infarcts that interrupt cellular migration or exposure to teratogenic drugs or neurotoxins. Defective timing interferes with the synchrony between different developmental processes, such as synaptogenesis in relation to other aspects of neuronal maturation. Timing may be delay or arrest development at an immature stage or may be precocious but asynchronous with other developmental features. Focal cortical dysplasia (FCD) types 1 and 3 may be arrested maturation of the radial microcolumnar neocortical architecture that is normal in the first half of gestation, without the expected transition to a horizontal or tangential laminar cortical architecture in the second half. Lack of lamination in either the vertical or horizontal planes may be due to defective extracellular adhesion molecules that cause detachment of migratory neuroblasts from their radial glial guide fibers, to enable recently arriving neuroblasts to bypass those already in place within the cortical plate for the programmed inside-out neuronal arrangement. FCD type 2 has a different pathogenesis. The megalocytic and dysmorphic neurons may result from somatic mutations of some, but not all, neuronal precursors in the periventricular region. FCD2 and hemimegalencephaly (HME) may have the same pathogenesis, the principal difference being timing of onset within the 33 mitotic cycles of the periventricular neuroepithelium to exponentially produce the total neuronal population of the cerebral cortex: if the mutation occurs during the late mitotic cycles, FCD2 results as a small dysgenesis; if the mutation occurs in the early

  10. Genetic disruption of KSHV major latent nuclear antigen LANA enhances viral lytic transcriptional program

    SciTech Connect

    Li Qiuhua; Zhou Fuchun; Ye Fengchun; Gao Shoujiang

    2008-09-30

    Following primary infection, KSHV establishes a lifelong persistent latent infection in the host. The mechanism of KSHV latency is not fully understood. The latent nuclear antigen (LANA or LNA) encoded by ORF73 is one of a few viral genes expressed during KSHV latency, and is consistently detected in all KSHV-related malignancies. LANA is essential for KSHV episome persistence, and regulates the expression of viral lytic genes through epigenetic silencing, and inhibition of the expression and transactivation function of the key KSHV lytic replication initiator RTA (ORF50). In this study, we used a genetic approach to examine the role of LANA in regulating KSHV lytic replication program. Deletion of LANA did not affect the expression of its adjacent genes vCyclin (ORF72) and vFLIP (ORF71). In contrast, the expression levels of viral lytic genes including immediate-early gene RTA, early genes MTA (ORF57), vIL-6 (ORF-K2) and ORF59, and late gene ORF-K8.1 were increased before and after viral lytic induction with 12-O-tetradecanoyl-phorbol-13-acetate and sodium butyrate. This enhanced expression of viral lytic genes was also observed following overexpression of RTA with or without simultaneous chemical induction. Consistent with these results, the LANA mutant cells produced more infectious virions than the wild-type virus cells did. Furthermore, genetic repair of the mutant virus reverted the phenotypes to those of wild-type virus. Together, these results have demonstrated that, in the context of viral genome, LANA contributes to KSHV latency by regulating the expression of RTA and its downstream genes.

  11. Genetic modeling of gliomas in mice: new tools to tackle old problems

    PubMed Central

    Hambardzumyan, Dolores; Parada, Luis F.; Holland, Eric C.; Charest, Al

    2011-01-01

    The recently published comprehensive profiles of genomic alterations in glioma have led to a refinement in our understanding of the molecular events that underlie this cancer. Using state-of-the-art genomic tools, several laboratories have created and characterized accurate genetically engineered mouse models of glioma based on specific genetic alterations observed in human tumors. These in vivo brain tumor models faithfully recapitulate the histopathology, etiology, and biology of gliomas and provide an exceptional experimental system to discover novel therapeutic targets and test therapeutic agents. This review focuses on mouse models of glioma with a special emphasis on genetically engineered models developed around key genetic glioma signature mutations in the PDGFR, EGFR and NF1 genes and pathways. The resulting animal models have provided insight into many fundamental and mechanistic facets of tumor initiation, maintenance and resistance to therapeutic intervention and will continue to do so in the future. PMID:21305617

  12. Human operator identification model and related computer programs

    NASA Technical Reports Server (NTRS)

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

    1978-01-01

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

  13. Linking genetic algorithms with stochastic dynamic programming to the long-term operation of a multireservoir system

    NASA Astrophysics Data System (ADS)

    Huang, Wen-Cheng; Yuan, Lun-Chin; Lee, Chi-Ming

    2002-12-01

    The objective of this paper is to present a genetic algorithm-based stochastic dynamic programming (GA-based SDP) to cope with the dimensionality problem of a multiple-reservoir system. The joint long-term operation of a parallel reservoir system in the Feitsui and Shihmen reservoirs in northern Taiwan demonstrates the successful application of the proposed GA-based SDP model. Within the case study system it is believed that GA is a useful technique in supporting optimization. Though the employment of GA-based SDP may be time consuming as it proceeds through generation by generation, the model can overcome the "dimensionality curse" in searching solutions. Simulation results show Feitsui's surplus water can be utilized efficiently to fill Shihmen's deficit water without affecting Feitsui's main purpose as Taipei city's water supply. The optimal joint operation suggests that Feitsui, on average, can provide 650,000 m3/day and 920,000 m3/day to Shihmen during the wet season and dry season, respectively.

  14. Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS)

    NASA Astrophysics Data System (ADS)

    Emamgolizadeh, S.; Bateni, S. M.; Shahsavani, D.; Ashrafi, T.; Ghorbani, H.

    2015-10-01

    The soil cation exchange capacity (CEC) is one of the main soil chemical properties, which is required in various fields such as environmental and agricultural engineering as well as soil science. In situ measurement of CEC is time consuming and costly. Hence, numerous studies have used traditional regression-based techniques to estimate CEC from more easily measurable soil parameters (e.g., soil texture, organic matter (OM), and pH). However, these models may not be able to adequately capture the complex and highly nonlinear relationship between CEC and its influential soil variables. In this study, Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) were employed to estimate CEC from more readily measurable soil physical and chemical variables (e.g., OM, clay, and pH) by developing functional relations. The GEP- and MARS-based functional relations were tested at two field sites in Iran. Results showed that GEP and MARS can provide reliable estimates of CEC. Also, it was found that the MARS model (with root-mean-square-error (RMSE) of 0.318 Cmol+ kg-1 and correlation coefficient (R2) of 0.864) generated slightly better results than the GEP model (with RMSE of 0.270 Cmol+ kg-1 and R2 of 0.807). The performance of GEP and MARS models was compared with two existing approaches, namely artificial neural network (ANN) and multiple linear regression (MLR). The comparison indicated that MARS and GEP outperformed the MLP model, but they did not perform as good as ANN. Finally, a sensitivity analysis was conducted to determine the most and the least influential variables affecting CEC. It was found that OM and pH have the most and least significant effect on CEC, respectively.

  15. Genetic Model of an Aborted Porphyry-copper System

    NASA Astrophysics Data System (ADS)

    Papp, D. C.; Nitoi, E.; Szakacs, A.

    2009-05-01

    The Neogene Sturzii shallow intrusion from the East Carpathians (Bargau Mts., Romania), hosted by Paleogene-Miocene sediments of the Transcarpatian Flysch, developed as an immature porphyry copper structure. It consists of small volumes of dacites, andesites and related contact breccias, having a surface exposure of a few km2. Hydrothermal alteration occurred in the inner part of the intrusive body. The related mineralization consists of pyrite and chalcopyrite either as small veins or disseminated within the rock. A genetic model of the intrusive structure has been developed based on an integrated petrographic, geochemical, isotopic, fluid inclusion and geophysical study. The rapidly ascending calc-alkaline magmas that generated the intrusion are mantle-derived and contaminated with lower-crustal material. Pressure estimations for amphibole reveal significant differences between values corresponding to the crystal cores and rims, suggesting that decompression occurred during its crystallization. The occurrence of exploded fluid inclusions, as well as of primary igneous garnet, also indicate decompression regime during magma uplift and/or storage. All fluid inclusions identified in dacites are aqueous; C-N-S species were not detected. The general evolution of the fluids is toward decreasing salinity with decreasing temperature. Early high-T, high salinity fluids, most likely of magmatic origin, were subjected to a boiling event, related to a change of fluid pressure from litho- to hydrostatic, and followed by dilution with meteoric fluids as indicated by low salinities. These characteristics of the fluids suggest the tendency of the intrusion to evolve towards a porphyry copper system. We estimate that the evolution stopped due to decompression that allowed cold and dilute external fluids to enter the system and because of the small size of the intrusion that cooled down rapidly and could not induce extensive and long-lasting fluid circulation. Since there is no

  16. A Continuous Correlated Beta Process Model for Genetic Ancestry in Admixed Populations

    PubMed Central

    Gompert, Zachariah

    2016-01-01

    Admixture and recombination create populations and genomes with genetic ancestry from multiple source populations. Analyses of genetic ancestry in admixed populations are relevant for trait and disease mapping, studies of speciation, and conservation efforts. Consequently, many methods have been developed to infer genome-average ancestry and to deconvolute ancestry into continuous local ancestry blocks or tracts within individuals. Current methods for local ancestry inference perform well when admixture occurred recently or hybridization is ongoing, or when admixture occurred in the distant past such that local ancestry blocks have fixed in the admixed population. However, methods to infer local ancestry frequencies in isolated admixed populations still segregating for ancestry do not exist. In the current paper, I develop and test a continuous correlated beta process model to fill this analytical gap. The method explicitly models autocorrelations in ancestry frequencies at the population-level and uses discriminant analysis of SNP windows to take advantage of ancestry blocks within individuals. Analyses of simulated data sets show that the method is generally accurate such that ancestry frequency estimates exhibited low root-mean-square error and were highly correlated with the true values, particularly when large (±10 or ±20) SNP windows were used. Along these lines, the proposed method outperformed post hoc inference of ancestry frequencies from a traditional hidden Markov model (i.e., the linkage model in structure), particularly when admixture occurred more distantly in the past with little on-going gene flow or was followed by natural selection. The reliability and utility of the method was further assessed by analyzing genetic ancestry in an admixed human population (Uyghur) and three populations from a hybrid zone between Mus domesticus and M. musculus. Considerable variation in ancestry frequencies was detected within and among chromosomes in the Uyghur

  17. Modeling genetic connectivity in sticklebacks as a guideline for river restoration.

    PubMed

    Raeymaekers, Joost A M; Maes, Gregory E; Geldof, Sarah; Hontis, Ingrid; Nackaerts, Kris; Volckaert, Filip A M

    2008-08-01

    Estimating genetic connectivity in disturbed riverine landscapes is of key importance for river restoration. However, few species of the disturbed riverine fauna may provide a detailed and basin-wide picture of the human impact on the population genetics of riverine organisms. Here we used the most abundant native fish, the three-spined stickleback (Gasterosteus aculeatus L.), to detect the geographical determinants of genetic connectivity in the eastern part of the Scheldt basin in Belgium. Anthropogenic structures came out as the strongest determinant of population structure, when evaluated against a geographically well-documented baseline model accounting for natural effects. These barriers not only affected genetic diversity, but they also controlled the balance between gene flow and genetic drift, and therefore may crucially disrupt the population structure of sticklebacks. Landscape models explained a high percentage of variation (allelic richness: adjusted R (2) = 0.78; pairwise F ST: adjusted R (2) = 0.60), and likely apply to other species as well. River restoration and conservation genetics may highly benefit from riverine landscape genetics, including model building, the detection of outlier populations, and a specific test for the geographical factors controlling the balance between gene flow and genetic drift. PMID:25567729

  18. Modeling genetic connectivity in sticklebacks as a guideline for river restoration

    PubMed Central

    Raeymaekers, Joost A M; Maes, Gregory E; Geldof, Sarah; Hontis, Ingrid; Nackaerts, Kris; Volckaert, Filip A M

    2008-01-01

    Estimating genetic connectivity in disturbed riverine landscapes is of key importance for river restoration. However, few species of the disturbed riverine fauna may provide a detailed and basin-wide picture of the human impact on the population genetics of riverine organisms. Here we used the most abundant native fish, the three-spined stickleback (Gasterosteus aculeatus L.), to detect the geographical determinants of genetic connectivity in the eastern part of the Scheldt basin in Belgium. Anthropogenic structures came out as the strongest determinant of population structure, when evaluated against a geographically well-documented baseline model accounting for natural effects. These barriers not only affected genetic diversity, but they also controlled the balance between gene flow and genetic drift, and therefore may crucially disrupt the population structure of sticklebacks. Landscape models explained a high percentage of variation (allelic richness: adjusted R2 = 0.78; pairwise FST: adjusted R2 = 0.60), and likely apply to other species as well. River restoration and conservation genetics may highly benefit from riverine landscape genetics, including model building, the detection of outlier populations, and a specific test for the geographical factors controlling the balance between gene flow and genetic drift. PMID:25567729

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

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

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

  20. Modeling the evolution of complex genetic systems: the gene network family tree.

    PubMed

    Fierst, Janna L; Phillips, Patrick C

    2015-01-01

    In 1994 and 1996, Andreas Wagner introduced a novel model in two papers addressing the evolution of genetic regulatory networks. This work, and a suite of papers that followed using similar models, helped integrate network thinking into biology and motivate research focused on the evolution of genetic networks. The Wagner network has its mathematical roots in the Ising model, a statistical physics model describing the activity of atoms on a lattice, and in neural networks. These models have given rise to two branches of applications, one in physics and biology and one in artificial intelligence and machine learning. Here, we review development along these branches, outline similarities and differences between biological models of genetic regulatory circuits and neural circuits models used in machine learning, and identify ways in which these models can provide novel insights into biological systems. PMID:25504926

  1. Linguini Models of Molecular Genetic Mapping and Fingerprinting.

    ERIC Educational Resources Information Center

    Thompson, James N., Jr.; Gray, Stanton B.; Hellack, Jenna J.

    1997-01-01

    Presents an exercise using linguini noodles to demonstrate an aspect of DNA fingerprinting. DNA maps that show genetic differences can be produced by digesting a certain piece of DNA with two or more restriction enzymes both individually and in combination. By rearranging and matching linguini fragments, students can recreate the original pattern…

  2. The case for developing a lacewing genetic model organism

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Lacewings (Chrysopidae: Neuroptera) are a family of insect predators, also called aphidlions because of their voracious feeding on aphids. While lacewings have been popular with growers, gardeners, and biological control scientists, they have had little visibility in the world of genetics. Generalis...

  3. USE OF MODELING APPROACHES TO UNDERSTAND POTENTIAL IMPACTS OF GENETICALLY MODIFIED PLANTS ON PLANT COMMUNITIES

    EPA Science Inventory

    Model development is of interest to ecologists, regulators and developers, since it may assist theoretical understanding, decision making in experimental design, product development and risk assessment. In order to predict the potential impacts of genetically modified (GM) plants...

  4. Optimization Research of Generation Investment Based on Linear Programming Model

    NASA Astrophysics Data System (ADS)

    Wu, Juan; Ge, Xueqian

    Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.

  5. Automatic Compilation from High-Level Biologically-Oriented Programming Language to Genetic Regulatory Networks

    PubMed Central

    Beal, Jacob; Lu, Ting; Weiss, Ron

    2011-01-01

    Background The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. Methodology/Principal Findings To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes () and latency of the optimized engineered gene networks. Conclusions/Significance Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems. PMID:21850228

  6. Drosophila melanogaster as a genetic model system to study neurotransmitter transporters

    PubMed Central

    Martin, Ciara A.; Krantz, David E.

    2014-01-01

    The model genetic organism Drosophila melanogaster, commonly known as the fruit fly, uses many of the same neurotransmitters as mammals and very similar mechanisms of neurotransmitter storage, release and recycling. This system offers a variety of powerful molecular-genetic methods for the study of transporters, many of which would be difficult in mammalian models. We review here progress made using Drosophila to understand the function and regulation of neurotransmitter transporters and discuss future directions for its use. PMID:24704795

  7. Genetic program based data mining of fuzzy decision trees and methods of improving convergence and reducing bloat

    NASA Astrophysics Data System (ADS)

    Smith, James F., III; Nguyen, ThanhVu H.

    2007-04-01

    A data mining procedure for automatic determination of fuzzy decision tree structure using a genetic program (GP) is discussed. A GP is an algorithm that evolves other algorithms or mathematical expressions. Innovative methods for accelerating convergence of the data mining procedure and reducing bloat are given. In genetic programming, bloat refers to excessive tree growth. It has been observed that the trees in the evolving GP population will grow by a factor of three every 50 generations. When evolving mathematical expressions much of the bloat is due to the expressions not being in algebraically simplest form. So a bloat reduction method based on automated computer algebra has been introduced. The effectiveness of this procedure is discussed. Also, rules based on fuzzy logic have been introduced into the GP to accelerate convergence, reduce bloat and produce a solution more readily understood by the human user. These rules are discussed as well as other techniques for convergence improvement and bloat control. Comparisons between trees created using a genetic program and those constructed solely by interviewing experts are made. A new co-evolutionary method that improves the control logic evolved by the GP by having a genetic algorithm evolve pathological scenarios is discussed. The effect on the control logic is considered. Finally, additional methods that have been used to validate the data mining algorithm are referenced.

  8. Genetic mouse models to study blood–brain barrier development and function

    PubMed Central

    2013-01-01

    The blood–brain barrier (BBB) is a complex physiological structure formed by the blood vessels of the central nervous system (CNS) that tightly regulates the movement of substances between the blood and the neural tissue. Recently, the generation and analysis of different genetic mouse models has allowed for greater understanding of BBB development, how the barrier is regulated during health, and its response to disease. Here we discuss: 1) Genetic mouse models that have been used to study the BBB, 2) Available mouse genetic tools that can aid in the study of the BBB, and 3) Potential tools that if generated could greatly aid in our understanding of the BBB. PMID:23305182

  9. 76 FR 68011 - Medicare Program; Advanced Payment Model

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-11-02

    ... Medicare Program; Advanced Payment Model; Notice #0;#0;Federal Register / Vol. 76, No. 212 / Wednesday... Services Medicare Program; Advanced Payment Model AGENCY: Centers for Medicare & Medicaid Services (CMS), HHS. ACTION: Notice. SUMMARY: This notice announces the testing of the Advance Payment Model...

  10. Object detection via feature synthesis using MDL-based genetic programming.

    PubMed

    Lin, Yingqiang; Bhanu, Bir

    2005-06-01

    In this paper, we use genetic programming (GP) to synthesize composite operators and composite features from combinations of primitive operations and primitive features for object detection. The motivation for using GP is to overcome the human experts' limitations of focusing only on conventional combinations of primitive image processing operations in the feature synthesis. GP attempts many unconventional combinations that in some cases yield exceptionally good results. To improve the efficiency of GP and prevent its well-known code bloat problem without imposing severe restriction on the GP search, we design a new fitness function based on minimum description length principle to incorporate both the pixel labeling error and the size of a composite operator into the fitness evaluation process. To further improve the efficiency of GP, smart crossover, smart mutation and a public library ideas are incorporated to identify and keep the effective components of composite operators. Our experiments, which are performed on selected training regions of a training image to reduce the training time, show that compared to normal GP, our GP algorithm finds effective composite operators more quickly and the learned composite operators can be applied to the whole training image and other similar testing images. Also, compared to a traditional region-of-interest extraction algorithm, the composite operators learned by GP are more effective and efficient for object detection. PMID:15971921

  11. Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem

    PubMed Central

    Lu, Qiang; Ren, Jun; Wang, Zhiguang

    2016-01-01

    A researcher can infer mathematical expressions of functions quickly by using his professional knowledge (called Prior Knowledge). But the results he finds may be biased and restricted to his research field due to limitation of his knowledge. In contrast, Genetic Programming method can discover fitted mathematical expressions from the huge search space through running evolutionary algorithms. And its results can be generalized to accommodate different fields of knowledge. However, since GP has to search a huge space, its speed of finding the results is rather slow. Therefore, in this paper, a framework of connection between Prior Formula Knowledge and GP (PFK-GP) is proposed to reduce the space of GP searching. The PFK is built based on the Deep Belief Network (DBN) which can identify candidate formulas that are consistent with the features of experimental data. By using these candidate formulas as the seed of a randomly generated population, PFK-GP finds the right formulas quickly by exploring the search space of data features. We have compared PFK-GP with Pareto GP on regression of eight benchmark problems. The experimental results confirm that the PFK-GP can reduce the search space and obtain the significant improvement in the quality of SR. PMID:26819577

  12. The genetic program for cartilage development has deep homology within Bilateria.

    PubMed

    Tarazona, Oscar A; Slota, Leslie A; Lopez, Davys H; Zhang, GuangJun; Cohn, Martin J

    2016-05-01

    The evolution of novel cell types led to the emergence of new tissues and organs during the diversification of animals. The origin of the chondrocyte, the cell type that synthesizes cartilage matrix, was central to the evolution of the vertebrate endoskeleton. Cartilage-like tissues also exist outside the vertebrates, although their relationship to vertebrate cartilage is enigmatic. Here we show that protostome and deuterostome cartilage share structural and chemical properties, and that the mechanisms of cartilage development are extensively conserved--from induction of chondroprogenitor cells by Hedgehog and β-catenin signalling, to chondrocyte differentiation and matrix synthesis by SoxE and SoxD regulation of clade A fibrillar collagen (ColA) genes--suggesting that the chondrogenic gene regulatory network evolved in the common ancestor of Bilateria. These results reveal deep homology of the genetic program for cartilage development in Bilateria and suggest that activation of this ancient core chondrogenic network underlies the parallel evolution of cartilage tissues in Ecdysozoa, Lophotrochozoa and Deuterostomia. PMID:27111511

  13. Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach.

    PubMed

    Liu, Li; Shao, Ling; Li, Xuelong; Lu, Ke

    2016-01-01

    Extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. In this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. This is achieved via an evolutionary method, i.e., genetic programming (GP), which evolves the motion feature descriptor on a population of primitive 3D operators (e.g., 3D-Gabor and wavelet). In this way, the scale and shift invariant features can be effectively extracted from both color and optical flow sequences. We intend to learn data adaptive descriptors for different datasets with multiple layers, which makes fully use of the knowledge to mimic the physical structure of the human visual cortex for action recognition and simultaneously reduce the GP searching space to effectively accelerate the convergence of optimal solutions. In our evolutionary architecture, the average cross-validation classification error, which is calculated by an support-vector-machine classifier on the training set, is adopted as the evaluation criterion for the GP fitness function. After the entire evolution procedure finishes, the best-so-far solution selected by GP is regarded as the (near-)optimal action descriptor obtained. The GP-evolving feature extraction method is evaluated on four popular action datasets, namely KTH, HMDB51, UCF YouTube, and Hollywood2. Experimental results show that our method significantly outperforms other types of features, either hand-designed or machine-learned. PMID:25700480

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

    NASA Astrophysics Data System (ADS)

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

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

  15. Genetic and epigenetic catalysts in early-life programming of adult cardiometabolic disorders

    PubMed Central

    Estampador, Angela C; Franks, Paul W

    2014-01-01

    Evidence has emerged across the past few decades that the lifetime risk of developing morbidities like type 2 diabetes, obesity, and cardiovascular disease may be influenced by exposures that occur in utero and in childhood. Developmental abnormalities are known to occur at various stages in fetal growth. Epidemiological and mechanistic studies have sought to delineate developmental processes and plausible risk factors influencing pregnancy outcomes and later health. Whether these observations reflect causal processes or are confounded by genetic and social factors remains unclear, although animal (and some human) studies suggest that epigenetic programming events may be involved. Regardless of the causal basis to observations of early-life risk factors and later disease risk, the fact that such associations exist and that they are of a fairly large magnitude justifies further research around this topic. Furthermore, additional information is needed to substantiate public health guidelines on lifestyle behaviors during pregnancy to improve infant health outcomes. Indeed, lifestyle intervention clinical trials in pregnancy are now coming online, where materials and data are being collected that should facilitate understanding of the causal nature of intrauterine exposures related with gestational weight gain, such as elevated maternal blood glucose concentrations. In this review, we provide an overview of these concepts. PMID:25489250

  16. Genetic models reveal historical patterns of sea lamprey population fluctuations within Lake Champlain

    PubMed Central

    Azodi, Christina B.; Sheldon, Sallie P.; Trombulak, Stephen C.; Ardren, William R.

    2015-01-01

    The origin of sea lamprey (Petromyzon marinus) in Lake Champlain has been heavily debated over the past decade. Given the lack of historical documentation, two competing hypotheses have emerged in the literature. First, it has been argued that the relatively recent population size increase and concomitant rise in wounding rates on prey populations are indicative of an invasive population that entered the lake through the Champlain Canal. Second, recent genetic evidence suggests a post-glacial colonization at the end of the Pleistocene, approximately 11,000 years ago. One limitation to resolving the origin of sea lamprey in Lake Champlain is a lack of historical and current measures of population size. In this study, the issue of population size was explicitly addressed using nuclear (nDNA) and mitochondrial DNA (mtDNA) markers to estimate historical demography with genetic models. Haplotype network analysis, mismatch analysis, and summary statistics based on mtDNA noncoding sequences for NCI (479 bp) and NCII (173 bp) all indicate a recent population expansion. Coalescent models based on mtDNA and nDNA identified two potential demographic events: a population decline followed by a very recent population expansion. The decline in effective population size may correlate with land-use and fishing pressure changes post-European settlement, while the recent expansion may be associated with the implementation of the salmonid stocking program in the 1970s. These results are most consistent with the hypothesis that sea lamprey are native to Lake Champlain; however, the credibility intervals around parameter estimates demonstrate that there is uncertainty regarding the magnitude and timing of past demographic events. PMID:26539334

  17. Genetic models reveal historical patterns of sea lamprey population fluctuations within Lake Champlain.

    PubMed

    D'Aloia, Cassidy C; Azodi, Christina B; Sheldon, Sallie P; Trombulak, Stephen C; Ardren, William R

    2015-01-01

    The origin of sea lamprey (Petromyzon marinus) in Lake Champlain has been heavily debated over the past decade. Given the lack of historical documentation, two competing hypotheses have emerged in the literature. First, it has been argued that the relatively recent population size increase and concomitant rise in wounding rates on prey populations are indicative of an invasive population that entered the lake through the Champlain Canal. Second, recent genetic evidence suggests a post-glacial colonization at the end of the Pleistocene, approximately 11,000 years ago. One limitation to resolving the origin of sea lamprey in Lake Champlain is a lack of historical and current measures of population size. In this study, the issue of population size was explicitly addressed using nuclear (nDNA) and mitochondrial DNA (mtDNA) markers to estimate historical demography with genetic models. Haplotype network analysis, mismatch analysis, and summary statistics based on mtDNA noncoding sequences for NCI (479 bp) and NCII (173 bp) all indicate a recent population expansion. Coalescent models based on mtDNA and nDNA identified two potential demographic events: a population decline followed by a very recent population expansion. The decline in effective population size may correlate with land-use and fishing pressure changes post-European settlement, while the recent expansion may be associated with the implementation of the salmonid stocking program in the 1970s. These results are most consistent with the hypothesis that sea lamprey are native to Lake Champlain; however, the credibility intervals around parameter estimates demonstrate that there is uncertainty regarding the magnitude and timing of past demographic events. PMID:26539334

  18. Programming models for energy-aware systems

    NASA Astrophysics Data System (ADS)

    Zhu, Haitao

    Energy efficiency is an important goal of modern computing, with direct impact on system operational cost, reliability, usability and environmental sustainability. This dissertation describes the design and implementation of two innovative programming languages for constructing energy-aware systems. First, it introduces ET, a strongly typed programming language to promote and facilitate energy-aware programming, with a novel type system design called Energy Types. Energy Types is built upon a key insight into today's energy-efficient systems and applications: despite the popular perception that energy and power can only be described in joules and watts, real-world energy management is often based on discrete phases and modes, which in turn can be reasoned about by type systems very effectively. A phase characterizes a distinct pattern of program workload, and a mode represents an energy state the program is expected to execute in. Energy Types is designed to reason about energy phases and energy modes, bringing programmers into the optimization of energy management. Second, the dissertation develops Eco, an energy-aware programming language centering around sustainability. A sustainable program built from Eco is able to adaptively adjusts its own behaviors to stay on a given energy budget, avoiding both deficit that would lead to battery drain or CPU overheating, and surplus that could have been used to improve the quality of the program output. Sustainability is viewed as a form of supply and demand matching, and a sustainable program consistently maintains the equilibrium between supply and demand. ET is implemented as a prototyped compiler for smartphone programming on Android, and Eco is implemented as a minimal extension to Java. Programming practices and benchmarking experiments in these two new languages showed that ET can lead to significant energy savings for Android Apps and Eco can efficiently promote battery awareness and temperature awareness in real

  19. Discrete time spatial models arising in genetics, evolutionary game theory, and branching processes.

    PubMed

    Radcliffe, J; Rass, L

    1997-03-01

    A saddle point method is used to obtain the speed of first spread of new genotypes in genetic models and of new strategies in game theoretic models. It is also used to obtain the speed of the forward tail of the distribution of farthest spread for branching process models. The technique is applicable to a wide range of models. They include multiple allele and sex-linked models in genetics, multistrategy and bimatrix evolutionary games, and multitype and demographic branching processes. The speed of propagation has been obtained for genetics models (in simple cases only) by Weinberger and Lui, using exact analytical methods. The exact results were obtained only for two-allele, single-locus genetic models. The saddle point method agrees in these very simple cases with the results obtained by using the exact analytic methods. Of course, it can also be used in much more general situations far less tractable to exact analysis. The connection between genetic and game theoretic models is also briefly considered, as is the extent to which the exact analytic methods yield results for simple models in game theory. PMID:9046771

  20. Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity

    USGS Publications Warehouse

    Louis, S.J.; Raines, G.L.

    2003-01-01

    We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic algorithm searches through a space of transition rule parameters of a two dimensional cellular automata model to find rule parameters that fit observed mining activity data. Previous work by one of the authors in calibrating the cellular automaton took weeks - the genetic algorithm takes a day and produces rules leading to about the same (or better) fit to observed data. These preliminary results indicate that genetic algorithms are a viable tool in calibrating cellular automata for this application. Experience gained during the calibration of this cellular automata suggests that mineral resource information is a critical factor in the quality of the results. With automated calibration, further refinements of how the mineral-resource information is provided to the cellular automaton will probably improve our model.

  1. Identifying future models for delivering genetic services: a nominal group study in primary care

    PubMed Central

    Elwyn, Glyn; Edwards, Adrian; Iredale, Rachel; Davies, Peter; Gray, Jonathon

    2005-01-01

    Background To enable primary care medical practitioners to generate a range of possible service delivery models for genetic counselling services and critically assess their suitability. Methods Modified nominal group technique using in primary care professional development workshops. Results 37 general practitioners in Wales, United Kingdom too part in the nominal group process. The practitioners who attended did not believe current systems were sufficient to meet anticipated demand for genetic services. A wide range of different service models was proposed, although no single option emerged as a clear preference. No argument was put forward for genetic assessment and counselling being central to family practice, neither was there a voice for the view that the family doctor should become skilled at advising patients about predictive genetic testing and be able to counsel patients about the wider implications of genetic testing for patients and their family members, even for areas such as common cancers. Nevertheless, all the preferred models put a high priority on providing the service in the community, and often co-located in primary care, by clinicians who had developed expertise. Conclusion There is a need for a wider debate about how healthcare systems address individual concerns about genetic concerns and risk, especially given the increasing commercial marketing of genetic tests. PMID:15831099

  2. Ocean circulation model predicts high genetic structure observed in a long-lived pelagic developer.

    PubMed

    Sunday, J M; Popovic, I; Palen, W J; Foreman, M G G; Hart, M W

    2014-10-01

    Understanding the movement of genes and individuals across marine seascapes is a long-standing challenge in marine ecology and can inform our understanding of local adaptation, the persistence and movement of populations, and the spatial scale of effective management. Patterns of gene flow in the ocean are often inferred based on population genetic analyses coupled with knowledge of species' dispersive life histories. However, genetic structure is the result of time-integrated processes and may not capture present-day connectivity between populations. Here, we use a high-resolution oceanographic circulation model to predict larval dispersal along the complex coastline of western Canada that includes the transition between two well-studied zoogeographic provinces. We simulate dispersal in a benthic sea star with a 6-10 week pelagic larval phase and test predictions of this model against previously observed genetic structure including a strong phylogeographic break within the zoogeographical transition zone. We also test predictions with new genetic sampling in a site within the phylogeographic break. We find that the coupled genetic and circulation model predicts the high degree of genetic structure observed in this species, despite its long pelagic duration. High genetic structure on this complex coastline can thus be explained through ocean circulation patterns, which tend to retain passive larvae within 20-50 km of their parents, suggesting a necessity for close-knit design of Marine Protected Area networks. PMID:25231198

  3. Coupling genetic and ecological-niche models to examine how past population distributions contribute to divergence.

    PubMed

    Knowles, L Lacey; Carstens, Bryan C; Keat, Marcia L

    2007-06-01

    Understanding the impact of climate-induced distributional shifts on species divergence, like those accompanying the Pleistocene glacial cycles [1, 2], requires tools that explicitly incorporate the geographic configuration of past distributions into analyses of genetic differentiation. Depending on the historical distribution of species, genetic differences may accumulate among ancestral source populations, but there is long-standing debate whether displacements into glacial refugia promoted divergence. Here we integrate coalescent-based genetic models [3, 4] with ecological-niche modeling [5, 6] to generate expectations for patterns of genetic variation based on an inferred past distribution of a species. Reconstruction of the distribution of a montane grasshopper species during the last glacial maximum suggests that Melanoplus marshalli populations from the sky islands of Colorado and Utah were likely colonized from multiple ancestral source populations. The genetic analyses provide compelling evidence that the historical distribution of M. marshalli-namely, spatial separation of multiple refugia-was conducive to genetic differentiation. The coupling of genetic and ecological-niche modeling provides a new and flexible tool for integrating paleoenvironmental details into species-specific predictions of population structure that can increase our understanding of why the glacial cycles promoted speciation in some taxa and yet inhibited diversification in others [7, 8]. PMID:17475496

  4. Genetic Parameters for Milk Yield and Lactation Persistency Using Random Regression Models in Girolando Cattle

    PubMed Central

    Canaza-Cayo, Ali William; Lopes, Paulo Sávio; da Silva, Marcos Vinicius Gualberto Barbosa; de Almeida Torres, Robledo; Martins, Marta Fonseca; Arbex, Wagner Antonio; Cobuci, Jaime Araujo

    2015-01-01

    A total of 32,817 test-day milk yield (TDMY) records of the first lactation of 4,056 Girolando cows daughters of 276 sires, collected from 118 herds between 2000 and 2011 were utilized to estimate the genetic parameters for TDMY via random regression models (RRM) using Legendre’s polynomial functions whose orders varied from 3 to 5. In addition, nine measures of persistency in milk yield (PSi) and the genetic trend of 305-day milk yield (305MY) were evaluated. The fit quality criteria used indicated RRM employing the Legendre’s polynomial of orders 3 and 5 for fitting the genetic additive and permanent environment effects, respectively, as the best model. The heritability and genetic correlation for TDMY throughout the lactation, obtained with the best model, varied from 0.18 to 0.23 and from −0.03 to 1.00, respectively. The heritability and genetic correlation for persistency and 305MY varied from 0.10 to 0.33 and from −0.98 to 1.00, respectively. The use of PS7 would be the most suitable option for the evaluation of Girolando cattle. The estimated breeding values for 305MY of sires and cows showed significant and positive genetic trends. Thus, the use of selection indices would be indicated in the genetic evaluation of Girolando cattle for both traits. PMID:26323397

  5. Inference of S-system models of genetic networks by solving one-dimensional function optimization problems.

    PubMed

    Kimura, S; Araki, D; Matsumura, K; Okada-Hatakeyama, M

    2012-02-01

    Voit and Almeida have proposed the decoupling approach as a method for inferring the S-system models of genetic networks. The decoupling approach defines the inference of a genetic network as a problem requiring the solutions of sets of algebraic equations. The computation can be accomplished in a very short time, as the approach estimates S-system parameters without solving any of the differential equations. Yet the defined algebraic equations are non-linear, which sometimes prevents us from finding reasonable S-system parameters. In this study, we propose a new technique to overcome this drawback of the decoupling approach. This technique transforms the problem of solving each set of algebraic equations into a one-dimensional function optimization problem. The computation can still be accomplished in a relatively short time, as the problem is transformed by solving a linear programming problem. We confirm the effectiveness of the proposed approach through numerical experiments. PMID:22155075

  6. Norrin, Frizzled4, and Lrp5 signaling in endothelial cells controls a genetic program for retinal vascularization

    PubMed Central

    Ye, Xin; Wang, Yanshu; Cahill, Hugh; Yu, Minzhong; Badea, Tudor C.; Smallwood, Philip M.; Peachey, Neal S.; Nathans, Jeremy

    2009-01-01

    SUMMARY Disorders of vascular structure and function play a central role in a wide variety of CNS diseases. Mutations in the Frizzled4 (Fz4) receptor, Lrp5 co-receptor, or Norrin ligand cause retinal hypovascularization, but the role of Norrin/Fz4/Lrp signaling in vascular development has not been defined. Using mouse genetic and cell culture models, we show that loss of Fz4 signaling in endothelial cells causes defective vascular growth, which leads to chronic but reversible silencing of retinal neurons. Loss of Fz4 in all endothelial cells disrupts the blood brain barrier in the cerebellum, while excessive Fz4 signaling disrupts embryonic angiogenesis. Sox17, a transcription factor that is up-regulated by Norrin/Fz4/Lrp signaling, plays a central role in inducing the angiogenic program controlled by Norrin/Fz4/Lrp. These experiments establish a cellular basis for retinal hypovascularization diseases due to insufficient Frizzled signaling, and they suggest a broader role for Frizzled signaling in vascular growth, remodeling, maintenance, and disease. PMID:19837032

  7. Programs.

    ERIC Educational Resources Information Center

    Community College Journal, 1996

    1996-01-01

    Includes a collection of eight short articles describing model community college programs. Discusses a literacy program, a mobile computer classroom, a support program for at-risk students, a timber-harvesting program, a multimedia presentation on successful women graduates, a career center, a collaboration with NASA, and an Israeli engineering…

  8. An Organizational Strategies Program Model. Rural Isolated Schools Program.

    ERIC Educational Resources Information Center

    Southeastern Education Lab., Atlanta, GA.

    This report on an educational model termed Harold County, Tappan, which was developed under the 1965 ESEA, is in five parts. Part I, Statistical, gives projections of basic ESEA statistical data. Part II, Narrative, discusses the following: the community--population of the area to be served and its location; statement of need--educational and…

  9. Model Program: West Springfield High School, Virginia

    ERIC Educational Resources Information Center

    Alukonis, J. T.; Settar, Scott

    2008-01-01

    West Springfield High School sits on the outskirts of Washington, D.C., in Fairfax County, Virginia. WSHS is home to a thriving and growing technology education program. In recent years, the program has exploded from less than 175 students to well over 425. These numbers are expected to continue to grow in the foreseeable future. In 2002, the…

  10. Visual Teaching Model for Introducing Programming Languages

    ERIC Educational Resources Information Center

    Shehane, Ronald; Sherman, Steven

    2014-01-01

    This study examines detailed usage of online training videos that were designed to address specific course problems that were encountered in an online computer programming course. The study presents the specifics of a programming course where training videos were used to provide students with a quick start path to learning a new programming…

  11. Model Teacher - School Dental Hygiene Program.

    ERIC Educational Resources Information Center

    Smith, Lowell W.

    The purpose of this study, which was carried out during the 1972-73 school year at three parochial schools in the Houston area, was to determine the effectiveness of the Toothkeeper Program, a multimedia program of oral hygiene training carefully developed and packaged to establish effective long-term dental hygiene practice. The study population…

  12. Evaluating Individualized Reading Programs: A Bayesian Model.

    ERIC Educational Resources Information Center

    Maxwell, Martha

    Simple Bayesian approaches can be applied to answer specific questions in evaluating an individualized reading program. A small reading and study skills program located in the counseling center of a major research university collected and compiled data on student characteristics such as class, number of sessions attended, grade point average, and…

  13. A Model Program for International Commerce Education.

    ERIC Educational Resources Information Center

    Funston, Richard

    To address the economy's growing reliance on international business, San Diego State University has recently introduced a program in international commerce. The program was developed by packaging coursework in three existing areas: business administration, language training, and area studies. Although still in its infancy, the international…

  14. Calculation of exact p-values when SNPs are tested using multiple genetic models

    PubMed Central

    2014-01-01

    Background Several methods have been proposed to account for multiple comparisons in genetic association studies. However, investigators typically test each of the SNPs using multiple genetic models. Association testing using the Cochran-Armitage test for trend assuming an additive, dominant, or recessive genetic model, is commonly performed. Thus, each SNP is tested three times. Some investigators report the smallest p-value obtained from the three tests corresponding to the three genetic models, but such an approach inherently leads to inflated type 1 errors. Because of the small number of tests (three) and high correlation (functional dependence) among these tests, the procedures available for accounting for multiple tests are either too conservative or fail to meet the underlying assumptions (e.g., asymptotic multivariate normality or independence among the tests). Results We propose a method to calculate the exact p-value for each SNP using different genetic models. We performed simulations, which demonstrated the control of type 1 error and power gains using the proposed approach. We applied the proposed method to compute p-value for a polymorphism eNOS -786T>C which was shown to be associated with breast cancer risk. Conclusions Our findings indicate that the proposed method should be used to maximize power and control type 1 errors when analyzing genetic data using additive, dominant, and recessive models. PMID:24950707

  15. Transposon mouse models to elucidate the genetic mechanisms of hepatitis B viral induced hepatocellular carcinoma.

    PubMed

    Chiu, Amy P; Tschida, Barbara R; Lo, Lilian H; Moriarity, Branden S; Rowlands, Dewi K; Largaespada, David A; Keng, Vincent W

    2015-11-14

    The major type of human liver cancer is hepatocellular carcinoma (HCC), and there are currently many risk factors that contribute to this deadly disease. The majority of HCC occurrences are associated with chronic hepatitis viral infection, and hepatitis B viral (HBV) infection is currently a major health problem in Eastern Asia. Elucidating the genetic mechanisms associated with HBV-induced HCC has been difficult due to the heterogeneity and genetic complexity associated with this disease. A repertoire of animal models has been broadly used to study the pathophysiology and to develop potential treatment regimens for HBV-associated HCC. The use of these animal models has provided valuable genetic information and has been an important contributor to uncovering the factors involved in liver malignant transformation, invasion and metastasis. Recently, transposon-based mouse models are becoming more widely used in liver cancer research to interrogate the genome by forward genetics and also used to validate genes rapidly in a reverse genetic manner. Importantly, these transposon-based rapid reverse genetic mouse models could become crucial in testing potential therapeutic agents before proceeding to clinical trials in human. Therefore, this review will cover the use of transposon-based mouse models to address the problems of liver cancer, especially HBV-associated HCC occurrences in Asia. PMID:26576100

  16. Transposon mouse models to elucidate the genetic mechanisms of hepatitis B viral induced hepatocellular carcinoma

    PubMed Central

    Chiu, Amy P; Tschida, Barbara R; Lo, Lilian H; Moriarity, Branden S; Rowlands, Dewi K; Largaespada, David A; Keng, Vincent W

    2015-01-01

    The major type of human liver cancer is hepatocellular carcinoma (HCC), and there are currently many risk factors that contribute to this deadly disease. The majority of HCC occurrences are associated with chronic hepatitis viral infection, and hepatitis B viral (HBV) infection is currently a major health problem in Eastern Asia. Elucidating the genetic mechanisms associated with HBV-induced HCC has been difficult due to the heterogeneity and genetic complexity associated with this disease. A repertoire of animal models has been broadly used to study the pathophysiology and to develop potential treatment regimens for HBV-associated HCC. The use of these animal models has provided valuable genetic information and has been an important contributor to uncovering the factors involved in liver malignant transformation, invasion and metastasis. Recently, transposon-based mouse models are becoming more widely used in liver cancer research to interrogate the genome by forward genetics and also used to validate genes rapidly in a reverse genetic manner. Importantly, these transposon-based rapid reverse genetic mouse models could become crucial in testing potential therapeutic agents before proceeding to clinical trials in human. Therefore, this review will cover the use of transposon-based mouse models to address the problems of liver cancer, especially HBV-associated HCC occurrences in Asia. PMID:26576100

  17. Cycles of Exploration, Reflection, and Consolidation in Model-Based Learning of Genetics

    ERIC Educational Resources Information Center

    Kim, Beaumie; Pathak, Suneeta A.; Jacobson, Michael J.; Zhang, Baohui; Gobert, Janice D.

    2015-01-01

    Model-based reasoning has been introduced as an authentic way of learning science, and many researchers have developed technological tools for learning with models. This paper describes how a model-based tool, "BioLogica"™, was used to facilitate genetics learning in secondary 3-level biology in Singapore. The research team co-designed…

  18. Building an advanced climate model: Program plan for the CHAMMP (Computer Hardware, Advanced Mathematics, and Model Physics) Climate Modeling Program

    SciTech Connect

    Not Available

    1990-12-01

    The issue of global warming and related climatic changes from increasing concentrations of greenhouse gases in the atmosphere has received prominent attention during the past few years. The Computer Hardware, Advanced Mathematics, and Model Physics (CHAMMP) Climate Modeling Program is designed to contribute directly to this rapid improvement. The goal of the CHAMMP Climate Modeling Program is to develop, verify, and apply a new generation of climate models within a coordinated framework that incorporates the best available scientific and numerical approaches to represent physical, biogeochemical, and ecological processes, that fully utilizes the hardware and software capabilities of new computer architectures, that probes the limits of climate predictability, and finally that can be used to address the challenging problem of understanding the greenhouse climate issue through the ability of the models to simulate time-dependent climatic changes over extended times and with regional resolution.

  19. Logics and properties of a genetic regulatory program that drives embryonic muscle development in an echinoderm

    PubMed Central

    Andrikou, Carmen; Pai, Chih-Yu; Su, Yi-Hsien; Arnone, Maria Ina

    2015-01-01

    Evolutionary origin of muscle is a central question when discussing mesoderm evolution. Developmental mechanisms underlying somatic muscle development have mostly been studied in vertebrates and fly where multiple signals and hierarchic genetic regulatory cascades selectively specify myoblasts from a pool of naive mesodermal progenitors. However, due to the increased organismic complexity and distant phylogenetic position of the two systems, a general mechanistic understanding of myogenesis is still lacking. In this study, we propose a gene regulatory network (GRN) model that promotes myogenesis in the sea urchin embryo, an early branching deuterostome. A fibroblast growth factor signaling and four Forkhead transcription factors consist the central part of our model and appear to orchestrate the myogenic process. The topological properties of the network reveal dense gene interwiring and a multilevel transcriptional regulation of conserved and novel myogenic genes. Finally, the comparison of the myogenic network architecture among different animal groups highlights the evolutionary plasticity of developmental GRNs. DOI: http://dx.doi.org/10.7554/eLife.07343.001 PMID:26218224

  20. Challenges in Disseminating Model Programs: A Qualitative Analysis of the Strengthening Washington DC Families Program

    ERIC Educational Resources Information Center

    Fox, Danielle Polizzi; Gottfredson, Denise C.; Kumpfer, Karol K.; Beatty, Penny D.

    2004-01-01

    This article discusses the challenges faced when a popular model program, the Strengthening Families Program, which in the past has been implemented on a smaller scale in single organizations, moves to a larger, multiorganization endeavor. On the basis of 42 interviews conducted with program staff, the results highlight two main themes that…

  1. Developing Programs in Adult Education: A Conceptual Programming Model. Second Edition.

    ERIC Educational Resources Information Center

    Boone, Edgar J.; Safrit, R. Dale; Jones, Jo

    This guide for current and prospective adult educators is a comprehensive, practical conceptual framework for planning, designing/implementing, and evaluating/accounting for adult education programs. Chapter 1 describes the programming process. Chapter 2 describes 13 nationally recognized models of the adult education programming process, the…

  2. Optimisation of contribution of candidate parents to maximise genetic gain and restricting inbreeding using semidefinite programming (Open Access publication)

    PubMed Central

    Pong-Wong, Ricardo; Woolliams, John A

    2007-01-01

    An approach for optimising genetic contributions of candidates to control inbreeding in the offspring generation using semidefinite programming (SDP) was proposed. Formulations were done for maximising genetic gain while restricting inbreeding to a preset value and for minimising inbreeding without regard of gain. Adaptations to account for candidates with fixed contributions were also shown. Using small but traceable numerical examples, the SDP method was compared with an alternative based upon Lagrangian multipliers (RSRO). The SDP method always found the optimum solution that maximises genetic gain at any level of restriction imposed on inbreeding, unlike RSRO which failed to do so in several situations. For these situations, the expected gains from the solution obtained with RSRO were between 1.5–9% lower than those expected from the optimum solution found with SDP with assigned contributions varying widely. In conclusion SDP is a reliable and flexible method for solving contribution problems. PMID:17212945

  3. COE Loss-of-Function Analysis Reveals a Genetic Program Underlying Maintenance and Regeneration of the Nervous System in Planarians

    PubMed Central

    Cowles, Martis W.; Omuro, Kerilyn C.; Stanley, Brianna N.; Quintanilla, Carlo G.; Zayas, Ricardo M.

    2014-01-01

    Members of the COE family of transcription factors are required for central nervous system (CNS) development. However, the function of COE in the post-embryonic CNS remains largely unknown. An excellent model for investigating gene function in the adult CNS is the freshwater planarian. This animal is capable of regenerating neurons from an adult pluripotent stem cell population and regaining normal function. We previously showed that planarian coe is expressed in differentiating and mature neurons and that its function is required for proper CNS regeneration. Here, we show that coe is essential to maintain nervous system architecture and patterning in intact (uninjured) planarians. We took advantage of the robust phenotype in intact animals to investigate the genetic programs coe regulates in the CNS. We compared the transcriptional profiles of control and coe RNAi planarians using RNA sequencing and identified approximately 900 differentially expressed genes in coe knockdown animals, including 397 downregulated genes that were enriched for nervous system functional annotations. Next, we validated a subset of the downregulated transcripts by analyzing their expression in coe-deficient planarians and testing if the mRNAs could be detected in coe+ cells. These experiments revealed novel candidate targets of coe in the CNS such as ion channel, neuropeptide, and neurotransmitter genes. Finally, to determine if loss of any of the validated transcripts underscores the coe knockdown phenotype, we knocked down their expression by RNAi and uncovered a set of coe-regulated genes implicated in CNS regeneration and patterning, including orthologs of sodium channel alpha-subunit and pou4. Our study broadens the knowledge of gene expression programs regulated by COE that are required for maintenance of neural subtypes and nervous system architecture in adult animals. PMID:25356635

  4. COE loss-of-function analysis reveals a genetic program underlying maintenance and regeneration of the nervous system in planarians.

    PubMed

    Cowles, Martis W; Omuro, Kerilyn C; Stanley, Brianna N; Quintanilla, Carlo G; Zayas, Ricardo M

    2014-10-01

    Members of the COE family of transcription factors are required for central nervous system (CNS) development. However, the function of COE in the post-embryonic CNS remains largely unknown. An excellent model for investigating gene function in the adult CNS is the freshwater planarian. This animal is capable of regenerating neurons from an adult pluripotent stem cell population and regaining normal function. We previously showed that planarian coe is expressed in differentiating and mature neurons and that its function is required for proper CNS regeneration. Here, we show that coe is essential to maintain nervous system architecture and patterning in intact (uninjured) planarians. We took advantage of the robust phenotype in intact animals to investigate the genetic programs coe regulates in the CNS. We compared the transcriptional profiles of control and coe RNAi planarians using RNA sequencing and identified approximately 900 differentially expressed genes in coe knockdown animals, including 397 downregulated genes that were enriched for nervous system functional annotations. Next, we validated a subset of the downregulated transcripts by analyzing their expression in coe-deficient planarians and testing if the mRNAs could be detected in coe+ cells. These experiments revealed novel candidate targets of coe in the CNS such as ion channel, neuropeptide, and neurotransmitter genes. Finally, to determine if loss of any of the validated transcripts underscores the coe knockdown phenotype, we knocked down their expression by RNAi and uncovered a set of coe-regulated genes implicated in CNS regeneration and patterning, including orthologs of sodium channel alpha-subunit and pou4. Our study broadens the knowledge of gene expression programs regulated by COE that are required for maintenance of neural subtypes and nervous system architecture in adult animals. PMID:25356635

  5. Energy efficiency in nonprofit agencies: Creating effective program models

    SciTech Connect

    Brown, M.A.; Prindle, B.; Scherr, M.I.; White, D.L.

    1990-08-01

    Nonprofit agencies are a critical component of the health and human services system in the US. It has been clearly demonstrated by programs that offer energy efficiency services to nonprofits that, with minimal investment, they can educe their energy consumption by ten to thirty percent. This energy conservation potential motivated the Department of Energy and Oak Ridge National Laboratory to conceive a project to help states develop energy efficiency programs for nonprofits. The purpose of the project was two-fold: (1) to analyze existing programs to determine which design and delivery mechanisms are particularly effective, and (2) to create model programs for states to follow in tailoring their own plans for helping nonprofits with energy efficiency programs. Twelve existing programs were reviewed, and three model programs were devised and put into operation. The model programs provide various forms of financial assistance to nonprofits and serve as a source of information on energy efficiency as well. After examining the results from the model programs (which are still on-going) and from the existing programs, several replicability factors'' were developed for use in the implementation of programs by other states. These factors -- some concrete and practical, others more generalized -- serve as guidelines for states devising program based on their own particular needs and resources.

  6. Support for Career Development in Youth: Program Models and Evaluations

    ERIC Educational Resources Information Center

    Mekinda, Megan A.

    2012-01-01

    This article examines four influential programs--Citizen Schools, After School Matters, career academies, and Job Corps--to demonstrate the diversity of approaches to career programming for youth. It compares the specific program models and draws from the evaluation literature to discuss strengths and weaknesses of each. The article highlights…

  7. A Model Program for Dental Assisting Education in California.

    ERIC Educational Resources Information Center

    California State Dept. of Education, Sacramento. Bureau of Industrial Education.

    Intended to provide assistance for developing new programs and improving existing ones, the guide was constructed by dental assisting instructors and other professional participants in a 196 5 workshop conference. Elements of the model program were derived from a statistical analysis of California junior colleg e programs in dental assisting and…

  8. Evaluating an English Language Teacher Education Program through Peacock's Model

    ERIC Educational Resources Information Center

    Coskun, Abdullah; Daloglu, Aysegul

    2010-01-01

    The main aim of this study is to draw attention to the importance of program evaluation for teacher education programs and to reveal the pre-service English teacher education program components that are in need of improvement or maintenance both from teachers' and students' perspectives by using Peacock's (2009) recent evaluation model in a…

  9. Subscale Beryllium Mirrors Demonstrator (SBMD) Program Summary and Ball Modeling

    NASA Technical Reports Server (NTRS)

    Kendrick, Stephen; Brown, Robert; Stahl, Philip (Technical Monitor)

    2001-01-01

    The SBMD Program was to design, fabricate, and test a 0.5-m beryllium lightweighted mirror applicable to space deployable systems with demanding optical and areal density requirements. This presentation summarizes the program's objectives and the mirror's tested technical performance along with lessons learned. In addition, test results are compared to modeling predictions. The SBMD Program was funded by NASA MSFC.

  10. A genetic algorithm for a bi-objective mathematical model for dynamic virtual cell formation problem

    NASA Astrophysics Data System (ADS)

    Moradgholi, Mostafa; Paydar, Mohammad Mahdi; Mahdavi, Iraj; Jouzdani, Javid

    2016-05-01

    Nowadays, with the increasing pressure of the competitive business environment and demand for diverse products, manufacturers are force to seek for solutions that reduce production costs and rise product quality. Cellular manufacturing system (CMS), as a means to this end, has been a point of attraction to both researchers and practitioners. Limitations of cell formation problem (CFP), as one of important topics in CMS, have led to the introduction of virtual CMS (VCMS). This research addresses a bi-objective dynamic virtual cell formation problem (DVCFP) with the objective of finding the optimal formation of cells, considering the material handling costs, fixed machine installation costs and variable production costs of machines and workforce. Furthermore, we consider different skills on different machines in workforce assignment in a multi-period planning horizon. The bi-objective model is transformed to a single-objective fuzzy goal programming model and to show its performance; numerical examples are solved using the LINGO software. In addition, genetic algorithm (GA) is customized to tackle large-scale instances of the problems to show the performance of the solution method.

  11. Program evaluation models and related theories: AMEE guide no. 67.

    PubMed

    Frye, Ann W; Hemmer, Paul A

    2012-01-01

    This Guide reviews theories of science that have influenced the development of common educational evaluation models. Educators can be more confident when choosing an appropriate evaluation model if they first consider the model's theoretical basis against their program's complexity and their own evaluation needs. Reductionism, system theory, and (most recently) complexity theory have inspired the development of models commonly applied in evaluation studies today. This Guide describes experimental and quasi-experimental models, Kirkpatrick's four-level model, the Logic Model, and the CIPP (Context/Input/Process/Product) model in the context of the theories that influenced their development and that limit or support their ability to do what educators need. The goal of this Guide is for educators to become more competent and confident in being able to design educational program evaluations that support intentional program improvement while adequately documenting or describing the changes and outcomes-intended and unintended-associated with their programs. PMID:22515309

  12. SMP: A solid modeling program version 2.0

    NASA Technical Reports Server (NTRS)

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

    1986-01-01

    The Solid Modeling Program (SMP) provides the capability to model complex solid objects through the composition of primitive geometric entities. In addition to the construction of solid models, SMP has extensive facilities for model editing, display, and analysis. The geometric model produced by the software system can be output in a format compatible with existing analysis programs such as PATRAN-G. The present version of the SMP software supports six primitives: boxes, cones, spheres, paraboloids, tori, and trusses. The details for creating each of the major primitive types is presented. The analysis capabilities of SMP, including interfaces to existing analysis programs, are discussed.

  13. Genetics of complex neurological disease: challenges and opportunities for modeling epilepsy in mice and rats.

    PubMed

    Frankel, Wayne N

    2009-08-01

    Currently, approximately 20 genetic variants are known to cause Mendelian forms of human epilepsy, leaving a vast heritability undefined. Rodent models for genetically complex epilepsy have been studied for many years, but only recently have strong candidate genes emerged, including Cacna1 g in the GAERS rat model of absence epilepsy and Kcnj10 in the low seizure threshold of DBA/2 mice. In parallel, a growing number of mouse mutations studied on multiple strain backgrounds reveal the impact of genetic modifiers on seizure severity, incidence or form--perhaps mimicking the complexity seen in humans. The field of experimental genetics in rodents is poised to study discrete epilepsy mutations on a diverse choice of strain backgrounds to develop better models and identify modifiers. But, it must find the right balance between embracing the strain diversity available, with the ability to detect and characterize genetic effects. Using alternative strain backgrounds when studying epilepsy mutations will enhance the modeling of epilepsy as a complex genetic disease. PMID:19665252

  14. A Comparison of Three Programming Models for Adaptive Applications

    NASA Technical Reports Server (NTRS)

    Shan, Hong-Zhang; Singh, Jaswinder Pal; Oliker, Leonid; Biswa, Rupak; Kwak, Dochan (Technical Monitor)

    2000-01-01

    We study the performance and programming effort for two major classes of adaptive applications under three leading parallel programming models. We find that all three models can achieve scalable performance on the state-of-the-art multiprocessor machines. The basic parallel algorithms needed for different programming models to deliver their best performance are similar, but the implementations differ greatly, far beyond the fact of using explicit messages versus implicit loads/stores. Compared with MPI and SHMEM, CC-SAS (cache-coherent shared address space) provides substantial ease of programming at the conceptual and program orchestration level, which often leads to the performance gain. However it may also suffer from the poor spatial locality of physically distributed shared data on large number of processors. Our CC-SAS implementation of the PARMETIS partitioner itself runs faster than in the other two programming models, and generates more balanced result for our application.

  15. A stochastic dynamic simulation model including multi-trait genetics to estimate genetic, technical and financial consequences of dairy farm reproduction and selection strategies

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The objective of this study was to develop a daily stochastic dynamic dairy simulation model which included multi-trait genetics, and to evaluate the effects of various reproduction and selection strategies on the genetic, technical and financial performance of a dairy herd. The 12 correlated geneti...

  16. 76 FR 72424 - Submission for OMB Review; Comment Request Information Program on the Genetic Testing Registry

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-11-23

    ... information collection was previously published in the Federal Register on July 27, 2011, (76 FR 44937) and... information about genetic tests. The overarching goal of the GTR is to advance the public health and research... tests; and (3) facilitating genetic and genomic data-sharing for research and new scientific...

  17. Comparison of genetic gains per year for carcass traits among breeding programs in the Japanese Brown and the Japanese Black cattle.

    PubMed

    Sasaki, Y; Miyake, T; Gaillard, C; Oguni, T; Matsumoto, M; Ito, M; Kurahara, T; Sasae, Y; Fujinaka, K; Ohtagaki, S; Dougo, T

    2006-02-01

    The breeding program for beef cattle in Japan has changed dramatically over 4 decades. Visual judging was done initially, but progeny testing in test stations began in 1968. In the 1980s, the genetic evaluation program using field records, so-called on-farm progeny testing, was first adopted in Oita, Hyogo, and Kumamoto prefectures. In this study, genetic trends for carcass traits in these 3 Wagyu populations were estimated, and genetic gains per year were compared among the 3 different beef cattle breeding programs. The field carcass records used were collected between 1988 and 2003. The traits analyzed were carcass weight, LM area, rib thickness, s.c. fat thickness, and beef marbling standard number. The average breeding values of reproducing dams born the same year were used to estimate the genetic trends for the carcass traits. For comparison of the 3 breeding programs, birth years of the dams were divided into 3 periods reflecting each program. Positive genetic trends for beef marbling standard number were clearly shown in all populations. The genetic gains per year for all carcass traits were significantly enhanced by adopting the on-farm progeny testing program. These results indicate that the on-farm progeny testing program with BLUP is a very powerful approach for genetic improvement of carcass traits in Japanese Wagyu beef cattle. PMID:16424259

  18. Optimization of Volumetric Computed Tomography for Skeletal Analysis of Model Genetic Organisms

    PubMed Central

    Vasquez, Sergio X.; Hansen, Mark S.; Bahadur, Ali N.; Hockin, Matthew F.; Kindlmann, Gordon L.; Nevell, Lisa; Wu, Isabel Q.; Grunwald, David J.; Weinstein, David M.; Jones, Greg M.; Johnson, Christopher R.; Vandeberg, John L.; Capecchi, Mario R.; Keller, Charles

    2011-01-01

    Forward and reverse genetics now allow researchers to understand embryonic and postnatal gene function in a broad range of species. Although some genetic mutations cause obvious morphological change, other mutations can be more subtle and, without adequate observation and quantification, might be overlooked. For the increasing number of genetic model organisms examined by the growing field of phenomics, standardized but sensitive methods for quantitative analysis need to be incorporated into routine practice to effectively acquire and analyze ever-increasing quantities of phenotypic data. In this study, we present platform-independent parameters for the use of microscopic x-ray computed tomography (microCT) for phenotyping species-specific skeletal morphology of a variety of different genetic model organisms. We show that microCT is suitable for phenotypic characterization for prenatal and postnatal specimens across multiple species. PMID:18286615

  19. A Generalized Evaluation Model for Primary Prevention Programs.

    ERIC Educational Resources Information Center

    Barling, Phillip W.; Cramer, Kathryn D.

    A generalized evaluation model (GEM) has been developed to evaluate primary prevention program impact. The GEM model views primary prevention dynamically; delineating four structural components (program, organization, target population, system) and four developmental stages (initiation, establishment, integration, continuation). The interaction of…

  20. An Alternative Feedback/Evaluation Model for Outdoor Wilderness Programs.

    ERIC Educational Resources Information Center

    Dawson, R.

    Project D.A.R.E. (Development through Adventure, Responsibility and Education), an adventure-based outdoor program, uses a feedback/evaluation model, combining a learning component with a two-part participant observational model. The first phase focuses on evaluation of the child and progress made while he is in the program (stages one to four);…

  1. Evolving Leaders. A Model for Promoting Leadership Development in Programs.

    ERIC Educational Resources Information Center

    Palus, Charles J.; Drath, Wilfred H.

    A new model for promoting leadership development in programs emphasizes individuals' psychological development. The model, which is intended for use by individuals responsible for leadership development in organizations or by leadership development program planners/evaluators, is based on a cyclic process of three time-linked categories: readiness…

  2. A Model for Integrating Program Development and Evaluation.

    ERIC Educational Resources Information Center

    Brown, J. Lynne; Kiernan, Nancy Ellen

    1998-01-01

    A communication model consisting of input from target audience, program delivery, and outcomes (receivers' perception of message) was applied to an osteoporosis-prevention program for working mothers ages 21 to 45. Due to poor completion rate on evaluation instruments and failure of participants to learn key concepts, the model was used to improve…

  3. Model Preservation Program for a Small University Library.

    ERIC Educational Resources Information Center

    Robbins, Louise S.

    This report proposes a preservation program assuming a model of a university library serving 5,000 or fewer students and 350 or fewer faculty members. The model program is not for a comprehensive university or research institution, and the library's collection is one developed and used as a curriculum-support collection. The goal of the…

  4. A Model Acquaintance Rape Prevention Program for Men.

    ERIC Educational Resources Information Center

    Berkowitz, Alan D.

    1994-01-01

    Describes the underlying philosophy, content and evaluation issues, and selection and training of facilitators for a model acquaintance rape prevention program for male students conducted at Hobart College. Provides guidelines to help other colleges and universities implement a model rape prevention program for men that uses small-group discussion…

  5. Alternate Service Delivery Models in Cancer Genetic Counseling: A Mini-Review.

    PubMed

    Buchanan, Adam Hudson; Rahm, Alanna Kulchak; Williams, Janet L

    2016-01-01

    Demand for cancer genetic counseling has grown rapidly in recent years as germline genomic information has become increasingly incorporated into cancer care, and the field has entered the public consciousness through high-profile celebrity publications. Increased demand and existing variability in the availability of trained cancer genetics clinicians place a priority on developing and evaluating alternate service delivery models for genetic counseling. This mini-review summarizes the state of science regarding service delivery models, such as telephone counseling, telegenetics, and group counseling. Research on comparative effectiveness of these models in traditional individual, in-person genetic counseling has been promising for improving access to care in a manner acceptable to patients. Yet, it has not fully evaluated the short- and long-term patient- and system-level outcomes that will help answer the question of whether these models achieve the same beneficial psychosocial and behavioral outcomes as traditional cancer genetic counseling. We propose a research agenda focused on comparative effectiveness of available service delivery models and how to match models to patients and practice settings. Only through this rigorous research can clinicians and systems find the optimal balance of clinical quality, ready and secure access to care, and financial sustainability. Such research will be integral to achieving the promise of genomic medicine in oncology. PMID:27242960

  6. Alternate Service Delivery Models in Cancer Genetic Counseling: A Mini-Review

    PubMed Central

    Buchanan, Adam Hudson; Rahm, Alanna Kulchak; Williams, Janet L.

    2016-01-01

    Demand for cancer genetic counseling has grown rapidly in recent years as germline genomic information has become increasingly incorporated into cancer care, and the field has entered the public consciousness through high-profile celebrity publications. Increased demand and existing variability in the availability of trained cancer genetics clinicians place a priority on developing and evaluating alternate service delivery models for genetic counseling. This mini-review summarizes the state of science regarding service delivery models, such as telephone counseling, telegenetics, and group counseling. Research on comparative effectiveness of these models in traditional individual, in-person genetic counseling has been promising for improving access to care in a manner acceptable to patients. Yet, it has not fully evaluated the short- and long-term patient- and system-level outcomes that will help answer the question of whether these models achieve the same beneficial psychosocial and behavioral outcomes as traditional cancer genetic counseling. We propose a research agenda focused on comparative effectiveness of available service delivery models and how to match models to patients and practice settings. Only through this rigorous research can clinicians and systems find the optimal balance of clinical quality, ready and secure access to care, and financial sustainability. Such research will be integral to achieving the promise of genomic medicine in oncology. PMID:27242960

  7. Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

    PubMed Central

    Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.

    2013-01-01

    Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933

  8. Investigating the genetic architecture of conditional strategies using the environmental threshold model.

    PubMed

    Buzatto, Bruno A; Buoro, Mathieu; Hazel, Wade N; Tomkins, Joseph L

    2015-12-22

    The threshold expression of dichotomous phenotypes that are environmentally cued or induced comprise the vast majority of phenotypic dimorphisms in colour, morphology, behaviour and life history. Modelled as conditional strategies under the framework of evolutionary game theory, the quantitative genetic basis of these traits is a challenge to estimate. The challenge exists firstly because the phenotypic expression of the trait is dichotomous and secondly because the apparent environmental cue is separate from the biological signal pathway that induces the switch between phenotypes. It is the cryptic variation underlying the translation of cue to phenotype that we address here. With a 'half-sib common environment' and a 'family-level split environment' experiment, we examine the environmental and genetic influences that underlie male dimorphism in the earwig Forficula auricularia. From the conceptual framework of the latent environmental threshold (LET) model, we use pedigree information to dissect the genetic architecture of the threshold expression of forceps length. We investigate for the first time the strength of the correlation between observable and cryptic 'proximate' cues. Furthermore, in support of the environmental threshold model, we found no evidence for a genetic correlation between cue and the threshold between phenotypes. Our results show strong correlations between observable and proximate cues and less genetic variation for thresholds than previous studies have suggested. We discuss the importance of generating better estimates of the genetic variation for thresholds when investigating the genetic architecture and heritability of threshold traits. By investigating genetic architecture by means of the LET model, our study supports several key evolutionary ideas related to conditional strategies and improves our understanding of environmentally cued decisions. PMID:26674955

  9. Protein folding simulations of the hydrophobic-hydrophilic model by combining tabu search with genetic algorithms

    NASA Astrophysics Data System (ADS)

    Jiang, Tianzi; Cui, Qinghua; Shi, Guihua; Ma, Songde

    2003-08-01

    In this paper, a novel hybrid algorithm combining genetic algorithms and tabu search is presented. In the proposed hybrid algorithm, the idea of tabu search is applied to the crossover operator. We demonstrate that the hybrid algorithm can be applied successfully to the protein folding problem based on a hydrophobic-hydrophilic lattice model. The results show that in all cases the hybrid algorithm works better than a genetic algorithm alone. A comparison with other methods is also made.

  10. Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops

    PubMed Central

    Yabe, Shiori; Yamasaki, Masanori; Ebana, Kaworu; Hayashi, Takeshi; Iwata, Hiroyoshi

    2016-01-01

    Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an “island model” inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of

  11. Semiparametric regression of multidimensional genetic pathway data: least-squares kernel machines and linear mixed models.

    PubMed

    Liu, Dawei; Lin, Xihong; Ghosh, Debashis

    2007-12-01

    We consider a semiparametric regression model that relates a normal outcome to covariates and a genetic pathway, where the covariate effects are modeled parametrically and the pathway effect of multiple gene expressions is modeled parametrically or nonparametrically using least-squares kernel machines (LSKMs). This unified framework allows a flexible function for the joint effect of multiple genes within a pathway by specifying a kernel function and allows for the possibility that each gene expression effect might be nonlinear and the genes within the same pathway are likely to interact with each other in a complicated way. This semiparametric model also makes it possible to test for the overall genetic pathway effect. We show that the LSKM semiparametric regression can be formulated using a linear mixed model. Estimation and inference hence can proceed within the linear mixed model framework using standard mixed model software. Both the regression coefficients of the covariate effects and the LSKM estimator of the genetic pathway effect can be obtained using the best linear unbiased predictor in the corresponding linear mixed model formulation. The smoothing parameter and the kernel parameter can be estimated as variance components using restricted maximum likelihood. A score test is developed to test for the genetic pathway effect. Model/variable selection within the LSKM framework is discussed. The methods are illustrated using a prostate cancer data set and evaluated using simulations. PMID:18078480

  12. Physical Model for the Evolution of the Genetic Code

    NASA Astrophysics Data System (ADS)

    Yamashita, Tatsuro; Narikiyo, Osamu

    2011-12-01

    Using the shape space of codons and tRNAs we give a physical description of the genetic code evolution on the basis of the codon capture and ambiguous intermediate scenarios in a consistent manner. In the lowest dimensional version of our description, a physical quantity, codon level is introduced. In terms of the codon levels two scenarios are typically classified into two different routes of the evolutional process. In the case of the ambiguous intermediate scenario we perform an evolutional simulation implemented cost selection of amino acids and confirm a rapid transition of the code change. Such rapidness reduces uncomfortableness of the non-unique translation of the code at intermediate state that is the weakness of the scenario. In the case of the codon capture scenario the survival against mutations under the mutational pressure minimizing GC content in genomes is simulated and it is demonstrated that cells which experience only neutral mutations survive.

  13. Risk estimation and value-of-information analysis for three proposed genetic screening programs for chronic beryllium disease prevention

    SciTech Connect

    Bartell, S.M.; Ponce, R.A.; Takaro, T.K.; Zerbe, R.O.; Omenn, G.S.; Faustman, E.M.

    2000-02-01

    Genetic differences (polymorphisms) among members of a population are thought to influence susceptibility to various environmental exposures. In practice, however, this information is rarely incorporated into quantitative risk assessment and risk management. The authors describe an analytic framework for predicting the risk reduction and value-of-information (VOI) resulting from specific risk management applications of genetic biomarkers, and they apply the framework to the example of occupational chronic beryllium disease (CBD), an immune-mediated pulmonary granulomatous disease. One described Human Leukocyte Antigen gene variant, HLA-DP{beta}1*0201, contains a substitution of glutamate for lysine at position 69 that appears to have high sensitivity ({approximately}94%) but low specificity ({approximately}70%) with respect to CBD among individuals occupationally exposed to respirable beryllium. The expected postintervention CBD prevalence rates for using the genetic variant (1) as a required job placement screen, (2) as a medical screen for semiannual in place of annual lymphocyte proliferation testing, or (3) as a voluntary job placement screen are 0.08%, 0.8%, and 0.6%, respectively, in a hypothetical cohort with 1% baseline CBD prevalence. VOI analysis is used to examine the reduction in total social cost, calculated as the net value of disease reduction and financial expenditures, expected for proposed CBD intervention programs based on the genetic susceptibility test. For the example cohort the expected net VOI per beryllium worker for genetically based testing and intervention is $13,000, $1,800, and $5,100, respectively, based on a health valuation of $1.45 million per CBD case avoided. VOI results for alternative CBD valuations are also presented. Despite large parameter uncertainty, probabilistic analysis predicts generally positive utility for each of the three evaluated programs when avoidance of a CBD case is valued at $1 million or higher. Although

  14. Optimization of simulation models with GADELO: a multi-population genetic algorithm.

    PubMed

    Elketroussi, M; Fan, D P

    1994-02-01

    In this paper, a new Genetic Algorithm based on the Dynamic Exploration of Local Optima (GADELO) was used to estimate the parameters of the MRD (Micro-population model of Risk-group Dynamics) micro-population model for smoking cessation by minimizing a deviation function between the model's predictions and the smoking cessation data of the Multiple Risk Factor Intervention Trial (MRFIT). The efficiency and accuracy of the GADELO estimations were consistently superior to those obtained using the standard genetic algorithm or the simplex algorithm of Nelder-Mead. PMID:8175209

  15. 2015 Guidelines for Establishing Genetically Modified Rat Models for Cardiovascular Research

    PubMed Central

    Flister, Michael J.; Prokop, Jeremy W.; Lazar, Jozef; Shimoyama, Mary; Dwinell, Melinda; Geurts, Aron

    2015-01-01

    The rat has long been a key physiological model for cardiovascular research; most of the inbred strains having been previously selected for susceptibility or resistance to various cardiovascular diseases (CVD). These CVD rat models offer a physiologically relevant background on which candidates of human CVD can be tested in a more clinically translatable experimental setting. However, a diverse toolbox for genetically modifying the rat genome to test molecular mechanisms has only recently become available. Here, we provide a high-level description of several strategies for developing genetically modified rat models of CVD. PMID:25920443

  16. Initial assessment of a model relating intratumoral genetic heterogeneity to radiological morphology

    PubMed Central

    Noterdaeme, O; Kelly, M; Friend, P; Soonowalla, Z; Steers, G; Brady, M

    2010-01-01

    Tumour heterogeneity has major implications for tumour development and response to therapy. Tumour heterogeneity results from mutations in the genes responsible for mismatch repair or maintenance of chromosomal stability. Cells with different genetic properties may grow at different rates and exhibit different resistance to therapeutic interventions. To date, there exists no approach to non-invasively assess tumour heterogeneity. Here we present a biologically inspired model of tumour growth, which relates intratumoral genetic heterogeneity to gross morphology visible on radiological images. The model represents the development of a tumour as a set of expanding spheres, each sphere representing a distinct clonal centre, with the sprouting of new spheres corresponding to new clonal centres. Each clonal centre may possess different characteristics relating to genetic composition, growth rate and response to treatment. We present a clinical example for which the model accurately tracks tumour growth and shows the correspondence to genetic variation (as determined by array comparative genomic hybridisation). One clinical implication of our work is that the assessment of heterogeneous tumours using Response Evaluation Criteria In Solid Tumours (RECIST) or volume measurements may not accurately reflect tumour growth, stability or the response to treatment. We believe that this is the first model linking the macro-scale appearance of tumours to their genetic composition. We anticipate that our model will provide a more informative way to assess the response of heterogeneous tumours to treatment, which is of increasing importance with the development of novel targeted anti-cancer treatments. PMID:19690073

  17. Genetic evaluation of calf and heifer survival in Iranian Holstein cattle using linear and threshold models.

    PubMed

    Forutan, M; Ansari Mahyari, S; Sargolzaei, M

    2015-02-01

    Calf and heifer survival are important traits in dairy cattle affecting profitability. This study was carried out to estimate genetic parameters of survival traits in female calves at different age periods, until nearly the first calving. Records of 49,583 female calves born during 1998 and 2009 were considered in five age periods as days 1-30, 31-180, 181-365, 366-760 and full period (day 1-760). Genetic components were estimated based on linear and threshold sire models and linear animal models. The models included both fixed effects (month of birth, dam's parity number, calving ease and twin/single) and random effects (herd-year, genetic effect of sire or animal and residual). Rates of death were 2.21, 3.37, 1.97, 4.14 and 12.4% for the above periods, respectively. Heritability estimates were very low ranging from 0.48 to 3.04, 0.62 to 3.51 and 0.50 to 4.24% for linear sire model, animal model and threshold sire model, respectively. Rank correlations between random effects of sires obtained with linear and threshold sire models and with linear animal and sire models were 0.82-0.95 and 0.61-0.83, respectively. The estimated genetic correlations between the five different periods were moderate and only significant for 31-180 and 181-365 (r(g) = 0.59), 31-180 and 366-760 (r(g) = 0.52), and 181-365 and 366-760 (r(g) = 0.42). The low genetic correlations in current study would suggest that survival at different periods may be affected by the same genes with different expression or by different genes. Even though the additive genetic variations of survival traits were small, it might be possible to improve these traits by traditional or genomic selection. PMID:25100295

  18. Seismic Modeling on a GPU with Modest Programming Effort

    NASA Astrophysics Data System (ADS)

    Unat, D.

    2012-12-01

    We demonstrate an effective approach to porting a production code of anelastic wave propagation using a directive-based programming model, called Mint. Mint generated code realized about 80% of the performance of hand-coded CUDA on the Nvidia 200 and 400 series of GPUs. We think this performance gap is reasonable in light of the simplified programming model enjoyed by Mint's pragma-based model. Compared with writing CUDA by hand, Mint provides the flexibility to explore different performance variants of the same program through annotations and compiler options which saves considerable amount of programming effort. In this talk, we discuss how Mint enables the programmer to tune performance at a high level and evaluate various performance programming strategies realized by Mint programs and translator optimization options.

  19. A simulation model for wind energy storage systems. Volume 3: Program descriptions

    NASA Technical Reports Server (NTRS)

    Warren, A. W.; Edsinger, R. W.; Burroughs, J. D.

    1977-01-01

    Program descriptions, flow charts, and program listings for the SIMWEST model generation program, the simulation program, the file maintenance program, and the printer plotter program are given. For Vol 2, see .

  20. DSN RFI susceptibility models development program overview

    NASA Technical Reports Server (NTRS)

    Sue, M. K.

    1982-01-01

    The RFI model development described intended to provide an understanding of the interference susceptibility of DSN receivers. An overview of interference types and effects, analytic modelling and experimental verification is presented.