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Sample records for genetic programming model

  1. Genetic Programming for Automatic Hydrological Modelling

    NASA Astrophysics Data System (ADS)

    Chadalawada, Jayashree; Babovic, Vladan

    2017-04-01

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

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

    PubMed

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

    2017-03-01

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

  3. Regionalization of runoff models derived by genetic programming

    NASA Astrophysics Data System (ADS)

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

    2017-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-07-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

  6. Mathematical Modeling of Intestinal Iron Absorption Using Genetic Programming

    PubMed Central

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

    2017-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2007-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Bellucci, Michael A.; Coker, David F.

    2011-07-01

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

  9. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

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

    PubMed

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

    2015-03-01

    A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. 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.

  13. 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

  14. 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.

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

    NASA Astrophysics Data System (ADS)

    Ravansalar, Masoud; Rajaee, Taher; Kisi, Ozgur

    2017-06-01

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

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

    NASA Astrophysics Data System (ADS)

    Ahalpara, Dilip P.; Parikh, Jitendra C.

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

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

    NASA Astrophysics Data System (ADS)

    Danandeh Mehr, Ali; Kahya, Ercan

    2017-06-01

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

  18. 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.

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

    NASA Astrophysics Data System (ADS)

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

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-08-01

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

  1. 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.

  2. 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

    2017-08-01

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

  3. Evaluation of genetic programming-based models for simulating friction factor in alluvial channels

    NASA Astrophysics Data System (ADS)

    Roushangar, Kiyoumars; Mouaze, Dominique; Shiri, Jalal

    2014-09-01

    The bed resistance is one of the most complex aspects of water flow studies in natural streams. Most of the existing non-linear formulas for describing alluvial channel flows are based on dimensional analysis and statistical fitting of data to the parameters considered in the functional relationships implicitly, which are partially valid. The present study aims at developing genetic programming (GP) - based formulation of Manning roughness coefficient in alluvial channels. The training and testing data are selected from original experiments, performed in a hydraulic flume using a sand mobile bed. A comparison was also made between GP and traditional nonlinear approaches of resistance modeling. The obtained results revealed the GP capability in modeling resistance coefficient of alluvial channels' bed.

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

    NASA Astrophysics Data System (ADS)

    Coelho, Leandro dos Santos; Pessôa, Marcelo Wicthoff

    2009-07-01

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

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

    NASA Astrophysics Data System (ADS)

    Mori, Shigeo; Hirasawa, Kotaro; Hu, Jinglu

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

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

    PubMed

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

    2013-01-01

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

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

    PubMed

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

    2009-01-01

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

  8. 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.

  9. gesp: A computer program for modelling genetic effective population size, inbreeding and divergence in substructured populations.

    PubMed

    Olsson, Fredrik; Laikre, Linda; Hössjer, Ola; Ryman, Nils

    2017-03-24

    The genetically effective population size (Ne ) is of key importance for quantifying rates of inbreeding and genetic drift and is often used in conservation management to set targets for genetic viability. The concept was developed for single, isolated populations and the mathematical means for analysing the expected Ne in complex, subdivided populations have previously not been available. We recently developed such analytical theory and central parts of that work have now been incorporated into a freely available software tool presented here. gesp (Genetic Effective population size, inbreeding and divergence in Substructured Populations) is R-based and designed to model short- and long-term patterns of genetic differentiation and effective population size of subdivided populations. The algorithms performed by gesp allow exact computation of global and local inbreeding and eigenvalue effective population size, predictions of genetic divergence among populations (GST ) as well as departures from random mating (FIS , FIT ) while varying (i) subpopulation census and effective size, separately or including trend of the global population size, (ii) rate and direction of migration between all pairs of subpopulations, (iii) degree of relatedness and divergence among subpopulations, (iv) ploidy (haploid or diploid) and (v) degree of selfing. Here, we describe gesp and exemplify its use in conservation genetics modelling. © 2017 John Wiley & Sons Ltd.

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

    PubMed

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

    2014-04-01

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

  11. 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

  12. National Dairy Genetic Evaluation Program

    USDA-ARS?s Scientific Manuscript database

    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...

  13. A Synthetic Genetic Edge Detection Program

    PubMed Central

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

    2009-01-01

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

  14. A synthetic genetic edge detection program.

    PubMed

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

    2009-06-26

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

  15. Texture segmentation by genetic programming.

    PubMed

    Song, Andy; Ciesielski, Vic

    2008-01-01

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

  16. 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.

  17. Development and validation of clinical prediction models: marginal differences between logistic regression, penalized maximum likelihood estimation, and genetic programming.

    PubMed

    Janssen, Kristel J M; Siccama, Ivar; Vergouwe, Yvonne; Koffijberg, Hendrik; Debray, T P A; Keijzer, Maarten; Grobbee, Diederick E; Moons, Karel G M

    2012-04-01

    Many prediction models are developed by multivariable logistic regression. However, there are several alternative methods to develop prediction models. We compared the accuracy of a model that predicts the presence of deep venous thrombosis (DVT) when developed by four different methods. We used the data of 2,086 primary care patients suspected of DVT, which included 21 candidate predictors. The cohort was split into a derivation set (1,668 patients, 329 with DVT) and a validation set (418 patients, 86 with DVT). Also, 100 cross-validations were conducted in the full cohort. The models were developed by logistic regression, logistic regression with shrinkage by bootstrapping techniques, logistic regression with shrinkage by penalized maximum likelihood estimation, and genetic programming. The accuracy of the models was tested by assessing discrimination and calibration. There were only marginal differences in the discrimination and calibration of the models in the validation set and cross-validations. The accuracy measures of the models developed by the four different methods were only slightly different, and the 95% confidence intervals were mostly overlapped. We have shown that models with good predictive accuracy are most likely developed by sensible modeling strategies rather than by complex development methods. Copyright © 2012 Elsevier Inc. All rights reserved.

  18. 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

  19. 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

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

    PubMed

    Mendyk, Aleksander; Güres, Sinan; Jachowicz, Renata; Szlęk, Jakub; Polak, Sebastian; Wiśniowska, Barbara; Kleinebudde, Peter

    2015-01-01

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

  1. 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.

  2. Evolving evolutionary algorithms using linear genetic programming.

    PubMed

    Oltean, Mihai

    2005-01-01

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

  3. Evaluation of liquefaction potential of soil based on standard penetration test using multi-gene genetic programming model

    NASA Astrophysics Data System (ADS)

    Muduli, Pradyut; Das, Sarat

    2014-06-01

    This paper discusses the evaluation of liquefaction potential of soil based on standard penetration test (SPT) dataset using evolutionary artificial intelligence technique, multi-gene genetic programming (MGGP). The liquefaction classification accuracy (94.19%) of the developed liquefaction index (LI) model is found to be better than that of available artificial neural network (ANN) model (88.37%) and at par with the available support vector machine (SVM) model (94.19%) on the basis of the testing data. Further, an empirical equation is presented using MGGP to approximate the unknown limit state function representing the cyclic resistance ratio (CRR) of soil based on developed LI model. Using an independent database of 227 cases, the overall rates of successful prediction of occurrence of liquefaction and non-liquefaction are found to be 87, 86, and 84% by the developed MGGP based model, available ANN and the statistical models, respectively, on the basis of calculated factor of safety (F s) against the liquefaction occurrence.

  4. 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. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

    PubMed

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

    2016-10-26

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

  6. Modeling of constructed wetland performance in BOD5 removal for domestic wastewater under changes in relative humidity using genetic programming.

    PubMed

    Sankararajan, Vanitha; Neelakandhan, Nampoothiri; Chandrasekaran, Sivapragasam

    2017-04-01

    Despite the extensive use of constructed wetland (CW) as an effective method for domestic wastewater treatment, there is lack of clarity in arriving at well-defined design guidelines. This is particularly due to the fact that the design of CW is dependent on many inter-connected parameters which interact in a complex manner. Consequently, different researchers in the past have tried to address different aspects of this complexity. In this study, an attempt is made to model the influence of relative humidity (RH) in the effectiveness of BOD5 removal. Since it is an accepted fact that plants respond to change in humidity, it is necessary to take this parameter into consideration particularly when the CW is to be designed involving changes in relative humidity over a shorter time horizon (say a couple of months). This study reveals that BOD5out depends on the ratio of BOD5in and relative humidity. An attempt is also made to model the outlet BOD5 using genetic programming with inlet BOD5 and relative humidity as input parameters.

  7. Dynamical genetic programming in XCSF.

    PubMed

    Preen, Richard J; Bull, Larry

    2013-01-01

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

  8. Short-term streamflow forecasting with global climate change implications A comparative study between genetic programming and neural network models

    NASA Astrophysics Data System (ADS)

    Makkeasorn, A.; Chang, N. B.; Zhou, X.

    2008-05-01

    SummarySustainable water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods may also result in property damages and the loss of life. To more efficiently use the limited amount of water under the changing world or to resourcefully provide adequate time for flood warning, the issues have led us to seek advanced techniques for improving streamflow forecasting on a short-term basis. This study emphasizes the inclusion of sea surface temperature (SST) in addition to the spatio-temporal rainfall distribution via the Next Generation Radar (NEXRAD), meteorological data via local weather stations, and historical stream data via USGS gage stations to collectively forecast discharges in a semi-arid watershed in south Texas. Two types of artificial intelligence models, including genetic programming (GP) and neural network (NN) models, were employed comparatively. Four numerical evaluators were used to evaluate the validity of a suite of forecasting models. Research findings indicate that GP-derived streamflow forecasting models were generally favored in the assessment in which both SST and meteorological data significantly improve the accuracy of forecasting. Among several scenarios, NEXRAD rainfall data were proven its most effectiveness for a 3-day forecast, and SST Gulf-to-Atlantic index shows larger impacts than the SST Gulf-to-Pacific index on the streamflow forecasts. The most forward looking GP-derived models can even perform a 30-day streamflow forecast ahead of time with an r-square of 0.84 and RMS error 5.4 in our study.

  9. Solving Classification Problems Using Genetic Programming Algorithms on GPUs

    NASA Astrophysics Data System (ADS)

    Cano, Alberto; Zafra, Amelia; Ventura, Sebastián

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

  10. 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.

  11. 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)

  12. 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)

  13. Applications of genetic programming in cancer research.

    PubMed

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

    2009-02-01

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

  14. 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

  15. 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.

  16. Genetic algorithms using SISAL parallel programming language

    SciTech Connect

    Tejada, S.

    1994-05-06

    Genetic algorithms are a mathematical optimization technique developed by John Holland at the University of Michigan [1]. The SISAL programming language possesses many of the characteristics desired to implement genetic algorithms. SISAL is a deterministic, functional programming language which is inherently parallel. Because SISAL is functional and based on mathematical concepts, genetic algorithms can be efficiently translated into the language. Several of the steps involved in genetic algorithms, such as mutation, crossover, and fitness evaluation, can be parallelized using SISAL. In this paper I will l discuss the implementation and performance of parallel genetic algorithms in SISAL.

  17. LIGO detector characterization with genetic programming

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

  18. Genetics and the unity of biology. Program

    SciTech Connect

    Not Available

    1988-12-31

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

  19. 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.

  20. 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.

  1. Genetic Parallel Programming: design and implementation.

    PubMed

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

    2006-01-01

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

  2. 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

  3. Improving Search Properties in Genetic Programming

    NASA Technical Reports Server (NTRS)

    Janikow, Cezary Z.; DeWeese, Scott

    1997-01-01

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

  4. Successful technical trading agents using genetic programming.

    SciTech Connect

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

    2004-10-01

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

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

    PubMed

    Clancy, Kevin; Voigt, Christopher A

    2010-08-01

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

  6. Prenatal programming of postnatal plasticity for externalizing behavior: Testing an integrated developmental model of genetic and temperamental sensitivity to the environment.

    PubMed

    Tung, Irene; Morgan, Julia E; Noroña, Amanda N; Lee, Steve S

    2017-08-21

    Although both gene- and temperament-environment interactions contribute to the development of youth externalizing problems, it is unclear how these factors jointly affect environmental sensitivity over time. In a 7-year longitudinal study of 232 children (aged 5-10) with and without ADHD, we employed moderated mediation to test a developmentally sensitive mechanistic model of genetic and temperamental sensitivity to prenatal and postnatal environmental factors. Birth weight, a global measure of the prenatal environment, moderated predictions of child negative emotionality from a composite of dopaminergic polymorphisms (i.e., DRD4 and DAT1), such that birth weight inversely predicted negative emotionality only for children with genetic plasticity. Negative emotionality, in turn, predicted externalizing behavior 4-5 years later, beyond genetic and postnatal parenting effects. Finally, birth weight moderated the indirect effect of dopaminergic genotypes on externalizing problems through negative emotionality, partially supporting a prenatal programming model. We discuss theoretical and empirical implications for models of environmental sensitivity. © 2017 Wiley Periodicals, Inc.

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

    PubMed

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

    2012-10-01

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

  8. Allele-specific programming of Npy and epigenetic effects of physical activity in a genetic model of depression.

    PubMed

    Melas, P A; Lennartsson, A; Vakifahmetoglu-Norberg, H; Wei, Y; Åberg, E; Werme, M; Rogdaki, M; Mannervik, M; Wegener, G; Brené, S; Mathé, A A; Lavebratt, C

    2013-05-07

    Neuropeptide Y (NPY) has been implicated in depression, emotional processing and stress response. Part of this evidence originates from human single-nucleotide polymorphism (SNP) studies. In the present study, we report that a SNP in the rat Npy promoter (C/T; rs105431668) affects in vitro transcription and DNA-protein interactions. Genotyping studies showed that the C-allele of rs105431668 is present in a genetic rat model of depression (Flinders sensitive line; FSL), while the SNP's T-allele is present in its controls (Flinders resistant line; FRL). In vivo experiments revealed binding of a transcription factor (CREB2) and a histone acetyltransferase (Ep300) only at the SNP locus of the FRL. Accordingly, the FRL had increased hippocampal levels of Npy mRNA and H3K18 acetylation; a gene-activating histone modification maintained by Ep300. Next, based on previous studies showing antidepressant-like effects of physical activity in the FSL, we hypothesized that physical activity may affect Npy's epigenetic status. In line with this assumption, physical activity was associated with increased levels of Npy mRNA and H3K18 acetylation. Physical activity was also associated with reduced mRNA levels of a histone deacetylase (Hdac5). Conclusively, the rat rs105431668 appears to be a functional Npy SNP that may underlie depression-like characteristics. In addition, the achieved epigenetic reprogramming of Npy provides molecular support for the putative effectiveness of physical activity as a non-pharmacological antidepressant.

  9. Allele-specific programming of Npy and epigenetic effects of physical activity in a genetic model of depression

    PubMed Central

    Melas, P A; Lennartsson, A; Vakifahmetoglu-Norberg, H; Wei, Y; Åberg, E; Werme, M; Rogdaki, M; Mannervik, M; Wegener, G; Brené, S; Mathé, A A; Lavebratt, C

    2013-01-01

    Neuropeptide Y (NPY) has been implicated in depression, emotional processing and stress response. Part of this evidence originates from human single-nucleotide polymorphism (SNP) studies. In the present study, we report that a SNP in the rat Npy promoter (C/T; rs105431668) affects in vitro transcription and DNA–protein interactions. Genotyping studies showed that the C-allele of rs105431668 is present in a genetic rat model of depression (Flinders sensitive line; FSL), while the SNP's T-allele is present in its controls (Flinders resistant line; FRL). In vivo experiments revealed binding of a transcription factor (CREB2) and a histone acetyltransferase (Ep300) only at the SNP locus of the FRL. Accordingly, the FRL had increased hippocampal levels of Npy mRNA and H3K18 acetylation; a gene-activating histone modification maintained by Ep300. Next, based on previous studies showing antidepressant-like effects of physical activity in the FSL, we hypothesized that physical activity may affect Npy's epigenetic status. In line with this assumption, physical activity was associated with increased levels of Npy mRNA and H3K18 acetylation. Physical activity was also associated with reduced mRNA levels of a histone deacetylase (Hdac5). Conclusively, the rat rs105431668 appears to be a functional Npy SNP that may underlie depression-like characteristics. In addition, the achieved epigenetic reprogramming of Npy provides molecular support for the putative effectiveness of physical activity as a non-pharmacological antidepressant. PMID:23652932

  10. Genetic Network Programming with Reconstructed Individuals

    NASA Astrophysics Data System (ADS)

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

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

  11. Programming Models in HPC

    SciTech Connect

    Shipman, Galen M.

    2016-06-13

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

  12. 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.

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

    PubMed

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

    2012-03-01

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

  14. Expanding the genetic counseling workforce: program directors' views on increasing the size of genetic counseling graduate programs.

    PubMed

    Pan, Vivian; Yashar, Beverly M; Pothast, Rachel; Wicklund, Catherine

    2016-08-01

    Although there is an anticipated need for more genetic counselors, little is known about limitations at the graduate training level. We evaluated opportunities for growth of the genetic counseling (GC) workforce by exploring program directors' perspectives on increasing number of graduate trainees. Thirty US-based GC program directors (PDs) were recruited through the Association of Genetic Counseling Program Directors' listserv. Online surveys and semistructured phone interviews were used to explore factors impacting the expansion of the GC workforce. Twenty-five PDs completed the survey; 18 interviews were conducted. Seventy-three percent said they believe that the workforce is growing too slowly and the number of graduates should increase. Attitudes were mixed regarding whether the job market should be the main factor driving workforce expansion. Thematic analysis of transcripts identified barriers to program expansion in six categories: funding, accreditation requirements, clinical sites, faculty availability, applicant pool, and physical space. General consensus among participants indicates the importance of increasing the capacity of the GC workforce pipeline. Addressing funding issues, examining current accreditation requirements, and reevaluating current education models may be effective strategies to expanding GC program size. Future research on increasing the number of GC programs and a needs assessment for GC services are suggested.Genet Med 18 8, 842-849.

  15. 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)

  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. 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…

  18. 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…

  19. Program management model study

    NASA Technical Reports Server (NTRS)

    Connelly, J. J.; Russell, J. E.; Seline, J. R.; Sumner, N. R., Jr.

    1972-01-01

    Two models, a system performance model and a program assessment model, have been developed to assist NASA management in the evaluation of development alternatives for the Earth Observations Program. Two computer models were developed and demonstrated on the Goddard Space Flight Center Computer Facility. Procedures have been outlined to guide the user of the models through specific evaluation processes, and the preparation of inputs describing earth observation needs and earth observation technology. These models are intended to assist NASA in increasing the effectiveness of the overall Earth Observation Program by providing a broader view of system and program development alternatives.

  20. 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.

  1. 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.

  2. Genetically programmed superparamagnetic behavior of mammalian cells.

    PubMed

    Kim, Taeuk; Moore, David; Fussenegger, Martin

    2012-12-31

    Although magnetic fields and paramagnetic inorganic materials were abundant on planet earth during the entire evolution of living species the interaction of organisms with these physical forces remains a little-understood phenomenon. Interestingly, rather than being genetically encoded, organisms seem to accumulate and take advantage of inorganic nanoparticles to sense or react to magnetic fields. Using a synthetic biology-inspired approach we have genetically programmed mammalian cells to show superparamagnetic behavior. The combination of ectopic production of the human ferritin heavy chain 1 (hFTH1), engineering the cells for expression of an iron importer, the divalent metal ion transferase 1 (DMT1) and the design of an iron-loading culture medium to maximize cellular iron uptake enabled efficient iron mineralization in intracellular ferritin particles and conferred superparamagnetic behavior to the entire cell. When captured by a magnetic field the superparamagnetic cells reached attraction velocities of up to 30 μm/s and could be efficiently separated from complex cell mixtures using standard magnetic cell separation equipment. Technology that enables magnetic separation of genetically programmed superparamagnetic cells in the absence of inorganic particles could foster novel opportunities in diagnostics and cell-based therapies. Copyright © 2012 Elsevier B.V. All rights reserved.

  3. 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.

  4. Los Alamos Programming Models

    SciTech Connect

    Bergen, Benjamin Karl

    2016-07-07

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

  5. Genetic programming applied to RFI mitigation in radio astronomy

    NASA Astrophysics Data System (ADS)

    Staats, K.

    2016-12-01

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

  6. 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.

  7. Restoration of degraded images using genetic programming

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

  8. Augmented Evolutionary Computation Using Genetic Programming

    NASA Astrophysics Data System (ADS)

    Ae, Tadashi; Kamitani, Motoki

    2006-06-01

    Evolutionary computation is an anticipatory computation for generation of creative sets including the set of sequences. The Interactive Evolutionary Computation (IEC, in short) is known as one of evolutionary computations, but it is not necessarily efficient because it may make the user tired. Therefore, we propose an improved method, that is, Augmented Interactive Evolutionary Computation (AIEC, in short), where the hypothesis/verification is applied for the generative agent instead of the objective element. We will state this type of evolutionary computation which is realized by a Genetic Programming.

  9. Genetic Programming as Alternative for Predicting Development Effort of Individual Software Projects

    PubMed Central

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

    2012-01-01

    Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects. Accuracy obtained from a genetic programming model was compared against one generated from the application of a statistical regression model. A sample of 219 projects developed by 71 practitioners was used for generating the two models, whereas another sample of 130 projects developed by 38 practitioners was used for validating them. The models used two kinds of lines of code as well as programming language experience as independent variables. Accuracy results from the model obtained with genetic programming suggest that it could be used to predict the software development effort of individual projects when these projects have been developed in a disciplined manner within a development-controlled environment. PMID:23226305

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

    PubMed

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

    2012-01-01

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

  11. Genetic programming for detecting target motions

    NASA Astrophysics Data System (ADS)

    Song, Andy; Zhang, Mengjie

    2012-09-01

    This study presents a selective motion detection methodology which is based on genetic programming (GP), an evolutionary search strategy. By this approach, motion detection programs can be automatically evolved instead of manually coded. This study investigates the suitable GP representation for motion detection as well as explores the advantages of this method. Unlike conventional methods, this evolutionary approach can generate programs which are able to mark target motions. The stationary background and the uninteresting or irrelevant motions such as swaying trees, noises are all ignored. Furthermore, programs can be trained to detect target motions from a moving background. They are capable of distinguishing different kinds of motions. Such differentiation can be based on the type of motions as well, for example, fast moving targets are captured, while slow moving targets are ignored. One of the characteristics of this method is that no modification or additional process is required when different types of motions are introduced. Moreover, real-time performance can be achieved by this GP motion detection method.

  12. Discovering Knowledge from Noisy Databases Using Genetic Programming.

    ERIC Educational Resources Information Center

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

    2000-01-01

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

  13. 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.

  14. 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.

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

    PubMed

    Bruhn, Peter; Geyer-Schulz, Andreas

    2002-01-01

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

  16. 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.

  17. Genetic models of focal epilepsies.

    PubMed

    Boillot, Morgane; Baulac, Stéphanie

    2016-02-15

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

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

    Treesearch

    Michael K. Schwartz

    2005-01-01

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

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

    PubMed Central

    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

  20. 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.

  1. Modeling RASopathies with Genetically Modified Mouse Models.

    PubMed

    Hernández-Porras, Isabel; Guerra, Carmen

    2017-01-01

    The RAS/MAPK signaling pathway plays key roles in development, cell survival and proliferation, as well as in cancer pathogenesis. Molecular genetic studies have identified a group of developmental syndromes, the RASopathies, caused by germ line mutations in this pathway. The syndromes included within this classification are neurofibromatosis type 1 (NF1), Noonan syndrome (NS), Noonan syndrome with multiple lentigines (NS-ML, formerly known as LEOPARD syndrome), Costello syndrome (CS), cardio-facio-cutaneous syndrome (CFC), Legius syndrome (LS, NF1-like syndrome), capillary malformation-arteriovenous malformation syndrome (CM-AVM), and hereditary gingival fibromatosis (HGF) type 1. Although these syndromes present specific molecular alterations, they are characterized by a large spectrum of functional and morphological abnormalities, which include heart defects, short stature, neurocognitive impairment, craniofacial malformations, and, in some cases, cancer predisposition. The development of genetically modified animals, such as mice (Mus musculus), flies (Drosophila melanogaster), and zebrafish (Danio rerio), has been instrumental in elucidating the molecular and cellular bases of these syndromes. Moreover, these models can also be used to determine tumor predisposition, the impact of different genetic backgrounds on the variable phenotypes found among the patients and to evaluate preventative and therapeutic strategies. Here, we review a wide range of genetically modified mouse models used in the study of RASopathies and the potential application of novel technologies, which hopefully will help us resolve open questions in the field.

  2. 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.

  3. Genetic mouse models of depression.

    PubMed

    Barkus, Christopher

    2013-01-01

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

  4. 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.

  5. Alternative Living Kidney Donation Programs Boost Genetically Unrelated Donation

    PubMed Central

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

    2015-01-01

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

  6. 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.

  7. Using genetic programming to discover nonlinear variable interactions.

    PubMed

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

    2003-05-01

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

  8. Developmental hematopoiesis: ontogeny, genetic programming and conservation.

    PubMed

    Ciau-Uitz, Aldo; Monteiro, Rui; Kirmizitas, Arif; Patient, Roger

    2014-08-01

    Hematopoietic stem cells (HSCs) sustain blood production throughout life and are of pivotal importance in regenerative medicine. Although HSC generation from pluripotent stem cells would resolve their shortage for clinical applications, this has not yet been achieved mainly because of the poor mechanistic understanding of their programming. Bone marrow HSCs are first created during embryogenesis in the dorsal aorta (DA) of the midgestation conceptus, from where they migrate to the fetal liver and, eventually, the bone marrow. It is currently accepted that HSCs emerge from specialized endothelium, the hemogenic endothelium, localized in the ventral wall of the DA through an evolutionarily conserved process called the endothelial-to-hematopoietic transition. However, the endothelial-to-hematopoietic transition represents one of the last steps in HSC creation, and an understanding of earlier events in the specification of their progenitors is required if we are to create them from naïve pluripotent cells. Because of their ready availability and external development, zebrafish and Xenopus embryos have enormously facilitated our understanding of the early developmental processes leading to the programming of HSCs from nascent lateral plate mesoderm to hemogenic endothelium in the DA. The amenity of the Xenopus model to lineage tracing experiments has also contributed to the establishment of the distinct origins of embryonic (yolk sac) and adult (HSC) hematopoiesis, whereas the transparency of the zebrafish has allowed in vivo imaging of developing blood cells, particularly during and after the emergence of HSCs in the DA. Here, we discuss the key contributions of these model organisms to our understanding of developmental hematopoiesis. Copyright © 2014 ISEH - International Society for Experimental Hematology. Published by Elsevier Inc. All rights reserved.

  9. Genetic mouse models of Alzheimer's disease.

    PubMed

    Mineur, Yann S; McLoughlin, Declan; Crusio, Wim E; Sluyter, Frans

    2005-01-01

    In the current minireview, we focus on genetic mouse models of Alzheimer's disease (AD). Because various excellent, up-to-date reviews, special issues, and reliable websites are already dedicated to the genetics of Alzheimer's disease in general and of animal models in particular, this review is not meant to be comprehensive. Rather, we aim to steer the Alzheimer's novice through the recent mouse literature on AD. Special attention will be paid to genetic models that have been tested behaviorally.

  10. Genetic Mouse Models of Alzheimer's Disease

    PubMed Central

    Mineur, Yann S.; McLoughlin, Declan; Crusio, Wim E.; Sluyter, Frans

    2005-01-01

    In the current minireview, we focus on genetic mouse models of Alzheimer's disease (AD). Because various excellent, up-to-date reviews, special issues, and reliable websites are already dedicated to the genetics of Alzheimer's disease in general and of animal models in particular, this review is not meant to be comprehensive. Rather, we aim to steer the Alzheimer's novice through the recent mouse literature on AD. Special attention will be paid to genetic models that have been tested behaviorally. PMID:16444901

  11. Predicting the Structure of Covert Networks using Genetic Programming, Cognitive Work Analysis and Social Network Analysis

    DTIC Science & Technology

    2009-10-01

    RTO-MP-MSG-069 15 - 1 Predicting the Structure of Covert Networks using Genetic Programming, Cognitive Work Analysis and Social Network...collation of intelligence covering types of mission, in terms of actors and goals; phase two involves the building of task models, based on Cognitive ...REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE Predicting the Structure of Covert Networks using Genetic Programming, Cognitive Work

  12. Guiding Genetic Program Based Data Mining Using Fuzzy Rules

    DTIC Science & Technology

    2006-09-01

    A data mining procedure for automatic determination of fuzzy decision tree structure using a genetic program is discussed. A genetic program (GP) is...an algorithm that evolves other algorithms or mathematical expressions. Methods for accelerating convergence of the data mining procedure are examined...Connections to past GP based data mining procedures for evolving fuzzy decision trees are established. Finally, experimental methods that have been used to validate the data mining algorithm are discussed.

  13. Divergent selection on home pen locomotor activity in a chicken model: Selection program, genetic parameters and direct response on activity and body weight

    PubMed Central

    2017-01-01

    General locomotor activity (GLA) in poultry has attracted attention, as it negatively influences production costs (energy expenditure and feed consumption) and welfare parameters (bone strength, litter quality, feather pecking and cannibalism). Laying hen lines diverging in the average level of spontaneous locomotor activity in the home pen were developed by genetic selection using the founder New Hampshire line. Activity was recorded using RFID technology at around five weeks of age during four to five days in the home pen. After initial phenotyping, the least active birds were selected for the low activity line and the most active for the high activity line, with no gene transfer between lines. In each of six generations, approximately ten sires were mated to twenty dams producing 158 to 334 offspring per line per generation. The response to selection was rapid and of a considerable magnitude. In sixth generation, the level of GLA was approximately halved in the low and doubled in the high line compared to the control (7.2, 14.9 and 28.7 recordings/h). Estimated heritability of locomotor activity in the low and high line was 0.38 and 0.33, respectively. Males, in general, were more active than females. High line birds were significantly heavier than low line birds. In fourth, fifth, and sixth generation, low as well as high line birds were lighter than control line birds. This selection experiment demonstrates variation in heritability for GLA and, as a result, genetically diverged lines have been developed. These lines can be used as models for further studies of underlying physiological, neural and molecular genetic mechanisms of spontaneous locomotor activity. PMID:28796792

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

    Treesearch

    Erin L. Landguth; S. A. Cushman

    2010-01-01

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

  15. Aerothermal modeling program

    NASA Technical Reports Server (NTRS)

    Kenworthy, M.

    1982-01-01

    Some significant features of the approach adopted for the combustor aerothermal modeling program are described. The individual computerized models utilized in the aero design approach are characterized. The preliminary design module provides the overall envelope definition of the burner. The diffuser module provides the detailed contours of the diffuser and combustor cowl region, as well as the pressure loss characteristics into each of the individual flow passages into the dome and around the combustor. The flow distribution module provides the air entry quantities through each of the aperatures and the overall pressure drop. The heat transfer module provides detailed metal temperature distribution throughout the metal structure as input to stress and life analysis that are not part of the aerothermo design effort. Finally, the internal flow module, INTFLOW, is described and the approach for model evaluation using laboratory data is discussed.

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

    SciTech Connect

    Koza, J.R.

    1994-12-31

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

  17. Genetically modified pig models for human diseases.

    PubMed

    Fan, Nana; Lai, Liangxue

    2013-02-20

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

  18. 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…

  19. Genetic conservation in applied tree breeding programs.

    Treesearch

    R. Johnson; B. St. Clair; S. Lipow

    2001-01-01

    This paper reviews how population size and structure impacts the maintenance of genetic variation in breeding and gene resource populations. We discuss appropriate population sizes for low frequency alleles and point out some examples of low frequency alleles in the literature. Development of appropriate breeding populations and gene resource populations are discussed...

  20. Aging is not programmed: genetic pseudo-program is a shadow of developmental growth.

    PubMed

    Blagosklonny, Mikhail V

    2013-12-15

    Aging is not and cannot be programmed. Instead, aging is a continuation of developmental growth, driven by genetic pathways such as mTOR. Ironically, this is often misunderstood as a sort of programmed aging. In contrast, aging is a purposeless quasi-program or, figuratively, a shadow of actual programs.

  1. 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.

  2. Genetic network models: a comparative study

    NASA Astrophysics Data System (ADS)

    van Someren, Eugene P.; Wessels, Lodewyk F. A.; Reinders, Marcel J. T.

    2001-06-01

    Currently, the need arises for tools capable of unraveling the functionality of genes based on the analysis of microarray measurements. Modeling genetic interactions by means of genetic network models provides a methodology to infer functional relationships between genes. Although a wide variety of different models have been introduced so far, it remains, in general, unclear what the strengths and weaknesses of each of these approaches are and where these models overlap and differ. This paper compares different genetic modeling approaches that attempt to extract the gene regulation matrix from expression data. A taxonomy of continuous genetic network models is proposed and the following important characteristics are suggested and employed to compare the models: inferential power; predictive power; robustness; consistency; stability and computational cost. Where possible, synthetic time series data are employed to investigate some of these properties. The comparison shows that although genetic network modeling might provide valuable information regarding genetic interactions, current models show disappointing results on simple artificial problems. For now, the simplest models are favored because they generalize better, but more complex models will probably prevail once their bias is more thoroughly understood and their variance is better controlled.

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

    PubMed

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

    2014-01-01

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

  4. 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

  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. SPAGHETTI: simulation software to test genetic mapping programs.

    PubMed

    Tinker, Nicholas A

    2010-01-01

    SPAGHETTI is a computer program to simulate genetic populations with segregating molecular markers that are influenced by "real-life" complications such as duplicated loci and segregation distortion. It produces output that is readable by popular mapping software, and it can be used to demonstrate or test methods for constructing genetic linkage maps. The source code, sample files, instructions for use, and an executable version compatible with MS Windows are available for free from http://gnomad.agr.ca/software/spaghetti.

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

    NASA Astrophysics Data System (ADS)

    Matsudaira, Tomomi; Hagiwara, Masafumi

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

  10. 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

  11. 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.

  12. 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

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

    PubMed

    Abbey, Antonia

    2014-04-01

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

  14. 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.

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

    PubMed

    Catania, Francesco; Schmitz, Jürgen

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2006-04-01

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

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

    PubMed

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

    2012-11-01

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

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

    PubMed Central

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

    2012-01-01

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

  20. Considering genetic characteristics in German Holstein breeding programs.

    PubMed

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

    2016-01-01

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

  1. 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

  2. Genetic model compensation: Theory and applications

    NASA Astrophysics Data System (ADS)

    Cruickshank, David Raymond

    1998-12-01

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

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

    PubMed

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

    2004-11-01

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

  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. 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.

  6. 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. Copyright © 2015 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

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

    USGS Publications Warehouse

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

    2005-01-01

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

  8. 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

  9. Genetic models of homosexuality: generating testable predictions.

    PubMed

    Gavrilets, Sergey; Rice, William R

    2006-12-22

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

  10. A Spatial Statistical Model for Landscape Genetics

    PubMed Central

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

    2005-01-01

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

  11. 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…

  12. 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…

  13. Initialization Method for Grammar-Guided Genetic Programming

    NASA Astrophysics Data System (ADS)

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

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

  14. ONMCGP: Orthogonal Neighbourhood Mutation Cartesian Genetic Programming for Evolvable Hardware

    NASA Astrophysics Data System (ADS)

    I, Fuchuan N.; I, Yuanxiang L.; E, Peng K.

    2014-03-01

    Evolvable Hardware is facing the problems of scalability and stalling effect. This paper proposed a novel Orthogonal Neighbourhood Mutation (ONM) operator in Cartesian genetic programming (CGP), to reduce the stalling effect in CGP and improve the efficiency of the algorithms.The method incorporates with Differential Evolution strategy. Demonstrated by experiments on benchmark, the proposed Orthogonal Neighbourhood Search can jump out of Local optima, reduce the stalling effect in CGP and the algorithm convergence faster.

  15. Genetic Programming of Conventional Features to Detect Seizure Precursors

    PubMed Central

    Smart, Otis; Firpi, Hiram; Vachtsevanos, George

    2008-01-01

    This paper presents an application of genetic programming (GP) to optimally select and fuse conventional features (C-features) for the detection of epileptic waveforms within intracranial electroencephalogram (IEEG) recordings that precede seizures, known as seizure-precursors. Evidence suggests that seizure-precursors may localize regions important to seizure generation on the IEEG and epilepsy treatment. However, current methods to detect epileptic precursors lack a sound approach to automatically select and combine C-features that best distinguish epileptic events from background, relying on visual review predominantly. This work suggests GP as an optimal alternative to create a single feature after evaluating the performance of a binary detector that uses: 1) genetically programmed features; 2) features selected via GP; 3) forward sequentially selected features; and 4) visually selected features. Results demonstrate that a detector with a genetically programmed feature outperforms the other three approaches, achieving over 78.5% positive predictive value, 83.5% sensitivity, and 93% specificity at the 95% level of confidence. PMID:19050744

  16. Genetic Programming of Conventional Features to Detect Seizure Precursors.

    PubMed

    Smart, Otis; Firpi, Hiram; Vachtsevanos, George

    2007-12-01

    This paper presents an application of genetic programming (GP) to optimally select and fuse conventional features (C-features) for the detection of epileptic waveforms within intracranial electroencephalogram (IEEG) recordings that precede seizures, known as seizure-precursors. Evidence suggests that seizure-precursors may localize regions important to seizure generation on the IEEG and epilepsy treatment. However, current methods to detect epileptic precursors lack a sound approach to automatically select and combine C-features that best distinguish epileptic events from background, relying on visual review predominantly. This work suggests GP as an optimal alternative to create a single feature after evaluating the performance of a binary detector that uses: 1) genetically programmed features; 2) features selected via GP; 3) forward sequentially selected features; and 4) visually selected features. Results demonstrate that a detector with a genetically programmed feature outperforms the other three approaches, achieving over 78.5% positive predictive value, 83.5% sensitivity, and 93% specificity at the 95% level of confidence.

  17. 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.

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

    PubMed

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

    2017-02-01

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

  19. A Model for Integrating Genetics into Nursing Education.

    ERIC Educational Resources Information Center

    Zamerowski, Suzanne Tracey

    2000-01-01

    Describes essential components for including genetics into undergraduate nursing education: a required basic science course in genetics and a model curriculum that integrates genetics content into nursing courses. (SK)

  20. Genetic demographic networks: Mathematical model and applications.

    PubMed

    Kimmel, Marek; Wojdyła, Tomasz

    2016-10-01

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

  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. 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

  6. A genetic programming approach to oral cancer prognosis

    PubMed Central

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

    2016-01-01

    Background The potential of genetic programming (GP) on various fields has been attained in recent years. In bio-medical field, many researches in GP are focused on the recognition of cancerous cells and also on gene expression profiling data. In this research, the aim is to study the performance of GP on the survival prediction of a small sample size of oral cancer prognosis dataset, which is the first study in the field of oral cancer prognosis. Method GP is applied on an oral cancer dataset that contains 31 cases collected from the Malaysia Oral Cancer Database and Tissue Bank System (MOCDTBS). The feature subsets that is automatically selected through GP were noted and the influences of this subset on the results of GP were recorded. In addition, a comparison between the GP performance and that of the Support Vector Machine (SVM) and logistic regression (LR) are also done in order to verify the predictive capabilities of the GP. Result The result shows that GP performed the best (average accuracy of 83.87% and average AUROC of 0.8341) when the features selected are smoking, drinking, chewing, histological differentiation of SCC, and oncogene p63. In addition, based on the comparison results, we found that the GP outperformed the SVM and LR in oral cancer prognosis. Discussion Some of the features in the dataset are found to be statistically co-related. This is because the accuracy of the GP prediction drops when one of the feature in the best feature subset is excluded. Thus, GP provides an automatic feature selection function, which chooses features that are highly correlated to the prognosis of oral cancer. This makes GP an ideal prediction model for cancer clinical and genomic data that can be used to aid physicians in their decision making stage of diagnosis or prognosis. PMID:27688975

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

    PubMed

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

    2011-08-01

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

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

    PubMed

    Thomas, B

    1985-08-01

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

  9. Computational Modeling Program

    NASA Technical Reports Server (NTRS)

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

    1998-01-01

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

  10. 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.

  11. Animal models for genetic neuromuscular diseases.

    PubMed

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

    2008-03-01

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

  12. New genetic model for predicting phenotype traits in sports.

    PubMed

    Massidda, Myosotis; Scorcu, Marco; Calò, Carla M

    2014-05-01

    The aim of the current study was to construct a genetic model with a new algorithm for predicting athletic-performance variability based on genetic variations. The influence of 6 polymorphisms (ACE, ACTN-3, BDKRB2, VDR-ApaI, VDR-BsmI, and VDR-FokI) on vertical jump was studied in top-level male Italian soccer players (n = 90). First, the authors calculated the traditional total genotype score and then determined the total weighting genotype score (TWGS), which accounts for the proportion of significant phenotypic variance predicted by the polymorphisms. Genomic DNA was extracted from saliva samples using a standard protocol. Genotyping was performed using polymerase chain reaction (PCR). The results obtained from the new genetic model (TWGS) showed that only 3 polymorphisms entered the regression equation (ACTN-3, ACE, and BDKRB2), and these polymorphisms explained 17.68-24.24% of the vertical-jump variance. With the weighting given to each polymorphism, it may be possible to identify a polygenic profile that more accurately explains, at least in part, the individual variance of athletic-performance traits. This model may be used to create individualized training programs based on a player's genetic predispositions, as well as to identify athletes who need an adapted training routine to account for individual susceptibility to injury.

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

    PubMed

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

    2012-11-08

    Genetic programs function to integrate environmental sensors, implement signal processing algorithms and control expression dynamics. These programs consist of integrated genetic circuits that individually implement operations ranging from digital logic to dynamic circuits, 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 circuits. 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.

  14. 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.

  15. 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.

  16. Genetically modified pigs to model human diseases.

    PubMed

    Flisikowska, Tatiana; Kind, Alexander; Schnieke, Angelika

    2014-02-01

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

  17. 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.

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

    PubMed

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

    2011-07-01

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

  19. A Genetic Porcine Model of Cancer

    PubMed Central

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

    2015-01-01

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

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

    PubMed

    Halloran, M Elizabeth; Holmes, Edward C

    2009-12-15

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

  1. 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

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

    PubMed

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

    2013-08-01

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

  3. Population genetics models of local ancestry.

    PubMed

    Gravel, Simon

    2012-06-01

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

  4. 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.

  5. Model Site Program. Final Report.

    ERIC Educational Resources Information Center

    Davis, Jerome; And Others

    The report covers two activities of the Model Site Program of the National Committee: Arts for the Handicapped (NCAH). The first section is devoted to a review of the needs assessment survey which was completed by 703 special education professionals. Data from the survey is summarized according to such topics as the extent to which arts…

  6. Genetically-defined ovarian cancer mouse models.

    PubMed

    Morin, Patrice J; Weeraratna, Ashani T

    2016-01-01

    Epithelial ovarian cancer (EOC), the deadliest of gynaecological cancers, is a disease that remains difficult to detect early and treat efficiently. A significant challenge for researchers in the field is that the aetiology of EOC and the molecular pathways important for its development are poorly understood. Moreover, precursor lesions have not been readily identifiable, making the mechanisms of EOC progression difficult to delineate. In order to address these issues, several genetically-defined ovarian mouse models have been generated in the past 15 years. However, because of the recent suggestion that most EOCs may not originate from the ovarian surface 'epithelium', but from other tissues of the female genital tract, some models may need to be re-evaluated within this new paradigm. In this review, we examine several genetically-defined EOC models and discuss how the new paradigm may explain some of the features of these models. A better understanding of the strengths and limitations of the current EOC mouse models will undoubtedly allow us to utilize these tools to better understand the biology of the disease and develop new approaches for EOC prevention, detection, and treatment. Copyright © 2015 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

  7. Brachypodium distachyon as a Genetic Model System.

    PubMed

    Kellogg, Elizabeth A

    2015-01-01

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

  8. 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.

  9. Defining Our Clinical Practice: The Identification of Genetic Counseling Outcomes Utilizing the Reciprocal Engagement Model.

    PubMed

    Redlinger-Grosse, Krista; Veach, Patricia McCarthy; Cohen, Stephanie; LeRoy, Bonnie S; MacFarlane, Ian M; Zierhut, Heather

    2016-04-01

    The need for evidence-based medicine, including comparative effectiveness studies and patient-centered outcomes research, has become a major healthcare focus. To date, a comprehensive list of genetic counseling outcomes, as espoused by genetic counselors, has not been established and thus, identification of outcomes unique to genetic counseling services has become a priority for the National Society of Genetic Counselors (NSGC). The purpose of this study was to take a critical first step at identifying a more comprehensive list of genetic counseling outcomes. This paper describes the results of a focus group study using the Reciprocal-Engagement Model (REM) as a framework to characterize patient-centered outcomes of genetic counseling clinical practice. Five focus groups were conducted with 27 peer nominated participants who were clinical genetic counselors, genetic counseling program directors, and/or outcomes researchers in genetic counseling. Members of each focus group were asked to identify genetic counseling outcomes for four to five of the 17 goals of the REM. A theory-driven, thematic analysis of focus group data yielded 194 genetic counseling outcomes across the 17 goals. Participants noted some concerns about how genetic counseling outcomes will be measured and evaluated given varying stakeholders and the long-term nature of genetic concerns. The present results provide a list of outcomes for use in future genetic counseling outcomes research and for empirically-supported clinical interventions.

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

    DTIC Science & Technology

    1996-12-10

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

  11. Interactive computer program for learning genetic principles of segregation and independent assortment through meiosis.

    PubMed

    Yang, Xiaoli; Ge, Rong; Yang, Yufei; Shen, Hao; Li, Yingjie; Tseng, Charles C

    2009-01-01

    Teaching fundamental principles of genetics such as segregation and independent assortment of genes could be challenging for high school and college biology instructors. Students without thorough knowledge in meiosis often end up of frustration and failure in genetics courses. Although all textbooks and laboratory manuals have excellent graphic demonstrations and photographs of meiotic process, students may not always master the concept due to the lack of hands-on exercise. In response to the need for an effective lab exercise to understand the segregation of allelic genes and the independent assortment of the unlinked genes, we developed an interactive program for students to manually manipulate chromosome models and visualize each major step of meiosis so that these two genetic principles can be thoroughly understood.

  12. Latent spatial models and sampling design for landscape genetics

    USGS Publications Warehouse

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

    2016-01-01

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

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

    PubMed

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

    2017-03-01

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

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

    PubMed

    Morton, A Jennifer; Howland, David S

    2013-01-01

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

  15. Evolutionary model with genetics, aging, and knowledge.

    PubMed

    Bustillos, Armando Ticona; de Oliveira, Paulo Murilo C

    2004-02-01

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

  16. 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.

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

    PubMed

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

    2011-01-01

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

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

    PubMed

    Zhang, Mengjie; Gao, Xiaoying; Lou, Weijun

    2007-10-01

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

  19. 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.

  20. 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

  1. 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.

  2. 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.

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

    PubMed

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

    1996-09-01

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

  4. Latent spatial models and sampling design for landscape genetics

    Treesearch

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

    2016-01-01

    We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial...

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

    NASA Astrophysics Data System (ADS)

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

    2017-06-01

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

  6. 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.

  7. 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.

  8. 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.

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

    PubMed

    Lewis, Linwood J

    2002-06-01

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

  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. Relieving the Bottleneck: An Investigation of Barriers to Expansion of Supervision Networks at Genetic Counseling Training Programs.

    PubMed

    Berg, Jordan; Hoskovec, Jennifer; Hashmi, S Shahrukh; McCarthy Veach, Patricia; Ownby, Allison; Singletary, Claire N

    2017-09-06

    Rapid growth in the demand for genetic counselors has led to a workforce shortage. There is a prevailing assumption that the number of training slots for genetic counseling students is linked to the availability of clinical supervisors. This study aimed to determine and compare barriers to expansion of supervision networks at genetic counseling training programs as perceived by supervisors, non-supervisors, and Program Directors. Genetic counselors were recruited via National Society of Genetic Counselors e-blast; Program Directors received personal emails. Online surveys were completed by 216 supervisors, 98 non-supervisors, and 23 Program Directors. Respondents rated impact of 35 barriers; comparisons were made using Kruskal-Wallis and Wilcoxon ranked sum tests. Half of supervisors (51%) indicated willingness to increase supervision. All non-supervisors were willing to supervise. However, all agreed that being too busy impacted ability to supervise, highlighted by supervisors' most impactful barriers: lack of time, other responsibilities, intensive nature of supervision, desire for breaks, and unfilled positions. Non-supervisors noted unique barriers: distance, institutional barriers, and non-clinical roles. Program Directors' perceptions were congruent with those of genetic counselors with three exceptions they rated as impactful: lack of money, prefer not to supervise, and never been asked. In order to expand supervision networks and provide comprehensive student experiences, the profession must examine service delivery models to increase workplace efficiency, reconsider the supervision paradigm, and redefine what constitutes a countable case or place value on non-direct patient care experiences.

  12. 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.

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

    PubMed

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

    2015-09-02

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

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

    ERIC Educational Resources Information Center

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

    2003-01-01

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

  15. 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.

  16. 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

  17. 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

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

    SciTech Connect

    Handley, S.

    1995-12-31

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

  19. Towards programming languages for genetic engineering of living cells

    PubMed Central

    Pedersen, Michael; Phillips, Andrew

    2009-01-01

    Synthetic biology aims at producing novel biological systems to carry out some desired and well-defined functions. An ultimate dream is to design these systems at a high level of abstraction using engineering-based tools and programming languages, press a button, and have the design translated to DNA sequences that can be synthesized and put to work in living cells. We introduce such a programming language, which allows logical interactions between potentially undetermined proteins and genes to be expressed in a modular manner. Programs can be translated by a compiler into sequences of standard biological parts, a process that relies on logic programming and prototype databases that contain known biological parts and protein interactions. Programs can also be translated to reactions, allowing simulations to be carried out. While current limitations on available data prevent full use of the language in practical applications, the language can be used to develop formal models of synthetic systems, which are otherwise often presented by informal notations. The language can also serve as a concrete proposal on which future language designs can be discussed, and can help to guide the emerging standard of biological parts which so far has focused on biological, rather than logical, properties of parts. PMID:19369220

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

    PubMed

    Pedersen, Michael; Phillips, Andrew

    2009-08-06

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

  1. Occupational-Technical Program Need Assessment Model.

    ERIC Educational Resources Information Center

    Reed, Lester W., Jr.

    An occupational/technical program needs assessment model is described by which colleges can alter existing programs or develop new ones in anticipation of job market requirements. The report first discusses the two-part (existing and potential programs), tri-level (initial evaluation, individual program screening, and individual program planning),…

  2. 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. Copyright 2009 Elsevier Ltd. All rights reserved.

  3. 76 FR 72424 - Submission for OMB Review; Comment Request Information Program on the Genetic Testing Registry

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-11-23

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

  4. Network growth models and genetic regulatory networks

    NASA Astrophysics Data System (ADS)

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

    2006-03-01

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

  5. Network growth models and genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Socolar, Joshua; Foster, David; Kauffman, Stuart

    2006-03-01

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

  6. 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.

  7. 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.

  8. 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.

  9. 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…

  10. Science: Ohio's Model Competency-Based Program.

    ERIC Educational Resources Information Center

    Ohio State Dept. of Education, Columbus.

    Ohio's Model Competency-Based Science Program is designed to provide direction for school districts in developing local competency-based science education programs. The model is designed to be used to guide the development of district curriculum. The ultimate purpose of Ohio's Model Competency-Based Science Program is to move Ohio towards the…

  11. Genetically engineered murine models – Contribution to our understanding of the genetics, molecular pathology and therapeutic targeting of neuroblastoma

    PubMed Central

    Chesler, Louis; Weiss, William A.

    2012-01-01

    Genetically engineered mouse models (GEMM) have made major contributions to a molecular understanding of several adult cancers and these results are increasingly being translated into the pre-clinical setting where GEMM will very likely make a major impact on the development of targeted therapeutics in the near future. The relationship of pediatric cancers to altered developmental programs, and their genetic simplicity relative to adult cancers provides unique opportunities for the application of new advances in GEMM technology. In neuroblastoma the well-characterized TH-MYCN GEMM is increasingly used for a variety of molecular-genetic, developmental and pre-clinical therapeutics applications. We discuss: the present and historical application of GEMM to neuroblastoma research, future opportunities, and relevant targets suitable for new GEMM strategies in neuroblastoma. We review the potential of these models to contribute both to an understanding of the developmental nature of neuroblastoma and to improved therapy for this disease. PMID:21958944

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

    EPA Science Inventory

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

  13. 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...

  14. 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

  15. Human Genetics Education in the High School: A Pilot Program.

    ERIC Educational Resources Information Center

    Haddow, Paula K.

    1982-01-01

    Describes and evaluates a two-day workshop on human genetics for high school biology teachers which involved: (1) a series of lectures by professionals in medical genetics, ethics, and genetic counseling; (2) demonstrations; (3) question-and-answer sessions; (4) a curriculum packet; and (5) a follow-up bimonthly newsletter. (DC)

  16. Genetically engineered mouse models of pancreatic adenocarcinoma.

    PubMed

    Guerra, Carmen; Barbacid, Mariano

    2013-04-01

    Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal types of human cancer for which there are no effective therapies. Deep sequencing of PDAC tumors has revealed the presence of a high number of mutations (>50) that affect at least a dozen key signaling pathways. This scenario highlights the urgent need to develop experimental models that faithfully reproduce the natural history of these human tumors in order to understand their biology and to design therapeutic approaches that might effectively interfere with their multiple mutated pathways. Over the last decade, several models, primarily based on the genetic activation of resident KRas oncogenes knocked-in within the endogenous KRas locus have been generated. These models faithfully reproduce the histological lesions that characterize human pancreatic tumors. Decoration of these models with additional mutations, primarily involving tumor suppressor loci known to be also mutated in human PDAC tumors, results in accelerated tumor progression and in the induction of invasive and metastatic malignancies. Mouse PDACs also display a desmoplastic stroma and inflammatory responses that closely resemble those observed in human patients. Interestingly, adult mice appear to be resistant to PDAC development unless the animals undergo pancreatic damage, mainly in the form of acute, chronic or even temporary pancreatitis. In this review, we describe the most representative models available to date and how their detailed characterization is allowing us to understand their cellular origin as well as the events involved in tumor progression. Moreover, their molecular dissection is starting to unveil novel therapeutic strategies that could be translated to the clinic in the very near future. Copyright © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

  17. 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

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

    ERIC Educational Resources Information Center

    Tsai, Bor-sheng

    1994-01-01

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

  19. 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…

  20. Optimal In Silico Target Gene Deletion through Nonlinear Programming for Genetic Engineering

    PubMed Central

    Hong, Chung-Chien; Song, Mingzhou

    2010-01-01

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

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

    PubMed

    Hong, Chung-Chien; Song, Mingzhou

    2010-02-24

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

  2. Genetic programming approach to evaluate complexity of texture images

    NASA Astrophysics Data System (ADS)

    Ciocca, Gianluigi; Corchs, Silvia; Gasparini, Francesca

    2016-11-01

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

  3. 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.

  4. 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.

  5. Genetic programming assisted stochastic optimization strategies for optimization of glucose to gluconic acid fermentation.

    PubMed

    Cheema, Jitender Jit Singh; Sankpal, Narendra V; Tambe, Sanjeev S; Kulkarni, Bhaskar D

    2002-01-01

    This article presents two hybrid strategies for the modeling and optimization of the glucose to gluconic acid batch bioprocess. In the hybrid approaches, first a novel artificial intelligence formalism, namely, genetic programming (GP), is used to develop a process model solely from the historic process input-output data. In the next step, the input space of the GP-based model, representing process operating conditions, is optimized using two stochastic optimization (SO) formalisms, viz., genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). These SO formalisms possess certain unique advantages over the commonly used gradient-based optimization techniques. The principal advantage of the GP-GA and GP-SPSA hybrid techniques is that process modeling and optimization can be performed exclusively from the process input-output data without invoking the detailed knowledge of the process phenomenology. The GP-GA and GP-SPSA techniques have been employed for modeling and optimization of the glucose to gluconic acid bioprocess, and the optimized process operating conditions obtained thereby have been compared with those obtained using two other hybrid modeling-optimization paradigms integrating artificial neural networks (ANNs) and GA/SPSA formalisms. Finally, the overall optimized operating conditions given by the GP-GA method, when verified experimentally resulted in a significant improvement in the gluconic acid yield. The hybrid strategies presented here are generic in nature and can be employed for modeling and optimization of a wide variety of batch and continuous bioprocesses.

  6. Genetic adaptation to captivity in species conservation programs.

    PubMed

    Frankham, Richard

    2008-01-01

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

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

    SciTech Connect

    Russell, Liane B.

    2013-10-01

    The large mouse genetics program at the Oak Ridge National Lab is often re-membered chiefly for the germ-cell mutation-rate data it generated and their uses in estimating the risk of heritable radiation damage. In fact, it soon became a multi-faceted research effort that, over a period of almost 60 years, generated a wealth of information in the areas of mammalian mutagenesis, basic genetics (later enriched by molecular techniques), cytogenetics, reproductive biology, biochemistry of germ cells, and teratology. Research in the area of germ-cell mutagenesis explored the important physical and biological factors that affect the frequency and nature of induced mutations and made several unexpected discoveries, such as the major importance of the perigametic interval (the zygote stage) for the origin of spontaneous mutations and for the sensitivity to induced genetic change. Of practical value was the discovery that ethylnitrosourea was a supermutagen for point mutations, making high-efficiency mutagenesis in the mouse feasible worldwide. Teratogenesis findings resulted in recommendations still generally accepted in radiological practice. Studies supporting the mutagenesis research added whole bodies of information about mammalian germ-cell development and about molecular targets in germ cells. The early decision to not merely count but propagate genetic variants of all sorts made possible further discoveries, such as the Y-Chromosome s importance in mammalian sex determination and the identification of rare X-autosome translocations, which, in turn, led to the formulation of the single-active-X hypothesis and provided tools for studies of functional mosaicism for autosomal genes, male sterility, and chromosome-pairing mechanism. Extensive genetic and then molecular analyses of large numbers of induced specific-locus mutants resulted in fine-structure physical and correlated functional mapping of significant portions of the mouse genome and constituted a valuable

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

    PubMed

    Russell, Liane B

    2013-01-01

    The large mouse genetics program at the Oak Ridge National Laboratory (ORNL) is often remembered chiefly for the germ-cell mutation-rate data it generated and their uses in estimating the risk of heritable radiation damage. In fact, it soon became a multi-faceted research effort that, over a period of almost 60 years, generated a wealth of information in the areas of mammalian mutagenesis, basic genetics (later enriched by molecular techniques), cytogenetics, reproductive biology, biochemistry of germ cells, and teratology. Research in the area of germ-cell mutagenesis explored the important physical and biological factors that affect the frequency and nature of induced mutations and made several unexpected discoveries, such as the major importance of the perigametic interval (the zygote stage) for the origin of spontaneous mutations and for the sensitivity to induced genetic change. Of practical value was the discovery that ethylnitrosourea was a supermutagen for point mutations, making high-efficiency mutagenesis in the mouse feasible worldwide. Teratogenesis findings resulted in recommendations still generally accepted in radiological practice. Studies supporting the mutagenesis research added whole bodies of information about mammalian germ-cell development and about molecular targets in germ cells. The early decision to not merely count but propagate genetic variants of all sorts made possible further discoveries, such as the Y-chromosome's importance in mammalian sex determination and the identification of rare X-autosome translocations, which, in turn, led to the formulation of the single-active-X hypothesis and provided tools for studies of functional mosaicism for autosomal genes, male sterility, and chromosome-pairing mechanism. Extensive genetic and then molecular analyses of large numbers of induced specific-locus mutants resulted in fine-structure physical and correlated functional mapping of significant portions of the mouse genome and constituted a

  9. 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.

  10. 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

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

    PubMed

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

    2013-08-01

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

  12. Model Energy Efficiency Program Impact Evaluation Guide

    EPA Pesticide Factsheets

    This document provides guidance on model approaches for calculating energy, demand, and emissions savings resulting from energy efficiency programs. It describes several standard approaches that can be used in order to make these programs more efficient.

  13. Genetically modified mouse models addressing gonadotropin function.

    PubMed

    Ratner, Laura D; Rulli, Susana B; Huhtaniemi, Ilpo T

    2014-03-01

    The development of genetically modified animals has been useful to understand the mechanisms involved in the regulation of the gonadotropin function. It is well known that alterations in the secretion of a single hormone is capable of producing profound reproductive abnormalities. Human chorionic gonadotropin (hCG) is a glycoprotein hormone normally secreted by the human placenta, and structurally and functionally it is related to pituitary LH. LH and hCG bind to the same LH/hCG receptor, and hCG is often used as an analog of LH to boost gonadotropin action. There are many physiological and pathological conditions where LH/hCG levels and actions are elevated. In order to understand how elevated LH/hCG levels may impact on the hypothalamic-pituitary-gonadal axis we have developed a transgenic mouse model with chronic hCG hypersecretion. Female mice develop many gonadal and extragonadal phenotypes including obesity, infertility, hyperprolactinemia, and pituitary and mammary gland tumors. This article summarizes recent findings on the mechanisms involved in pituitary gland tumorigenesis and hyperprolactinemia in the female mice hypersecreting hCG, in particular the relationship of progesterone with the hyperprolactinemic condition of the model. In addition, we describe the role of hyperprolactinemia as the main cause of infertility and the phenotypic abnormalities in these mice, and the use of dopamine agonists bromocriptine and cabergoline to normalize these conditions. Copyright © 2014 Society for Biology of Reproduction & the Institute of Animal Reproduction and Food Research of Polish Academy of Sciences in Olsztyn. Published by Elsevier Urban & Partner Sp. z o.o. All rights reserved.

  14. Addressing Dynamic Issues of Program Model Checking

    NASA Technical Reports Server (NTRS)

    Lerda, Flavio; Visser, Willem

    2001-01-01

    Model checking real programs has recently become an active research area. Programs however exhibit two characteristics that make model checking difficult: the complexity of their state and the dynamic nature of many programs. Here we address both these issues within the context of the Java PathFinder (JPF) model checker. Firstly, we will show how the state of a Java program can be encoded efficiently and how this encoding can be exploited to improve model checking. Next we show how to use symmetry reductions to alleviate some of the problems introduced by the dynamic nature of Java programs. Lastly, we show how distributed model checking of a dynamic program can be achieved, and furthermore, how dynamic partitions of the state space can improve model checking. We support all our findings with results from applying these techniques within the JPF model checker.

  15. 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.…

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

    ERIC Educational Resources Information Center

    Devor, Eric J.

    1994-01-01

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

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2014-12-01

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

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

    PubMed

    Shugar, Andrea

    2017-04-01

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

  1. 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)

  2. 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)

  3. Genetic programming of polynomial harmonic networks using the discrete Fourier transform.

    PubMed

    Nikolaev, Nikolay Y; Iba, Hitoshi

    2002-10-01

    This paper presents a genetic programming system that evolves polynomial harmonic networks. These are multilayer feed-forward neural networks with polynomial activation functions. The novel hybrids assume that harmonics with non-multiple frequencies may enter as inputs the activation polynomials. The harmonics with non-multiple, irregular frequencies are derived analytically using the discrete Fourier transform. The polynomial harmonic networks have tree-structured topology which makes them especially suitable for evolutionary structural search. Empirical results show that this hybrid genetic programming system outperforms an evolutionary system manipulating polynomials, the traditional Koza-style genetic programming, and the harmonic GMDH network algorithm on processing time series.

  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. Copyright © 2016 by the Genetics Society of America.

  5. Mendel: the Swiss army knife of genetic analysis programs.

    PubMed

    Lange, Kenneth; Papp, Jeanette C; Sinsheimer, Janet S; Sripracha, Ram; Zhou, Hua; Sobel, Eric M

    2013-06-15

    Mendel is one of the few statistical genetics packages that provide a full spectrum of gene mapping methods, ranging from parametric linkage in large pedigrees to genome-wide association with rare variants. Our latest additions to Mendel anticipate and respond to the needs of the genetics community. Compared with earlier versions, Mendel is faster and easier to use and has a wider range of applications. Supported platforms include Linux, MacOS and Windows. Free from www.genetics.ucla.edu/software/mendel.

  6. Mendel: the Swiss army knife of genetic analysis programs

    PubMed Central

    Lange, Kenneth; Papp, Jeanette C.; Sinsheimer, Janet S.; Sripracha, Ram; Zhou, Hua; Sobel, Eric M.

    2013-01-01

    Summary: Mendel is one of the few statistical genetics packages that provide a full spectrum of gene mapping methods, ranging from parametric linkage in large pedigrees to genome-wide association with rare variants. Our latest additions to Mendel anticipate and respond to the needs of the genetics community. Compared with earlier versions, Mendel is faster and easier to use and has a wider range of applications. Supported platforms include Linux, MacOS and Windows. Availability: Free from www.genetics.ucla.edu/software/mendel Contact: klange@ucla.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23610370

  7. 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

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

    PubMed

    Thorvaldsen, Steinar

    2016-01-01

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

  9. Differential program evaluation model in child protection.

    PubMed

    Lalayants, Marina

    2012-01-01

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

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

    PubMed

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

    2017-08-01

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

  11. 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.

  12. Identification of Student Misconceptions in Genetics Problem Solving via Computer Program.

    ERIC Educational Resources Information Center

    Browning, Mark E.; Lehman, James D.

    1988-01-01

    Describes a computer program presenting four genetics problems to monitor the problem solving process of college students. Identifies three main areas of difficulty: computational skills; determination of gametes; and application of previous learning to new situations. (Author/YP)

  13. Mathematical Programming Models in Educational Planning.

    ERIC Educational Resources Information Center

    McNamara, James F.

    This document begins by defining and discussing educational planning. A brief overview of mathematical programing with an explanation of the general linear programing model is then provided. Some recent applications of mathematical programing techniques to educational planning problems are reviewed, and their implications for educational research…

  14. 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…

  15. 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…

  16. Genetically Engineered Humanized Mouse Models for Preclinical Antibody Studies

    PubMed Central

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

    2015-01-01

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

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

    ERIC Educational Resources Information Center

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

    1999-01-01

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

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

    DTIC Science & Technology

    2004-03-01

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

  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. Simulating pattern-process relationships to validate landscape genetic models

    Treesearch

    A. J. Shirk; S. A. Cushman; E. L. Landguth

    2012-01-01

    Landscapes may resist gene flow and thereby give rise to a pattern of genetic isolation within a population. The mechanism by which a landscape resists gene flow can be inferred by evaluating the relationship between landscape models and an observed pattern of genetic isolation. This approach risks false inferences because researchers can never feasibly test all...

  1. Cognitive Modelling and the Behaviour Genetics of Reading

    ERIC Educational Resources Information Center

    Castles, Anne; Bates, Timothy; Coltheart, Max; Luciano, Michelle; Martin, Nicholas G.

    2006-01-01

    While it is well known that reading is highly heritable, less has been understood about the bases of these genetic influences. In this paper, we review the research that we have been conducting in recent years to examine genetic and environmental influences on the particular reading processes specified in the "dual-route" cognitive model of…

  2. 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

  3. 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)

  4. 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)

  5. 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. Copyright © 2015 by the Genetics Society of America.

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

    PubMed

    Legarra, Andres

    2016-02-01

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

  7. 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.

  8. Statistical Inference of Biometrical Genetic Model With Cultural Transmission.

    PubMed

    Guo, Xiaobo; Ji, Tian; Wang, Xueqin; Zhang, Heping; Zhong, Shouqiang

    2013-01-01

    Twin and family studies establish the foundation for studying the genetic, environmental and cultural transmission effects for phenotypes. In this work, we make use of the well established statistical methods and theory for mixed models to assess cultural transmission in twin and family studies. Specifically, we address two critical yet poorly understood issues: the model identifiability in assessing cultural transmission for twin and family data and the biases in the estimates when sub-models are used. We apply our models and theory to two real data sets. A simulation is conducted to verify the bias in the estimates of genetic effects when the working model is a sub-model.

  9. Program Development and Evaluation: A Modeling Process.

    ERIC Educational Resources Information Center

    Green, Donald W.; Corgiat, RayLene

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

  10. 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…

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

    PubMed

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

    2014-08-01

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

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

    USDA-ARS?s Scientific Manuscript database

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  15. 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.

  16. Model Checking JAVA Programs Using Java Pathfinder

    NASA Technical Reports Server (NTRS)

    Havelund, Klaus; Pressburger, Thomas

    2000-01-01

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

  17. 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.

  18. 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

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

    PubMed

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

    2014-02-01

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

  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. Geometric Modeling Application Interface Program

    DTIC Science & Technology

    1990-11-01

    run-time environment must satisfy the interlanguage communication requirements of all the languages involved. This appendix discusses the HAS... interlanguage environment conventions and the composition of the PASCAL dynamic storage areas. Examples are given for a FORTRAN program that uses HAS routines... INTERLANGUAGE CONVENTIONS When the HAS subprograms were compiled, they were defined as PROCEDUREs using SUBPROGRAM declarations. The subprogram

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

    PubMed

    Moore, J H

    1995-06-01

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

  3. Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models.

    PubMed

    Zeng, Ping; Zhou, Xiang

    2017-09-06

    Using genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling all genetic variants together via polygenic methods. Here, we develop such a polygenic method, which we refer to as the latent Dirichlet process regression model. Dirichlet process regression is non-parametric in nature, relies on the Dirichlet process to flexibly and adaptively model the effect size distribution, and thus enjoys robust prediction performance across a broad spectrum of genetic architectures. We compare Dirichlet process regression with several commonly used prediction methods with simulations. We further apply Dirichlet process regression to predict gene expressions, to conduct PrediXcan based gene set test, to perform genomic selection of four traits in two species, and to predict eight complex traits in a human cohort.Genetic prediction of complex traits with polygenic architecture has wide application from animal breeding to disease prevention. Here, Zeng and Zhou develop a non-parametric genetic prediction method based on latent Dirichlet Process regression models.

  4. 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…

  5. 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…

  6. 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

  7. 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.

  8. Stationary phase mutagenesis in B. subtilis: a paradigm to study genetic diversity programs in cells under stress.

    PubMed

    Robleto, Eduardo A; Yasbin, Ronald; Ross, Christian; Pedraza-Reyes, Mario

    2007-01-01

    One of the experimental platforms to study programs increasing genetic diversity in cells under stressful or nondividing conditions is adaptive mutagenesis, also called stationary phase mutagenesis or stress-induced mutagenesis. In some model systems, there is evidence that mutagenesis occurs in genes that are actively transcribed. Some of those genes may be actively transcribed as a result of environmental stress giving the appearance of directed mutation. That is, cells under conditions of starvation or other stresses accumulate mutations in transcribed genes, including those transcribed because of the selective pressure. An important question concerns how, within the context of stochastic processes, a cell biases mutation to genes under selection pressure? Because the mechanisms underlying DNA transactions in prokaryotic cells are well conserved among the three domains of life, these studies are likely to apply to the examination of genetic programs in eukaryotes. In eukaryotes, increasing genetic diversity in differentiated cells has been implicated in neoplasia and cell aging. Historically, Escherichia coli has been the paradigm used to discern the cellular processes driving the generation of adaptive mutations; however, examining adaptive mutation in Bacillus subtilis has contributed new insights. One noteworthy contribution is that the B. subtilis' ability to accumulate chromosomal mutations under conditions of starvation is influenced by cell differentiation and transcriptional derepression, as well as by proteins homologous to transcription and repair factors. Here we revise and discuss concepts pertaining to genetic programs that increase diversity in B. subtilis cells under nutritional stress.

  9. Program Model Checking: A Practitioner's Guide

    NASA Technical Reports Server (NTRS)

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

    2008-01-01

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

  10. Genetic algorithms for modelling and optimisation

    NASA Astrophysics Data System (ADS)

    McCall, John

    2005-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-05-01

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

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

    PubMed Central

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

    2016-01-01

    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

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

    ERIC Educational Resources Information Center

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

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

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

    PubMed

    Detilleux, Johann C

    2005-01-01

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

  15. 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

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

    SciTech Connect

    Handley, S.

    1994-12-31

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

  17. Genetic analysis of the temperament of Nellore cattle using linear and threshold models.

    PubMed

    Lucena, C R S; Neves, H H R; Carvalheiro, R; Oliveira, J A; Queiroz, S A

    2015-03-01

    Temperament is an important trait for the management and welfare of animals and for reducing accidents involving people who work with cattle. The present study aimed to estimate the genetic parameters related to the temperament score (T) and weaning weight (WW) of Nellore cattle, reared in a beef cattle breeding program in Brazil. Data were analyzed using two different two-trait statistical models, both considering WW and T: (1) a linear-linear model in which variance components (VCs) were estimated using restricted maximum likelihood; and (2) a linear-threshold model in which VCs were estimated via Bayesian inference. WW was included in the analyses of T to minimize any possible effects of sequential selection and to allow for estimation of the genetic correlation between these two traits. The heritability estimates for T were 0.21 ± 0.003 (model 1) and 0.26 (model 2, with a 95% credibility interval (95% CI) of 0.21 to 0.32). The estimated genetic correlations between WW and T were of a moderate magnitude (-0.33 ± 0.01 (model 1) and -0.34 (95% CI: -0.40, -0.28, model 2). The genetic correlations between the estimated breeding values (EBVs) obtained for the animals based on the two models were high (>0.92). The use of different models had little influence on the classification of animals based on EBVs or the accuracy of the EBVs.

  18. Requirements Modeling with Agent Programming

    NASA Astrophysics Data System (ADS)

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

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

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

    PubMed

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

    2016-10-01

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

  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. Cucumber as a Model for Organellar Genetics

    USDA-ARS?s Scientific Manuscript database

    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...

  2. Budding yeast as a model organism for population genetics.

    PubMed

    Zeyl, C

    2000-06-15

    Population genetics is a highly theoretical field in which many models and theories of broad significance have received little experimental testing. Microbes are well-suited for empirical population genetics since populations of almost any size may be studied genetically, and because many have easily controlled life cycles. Saccharomyces cerevisiae is almost ideal for such studies as the growing body of knowledge and techniques that have made it the best characterized eukaryote genome also allow the experimental manipulation and analysis of its population genetics. In experiments to date, the evolution of laboratory yeast populations has been observed for up to 1000 generations. In several cases, adaptation has occurred by gene duplications. The interaction between mutation, selection and genetic drift at varying population sizes is a major area of theoretical study in which yeast experiments can provide particularly valuable data. Conflicts between gene-level and among-cell selection, and co-evolution between genes within a genome, are additional topics in which a population genetics perspective may be particularly helpful. The growing field of genomics is increasingly complementary with that of population genetics. The characterization of the yeast genome presents unprecedented opportunities for the detailed study of evolutionary and population genetics. Conversely, the redundancy of the yeast genome means that, for many open reading frames, deletion has only a quantitative effect that is most readily observed in competitions with a wild-type strain. Copyright 2000 John Wiley & Sons, Ltd.

  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. 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…

  5. 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…

  6. 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.

  7. 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

  8. Tracking the genetic stability of a honeybee breeding program with genetic markers

    USDA-ARS?s Scientific Manuscript database

    A genetic stock identification (GSI) assay was developed in 2008 to distinguish Russian honey bees from other honey bee stocks that are commercially produced in the United States. Probability of assignment (POA) values have been collected and maintained since the stock release in 2008 to the Russian...

  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. Copyright © 2016 Elsevier Inc. All rights reserved.

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

    PubMed

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

    2010-08-01

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

  11. [Cost-effectiveness analysis of a genetic screening program in the close relatives of Spanish patients with familial hypercholesterolemia].

    PubMed

    Oliva, Juan; López-Bastida, Julio; Moreno, Santiago G; Mata, Pedro; Alonso, Rodrigo

    2009-01-01

    The aim was to assess the cost-effectiveness of a genetic screening program for first-degree relatives of patients with familial hypercholesterolemia (FH), followed by treatment when necessary, compared with the alternative of no screening. The cost-effectiveness analysis modeled the effect of statin treatment on individuals who were diagnosed with FH after genetic screening. The impact of uncertainty was evaluated using univariate probabilistic sensitivity analysis. The alternate strategy considered was no screening. In the cost-effectiveness analysis, the number of life-years gained (LYG) was regarded as the health outcome and the costs of screening, statin treatment, specialist consultations and hospital visits were all included. In addition, the expected value of perfect information was calculated as part of the sensitivity analysis. In the base case, the incremental cost of the screening program for close relatives was 3423 euros per LYG. Although the sensitivity analysis gave a range of results, the conclusions were not affected by changes in the parameters considered. The screening program was found to be better than the alternative considered at a probability level of 95% if the acceptable level of health-care costs was at least 7400 euros per LYG. This analysis indicates that a genetic screening program, supplemented by treatment, for the close relatives of individuals with FH is preferable to the alternative of no screening in terms of incremental cost-effectiveness.

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

    PubMed

    Keren, Naomi; Peled, Noam; Korngreen, Alon

    2005-12-01

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

  13. 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. © 2014 John Wiley & Sons Ltd.

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

    PubMed

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

    2005-10-01

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

  15. Data-Generating Program for ASKA Modeling

    NASA Technical Reports Server (NTRS)

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

    1985-01-01

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

  16. Programming model for distributed intelligent systems

    NASA Technical Reports Server (NTRS)

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

    1988-01-01

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

  17. 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.

  18. Practical implications for genetic modeling in the genomics era

    USDA-ARS?s Scientific Manuscript database

    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...

  19. Disaggregating sorghum yield reductions under warming scenarios exposes narrow genetic diversity in US breeding programs.

    PubMed

    Tack, Jesse; Lingenfelser, Jane; Jagadish, S V Krishna

    2017-08-29

    Historical adaptation of sorghum production to arid and semiarid conditions has provided promise regarding its sustained productivity under future warming scenarios. Using Kansas field-trial sorghum data collected from 1985 to 2014 and spanning 408 hybrid cultivars, we show that sorghum productivity under increasing warming scenarios breaks down. Through extensive regression modeling, we identify a temperature threshold of 33 °C, beyond which yields start to decline. We show that this decline is robust across both field-trial and on-farm data. Moderate and higher warming scenarios of 2 °C and 4 °C resulted in roughly 17% and 44% yield reductions, respectively. The average reduction across warming scenarios from 1 to 5 °C is 10% per degree Celsius. Breeding efforts over the last few decades have developed high-yielding cultivars with considerable variability in heat resilience, but even the most tolerant cultivars did not offer much resilience to warming temperatures. This outcome points to two concerns regarding adaption to global warming, the first being that adaptation will not be as simple as producers' switching among currently available cultivars and the second being that there is currently narrow genetic diversity for heat resilience in US breeding programs. Using observed flowering dates and disaggregating heat-stress impacts, both pre- and postflowering stages were identified to be equally important for overall yields. These findings suggest the adaptation potential for sorghum under climate change would be greatly facilitated by introducing wider genetic diversity for heat resilience into ongoing breeding programs, and that there should be additional efforts to improve resilience during the preflowering phase.

  20. Disaggregating sorghum yield reductions under warming scenarios exposes narrow genetic diversity in US breeding programs

    PubMed Central

    Tack, Jesse; Lingenfelser, Jane; Jagadish, S. V. Krishna

    2017-01-01

    Historical adaptation of sorghum production to arid and semiarid conditions has provided promise regarding its sustained productivity under future warming scenarios. Using Kansas field-trial sorghum data collected from 1985 to 2014 and spanning 408 hybrid cultivars, we show that sorghum productivity under increasing warming scenarios breaks down. Through extensive regression modeling, we identify a temperature threshold of 33 °C, beyond which yields start to decline. We show that this decline is robust across both field-trial and on-farm data. Moderate and higher warming scenarios of 2 °C and 4 °C resulted in roughly 17% and 44% yield reductions, respectively. The average reduction across warming scenarios from 1 to 5 °C is 10% per degree Celsius. Breeding efforts over the last few decades have developed high-yielding cultivars with considerable variability in heat resilience, but even the most tolerant cultivars did not offer much resilience to warming temperatures. This outcome points to two concerns regarding adaption to global warming, the first being that adaptation will not be as simple as producers’ switching among currently available cultivars and the second being that there is currently narrow genetic diversity for heat resilience in US breeding programs. Using observed flowering dates and disaggregating heat-stress impacts, both pre- and postflowering stages were identified to be equally important for overall yields. These findings suggest the adaptation potential for sorghum under climate change would be greatly facilitated by introducing wider genetic diversity for heat resilience into ongoing breeding programs, and that there should be additional efforts to improve resilience during the preflowering phase. PMID:28808013

  1. Modeling hygroelastic properties of genetically modified aspen

    Treesearch

    Laszlo Horvath; Perry Peralta; Ilona Peszlen; Levente Csoka; Balazs Horvath; Joseph Jakes

    2012-01-01

    Numerical and three-dimensional finite element models were developed to improve understanding of major factors affecting hygroelastic wood properties. Effects of chemical composition, microfibril angle, crystallinity, structure of microfibrils, moisture content, and hydrophilicity of the cell wall were included in the model. Wood from wild-type and decreased-lignin...

  2. 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…

  3. 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…

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

    PubMed Central

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

    2015-01-01

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

  5. Genetic and economic effects of the increase in female paternal filiations by parentage assignment in sheep and goat breeding programs.

    PubMed

    Raoul, J; Palhière, I; Astruc, J M; Elsen, J M

    2016-09-01

    In sheep and goat breeding programs, the proportion of females for which the sire is known (known paternity rate [KPR]) can be very low. In this context, paternity assignment using SNP is an attractive tool. The annual genetic gain (AGG) is impacted by the accuracy of the EBV. In populations with a low KPR, the number of known relatives for a given individual is low, and the EBV that are based on this information are imprecise. However, the impact of partially known paternal filiations, in terms of potential genetic and economic losses, has never been quantitatively evaluated in situations where natural mating is the main reproductive mode. A deterministic model was developed to assess, for a panel of real breeding programs, the influence of the female KPR on the AGG and economic benefit. First, males were divided into categories according to their status (natural mating or AI sire) and breeding cycle and females according to parity, sire status (including unknown sire), and breeding cycle of the sire. Second, a demographic model described, for each category, the accumulation of known records for individuals and their close relatives. The output from this model was used to compute the average accuracy of the EBV per category. Then, a genetic model based on the gene flow between categories over time was described. Using the average accuracy of EBV per category, it provided the asymptotic AGG of the nucleus given its KPR. In the economic studies, changes to the mean genetic values in the nucleus and the commercial population after an increase in KPR and various gain:cost ratios (monetary gain due to an extra genetic SD of the selected trait divided by the cost of 1 assignment) were considered. Relative profit and payback periods were computed. We showed that SNP-based parentage assignment aimed at increasing the female KPR was not always profitable and that the type of breeding program and the size of the commercial population should be taken into consideration

  6. Modeling genetic inheritance of copy number variations

    PubMed Central

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

    2008-01-01

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

  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.

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

    PubMed Central

    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

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

    PubMed

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

    2015-06-01

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

  10. 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.

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

    PubMed

    Khan, Mohammad Wahab; Alam, Mansaf

    2012-08-01

    The aim of this paper is to provide an introduction to the rapidly developing field of genetic programming (GP). Particular emphasis is placed on the application of GP to genomics. First, the basic methodology of GP is introduced. This is followed by a review of applications in the areas of gene network inference, gene expression data analysis, SNP analysis, epistasis analysis and gene annotation. Finally this paper concluded by suggesting potential avenues of possible future research on genetic programming, opportunities to extend the technique, and areas for possible practical applications. Copyright © 2012 Elsevier Inc. All rights reserved.

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

    PubMed

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

    2017-02-01

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

  13. 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

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

    PubMed Central

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

    2013-01-01

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

  15. 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.

  16. 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.

  17. Predicting Diabetic Nephropathy Using a Multifactorial Genetic Model

    PubMed Central

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

    2011-01-01

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

  18. 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.

  19. 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

  20. 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.

  1. Genetic Network Programming-Sarsa with Multi-Subroutines for Trading Rules on Stock Markets

    NASA Astrophysics Data System (ADS)

    Yang, Yang; Gu, Yunqing; Mabu, Shingo; Hirasawa, Kotaro

    In this paper, a stock trading model is proposed using a graph-based evolutionary algorithm named Genetic Network Programming-Sarsa (GNP-Sarsa) with multi-subroutines. The method is developed for discovering the frequent transitions of GNP, which can be seen as the repetitive subgraphs, i.e., building blocks with useful knowledge over the entire graph structure, and modularizing them as subroutines. The important points of the subroutines mechanism are as follows: First, the nodes and node connections discovered in the subroutines are reused to create effective trading rules. Second, the evolution can be achieved so quickly by narrowing the search space with subroutines. Last, as the kinds of subroutines increase, the generalization ability is improved since more generalized frequent transitions of GNP, i.e., building blocks are found instead of precisely modeling the training data, which leads to the overiftting problem. The following two experiments are discussed: 1) varying the number of subroutine nodes in the main GNP and 2) varying the kinds of subroutines to be generated. Simulation results on the stock markets show that the proposed method can generate more efficient and generalized trading models and obtain much higher profits.

  2. Programming Models for Heterogeneous Multicore Systems

    DTIC Science & Technology

    2011-08-08

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

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

    PubMed

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

    1999-01-01

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

  4. ENU mutagenesis to generate genetically modified rat models.

    PubMed

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

    2010-01-01

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

  5. Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies

    PubMed Central

    Lawrence, Neil D.

    2012-01-01

    Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown subtle environmental perturbations. These factors can induce a pronounced artifactual correlation structure in the expression profiles, which may create spurious false associations or mask real genetic association signals. Here, we report PANAMA (Probabilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors within an eQTL analysis. In contrast to previous methods, PANAMA learns hidden factors jointly with the effect of prominent genetic regulators. As a result, this new model can more accurately distinguish true genetic association signals from confounding variation. We applied our model and compared it to existing methods on different datasets and biological systems. PANAMA consistently performs better than alternative methods, and finds in particular substantially more trans regulators. Importantly, our approach not only identifies a greater number of associations, but also yields hits that are biologically more plausible and can be better reproduced between independent studies. A software implementation of PANAMA is freely available online at http://ml.sheffield.ac.uk/qtl/. PMID:22241974

  6. 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 ...

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

    PubMed

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

    2017-01-01

    We examined the hypothesis that ecological niche models (ENMs) more accurately predict species distributions when they incorporate information on population genetic structure, and concomitantly, local adaptation. Local adaptation is common in species that span a range of environmental gradients (e.g., soils and climate). Moreover, common garden studies have demonstrated a covariance between neutral markers and functional traits associated with a species' ability to adapt to environmental change. We therefore predicted that genetically distinct populations would respond differently to climate change, resulting in predicted distributions with little overlap. To test whether genetic information improves our ability to predict a species' niche space, we created genetically informed ecological niche models (gENMs) using Populus fremontii (Salicaceae), a widespread tree species in which prior common garden experiments demonstrate strong evidence for local adaptation. Four major findings emerged: (i) gENMs predicted population occurrences with up to 12-fold greater accuracy than models without genetic information; (ii) tests of niche similarity revealed that three ecotypes, identified on the basis of neutral genetic markers and locally adapted populations, are associated with differences in climate; (iii) our forecasts indicate that ongoing climate change will likely shift these ecotypes further apart in geographic space, resulting in greater niche divergence; (iv) ecotypes that currently exhibit the largest geographic distribution and niche breadth appear to be buffered the most from climate change. As diverse agents of selection shape genetic variability and structure within species, we argue that gENMs will lead to more accurate predictions of species distributions under climate change. © 2016 John Wiley & Sons Ltd.

  8. Identification of biochemical networks by S-tree based genetic programming.

    PubMed

    Cho, Dong-Yeon; Cho, Kwang-Hyun; Zhang, Byoung-Tak

    2006-07-01

    Most previous approaches to model biochemical networks have focused either on the characterization of a network structure with a number of components or on the estimation of kinetic parameters of a network with a relatively small number of components. For system-level understanding, however, we should examine both the interactions among the components and the dynamic behaviors of the components. A key obstacle to this simultaneous identification of the structure and parameters is the lack of data compared with the relatively large number of parameters to be estimated. Hence, there are many plausible networks for the given data, but most of them are not likely to exist in the real system. We propose a new representation named S-trees for both the structural and dynamical modeling of a biochemical network within a unified scheme. We further present S-tree based genetic programming to identify the structure of a biochemical network and to estimate the corresponding parameter values at the same time. While other evolutionary algorithms require additional techniques for sparse structure identification, our approach can automatically assemble the sparse primitives of a biochemical network in an efficient way. We evaluate our algorithm on the dynamic profiles of an artificial genetic network. In 20 trials for four settings, we obtain the true structure and their relative squared errors are <5% regardless of releasing constraints about structural sparseness. In addition, we confirm that the proposed algorithm is robust within +/-10% noise ratio. Furthermore, the proposed approach ensures a reasonable estimate of a real yeast fermentation pathway. The comparatively less important connections with non-zero parameters can be detected even though their orders are below 10(-2). To demonstrate the usefulness of the proposed algorithm for real experimental biological data, we provide an additional example on the transcriptional network of SOS response to DNA damage in Escherichia

  9. Intrasystem Analysis Program (IAP) Model Improvement.

    DTIC Science & Technology

    1982-02-01

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

  10. 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)

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

    ERIC Educational Resources Information Center

    Dewhurst, D. G.; And Others

    1989-01-01

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

  12. 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.

  13. An Integrative Bayesian Modeling Approach to Imaging Genetics

    PubMed Central

    Stingo, Francesco C.; Guindani, Michele; Vannucci, Marina; Calhoun, Vince D.

    2013-01-01

    In this paper we present a Bayesian hierarchical modeling approach for imaging genetics, where the interest lies in linking brain connectivity across multiple individuals to their genetic information. We have available data from a functional magnetic resonance (fMRI) study on schizophrenia. Our goals are to identify brain regions of interest (ROIs) with discriminating activation patterns between schizophrenic patients and healthy controls, and to relate the ROIs’ activations with available genetic information from single nucleotide polymorphisms (SNPs) on the subjects. For this task we develop a hierarchical mixture model that includes several innovative characteristics: it incorporates the selection of ROIs that discriminate the subjects into separate groups; it allows the mixture components to depend on selected covariates; it includes prior models that capture structural dependencies among the ROIs. Applied to the schizophrenia data set, the model leads to the simultaneous selection of a set of discriminatory ROIs and the relevant SNPs, together with the reconstruction of the correlation structure of the selected regions. To the best of our knowledge, our work represents the first attempt at a rigorous modeling strategy for imaging genetics data that incorporates all such features. PMID:24298194

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

    PubMed

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

    2006-02-01

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

  15. 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. Copyright © 2016 by the Genetics Society of America.

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

    PubMed

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

    2016-08-01

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

  17. 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.

  18. Evolving complex dynamics in electronic models of genetic networks

    NASA Astrophysics Data System (ADS)

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

    2004-09-01

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

  19. 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

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

    PubMed

    Bikard, David; Marraffini, Luciano A

    2012-02-01

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

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

    USDA-ARS?s Scientific Manuscript database

    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...

  2. 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…

  3. 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

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

    PubMed

    Schulz, Angela; Kreutz, Reinhold

    2012-07-01

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

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

    PubMed Central

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

    2017-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-03-01

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

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

    PubMed

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

    2007-01-01

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

  8. 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.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  10. Genetically selected alcohol preferring rats to model human alcoholism

    PubMed Central

    Ciccocioppo, Roberto

    2016-01-01

    Animal models have been successfully developed to mimic and study alcoholism. These models have the unique feature of allowing the researcher to control for the genetic characteristics of the animal, alcohol exposure and environment. Moreover, these animal models allow pharmacological, neurochemical and behavioural manipulations otherwise impossible. Unquestionably, one of the major contributions to the understanding of the neurobiological basis of alcoholism comes from data that have been obtained from the study of genetically selected alcohol-preferring rat lines and from the consequences that alcohol drinking and environmental manipulations (/i.e., protracted alcohol drinking, intoxication, exposure to stress etc) have on them. In fact, if on the one hand genetic factors may account for about 50–60% of the risk of developing alcohol dependence, on the other hand protracted alcohol exposure is a necessary precondition to actually develop the disease, while environmental vulnerability factors may be crucial for disease progression. The present article will offer an overview of the different genetically selected alcohol preferring rat lines developed and used to study alcoholism. The predictive, face and construct validity of these animal models and the translational significance of findings achieved through their use will be critically discussed. PMID:22328453

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

    ERIC Educational Resources Information Center

    Dade County Public Schools, Miami, FL.

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

  12. 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.

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

    PubMed Central

    Monir, Md. Mamun; Zhu, Jun

    2017-01-01

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

  14. Academic program models for undergraduate biomedical engineering.

    PubMed

    Krishnan, Shankar M

    2014-01-01

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

  15. Sex differences in developmental programming models.

    PubMed

    Aiken, Catherine E; Ozanne, Susan E

    2013-01-01

    The theory of developmental programming suggests that diseases such as the metabolic syndrome may be 'programmed' by exposure to adverse stimuli during early development. The developmental programming literature encompasses the study of a wide range of suboptimal intrauterine environments in a variety of species and correlates these with diverse phenotypic outcomes in the offspring. At a molecular level, a large number of variables have been measured and suggested as the basis of the programmed phenotype. The range of both dependent and independent variables studied often makes the developmental programming literature complex to interpret and the drawing of definitive conclusions difficult. A common, though under-explored, theme of many developmental programming models is a sex difference in offspring outcomes. This holds true across a range of interventions, including dietary, hypoxic, and surgical models. The molecular and phenotypic outcomes of adverse in utero conditions are often more prominent in male than female offspring, although there is little consideration given to the basis for this observation in most studies. We review the evidence that maternal energy investment in male and female conceptuses may not be equal and may be environment dependent. It is suggested that male and female development could be viewed as separate processes from the time of conception, with differences in both timing and outcomes.

  16. Genetic code: an alternative model of translation.

    PubMed

    Damjanović, Zvonimir M; Rakocević, Miloje M

    2005-06-01

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

  17. 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

  18. Supermultiplicative Speedups of Probabilistic Model-Building Genetic Algorithms

    DTIC Science & Technology

    2009-02-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

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

  20. The significance of genetics in pathophysiologic models of premature birth.

    PubMed

    Uberos, Jose

    2017-05-31

    Prematurity is a major health problem in all countries, especially in certain ethic groups and increasing recurrence imply the influence of genetic factors. Published genetic polymorphisms are identified in relation to the 4 pathophysiological models of prematurity described: Chorioamniotic-decidual inflammation, premature contraction pathway, decidual haemorrhage and susceptibility to environmental toxins. 240 articles are identified, 52 articles are excluded because they are not original, not written in English or duplicated. From them 125 articles were included in qualitative analysis This review aims to update recent knowledge about genes associated with premature birth.

  1. 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

  2. Investigating the connectivity between emissions of BVOC and rainfall formation in Amazonia using Genetic Programming

    NASA Astrophysics Data System (ADS)

    Von Randow, Celso; Sanches, Marcos B.; Santos, Rosa Maria N.; Chamecki, Marcelo; Fuentes, Jose D.

    2017-04-01

    A detailed field experiment measuring turbulent properties, trace gases and BVOCs was carried out from April 2014 to January 2015 within and above a central Amazonian rainforest, with the objective of understanding the role of emissions and reactions of BVOCs, formation and transport of aerosols out of the boundary layer on cloud formation and precipitation triggers. Our measurements show two-way aspects of connectivity: mesoscale convective systems transport ozone down from the middle troposphere, enriching the atmospheric boundary layer as well as the forest canopy and surface layer, and, through multiple chemical transformations, an ozone-enriched atmospheric surface layer that can oxidize rainforest-emitted hydrocarbons and generate aerosols that subsequently activate into cloud condensation nuclei, thereby possibly influencing the formation of new convective precipitation. Qualitatively, we address the connectivity between emissions of BVOCs near the surface and rainfall generation, using the technique of Genetic Programing (GP), introduced by Koza (1992), based on the concepts of natural selection and genetics. The technique involves finding a mathematical expression that fits a given set of data, and constructing a population of mathematical models from different combinations of variables, constants and operators. An advantage of GP is that it can flexibly incorporate multivariate non-linear relations, and obtained numeric solutions are possibly interpreted and checked for physical consistency. A number of state variables (for example, surface fluxes, meteorological conditions, boundary layer stability conditions, BVOC and Ozone vertical profiles, etc), representing possible influences on BVOC emissions and their interrelations along the way through secondary organic aerosol and CCN formation to rainfall will be used.

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

    PubMed

    Zhao, Weidong; Liu, Xi; Dai, Weihui

    2014-01-01

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

  4. 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.

  5. Influence of Genetic Counseling Graduate Program Websites on Student Application Decisions.

    PubMed

    Ivan, Kristina M; Hassed, Susan; Darden, Alix G; Aston, Christopher E; Guy, Carrie

    2017-04-19

    This study investigated how genetic counseling educational program websites affect application decisions via an online survey sent to current students and recent graduates. Program leadership: directors, assistant directors, associate directors, were also surveyed to determine where their opinions coincided or differed from those reported by students and recent graduates. Chi square analysis and t-tests were used to determine significance of results. A two-sample t-test was used to compare factors students identified as important on a 5-point Likert scale with those identified by directors. Thematic analysis revealed three major themes students consider important for program websites: easy navigation, website content, and website impression. Directors were interested in how prospective students use their program website and what information they found most useful. Students indicated there were specific programs they chose not to apply to due to the difficulty of using the website for that program. Directors significantly underestimated how important information about application requirements was to students in making application decisions. The information reported herein will help individual genetic counseling graduate programs improve website functionality and retain interested applicants.

  6. Logic integer programming models for signaling networks.

    PubMed

    Haus, Utz-Uwe; Niermann, Kathrin; Truemper, Klaus; Weismantel, Robert

    2009-05-01

    We propose a static and a dynamic approach to model biological signaling networks, and show how each can be used to answer relevant biological questions. For this, we use the two different mathematical tools of Propositional Logic and Integer Programming. The power of discrete mathematics for handling qualitative as well as quantitative data has so far not been exploited in molecular biology, which is mostly driven by experimental research, relying on first-order or statistical models. The arising logic statements and integer programs are analyzed and can be solved with standard software. For a restricted class of problems the logic models reduce to a polynomial-time solvable satisfiability algorithm. Additionally, a more dynamic model enables enumeration of possible time resolutions in poly-logarithmic time. Computational experiments are included.

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

    PubMed

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

    2017-01-04

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

  8. Mining and modeling human genetics for autism therapeutics.

    PubMed

    Smith, Daniel G; Ehlers, Michael D

    2012-10-01

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

  9. The Genetics of Sleep: Insight from Rodent Models

    PubMed Central

    Summa, Keith C.; Turek, Fred W.

    2011-01-01

    Summary: Sleep is a fundamental behavior in higher animals that has been firmly established to be under substantial genetic control. However, the identification of individual genes responsible for primary sleep-wake traits has largely eluded researchers. Genetic studies in animal models have uncovered a variety of genomic loci associated with specific traits, validated the role of key neurotransmitter systems (i.e., monoamines) in sleep-wake regulation, identified novel and unexpected genes responsible for controlling sleep-wake traits, and demonstrated substantial genetic overlap in the regulation of sleep and circadian rhythms. Future studies are expected to reveal additional genes and gene networks underlying certain sleep-wake traits, thereby advancing our understanding of the molecular basis of sleep, which may suggest answers to the ultimate question of why we sleep as well as provide unique insight into the relationship between sleep and chronic diseases. PMID:21765816

  10. Genetic demixing and evolution in linear stepping stone models

    PubMed Central

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

    2010-01-01

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

  11. 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

  12. Math and Science Model Programs Manual.

    ERIC Educational Resources Information Center

    Sawyer, Donna, Comp.; And Others

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

  13. 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…

  14. 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.

  15. 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…

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

    PubMed

    Shao, Yunru; Liu, Shuling; Grinzaid, Karen

    2015-04-01

    Improvements in genetic testing technologies have led to the development of expanded carrier screening panels for the Ashkenazi Jewish population; however, there are major inconsistencies in current screening practices. A 2-year pilot program was launched in Atlanta in 2010 to promote and facilitate screening for 19 Jewish genetic diseases. We analyzed data from this program, including participant demographics and outreach efforts. This retrospective analysis is based on a de-identified dataset of 724 screenees. Data were obtained through medical chart review and questionnaires and included demographic information, screening results, response to outreach efforts, and follow-up behavior and preferences. We applied descriptive analysis, chi-square tests, and logistic regression to analyze the data and compare findings with published literature. The majority of participants indicated that they were not pregnant or did not have a partner who was pregnant were affiliated with Jewish organizations and reported 100 % AJ ancestry. Overall, carrier frequency was 1 in 3.9. Friends, rabbis, and family members were the most common influencers of the decision to receive screening. People who were older, had a history of pregnancy, and had been previously screened were more likely to educate others (all p < 0.05). Analysis of this 2-year program indicated that people who are ready to have children or expand their families are more likely to get screened and encourage others to be screened. The most effective outreach efforts targeted influencers who then encouraged screening in the target population. Educating influencers and increasing overall awareness were the most effective outreach strategies.

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

    PubMed

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

    2012-06-01

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

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

    PubMed Central

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

    2012-01-01

    Sensitivity to reinforcement from methamphetamine (MA) likely influences risk for MA addiction, and genetic differences are one source of individual variation. Generation of two sets of selectively bred mouse lines for high and low MA drinking has shown that genetic factors influence MA intake, and pronounced differences in sensitivity to rewarding and aversive effects of MA play a significant role. Further validation of these lines as a unique genetic model relevant to MA addiction was obtained using operant methods to study MA reinforcement. High and low MA drinking line mice were used to test the hypotheses that: 1) oral and intracerebroventricular (ICV) MA serve as behavioral reinforcers, and 2) MA exhibits greater reinforcing efficacy in high than low MA drinking mice. Operant responses resulted in access to an MA or non-MA drinking tube or intracranial delivery of MA. Behavioral activation consequent to orally consumed MA was determined. MA available for consumption maintained higher levels of reinforced instrumental responding in high than low MA drinking line mice, and MA intake in the oral operant procedure was greater in high than low MA drinking line mice. Behavioral activation was associated with amount of MA consumed during operant sessions. High line mice delivered more MA via ICV infusion than did low line mice across a range of doses. Thus, genetic risk factors play a critical role in the reinforcing efficacy of MA and the oral self-administration procedure is suitable for delineating genetic contributions to MA reinforcement. PMID:22280875

  19. 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

  20. 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.

  1. The Use of the Linear Mixed Model in Human Genetics.

    PubMed

    Dandine-Roulland, Claire; Perdry, Hervé

    2015-01-01

    We give a short but detailed review of the methods used to deal with linear mixed models (restricted likelihood, AIREML algorithm, best linear unbiased predictors, etc.), with a few original points. Then we describe three common applications of the linear mixed model in contemporary human genetics: association testing (pathways analysis or rare variants association tests), genomic heritability estimates, and correction for population stratification in genome-wide association studies. We also consider the performance of best linear unbiased predictors for prediction in this context, through a simulation study for rare variants in a short genomic region, and through a short theoretical development for genome-wide data. For each of these applications, we discuss the relevance and the impact of modeling genetic effects as random effects. © 2016 S. Karger AG, Basel.

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

    PubMed

    Crews, D H; Wang, Z

    2007-07-01

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

  3. Modeling the Diagnostic Criteria for Alcohol Dependence with Genetic Animal Models

    PubMed Central

    Kendler, Kenneth S.; Hitzemann, Robert J.

    2012-01-01

    A diagnosis of alcohol dependence (AD) using the DSM-IV-R is categorical, based on an individual’s manifestation of three or more symptoms from a list of seven. AD risk can be traced to both genetic and environmental sources. Most genetic studies of AD risk implicitly assume that an AD diagnosis represents a single underlying genetic factor. We recently found that the criteria for an AD diagnosis represent three somewhat distinct genetic paths to individual risk. Specifically, heavy use and tolerance versus withdrawal and continued use despite problems reflected separate genetic factors. However, some data suggest that genetic risk for AD is adequately described with a single underlying genetic risk factor. Rodent animal models for alcohol-related phenotypes typically target discrete aspects of the complex human AD diagnosis. Here, we review the literature derived from genetic animal models in an attempt to determine whether they support a single-factor or multiple-factor genetic structure. We conclude that there is modest support in the animal literature that alcohol tolerance and withdrawal reflect distinct genetic risk factors, in agreement with our human data. We suggest areas where more research could clarify this attempt to align the rodent and human data. PMID:21910077

  4. 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

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

    PubMed

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

    2005-12-01

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

  6. 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.

  7. 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.

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

    PubMed Central

    Phillips, Tamara J.; Shabani, Shkelzen

    2015-01-01

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

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

    PubMed

    Zhang, Yuan-Zhi; Zhong, Nanbert

    2006-11-01

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

  10. Cost-effectiveness of a school-based Tay-Sachs and cystic fibrosis genetic carrier screening program.

    PubMed

    Warren, Emma; Anderson, Rob; Proos, Anné L; Burnett, Leslie B; Barlow-Stewart, Kris; Hall, Jane

    2005-09-01

    To explore the cost-effectiveness of school-based multi-disease genetic carrier screening. Decision analysis of the cost-effectiveness of a school-based Tay-Sachs disease and cystic fibrosis genetic carrier screening program, relative to no screening. Data relating to ethnicity profile, test-accepting behavior, and screening program cost were sourced from an existing program in Sydney, Australia. Compared to no screening, the incremental cost-effectiveness of the screening program is A dollar 5,834 per additional carrier detected. This cost-effectiveness ratio is most sensitive to changes in genetic test accuracy, and the cost of laboratory assays. The results imply a cost per affected birth avoided of approximately A dollar 530,000 (approximately US dollar 371,000). This preconceptional genetic carrier screening program offers comparable cost-effectiveness to prenatal screening programs for cystic fibrosis.

  11. 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.

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

    PubMed

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

    2017-09-25

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

  13. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Matsumura, Koki; Goto, Michihiko; Hamamatsu, Yoshio

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

  16. ZATPAC: a model consortium evaluates teen programs.

    PubMed

    Owen, Kathryn; Murphy, Dana; Parsons, Chris

    2009-09-01

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

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

    PubMed Central

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

    2014-01-01

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

  18. A Linguistic Model in Component Oriented Programming

    NASA Astrophysics Data System (ADS)

    Crăciunean, Daniel Cristian; Crăciunean, Vasile

    2016-12-01

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

  19. A unified model of the standard genetic code

    PubMed Central

    Morgado, Eberto R.

    2017-01-01

    The Rodin–Ohno (RO) and the Delarue models divide the table of the genetic code into two classes of aminoacyl-tRNA synthetases (aaRSs I and II) with recognition from the minor or major groove sides of the tRNA acceptor stem, respectively. These models are asymmetric but they are biologically meaningful. On the other hand, the standard genetic code (SGC) can be derived from the primeval RNY code (R stands for purines, Y for pyrimidines and N any of them). In this work, the RO-model is derived by means of group actions, namely, symmetries represented by automorphisms, assuming that the SGC originated from a primeval RNY code. It turns out that the RO-model is symmetric in a six-dimensional (6D) hypercube. Conversely, using the same automorphisms, we show that the RO-model can lead to the SGC. In addition, the asymmetric Delarue model becomes symmetric by means of quotient group operations. We formulate isometric functions that convert the class aaRS I into the class aaRS II and vice versa. We show that the four polar requirement categories display a symmetrical arrangement in our 6D hypercube. Altogether these results cannot be attained, neither in two nor in three dimensions. We discuss the present unified 6D algebraic model, which is compatible with both the SGC (based upon the primeval RNY code) and the RO-model. PMID:28405378

  20. A unified model of the standard genetic code.

    PubMed

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

    2017-03-01

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

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

    PubMed

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

    2016-02-26

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

  2. A Genetic Algorithm Approach for Modeling a Grounding Electrode

    NASA Astrophysics Data System (ADS)

    Mishra, Arbind Kumar; Nagaoka, Naoto; Ametani, Akihiro

    This paper has proposed a genetic algorithm based approach to determine a grounding electrode model circuit composed of resistances, inductances and capacitances. The proposed methodology determines the model circuit parameters based on a general ladder circuit directly from a measured result. Transient voltages of some electrodes were measured when applying a step like current. An EMTP simulation of a transient voltage on the grounding electrode has been carried out by adopting the proposed model circuits. The accuracy of the proposed method has been confirmed to be high in comparison with the measured transient voltage.

  3. 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…

  4. 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…

  5. 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,…

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

    PubMed

    Fajfar, Iztok; Puhan, Janez; Bűrmen, Árpád

    2016-01-25

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

  7. Sleep and Development in Genetically Tractable Model Organisms

    PubMed Central

    Kayser, Matthew S.; Biron, David

    2016-01-01

    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

  8. Genetic Animal Models of Malformations of Cortical Development and Epilepsy

    PubMed Central

    Wong, Michael; Roper, Steven N.

    2015-01-01

    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, hetereotopias, 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

  9. 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

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

    PubMed

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

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

  11. 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

  12. 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.

  13. 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.

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

    PubMed

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

    2012-02-01

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

  15. 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

  16. The influence of genetic background versus commercial breeding programs on chicken immunocompetence.

    PubMed

    Emam, Mehdi; Mehrabani-Yeganeh, Hassan; Barjesteh, Neda; Nikbakht, Gholamreza; Thompson-Crispi, Kathleen; Charkhkar, Saeid; Mallard, Bonnie

    2014-01-01

    Immunocompetence of livestock plays an important role in farm profitability because it directly affects health maintenance. Genetics significantly influences the immune system, and the genotypic structure of modern fast-growing chickens has been changed, particularly after decades of breeding for higher production. Therefore, this study was designed to help determine if intensive breeding programs have adversely affected immunocompetence or whether the immune response profiles are controlled to greater extent by genetic background. Thus, 3 indigenous chicken populations from different genetic backgrounds and 2 globally available modern broiler strains, Ross 308 and Cobb 500, were evaluated for various aspects of immune response. These included antibody responses against sheep red blood cells and Brucella abortus antigen, as well as some aspects of cell-mediated immunocompetence by toe web swelling test and in vitro blood mononuclear cell proliferation. Significant differences (P < 0.05) in antibody responses to both antigens and cellular proliferation were observed among populations but not consistently between modern commercial strains versus the indigenous populations. In fact, the immune response profiles of Cobb 500 were similar to the indigenous populations, but varied compared with the other commercial strain. In addition, considerable variation was recorded between indigenous populations for all responses measured in this study. The results of this study suggest that the variation observed in immune responses between these strains of chickens is most likely due to differences in the genetic background between each strain of chicken rather than by commercial selection programs for high production.

  17. Genetic mouse models for bone studies—Strengths and limitations

    PubMed Central

    Elefteriou, Florent; Yang, Xiangli

    2012-01-01

    Mice have become a preferred model system for bone research because of their genetic and pathophysiological similarities to humans: a relatively short reproductive period, leading to relatively low cost of maintenance and the availability of the entire mouse genome sequence information. The success in producing the first transgenic mouse line that expressed rabbit β-globin protein in mouse erythrocytes three decades ago marked the beginning of the use of genetically engineered mice as model system to study human diseases. Soon afterward the development of cultured pluripotent embryonic stem cells provided the possibility of gene replacement or gene deletion in mice. These technologies have been critical to identify new genes involved in bone development, growth, remodeling, repair, and diseases, but like many other approaches, they have limitations. This review will introduce the approaches that allow the generation of transgenic mice and global or conditional (tissue-specific and inducible) mutant mice. A list of the various promoters used to achieve bone-specific gene deletion or overexpression is included. The limitations of these approaches are discussed, and general guidelines related to the analysis of genetic mouse models are provided. PMID:21907838

  18. 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.

  19. Inferring modulators of genetic interactions with epistatic nested effects models.

    PubMed

    Pirkl, Martin; Diekmann, Madeline; van der Wees, Marlies; Beerenwinkel, Niko; Fröhlich, Holger; Markowetz, Florian

    2017-04-01

    Maps of genetic interactions can dissect functional redundancies in cellular networks. Gene expression profiles as high-dimensional molecular readouts of combinatorial perturbations provide a detailed view of genetic interactions, but can be hard to interpret if different gene sets respond in different ways (called mixed epistasis). Here we test the hypothesis that mixed epistasis between a gene pair can be explained by the action of a third gene that modulates the interaction. We have extended the framework of Nested Effects Models (NEMs), a type of graphical model specifically tailored to analyze high-dimensional gene perturbation data, to incorporate logical functions that describe interactions between regulators on downstream genes and proteins. We benchmark our approach in the controlled setting of a simulation study and show high accuracy in inferring the correct model. In an application to data from deletion mutants of kinases and phosphatases in S. cerevisiae we show that epistatic NEMs can point to modulators of genetic interactions. Our approach is implemented in the R-package 'epiNEM' available from https://github.com/cbg-ethz/epiNEM and https://bioconductor.org/packages/epiNEM/.

  20. A statistical model for dissecting genomic imprinting through genetic mapping.

    PubMed

    Cui, Yuehua; Cheverud, James M; Wu, Rongling

    2007-07-01

    As a result of nonequivalent genetic contribution of maternal and paternal genomes to offsprings, genomic imprinting or called parent-of-origin effect, has been broadly identified in plants, animals and humans. Its role in shaping organism's development has been unanimously recognized. However, statistical methods for identifying imprinted quantitative trait loci (iQTL) and estimating the imprinted effect have not been well developed. In this article, we propose an efficient statistical procedure for genomewide estimating and testing the effects of significant iQTL underlying the quantitative variation of interested traits. The developed model can be applied to two different genetic cross designs, backcross and F(2) families derived from inbred lines. The proposed procedure is built within the maximum likelihood framework and implemented with the EM algorithm. Extensive simulation studies show that the proposed model is well performed in a variety of situations. To demonstrate the usefulness of the proposed approach, we apply the model to a published data in an F(2) family derived from LG/S and SM/S mouse stains. Two partially maternal imprinting iQTL are identified which regulate the growth of body weight. Our approach provides a testable framework for identifying and estimating iQTL involved in the genetic control of complex traits.

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

    PubMed

    Calhoun, Jean; Admire, Kaye S

    2005-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Gather, Malte C.; Yun, Seok Hyun

    2011-08-01

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

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

    PubMed

    Gather, Malte C; Yun, Seok Hyun

    2011-08-15

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

  4. Human Factors Engineering Program Review Model

    DTIC Science & Technology

    2004-02-01

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

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

    PubMed

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

    2015-04-08

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

  6. Exploratory Bayesian model selection for serial genetics data.

    PubMed

    Zhao, Jing X; Foulkes, Andrea S; George, Edward I

    2005-06-01

    Characterizing the process by which molecular and cellular level changes occur over time will have broad implications for clinical decision making and help further our knowledge of disease etiology across many complex diseases. However, this presents an analytic challenge due to the large number of potentially relevant biomarkers and the complex, uncharacterized relationships among them. We propose an exploratory Bayesian model selection procedure that searches for model simplicity through independence testing of multiple discrete biomarkers measured over time. Bayes factor calculations are used to identify and compare models that are best supported by the data. For large model spaces, i.e., a large number of multi-leveled biomarkers, we propose a Markov chain Monte Carlo (MCMC) stochastic search algorithm for finding promising models. We apply our procedure to explore the extent to which HIV-1 genetic changes occur independently over time.

  7. Hierarchical Stochastic Simulation Algorithm for SBML Models of Genetic Circuits

    PubMed Central

    Watanabe, Leandro H.; Myers, Chris J.

    2014-01-01

    This paper describes a hierarchical stochastic simulation algorithm, which has been implemented within iBioSim, a tool used to model, analyze, and visualize genetic circuits. Many biological analysis tools flatten out hierarchy before simulation, but there are many disadvantages associated with this approach. First, the memory required to represent the model can quickly expand in the process. Second, the flattening process is computationally expensive. Finally, when modeling a dynamic cellular population within iBioSim, inlining the hierarchy of the model is inefficient since models must grow dynamically over time. This paper discusses a new approach to handle hierarchy on the fly to make the tool faster and more memory-efficient. This approach yields significant performance improvements as compared to the former flat analysis method. PMID:25506588

  8. Multiple Magnetic Dipole Modeling Coupled with a Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Lientschnig, G.

    2012-05-01

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

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

    PubMed Central

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

    2012-01-01

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

  10. 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

  11. Identification of epilepsy stages from ECoG using genetic programming classifiers.

    PubMed

    Sotelo, Arturo; Guijarro, Enrique; Trujillo, Leonardo; Coria, Luis N; Martínez, Yuliana

    2013-11-01

    Epilepsy is a common neurological disorder, for which a great deal of research has been devoted to analyze and characterize brain activity during seizures. While this can be done by a human expert, automatic methods still lag behind. This paper analyzes neural activity captured with Electrocorticogram (ECoG), recorded through intracranial implants from Kindling model test subjects. The goal is to automatically identify the main seizure stages: Pre-Ictal, Ictal and Post-Ictal. While visually differentiating each stage can be done by an expert if the complete time-series is available, the goal here is to automatically identify the corresponding stage of short signal segments. The proposal is to pose the above task as a supervised classification problem and derive a mapping function that classifies each signal segment. Given the complexity of the signal patterns, it is difficult to a priori choose any particular classifier. Therefore, Genetic Programming (GP), a population based meta-heuristic for automatic program induction, is used to automatically search for the mapping functions. Two GP-based classifiers are used and extensively evaluated. The signals from epileptic seizures are obtained using the Kindling model of elicited epilepsy in rodent test subjects, for which a seizure was elicited and recorded on four separate days. Results show that signal segments from a single seizure can be used to derive accurate classifiers that generalize when tested on different signals from the same subject; i.e., GP can automatically produce accurate mapping functions for intra-subject classification. A large number of experiments are performed with the GP classifiers achieving good performance based on standard performance metrics. Moreover, a proof-of-concept real-world prototype is presented, where a GP classifier is transferred and hard-coded on an embedded system using a digital-to-analogue converter and a field programmable gate array, achieving a low average

  12. 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.

  13. 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

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

    PubMed

    Watkinson-Powell, Benjamin; Alphey, Nina

    2017-01-21

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

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

    PubMed

    Bilkei-Gorzo, Andras

    2014-05-01

    Progression of brain ageing is influenced by a complex interaction of genetic and environmental factors. Analysis of genetically modified animals with uniform genetic backgrounds in a standardised, controlled environment enables the dissection of critical determinants of brain ageing on a molecular level. Human and animal studies suggest that increased load of damaged macromolecules, efficacy of DNA maintenance, mitochondrial activity, and cellular stress defences are critical determinants of brain ageing. Surprisingly, mouse lines with genetic impairment of anti-oxidative capacity generally did not show enhanced cognitive ageing but rather an increased sensitivity to oxidative challenge. Mouse lines with impaired mitochondrial activity had critically short life spans or severe and rapidly progressing neurodegeneration. Strains with impaired clearance in damaged macromolecules or defects in the regulation of cellular stress defences showed alterations in the onset and progression of cognitive decline. Importantly, reduced insulin/insulin-like growth factor signalling generally increased life span but impaired cognitive functions revealing a complex interaction between ageing of the brain and of the body. Brain ageing is accompanied by an increased risk of developing Alzheimer's disease. Transgenic mouse models expressing high levels of mutant human amyloid precursor protein showed a number of symptoms and pathophysiological processes typical for early phase of Alzheimer's disease. Generally, therapeutic strategies effective against Alzheimer's disease in humans were also active in the Tg2576, APP23, APP/PS1 and 5xFAD lines, but a large number of false positive findings were also reported. The 3xtg AD model likely has the highest face and construct validity but further studies are needed. Copyright © 2013 Elsevier Inc. All rights reserved.

  16. Analysis of genetic effects of nuclear-cytoplasmic interaction on quantitative traits: genetic model for diploid plants.

    PubMed

    Han, Lide; Yang, Jian; Zhu, Jun

    2007-06-01

    A genetic model was proposed for simultaneously analyzing genetic effects of nuclear, cytoplasm, and nuclear-cytoplasmic interaction (NCI) as well as their genotype by environment (GE) interaction for quantitative traits of diploid plants. In the model, the NCI effects were further partitioned into additive and dominance nuclear-cytoplasmic interaction components. Mixed linear model approaches were used for statistical analysis. On the basis of diallel cross designs, Monte Carlo simulations showed that the genetic model was robust for estimating variance components under several situations without specific effects. Random genetic effects were predicted by an adjusted unbiased prediction (AUP) method. Data on four quantitative traits (boll number, lint percentage, fiber length, and micronaire) in Upland cotton (Gossypium hirsutum L.) were analyzed as a worked example to show the effectiveness of the model.

  17. 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

  18. Genetic and genomic approaches to assess adaptive genetic variation in plants: forest trees as a model.

    PubMed

    Gailing, Oliver; Vornam, Barbara; Leinemann, Ludger; Finkeldey, Reiner

    2009-12-01

    With the increasing availability of sequence information at putatively important genes or regulatory regions, the characterization of adaptive genetic diversity and their association with phenotypic trait variation becomes feasible for many non-model organisms such as forest trees. Especially in predominantly outcrossing forest tree populations with large effective size, a high genetic variation in relevant genes is maintained, that is the raw material for the adaptation to changing and variable environments, and likewise for plant breeding. Oaks (Quercus spp.) are excellent model species to study the adaptation of forest trees to changing environments. They show a wide geographic distribution in Europe as dominant tree species in many forests and grow under a wide range of climatic and edaphic conditions. With the availability of a growing amount of functional and expressional candidate genes, we are now able to test the functional importance of single nucleotide polymorphisms (SNPs) by associating nucleotide variation in these genes with phenotypic variation in adaptive traits in segregating or natural populations. Here, we report on quantitative trait locus (QTL), candidate gene and association mapping approaches that are applicable to characterize gene markers and SNPs associated with variation in adaptive traits, such as bud burst, drought resistance and other traits showing selective responses to environmental change and stress. Because genome-wide association mapping studies are not feasible because of the enormous amount of SNP markers required in outcrossing trees with high recombination rates, the success of such an approach depends largely on the reasonable selection of candidate genes.

  19. The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis.

    PubMed

    Malosetti, Marcos; Ribaut, Jean-Marcel; van Eeuwijk, Fred A

    2013-01-01

    Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay-Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as "Appendix."

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

    PubMed

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

    2009-01-01

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

  1. Behavioral phenotypes of genetic mouse models of autism.

    PubMed

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

    2016-01-01

    More than a hundred de novo single gene mutations and copy-number variants have been implicated in autism, each occurring in a small subset of cases. Mutant mouse models with syntenic mutations offer research tools to gain an understanding of the role of each gene in modulating biological and behavioral phenotypes relevant to autism. Knockout, knockin and transgenic mice incorporating risk gene mutations detected in autism spectrum disorder and comorbid neurodevelopmental disorders are now widely available. At present, autism spectrum disorder is diagnosed solely by behavioral criteria. We developed a constellation of mouse behavioral assays designed to maximize face validity to the types of social deficits and repetitive behaviors that are central to an autism diagnosis. Mouse behavioral assays for associated symptoms of autism, which include cognitive inflexibility, anxiety, hyperactivity, and unusual reactivity to sensory stimuli, are frequently included in the phenotypic analyses. Over the past 10 years, we and many other laboratories around the world have employed these and additional behavioral tests to phenotype a large number of mutant mouse models of autism. In this review, we highlight mouse models with mutations in genes that have been identified as risk genes for autism, which work through synaptic mechanisms and through the mTOR signaling pathway. Robust, replicated autism-relevant behavioral outcomes in a genetic mouse model lend credence to a causal role for specific gene contributions and downstream biological mechanisms in the etiology of autism. © 2015 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

  2. Effective Genetic-Risk Prediction Using Mixed Models

    PubMed Central

    Golan, David; Rosset, Saharon

    2014-01-01

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

  3. Unified Program Design: Organizing Existing Programming Models, Delivery Options, and Curriculum

    ERIC Educational Resources Information Center

    Rubenstein, Lisa DaVia; Ridgley, Lisa M.

    2017-01-01

    A persistent problem in the field of gifted education has been the lack of categorization and delineation of gifted programming options. To address this issue, we propose Unified Program Design as a structural framework for gifted program models. This framework defines gifted programs as the combination of delivery methods and curriculum models.…

  4. The National Animal Germplasm Program: challenges and opportunities for poultry genetic resources.

    PubMed

    Blackburn, H D

    2006-02-01

    In the United States, poultry genetic resources have consolidated because of economic pressures. Such consolidations can potentially jeopardize the poultry industry and the ability of research communities to respond to future challenges. To address the loss of genetic resources for all livestock and aquatic species, USDA established the National Animal Germplasm Program (NAGP) in 1999. Since the initiation of NAGP, population surveys have been conducted on nonindustrial chicken and turkey breeds. These surveys not only provide insight into breed status, but also serve as a benchmark for future comparisons. The survey results revealed that 20 chicken breeds and 9 turkey breeds were in various stages of being lost. The NAGP has initiated an ex situ repository for cryopreserved germplasm and tissue that already contains 59 chicken lines and 2,915 tissue samples. As the NAGP, along with its industry and university partners, continues developing the ex situ collection, there are research opportunities in cryopreserved tissue utilization and studies of genetic diversity. For cryopreserved tissues, several key research areas include improving the cryopreservation protocols for rooster and tom semen by using cryoprotectants other than glycerol and utilizing embryonic cells. Although surveys have been conducted on public research lines and rare breeds, there is a void in understanding the level of genetic diversity present in U.S. poultry populations. Therefore, an opportunity exists to perform a series of genetic diversity studies using molecular- based approaches. Such an evaluation can help clarify population differences between research lines and rare breeds and, thereby, facilitate conservation strategies. There appears to be growing consumer interest in poultry products derived from heritage breeds and/or poultry raised in nonindustrial production systems. Although the depth of such market trends is unknown, such an interest may provide an important niche for rare

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

    PubMed

    Jamieson, Ian G

    2011-02-01

    The number of individuals translocated and released as part of a reintroduction is often small, as is the final established population, because the reintroduction site is typically small. Small founder and small resulting populations can result in population bottlenecks, which are associated with increased rates of inbreeding and loss of genetic diversity, both of which can affect the long-term viability of reintroduced populations. I used information derived from pedigrees of four monogamous bird species reintroduced onto two different islands (220 and 259 ha) in New Zealand to compare the pattern of inbreeding and loss of genetic diversity among the reintroduced populations. Although reintroduced populations founded with few individuals had higher levels of inbreeding, as predicted, other factors, including biased sex ratio and skewed breeding success, contributed to high levels of inbreeding and loss of genetic diversity. Of the 10-58 individuals released, 4-25 genetic founders contributed at least one living descendent and yielded approximately 3-11 founder-genome equivalents (number of genetic founders assuming an equal contribution of offspring and no random loss of alleles across generations) after seven breeding seasons. This range is much lower than the 20 founder-genome equivalents recommended for captive-bred populations. Although the level of inbreeding in one reintroduced population initially reached three times that of a closely related species, the long-term estimated rate of inbreeding of this one population was approximately one-third that of the other species due to differences in carrying capacities of the respective reintroduction sites. The increasing number of reintroductions to suitable areas that are smaller than those I examined here suggests that it might be useful to develop long-term strategies and guidelines for reintroduction programs, which would minimize inbreeding and maintain genetic diversity. ©2010 Society for Conservation

  6. 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…

  7. 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…

  8. 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…

  9. 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.

  10. Cost-effectiveness analysis of the genetic screening program for familial hypercholesterolemia in The Netherlands.

    PubMed

    Wonderling, David; Umans-Eckenhausen, Marina A W; Marks, Dalya; Defesche, Joep C; Kastelein, John J P; Thorogood, Margaret

    2004-02-01

    Familial hypercholesterolemia (FH) is associated with pronounced atherosclerosis leading to premature cardiovascular disease and untimely death. Despite the availability of effective preventative drug treatments, many affected individuals remain undiagnosed and untreated until they become symptomatic with cardiovascular disease. To assess the cost-effectiveness of systematic genetic screening of family members of persons diagnosed with FH, an analysis was conducted using data from a nationwide screening program for the identification of individuals with FH, instituted in The Netherlands in 1994, and from other sources. There was DNA testing of families with a known genetic defect to identify new cases of FH in the presymptomatic stage of the disease. After identification, most newly identified patients were started on cholesterol-lowering statin treatment. On average, new cases diagnosed by the screening program gained 3.3 years of life each. Twenty-six myocardial infarctions would be avoided for every 100 persons treated with statins between the ages of 18 and 60 years. The average total lifetime incremental costs, over all age ranges and both sexes, including costs for screening and testing, lifetime drug treatment, and treatment of cardiovascular events, was US dollars 7500 per new case identified. Cost per life-year gained was US dollars 8700. Therefore, systematic genetic screening of family members of persons diagnosed with FH is cost-effective in The Netherlands and should be considered for other settings.

  11. 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.

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

    PubMed

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

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

  13. Invited review: Genetics and modeling of milk coagulation properties.

    PubMed

    Bittante, G; Penasa, M; Cecchinato, A

    2012-12-01

    Milk coagulation properties (MCP) are conventionally measured using computerized renneting meters, mechanical or optical devices that record curd firmness over time (CF(t)). The traditional MCP are rennet coagulation time (RCT, min), curd firmness (a(30), mm), and curd-firming time (k(20), min). The milk of different ruminant species varies in terms of CF(t) pattern. Milk from Holstein-Friesian and some Scandinavian cattle breeds yields higher proportions of noncoagulating samples, samples with longer RCT and lower a(30), and samples for which k(20) is not estimable, than does milk from Brown Swiss, Simmental, and other local Alpine breeds. The amount, proportion, and genetic variants (especially κ-casein) of milk protein fractions strongly influence MCP and explain variable proportions of the observed differences among breeds and among individuals of the same breed. In addition, other major genes have been shown to affect MCP. Individual repeatability of MCP is high, whereas any herd effect is low; thus, the improvement of MCP should be based principally on selection. Exploitable additive genetic variation in MCP exists and has been assessed using different breeds in various countries. Several models have been formulated that either handle noncoagulating samples or not. The heritability of MCP is similar to that of other milk quality traits and is higher than the heritability of milk yield. Rennet coagulation time and a(30) are highly correlated, both phenotypically and genetically. This means that the use of a(30) data does not add valuable information to that obtainable from RCT; both traits are genetically correlated mainly with milk acidity. Moreover, a(30) is correlated with casein content. The major limitations of traditional MCP can be overcome by prolonging the observation period and by using a novel CF(t) modeling, which uses all available information provided by computerized renneting meters and allows the estimation of RCT, the potential asymptotic

  14. 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.

  15. Modelling of genetically engineered microorganisms introduction in closed artificial microcosms.

    PubMed

    Pechurkin, N S; Brilkov, A V; Ganusov, V V; Kargatova, T V; Maksimova, E E; Popova LYu

    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.

  16. Annual fish as a genetic model for aging.

    PubMed

    Herrera, Michael; Jagadeeswaran, Pudur

    2004-02-01

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

  17. In vivo Drosophilia genetic model for calcium oxalate nephrolithiasis

    PubMed Central

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

    2012-01-01

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

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

    PubMed

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

    2012-12-01

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

  19. 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.

  20. 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.