Genetic Programming for Sea Level Predictions in an Island Environment

Directory of Open Access Journals (Sweden)

M.A. Ghorbani

2010-03-01

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

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

Science.gov (United States)

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

2018-06-04

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

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

Science.gov (United States)

Ravansalar, Masoud; Rajaee, Taher; Kisi, Ozgur

2017-06-01

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

Genetic programming theory and practice XII

CERN Document Server

Riolo, Rick; Kotanchek, Mark

2015-01-01

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: gene expression regulation, novel genetic models for glaucoma, inheritable epigenetics, combinators in genetic programming, sequential symbolic regression, system dynamics, sliding window symbolic regression, large feature problems, alignment in the error space, HUMIE winners, Boolean multiplexer funct

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

Science.gov (United States)

Moore, J H

1995-06-01

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

Evolving Rule-Based Systems in two Medical Domains using Genetic Programming

DEFF Research Database (Denmark)

Tsakonas, A.; Dounias, G.; Jantzen, Jan

2004-01-01

We demonstrate, compare and discuss the application of two genetic programming methodologies for the construction of rule-based systems in two medical domains: the diagnosis of Aphasia's subtypes and the classification of Pap-Smear Test examinations. The first approach consists of a scheme...

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

Science.gov (United States)

Dewhurst, D. G.; And Others

1989-01-01

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

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

Science.gov (United States)

Shao, Yunru; Liu, Shuling; Grinzaid, Karen

2015-04-01

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

Multigene Genetic Programming for Estimation of Elastic Modulus of Concrete

Directory of Open Access Journals (Sweden)

Alireza Mohammadi Bayazidi

2014-01-01

Full Text Available This paper presents a new multigene genetic programming (MGGP approach for estimation of elastic modulus of concrete. The MGGP technique models the elastic modulus behavior by integrating the capabilities of standard genetic programming and classical regression. The main aim is to derive precise relationships between the tangent elastic moduli of normal and high strength concrete and the corresponding compressive strength values. Another important contribution of this study is to develop a generalized prediction model for the elastic moduli of both normal and high strength concrete. Numerous concrete compressive strength test results are obtained from the literature to develop the models. A comprehensive comparative study is conducted to verify the performance of the models. The proposed models perform superior to the existing traditional models, as well as those derived using other powerful soft computing tools.

A genetic algorithm approach to recognition and data mining

Energy Technology Data Exchange (ETDEWEB)

Punch, W.F.; Goodman, E.D.; Min, Pei [Michigan State Univ., East Lansing, MI (United States)] [and others

1996-12-31

We review here our use of genetic algorithm (GA) and genetic programming (GP) techniques to perform {open_quotes}data mining,{close_quotes} the discovery of particular/important data within large datasets, by finding optimal data classifications using known examples. Our first experiments concentrated on the use of a K-nearest neighbor algorithm in combination with a GA. The GA selected weights for each feature so as to optimize knn classification based on a linear combination of features. This combined GA-knn approach was successfully applied to both generated and real-world data. We later extended this work by substituting a GP for the GA. The GP-knn could not only optimize data classification via linear combinations of features but also determine functional relationships among the features. This allowed for improved performance and new information on important relationships among features. We review the effectiveness of the overall approach on examples from biology and compare the effectiveness of the GA and GP.

Fuzzy Multi-objective Linear Programming Approach

Directory of Open Access Journals (Sweden)

Amna Rehmat

2007-07-01

Full Text Available Traveling salesman problem (TSP is one of the challenging real-life problems, attracting researchers of many fields including Artificial Intelligence, Operations Research, and Algorithm Design and Analysis. The problem has been well studied till now under different headings and has been solved with different approaches including genetic algorithms and linear programming. Conventional linear programming is designed to deal with crisp parameters, but information about real life systems is often available in the form of vague descriptions. Fuzzy methods are designed to handle vague terms, and are most suited to finding optimal solutions to problems with vague parameters. Fuzzy multi-objective linear programming, an amalgamation of fuzzy logic and multi-objective linear programming, deals with flexible aspiration levels or goals and fuzzy constraints with acceptable deviations. In this paper, a methodology, for solving a TSP with imprecise parameters, is deployed using fuzzy multi-objective linear programming. An example of TSP with multiple objectives and vague parameters is discussed.

Developing robotic behavior using a genetic programming model

International Nuclear Information System (INIS)

Pryor, R.J.

1998-01-01

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

Modeling the Isentropic Head Value of Centrifugal Gas Compressor using Genetic Programming

Directory of Open Access Journals (Sweden)

Safiyullah Ferozkhan

2016-01-01

Full Text Available Gas compressor performance is vital in oil and gas industry because of the equipment criticality which requires continuous operations. Plant operators often face difficulties in predicting appropriate time for maintenance and would usually rely on time based predictive maintenance intervals as recommended by original equipment manufacturer (OEM. The objective of this work is to develop the computational model to find the isentropic head value using genetic programming. The isentropic head value is calculated from the OEM performance chart. Inlet mass flow rate and speed of the compressor are taken as the input value. The obtained results from the GP computational models show good agreement with experimental and target data with the average prediction error of 1.318%. The genetic programming computational model will assist machinery engineers to quantify performance deterioration of gas compressor and the results from this study will be then utilized to estimate future maintenance requirements based on the historical data. In general, this genetic programming modelling provides a powerful solution for gas compressor operators to realize predictive maintenance approach in their operations.

Multiobjective genetic algorithm approaches to project scheduling under risk

OpenAIRE

Kılıç, Murat; Kilic, Murat

2003-01-01

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

Testing the Structure of Hydrological Models using Genetic Programming

Science.gov (United States)

Selle, B.; Muttil, N.

2009-04-01

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

Testing the structure of a hydrological model using Genetic Programming

Science.gov (United States)

Selle, Benny; Muttil, Nitin

2011-01-01

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

Extracting classification rules from an informatic security incidents repository by genetic programming

Directory of Open Access Journals (Sweden)

Carlos Javier Carvajal Montealegre

2015-04-01

Full Text Available This paper describes the data mining process to obtain classification rules over an information security incident data collection, explaining in detail the use of genetic programming as a mean to model the incidents behavior and representing such rules as decision trees. The described mining process includes several tasks, such as the GP (Genetic Programming approach evaluation, the individual's representation and the algorithm parameters tuning to upgrade the performance. The paper concludes with the result analysis and the description of the rules obtained, suggesting measures to avoid the occurrence of new informatics attacks. This paper is a part of the thesis work degree: Information Security Incident Analytics by Data Mining for Behavioral Modeling and Pattern Recognition (Carvajal, 2012.

Genetic Programming for the Generation of Crisp and Fuzzy Rule Bases in Classification and Diagnosis of Medical Data

DEFF Research Database (Denmark)

Dounias, George; Tsakonas, Athanasios; Jantzen, Jan

2002-01-01

This paper demonstrates two methodologies for the construction of rule-based systems in medical decision making. The first approach consists of a method combining genetic programming and heuristic hierarchical rule-base construction. The second model is composed by a strongly-typed genetic...

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

Directory of Open Access Journals (Sweden)

Turky N. Alotaiby

2017-01-01

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

Genetics Education in Nurse Residency Programs: A Natural Fit.

Science.gov (United States)

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

2017-08-01

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

Extraction of Static and Dynamic Reservoir Operation Rules by Genetic Programming

Directory of Open Access Journals (Sweden)

Habib Akbari Alashti

2014-11-01

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

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

Science.gov (United States)

Danandeh Mehr, Ali; Kahya, Ercan

2017-06-01

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

A “genetics first” approach to selection

Science.gov (United States)

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

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

Science.gov (United States)

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

2017-01-01

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

Genetic approaches in comparative and evolutionary physiology

Science.gov (United States)

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

2015-01-01

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

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

NARCIS (Netherlands)

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

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

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

Science.gov (United States)

Kenen, R H; Schmidt, R M

1978-01-01

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

Simulation Approach for Timing Analysis of Genetic Logic Circuits

DEFF Research Database (Denmark)

Baig, Hasan; Madsen, Jan

2017-01-01

in a manner similar to electronic logic circuits, but they are much more stochastic and hence much harder to characterize. In this article, we introduce an approach to analyze the threshold value and timing of genetic logic circuits. We show how this approach can be used to analyze the timing behavior...... of single and cascaded genetic logic circuits. We further analyze the timing sensitivity of circuits by varying the degradation rates and concentrations. Our approach can be used not only to characterize the timing behavior but also to analyze the timing constraints of cascaded genetic logic circuits...

A genetic programming approach to oral cancer prognosis.

Science.gov (United States)

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

2016-01-01

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

A genetic programming approach to oral cancer prognosis

Directory of Open Access Journals (Sweden)

Mei Sze Tan

2016-09-01

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

Routine human-competitive machine intelligence by means of genetic programming

Science.gov (United States)

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

2004-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

Pryor, Richard J.; Schaller, Mark J.

2003-10-01

Genetic programming is a powerful methodology for automatically producing solutions to problems in a variety of domains. It has been used successfully to develop behaviors for RoboCup soccer players and simple combat agents. We will attempt to use genetic programming to solve a problem in the domain of strategic combat, keeping in mind the end goal of developing sophisticated behaviors for compound defense and infiltration. The simplified problem at hand is that of two armed agents in a small room, containing obstacles, fighting against each other for survival. The base case and three changes are considered: a memory of positions using stacks, context-dependent genetic programming, and strongly typed genetic programming. Our work demonstrates slight improvements from the first two techniques, and no significant improvement from the last.

A multiobjective approach to the genetic code adaptability problem.

Science.gov (United States)

de Oliveira, Lariza Laura; de Oliveira, Paulo S L; Tinós, Renato

2015-02-19

The organization of the canonical code has intrigued researches since it was first described. If we consider all codes mapping the 64 codes into 20 amino acids and one stop codon, there are more than 1.51×10(84) possible genetic codes. The main question related to the organization of the genetic code is why exactly the canonical code was selected among this huge number of possible genetic codes. Many researchers argue that the organization of the canonical code is a product of natural selection and that the code's robustness against mutations would support this hypothesis. In order to investigate the natural selection hypothesis, some researches employ optimization algorithms to identify regions of the genetic code space where best codes, according to a given evaluation function, can be found (engineering approach). The optimization process uses only one objective to evaluate the codes, generally based on the robustness for an amino acid property. Only one objective is also employed in the statistical approach for the comparison of the canonical code with random codes. We propose a multiobjective approach where two or more objectives are considered simultaneously to evaluate the genetic codes. In order to test our hypothesis that the multiobjective approach is useful for the analysis of the genetic code adaptability, we implemented a multiobjective optimization algorithm where two objectives are simultaneously optimized. Using as objectives the robustness against mutation with the amino acids properties polar requirement (objective 1) and robustness with respect to hydropathy index or molecular volume (objective 2), we found solutions closer to the canonical genetic code in terms of robustness, when compared with the results using only one objective reported by other authors. Using more objectives, more optimal solutions are obtained and, as a consequence, more information can be used to investigate the adaptability of the genetic code. The multiobjective approach

Applications of genetic programming in cancer research.

Science.gov (United States)

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

2009-02-01

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

Therapeutic approaches to genetic disorders

African Journals Online (AJOL)

salah

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

A Heuristics Approach for Classroom Scheduling Using Genetic Algorithm Technique

Science.gov (United States)

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

2018-04-01

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

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

OpenAIRE

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

2018-01-01

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

A Constraint programming-based genetic algorithm for capacity output optimization

Directory of Open Access Journals (Sweden)

Kate Ean Nee Goh

2014-10-01

Full Text Available Purpose: The manuscript presents an investigation into a constraint programming-based genetic algorithm for capacity output optimization in a back-end semiconductor manufacturing company.Design/methodology/approach: In the first stage, constraint programming defining the relationships between variables was formulated into the objective function. A genetic algorithm model was created in the second stage to optimize capacity output. Three demand scenarios were applied to test the robustness of the proposed algorithm.Findings: CPGA improved both the machine utilization and capacity output once the minimum requirements of a demand scenario were fulfilled. Capacity outputs of the three scenarios were improved by 157%, 7%, and 69%, respectively.Research limitations/implications: The work relates to aggregate planning of machine capacity in a single case study. The constraints and constructed scenarios were therefore industry-specific.Practical implications: Capacity planning in a semiconductor manufacturing facility need to consider multiple mutually influenced constraints in resource availability, process flow and product demand. The findings prove that CPGA is a practical and an efficient alternative to optimize the capacity output and to allow the company to review its capacity with quick feedback.Originality/value: The work integrates two contemporary computational methods for a real industry application conventionally reliant on human judgement.

Comparative Approaches to Genetic Discrimination: Chasing Shadows?

Science.gov (United States)

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

2017-05-01

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

Dynamic traffic assignment : genetic algorithms approach

Science.gov (United States)

1997-01-01

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

Experimental control of a fluidic pinball using genetic programming

Science.gov (United States)

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

2017-11-01

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

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

Science.gov (United States)

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

2018-02-01

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

An imaging genetics approach to understanding social influence

Directory of Open Access Journals (Sweden)

Emily eFalk

2012-06-01

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

An imaging genetics approach to understanding social influence.

Science.gov (United States)

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

2012-01-01

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

A Genetic Programming Method for the Identification of Signal Peptides and Prediction of Their Cleavage Sites

Directory of Open Access Journals (Sweden)

David Lennartsson

2004-01-01

Full Text Available A novel approach to signal peptide identification is presented. We use an evolutionary algorithm for automatic evolution of classification programs, so-called programmatic motifs. The variant of evolutionary algorithm used is called genetic programming where a population of solution candidates in the form of full computer programs is evolved, based on training examples consisting of signal peptide sequences. The method is compared with a previous work using artificial neural network (ANN approaches. Some advantages compared to ANNs are noted. The programmatic motif can perform computational tasks beyond that of feed-forward neural networks and has also other advantages such as readability. The best motif evolved was analyzed and shown to detect the h-region of the signal peptide. A powerful parallel computer cluster was used for the experiment.

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Directory of Open Access Journals (Sweden)

Chandra Nagasuma R

2009-02-01

Full Text Available Abstract Background A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN from transcript profiling data. Results The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting problem and solved finally by formulating a Linear Program (LP. A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known

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

Energy Technology Data Exchange (ETDEWEB)

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

2006-09-15

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

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

International Nuclear Information System (INIS)

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

2006-01-01

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

The sheep blowfly genetic control program in Australia

International Nuclear Information System (INIS)

Foster, Geoffrey G.

1989-01-01

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

Primer on molecular genetics. DOE Human Genome Program

Energy Technology Data Exchange (ETDEWEB)

1992-04-01

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

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

Science.gov (United States)

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

2017-09-01

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

Guidelines on the use of molecular genetics in reintroduction programs

Science.gov (United States)

Michael K. Schwartz

2005-01-01

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

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

International Nuclear Information System (INIS)

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

2017-01-01

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

A Hybrid Genetic Algorithm Approach for Optimal Power Flow

Directory of Open Access Journals (Sweden)

Sydulu Maheswarapu

2011-08-01

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

Non-linear nuclear engineering models as genetic programming application

International Nuclear Information System (INIS)

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

1997-01-01

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

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

Science.gov (United States)

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

1995-11-01

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

Considering genetic characteristics in German Holstein breeding programs.

Science.gov (United States)

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

2016-01-01

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

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

Science.gov (United States)

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

2018-03-01

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

Intra-Day Trading System Design Based on the Integrated Model of Wavelet De-Noise and Genetic Programming

Directory of Open Access Journals (Sweden)

Hongguang Liu

2016-12-01

Full Text Available Technical analysis has been proved to be capable of exploiting short-term fluctuations in financial markets. Recent results indicate that the market timing approach beats many traditional buy-and-hold approaches in most of the short-term trading periods. Genetic programming (GP was used to generate short-term trade rules on the stock markets during the last few decades. However, few of the related studies on the analysis of financial time series with genetic programming considered the non-stationary and noisy characteristics of the time series. In this paper, to de-noise the original financial time series and to search profitable trading rules, an integrated method is proposed based on the Wavelet Threshold (WT method and GP. Since relevant information that affects the movement of the time series is assumed to be fully digested during the market closed periods, to avoid the jumping points of the daily or monthly data, in this paper, intra-day high-frequency time series are used to fully exploit the short-term forecasting advantage of technical analysis. To validate the proposed integrated approach, an empirical study is conducted based on the China Securities Index (CSI 300 futures in the emerging China Financial Futures Exchange (CFFEX market. The analysis outcomes show that the wavelet de-noise approach outperforms many comparative models.

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

Science.gov (United States)

Powell, Jeffrey R; Tabachnick, Walter J

2014-06-01

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

Polyglot programming in applications used for genetic data analysis.

Science.gov (United States)

Nowak, Robert M

2014-01-01

Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development.

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

Directory of Open Access Journals (Sweden)

Mauro Castelli

2015-01-01

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

Electrochemical impedance spectroscopy of supercapacitors: A novel analysis approach using evolutionary programming

Science.gov (United States)

Oz, Alon; Hershkovitz, Shany; Tsur, Yoed

2014-11-01

In this contribution we present a novel approach to analyze impedance spectroscopy measurements of supercapacitors. Transforming the impedance data into frequency-dependent capacitance allows us to use Impedance Spectroscopy Genetic Programming (ISGP) in order to find the distribution function of relaxation times (DFRT) of the processes taking place in the tested device. Synthetic data was generated in order to demonstrate this technique and a model for supercapacitor ageing process has been obtained.

Genetic variability of broodstocks of restocking programs in Brazil

Directory of Open Access Journals (Sweden)

Nelson Lopera-Barrero

2015-09-01

Full Text Available Objective. The aim of this study was evaluate the genetic diversity of the following broodstocks: piapara (Leporinus elongatus, dourado (Salminus brasiliensis, jundiá (Rhamdia quelen and cachara (Pseudoplatystoma fasciatum already useful for restocking programs in the Paranapanema, Iguaçu and Paraná Brazilian Rivers. Materials and methods. Samples from the caudal fin of 122 fish were analyzed. DNA was extracted by NaCl protocol. PCR products were separated by a horizontal agarose gel electrophoresis. The fragments were visualized by staining with ethidium bromide. Results. The amplification of 25 primers generated different fragments in studied species that allowed characterizing 440 fragments of 100-2900 bp. High percentage of polymorphic fragments (66.67 to 86.29, Shannon index (0.365 to 0.486 and genetic diversity of Nei (0.248 to 0.331 were detected. Conclusions. The level of genetic variability in the broodstocks was adequate for allowing their use in restocking programs in the studied Rivers. However, periodical monitoring studies of genetic variability in these stocks, the mating system, reproductive system and general management must be made to guarantee the preservation of wild populations.

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

OpenAIRE

Zhao, Kai; Yang, Shengxiang; Wang, Dingwei

1998-01-01

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

Feature extraction from multiple data sources using genetic programming.

Energy Technology Data Exchange (ETDEWEB)

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

2002-01-01

Feature extration from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. The tool used is the GENetic Imagery Exploitation (GENIE) software, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land-cover features including towns, grasslands, wild fire burn scars, and several types of forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.

System Response Analysis and Model Order Reduction, Using Conventional Method, Bond Graph Technique and Genetic Programming

Directory of Open Access Journals (Sweden)

Lubna Moin

2009-04-01

Full Text Available This research paper basically explores and compares the different modeling and analysis techniques and than it also explores the model order reduction approach and significance. The traditional modeling and simulation techniques for dynamic systems are generally adequate for single-domain systems only, but the Bond Graph technique provides new strategies for reliable solutions of multi-domain system. They are also used for analyzing linear and non linear dynamic production system, artificial intelligence, image processing, robotics and industrial automation. This paper describes a unique technique of generating the Genetic design from the tree structured transfer function obtained from Bond Graph. This research work combines bond graphs for model representation with Genetic programming for exploring different ideas on design space tree structured transfer function result from replacing typical bond graph element with their impedance equivalent specifying impedance lows for Bond Graph multiport. This tree structured form thus obtained from Bond Graph is applied for generating the Genetic Tree. Application studies will identify key issues and importance for advancing this approach towards becoming on effective and efficient design tool for synthesizing design for Electrical system. In the first phase, the system is modeled using Bond Graph technique. Its system response and transfer function with conventional and Bond Graph method is analyzed and then a approach towards model order reduction is observed. The suggested algorithm and other known modern model order reduction techniques are applied to a 11th order high pass filter [1], with different approach. The model order reduction technique developed in this paper has least reduction errors and secondly the final model retains structural information. The system response and the stability analysis of the system transfer function taken by conventional and by Bond Graph method is compared and

Using genetic programming to find Lyapunov functions

NARCIS (Netherlands)

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

2001-01-01

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

Academic training: From Evolution Theory to Parallel and Distributed Genetic Programming

CERN Multimedia

2007-01-01

2006-2007 ACADEMIC TRAINING PROGRAMME LECTURE SERIES 15, 16 March From 11:00 to 12:00 - Main Auditorium, bldg. 500 From Evolution Theory to Parallel and Distributed Genetic Programming F. FERNANDEZ DE VEGA / Univ. of Extremadura, SP Lecture No. 1: From Evolution Theory to Evolutionary Computation Evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) involving combinatorial optimization problems, which are based to some degree on the evolution of biological life in the natural world. In this tutorial we will review the source of inspiration for this metaheuristic and its capability for solving problems. We will show the main flavours within the field, and different problems that have been successfully solved employing this kind of techniques. Lecture No. 2: Parallel and Distributed Genetic Programming The successful application of Genetic Programming (GP, one of the available Evolutionary Algorithms) to optimization problems has encouraged an ...

Tracking the Genetic Stability of a Honey Bee (Hymenoptera: Apidae) Breeding Program With Genetic Markers.

Science.gov (United States)

Bourgeois, Lelania; Beaman, Lorraine

2017-08-01

A genetic stock identification (GSI) assay was developed in 2008 to distinguish Russian honey bees from other honey bee stocks that are commercially produced in the United States. Probability of assignment (POA) values have been collected and maintained since the stock release in 2008 to the Russian Honey Bee Breeders Association. These data were used to assess stability of the breeding program and the diversity levels of the contemporary breeding stock through comparison of POA values and genetic diversity parameters from the initial release to current values. POA values fluctuated throughout 2010-2016, but have recovered to statistically similar levels in 2016 (POA(2010) = 0.82, POA(2016) = 0.74; P = 0.33). Genetic diversity parameters (i.e., allelic richness and gene diversity) in 2016 also remained at similar levels when compared to those in 2010. Estimates of genetic structure revealed stability (FST(2009/2016) = 0.0058) with a small increase in the estimate of the inbreeding coefficient (FIS(2010) = 0.078, FIS(2016) = 0.149). The relationship among breeding lines, based on genetic distance measurement, was similar in 2008 and 2016 populations, but with increased homogeneity among lines (i.e., decreased genetic distance). This was expected based on the closed breeding system used for Russian honey bees. The successful application of the GSI assay in a commercial breeding program demonstrates the utility and stability of such technology to contribute to and monitor the genetic integrity of a breeding stock of an insect species. Published by Oxford University Press on behalf of Entomological Society of America 2017. This work is written by US Government employees and is in the public domain in the US.

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

Science.gov (United States)

Ahmed, Soha; Zhang, Mengjie; Peng, Lifeng

2014-07-01

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

Education and certification of genetic counselors.

Science.gov (United States)

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

1999-01-01

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

Programming peptidomimetic syntheses by translating genetic codes designed de novo.

Science.gov (United States)

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

2003-05-27

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

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

International Nuclear Information System (INIS)

Caldas, Gustavo Henrique Flores; Schirru, Roberto

2002-01-01

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

Genetic programming theory and practice X

CERN Document Server

Riolo, Rick; Ritchie, Marylyn D; Moore, Jason H

2013-01-01

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: evolutionary constraints, relaxation of selection mechanisms, diversity preservation strategies, flexing fitness evaluation, evolution in dynamic environments, multi-objective and multi-modal selection, foundations of evolvability, evolvable and adaptive evolutionary operators, foundation of  injecting

Review: domestic animal forensic genetics - biological evidence, genetic markers, analytical approaches and challenges.

Science.gov (United States)

Kanthaswamy, S

2015-10-01

This review highlights the importance of domestic animal genetic evidence sources, genetic testing, markers and analytical approaches as well as the challenges this field is facing in view of the de facto 'gold standard' human DNA identification. Because of the genetic similarity between humans and domestic animals, genetic analysis of domestic animal hair, saliva, urine, blood and other biological material has generated vital investigative leads that have been admitted into a variety of court proceedings, including criminal and civil litigation. Information on validated short tandem repeat, single nucleotide polymorphism and mitochondrial DNA markers and public access to genetic databases for forensic DNA analysis is becoming readily available. Although the fundamental aspects of animal forensic genetic testing may be reliable and acceptable, animal forensic testing still lacks the standardized testing protocols that human genetic profiling requires, probably because of the absence of monetary support from government agencies and the difficulty in promoting cooperation among competing laboratories. Moreover, there is a lack in consensus about how to best present the results and expert opinion to comply with court standards and bear judicial scrutiny. This has been the single most persistent challenge ever since the earliest use of domestic animal forensic genetic testing in a criminal case in the mid-1990s. Crime laboratory accreditation ensures that genetic test results have the courts' confidence. Because accreditation requires significant commitments of effort, time and resources, the vast majority of animal forensic genetic laboratories are not accredited nor are their analysts certified forensic examiners. The relevance of domestic animal forensic genetics in the criminal justice system is undeniable. However, further improvements are needed in a wide range of supporting resources, including standardized quality assurance and control protocols for sample

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

Science.gov (United States)

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

2018-02-01

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

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

Science.gov (United States)

Beaver, Kevin M; Jackson, Dylan B; Flesher, Dillon

2014-01-01

During the past couple of decades, the amount of research examining the genetic underpinnings to antisocial behaviors, including crime, has exploded. Findings from this body of work have generated a great deal of information linking genetics to criminal involvement. As a partial result, there is now a considerable amount of interest in how these findings should be integrated into the criminal justice system. In the current paper, we outline the potential ways that genetic information can be used to increase the effectiveness of treatment programs designed to reduce recidivism among offenders. We conclude by drawing attention to how genetic information can be used by rehabilitation programs to increase program effectiveness, reduce offender recidivism rates, and enhance public safety.

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

Science.gov (United States)

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

2017-12-16

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

On Generating Optimal Signal Probabilities for Random Tests: A Genetic Approach

Directory of Open Access Journals (Sweden)

M. Srinivas

1996-01-01

Full Text Available Genetic Algorithms are robust search and optimization techniques. A Genetic Algorithm based approach for determining the optimal input distributions for generating random test vectors is proposed in the paper. A cost function based on the COP testability measure for determining the efficacy of the input distributions is discussed. A brief overview of Genetic Algorithms (GAs and the specific details of our implementation are described. Experimental results based on ISCAS-85 benchmark circuits are presented. The performance of our GAbased approach is compared with previous results. While the GA generates more efficient input distributions than the previous methods which are based on gradient descent search, the overheads of the GA in computing the input distributions are larger.

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

International Nuclear Information System (INIS)

Telnov, I.

2006-01-01

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

Forecasting Shaharchay River Flow in Lake Urmia Basin using Genetic Programming and M5 Model Tree

Directory of Open Access Journals (Sweden)

S. Samadianfard

2017-01-01

Full Text Available Introduction: Precise prediction of river flows is the key factor for proper planning and management of water resources. Thus, obtaining the reliable methods for predicting river flows has great importance in water resource engineering. In the recent years, applications of intelligent methods such as artificial neural networks, fuzzy systems and genetic programming in water science and engineering have been grown extensively. These mentioned methods are able to model nonlinear process of river flows without any need to geometric properties. A huge number of studies have been reported in the field of using intelligent methods in water resource engineering. For example, Noorani and Salehi (23 presented a model for predicting runoff in Lighvan basin using adaptive neuro-fuzzy network and compared the performance of it with neural network and fuzzy inference methods in east Azerbaijan, Iran. Nabizadeh et al. (21 used fuzzy inference system and adaptive neuro-fuzzy inference system in order to predict river flow in Lighvan river. Khalili et al. (13 proposed a BL-ARCH method for prediction of flows in Shaharchay River in Urmia. Khu et al. (16 used genetic programming for runoff prediction in Orgeval catchment in France. Firat and Gungor (11 evaluated the fuzzy-neural model for predicting Mendes river flow in Turkey. The goal of present study is comparing the performance of genetic programming and M5 model trees for prediction of Shaharchay river flow in the basin of Lake Urmia and obtaining a comprehensive insight of their abilities. Materials and Methods: Shaharchay river as a main source of providing drinking water of Urmia city and agricultural needs of surrounding lands and finally one of the main input sources of Lake Urmia is quite important in the region. For obtaining the predetermined goals of present study, average monthly flows of Shaharchay River in Band hydrometric station has been gathered from 1951 to 2011. Then, two third of mentioned

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

OpenAIRE

Powell, Jeffrey R.; Tabachnick, Walter J.

2014-01-01

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

Applying genetic algorithms for programming manufactoring cell tasks

Directory of Open Access Journals (Sweden)

Efredy Delgado

2005-05-01

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

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

Science.gov (United States)

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

2015-03-01

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

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

International Nuclear Information System (INIS)

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

1998-01-01

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

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

DEFF Research Database (Denmark)

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

2013-01-01

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

Genetic Programming for Automatic Hydrological Modelling

Science.gov (United States)

Chadalawada, Jayashree; Babovic, Vladan

2017-04-01

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

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

Directory of Open Access Journals (Sweden)

M. Passarella

2018-02-01

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

A Unified Approach to Modeling and Programming

DEFF Research Database (Denmark)

Madsen, Ole Lehrmann; Møller-Pedersen, Birger

2010-01-01

of this paper is to go back to the future and get inspiration from SIMULA and propose a unied approach. In addition to reintroducing the contributions of SIMULA and the Scandinavian approach to object-oriented programming, we do this by discussing a number of issues in modeling and programming and argue3 why we......SIMULA was a language for modeling and programming and provided a unied approach to modeling and programming in contrast to methodologies based on structured analysis and design. The current development seems to be going in the direction of separation of modeling and programming. The goal...

A genetic ensemble approach for gene-gene interaction identification

Directory of Open Access Journals (Sweden)

Ho Joshua WK

2010-10-01

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

A propensity score approach to correction for bias due to population stratification using genetic and non-genetic factors.

Science.gov (United States)

Zhao, Huaqing; Rebbeck, Timothy R; Mitra, Nandita

2009-12-01

Confounding due to population stratification (PS) arises when differences in both allele and disease frequencies exist in a population of mixed racial/ethnic subpopulations. Genomic control, structured association, principal components analysis (PCA), and multidimensional scaling (MDS) approaches have been proposed to address this bias using genetic markers. However, confounding due to PS can also be due to non-genetic factors. Propensity scores are widely used to address confounding in observational studies but have not been adapted to deal with PS in genetic association studies. We propose a genomic propensity score (GPS) approach to correct for bias due to PS that considers both genetic and non-genetic factors. We compare the GPS method with PCA and MDS using simulation studies. Our results show that GPS can adequately adjust and consistently correct for bias due to PS. Under no/mild, moderate, and severe PS, GPS yielded estimated with bias close to 0 (mean=-0.0044, standard error=0.0087). Under moderate or severe PS, the GPS method consistently outperforms the PCA method in terms of bias, coverage probability (CP), and type I error. Under moderate PS, the GPS method consistently outperforms the MDS method in terms of CP. PCA maintains relatively high power compared to both MDS and GPS methods under the simulated situations. GPS and MDS are comparable in terms of statistical properties such as bias, type I error, and power. The GPS method provides a novel and robust tool for obtaining less-biased estimates of genetic associations that can consider both genetic and non-genetic factors. 2009 Wiley-Liss, Inc.

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

Science.gov (United States)

Sastry, Kumara Narasimha

2007-03-01

Effective and efficient rnultiscale modeling is essential to advance both the science and synthesis in a, wide array of fields such as physics, chemistry, materials science; biology, biotechnology and pharmacology. This study investigates the efficacy and potential of rising genetic algorithms for rnultiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately. In particular, this thesis demonstrates the use of genetic algorithms (GAs) and genetic programming (GP) in multiscale modeling with the help of two non-trivial case studies in materials science and chemistry. The first case study explores the utility of genetic programming (GP) in multi-timescaling alloy kinetics simulations. In essence, GP is used to bridge molecular dynamics and kinetic Monte Carlo methods to span orders-of-magnitude in simulation time. Specifically, GP is used to regress symbolically an inline barrier function from a limited set of molecular dynamics simulations to enable kinetic Monte Carlo that simulate seconds of real time. Results on a non-trivial example of vacancy-assisted migration on a surface of a face-centered cubic (fcc) Copper-Cobalt (CuxCo 1-x) alloy show that GP predicts all barriers with 0.1% error from calculations for less than 3% of active configurations, independent of type of potentials used to obtain the learning set of barriers via molecular dynamics. The resulting method enables 2--9 orders-of-magnitude increase in real-time dynamics simulations taking 4--7 orders-of-magnitude less CPU time. The second case study presents the application of multiobjective genetic algorithms (MOGAs) in multiscaling quantum chemistry simulations. Specifically, MOGAs are used to bridge high-level quantum chemistry and semiempirical methods to provide accurate representation of complex molecular excited-state and ground-state behavior. Results on ethylene and benzene---two common

A holistic approach to genetic conservation of Pinus strobiformis

Science.gov (United States)

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

2017-01-01

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

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

Directory of Open Access Journals (Sweden)

Svetlana Gorokhova

2015-12-01

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

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

International Nuclear Information System (INIS)

Singh, Sonveer; Agrawal, Sanjay

2015-01-01

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

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

Science.gov (United States)

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

2010-01-01

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

Molecular genetics and livestock selection. Approaches, opportunities and risks

International Nuclear Information System (INIS)

Williams, J.L.

2005-01-01

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

A NEW MUTATION OPERATOR IN GENETIC PROGRAMMING

Directory of Open Access Journals (Sweden)

Anuradha Purohit

2013-01-01

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

Landscape genetic approaches to guide native plant restoration in the Mojave Desert

Science.gov (United States)

Shryock, Daniel F.; Havrilla, Caroline A.; DeFalco, Lesley; Esque, Todd C.; Custer, Nathan; Wood, Troy E.

2016-01-01

Restoring dryland ecosystems is a global challenge due to synergistic drivers of disturbance coupled with unpredictable environmental conditions. Dryland plant species have evolved complex life-history strategies to cope with fluctuating resources and climatic extremes. Although rarely quantified, local adaptation is likely widespread among these species and potentially influences restoration outcomes. The common practice of reintroducing propagules to restore dryland ecosystems, often across large spatial scales, compels evaluation of adaptive divergence within these species. Such evaluations are critical to understanding the consequences of large-scale manipulation of gene flow and to predicting success of restoration efforts. However, genetic information for species of interest can be difficult and expensive to obtain through traditional common garden experiments. Recent advances in landscape genetics offer marker-based approaches for identifying environmental drivers of adaptive genetic variability in non-model species, but tools are still needed to link these approaches with practical aspects of ecological restoration. Here, we combine spatially-explicit landscape genetics models with flexible visualization tools to demonstrate how cost-effective evaluations of adaptive genetic divergence can facilitate implementation of different seed sourcing strategies in ecological restoration. We apply these methods to Amplified Fragment Length Polymorphism (AFLP) markers genotyped in two Mojave Desert shrub species of high restoration importance: the long-lived, wind-pollinated gymnosperm Ephedra nevadensis, and the short-lived, insect-pollinated angiosperm Sphaeralcea ambigua. Mean annual temperature was identified as an important driver of adaptive genetic divergence for both species. Ephedra showed stronger adaptive divergence with respect to precipitation variability, while temperature variability and precipitation averages explained a larger fraction of adaptive

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

Science.gov (United States)

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

2015-10-01

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

Forward Genetic Approaches for Elucidation of Novel Regulators of Lyme Arthritis Severity

Directory of Open Access Journals (Sweden)

Kenneth K.C. Bramwell

2014-06-01

Full Text Available Patients experiencing natural infection with Borrelia burgdorferi display a spectrum of associated symptoms and severity, strongly implicating the impact of genetically determined host factors in the pathogenesis of Lyme disease. Herein, we provide a summary of the host genetic factors that have been demonstrated to influence the severity and chronicity of Lyme arthritis symptoms, and a review of the resources available, current progress, and added value of a forward genetic approach for identification of novel genetic regulators.

Reverse Genetics Approaches for the Development of Influenza Vaccines

Science.gov (United States)

Nogales, Aitor; Martínez-Sobrido, Luis

2016-01-01

Influenza viruses cause annual seasonal epidemics and occasional pandemics of human respiratory disease. Influenza virus infections represent a serious public health and economic problem, which are most effectively prevented through vaccination. However, influenza viruses undergo continual antigenic variation, which requires either the annual reformulation of seasonal influenza vaccines or the rapid generation of vaccines against potential pandemic virus strains. The segmented nature of influenza virus allows for the reassortment between two or more viruses within a co-infected cell, and this characteristic has also been harnessed in the laboratory to generate reassortant viruses for their use as either inactivated or live-attenuated influenza vaccines. With the implementation of plasmid-based reverse genetics techniques, it is now possible to engineer recombinant influenza viruses entirely from full-length complementary DNA copies of the viral genome by transfection of susceptible cells. These reverse genetics systems have provided investigators with novel and powerful approaches to answer important questions about the biology of influenza viruses, including the function of viral proteins, their interaction with cellular host factors and the mechanisms of influenza virus transmission and pathogenesis. In addition, reverse genetics techniques have allowed the generation of recombinant influenza viruses, providing a powerful technology to develop both inactivated and live-attenuated influenza vaccines. In this review, we will summarize the current knowledge of state-of-the-art, plasmid-based, influenza reverse genetics approaches and their implementation to provide rapid, convenient, safe and more effective influenza inactivated or live-attenuated vaccines. PMID:28025504

Earthquake—explosion discrimination using genetic algorithm-based boosting approach

Science.gov (United States)

Orlic, Niksa; Loncaric, Sven

2010-02-01

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

WONOEP appraisal: new genetic approaches to study epilepsy

Science.gov (United States)

Rossignol, Elsa; Kobow, Katja; Simonato, Michele; Loeb, Jeffrey A.; Grisar, Thierry; Gilby, Krista L.; Vinet, Jonathan; Kadam, Shilpa D.; Becker, Albert J.

2014-01-01

Objective New genetic investigation techniques, including next-generation sequencing, epigenetic profiling, cell lineage mapping, targeted genetic manipulation of specific neuronal cell types, stem cell reprogramming and optogenetic manipulations within epileptic networks are progressively unravelling the mysteries of epileptogenesis and ictogenesis. These techniques have opened new avenues to discover the molecular basis of epileptogenesis and to study the physiological impacts of mutations in epilepsy-associated genes on a multilayer level, from cells to circuits. Methods This manuscript reviews recently published applications of these new genetic technologies in the study of epilepsy, as well as work presented by the authors at the genetic session of the XII Workshop on the Neurobiology of Epilepsy in Quebec, Canada. Results Next-generation sequencing is providing investigators with an unbiased means to assess the molecular causes of sporadic forms of epilepsy and have revealed the complexity and genetic heterogeneity of sporadic epilepsy disorders. To assess the functional impact of mutations in these newly identified genes on specific neuronal cell-types during brain development, new modeling strategies in animals, including conditional genetics in mice and in utero knockdown approaches, are enabling functional validation with exquisite cell-type and temporal specificity. In addition, optogenetics, using cell-type specific Cre recombinase driver lines, is enabling investigators to dissect networks involved in epilepsy. Genetically-encoded cell-type labeling is also providing new means to assess the role of the non-neuronal components of epileptic networks such as glial cells. Furthermore, beyond its role in revealing coding variants involved in epileptogenesis, next-generation sequencing can be used to assess the epigenetic modifications that lead to sustained network hyperexcitability in epilepsy, including methylation changes in gene promoters and non

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

Science.gov (United States)

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

2014-01-01

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

A systems genetics approach provides a bridge from discovered genetic variants to biological pathways in rheumatoid arthritis.

Directory of Open Access Journals (Sweden)

Hirofumi Nakaoka

Full Text Available Genome-wide association studies (GWAS have yielded novel genetic loci underlying common diseases. We propose a systems genetics approach to utilize these discoveries for better understanding of the genetic architecture of rheumatoid arthritis (RA. Current evidence of genetic associations with RA was sought through PubMed and the NHGRI GWAS catalog. The associations of 15 single nucleotide polymorphisms and HLA-DRB1 alleles were confirmed in 1,287 cases and 1,500 controls of Japanese subjects. Among these, HLA-DRB1 alleles and eight SNPs showed significant associations and all but one of the variants had the same direction of effect as identified in the previous studies, indicating that the genetic risk factors underlying RA are shared across populations. By receiver operating characteristic curve analysis, the area under the curve (AUC for the genetic risk score based on the selected variants was 68.4%. For seropositive RA patients only, the AUC improved to 70.9%, indicating good but suboptimal predictive ability. A simulation study shows that more than 200 additional loci with similar effect size as recent GWAS findings or 20 rare variants with intermediate effects are needed to achieve AUC = 80.0%. We performed the random walk with restart (RWR algorithm to prioritize genes for future mapping studies. The performance of the algorithm was confirmed by leave-one-out cross-validation. The RWR algorithm pointed to ZAP70 in the first rank, in which mutation causes RA-like autoimmune arthritis in mice. By applying the hierarchical clustering method to a subnetwork comprising RA-associated genes and top-ranked genes by the RWR, we found three functional modules relevant to RA etiology: "leukocyte activation and differentiation", "pattern-recognition receptor signaling pathway", and "chemokines and their receptors".These results suggest that the systems genetics approach is useful to find directions of future mapping strategies to illuminate

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

Directory of Open Access Journals (Sweden)

Šijačić-Nikolić Mirjana

2017-01-01

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

Process planning optimization on turning machine tool using a hybrid genetic algorithm with local search approach

Directory of Open Access Journals (Sweden)

Yuliang Su

2015-04-01

Full Text Available A turning machine tool is a kind of new type of machine tool that is equipped with more than one spindle and turret. The distinctive simultaneous and parallel processing abilities of turning machine tool increase the complexity of process planning. The operations would not only be sequenced and satisfy precedence constraints, but also should be scheduled with multiple objectives such as minimizing machining cost, maximizing utilization of turning machine tool, and so on. To solve this problem, a hybrid genetic algorithm was proposed to generate optimal process plans based on a mixed 0-1 integer programming model. An operation precedence graph is used to represent precedence constraints and help generate a feasible initial population of hybrid genetic algorithm. Encoding strategy based on data structure was developed to represent process plans digitally in order to form the solution space. In addition, a local search approach for optimizing the assignments of available turrets would be added to incorporate scheduling with process planning. A real-world case is used to prove that the proposed approach could avoid infeasible solutions and effectively generate a global optimal process plan.

Which Introductory Programming Approach Is Most Suitable for Students: Procedural or Visual Programming?

Science.gov (United States)

Eid, Chaker; Millham, Richard

2012-01-01

In this paper, we discuss the visual programming approach to teaching introductory programming courses and then compare this approach with that of procedural programming. The involved cognitive levels of students, as beginning students are introduced to different types of programming concepts, are correlated to the learning processes of…

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

Science.gov (United States)

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

2017-02-01

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

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

Science.gov (United States)

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

2017-09-25

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

Developmental Programming of Renal Function and Re-Programming Approaches.

Science.gov (United States)

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

2018-01-01

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

Food control and a citizen science approach for improving teaching of Genetics in universities.

Science.gov (United States)

Borrell, Y J; Muñoz-Colmenero, A M; Dopico, E; Miralles, L; Garcia-Vazquez, E

2016-09-10

A Citizen Science approach was implemented in the laboratory practices of Genetics at the University of Oviedo, related with the engaging topic of Food Control. Real samples of food products consumed by students at home (students as samplers) were employed as teaching material in three different courses of Genetics during the academic year 2014-2015: Experimental Methods in Food Production (MBTA) (Master level), and Applied Molecular Biology (BMA) and Conservation Genetics and Breeding (COMGE) (Bachelor/Degree level). Molecular genetics based on PCR amplification of DNA markers was employed for species identification of 22 seafood products in COMGE and MBTA, and for detection of genetically modified (GM) maize from nine products in BMA. In total six seafood products incorrectly labeled (27%), and two undeclared GM maize (22%) were found. A post-Laboratory survey was applied for assessing the efficacy of the approach for improving motivation in the Laboratory Practices of Genetics. Results confirmed that students that worked on their own samples from local markets were significantly more motivated and better evaluated their Genetic laboratory practices than control students (χ(2)  = 12.11 p = 0.033). Our results suggest that citizen science approaches could not be only useful for improving teaching of Genetics in universities but also to incorporate students and citizens as active agents in food control. © 2016 by The International Union of Biochemistry and Molecular Biology, 44(5):450-462, 2016. © 2016 The International Union of Biochemistry and Molecular Biology.

Geometric Semantic Genetic Programming Algorithm and Slump Prediction

OpenAIRE

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

2017-01-01

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

Fuzzy Information Retrieval Using Genetic Algorithms and Relevance Feedback.

Science.gov (United States)

Petry, Frederick E.; And Others

1993-01-01

Describes an approach that combines concepts from information retrieval, fuzzy set theory, and genetic programing to improve weighted Boolean query formulation via relevance feedback. Highlights include background on information retrieval systems; genetic algorithms; subproblem formulation; and preliminary results based on a testbed. (Contains 12…

Applying new genetic approaches to improve quality of population assessment of green and loggerhead turtles

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — As the NOAA-Fisheries? National Sea Turtle Genetics Lab, the SWFSC Marine Turtle Genetics Program has the lead responsibility for generating, analyzing and...

Management intensity and genetics affect loblolly pine seedling performance

Science.gov (United States)

Scott D. Roberts; Randall J. Rousseau; B. Landis Herrin

2012-01-01

Capturing potential genetic gains from tree improvement programs requires selection of the appropriate genetic stock and application of appropriate silvicultural management techniques. Limited information is available on how specific loblolly pine varietal genotypes perform under differing growing environments and management approaches. This study was established in...

Developmental Programming of Renal Function and Re-Programming Approaches

Science.gov (United States)

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

2018-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

Wichapa, Narong; Khokhajaikiat, Porntep

2017-07-01

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

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

International Nuclear Information System (INIS)

Wichapa, Narong; Khokhajaikiat, Porntep

2017-01-01

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

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

Science.gov (United States)

2011-11-23

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

Understanding the Science-Learning Environment: A Genetically Sensitive Approach

Science.gov (United States)

Haworth, Claire M. A.; Davis, Oliver S. P.; Hanscombe, Ken B.; Kovas, Yulia; Dale, Philip S.; Plomin, Robert

2013-01-01

Previous studies have shown that environmental influences on school science performance increase in importance from primary to secondary school. Here we assess for the first time the relationship between the science-learning environment and science performance using a genetically sensitive approach to investigate the aetiology of this link. 3000…

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

National Research Council Canada - National Science Library

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

2004-01-01

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

Rationale for an integrated approach to genetic epidemiology.

Science.gov (United States)

Laberge, Claude M; Knoppers, Bartha Maria

1992-10-01

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

Applications of a formal approach to decipher discrete genetic networks.

Science.gov (United States)

Corblin, Fabien; Fanchon, Eric; Trilling, Laurent

2010-07-20

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

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

Science.gov (United States)

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

2012-02-01

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

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

Science.gov (United States)

Tripathi, Niraj; Khare, Dhirendra

2016-10-01

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

Developmental Programming of Renal Function and Re-Programming Approaches

Directory of Open Access Journals (Sweden)

Eva Nüsken

2018-02-01

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

Flow discharge prediction in compound channels using linear genetic programming

Science.gov (United States)

Azamathulla, H. Md.; Zahiri, A.

2012-08-01

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

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

Science.gov (United States)

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

2014-03-01

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

Model-based problem solving through symbolic regression via pareto genetic programming

NARCIS (Netherlands)

Vladislavleva, E.

2008-01-01

Pareto genetic programming methodology is extended by additional generic model selection and generation strategies that (1) drive the modeling engine to creation of models of reduced non-linearity and increased generalization capabilities, and (2) improve the effectiveness of the search for robust

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

Science.gov (United States)

Kassa, Biniyam Asmare

2013-01-01

This paper reports a linear programming approach for placement of applicants to study programs developed and implemented at the college of Business & Economics, Bahir Dar University, Bahir Dar, Ethiopia. The approach is estimated to significantly streamline the placement decision process at the college by reducing required man hour as well as the time it takes to announce placement decisions. Compared to the previous manual system where only one or two placement criteria were considered, the new approach allows the college's management to easily incorporate additional placement criteria, if needed. Comparison of our approach against manually constructed placement decisions based on actual data for the 2012/13 academic year suggested that about 93 percent of the placements from our model concur with the actual placement decisions. For the remaining 7 percent of placements, however, the actual placements made by the manual system display inconsistencies of decisions judged against the very criteria intended to guide placement decisions by the college's program management office. Overall, the new approach proves to be a significant improvement over the manual system in terms of efficiency of the placement process and the quality of placement decisions.

Genetic and neural approaches to nuclear transient identification

International Nuclear Information System (INIS)

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

2005-01-01

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

An imaging genetics approach to understanding social influence

OpenAIRE

Emily eFalk; Emily eFalk; Baldwin eWay; Agnes eJasinska

2012-01-01

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

An imaging genetics approach to understanding social influence

OpenAIRE

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

2012-01-01

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

A linear programming approach for placement of applicants to academic programs

OpenAIRE

Kassa, Biniyam Asmare

2013-01-01

This paper reports a linear programming approach for placement of applicants to study programs developed and implemented at the college of Business & Economics, Bahir Dar University, Bahir Dar, Ethiopia. The approach is estimated to significantly streamline the placement decision process at the college by reducing required man hour as well as the time it takes to announce placement decisions. Compared to the previous manual system where only one or two placement criteria were considered, the ...

Hunter disease eClinic: interactive, computer-assisted, problem-based approach to independent learning about a rare genetic disease

Directory of Open Access Journals (Sweden)

Moldovan Laura

2010-10-01

Hunter disease eClinic employs a CBT model providing the trainee with realistic clinical problems, coupled with comprehensive basic and clinical reference information by instantaneous access to an electronic textbook, the eBook. The program was rated highly by attendees at national and international presentations. It provides a potential model for use as an educational approach to other rare genetic diseases.

Hunter disease eClinic: interactive, computer-assisted, problem-based approach to independent learning about a rare genetic disease.

Science.gov (United States)

Al-Jasmi, Fatma; Moldovan, Laura; Clarke, Joe T R

2010-10-25

clinical problems, coupled with comprehensive basic and clinical reference information by instantaneous access to an electronic textbook, the eBook. The program was rated highly by attendees at national and international presentations. It provides a potential model for use as an educational approach to other rare genetic diseases.

Beginning Java programming the object-oriented approach

CERN Document Server

Baesens, Bart; vanden Broucke, Seppe

2015-01-01

A comprehensive Java guide, with samples, exercises, case studies, and step-by-step instruction Beginning Java Programming: The Object Oriented Approach is a straightforward resource for getting started with one of the world's most enduringly popular programming languages. Based on classes taught by the authors, the book starts with the basics and gradually builds into more advanced concepts. The approach utilizes an integrated development environment that allows readers to immediately apply what they learn, and includes step-by-step instruction with plenty of sample programs. Each chapter c

A reliability program approach to operational safety

International Nuclear Information System (INIS)

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

1985-01-01

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

Order Batching in Warehouses by Minimizing Total Tardiness: A Hybrid Approach of Weighted Association Rule Mining and Genetic Algorithms

Directory of Open Access Journals (Sweden)

Amir Hossein Azadnia

2013-01-01

Full Text Available One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach.

Genetic diversity of tambaqui broodstocks in stock enhancement programs

Directory of Open Access Journals (Sweden)

Americo Moraes Neto

2017-06-01

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

Towards Merging Binary Integer Programming Techniques with Genetic Algorithms

Directory of Open Access Journals (Sweden)

Reza Zamani

2017-01-01

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

Genetics of healthy aging in Europe: the EU-integrated project GEHA (GEnetics of Healthy Aging)

DEFF Research Database (Denmark)

Franceschi, Claudio; Bezrukov, Vladyslav; Blanché, Hélène

2007-01-01

The aim of the 5-year European Union (EU)-Integrated Project GEnetics of Healthy Aging (GEHA), constituted by 25 partners (24 from Europe plus the Beijing Genomics Institute from China), is to identify genes involved in healthy aging and longevity, which allow individuals to survive to advanced old......DNA). The genetic analysis will be performed by 9 high-throughput platforms, within the framework of centralized databases for phenotypic, genetic, and mtDNA data. Additional advanced approaches (bioinformatics, advanced statistics, mathematical modeling, functional genomics and proteomics, molecular biology...... age in good cognitive and physical function and in the absence of major age-related diseases. To achieve this aim a coherent, tightly integrated program of research that unites demographers, geriatricians, geneticists, genetic epidemiologists, molecular biologists, bioinfomaticians, and statisticians...

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

International Nuclear Information System (INIS)

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

2015-01-01

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

Approaches to quality management and accreditation in a genetic testing laboratory

Science.gov (United States)

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

2010-01-01

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

Genetics and caries: prospects

Directory of Open Access Journals (Sweden)

Alexandre Rezende Vieira

2012-01-01

Full Text Available Caries remains the most prevalent non-contagious infectious disease in humans. It is clear that the current approaches to decrease the prevalence of caries in human populations, including water fluoridation and school-based programs, are not enough to protect everyone. The scientific community has suggested the need for innovative work in a number of areas in cariology, encompassing disease etiology, epidemiology, definition, prevention, and treatment. We have pioneered the work on genetic studies to identify genes and genetic markers of diagnostic, prognostic, and therapeutic value. This paper summarizes a presentation that elaborated on these initial findings.

Modeling AEC—New Approaches to Study Rare Genetic Disorders

Science.gov (United States)

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

2015-01-01

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

Food Control and a Citizen Science Approach for Improving Teaching of Genetics in Universities

Science.gov (United States)

Borrell, Y. J.; Muñoz-Colmenero, A. M.; Dopico, E.; Miralles, L.; Garcia-Vazquez, E.

2016-01-01

A Citizen Science approach was implemented in the laboratory practices of Genetics at the University of Oviedo, related with the engaging topic of Food Control. Real samples of food products consumed by students at home ("students as samplers") were employed as teaching material in three different courses of Genetics during the academic…

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

Energy Technology Data Exchange (ETDEWEB)

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

1999-08-01

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

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

Science.gov (United States)

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

2010-01-01

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

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

Science.gov (United States)

Olwi, Duaa; Merdad, Leena; Ramadan, Eman

2016-01-01

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

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

Science.gov (United States)

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

2016-01-01

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

An Airborne Conflict Resolution Approach Using a Genetic Algorithm

Science.gov (United States)

Mondoloni, Stephane; Conway, Sheila

2001-01-01

An airborne conflict resolution approach is presented that is capable of providing flight plans forecast to be conflict-free with both area and traffic hazards. This approach is capable of meeting constraints on the flight plan such as required times of arrival (RTA) at a fix. The conflict resolution algorithm is based upon a genetic algorithm, and can thus seek conflict-free flight plans meeting broader flight planning objectives such as minimum time, fuel or total cost. The method has been applied to conflicts occurring 6 to 25 minutes in the future in climb, cruise and descent phases of flight. The conflict resolution approach separates the detection, trajectory generation and flight rules function from the resolution algorithm. The method is capable of supporting pilot-constructed resolutions, cooperative and non-cooperative maneuvers, and also providing conflict resolution on trajectories forecast by an onboard FMC.

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

International Nuclear Information System (INIS)

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

2009-01-01

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

School District Program Cost Accounting: An Alternative Approach

Science.gov (United States)

Hentschke, Guilbert C.

1975-01-01

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

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

Science.gov (United States)

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

2018-03-01

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

Rethink, Reform, Reenter: An Entrepreneurial Approach to Prison Programming.

Science.gov (United States)

Keena, Linda; Simmons, Chris

2015-07-01

The purpose of this article was to present a description and first-stage evaluation of the impact of the Ice House Entrepreneurship Program on the learning experience of participating prerelease inmates at a Mississippi maximum-security prison and their perception of the transfer of skills learned in program into securing employment upon reentry. The Ice House Entrepreneurship Program is a 12-week program facilitated by volunteer university professors to inmates in a prerelease unit of a maximum-security prison in Mississippi. Participants' perspectives were examined through content analysis of inmates' answers to program Reflection and Response Assignments and interviews. The analyses were conducted according to the constant comparative method. Findings reveal the emergent of eight life-lessons and suggest that this is a promising approach to prison programming for prerelease inmates. This study discusses three approaches to better prepare inmates for a mindset change. The rethink, reform, and reenter approaches help break the traditional cycle of release, reoffend, and return. © The Author(s) 2014.

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

Directory of Open Access Journals (Sweden)

M. Abdul-Niby

2016-04-01

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

Evolutionary Data Mining Approach to Creating Digital Logic

Science.gov (United States)

2010-01-01

To deal with this problem a genetic program (GP) based data mining ( DM ) procedure has been invented (Smith 2005). A genetic program is an algorithm...that can operate on the variables. When a GP was used as a DM function in the past to automatically create fuzzy decision trees, the Report...rules represents an approach to the determining the effect of linguistic imprecision, i.e., the inability of experts to provide crisp rules. The

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

Directory of Open Access Journals (Sweden)

Krawiec Krzysztof

2017-12-01

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

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

International Nuclear Information System (INIS)

Sahoo, N.C.; Prasad, K.

2006-01-01

This paper presents a fuzzy genetic approach for reconfiguration of radial distribution systems (RDS) so as to maximize the voltage stability of the network for a specific set of loads. The network reconfiguration involves a mechanism for selection of the best set of branches to be opened, one from each loop, such that the reconfigured RDS possesses desired performance characteristics. This discrete solution space is better handled by the proposed scheme, which maximizes a suitable optimizing function (computed using two different approaches). In the first approach, this function is chosen as the average of a voltage stability index of all the buses in the RDS, while in the second approach, the complete RDS is reduced to a two bus equivalent system and the optimizing function is the voltage stability index of this reduced two bus system. The fuzzy genetic algorithm uses a suitable coding and decoding scheme for maintaining the radial nature of the network at every stage of genetic evolution, and it also uses a fuzzy rule based mutation controller for efficient search of the solution space. This method, tested on 69 bus and 33 bus RDSs, shows promising results for the both approaches. It is also observed that the network losses are reduced when the voltage stability is enhanced by the network reconfiguration

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

Science.gov (United States)

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

2017-05-01

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

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

Science.gov (United States)

2015-12-01

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

Non-Genetic Engineering Approaches for Isolating and Generating Novel Yeasts for Industrial Applications

Science.gov (United States)

Chambers, P. J.; Bellon, J. R.; Schmidt, S. A.; Varela, C.; Pretorius, I. S.

Generating novel yeast strains for industrial applications should be quite straightforward; after all, research into the genetics, biochemistry and physiology of Baker's Yeast, Saccharomyces cerevisiae, has paved the way for many advances in the modern biological sciences. We probably know more about this humble eukaryote than any other, and it is the most tractable of organisms for manipulation using modern genetic engineering approaches. In many countries, however, there are restrictions on the use of genetically-modified organisms (GMOs), particularly in foods and beverages, and the level of consumer acceptance of GMOs is, at best, variable. Thus, many researchers working with industrial yeasts use genetic engineering techniques primarily as research tools, and strain development continues to rely on non-GM technologies. This chapter explores the non-GM tools and strategies available to such researchers.

Comparing approaches to generic programming in Haskell

NARCIS (Netherlands)

Hinze, R.; Jeuring, J.T.; Löh, A.

2006-01-01

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

Basic Genetics: A Human Approach.

Science.gov (United States)

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

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

Stream Flow Prediction by Remote Sensing and Genetic Programming

Science.gov (United States)

Chang, Ni-Bin

2009-01-01

A genetic programming (GP)-based, nonlinear modeling structure relates soil moisture with synthetic-aperture-radar (SAR) images to present representative soil moisture estimates at the watershed scale. Surface soil moisture measurement is difficult to obtain over a large area due to a variety of soil permeability values and soil textures. Point measurements can be used on a small-scale area, but it is impossible to acquire such information effectively in large-scale watersheds. This model exhibits the capacity to assimilate SAR images and relevant geoenvironmental parameters to measure soil moisture.

Comparing approaches to generic programming in Haskell

NARCIS (Netherlands)

Hinze, R.; Jeuring, J.T.; Löh, A.

2006-01-01

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

Prediksi Nilai Tukar Rupiah Terhadap US Dollar Menggunakan Metode Genetic Programming

Directory of Open Access Journals (Sweden)

Daneswara Jauhari

2016-12-01

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

A programming approach to computability

CERN Document Server

Kfoury, A J; Arbib, Michael A

1982-01-01

Computability theory is at the heart of theoretical computer science. Yet, ironically, many of its basic results were discovered by mathematical logicians prior to the development of the first stored-program computer. As a result, many texts on computability theory strike today's computer science students as far removed from their concerns. To remedy this, we base our approach to computability on the language of while-programs, a lean subset of PASCAL, and postpone consideration of such classic models as Turing machines, string-rewriting systems, and p. -recursive functions till the final chapter. Moreover, we balance the presentation of un solvability results such as the unsolvability of the Halting Problem with a presentation of the positive results of modern programming methodology, including the use of proof rules, and the denotational semantics of programs. Computer science seeks to provide a scientific basis for the study of information processing, the solution of problems by algorithms, and the design ...

An Approach for Solving Linear Fractional Programming Problems

OpenAIRE

Andrew Oyakhobo Odior

2012-01-01

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

Mouse genetic approaches applied to the normal tissue radiation response

International Nuclear Information System (INIS)

Haston, Christina K.

2012-01-01

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

Branch-pipe-routing approach for ships using improved genetic algorithm

Science.gov (United States)

Sui, Haiteng; Niu, Wentie

2016-09-01

Branch-pipe routing plays fundamental and critical roles in ship-pipe design. The branch-pipe-routing problem is a complex combinatorial optimization problem and is thus difficult to solve when depending only on human experts. A modified genetic-algorithm-based approach is proposed in this paper to solve this problem. The simplified layout space is first divided into threedimensional (3D) grids to build its mathematical model. Branch pipes in layout space are regarded as a combination of several two-point pipes, and the pipe route between two connection points is generated using an improved maze algorithm. The coding of branch pipes is then defined, and the genetic operators are devised, especially the complete crossover strategy that greatly accelerates the convergence speed. Finally, simulation tests demonstrate the performance of proposed method.

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

Energy Technology Data Exchange (ETDEWEB)

Sahoo, N.C. [Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450 Melaka (Malaysia); Prasad, K. [Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450 Melaka (Malaysia)

2006-11-15

This paper presents a fuzzy genetic approach for reconfiguration of radial distribution systems (RDS) so as to maximize the voltage stability of the network for a specific set of loads. The network reconfiguration involves a mechanism for selection of the best set of branches to be opened, one from each loop, such that the reconfigured RDS possesses desired performance characteristics. This discrete solution space is better handled by the proposed scheme, which maximizes a suitable optimizing function (computed using two different approaches). In the first approach, this function is chosen as the average of a voltage stability index of all the buses in the RDS, while in the second approach, the complete RDS is reduced to a two bus equivalent system and the optimizing function is the voltage stability index of this reduced two bus system. The fuzzy genetic algorithm uses a suitable coding and decoding scheme for maintaining the radial nature of the network at every stage of genetic evolution, and it also uses a fuzzy rule based mutation controller for efficient search of the solution space. This method, tested on 69 bus and 33 bus RDSs, shows promising results for the both approaches. It is also observed that the network losses are reduced when the voltage stability is enhanced by the network reconfiguration. (author)

Assembling networks of microbial genomes using linear programming.

Science.gov (United States)

Holloway, Catherine; Beiko, Robert G

2010-11-20

Microbial genomes exhibit complex sets of genetic affinities due to lateral genetic transfer. Assessing the relative contributions of parent-to-offspring inheritance and gene sharing is a vital step in understanding the evolutionary origins and modern-day function of an organism, but recovering and showing these relationships is a challenging problem. We have developed a new approach that uses linear programming to find between-genome relationships, by treating tables of genetic affinities (here, represented by transformed BLAST e-values) as an optimization problem. Validation trials on simulated data demonstrate the effectiveness of the approach in recovering and representing vertical and lateral relationships among genomes. Application of the technique to a set comprising Aquifex aeolicus and 75 other thermophiles showed an important role for large genomes as 'hubs' in the gene sharing network, and suggested that genes are preferentially shared between organisms with similar optimal growth temperatures. We were also able to discover distinct and common genetic contributors to each sequenced representative of genus Pseudomonas. The linear programming approach we have developed can serve as an effective inference tool in its own right, and can be an efficient first step in a more-intensive phylogenomic analysis.

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

Energy Technology Data Exchange (ETDEWEB)

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

2013-05-15

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

A Novel Approach for Solving Semidefinite Programs

Directory of Open Access Journals (Sweden)

Hong-Wei Jiao

2014-01-01

Full Text Available A novel linearizing alternating direction augmented Lagrangian approach is proposed for effectively solving semidefinite programs (SDP. For every iteration, by fixing the other variables, the proposed approach alternatively optimizes the dual variables and the dual slack variables; then the primal variables, that is, Lagrange multipliers, are updated. In addition, the proposed approach renews all the variables in closed forms without solving any system of linear equations. Global convergence of the proposed approach is proved under mild conditions, and two numerical problems are given to demonstrate the effectiveness of the presented approach.

HeurAA: accurate and fast detection of genetic variations with a novel heuristic amplicon aligner program for next generation sequencing.

Directory of Open Access Journals (Sweden)

Lőrinc S Pongor

Full Text Available Next generation sequencing (NGS of PCR amplicons is a standard approach to detect genetic variations in personalized medicine such as cancer diagnostics. Computer programs used in the NGS community often miss insertions and deletions (indels that constitute a large part of known human mutations. We have developed HeurAA, an open source, heuristic amplicon aligner program. We tested the program on simulated datasets as well as experimental data from multiplex sequencing of 40 amplicons in 12 oncogenes collected on a 454 Genome Sequencer from lung cancer cell lines. We found that HeurAA can accurately detect all indels, and is more than an order of magnitude faster than previous programs. HeurAA can compare reads and reference sequences up to several thousand base pairs in length, and it can evaluate data from complex mixtures containing reads of different gene-segments from different samples. HeurAA is written in C and Perl for Linux operating systems, the code and the documentation are available for research applications at http://sourceforge.net/projects/heuraa/

A High Precision Comprehensive Evaluation Method for Flood Disaster Loss Based on Improved Genetic Programming

Institute of Scientific and Technical Information of China (English)

ZHOU Yuliang; LU Guihua; JIN Juliang; TONG Fang; ZHOU Ping

2006-01-01

Precise comprehensive evaluation of flood disaster loss is significant for the prevention and mitigation of flood disasters. Here, one of the difficulties involved is how to establish a model capable of describing the complex relation between the input and output data of the system of flood disaster loss. Genetic programming (GP) solves problems by using ideas from genetic algorithm and generates computer programs automatically. In this study a new method named the evaluation of the grade of flood disaster loss (EGFD) on the basis of improved genetic programming (IGP) is presented (IGPEGFD). The flood disaster area and the direct economic loss are taken as the evaluation indexes of flood disaster loss. Obviously that the larger the evaluation index value, the larger the corresponding value of the grade of flood disaster loss is. Consequently the IGP code is designed to make the value of the grade of flood disaster be an increasing function of the index value. The result of the application of the IGP-EGFD model to Henan Province shows that a good function expression can be obtained within a bigger searched function space; and the model is of high precision and considerable practical significance.Thus, IGP-EGFD can be widely used in automatic modeling and other evaluation systems.

A possibilistic approach for transient identification with 'don't know' response capability optimized by genetic algorithm

International Nuclear Information System (INIS)

Almeida, Jose Carlos S. de; Schirru, Roberto; Pereira, Claudio M.N.A.; Universidade Federal, Rio de Janeiro, RJ

2002-01-01

This work describes a possibilistic approach for transient identification based on the minimum centroids set method, proposed in previous work, optimized by genetic algorithm. The idea behind this method is to split the complex classification problem into small and simple ones, so that the performance in the classification can be increased. In order to accomplish that, a genetic algorithm is used to learn, from realistic simulated data, the optimized time partitions, which the robustness and correctness in the classification are maximized. The use of a possibilistic classification approach propitiates natural and consistent classification rules, leading naturally to a good heuristic to handle the 'don't know 'response, in case of unrecognized transient, which is fairly desirable in transient classification systems where safety is critical. Application of the proposed approach to a nuclear transient indentification problem reveals good capability of the genetic algorithm in learning optimized possibilistic classification rules for efficient diagnosis including 'don't know' response. Obtained results are shown and commented. (author)

Comprehensive bidding strategies with genetic programming/finite state automata

International Nuclear Information System (INIS)

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

1999-01-01

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

Marine biosurfaces research program

Science.gov (United States)

The Office of Naval Research (ONR) of the U.S. Navy is starting a basic research program to address the initial events that control colonization of surfaces by organisms in marine environments. The program “arises from the Navy's need to understand and ultimately control biofouling and biocorrosion in marine environments,” according to a Navy announcement.The program, “Biological Processes Controlling Surface Modification in the Marine Environment,” will emphasize the application of in situ techniques and modern molecular biological, biochemical, and biophysical approaches; it will also encourage the development of interdisciplinary projects. Specific areas of interest include sensing and response to environmental surface (physiology/physical chemistry), factors controlling movement to and retention at surfaces (behavior/hydrodynamics), genetic regulation of attachment (molecular genetics), and mechanisms of attachment (biochemistry/surface chemistry).

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

Science.gov (United States)

Lacková, Ľudmila; Matlach, Vladimír; Faltýnek, Dan

2017-12-01

Arbitrariness in the genetic code is one of the main reasons for a linguistic approach to molecular biology: the genetic code is usually understood as an arbitrary relation between amino acids and nucleobases. However, from a semiotic point of view, arbitrariness should not be the only condition for definition of a code, consequently it is not completely correct to talk about "code" in this case. Yet we suppose that there exist a code in the process of protein synthesis, but on a higher level than the nucleic bases chains. Semiotically, a code should be always associated with a function and we propose to define the genetic code not only relationally (in basis of relation between nucleobases and amino acids) but also in terms of function (function of a protein as meaning of the code). Even if the functional definition of meaning in the genetic code has been discussed in the field of biosemiotics, its further implications have not been considered. In fact, if the function of a protein represents the meaning of the genetic code (the sign's object), then it is crucial to reconsider the notion of its expression (the sign) as well. In our contribution, we will show that the actual model of the genetic code is not the only possible and we will propose a more appropriate model from a semiotic point of view.

DNA enrichment approaches to identify unauthorized genetically modified organisms (GMOs).

Science.gov (United States)

Arulandhu, Alfred J; van Dijk, Jeroen P; Dobnik, David; Holst-Jensen, Arne; Shi, Jianxin; Zel, Jana; Kok, Esther J

2016-07-01

With the increased global production of different genetically modified (GM) plant varieties, chances increase that unauthorized GM organisms (UGMOs) may enter the food chain. At the same time, the detection of UGMOs is a challenging task because of the limited sequence information that will generally be available. PCR-based methods are available to detect and quantify known UGMOs in specific cases. If this approach is not feasible, DNA enrichment of the unknown adjacent sequences of known GMO elements is one way to detect the presence of UGMOs in a food or feed product. These enrichment approaches are also known as chromosome walking or gene walking (GW). In recent years, enrichment approaches have been coupled with next generation sequencing (NGS) analysis and implemented in, amongst others, the medical and microbiological fields. The present review will provide an overview of these approaches and an evaluation of their applicability in the identification of UGMOs in complex food or feed samples.

Developmental psychopathology in an era of molecular genetics and neuroimaging: A developmental neurogenetics approach.

Science.gov (United States)

Hyde, Luke W

2015-05-01

The emerging field of neurogenetics seeks to model the complex pathways from gene to brain to behavior. This field has focused on imaging genetics techniques that examine how variability in common genetic polymorphisms predict differences in brain structure and function. These studies are informed by other complimentary techniques (e.g., animal models and multimodal imaging) and have recently begun to incorporate the environment through examination of Imaging Gene × Environment interactions. Though neurogenetics has the potential to inform our understanding of the development of psychopathology, there has been little integration between principles of neurogenetics and developmental psychopathology. The paper describes a neurogenetics and Imaging Gene × Environment approach and how these approaches have been usefully applied to the study of psychopathology. Six tenets of developmental psychopathology (the structure of phenotypes, the importance of exploring mechanisms, the conditional nature of risk, the complexity of multilevel pathways, the role of development, and the importance of who is studied) are identified, and how these principles can further neurogenetics applications to understanding the development of psychopathology is discussed. A major issue of this piece is how neurogenetics and current imaging and molecular genetics approaches can be incorporated into developmental psychopathology perspectives with a goal of providing models for better understanding pathways from among genes, environments, the brain, and behavior.

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

Science.gov (United States)

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

2013-04-26

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

A reverse genetics approach to study feline infectious peritonitis.

Science.gov (United States)

Tekes, Gergely; Spies, Danica; Bank-Wolf, Barbara; Thiel, Volker; Thiel, Heinz-Jürgen

2012-06-01

Feline infectious peritonitis (FIP) is a lethal immunopathological disease caused by feline coronaviruses (FCoVs). Here, we describe a reverse genetics approach to study FIP by assessing the pathogenicity of recombinant type I and type II and chimeric type I/type II FCoVs. All recombinant FCoVs established productive infection in cats, and recombinant type II FCoV (strain 79-1146) induced FIP. Virus sequence analyses from FIP-diseased cats revealed that the 3c gene stop codon of strain 79-1146 has changed to restore a full-length open reading frame (ORF).

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

International Nuclear Information System (INIS)

Pan, Indranil; Das, Saptarshi

2015-01-01

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

A statistical approach to quantification of genetically modified organisms (GMO) using frequency distributions.

Science.gov (United States)

Gerdes, Lars; Busch, Ulrich; Pecoraro, Sven

2014-12-14

According to Regulation (EU) No 619/2011, trace amounts of non-authorised genetically modified organisms (GMO) in feed are tolerated within the EU if certain prerequisites are met. Tolerable traces must not exceed the so-called 'minimum required performance limit' (MRPL), which was defined according to the mentioned regulation to correspond to 0.1% mass fraction per ingredient. Therefore, not yet authorised GMO (and some GMO whose approvals have expired) have to be quantified at very low level following the qualitative detection in genomic DNA extracted from feed samples. As the results of quantitative analysis can imply severe legal and financial consequences for producers or distributors of feed, the quantification results need to be utterly reliable. We developed a statistical approach to investigate the experimental measurement variability within one 96-well PCR plate. This approach visualises the frequency distribution as zygosity-corrected relative content of genetically modified material resulting from different combinations of transgene and reference gene Cq values. One application of it is the simulation of the consequences of varying parameters on measurement results. Parameters could be for example replicate numbers or baseline and threshold settings, measurement results could be for example median (class) and relative standard deviation (RSD). All calculations can be done using the built-in functions of Excel without any need for programming. The developed Excel spreadsheets are available (see section 'Availability of supporting data' for details). In most cases, the combination of four PCR replicates for each of the two DNA isolations already resulted in a relative standard deviation of 15% or less. The aims of the study are scientifically based suggestions for minimisation of uncertainty of measurement especially in -but not limited to- the field of GMO quantification at low concentration levels. Four PCR replicates for each of the two DNA isolations

Genetic diversity analysis in Malaysian giant prawns using expressed sequence tag microsatellite markers for stock improvement program.

Science.gov (United States)

Atin, K H; Christianus, A; Fatin, N; Lutas, A C; Shabanimofrad, M; Subha, B

2017-08-17

The Malaysian giant prawn is among the most commonly cultured species of the genus Macrobrachium. Stocks of giant prawns from four rivers in Peninsular Malaysia have been used for aquaculture over the past 25 years, which has led to repeated harvesting, restocking, and transplantation between rivers. Consequently, a stock improvement program is now important to avoid the depletion of wild stocks and the loss of genetic diversity. However, the success of such an improvement program depends on our knowledge of the genetic variation of these base populations. The aim of the current study was to estimate genetic variation and differentiation of these riverine sources using novel expressed sequence tag-microsatellite (EST-SSR) markers, which not only are informative on genetic diversity but also provide information on immune and metabolic traits. Our findings indicated that the tested stocks have inbreeding depression due to a significant deficiency in heterozygotes, and F IS was estimated as 0.15538 to 0.31938. An F-statistics analysis suggested that the stocks are composed of one large panmictic population. Among the four locations, stocks from Johor, in the southern region of the peninsular, showed higher allelic and genetic diversity than the other stocks. To overcome inbreeding problems, the Johor population could be used as a base population in a stock improvement program by crossing to the other populations. The study demonstrated that EST-SSR markers can be incorporated in future marker assisted breeding to aid the proper management of the stocks by breeders and stakeholders in Malaysia.

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

Science.gov (United States)

S. F. Shamoun; L. E. DeWald

2002-01-01

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

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

Science.gov (United States)

Zhang, Yang; Rockett, Peter I

2009-01-01

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

PSHED: a simplified approach to developing parallel programs

International Nuclear Information System (INIS)

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

1992-01-01

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

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

Science.gov (United States)

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

2010-12-01

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

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

Science.gov (United States)

Kassir, Yona; Stuart, David T

2017-01-01

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

A comprehensive approach to RCM-based preventive maintenance program development

International Nuclear Information System (INIS)

Hall, B.E.; Davis, T.; Pennington, A.J.

1988-01-01

In late 1986, Public Service Electric and Gas Company (PSE ampersand G) concluded that to support its vision and strategic planning it would be necessary to develop a consistent approach to maintenance for all nuclear units at the artificial island. General Physics Corporation was selected to lead a consultant team to support full-scale development of a preventive maintenance (PM) program for Salem and Hope Creek generating stations based on a reliability-centered maintenance (RCM) approach. RCM was selected because it represents a systematic approach to developing a PM program that provides a logical, consistent, and traceable methodology and produces a well-documented engineering basis for the program. Early in 1987, primary objectives for the PM program were defined. The Phase I tasks addressed key programmatic areas such as maintenance philosophy, procedures, condition monitoring, performance trending, equipment failure data base, ogranization, PM program effectiveness evaluation, RCM process, reliability/availability modeling, information management, training, spare parts, software/hardware, and commitments. Phase I of the PM program development project was completed in January 1988. Highlights of the Phase I work and the PM program manual are described

Dating Antarctic ice sheet collapse: Proposing a molecular genetic approach

Science.gov (United States)

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

2018-01-01

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

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

Science.gov (United States)

Lachhwani, Kailash; Poonia, Mahaveer Prasad

2012-08-01

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

Mathematical-programming approaches to test item pool design

NARCIS (Netherlands)

Veldkamp, Bernard P.; van der Linden, Willem J.; Ariel, A.

2002-01-01

This paper presents an approach to item pool design that has the potential to improve on the quality of current item pools in educational and psychological testing andhence to increase both measurement precision and validity. The approach consists of the application of mathematical programming

Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming

Czech Academy of Sciences Publication Activity Database

Křen, T.; Pilát, M.; Neruda, Roman

2017-01-01

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

A genetic epidemiology approach to cyber-security.

Science.gov (United States)

Gil, Santiago; Kott, Alexander; Barabási, Albert-László

2014-07-16

While much attention has been paid to the vulnerability of computer networks to node and link failure, there is limited systematic understanding of the factors that determine the likelihood that a node (computer) is compromised. We therefore collect threat log data in a university network to study the patterns of threat activity for individual hosts. We relate this information to the properties of each host as observed through network-wide scans, establishing associations between the network services a host is running and the kinds of threats to which it is susceptible. We propose a methodology to associate services to threats inspired by the tools used in genetics to identify statistical associations between mutations and diseases. The proposed approach allows us to determine probabilities of infection directly from observation, offering an automated high-throughput strategy to develop comprehensive metrics for cyber-security.

Mammalian genetics and biostatistics

International Nuclear Information System (INIS)

Grahn, D.; Carnes, B.A.; Farrington, B.H.; Lee, C.H.

1985-01-01

This program seeks to assess genetic hazards of single, weekly, and continuous doses of 60 Co gamma rays and single and weekly doses of fission neutrons to provide a basis for estimating relative biological effectiveness (RBE) of fission neutrons, to develop detailed dose-response data at low doses as a basis for studying relationships between linear energy transfer (LET) and the sensitivity of various cell stages, and to develop improved statistical approaches to analytical issues in chemical and radiation toxicology. 3 refs

Maximizing lifetime of wireless sensor networks using genetic approach

DEFF Research Database (Denmark)

Wagh, Sanjeev; Prasad, Ramjee

2014-01-01

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

Production of amino acids - Genetic and metabolic engineering approaches.

Science.gov (United States)

Lee, Jin-Ho; Wendisch, Volker F

2017-12-01

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

Plant Genetic Resources: Selected Issues from Genetic Erosion to Genetic Engineering

Directory of Open Access Journals (Sweden)

Karl Hammer

2008-04-01

Full Text Available Plant Genetic Resources (PGR continue to play an important role in the development of agriculture. The following aspects receive a special consideration:1. Definition. The term was coined in 1970. The genepool concept served as an important tool in the further development. Different approaches are discussed.2. Values of Genetic Resources. A short introduction is highlighting this problem and stressing the economic usfulness of PGR.3. Genetic Erosion. Already observed by E. Baur in 1914, this is now a key issue within PGR. The case studies cited include Ethiopia, Italy, China, S Korea, Greece and S. Africa. Modern approaches concentrate on allelic changes in varieties over time but neglect the landraces. The causes and consequences of genetic erosion are discussed.4. Genetic Resources Conservation. Because of genetic erosion there is a need for conservation. PGR should be consigned to the appropriate method of conservation (ex situ, in situ, on-farm according to the scientific basis of biodiversity (genetic diversity, species diversity, ecosystem diversity and the evolutionary status of plants (cultivated plants, weeds, related wild plants (crop wild relatives.5. GMO. The impact of genetically engineered plants on genetic diversity is discussed.6. The Conclusions and Recommendations stress the importance of PGR. Their conservation and use are urgent necessities for the present development and future survival of mankind.

Landscape genetics of the nonnative red fox of California.

Science.gov (United States)

Sacks, Benjamin N; Brazeal, Jennifer L; Lewis, Jeffrey C

2016-07-01

Invasive mammalian carnivores contribute disproportionately to declines in global biodiversity. In California, nonnative red foxes (Vulpes vulpes) have significantly impacted endangered ground-nesting birds and native canids. These foxes derive primarily from captive-reared animals associated with the fur-farming industry. Over the past five decades, the cumulative area occupied by nonnative red fox increased to cover much of central and southern California. We used a landscape-genetic approach involving mitochondrial DNA (mtDNA) sequences and 13 microsatellites of 402 nonnative red foxes removed in predator control programs to investigate source populations, contemporary connectivity, and metapopulation dynamics. Both markers indicated high population structuring consistent with origins from multiple introductions and low subsequent gene flow. Landscape-genetic modeling indicated that population connectivity was especially low among coastal sampling sites surrounded by mountainous wildlands but somewhat higher through topographically flat, urban and agricultural landscapes. The genetic composition of populations tended to be stable for multiple generations, indicating a degree of demographic resilience to predator removal programs. However, in two sites where intensive predator control reduced fox abundance, we observed increases in immigration, suggesting potential for recolonization to counter eradication attempts. These findings, along with continued genetic monitoring, can help guide localized management of foxes by identifying points of introductions and routes of spread and evaluating the relative importance of reproduction and immigration in maintaining populations. More generally, the study illustrates the utility of a landscape-genetic approach for understanding invasion dynamics and metapopulation structure of one of the world's most destructive invasive mammals, the red fox.

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

Science.gov (United States)

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

2016-01-01

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

Drag reduction of a car model by linear genetic programming control

Science.gov (United States)

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

2017-08-01

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

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

Science.gov (United States)

Tomlinson, Carol Ann

2009-01-01

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

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

OpenAIRE

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

2015-01-01

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

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

Science.gov (United States)

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

2011-09-01

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

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

Science.gov (United States)

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

2014-01-01

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

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

Science.gov (United States)

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

2018-03-01

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

High performance parallelism pearls 2 multicore and many-core programming approaches

CERN Document Server

Jeffers, Jim

2015-01-01

High Performance Parallelism Pearls Volume 2 offers another set of examples that demonstrate how to leverage parallelism. Similar to Volume 1, the techniques included here explain how to use processors and coprocessors with the same programming - illustrating the most effective ways to combine Xeon Phi coprocessors with Xeon and other multicore processors. The book includes examples of successful programming efforts, drawn from across industries and domains such as biomed, genetics, finance, manufacturing, imaging, and more. Each chapter in this edited work includes detailed explanations of t

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

Science.gov (United States)

Tsai, Yi-Hsin E

2011-05-01

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

An approach for solving linear fractional programming problems ...

African Journals Online (AJOL)

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

A Parallel Approach To Optimum Actuator Selection With a Genetic Algorithm

Science.gov (United States)

Rogers, James L.

2000-01-01

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

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

Science.gov (United States)

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

2013-11-01

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

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

Science.gov (United States)

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

2017-06-07

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

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

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Chitra Raghavan

2017-06-01

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

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

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Aleksander Mendyk

2015-01-01

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

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

Science.gov (United States)

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

2017-12-01

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

A New Approach to Programming Language Education for Beginners with Top-Down Learning

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Daisuke Saito

2013-12-01

Full Text Available There are two basic approaches in learning new programming language: a bottom-up approach and a top-down approach. It has been said that if a learner has already acquired one language, the top-down approach is more efficient to learn another while, for a person who has absolutely no knowledge of any programming languages; the bottom-up approach is preferable. The major problem of the bottom-up approach is that it requires longer period to acquire the language. For quicker learning, this paper applies a top-down approach for a beginners who has not yet acquired any programming languages.

Linear genetic programming application for successive-station monthly streamflow prediction

Science.gov (United States)

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

2014-09-01

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

A comparison of fitness-case sampling methods for genetic programming

Science.gov (United States)

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

2017-11-01

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

The underlying mechanisms of genetic innovation and speciation in the family Corynebacteriaceae: A phylogenomics approach.

Science.gov (United States)

Zhi, Xiao-Yang; Jiang, Zhao; Yang, Ling-Ling; Huang, Ying

2017-02-01

The pangenome of a bacterial species population is formed by genetic reduction and genetic expansion over the long course of evolution. Gene loss is a pervasive source of genetic reduction, and (exogenous and endogenous) gene gain is the main driver of genetic expansion. To understand the genetic innovation and speciation of the family Corynebacteriaceae, which cause a wide range of serious infections in humans and animals, we analyzed the pangenome of this family, and reconstructed its phylogeny using a phylogenomics approach. Genetic variations have occurred throughout the whole evolutionary history of the Corynebacteriaceae. Gene loss has been the primary force causing genetic changes, not only in terms of the number of protein families affected, but also because of its continuity on the time series. The variation in metabolism caused by these genetic changes mainly occurred for membrane transporters, two-component systems, and metabolism related to amino acids and carbohydrates. Interestingly, horizontal gene transfer (HGT) not only caused changes related to pathogenicity, but also triggered the acquisition of antimicrobial resistance. The Darwinian theory of evolution did not adequately explain the effects of dispersive HGT and/or gene loss in the evolution of the Corynebacteriaceae. These findings provide new insight into the evolution and speciation of Corynebacteriaceae and advance our understanding of the genetic innovation in microbial populations. Copyright © 2016 Elsevier Inc. All rights reserved.

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

International Nuclear Information System (INIS)

Tavakkoli-Moghaddam, R.

1999-01-01

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

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

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Alireza TaheriMoghadam

2014-05-01

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

Role-playing is an effective instructional strategy for genetic counseling training: an investigation and comparative study.

Science.gov (United States)

Xu, Xiao-Feng; Wang, Yan; Wang, Yan-Yan; Song, Ming; Xiao, Wen-Gang; Bai, Yun

2016-09-02

Genetic diseases represent a significant public health challenge in China that will need to be addressed by a correspondingly large number of professional genetic counselors. However, neither an official training program for genetic counseling, nor formal board certification, was available in China before 2015. In 2009, a genetic counseling training program based on role-playing was implemented as a pilot study at the Third Military Medical University to train third-year medical students. Questionnaires on participant attitudes to the program and role-playing were randomly administered to 324 students after they had finished their training. Pre- and post-training instructional tests, focusing on 42 key components of genetic counseling, were administered randomly to 200 participants to assess mastery of each component. Finally, scores in final examinations of 578 participants from 2009 to 2011 were compared to scores obtained by 614 non-participating students from 2006 to 2008 to further assess program efficacy. Both the training program and the instructional strategy of role-playing were accepted by most participants. Students believed that role-playing improved their practice of genetic counseling and medical genetics, enhanced their communication skills, and would likely contribute to future professional performance. The average understanding of 40 of the key points in genetic counseling was significantly improved, and most students approached excellent levels of mastery. Scores in final examinations and the percentages of students scoring above 90 were also significantly elevated. Role-playing is a feasible and effective instructional strategy for training genetic counselors in China as well as in other developing countries.

Multifaceted Approach to Designing an Online Masters Program.

Science.gov (United States)

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

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

Genetic Programming and Standardization in Water Temperature Modelling

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Maritza Arganis

2009-01-01

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

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

Science.gov (United States)

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

2010-03-01

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

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

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Ricardo Ribeiro

2013-11-01

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

Genetic divergence of rubber tree estimated by multivariate techniques and microsatellite markers

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Lígia Regina Lima Gouvêa

2010-01-01

Full Text Available Genetic diversity of 60 Hevea genotypes, consisting of Asiatic, Amazonian, African and IAC clones, and pertaining to the genetic breeding program of the Agronomic Institute (IAC, Brazil, was estimated. Analyses were based on phenotypic multivariate parameters and microsatellites. Five agronomic descriptors were employed in multivariate procedures, such as Standard Euclidian Distance, Tocher clustering and principal component analysis. Genetic variability among the genotypes was estimated with 68 selected polymorphic SSRs, by way of Modified Rogers Genetic Distance and UPGMA clustering. Structure software in a Bayesian approach was used in discriminating among groups. Genetic diversity was estimated through Nei's statistics. The genotypes were clustered into 12 groups according to the Tocher method, while the molecular analysis identified six groups. In the phenotypic and microsatellite analyses, the Amazonian and IAC genotypes were distributed in several groups, whereas the Asiatic were in only a few. Observed heterozygosity ranged from 0.05 to 0.96. Both high total diversity (H T' = 0.58 and high gene differentiation (Gst' = 0.61 were observed, and indicated high genetic variation among the 60 genotypes, which may be useful for breeding programs. The analyzed agronomic parameters and SSRs markers were effective in assessing genetic diversity among Hevea genotypes, besides proving to be useful for characterizing genetic variability.

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

Energy Technology Data Exchange (ETDEWEB)

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

2013-07-12

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

An Improved Dynamic Programming Decomposition Approach for Network Revenue Management

OpenAIRE

Dan Zhang

2011-01-01

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

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

Czech Academy of Sciences Publication Activity Database

Outrata, Jiří; Červinka, Michal

2009-01-01

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

A cutting- plane approach for semi- infinite mathematical programming

African Journals Online (AJOL)

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

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

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Konstantinos Salpasaranis

2012-01-01

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

AGROBACTERIUM-MEDIATED GENETIC TRANSFORMATION OF SORGHUM USING TISSUE CULTURE-BASED AND POLLEN-MEDIATED APPROACHES

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Elkonin L.A.

2012-08-01

Full Text Available Genetic transformation is a powerful tool for genetic improvement of arable crops. Genetic engineering approaches are especially important for modification of starch and protein contents, vitamin and micronutrient concentration, improvement of nutritive value of protein fractions, and increase tolerance to environmental stresses. Application of transgenic technologies for genetic improvement of sorghum, a highly productive heat tolerant and drought resistant crop, is extremely important since climate aridization in many regions all over the globe hampers sustainable production of traditional cereals, such as wheat, maize and barley. However, sorghum, in spite of great number of investigations, is one of the most recalcitrant crop species to genetic modification. The most frequently reported problems are a low frequency of transformation and silencing of transgenes. Using the A. tumefaciens strain AGL0/p35SGIB with the bar and gus-intron genes under the nos and CaMV35S promoters, respectively, we studied different methods of Agrobacterium-mediated genetic transformation of the grain sorghum: in vitro culture-based techniques, by inoculation of immature embryos or embryo-derived calli, and pollen-mediated approach, by inoculation of flowering panicles. Four lines of grain sorghum – Milo-10, [9E] Milo-10 (CMS-line, KVV-114, and KVV-45 – were used. In both approaches, for activation of vir-genes agrobacterial cell suspension was grown in the AB or modified AB media with acetosyringone at room temperature. In vitro culture approach was effective for obtaining transgenic plants in the lines Milo-10 and KVV-45, which were able to produce embryogenic callus from immature embryos after their co-cultivation with agrobacterial cell suspension. Callus cultures tolerant to glufosinate ammonium (GA and capable to plant regeneration were obtained. The frequency of immature embryos producing PCR-positive transgenic plants varied in different experiments

Enhancing genetic gain in the era of molecular breeding.

Science.gov (United States)

Xu, Yunbi; Li, Ping; Zou, Cheng; Lu, Yanli; Xie, Chuanxiao; Zhang, Xuecai; Prasanna, Boddupalli M; Olsen, Michael S

2017-05-17

As one of the important concepts in conventional quantitative genetics and breeding, genetic gain can be defined as the amount of increase in performance that is achieved annually through artificial selection. To develop pro ducts that meet the increasing demand of mankind, especially for food and feed, in addition to various industrial uses, breeders are challenged to enhance the potential of genetic gain continuously, at ever higher rates, while they close the gaps that remain between the yield potential in breeders' demonstration trials and the actual yield in farmers' fields. Factors affecting genetic gain include genetic variation available in breeding materials, heritability for traits of interest, selection intensity, and the time required to complete a breeding cycle. Genetic gain can be improved through enhancing the potential and closing the gaps, which has been evolving and complemented with modern breeding techniques and platforms, mainly driven by molecular and genomic tools, combined with improved agronomic practice. Several key strategies are reviewed in this article. Favorable genetic variation can be unlocked and created through molecular and genomic approaches including mutation, gene mapping and discovery, and transgene and genome editing. Estimation of heritability can be improved by refining field experiments through well-controlled and precisely assayed environmental factors or envirotyping, particularly for understanding and controlling spatial heterogeneity at the field level. Selection intensity can be significantly heightened through improvements in the scale and precision of genotyping and phenotyping. The breeding cycle time can be shortened by accelerating breeding procedures through integrated breeding approaches such as marker-assisted selection and doubled haploid development. All the strategies can be integrated with other widely used conventional approaches in breeding programs to enhance genetic gain. More transdisciplinary

Contemporary Genetics for Gender Researchers: Not Your Grandma's Genetics Anymore

Science.gov (United States)

Salk, Rachel H.; Hyde, Janet S.

2012-01-01

Over the past century, much of genetics was deterministic, and feminist researchers framed justified criticisms of genetics research. However, over the past two decades, genetics research has evolved remarkably and has moved far from earlier deterministic approaches. Our article provides a brief primer on modern genetics, emphasizing contemporary…

The Genetics of Obsessive-Compulsive Disorder and Tourette Syndrome: An Epidemiological and Pathway-Based Approach for Gene Discovery

Science.gov (United States)

Grados, Marco A.

2010-01-01

Objective: To provide a contemporary perspective on genetic discovery methods applied to obsessive-compulsive disorder (OCD) and Tourette syndrome (TS). Method: A review of research trends in genetics research in OCD and TS is conducted, with emphasis on novel approaches. Results: Genome-wide association studies (GWAS) are now in progress in OCD…

Adults' perceptions of genetic counseling and genetic testing.

Science.gov (United States)

Houfek, Julia Fisco; Soltis-Vaughan, Brigette S; Atwood, Jan R; Reiser, Gwendolyn M; Schaefer, G Bradley

2015-02-01

This study described the perceptions of genetic counseling and testing of adults (N = 116) attending a genetic education program. Understanding perceptions of genetic counseling, including the importance of counseling topics, will contribute to patient-focused care as clinical genetic applications for common, complex disorders evolve. Participants completed a survey addressing: the importance of genetic counseling topics, benefits and negative effects of genetic testing, and sharing test results. Topics addressing practical information about genetic conditions were rated most important; topics involving conceptual genetic/genomic principles were rated least important. The most frequently identified benefit and negative effect of testing were prevention/early detection/treatment and psychological distress. Participants perceived that they were more likely to share test results with first-degree than other relatives. Findings suggest providing patients with practical information about genetic testing and genetic contributions to disease, while also determining whether their self-care abilities would be enhanced by teaching genetic/genomic principles. Copyright © 2014 Elsevier Inc. All rights reserved.

Concurrent object-oriented programming: The MP-Eiffel approach

OpenAIRE

Silva, Miguel Augusto Mendes Oliveira e

2004-01-01

This article evaluates several possible approaches for integrating concurrency into object-oriented programming languages, presenting afterwards, a new language named MP-Eiffel. MP-Eiffel was designed attempting to include all the essential properties of both concurrent and object-oriented programming with simplicity and safety. A special care was taken to achieve the orthogonality of all the language mechanisms, allowing their joint use without unsafe side-effects (such as inh...

Simulating a base population in honey bee for molecular genetic studies.

Science.gov (United States)

Gupta, Pooja; Conrad, Tim; Spötter, Andreas; Reinsch, Norbert; Bienefeld, Kaspar

2012-06-27

Over the past years, reports have indicated that honey bee populations are declining and that infestation by an ecto-parasitic mite (Varroa destructor) is one of the main causes. Selective breeding of resistant bees can help to prevent losses due to the parasite, but it requires that a robust breeding program and genetic evaluation are implemented. Genomic selection has emerged as an important tool in animal breeding programs and simulation studies have shown that it yields more accurate breeding value estimates, higher genetic gain and low rates of inbreeding. Since genomic selection relies on marker data, simulations conducted on a genomic dataset are a pre-requisite before selection can be implemented. Although genomic datasets have been simulated in other species undergoing genetic evaluation, simulation of a genomic dataset specific to the honey bee is required since this species has a distinct genetic and reproductive biology. Our software program was aimed at constructing a base population by simulating a random mating honey bee population. A forward-time population simulation approach was applied since it allows modeling of genetic characteristics and reproductive behavior specific to the honey bee. Our software program yielded a genomic dataset for a base population in linkage disequilibrium. In addition, information was obtained on (1) the position of markers on each chromosome, (2) allele frequency, (3) χ(2) statistics for Hardy-Weinberg equilibrium, (4) a sorted list of markers with a minor allele frequency less than or equal to the input value, (5) average r(2) values of linkage disequilibrium between all simulated marker loci pair for all generations and (6) average r2 value of linkage disequilibrium in the last generation for selected markers with the highest minor allele frequency. We developed a software program that takes into account the genetic and reproductive biology specific to the honey bee and that can be used to constitute a genomic

Constraint Logic Programming approach to protein structure prediction

Directory of Open Access Journals (Sweden)

Fogolari Federico

2004-11-01

Full Text Available Abstract Background The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Results Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. Conclusions The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

Constraint Logic Programming approach to protein structure prediction.

Science.gov (United States)

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

2004-11-30

The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known) secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

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

Science.gov (United States)

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

2011-12-22

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

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

International Nuclear Information System (INIS)

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

1998-01-01

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

A Comparison of Genetic Programming Variants for Hyper-Heuristics

Energy Technology Data Exchange (ETDEWEB)

Harris, Sean [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-03-01

Modern society is faced with ever more complex problems, many of which can be formulated as generate-and-test optimization problems. General-purpose optimization algorithms are not well suited for real-world scenarios where many instances of the same problem class need to be repeatedly and efficiently solved, such as routing vehicles over highways with constantly changing traffic flows, because they are not targeted to a particular scenario. Hyper-heuristics automate the design of algorithms to create a custom algorithm for a particular scenario. Hyper-heuristics typically employ Genetic Programming (GP) and this project has investigated the relationship between the choice of GP and performance in Hyper-heuristics. Results are presented demonstrating the existence of problems for which there is a statistically significant performance differential between the use of different types of GP.

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

Science.gov (United States)

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

2018-05-01

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

A stochastic programming approach to manufacturing flow control

OpenAIRE

Haurie, Alain; Moresino, Francesco

2012-01-01

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

Linear decomposition approach for a class of nonconvex programming problems.

Science.gov (United States)

Shen, Peiping; Wang, Chunfeng

2017-01-01

This paper presents a linear decomposition approach for a class of nonconvex programming problems by dividing the input space into polynomially many grids. It shows that under certain assumptions the original problem can be transformed and decomposed into a polynomial number of equivalent linear programming subproblems. Based on solving a series of liner programming subproblems corresponding to those grid points we can obtain the near-optimal solution of the original problem. Compared to existing results in the literature, the proposed algorithm does not require the assumptions of quasi-concavity and differentiability of the objective function, and it differs significantly giving an interesting approach to solving the problem with a reduced running time.

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

Directory of Open Access Journals (Sweden)

Zibei Lin

2016-03-01

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

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

Science.gov (United States)

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

2016-03-01

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

The challenges and promises of genetic approaches for ballast water management

Science.gov (United States)

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

2018-03-01

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

Self-regulated learning in higher education: strategies adopted by computer programming students when supported by the SimProgramming approach

Directory of Open Access Journals (Sweden)

Daniela Pedrosa

Full Text Available Abstract The goal of the SimProgramming approach is to help students overcome their learning difficulties in the transition from entry-level to advanced computer programming, developing an appropriate set of learning strategies. We implemented it at the University of Trás-os-Montes e Alto Douro (Portugal, in two courses (PM3 and PM4 of the bachelor programmes in Informatics Engineering and ICT. We conducted semi-structured interviews with students (n=38 at the end of the courses, to identify the students’ strategies for self-regulation of learning in the assignment. We found that students changed some of their strategies from one course edition to the following one and that changes are related to the SimProgramming approach. We believe that changes to the educational approach were appropriate to support the assignment goals. We recommend applying the SimProgramming approach in other educational contexts, to improve educational practices by including techniques to help students in their learning.

Power assessment for genetic association study of human longevity using offspring of long-lived subjects

DEFF Research Database (Denmark)

Tan, Qihua; Zhao, Jing Hua; Li, Shuxia

2010-01-01

and the proportional hazard model for generating individual lifespan. Family genotype data is generated using a genetic linkage program for given SNP allele frequency. Power is estimated by setting the type I error rate at 0.05 and by calculating the Armitage's chi-squared test statistic for 200 replicate samples...... the direct approach. It also has low power in detecting non-additive effect genes. Indirect genetic association using offspring from families with both parents as nonagenarians is nearly as powerful as using offspring from families with one centenarian parent. In conclusion, the indirect design can be a good......Recently, an indirect genetic association approach that compares genotype frequencies in offspring of long-lived subjects and offspring from random families has been introduced to study gene-longevity associations. Although the indirect genetic association has certain advantages over the direct...

Search for major genes with progeny test data to accelerate the development of genetically superior loblolly pine

Energy Technology Data Exchange (ETDEWEB)

NCSU

2003-12-30

This research project is to develop a novel approach that fully utilized the current breeding materials and genetic test information available from the NCSU-Industry Cooperative Tree Improvement Program to identify major genes that are segregating for growth and disease resistance in loblolly pine. If major genes can be identified in the existing breeding population, they can be utilized directly in the conventional loblolly pine breeding program. With the putative genotypes of parents identified, tree breeders can make effective decisions on management of breeding populations and operational deployment of genetically superior trees. Forest productivity will be significantly enhanced if genetically superior genotypes with major genes for economically important traits could be deployed in an operational plantation program. The overall objective of the project is to develop genetic model and analytical methods for major gene detection with progeny test data and accelerate the development of genetically superior loblolly pine. Specifically, there are three main tasks: (1) Develop genetic models for major gene detection and implement statistical methods and develop computer software for screening progeny test data; (2) Confirm major gene segregation with molecular markers; and (3) Develop strategies for using major genes for tree breeding.

Nuclear power plant maintenance scheduling dilemma: a genetic algorithm approach

International Nuclear Information System (INIS)

Mahdavi, M.H.; Modarres, M.

2004-01-01

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

Non-linear nuclear engineering models as genetic programming application; Modelos nao-lineares de engenharia nuclear como aplicacao de programacao genetica

Energy Technology Data Exchange (ETDEWEB)

Domingos, Roberto P.; Schirru, Roberto; Martinez, Aquilino S. [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia

1997-12-01

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

Application of genetic programming in shape optimization of concrete gravity dams by metaheuristics

Directory of Open Access Journals (Sweden)

Abdolhossein Baghlani

2014-12-01

Full Text Available A gravity dam maintains its stability against the external loads by its massive size. Hence, minimization of the weight of the dam can remarkably reduce the construction costs. In this paper, a procedure for finding optimal shape of concrete gravity dams with a computationally efficient approach is introduced. Genetic programming (GP in conjunction with metaheuristics is used for this purpose. As a case study, shape optimization of the Bluestone dam is presented. Pseudo-dynamic analysis is carried out on a total number of 322 models in order to establish a database of the results. This database is then used to find appropriate relations based on GP for design criteria of the dam. This procedure eliminates the necessity of the time-consuming process of structural analyses in evolutionary optimization methods. The method is hybridized with three different metaheuristics, including particle swarm optimization, firefly algorithm (FA, and teaching–learning-based optimization, and a comparison is made. The results show that although all algorithms are very suitable, FA is slightly superior to other two algorithms in finding a lighter structure in less number of iterations. The proposed method reduces the weight of dam up to 14.6% with very low computational effort.

On Gene Concepts and Teaching Genetics: Episodes from Classical Genetics

Science.gov (United States)

Burian, Richard M.

2013-01-01

This paper addresses the teaching of advanced high school courses or undergraduate courses for non-biology majors about genetics or history of genetics. It will probably be difficult to take the approach described here in a high school science course, although the general approach could help improve such courses. It would be ideal for a college…

Unleashing the power of human genetic variation knowledge: New Zealand stakeholder perspectives.

Science.gov (United States)

Gu, Yulong; Warren, James Roy; Day, Karen Jean

2011-01-01

This study aimed to characterize the challenges in using genetic information in health care and to identify opportunities for improvement. Taking a grounded theory approach, semistructured interviews were conducted with 48 participants to collect multiple stakeholder perspectives on genetic services in New Zealand. Three themes emerged from the data: (1) four service delivery models were identified in operation, including both those expected models involving genetic counselors and variations that do not route through the formal genetic service program; (2) multiple barriers to sharing and using genetic information were perceived, including technological, organizational, institutional, legal, ethical, and social issues; and (3) impediments to wider use of genetic testing technology, including variable understanding of genetic test utilities among clinicians and the limited capacity of clinical genetic services. Targeting these problems, information technologies and knowledge management tools have the potential to support key tasks in genetic services delivery, improve knowledge processes, and enhance knowledge networks. Because of the effect of issues in genetic information and knowledge management, the potential of human genetic variation knowledge to enhance health care delivery has been put on a "leash."

On Gene Concepts and Teaching Genetics: Episodes from Classical Genetics

Science.gov (United States)

Burian, Richard M.

2013-02-01

This paper addresses the teaching of advanced high school courses or undergraduate courses for non-biology majors about genetics or history of genetics. It will probably be difficult to take the approach described here in a high school science course, although the general approach could help improve such courses. It would be ideal for a college course in history of genetics or a course designed to teach non-science majors how science works or the rudiments of the genetics in a way that will help them as citizens. The approach aims to teach the processes of discovery, correction, and validation by utilizing illustrative episodes from the history of genetics. The episodes are treated in way that should foster understanding of basic questions about genes, the sorts of techniques used to answer questions about the constitution and structure of genes, how they function, and what they determine, and some of the major biological disagreements that arose in dealing with these questions. The material covered here could be connected to social and political issues raised by genetics, but these connections are not surveyed here. As it is, to cover this much territory, the article is limited to four major episodes from Mendel's paper to the beginning of World War II. A sequel will deal with the molecularization of genetics and with molecular gene concepts through the Human Genome Project.

Implementation of inpatient models of pharmacogenetics programs.

Science.gov (United States)

Cavallari, Larisa H; Lee, Craig R; Duarte, Julio D; Nutescu, Edith A; Weitzel, Kristin W; Stouffer, George A; Johnson, Julie A

2016-12-01

The operational elements essential for establishing an inpatient pharmacogenetic service are reviewed, and the role of the pharmacist in the provision of genotype-guided drug therapy in pharmacogenetics programs at three institutions is highlighted. Pharmacists are well positioned to assume important roles in facilitating the clinical use of genetic information to optimize drug therapy given their expertise in clinical pharmacology and therapeutics. Pharmacists have assumed important roles in implementing inpatient pharmacogenetics programs. This includes programs designed to incorporate genetic test results to optimize antiplatelet drug selection after percutaneous coronary intervention and personalize warfarin dosing. Pharmacist involvement occurs on many levels, including championing and leading pharmacogenetics implementation efforts, establishing clinical processes to support genotype-guided therapy, assisting the clinical staff with interpreting genetic test results and applying them to prescribing decisions, and educating other healthcare providers and patients on genomic medicine. The three inpatient pharmacogenetics programs described use reactive versus preemptive genotyping, the most feasible approach under the current third-party payment structure. All three sites also follow Clinical Pharmacogenetics Implementation Consortium guidelines for drug therapy recommendations based on genetic test results. With the clinical emergence of pharmacogenetics into the inpatient setting, it is important that pharmacists caring for hospitalized patients are well prepared to serve as experts in interpreting and applying genetic test results to guide drug therapy decisions. Since genetic test results may not be available until after patient discharge, pharmacists practicing in the ambulatory care setting should also be prepared to assist with genotype-guided drug therapy as part of transitions in care. Copyright © 2016 by the American Society of Health

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

Science.gov (United States)

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

The key in stock trading model is to take the right actions for trading at the right time, primarily based on the accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to creating a stock trading model. In this paper, we propose a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are three important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the candlestick charts according to the real time stock prices. Second, we combine RTU-GNP with a Sarsa learning algorithm to create the programs efficiently. Also, sub-nodes are introduced in each judgment and processing node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. Third, a Real Time Updating system has been firstly introduced in our paper considering the change of the trend of stock prices. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without real time updating. We also compared the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.

Dynamic Programming Approach for Exact Decision Rule Optimization

KAUST Repository

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

2013-01-01

This chapter is devoted to the study of an extension of dynamic programming approach that allows sequential optimization of exact decision rules relative to the length and coverage. It contains also results of experiments with decision tables from

Structural health monitoring feature design by genetic programming

International Nuclear Information System (INIS)

Harvey, Dustin Y; Todd, Michael D

2014-01-01

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

Integrating modelling and phenotyping approaches to identify and screen complex traits - Illustration for transpiration efficiency in cereals.

Science.gov (United States)

Chenu, K; van Oosterom, E J; McLean, G; Deifel, K S; Fletcher, A; Geetika, G; Tirfessa, A; Mace, E S; Jordan, D R; Sulman, R; Hammer, G L

2018-02-21

Following advances in genetics, genomics, and phenotyping, trait selection in breeding is limited by our ability to understand interactions within the plants and with their environments, and to target traits of most relevance for the target population of environments. We propose an integrated approach that combines insights from crop modelling, physiology, genetics, and breeding to identify traits valuable for yield gain in the target population of environments, develop relevant high-throughput phenotyping platforms, and identify genetic controls and their values in production environments. This paper uses transpiration efficiency (biomass produced per unit of water used) as an example of a complex trait of interest to illustrate how the approach can guide modelling, phenotyping, and selection in a breeding program. We believe that this approach, by integrating insights from diverse disciplines, can increase the resource use efficiency of breeding programs for improving yield gains in target populations of environments.

Genetic algorithms and fuzzy multiobjective optimization

CERN Document Server

Sakawa, Masatoshi

2002-01-01

Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a w...

Genetic Approaches to Develop Salt Tolerant Germplasm

KAUST Repository

Tester, Mark A.

2015-08-19

Forty percent of the world\\'s food is produced under irrigation, and this is directly threatened by over-exploitation and changes in the global environment. One way to address this threat is to develop systems for increasing our ability to use lower quality water, in particular saline water. Low cost partial desalination of brackish water, use of saline water for cooling and increases in the salinity tolerance of crops can all contribute to the development of this new agricultural system. In this talk, the focus will be on the use of forward genetic approaches for discovery of genes related to salinity tolerance in barley and tomatoes. Rather than studying salinity tolerance as a trait in itself, we dissect salinity tolerance into a series of components that are hypothesised to contribute to overall salinity tolerance (following the paradigm of Munns & Tester, 2008). For example, one significant component of tolerance of most crop plants to moderate soil salinity is due to the ability to maintain low concentrations of Na+ in the leaves, and much analysis of this aspect has been done (e.g. Roy et al., 2013, 2014). A major site for the control of shoot Na+ accumulation is at the plasma membrane of the mature stele of the root. Alleles of HKT, a major gene underlying this transport process have been characterized and, in work led by Dr Rana Munns (CSIRO), have now been introgressed into commercial durum wheat and led to significantly increased yields in saline field conditions (Munns et al., 2012). The genotyping of mapping populations is now highly efficient. However, the ability to quantitatively phenotype these populations is now commonly limiting forward progress in plant science. The increasing power of digital imaging and computational technologies offers the opportunity to relieve this phenotyping bottleneck. The Plant Accelerator is a 4500m2 growth facility that provides non-destructive phenotyping of large populations of plants (http

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

OpenAIRE

Hulle, Van, Dirk

2015-01-01

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

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

International Nuclear Information System (INIS)

Almeida, Jose Carlos Soares de

2001-02-01

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

Integrating Genetic Studies of Nicotine Addiction into Public Health Practice: Stakeholder Views on Challenges, Barriers and Opportunities

Science.gov (United States)

Dingel, M.J.; Hicks, A.D.; Robinson, M.E.; Koenig, B.A.

2011-01-01

Objective: Will emerging genetic research strengthen tobacco control programs? In this empirical study, we interview stakeholders in tobacco control to illuminate debates about the role of genomics in public health. Methods: The authors performed open-ended interviews with 86 stakeholders from 5 areas of tobacco control: basic scientists, clinicians, tobacco prevention specialists, health payers, and pharmaceutical industry employees. Interviews were qualitatively analyzed using standard techniques. Results: The central tension is between the hope that an expanding genomic knowledge base will improve prevention and smoking cessation therapies and the fear that genetic research might siphon resources away from traditional and proven public health programs. While showing strong support for traditional public health approaches to tobacco control, stakeholders recognize weaknesses, specifically the difficulty of countering the powerful voice of the tobacco industry when mounting public campaigns and the problem of individuals who are resistant to treatment and continue smoking. Conclusions: In order for genetic research to be effectively translated into efforts to minimize the harm of smoking-related disease, the views of key stakeholders must be voiced and disagreements reconciled. Effective translation requires honest evaluation of both the strengths and limitations of genetic approaches. PMID:21757875

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

Science.gov (United States)

Xita, Nectaria; Tsatsoulis, Agathocles

2006-05-01

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

Diversity and genetic stability in banana genotypes in a breeding program using inter simple sequence repeats (ISSR) markers.

Science.gov (United States)

Silva, A V C; Nascimento, A L S; Vitória, M F; Rabbani, A R C; Soares, A N R; Lédo, A S

2017-02-23

Banana (Musa spp) is a fruit species frequently cultivated and consumed worldwide. Molecular markers are important for estimating genetic diversity in germplasm and between genotypes in breeding programs. The objective of this study was to analyze the genetic diversity of 21 banana genotypes (FHIA 23, PA42-44, Maçã, Pacovan Ken, Bucaneiro, YB42-47, Grand Naine, Tropical, FHIA 18, PA94-01, YB42-17, Enxerto, Japira, Pacovã, Prata-Anã, Maravilha, PV79-34, Caipira, Princesa, Garantida, and Thap Maeo), by using inter-simple sequence repeat (ISSR) markers. Material was generated from the banana breeding program of Embrapa Cassava & Fruits and evaluated at Embrapa Coastal Tablelands. The 12 primers used in this study generated 97.5% polymorphism. Four clusters were identified among the different genotypes studied, and the sum of the first two principal components was 48.91%. From the Unweighted Pair Group Method using Arithmetic averages (UPGMA) dendrogram, it was possible to identify two main clusters and subclusters. Two genotypes (Garantida and Thap Maeo) remained isolated from the others, both in the UPGMA clustering and in the principal cordinate analysis (PCoA). Using ISSR markers, we could analyze the genetic diversity of the studied material and state that these markers were efficient at detecting sufficient polymorphism to estimate the genetic variability in banana genotypes.

Training Program Handbook: A systematic approach to training

Energy Technology Data Exchange (ETDEWEB)

1994-08-01

This DOE handbook describes a systematic method for establishing and maintaining training programs that meet the requirements and expectations of DOE Orders 5480.18B and 5480.20. The systematic approach to training includes 5 phases: Analysis, design, development, implementation, and evaluation.

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

DEFF Research Database (Denmark)

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

2015-01-01

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

Genetic Parameters and the Impact of Off-Types for Theobroma cacao L. in a Breeding Program in Brazil

Science.gov (United States)

DuVal, Ashley; Gezan, Salvador A.; Mustiga, Guiliana; Stack, Conrad; Marelli, Jean-Philippe; Chaparro, José; Livingstone, Donald; Royaert, Stefan; Motamayor, Juan C.

2017-01-01

Breeding programs of cacao (Theobroma cacao L.) trees share the many challenges of breeding long-living perennial crops, and genetic progress is further constrained by both the limited understanding of the inheritance of complex traits and the prevalence of technical issues, such as mislabeled individuals (off-types). To better understand the genetic architecture of cacao, in this study, 13 years of phenotypic data collected from four progeny trials in Bahia, Brazil were analyzed jointly in a multisite analysis. Three separate analyses (multisite, single site with and without off-types) were performed to estimate genetic parameters from statistical models fitted on nine important agronomic traits (yield, seed index, pod index, % healthy pods, % pods infected with witches broom, % of pods other loss, vegetative brooms, diameter, and tree height). Genetic parameters were estimated along with variance components and heritabilities from the multisite analysis, and a trial was fingerprinted with low-density SNP markers to determine the impact of off-types on estimations. Heritabilities ranged from 0.37 to 0.64 for yield and its components and from 0.03 to 0.16 for disease resistance traits. A weighted index was used to make selections for clonal evaluation, and breeding values estimated for the parental selection and estimation of genetic gain. The impact of off-types to breeding progress in cacao was assessed for the first time. Even when present at <5% of the total population, off-types altered selections by 48%, and impacted heritability estimations for all nine of the traits analyzed, including a 41% difference in estimated heritability for yield. These results show that in a mixed model analysis, even a low level of pedigree error can significantly alter estimations of genetic parameters and selections in a breeding program. PMID:29250097

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

Science.gov (United States)

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

2015-11-01

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

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

OpenAIRE

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

2016-01-01

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

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling.

Science.gov (United States)

Kochunov, Peter; Jahanshad, Neda; Sprooten, Emma; Nichols, Thomas E; Mandl, René C; Almasy, Laura; Booth, Tom; Brouwer, Rachel M; Curran, Joanne E; de Zubicaray, Greig I; Dimitrova, Rali; Duggirala, Ravi; Fox, Peter T; Hong, L Elliot; Landman, Bennett A; Lemaitre, Hervé; Lopez, Lorna M; Martin, Nicholas G; McMahon, Katie L; Mitchell, Braxton D; Olvera, Rene L; Peterson, Charles P; Starr, John M; Sussmann, Jessika E; Toga, Arthur W; Wardlaw, Joanna M; Wright, Margaret J; Wright, Susan N; Bastin, Mark E; McIntosh, Andrew M; Boomsma, Dorret I; Kahn, René S; den Braber, Anouk; de Geus, Eco J C; Deary, Ian J; Hulshoff Pol, Hilleke E; Williamson, Douglas E; Blangero, John; van 't Ent, Dennis; Thompson, Paul M; Glahn, David C

2014-07-15

Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability. Copyright © 2014 Elsevier Inc. All rights reserved.

Automatic programming via iterated local search for dynamic job shop scheduling.

Science.gov (United States)

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

2015-01-01

Dispatching rules have been commonly used in practice for making sequencing and scheduling decisions. Due to specific characteristics of each manufacturing system, there is no universal dispatching rule that can dominate in all situations. Therefore, it is important to design specialized dispatching rules to enhance the scheduling performance for each manufacturing environment. Evolutionary computation approaches such as tree-based genetic programming (TGP) and gene expression programming (GEP) have been proposed to facilitate the design task through automatic design of dispatching rules. However, these methods are still limited by their high computational cost and low exploitation ability. To overcome this problem, we develop a new approach to automatic programming via iterated local search (APRILS) for dynamic job shop scheduling. The key idea of APRILS is to perform multiple local searches started with programs modified from the best obtained programs so far. The experiments show that APRILS outperforms TGP and GEP in most simulation scenarios in terms of effectiveness and efficiency. The analysis also shows that programs generated by APRILS are more compact than those obtained by genetic programming. An investigation of the behavior of APRILS suggests that the good performance of APRILS comes from the balance between exploration and exploitation in its search mechanism.

Forecasting tourist arrivals to balearic islands using genetic programming

Directory of Open Access Journals (Sweden)

Rosselló-Nadal, Jaume

2007-01-01

Full Text Available Traditionally, univariate time-series models have largely dominated forecasting for international tourism demand. In this paper, the ability of a Genetic Program (GP to predict monthly tourist arrivals from UK and Germany to Balearic Islands (Spain is explored. GP has already been employed satisfactorily in different scientific areas, including economics. The technique shows different advantages regarding to other forecasting methods. Firstly, it does not assume a priori a rigid functional form of the model. Secondly, it is more robust and easy-to-use than other non-parametric methods. Finally, it provides explicitly a mathematical equation which allows a simple ad hoc interpretation of the results. Comparing the performance of the proposed technique against other method commonly used in tourism forecasting (no-change model, Moving Average and ARIMA, the empirical results reveal that GP can be a valuable tool in this field.

Simulating a base population in honey bee for molecular genetic studies

Directory of Open Access Journals (Sweden)

Gupta Pooja

2012-06-01

Full Text Available Abstract Background Over the past years, reports have indicated that honey bee populations are declining and that infestation by an ecto-parasitic mite (Varroa destructor is one of the main causes. Selective breeding of resistant bees can help to prevent losses due to the parasite, but it requires that a robust breeding program and genetic evaluation are implemented. Genomic selection has emerged as an important tool in animal breeding programs and simulation studies have shown that it yields more accurate breeding value estimates, higher genetic gain and low rates of inbreeding. Since genomic selection relies on marker data, simulations conducted on a genomic dataset are a pre-requisite before selection can be implemented. Although genomic datasets have been simulated in other species undergoing genetic evaluation, simulation of a genomic dataset specific to the honey bee is required since this species has a distinct genetic and reproductive biology. Our software program was aimed at constructing a base population by simulating a random mating honey bee population. A forward-time population simulation approach was applied since it allows modeling of genetic characteristics and reproductive behavior specific to the honey bee. Results Our software program yielded a genomic dataset for a base population in linkage disequilibrium. In addition, information was obtained on (1 the position of markers on each chromosome, (2 allele frequency, (3 χ2 statistics for Hardy-Weinberg equilibrium, (4 a sorted list of markers with a minor allele frequency less than or equal to the input value, (5 average r2 values of linkage disequilibrium between all simulated marker loci pair for all generations and (6 average r2 value of linkage disequilibrium in the last generation for selected markers with the highest minor allele frequency. Conclusion We developed a software program that takes into account the genetic and reproductive biology specific to the honey bee

Genetic screening: programs, principles, and research--thirty years later. Reviewing the recommendations of the Committee for the Study of Inborn Errors of Metabolism (SIEM).

Science.gov (United States)

Simopoulos, A P

2009-01-01

Screening programs for genetic diseases and characteristics have multiplied in the last 50 years. 'Genetic Screening: Programs, Principles, and Research' is the report of the Committee for the Study of Inborn Errors of Metabolism (SIEM Committee) commissioned by the Division of Medical Sciences of the National Research Council at the National Academy of Sciences in Washington, DC, published in 1975. The report is considered a classic in the field worldwide, therefore it was thought appropriate 30 years later to present the Committee's modus operandi and bring the Committee's recommendations to the attention of those involved in genetics, including organizational, educational, legal, and research aspects of genetic screening. The Committee's report anticipated many of the legal, ethical, economic, social, medical, and policy aspects of genetic screening. The recommendations are current, and future committees should be familiar with them. In 1975 the Committee stated: 'As new screening tests are devised, they should be carefully reviewed. If the experimental rate of discovery of new genetic characteristics means an accelerating rate of appearance of new screening tests, now is the time to develop the medical and social apparatus to accommodate what later on may otherwise turn out to be unmanageable growth.' What a prophetic statement that was. If the Committee's recommendations had been implemented on time, there would be today a federal agency in existence, responsive and responsible to carry out the programs and support research on various aspects of genetic screening, including implementation of a federal law that protects consumers from discrimination by their employers and the insurance industry on the basis of genetic information. Copyright 2008 S. Karger AG, Basel.

Interactive Approach for Multi-Level Multi-Objective Fractional Programming Problems with Fuzzy Parameters

Directory of Open Access Journals (Sweden)

M.S. Osman

2018-03-01

Full Text Available In this paper, an interactive approach for solving multi-level multi-objective fractional programming (ML-MOFP problems with fuzzy parameters is presented. The proposed interactive approach makes an extended work of Shi and Xia (1997. In the first phase, the numerical crisp model of the ML-MOFP problem has been developed at a confidence level without changing the fuzzy gist of the problem. Then, the linear model for the ML-MOFP problem is formulated. In the second phase, the interactive approach simplifies the linear multi-level multi-objective model by converting it into separate multi-objective programming problems. Also, each separate multi-objective programming problem of the linear model is solved by the ∊-constraint method and the concept of satisfactoriness. Finally, illustrative examples and comparisons with the previous approaches are utilized to evince the feasibility of the proposed approach.

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

Science.gov (United States)

Russell, Liane B

2013-01-01

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

Genetic testing in congenital heart disease:A clinical approach

Institute of Scientific and Technical Information of China (English)

Marie A Chaix; Gregor Andelfinger; Paul Khairy

2016-01-01

Congenital heart disease(CHD) is the most common type of birth defect. Traditionally, a polygenic model defined by the interaction of multiple genes and environmental factors was hypothesized to account for different forms of CHD. It is now understood that the contribution of genetics to CHD extends beyond a single unified paradigm. For example, monogenic models and chromosomal abnormalities have been associated with various syndromic and non-syndromic forms of CHD. In such instances, genetic investigation and testing may potentially play an important role in clinical care. A family tree with a detailed phenotypic description serves as the initial screening tool to identify potentially inherited defects and to guide further genetic investigation. The selection of a genetic test is contingent upon the particular diagnostic hypothesis generated by clinical examination. Genetic investigation in CHD may carry the potential to improve prognosis by yielding valuable information with regards to personalized medical care, confidence in the clinical diagnosis, and/or targeted patient followup. Moreover, genetic assessment may serve as a tool to predict recurrence risk, define the pattern of inheritance within a family, and evaluate the need for further family screening. In some circumstances, prenatal or preimplantation genetic screening could identify fetuses or embryos at high risk for CHD. Although genetics may appear to constitute a highly specialized sector of cardiology, basic knowledge regarding inheritance patterns, recurrence risks, and available screening and diagnostic tools, including their strengths and limitations, could assist the treating physician in providing sound counsel.

PlayIt: Game Based Learning Approach for Teaching Programming Concepts

Science.gov (United States)

Mathrani, Anuradha; Christian, Shelly; Ponder-Sutton, Agate

2016-01-01

This study demonstrates a game-based learning (GBL) approach to engage students in learning and enhance their programming skills. The paper gives a detailed narrative of how an educational game was mapped with the curriculum of a prescribed programming course in a computing diploma study programme. Two separate student cohorts were invited to…

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: comparing meta and mega analytical approaches for data pooling

Science.gov (United States)

Kochunov, Peter; Jahanshad, Neda; Sprooten, Emma; Nichols, Thomas E.; Mandl, René C.; Almasy, Laura; Booth, Tom; Brouwer, Rachel M.; Curran, Joanne E.; de Zubicaray, Greig I.; Dimitrova, Rali; Duggirala, Ravi; Fox, Peter T.; Hong, L. Elliot; Landman, Bennett A.; Lemaitre, Hervé; Lopez, Lorna; Martin, Nicholas G.; McMahon, Katie L.; Mitchell, Braxton D.; Olvera, Rene L.; Peterson, Charles P.; Starr, John M.; Sussmann, Jessika E.; Toga, Arthur W.; Wardlaw, Joanna M.; Wright, Margaret J.; Wright, Susan N.; Bastin, Mark E.; McIntosh, Andrew M.; Boomsma, Dorret I.; Kahn, René S.; den Braber, Anouk; de Geus, Eco JC; Deary, Ian J.; Hulshoff Pol, Hilleke E.; Williamson, Douglas E.; Blangero, John; van ’t Ent, Dennis; Thompson, Paul M.; Glahn, David C.

2014-01-01

Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9–85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large “mega-family”. We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability. PMID:24657781

Genetic risks from radiation

International Nuclear Information System (INIS)

Selby, P.B.

Two widely-recognized committees, UNSCEAR and BEIR, have reevaluated their estimates of genetic risks from radiation. Their estimates for gene mutations are based on two different approaches, one being the doubling-dose approach and the other being a new direct approach based on an empirical determination of the amount of dominant induced damage in the skeletons of mice in the first generation following irradiation. The estimates made by these committees are in reasonably good agreement and suggest that the genetic risks from present exposures resultng from nuclear power production are small. There is room for much improvement in the reliability of the risk estimates. The relatively new approach of measuring the amount of induced damage to the mouse skeleton shows great promise of improving knowledge about how changes in the mutation frequency affect the incidence of genetic disorders. Such findings may have considerable influence on genetic risk estimates for radiation and on the development of risk estimates for other less-well-understood environmental mutagens. (author)

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

Science.gov (United States)

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

2016-04-18

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

[The role of the genetics history in genetics teaching].

Science.gov (United States)

Li, Ming-Hui

2006-08-01

The research of the scientific history and development status reflect the science and technology level of a nation. The genetic history is one of the branches of the life science and the 21st century is life science century. The genetics history in the teaching of genetics not only can help students get familiar with the birth and development of genetics, but also enhance their thinking ability and scientific qualities. The roles and approaches of teaching are discussed in this paper.

Interactive Fuzzy Goal Programming approach in multi-response stratified sample surveys

Directory of Open Access Journals (Sweden)

Gupta Neha

2016-01-01

Full Text Available In this paper, we applied an Interactive Fuzzy Goal Programming (IFGP approach with linear, exponential and hyperbolic membership functions, which focuses on maximizing the minimum membership values to determine the preferred compromise solution for the multi-response stratified surveys problem, formulated as a Multi- Objective Non Linear Programming Problem (MONLPP, and by linearizing the nonlinear objective functions at their individual optimum solution, the problem is approximated to an Integer Linear Programming Problem (ILPP. A numerical example based on real data is given, and comparison with some existing allocations viz. Cochran’s compromise allocation, Chatterjee’s compromise allocation and Khowaja’s compromise allocation is made to demonstrate the utility of the approach.

The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases.

Science.gov (United States)

Heidema, A Geert; Boer, Jolanda M A; Nagelkerke, Nico; Mariman, Edwin C M; van der A, Daphne L; Feskens, Edith J M

2006-04-21

Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analyzing the relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies. In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimized neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted. Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association

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

Science.gov (United States)

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

2014-01-01

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

Within a smoking-cessation program, what impact does genetic information on lung cancer need to have to demonstrate cost-effectiveness?

Directory of Open Access Journals (Sweden)

Gordon Louisa G

2010-09-01

Full Text Available Abstract Background Many smoking-cessation programs and pharmaceutical aids demonstrate substantial health gains for a relatively low allocation of resources. Genetic information represents a type of individualized or personal feedback regarding the risk of developing lung cancer, and hence the potential benefits from stopping smoking, may motivate the person to remain smoke-free. The purpose of this study was to explore what the impact of a genetic test needs to have within a typical smoking-cessation program aimed at heavy smokers in order to be cost-effective. Methods Two strategies were modelled for a hypothetical cohort of heavy smokers aged 50 years; individuals either received or did not receive a genetic test within the course of a usual smoking-cessation intervention comprising nicotine replacement therapy (NRT and counselling. A Markov model was constructed using evidence from published randomized controlled trials and meta-analyses for estimates on 12-month quit rates and long-term relapse rates. Epidemiological data were used for estimates on lung cancer risk stratified by time since quitting and smoking patterns. Extensive sensitivity analyses were used to explore parameter uncertainty. Results The discounted incremental cost per QALY was AU$34,687 (95% CI $12,483, $87,734 over 35 years. At a willingness-to-pay of AU$20,000 per QALY gained, the genetic testing strategy needs to produce a 12-month quit rate of at least 12.4% or a relapse rate 12% lower than NRT and counselling alone for it to be equally cost-effective. The likelihood that adding a genetic test to the usual smoking-cessation intervention is cost-effective was 20.6% however cost-effectiveness ratios were favourable in certain situations (e.g., applied to men only, a 60 year old cohort. Conclusions The findings were sensitive to small changes in critical variables such as the 12-month quit rates and relapse rates. As such, the cost-effectiveness of the genetic testing

Genetic testing in congenital heart disease: A clinical approach

Science.gov (United States)

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

2016-01-01

Congenital heart disease (CHD) is the most common type of birth defect. Traditionally, a polygenic model defined by the interaction of multiple genes and environmental factors was hypothesized to account for different forms of CHD. It is now understood that the contribution of genetics to CHD extends beyond a single unified paradigm. For example, monogenic models and chromosomal abnormalities have been associated with various syndromic and non-syndromic forms of CHD. In such instances, genetic investigation and testing may potentially play an important role in clinical care. A family tree with a detailed phenotypic description serves as the initial screening tool to identify potentially inherited defects and to guide further genetic investigation. The selection of a genetic test is contingent upon the particular diagnostic hypothesis generated by clinical examination. Genetic investigation in CHD may carry the potential to improve prognosis by yielding valuable information with regards to personalized medical care, confidence in the clinical diagnosis, and/or targeted patient follow-up. Moreover, genetic assessment may serve as a tool to predict recurrence risk, define the pattern of inheritance within a family, and evaluate the need for further family screening. In some circumstances, prenatal or preimplantation genetic screening could identify fetuses or embryos at high risk for CHD. Although genetics may appear to constitute a highly specialized sector of cardiology, basic knowledge regarding inheritance patterns, recurrence risks, and available screening and diagnostic tools, including their strengths and limitations, could assist the treating physician in providing sound counsel. PMID:26981213

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

DEFF Research Database (Denmark)

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

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

A genetic approach to shape reconstruction in limited data tomography

International Nuclear Information System (INIS)

Turcanu, C.; Craciunescu, T.

2001-01-01

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

Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach

Science.gov (United States)

Moeeni, Hamid; Bonakdari, Hossein; Ebtehaj, Isa

2017-03-01

Forecasting reservoir inflow is one of the most important components of water resources and hydroelectric systems operation management. Seasonal autoregressive integrated moving average (SARIMA) models have been frequently used for predicting river flow. SARIMA models are linear and do not consider the random component of statistical data. To overcome this shortcoming, monthly inflow is predicted in this study based on a combination of seasonal autoregressive integrated moving average (SARIMA) and gene expression programming (GEP) models, which is a new hybrid method (SARIMA-GEP). To this end, a four-step process is employed. First, the monthly inflow datasets are pre-processed. Second, the datasets are modelled linearly with SARIMA and in the third stage, the non-linearity of residual series caused by linear modelling is evaluated. After confirming the non-linearity, the residuals are modelled in the fourth step using a gene expression programming (GEP) method. The proposed hybrid model is employed to predict the monthly inflow to the Jamishan Dam in west Iran. Thirty years' worth of site measurements of monthly reservoir dam inflow with extreme seasonal variations are used. The results of this hybrid model (SARIMA-GEP) are compared with SARIMA, GEP, artificial neural network (ANN) and SARIMA-ANN models. The results indicate that the SARIMA-GEP model ( R 2=78.8, VAF =78.8, RMSE =0.89, MAPE =43.4, CRM =0.053) outperforms SARIMA and GEP and SARIMA-ANN ( R 2=68.3, VAF =66.4, RMSE =1.12, MAPE =56.6, CRM =0.032) displays better performance than the SARIMA and ANN models. A comparison of the two hybrid models indicates the superiority of SARIMA-GEP over the SARIMA-ANN model.

Genetic & epigenetic approach to human obesity

Directory of Open Access Journals (Sweden)

K Rajender Rao

2014-01-01

Full Text Available Obesity is an important clinical and public health challenge, epitomized by excess adipose tissue accumulation resulting from an imbalance in energy intake and energy expenditure. It is a forerunner for a variety of other diseases such as type-2-diabetes (T2D, cardiovascular diseases, some types of cancer, stroke, hyperlipidaemia and can be fatal leading to premature death. Obesity is highly heritable and arises from the interplay of multiple genes and environmental factors. Recent advancements in Genome-wide association studies (GWAS have shown important steps towards identifying genetic risks and identification of genetic markers for lifestyle diseases, especially for a metabolic disorder like obesity. According to the 12 th u0 pdate of Human Obesity Gene Map there are 253 quantity trait loci (QTL for obesity related phenotypes from 61 genome wide scan studies. Contribution of genetic propensity of individual ethnic and racial variations in obesity is an active area of research. Further, understanding its complexity as to how these variations could influence ones susceptibility to become or remain obese will lead us to a greater understanding of how obesity occurs and hopefully, how to prevent and treat this condition. In this review, various strategies adapted for such an analysis based on the recent advances in genome wide and functional variations in human obesity are discussed.

Disability training in the genetic counseling curricula: bridging the gap between genetic counselors and the disability community.

Science.gov (United States)

Sanborn, Erica; Patterson, Annette R

2014-08-01

Over the past two decades, disability activists, ethicists, and genetic counselors have examined the moral complexities inherent in prenatal genetic counseling and considered whether and in what ways genetic counseling may negatively affect individuals in the disability community. Many have expressed concerns about defining disability in the context of prenatal decision-making, as the definition presented may influence prenatal choices. In the past few years, publications have begun to explore the responsibility of counselors in presenting a balanced view of disability and have questioned the preparedness of counselors for this duty. Currently, the Accreditation Council for Genetic Counseling (ACGC) only minimally includes disability training in their competencies for genetic counselors, and in their accreditation requirements for training programs. In an attempt to describe current practice, this article details two studies that assess disability training in ABGC-accredited genetic counseling programs. Results from these studies demonstrate that experience with disability is not required by the majority of programs prior to matriculation. Though most program directors agree on the importance of including disability training in the curriculum, there is wide variability in the amount and types of training students receive. Hours dedicated to disability exposure among programs ranged from 10 to 600 hours. Eighty-five percent of program directors surveyed agree that skills for addressing disability should be added to the core competencies. Establishing a set of disability competencies would help to ensure that all graduates have the skills necessary to provide patients with an accurate understanding of disability that facilitates informed decision-making. © 2014 Wiley Periodicals, Inc.

Molecular marker systems for Oenothera genetics.

Science.gov (United States)

Rauwolf, Uwe; Golczyk, Hieronim; Meurer, Jörg; Herrmann, Reinhold G; Greiner, Stephan

2008-11-01

The genus Oenothera has an outstanding scientific tradition. It has been a model for studying aspects of chromosome evolution and speciation, including the impact of plastid nuclear co-evolution. A large collection of strains analyzed during a century of experimental work and unique genetic possibilities allow the exchange of genetically definable plastids, individual or multiple chromosomes, and/or entire haploid genomes (Renner complexes) between species. However, molecular genetic approaches for the genus are largely lacking. In this study, we describe the development of efficient PCR-based marker systems for both the nuclear genome and the plastome. They allow distinguishing individual chromosomes, Renner complexes, plastomes, and subplastomes. We demonstrate their application by monitoring interspecific exchanges of genomes, chromosome pairs, and/or plastids during crossing programs, e.g., to produce plastome-genome incompatible hybrids. Using an appropriate partial permanent translocation heterozygous hybrid, linkage group 7 of the molecular map could be assigned to chromosome 9.8 of the classical Oenothera map. Finally, we provide the first direct molecular evidence that homologous recombination and free segregation of chromosomes in permanent translocation heterozygous strains is suppressed.

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

Directory of Open Access Journals (Sweden)

Hyunseok Park

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

Dynamic Programming Approach for Exact Decision Rule Optimization

KAUST Repository

Amin, Talha

2013-01-01

This chapter is devoted to the study of an extension of dynamic programming approach that allows sequential optimization of exact decision rules relative to the length and coverage. It contains also results of experiments with decision tables from UCI Machine Learning Repository. © Springer-Verlag Berlin Heidelberg 2013.

A Mobile Device Based Serious Gaming Approach for Teaching and Learning Java Programming

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Tobias Jordine

2015-01-01

Full Text Available Most first year computer science students find that learning object-oriented programming is hard. Serious games have ever been used as one approach to handle this problem. But most of them cannot be played with mobile devices. This obviously does not suit the era of mobile computing that intends to allow students to learn programming skills in anytime anywhere. To enhance mobile teaching and learning, a research project started over a year ago and aims to create a mobile device based serious gaming approach along with a serious game for enhancing mobile teaching and learning for Java programming. So far the project has completed a literature review for understanding existing work and identifying problems in this area, conducted a survey for eliciting students’ requirements for mobile gaming approach, and then established a mobile-device based serious gaming approach with a developed prototype of the game. This paper introduces the project in details, and in particularly presents and discusses its current results. It is expected that the presented project will be helpful and useful to bring more efficient approaches with new mobile games into teaching object-oriented programming and to enhance students’ learning experiences.

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

Directory of Open Access Journals (Sweden)

JingRui Zhang

2015-03-01

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

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

Science.gov (United States)

Kashid, Satishkumar S.; Maity, Rajib

2012-08-01

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

Genetic conservation and paddlefish propagation

Science.gov (United States)

Sloss, Brian L.; Klumb, Robert A.; Heist, Edward J.

2009-01-01

The conservation of genetic diversity of our natural resources is overwhelmingly one of the central foci of 21st century management practices. Three recommendations related to the conservation of paddlefish Polyodon spathula genetic diversity are to (1) identify genetic diversity at both nuclear and mitochondrial DNA loci using a suggested list of 20 sampling locations, (2) use genetic diversity estimates to develop genetic management units, and (3) identify broodstock sources to minimize effects of supplemental stocking on the genetic integrity of native paddlefish populations. We review previous genetic work on paddlefish and described key principles and concepts associated with maintaining genetic diversity within and among paddlefish populations and also present a genetic case study of current paddlefish propagation at the U.S. Fish and Wildlife Service Gavins Point National Fish Hatchery. This study confirmed that three potential sources of broodfish were genetically indistinguishable at the loci examined, allowing the management agencies cooperating on this program flexibility in sampling gametes. This study also showed significant bias in the hatchery occurred in terms of male reproductive contribution, which resulted in a shift in the genetic diversity of progeny compared to the broodfish. This shift was shown to result from differential male contributions, partially attributed to the mode of egg fertilization. Genetic insights enable implementation of a paddlefish propagation program within an adaptive management strategy that conserves inherent genetic diversity while achieving demographic goals.

The Processual Programming Essentials – Criticism and New Options

Directory of Open Access Journals (Sweden)

Ion Gh. Rosca

2008-02-01

Full Text Available The Process Programming Basics: Priorities, Heuristic or Genetic Algorithms? This paper analyzes methods to optimize process programming, starting with the heuristic algorithms, then reviewing the current method previously advanced by the authors, the quantitative priorities, and finally approaches the problem with an innovative and promissory concept: the genetic algorithms and the “total costs and risks” optimization criterion, which is an alternative to both optimization with constraints and optimization with Lagrange multipliers. This new method emulates natural systems, thus borrowing from their robustness and adaptability. The method proves particularly useful in a turbulent and changing environment, requiring a realistic simulation model and also parallel processing in a high power computing grid.

Genetic engineering of industrial Saccharomyces cerevisiae strains using a selection/counter-selection approach.

Science.gov (United States)

Kutyna, Dariusz R; Cordente, Antonio G; Varela, Cristian

2014-01-01

Gene modification of laboratory yeast strains is currently a very straightforward task thanks to the availability of the entire yeast genome sequence and the high frequency with which yeast can incorporate exogenous DNA into its genome. Unfortunately, laboratory strains do not perform well in industrial settings, indicating the need for strategies to modify industrial strains to enable strain development for industrial applications. Here we describe approaches we have used to genetically modify industrial strains used in winemaking.

A non-genetic approach to labelling acute myeloid leukemia and bone marrow cells with quantum dots.

Science.gov (United States)

Zheng, Yanwen; Tan, Dongming; Chen, Zheng; Hu, Chenxi; Mao, Zhengwei J; Singleton, Timothy P; Zeng, Yan; Shao, Xuejun; Yin, Bin

2014-06-01

The difficulty in manipulation of leukemia cells has long hindered the dissection of leukemia pathogenesis. We have introduced a non-genetic approach of marking blood cells, using quantum dots. We compared quantum dots complexed with different vehicles, including a peptide Tat, cationic polymer Turbofect and liposome. Quantum dots-Tat showed the highest efficiency of marking hematopoietic cells among the three vehicles. Quantum dots-Tat could also label a panel of leukemia cell lines at varied efficiencies. More uniform intracellular distributions of quantum dots in mouse bone marrow and leukemia cells were obtained with quantum dots-Tat, compared with the granule-like formation obtained with quantum dots-liposome. Our results suggest that quantum dots have provided a photostable and non-genetic approach that labels normal and malignant hematopoietic cells, in a cell type-, vehicle-, and quantum dot concentration-dependent manner. We expect for potential applications of quantum dots as an easy and fast marking tool assisting investigations of various types of blood cells in the future.

Introduction to focus issue: quantitative approaches to genetic networks.

Science.gov (United States)

Albert, Réka; Collins, James J; Glass, Leon

2013-06-01

All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks

DNAStat, version 2.1--a computer program for processing genetic profile databases and biostatistical calculations.

Science.gov (United States)

Berent, Jarosław

2010-01-01

This paper presents the new DNAStat version 2.1 for processing genetic profile databases and biostatistical calculations. The popularization of DNA studies employed in the judicial system has led to the necessity of developing appropriate computer programs. Such programs must, above all, address two critical problems, i.e. the broadly understood data processing and data storage, and biostatistical calculations. Moreover, in case of terrorist attacks and mass natural disasters, the ability to identify victims by searching related individuals is very important. DNAStat version 2.1 is an adequate program for such purposes. The DNAStat version 1.0 was launched in 2005. In 2006, the program was updated to 1.1 and 1.2 versions. There were, however, slight differences between those versions and the original one. The DNAStat version 2.0 was launched in 2007 and the major program improvement was an introduction of the group calculation options with the potential application to personal identification of mass disasters and terrorism victims. The last 2.1 version has the option of language selection--Polish or English, which will enhance the usage and application of the program also in other countries.

Enhancing the effectiveness of biological control programs of invasive species through a more comprehensive pest management approach.

Science.gov (United States)

DiTomaso, Joseph M; Van Steenwyk, Robert A; Nowierski, Robert M; Vollmer, Jennifer L; Lane, Eric; Chilton, Earl; Burch, Patrick L; Cowan, Phil E; Zimmerman, Kenneth; Dionigi, Christopher P

2017-01-01

Invasive species are one of the greatest economic and ecological threats to agriculture and natural areas in the US and the world. Among the available management tools, biological control provides one of the most economical and long-term effective strategies for managing widespread and damaging invasive species populations of nearly all taxa. However, integrating biological control programs in a more complete integrated pest management approach that utilizes increased information and communication, post-release monitoring, adaptive management practices, long-term stewardship strategies, and new and innovative ecological and genetic technologies can greatly improve the effectiveness of biological control. In addition, expanding partnerships among relevant national, regional, and local agencies, as well as academic scientists and land managers, offers far greater opportunities for long-term success in the suppression of established invasive species. In this paper we direct our recommendations to federal agencies that oversee, fund, conduct research, and develop classical biological control programs for invasive species. By incorporating these recommendations into adaptive management strategies, private and public land managers will have far greater opportunities for long-term success in suppression of established invasive species. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

A study of concept-based similarity approaches for recommending program examples

Science.gov (United States)

Hosseini, Roya; Brusilovsky, Peter

2017-07-01

This paper investigates a range of concept-based example recommendation approaches that we developed to provide example-based problem-solving support in the domain of programming. The goal of these approaches is to offer students a set of most relevant remedial examples when they have trouble solving a code comprehension problem where students examine a program code to determine its output or the final value of a variable. In this paper, we use the ideas of semantic-level similarity-based linking developed in the area of intelligent hypertext to generate examples for the given problem. To determine the best-performing approach, we explored two groups of similarity approaches for selecting examples: non-structural approaches focusing on examples that are similar to the problem in terms of concept coverage and structural approaches focusing on examples that are similar to the problem by the structure of the content. We also explored the value of personalized example recommendation based on student's knowledge levels and learning goal of the exercise. The paper presents concept-based similarity approaches that we developed, explains the data collection studies and reports the result of comparative analysis. The results of our analysis showed better ranking performance of the personalized structural variant of cosine similarity approach.

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

Directory of Open Access Journals (Sweden)

Edén Bojórquez

2013-01-01

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

[Genetics factors in pathogenesis and clinical genetics of binge eating disorder].

Science.gov (United States)

Kibitov, А О; Мazo, G E

2016-01-01

Genetic studies have shown that binge eating disorder (ВЕD) aggregates in families, heritability was estimated as about 60% and additive genetic influences on BED up to 50%. Using a genetic approach has proved useful for verifying the diagnostic categories of BED using DSM-IV criteria and supporting the validity of considering this pathology as a separate nosological category. The results confirmed the genetic and pathogenic originality of BED as a separate psychopathological phenomenon, but not a subtype of obesity. It seems fruitful to considerate BED as a disease with hereditary predisposition with significant genetic influence and a complex psychopathological syndrome, including not only eating disorders, but also depressive and addictive component. A possible mechanism of pathogenesis of BED may be the interaction of the neuroendocrine and neurotransmitters systems including the active involvement of the reward system in response to a variety of chronic stress influences with the important modulatory role of specific personality traits. The high level of genetic influence on the certain clinical manifestations of BED confirms the ability to identify the subphenotypes of BED on genetic basis involving clinical criteria. It can not only contribute to further genetic studies, taking into account more homogeneous samples, but also help in finding differentiated therapeutic approaches.

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

Science.gov (United States)

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

2015-02-01

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

Dynamic programming approach to optimization of approximate decision rules

KAUST Repository

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

2013-01-01

This paper is devoted to the study of an extension of dynamic programming approach which allows sequential optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure R(T) which is the number

Genetic variability in local Brazilian horse lines using microsatellite markers.

Science.gov (United States)

Silva, A C M; Paiva, S R; Albuquerque, M S M; Egito, A A; Santos, S A; Lima, F C; Castro, S T; Mariante, A S; Correa, P S; McManus, C M

2012-04-10

Genetic variability at 11 microsatellite markers was analyzed in five naturalized/local Brazilian horse breeds or genetic groups. Blood samples were collected from 328 animals of the breeds Campeira (Santa Catarina State), Lavradeira (Roraima State), Pantaneira (Pantanal Mato-Grossense), Mangalarga Marchador (Minas Gerais State), as well as the genetic group Baixadeiro (Maranhão State), and the exotic breeds English Thoroughbred and Arab. We found significant genetic variability within evaluated microsatellite loci, with observed heterozygosis varying between 0.426 and 0.768 and polymorphism information content values of 0.751 to 0.914. All breeds showed high inbreeding coefficients and were not in Hardy-Weinberg equilibrium. The smallest genetic distance was seen between the Pantaneira and Arab breeds. The principal component analyzes and Bayesian approach demonstrated that the exotic breeds have had a significant influence on the genetic formation of the local breeds, with introgression of English Throroughbred in Pantaneira and Lavradeira, as well as genetic proximity between the Arab, Pantaneira and Mangalarga Marchador populations. This study shows the need to conserve traits acquired by naturalized horse breeds over centuries of natural selection in Brazil due to the genetic uniqueness of each group, suggesting a reduced gene flow between them. These results reinforce the need to include these herds in animal genetic resource conservation programs to maximize the genetic variability and conserve useful allele combinations.

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

Science.gov (United States)

McNamara, J P

2012-06-01

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

Isolation and genetic analysis of pure cells from forensic biological mixtures: The precision of a digital approach.

Science.gov (United States)

Fontana, F; Rapone, C; Bregola, G; Aversa, R; de Meo, A; Signorini, G; Sergio, M; Ferrarini, A; Lanzellotto, R; Medoro, G; Giorgini, G; Manaresi, N; Berti, A

2017-07-01

Latest genotyping technologies allow to achieve a reliable genetic profile for the offender identification even from extremely minute biological evidence. The ultimate challenge occurs when genetic profiles need to be retrieved from a mixture, which is composed of biological material from two or more individuals. In this case, DNA profiling will often result in a complex genetic profile, which is then subject matter for statistical analysis. In principle, when more individuals contribute to a mixture with different biological fluids, their single genetic profiles can be obtained by separating the distinct cell types (e.g. epithelial cells, blood cells, sperm), prior to genotyping. Different approaches have been investigated for this purpose, such as fluorescent-activated cell sorting (FACS) or laser capture microdissection (LCM), but currently none of these methods can guarantee the complete separation of different type of cells present in a mixture. In other fields of application, such as oncology, DEPArray™ technology, an image-based, microfluidic digital sorter, has been widely proven to enable the separation of pure cells, with single-cell precision. This study investigates the applicability of DEPArray™ technology to forensic samples analysis, focusing on the resolution of the forensic mixture problem. For the first time, we report here the development of an application-specific DEPArray™ workflow enabling the detection and recovery of pure homogeneous cell pools from simulated blood/saliva and semen/saliva mixtures, providing full genetic match with genetic profiles of corresponding donors. In addition, we assess the performance of standard forensic methods for DNA quantitation and genotyping on low-count, DEPArray™-isolated cells, showing that pure, almost complete profiles can be obtained from as few as ten haploid cells. Finally, we explore the applicability in real casework samples, demonstrating that the described approach provides complete

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

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Arunabha Majumdar

2018-02-01

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

Genetic diversity of six populations of red hybrid tilapia, using microsatellites genetic markers

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Boris Briñez R.

2011-05-01

Full Text Available Objective. To determine and evaluate the genetic diversity of six populations of red hybrid tilapia, with the purpose to assess the potential benefit of a future breeding program conducted at the Research Center for Aquaculture (Ceniacua, Colombia. Material and methods. A total of 300 individuals, representing a wide genetic variability, were genotyped using a fluorescent microsatellite marker set of 5 gene-based SSRs in 6 different farms belonging to 4 States of Colombia. Results. The result showed that the mean number of alleles per locus per population was 8.367. The population 5 had the highest mean number of alleles with 9.6 alleles, followed by population 4 with 9.4 alleles, population 2 with 9.2, population 3 with 8.0, population 1 with 7.2 and population 6 with 6.8 alleles. The analysis of the distribution of genetic variation was (17.32% among population, while among individuals within populations was (28.55% and within individuals was high (54.12%. The standard diversity indices showed that population 4 was the more variable (mean He=0.837 followed by population 1 (mean He=0.728, population 3 (mean He=0.721, population 5 (mean He=0.705, population 2 (mean He=0.690, population 6 (mean He=0.586. Highly significant deviations from Hardy–Weinberg, exhibited all of the populations, mostly due to deficits of heterozygotes. Genotype frequencies at loci UNH 106 of population 5 and loci UNH 172 of population 6 were Hardy-Weinberg equilibrium (HWE. Conclusions. The results of this study, contribute to the genetic breeding program of Tilapia, conduced by the Research Center for Aquaculture. The Fst distance showed that the samples are differentiated genetically and it is possible to use at the beginning of the genetic program. However, it is recommended to introduce others individuals to the crossbreeding program.

Going forward with genetics: recent technological advances and forward genetics in mice.

Science.gov (United States)

Moresco, Eva Marie Y; Li, Xiaohong; Beutler, Bruce

2013-05-01

Forward genetic analysis is an unbiased approach for identifying genes essential to defined biological phenomena. When applied to mice, it is one of the most powerful methods to facilitate understanding of the genetic basis of human biology and disease. The speed at which disease-causing mutations can be identified in mutagenized mice has been markedly increased by recent advances in DNA sequencing technology. Creating and analyzing mutant phenotypes may therefore become rate-limiting in forward genetic experimentation. We review the forward genetic approach and its future in the context of recent technological advances, in particular massively parallel DNA sequencing, induced pluripotent stem cells, and haploid embryonic stem cells. Copyright © 2013 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.

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

Science.gov (United States)

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

2017-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

Russell, Liane B.

2013-10-01

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

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

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Francis Oloo

2017-01-01

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

TEACCH and SIT Approach Program in Children with Autism Spectrum Disorders

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Maryam Abshirini

2016-10-01

Full Text Available Objective: Sensory Integration Therapy (SIT is one of the most commonly used treatment approaches for Autism Spectrum Disorders (ASD. Treatment and Education of Autistic and related Communication-handicapped Children (TEACCH is another less known approach in Iran. The aim of this study was to compare the effectiveness of SIT and TEACCH approaches in children with ASD. Design: The study design was quasi- experimental, which was conducted on 2014 in Autism center of Bushehr city, based in south of Iran. Method: Study participants were children aged 3 to 9 with normal IQ who were diagnosed with ASD. Intervention included SIT and TEACCH treatment approaches for a 6 months duration to two groups of children (n=20. One group did not receive any intervention during the 6 months. Main outcome was the total score of Autism Treatment Evaluation Checklist (ATEC. Results: There was no significant difference in ATEC score between the three groups at the base line. ATEC score was significantly different among three groups after intervention using one-way ANOVA test. Tukey test showed that TEACCH group had more improvement in autism score compared to SIT group. The results of ANCOVA test showed that 70% of variation in autism score is due to the interventional approaches. Conclusion: This study showed that TEACCH program was effective in Iranian culture as well, and can be used widely in Iranian Autism centers and TEACCH program was more effective than SIT program.

Genetic bases of the nutritional approach to migraine.

Science.gov (United States)

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

2018-03-08

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

Genetics of osteoarthritis.

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Rodriguez-Fontenla, Cristina; Gonzalez, Antonio

2015-01-01

Osteoarthritis (OA) is a complex disease caused by the interaction of multiple genetic and environmental factors. This review focuses on the studies that have contributed to the discovery of genetic susceptibility factors in OA. The most relevant associations discovered until now are discussed in detail: GDF-5, 7q22 locus, MCF2L, DOT1L, NCOA3 and also some important findings from the arcOGEN study. Moreover, the different approaches that can be used to minimize the specific problems of the study of OA genetics are discussed. These include the study of microsatellites, phenotype standardization and other methods such as meta-analysis of GWAS and gene-based analysis. It is expected that these new approaches contribute to finding new susceptibility genetic factors for OA. Copyright © 2014 Elsevier España, S.L.U. All rights reserved.

Genetics in Relation to Biology.

Science.gov (United States)

Stewart, J. Bird

1987-01-01

Claims that most instruction dealing with genetics is limited to sex education and personal hygiene. Suggests that the biology curriculum should begin to deal with other issues related to genetics, including genetic normality, prenatal diagnoses, race, and intelligence. Predicts these topics will begin to appear in British examination programs.…

A two-stage stochastic programming approach for operating multi-energy systems

DEFF Research Database (Denmark)

Zeng, Qing; Fang, Jiakun; Chen, Zhe

2017-01-01

This paper provides a two-stage stochastic programming approach for joint operating multi-energy systems under uncertainty. Simulation is carried out in a test system to demonstrate the feasibility and efficiency of the proposed approach. The test energy system includes a gas subsystem with a gas...

The Elements of Language Curriculum: A Systematic Approach to Program Development.

Science.gov (United States)

Brown, James Dean

A systematic approach to second language curriculum development is outlined, enumerating the phases and activities involved in developing and implementing a sound and effective language program. The first chapter describes a system whereby all language teaching activities can be classified into approaches, syllabuses, techniques, exercises, or…

Reverse genetics with animal viruses. NSV reverse genetics

International Nuclear Information System (INIS)

Mebatsion, T.

2005-01-01

New strategies to genetically manipulate the genomes of several important animal pathogens have been established in recent years. This article focuses on the reverse genetics techniques, which enables genetic manipulation of the genomes of non-segmented negative-sense RNA viruses. Recovery of a negative-sense RNA virus entirely from cDNA was first achieved for rabies virus in 1994. Since then, reverse genetic systems have been established for several pathogens of medical and veterinary importance. Based on the reverse genetics technique, it is now possible to design safe and more effective live attenuated vaccines against important viral agents. In addition, genetically tagged recombinant viruses can be designed to facilitate serological differentiation of vaccinated animals from infected animals. The approach of delivering protective immunogens of different pathogens using a single vector was made possible with the introduction of the reverse genetics system, and these novel broad-spectrum vaccine vectors have potential applications in improving animal health in developing countries. (author)

Comparison between goal programming and cointegration approaches in enhanced index tracking

Science.gov (United States)

Lam, Weng Siew; Jamaan, Saiful Hafizah Hj.

2013-04-01

Index tracking is a popular form of passive fund management in stock market. Passive management is a buy-and-hold strategy that aims to achieve rate of return similar to the market return. Index tracking problem is a problem of reproducing the performance of a stock market index, without purchasing all of the stocks that make up the index. This can be done by establishing an optimal portfolio that minimizes risk or tracking error. An improved index tracking (enhanced index tracking) is a dual-objective optimization problem, a trade-off between maximizing the mean return and minimizing the tracking error. Enhanced index tracking aims to generate excess return over the return achieved by the index. The objective of this study is to compare the portfolio compositions and performances by using two different approaches in enhanced index tracking problem, which are goal programming and cointegration. The result of this study shows that the optimal portfolios for both approaches are able to outperform the Malaysia market index which is Kuala Lumpur Composite Index. Both approaches give different optimal portfolio compositions. Besides, the cointegration approach outperforms the goal programming approach because the cointegration approach gives higher mean return and lower risk or tracking error. Therefore, the cointegration approach is more appropriate for the investors in Malaysia.

Latent spatial models and sampling design for landscape genetics

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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.

An analysis of the genetic diversity and genetic structure of ...

African Journals Online (AJOL)

Scientific approaches to conservation of threatened species depend on a good understanding of the genetic information of wild and artificial population. The genetic diversity and structure analysis of 10 Eucommia ulmoides population was analyzed using inter-simple sequence repeat (ISSR) markers in this paper.

Breeding, genetic and genomic of citrus for disease resistance

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Marcos A. Machado

2011-10-01

Full Text Available Although the citriculture is one of the most important economic activities in Brazil, it is based on a small number of varieties. This fact has contributed for the vulnerability of the culture regarding the phytosanitary problems. A higher number of varieties/genotypes with potential for commercial growing, either for the industry or fresh market, has been one of the main objectives of citrus breeding programs. The genetic breeding of citrus has improved, in the last decades, due to the possibility of an association between biotechnological tools and classical methods of breeding. The use of molecular markers for early selection of zygotic seedlings from controlled crosses resulted in the possibility of selection of a high number of new combination and, as a consequence, the establishment of a great number of hybrids in field experiments. The faster new tools are incorporated in the program, the faster is possibility to reach new genotypes that can be tested as a new variety. Good traits should be kept or incorporate, whereas bad traits have to be excluded or minimized in the new genotype. Scion and rootstock can not be considered separately, and graft compatibility, fruit quality and productivity are essential traits to be evaluated in the last stages of the program. The mapping of QTLs has favored breeding programs of several perennial species and in citrus it was possible to map several characteristics with qualitative and quantitative inheritance. The existence of linkage maps and QTLs already mapped, the development of EST and BAC library and the sequencing of the Citrus complete genome altogether make very demanding and urgent the exploration of such data to launch a wider genetic study of citrus. The rising of information on genome of several organisms has opened new approaches looking for integration between breeding, genetic and genome. Genome assisted selection (GAS involves more than gene or complete genome sequencing and is becoming

A Genetic Algorithms-based Approach for Optimized Self-protection in a Pervasive Service Middleware

DEFF Research Database (Denmark)

Zhang, Weishan; Ingstrup, Mads; Hansen, Klaus Marius

2009-01-01

With increasingly complex and heterogeneous systems in pervasive service computing, it becomes more and more important to provide self-protected services to end users. In order to achieve self-protection, the corresponding security should be provided in an optimized manner considering...... the constraints of heterogeneous devices and networks. In this paper, we present a Genetic Algorithms-based approach for obtaining optimized security configurations at run time, supported by a set of security OWL ontologies and an event-driven framework. This approach has been realized as a prototype for self-protection...... in the Hydra middleware, and is integrated with a framework for enforcing the computed solution at run time using security obligations. The experiments with the prototype on configuring security strategies for a pervasive service middleware show that this approach has acceptable performance, and could be used...

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

International Nuclear Information System (INIS)

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

1982-01-01

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

Molecular genetic approaches to the study of cellular senescence.

Science.gov (United States)

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

1994-01-01

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

Program Management Approach to the Territorial Development of Small Business

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Natalia Aleksandrovna Knysh

2016-06-01

Full Text Available This article presents the results of the research of the application on a state level of the program management approach to the territorial development of small business. Studying the main mechanism of the state policy implementation in the sphere of small business on a regional level, the authors have revealed the necessity to take into account the territorial specificity while the government programs of small business development are being formed. The analysis of the national practice of utilizing the program management mechanism in the regional system of the government support of small entrepreneurship was conducted on the example of Omsk region. The results of the analysis have shown the inefficiency of the current support system for small business and have determined the need to create an integrated model of territorial programming, which would not only contribute to the qualitative development of small business, but also provide the functioning efficiency of program management mechanism. As a result, the authors have created the two-level model of the programming of the territorial development of small business, which allows to satisfy purposefully the needs of entrepreneurship taking into account the specificity of the internal and external environment of the region. The first level of the model is methodological one and it is based on the marketing approach (the concepts of place marketing and relationship marketing to the operation of the program management mechanism. The second level of the model is methodical one. It offers the combination of the flexible methods of management of programming procedure (benchmarking, foresight, crowdsourcing and outsourcing. The given model raises the efficiency of the management decisions of the state structures in the sphere of small business. Therefore, it is interesting for the government authorities, which are responsible for the regional and municipal support programs of small business, as well

Human Metabolic Enzymes Deficiency: A Genetic Mutation Based Approach

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Swati Chaturvedi

2016-01-01

Full Text Available One of the extreme challenges in biology is to ameliorate the understanding of the mechanisms which emphasize metabolic enzyme deficiency (MED and how these pretend to have influence on human health. However, it has been manifested that MED could be either inherited as inborn error of metabolism (IEM or acquired, which carries a high risk of interrupted biochemical reactions. Enzyme deficiency results in accumulation of toxic compounds that may disrupt normal organ functions and cause failure in producing crucial biological compounds and other intermediates. The MED related disorders cover widespread clinical presentations and can involve almost any organ system. To sum up the causal factors of almost all the MED-associated disorders, we decided to embark on a less traveled but nonetheless relevant direction, by focusing our attention on associated gene family products, regulation of their expression, genetic mutation, and mutation types. In addition, the review also outlines the clinical presentations as well as diagnostic and therapeutic approaches.

[Efficacy of the program "Testas's (mis)adventures" to promote the deep approach to learning].

Science.gov (United States)

Rosário, Pedro; González-Pienda, Julio Antonio; Cerezo, Rebeca; Pinto, Ricardo; Ferreira, Pedro; Abilio, Lourenço; Paiva, Olimpia

2010-11-01

This paper provides information about the efficacy of a tutorial training program intended to enhance elementary fifth graders' study processes and foster their deep approaches to learning. The program "Testas's (mis)adventures" consists of a set of books in which Testas, a typical student, reveals and reflects upon his life experiences during school years. These life stories are nothing but an opportunity to present and train a wide range of learning strategies and self-regulatory processes, designed to insure students' deeper preparation for present and future learning challenges. The program has been developed along a school year, in a one hour weekly tutorial sessions. The training program had a semi-experimental design, included an experimental group (n=50) and a control one (n=50), and used pre- and posttest measures (learning strategies' declarative knowledge, learning approaches and academic achievement). Data suggest that the students enrolled in the training program, comparing with students in the control group, showed a significant improvement in their declarative knowledge of learning strategies and in their deep approach to learning, consequently lowering their use of a surface approach. In spite of this, in what concerns to academic achievement, no statistically significant differences have been found.

From observational to dynamic genetics

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Claire M. A. Haworth

2014-01-01

Full Text Available Twin and family studies have shown that most traits are at least moderately heritable. But what are the implications of finding genetic influence for the design of intervention and prevention programs? For complex traits, heritability does not mean immutability, and research has shown that genetic influences can change with age, context and in response to behavioural and drug interventions. The most significant implications for intervention will come when we move from observational genetics to investigating dynamic genetics, including genetically sensitive interventions. Future interventions should be designed to overcome genetic risk and draw upon genetic strengths by changing the environment.

Genetic Gain Increases by Applying the Usefulness Criterion with Improved Variance Prediction in Selection of Crosses.

Science.gov (United States)

Lehermeier, Christina; Teyssèdre, Simon; Schön, Chris-Carolin

2017-12-01

A crucial step in plant breeding is the selection and combination of parents to form new crosses. Genome-based prediction guides the selection of high-performing parental lines in many crop breeding programs which ensures a high mean performance of progeny. To warrant maximum selection progress, a new cross should also provide a large progeny variance. The usefulness concept as measure of the gain that can be obtained from a specific cross accounts for variation in progeny variance. Here, it is shown that genetic gain can be considerably increased when crosses are selected based on their genomic usefulness criterion compared to selection based on mean genomic estimated breeding values. An efficient and improved method to predict the genetic variance of a cross based on Markov chain Monte Carlo samples of marker effects from a whole-genome regression model is suggested. In simulations representing selection procedures in crop breeding programs, the performance of this novel approach is compared with existing methods, like selection based on mean genomic estimated breeding values and optimal haploid values. In all cases, higher genetic gain was obtained compared with previously suggested methods. When 1% of progenies per cross were selected, the genetic gain based on the estimated usefulness criterion increased by 0.14 genetic standard deviation compared to a selection based on mean genomic estimated breeding values. Analytical derivations of the progeny genotypic variance-covariance matrix based on parental genotypes and genetic map information make simulations of progeny dispensable, and allow fast implementation in large-scale breeding programs. Copyright © 2017 by the Genetics Society of America.

Selection on Optimal Haploid Value Increases Genetic Gain and Preserves More Genetic Diversity Relative to Genomic Selection.

Science.gov (United States)

Daetwyler, Hans D; Hayden, Matthew J; Spangenberg, German C; Hayes, Ben J

2015-08-01

Doubled haploids are routinely created and phenotypically selected in plant breeding programs to accelerate the breeding cycle. Genomic selection, which makes use of both phenotypes and genotypes, has been shown to further improve genetic gain through prediction of performance before or without phenotypic characterization of novel germplasm. Additional opportunities exist to combine genomic prediction methods with the creation of doubled haploids. Here we propose an extension to genomic selection, optimal haploid value (OHV) selection, which predicts the best doubled haploid that can be produced from a segregating plant. This method focuses selection on the haplotype and optimizes the breeding program toward its end goal of generating an elite fixed line. We rigorously tested OHV selection breeding programs, using computer simulation, and show that it results in up to 0.6 standard deviations more genetic gain than genomic selection. At the same time, OHV selection preserved a substantially greater amount of genetic diversity in the population than genomic selection, which is important to achieve long-term genetic gain in breeding populations. Copyright © 2015 by the Genetics Society of America.

Stroke genetics: prospects for personalized medicine

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Markus Hugh S

2012-09-01

Full Text Available Abstract Epidemiologic evidence supports a genetic predisposition to stroke. Recent advances, primarily using the genome-wide association study approach, are transforming what we know about the genetics of multifactorial stroke, and are identifying novel stroke genes. The current findings are consistent with different stroke subtypes having different genetic architecture. These discoveries may identify novel pathways involved in stroke pathogenesis, and suggest new treatment approaches. However, the already identified genetic variants explain only a small proportion of overall stroke risk, and therefore are not currently useful in predicting risk for the individual patient. Such risk prediction may become a reality as identification of a greater number of stroke risk variants that explain the majority of genetic risk proceeds, and perhaps when information on rare variants, identified by whole-genome sequencing, is also incorporated into risk algorithms. Pharmacogenomics may offer the potential for earlier implementation of 'personalized genetic' medicine. Genetic variants affecting clopidogrel and warfarin metabolism may identify non-responders and reduce side-effects, but these approaches have not yet been widely adopted in clinical practice.

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

Science.gov (United States)

Mabu, Shingo; Hirasawa, Kotaro; Furuzuki, Takayuki

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

Chance-constrained programming approach to natural-gas curtailment decisions

Energy Technology Data Exchange (ETDEWEB)

Guldmann, J M

1981-10-01

This paper presents a modeling methodology for the determination of optimal-curtailment decisions by a gas-distribution utility during a chronic gas-shortage situation. Based on the end-use priority approach, a linear-programming model is formulated, that reallocates the available gas supply among the utility's customers while minimizing fuel switching, unemployment, and utility operating costs. This model is then transformed into a chance-constrained program in order to account for the weather-related variability of the gas requirements. The methodology is applied to the East Ohio Gas Company. 16 references, 2 figures, 3 tables.

Public health approach to birth defects: the Argentine experience.

Science.gov (United States)

Bidondo, María Paz; Groisman, Boris; Barbero, Pablo; Liascovich, Rosa

2015-04-01

Birth defects are a global problem, but their impact is particularly severe in low and middle income countries, where the conditions for prevention, treatment, and rehabilitation are more critical. The epidemiological transition in the infant mortality causes, and the concern of the community and the mass media about the teratogenic risk of environmental pollutants, has made health authorities aware of the importance of birth defects in Argentina. The objective of this paper is to outline those actions specifically taken in Argentina aimed at the prevention of birth defects at a national level. Firstly, we focus on birth defects in Argentina on a general basis, and then we present different laws and actions taken in terms of surveillance and public health programs, primary, secondary, and tertiary prevention. Finally, we present the Teratology Information Service "Fetal Health Line", and the genetic services organization and health professionals training by the National Center of Medical Genetics and the National Program of Genetics Network. In conclusion, in the country, several programs focus on different approaches to the problem, and the challenge is to coordinate the teamwork between them. Finally, we list tips to address birth defects from the public health perspective.

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

Science.gov (United States)

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

2016-07-01

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

Genetic technologies to enhance the Sterile Insect Technique (SIT)

International Nuclear Information System (INIS)

Alphey, Luke; Baker, Pam; Condon, George C.; Condon, Kirsty C.; Dafa'alla, Tarig H.; Fu, Guoliang; Jin, Li; Labbe, Genevieve; Morrison, Neil M.; Nimmo, Derric D.; O'Connell, Sinead; Phillips, Caroline E.; Plackett, Andrew; Scaife, Sarah; Woods, Alexander; Burton, Rosemary S.; Epton, Matthew J.; Gong, Peng

2006-01-01

The Sterile Insect Technique (SIT) has been used very successfully against range of pest insects, including various tephritid fruit flies, several moths and a small number of livestock pests. However, modern genetics could potentially provide several improvements that would increase the cost-effectiveness of SIT, and extend the range of suitable species. These include improved identification of released individuals by incorporation of a stable, heritable, genetic marker; built-in sex separation (genetic sexing); reduction of the hazard posed by non-irradiated accidental releases from mass-rearing facility (fail-safe); elimination of the need for sterilization by irradiation (genetic sterilization). We discuss applications of these methods and the state of the art, at the time of this meeting, in developing suitable strains. We have demonstrated, in several key pest species, that the required strains can be constructed by introducing a repressible dominant lethal genetic system, a method known as RIDL(trade mark). Based on field experience with Medfly, incorporation of a genetic sexing system into SIT programs for other tephritids could potentially provide a very significant improvement in cost-effectiveness. We have now been able to make efficient female-lethal strains for Medfly. One advantage of our approach is that it should be possible rapidly to extend this technology to other fruit fly species; indeed we have recently been able also to make genetic sexing strains of Medfly (Anastrepha ludens). (author)

Genetic technologies to enhance the Sterile Insect Technique (SIT)

Energy Technology Data Exchange (ETDEWEB)

Alphey, Luke; Baker, Pam; Condon, George C; Condon, Kirsty C; Dafa' alla, Tarig H; Fu, Guoliang; Jin, Li; Labbe, Genevieve; Morrison, Neil M; Nimmo, Derric D; O' Connell, Sinead; Phillips, Caroline E; Plackett, Andrew; Scaife, Sarah; Woods, Alexander [Oxitec Ltd., Oxford (United Kingdom); Burton, Rosemary S; Epton, Matthew J; Gong, Peng [University of Oxford (United Kingdom). Dept. of Zoology

2006-07-01

The Sterile Insect Technique (SIT) has been used very successfully against range of pest insects, including various tephritid fruit flies, several moths and a small number of livestock pests. However, modern genetics could potentially provide several improvements that would increase the cost-effectiveness of SIT, and extend the range of suitable species. These include improved identification of released individuals by incorporation of a stable, heritable, genetic marker; built-in sex separation (genetic sexing); reduction of the hazard posed by non-irradiated accidental releases from mass-rearing facility (fail-safe); elimination of the need for sterilization by irradiation (genetic sterilization). We discuss applications of these methods and the state of the art, at the time of this meeting, in developing suitable strains. We have demonstrated, in several key pest species, that the required strains can be constructed by introducing a repressible dominant lethal genetic system, a method known as RIDL(trade mark). Based on field experience with Medfly, incorporation of a genetic sexing system into SIT programs for other tephritids could potentially provide a very significant improvement in cost-effectiveness. We have now been able to make efficient female-lethal strains for Medfly. One advantage of our approach is that it should be possible rapidly to extend this technology to other fruit fly species; indeed we have recently been able also to make genetic sexing strains of Medfly (Anastrepha ludens). (author)

Portfolio optimization in enhanced index tracking with goal programming approach

Science.gov (United States)

Siew, Lam Weng; Jaaman, Saiful Hafizah Hj.; Ismail, Hamizun bin

2014-09-01

Enhanced index tracking is a popular form of passive fund management in stock market. Enhanced index tracking aims to generate excess return over the return achieved by the market index without purchasing all of the stocks that make up the index. This can be done by establishing an optimal portfolio to maximize the mean return and minimize the risk. The objective of this paper is to determine the portfolio composition and performance using goal programming approach in enhanced index tracking and comparing it to the market index. Goal programming is a branch of multi-objective optimization which can handle decision problems that involve two different goals in enhanced index tracking, a trade-off between maximizing the mean return and minimizing the risk. The results of this study show that the optimal portfolio with goal programming approach is able to outperform the Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index because of higher mean return and lower risk without purchasing all the stocks in the market index.

Practical approaches to implementing facility wide equipment strengthening programs

International Nuclear Information System (INIS)

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

1989-01-01

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

On the Reliability of Nonlinear Modeling using Enhanced Genetic Programming Techniques

Science.gov (United States)

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

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

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

Science.gov (United States)

Endelman, Jeffrey B; Plomion, Christophe

2014-06-01

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

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

Science.gov (United States)

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

2014-06-01

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

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

Data.gov (United States)

National Aeronautics and Space Administration — This Phase I SBIR proposal describes, "A New Approach to Commercialization of NASA's Human Research Program Technologies." NASA has a powerful research program that...

Modelling Autistic Features in Mice Using Quantitative Genetic Approaches

NARCIS (Netherlands)

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

2017-01-01

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

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

Science.gov (United States)

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

2012-12-01

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

Marketing the dental hygiene program. A public relations approach.

Science.gov (United States)

Nielsen, C

1989-09-01

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

Genetic diversity of pacu and piapara broodstocks in restocking programs in the rivers Paraná and Paranapanema (Brazil

Directory of Open Access Journals (Sweden)

Nelson Mauricio Lopera-Barrero

2016-09-01

Full Text Available The genetic diversity of Piaractus mesopotamicus (pacu and Leporinus elongatus (piapara broodstocks used in restocking programs in the rivers Paraná and Paranapanema is analyzed. One hundred and twenty specimens (two broodstocks of each species from fish ponds in Palotina PR Brazil and in Salto Grande SP Brazil were assessed. Ten primers produced 96 fragments, comprising 68 (70.83% and 94 (97.92% polymorphic fragments for P. mesopotamicus and L. elongatus broodstocks, respectively. Differences (p < 0.05 in the frequency of 15 and 27 fragments were detected for each species, without exclusive fragments. Shannon Index (0.347 - 0.572 and the percentage of polymorphic fragments (57.3% - 94.8% revealed high intra-population genetic variability for all broodstocks. Results of molecular variance analyses (AMOVA showed that most variations do not lie between the broodstocks but within each broodstock (89%. Genetic (0.088 and 0.142 and identity (0.916 and 0.868 distance rates demonstrated similarity between the broodstocks of each species, corroborated by Fst (0.1023 and 010.27 and Nm (4.18 and 4.33 rates, with a slight genetic difference due to genic flux. High intrapopulation genetic variability and similarity between the broodstocks of each species was also detected, proving a common ancestry.

Electromagnetic energy as a bridge between atomic and cellular levels in the genetics approach to cancer treatment.

Science.gov (United States)

Tofani, Santi

2015-01-01

Literature on magnetic fields (MF) and gene expression, as well as on DNA damage, supports the hypothesis that electromagnetic energy may act at atomic level influencing genetic stability. According to quantum physics, MF act on the interconversion of singlet and triplet spin states, and therefore on genetic instability, activating oxidative processes connected to biological free radicals formation, particularly ROS. In the above frame, the results of in vitro and in vivo laboratory trials have been analyzed. The use of a static MF amplitude modulated by 50 Hz MF, with a time average total intensity of 5.5 mT, has been shown to influence tumor cell functions such as cell proliferation, apoptosis, p53 expression, inhibition of tumor growth and prolongation of survival in animals, evidence that MF can be more effective than chemotherapy (cyclophosphamide) in inhibiting metastatic spread and growth, having synergistic activity with chemotherapy (Cis-platin), and no observable side effects or toxicity in animals or in humans. The beneficial biological/clinical effects observed, without any adverse effects, have been confirmed by various authors and augur well for the potentiality of this new approach to treat genetically based diseases like cancer. Further studies are needed to develop a quantum physics approach to biology, allowing a stable bridge to be built between atomic and cellular levels, therefore developing quantum biology.

Genetic parameters for oocyte number and embryo production within a bovine ovum pick-up-in vitro production embryo-production program.

Science.gov (United States)

Merton, J S; Ask, B; Onkundi, D C; Mullaart, E; Colenbrander, B; Nielen, M

2009-10-15

Genetic factors influencing the outcome of bovine ovum pick-up-in vitro production (OPU-IVP) and its relation to female fertility were investigated. For the first time, genetic parameters were estimated for the number of cumulus-oocyte complexes (Ncoc), quality of cumulus-oocyte complexes (Qcoc), number and proportion of cleaved embryos at Day 4 (Ncleav(D4), Pcleav(D4)), and number and proportion of total and transferable embryos at Day 7 of culture (Nemb(D7), Pemb(D7) and NTemb(D7), PTemb(D7), respectively). Data were recorded by CRV (formally Holland Genetics) from the OPU-IVP program from January 1995 to March 2006. Data were collected from 1508 Holstein female donors, both cows and pregnant virgin heifers, with a total of 18,702 OPU sessions. Data were analyzed with repeated-measure sire models with permanent environment effect using ASREML (Holstein Friesian). Estimates of heritability were 0.25 for Ncoc, 0.09 for Qcoc, 0.19 for Ncleav(D4), 0.21 for Nemb(D7), 0.16 for NTemb(D7), 0.07 for Pcleav(D4), 0.12 for Pemb(D7), and 0.10 for PTemb(D7). Genetic correlation between Ncoc and Qcoc was close to zero, whereas genetic correlations between Ncoc and the number of embryos were positive and moderate to high for Nemb(D7) (0.47), NTemb(D7) (0.52), and Ncleav(D4) (0.85). Genetic correlations between Ncoc and percentages of embryos (Pcleav(D4), Pemb(D7), and PTemb(D7)) were all close to zero. Phenotypic correlations were in line with genetic correlations. Genetic and phenotypic correlations between Qcoc and all other traits were not significant except for the phenotypic correlations between Qcoc and number of embryos, which were negative and low to moderate for Nemb(D7) (-0.20), NTemb(D7) (-0.24), and Ncleav(D4) (-0.43). Results suggest that cumulus-oocyte complex (COC) quality, based on cumulus investment, is independent from the total number of COCs collected via OPU and that in general, a higher number of COCs will lead to a higher number of embryos produced. The

Dynamic Programming Approaches for the Traveling Salesman Problem with Drone

OpenAIRE

Bouman, Paul; Agatz, Niels; Schmidt, Marie

2017-01-01

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

Swarm, genetic and evolutionary programming algorithms applied to multiuser detection

Directory of Open Access Journals (Sweden)

Paul Jean Etienne Jeszensky

2005-02-01

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

Genetic View To Stroke Occurrence

Directory of Open Access Journals (Sweden)

Sadegh Yoosefee

2017-02-01

Full Text Available Stroke is the third leading cause of death. The role of genetics in the etiology and development of this disease is undeniable. As a result of inadequate previous research, more and more studies in the field of genetics are necessary to identify pathways involved in the pathogenesis of stroke, which in turn, may lead to new therapeutic approaches. However, due to the multifactorial nature of stroke and the few studies conducted in this field, genetic diversity is able to predict only a small fraction of the risk of disease. On the other hand, studies have shown genetically different architecture for different types of stroke, and finally pharmacogenomics as an important part of personalized medicine approach, is influenced by genetic studies, all of which confirm the need of addressing the topic by researchers.

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

OpenAIRE

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

2003-01-01

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

Optimal groundwater remediation using artificial neural networks and the genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Rogers, Leah L. [Stanford Univ., CA (United States)

1992-08-01

An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ``recycle`` or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models.

Optimal groundwater remediation using artificial neural networks and the genetic algorithm

International Nuclear Information System (INIS)

Rogers, L.L.

1992-08-01

An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ''recycle'' or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models

Unraveling the genetic landscape of autosomal recessive Charcot-Marie-Tooth neuropathies using a homozygosity mapping approach

Science.gov (United States)

Zimoń, Magdalena; Battaloǧlu, Esra; Parman, Yesim; Erdem, Sevim; Baets, Jonathan; De Vriendt, Els; Atkinson, Derek; Almeida-Souza, Leonardo; Deconinck, Tine; Ozes, Burcak; Goossens, Dirk; Cirak, Sebahattin; Van Damme, Philip; Shboul, Mohammad; Voit, Thomas; Van Maldergem, Lionel; Dan, Bernard; El-Khateeb, Mohammed S.; Guergueltcheva, Velina; Lopez-Laso, Eduardo; Goemans, Nathalie; Masri, Amira; Züchner, Stephan; Timmerman, Vincent; Topaloǧlu, Haluk; De Jonghe, Peter

2016-01-01

Autosomal recessive forms of Charcot-Marie-Tooth disease (ARCMT) are rare but severe disorders of the peripheral nervous system. Their molecular basis is poorly understood due to the extensive genetic and clinical heterogeneity, posing considerable challenges for patients, physicians, and researchers. We report on the genetic findings from a systematic study of a large collection of 174 independent ARCMT families. Initial sequencing of the three most common ARCMT genes (ganglioside-induced differentiation protein 1—GDAP1, SH3 domain and tetratricopeptide repeats-containing protein 2—SH3TC2, histidine-triad nucleotide binding protein 1—HINT1) identified pathogenic mutations in 41 patients. Subsequently, 87 selected nuclear families underwent single nucleotide polymorphism (SNP) genotyping and homozygosity mapping, followed by targeted screening of known ARCMT genes. This strategy provided molecular diagnosis to 22 % of the families. Altogether, our unbiased genetic approach identified pathogenic mutations in ten ARCMT genes in a total of 41.3 % patients. Apart from a newly described founder mutation in GDAP1, the majority of variants constitute private molecular defects. Since the gene testing was independent of the clinical phenotype of the patients, we identified mutations in patients with unusual or additional clinical features, extending the phenotypic spectrum of the SH3TC2 gene. Our study provides an overview of the ARCMT genetic landscape and proposes guidelines for tackling the genetic heterogeneity of this group of hereditary neuropathies. PMID:25231362

A tailored approach to BRAF and MLH1 methylation testing in a universal screening program for Lynch syndrome.

Science.gov (United States)

Adar, Tomer; Rodgers, Linda H; Shannon, Kristen M; Yoshida, Makoto; Ma, Tianle; Mattia, Anthony; Lauwers, Gregory Y; Iafrate, Anthony J; Chung, Daniel C

2017-03-01

To determine the correlation between BRAF genotype and MLH1 promoter methylation in a screening program for Lynch syndrome (LS), a universal screening program for LS was established in two medical centers. Tumors with abnormal MLH1 staining were evaluated for both BRAF V600E genotype and MLH1 promoter methylation. Tumors positive for both were considered sporadic, and genetic testing was recommended for all others. A total 1011 colorectal cancer cases were screened for Lynch syndrome, and 148 (14.6%) exhibited absent MLH1 immunostaining. Both BRAF and MLH1 methylation testing were completed in 126 cases. Concordant results (both positive or both negative) were obtained in 86 (68.3%) and 16 (12.7%) cases, respectively, with 81% concordance overall. The positive and negative predictive values for a BRAF mutation in predicting MLH1 promoter methylation were 98.9% and 41%, respectively, and the negative predictive value fell to 15% in patients ≥70 years old. Using BRAF genotyping as a sole test to evaluate cases with absent MLH1 staining would have increased referral rates for genetic testing by 2.3-fold compared with MLH1 methylation testing alone (31% vs 13.5%, respectively, PMLH1 methylation testing for BRAF wild-type cases only would significantly decrease the number of methylation assays performed and reduce the referral rate for genetic testing to 12.7%. A BRAF mutation has an excellent positive predictive value but poor negative predictive value in predicting MLH1 promoter methylation. A hybrid use of these tests may reduce the number of low-risk patients referred to genetic counseling and facilitate wider implementation of Lynch syndrome screening programs.

My Family-Study, Early-Onset Substance use Prevention Program: An Application of Intervention Mapping Approach

Directory of Open Access Journals (Sweden)

Mehdi Mirzaei-Alavijeh

2017-03-01

Full Text Available Background and Objectives: Based on different studies, substance use is one of the health problems in the Iranian society. The prevalence of substance use is on a growing trend; moreover, the age of the onset of substance use has declined to early adolescence and even lower. Regarding this, the present study aimed to develop a family-based early-onset substance use prevention program in children (My Family-Study by using intervention mapping approach. Materials and Methods: This study descirbes the research protocol during which the intervention mapping approach was used as a framework to develop My Family-Study. In this study, six steps of intervention mapping were completed. Interviews with experts and literature review fulfilled the need assessment. In the second step, the change objectivs were rewritten based on the intersection of the performance objectives and the determinants associated in the matrices. After designing the program and planning the implementation of the intervention, the evaluation plan of the program was accomplished. Results: The use of intervention mapping approach facilitated the develop-pment of a systematic as well as theory- and evidence-based program. Moreover, this approach was helful in the determination of outcomes, performance and change objectives, determinants, theoretical methods, practical application, intervention, dissemination, and evaluation program. Conclusions: The intervention mapping provided a systematic as well as theory- and evidence-based approach to develop a quality continuing health promotion program.

Modelling of the Elasticity Modulus for Rock Using Genetic Expression Programming

Directory of Open Access Journals (Sweden)

Umit Atici

2016-01-01

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

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

Science.gov (United States)

Taylan, Fatih

2011-04-01

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

Genetic Engineering Workshop Report, 2010

Energy Technology Data Exchange (ETDEWEB)

Allen, J; Slezak, T

2010-11-03

The Lawrence Livermore National Laboratory (LLNL) Bioinformatics group has recently taken on a role in DTRA's Transformation Medical Technologies (TMT) program. The high-level goal of TMT is to accelerate the development of broad-spectrum countermeasures. To achieve this goal, there is a need to assess the genetic engineering (GE) approaches, potential application as well as detection and mitigation strategies. LLNL was tasked to coordinate a workshop to determine the scope of investments that DTRA should make to stay current with the rapid advances in genetic engineering technologies, so that accidental or malicious uses of GE technologies could be adequately detected and characterized. Attachment A is an earlier report produced by LLNL for TMT that provides some relevant background on Genetic Engineering detection. A workshop was held on September 23-24, 2010 in Springfield, Virginia. It was attended by a total of 55 people (see Attachment B). Twenty four (44%) of the attendees were academic researchers involved in GE or bioinformatics technology, 6 (11%) were from DTRA or the TMT program management, 7 (13%) were current TMT performers (including Jonathan Allen and Tom Slezak of LLNL who hosted the workshop), 11 (20%) were from other Federal agencies, and 7 (13%) were from industries that are involved in genetic engineering. Several attendees could be placed in multiple categories. There were 26 attendees (47%) who were from out of the DC area and received travel assistance through Invitational Travel Orders (ITOs). We note that this workshop could not have been as successful without the ability to invite experts from outside of the Beltway region. This workshop was an unclassified discussion of the science behind current genetic engineering capabilities. US citizenship was not required for attendance. While this may have limited some discussions concerning risk, we felt that it was more important for this first workshop to focus on the scientific state of

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

Directory of Open Access Journals (Sweden)

K. Mohaideen Pitchai

2017-07-01

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

Genetic algorithm approach to thin film optical parameters determination

International Nuclear Information System (INIS)

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

2003-01-01

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

COMPETENCE-BASED APPROACH TO MODELLING STRUCTURES OF THE MAIN EDUCATIONAL PROGRAM

Directory of Open Access Journals (Sweden)

V. A. Gerasimova

2015-01-01

Full Text Available By the analysis results of scientific works in the field of competence-based approach in education authors proved need of computer support of the planning and development stage of the main educational program, they developed the main educational program structure automatic formation model on the graphs basis, offered the integrated criterion of an discipline assessment and developed a strategic map of a discipline complex assessment. The executed theoretical researches are a basis for creation of the main educational program planning and development support automated system.

Obesogenic environments: environmental approaches to obesity prevention.

Science.gov (United States)

Lipek, Tobias; Igel, Ulrike; Gausche, Ruth; Kiess, Wieland; Grande, Gesine

2015-05-01

Childhood obesity is a major concern for public health. There are multiple factors (e.g., genetic, social, and environmental) that contribute to unhealthy weight gain. Drawing from findings on "obesogenic environments" and core principles of preventive strategies to reduce health inequalities, this paper gives an overview of recent childhood prevention programs that target aspects of the physical environment ("environmental changes"). Out of the ten reviews we screened (including more than 300 studies), we identified very few that addressed aspects of the environment. We focus here on 14 programs that follow different approaches to environmental changes (e.g., access to/quality of playgrounds, changes in school cafeterias). Altering the environment offers opportunities for healthier behaviors and seems to be an effective strategy to prevent childhood obesity. However, the evaluation of those (mostly) multidimensional interventions does not allow drawing firm conclusions about the single effect of environmental changes. We conclude that obesity prevention programs should combine person-based and environmental approaches.

A nonlinear bi-level programming approach for product portfolio management.

Science.gov (United States)

Ma, Shuang

2016-01-01

Product portfolio management (PPM) is a critical decision-making for companies across various industries in today's competitive environment. Traditional studies on PPM problem have been motivated toward engineering feasibilities and marketing which relatively pay less attention to other competitors' actions and the competitive relations, especially in mathematical optimization domain. The key challenge lies in that how to construct a mathematical optimization model to describe this Stackelberg game-based leader-follower PPM problem and the competitive relations between them. The primary work of this paper is the representation of a decision framework and the optimization model to leverage the PPM problem of leader and follower. A nonlinear, integer bi-level programming model is developed based on the decision framework. Furthermore, a bi-level nested genetic algorithm is put forward to solve this nonlinear bi-level programming model for leader-follower PPM problem. A case study of notebook computer product portfolio optimization is reported. Results and analyses reveal that the leader-follower bi-level optimization model is robust and can empower product portfolio optimization.

CDPOP: A spatially explicit cost distance population genetics program

Science.gov (United States)

Erin L. Landguth; S. A. Cushman

2010-01-01

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

Multilevel approach to mentoring in the Research Experiences for Undergraduates programs

Science.gov (United States)

Bonine, K. E.; Dontsova, K.; Pavao-Zuckerman, M.; Paavo, B.; Hogan, D.; Oberg, E.; Gay, J.

2015-12-01

This presentation focuses on different types of mentoring for students participating in Research Experiences for Undergraduates programs with examples, including some new approaches, from The Environmental and Earth Systems Research Experiences for Undergraduates Program at Biosphere 2. While traditional faculty mentors play essential role in students' development as researchers and professionals, other formal and informal mentoring can be important component of the REU program and student experiences. Students receive mentoring from program directors, coordinators, and on site undergraduate advisors. While working on their research projects, REU students receive essential support and mentoring from undergraduate and graduate students and postdoctoral scientists in the research groups of their primary mentors. Cohort living and group activities give multiple opportunities for peer mentoring where each student brings their own strengths and experiences to the group. Biosphere 2 REU program puts strong emphasis on teaching students to effectively communicate their research to public. In order to help REUs learn needed skills the outreach personnel at Biosphere 2 mentor and advise students both in groups and individually, in lecture format and by personal example, on best outreach approaches in general and on individual outreach projects students develop. To further enhance and strengthen outreach mentoring we used a novel approach of blending cohort of REU students with the Cal Poly STAR (STEM Teacher And Researcher) Program fellows, future K-12 STEM teachers who are gaining research experience at Biosphere 2. STAR fellows live together with the REU students and participate with them in professional development activities, as well as perform research side by side. Educational background and experiences gives these students a different view and better preparation and tools to effectively communicate and adapt science to lay audiences, a challenge commonly facing

Future possibilities in migraine genetics

DEFF Research Database (Denmark)

Rudkjøbing, Laura Aviaja; Esserlind, Ann-Louise; Olesen, Jes

2012-01-01

Migraine with and without aura (MA and MO, respectively) have a strong genetic basis. Different approaches using linkage-, candidate gene- and genome-wide association studies have been explored, yielding limited results. This may indicate that the genetic component in migraine is due to rare...... variants; capturing these will require more detailed sequencing in order to be discovered. Next-generation sequencing (NGS) techniques such as whole exome and whole genome sequencing have been successful in finding genes in especially monogenic disorders. As the molecular genetics research progresses......, the technology will follow, rendering these approaches more applicable in the search for causative migraine genes in MO and MA. To date, no studies using NGS in migraine genetics have been published. In order to gain insight into the future possibilities of migraine genetics, we have looked at NGS studies...

Nurses' knowledge and educational needs regarding genetics.

Science.gov (United States)

Seven, Memnun; Akyüz, Aygül; Elbüken, Burcu; Skirton, Heather; Öztürk, Hatice

2015-03-01

Nurses now require a basic knowledge of genetics to provide patient care in a range of settings. To determine Turkish registered nurses' current knowledge and educational needs in relation to genetics. A descriptive, cross-sectional study. Turkish registered nurses working in a university hospital in Turkey were recruited. All registered nurses were invited to participate and 175 completed the study. The survey instrument, basic knowledge of health genetics, confidence in knowledge and the nurses' need for genetics education were used to collect data. The majority (81.1%, n=142) of participants indicated that genetics was not taught during their degree program, although 53.1% to 96% of respondents felt confident in defining different genetic concepts. The average genetics knowledge score was 6.89±1.99 of a possible 11 (range 0-11). The majority (70.3%) expressed a strong wish to attend a continuing nursing education program in genetics. The study shows that although Turkish nurses are not sufficiently knowledgeable to apply genetics in practice, they are willing to have more education to support their care of patients. Nurses need to have more education related to genetics in accordance with advances in human genetics to optimize health care. Copyright © 2014 Elsevier Ltd. All rights reserved.

Fuzzy multinomial logistic regression analysis: A multi-objective programming approach

Science.gov (United States)

Abdalla, Hesham A.; El-Sayed, Amany A.; Hamed, Ramadan

2017-05-01

Parameter estimation for multinomial logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets, Maximum Likelihood (ML) estimation is a satisfactory approach. Unfortunately, ML can fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. In this study, a new approach based on fuzzy concepts is proposed to estimate parameters of the multinomial logistic regression. The study assumes that the parameters of multinomial logistic regression are fuzzy. Based on the extension principle stated by Zadeh and Bárdossy's proposition, a multi-objective programming approach is suggested to estimate these fuzzy parameters. A simulation study is used to evaluate the performance of the new approach versus Maximum likelihood (ML) approach. Results show that the new proposed model outperforms ML in cases of small datasets.

Feline genetics: clinical applications and genetic testing.

Science.gov (United States)

Lyons, Leslie A

2010-11-01

DNA testing for domestic cat diseases and appearance traits is a rapidly growing asset for veterinary medicine. Approximately 33 genes contain 50 mutations that cause feline health problems or alterations in the cat's appearance. A variety of commercial laboratories can now perform cat genetic diagnostics, allowing both the veterinary clinician and the private owner to obtain DNA test results. DNA is easily obtained from a cat via a buccal swab with a standard cotton bud or cytological brush, allowing DNA samples to be easily sent to any laboratory in the world. The DNA test results identify carriers of the traits, predict the incidence of traits from breeding programs, and influence medical prognoses and treatments. An overall goal of identifying these genetic mutations is the correction of the defect via gene therapies and designer drug therapies. Thus, genetic testing is an effective preventative medicine and a potential ultimate cure. However, genetic diagnostic tests may still be novel for many veterinary practitioners and their application in the clinical setting needs to have the same scrutiny as any other diagnostic procedure. This article will review the genetic tests for the domestic cat, potential sources of error for genetic testing, and the pros and cons of DNA results in veterinary medicine. Highlighted are genetic tests specific to the individual cat, which are a part of the cat's internal genome. Copyright © 2010 Elsevier Inc. All rights reserved.

Cancer Genetics and Signaling | Center for Cancer Research

Science.gov (United States)

The Cancer, Genetics, and Signaling (CGS) Group at the National Cancer Institute at Frederick  offers a competitive postdoctoral training and mentoring program focusing on molecular and genetic aspects of cancer. The CGS Fellows Program is designed to attract and train exceptional postdoctoral fellows interested in pursuing independent research career tracks. CGS Fellows participate in a structured mentoring program designed for scientific and career development and transition to independent positions.

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

Directory of Open Access Journals (Sweden)

Aidin Delgoshaei

2016-09-01

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

Contribution of genetics to ecological restoration.

Science.gov (United States)

Mijangos, Jose Luis; Pacioni, Carlo; Spencer, Peter B S; Craig, Michael D

2015-01-01

Ecological restoration of degraded ecosystems has emerged as a critical tool in the fight to reverse and ameliorate the current loss of biodiversity and ecosystem services. Approaches derived from different genetic disciplines are extending the theoretical and applied frameworks on which ecological restoration is based. We performed a search of scientific articles and identified 160 articles that employed a genetic approach within a restoration context to shed light on the links between genetics and restoration. These articles were then classified on whether they examined association between genetics and fitness or the application of genetics in demographic studies, and on the way the studies informed restoration practice. Although genetic research in restoration is rapidly growing, we found that studies could make better use of the extensive toolbox developed by applied fields in genetics. Overall, 41% of reviewed studies used genetic information to evaluate or monitor restoration, and 59% provided genetic information to guide prerestoration decision-making processes. Reviewed studies suggest that restoration practitioners often overlook the importance of including genetic aspects within their restoration goals. Even though there is a genetic basis influencing the provision of ecosystem services, few studies explored this relationship. We provide a view of research gaps, future directions and challenges in the genetics of restoration. © 2014 John Wiley & Sons Ltd.