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Sample records for learning methods genetic

  1. A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.

    Science.gov (United States)

    Koo, Ching Lee; Liew, Mei Jing; Mohamad, Mohd Saberi; Salleh, Abdul Hakim Mohamed

    2013-01-01

    Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

  2. A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

    Directory of Open Access Journals (Sweden)

    Ching Lee Koo

    2013-01-01

    Full Text Available Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs, support vector machine (SVM, and random forests (RFs in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

  3. Genetic Science Learning Center

    Science.gov (United States)

    Genetic Science Learning Center Making science and health easy for everyone to understand Home News Our Team What We Do ... Collaboration Conferences Current Projects Publications Contact The Genetic Science Learning Center at The University of Utah is a ...

  4. Statistics for Learning Genetics

    Science.gov (United States)

    Charles, Abigail Sheena

    , although the necessity for infusing these quantitative subjects with genetics and, overall, the biological sciences is growing (topics including synthetic biology, molecular systems biology and phylogenetics) there remains little time in the semester to be dedicated to the consolidation of learning and understanding.

  5. A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia

    Directory of Open Access Journals (Sweden)

    Honghui Yang

    2010-10-01

    Full Text Available We demonstrate a hybrid machine learning method to classify schizophrenia patients and healthy controls, using functional magnetic resonance imaging (fMRI and single nucleotide polymorphism (SNP data. The method consists of four stages: (1 SNPs with the most discriminating information between the healthy controls and schizophrenia patients are selected to construct a support vector machine ensemble (SNP-SVME. (2 Voxels in the fMRI map contributing to classification are selected to build another SVME (Voxel-SVME. (3 Components of fMRI activation obtained with independent component analysis (ICA are used to construct a single SVM classifier (ICA-SVMC. (4 The above three models are combined into a single module using a majority voting approach to make a final decision (Combined SNP-fMRI. The method was evaluated by a fully-validated leave-one-out method using 40 subjects (20 patients and 20 controls. The classification accuracy was: 0.74 for SNP-SVME, 0.82 for Voxel-SVME, 0.83 for ICA-SVMC, and 0.87 for Combined SNP-fMRI. Experimental results show that better classification accuracy was achieved by combining genetic and fMRI data than using either alone, indicating that genetic and brain function representing different, but partially complementary aspects, of schizophrenia etiopathology. This study suggests an effective way to reassess biological classification of individuals with schizophrenia, which is also potentially useful for identifying diagnostically important markers for the disorder.

  6. Learning Intelligent Genetic Algorithms Using Japanese Nonograms

    Science.gov (United States)

    Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen

    2012-01-01

    An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…

  7. Genetic Learning Particle Swarm Optimization.

    Science.gov (United States)

    Gong, Yue-Jiao; Li, Jing-Jing; Zhou, Yicong; Li, Yun; Chung, Henry Shu-Hung; Shi, Yu-Hui; Zhang, Jun

    2016-10-01

    Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for "learning." This leads to a generalized "learning PSO" paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO.

  8. Deep Learning for Population Genetic Inference.

    Science.gov (United States)

    Sheehan, Sara; Song, Yun S

    2016-03-01

    Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.

  9. Deep Learning for Population Genetic Inference.

    Directory of Open Access Journals (Sweden)

    Sara Sheehan

    2016-03-01

    Full Text Available Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data to the output (e.g., population genetic parameters of interest. We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history. Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.

  10. Deep Learning for Population Genetic Inference

    Science.gov (United States)

    Sheehan, Sara; Song, Yun S.

    2016-01-01

    Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme. PMID:27018908

  11. A 100-Year Review: Methods and impact of genetic selection in dairy cattle-From daughter-dam comparisons to deep learning algorithms.

    Science.gov (United States)

    Weigel, K A; VanRaden, P M; Norman, H D; Grosu, H

    2017-12-01

    In the early 1900s, breed society herdbooks had been established and milk-recording programs were in their infancy. Farmers wanted to improve the productivity of their cattle, but the foundations of population genetics, quantitative genetics, and animal breeding had not been laid. Early animal breeders struggled to identify genetically superior families using performance records that were influenced by local environmental conditions and herd-specific management practices. Daughter-dam comparisons were used for more than 30 yr and, although genetic progress was minimal, the attention given to performance recording, genetic theory, and statistical methods paid off in future years. Contemporary (herdmate) comparison methods allowed more accurate accounting for environmental factors and genetic progress began to accelerate when these methods were coupled with artificial insemination and progeny testing. Advances in computing facilitated the implementation of mixed linear models that used pedigree and performance data optimally and enabled accurate selection decisions. Sequencing of the bovine genome led to a revolution in dairy cattle breeding, and the pace of scientific discovery and genetic progress accelerated rapidly. Pedigree-based models have given way to whole-genome prediction, and Bayesian regression models and machine learning algorithms have joined mixed linear models in the toolbox of modern animal breeders. Future developments will likely include elucidation of the mechanisms of genetic inheritance and epigenetic modification in key biological pathways, and genomic data will be used with data from on-farm sensors to facilitate precision management on modern dairy farms. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  12. Machine learning applications in genetics and genomics.

    Science.gov (United States)

    Libbrecht, Maxwell W; Noble, William Stafford

    2015-06-01

    The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

  13. COMPUTER METHODS OF GENETIC ANALYSIS.

    Directory of Open Access Journals (Sweden)

    A. L. Osipov

    2017-02-01

    Full Text Available The basic statistical methods used in conducting the genetic analysis of human traits. We studied by segregation analysis, linkage analysis and allelic associations. Developed software for the implementation of these methods support.

  14. Immune Genetic Learning of Fuzzy Cognitive Map

    Institute of Scientific and Technical Information of China (English)

    LIN Chun-mei; HE Yue; TANG Bing-yong

    2006-01-01

    This paper presents a hybrid methodology of automatically constructing fuzzy cognitive map (FCM). The method uses immune genetic algorithm to learn the connection matrix of FCM. In the algorithm, the DNA coding method is used and an immune operator based on immune mechanism is constructed. The characteristics of the system and the experts' knowledge are abstracted as vaccine for restraining the degenerative phenomena during evolution so as to improve the algorithmic efficiency. Finally, an illustrative example is provided, and its results suggest that the method is capable of automatically generating FCM model.

  15. Statistical methods in spatial genetics

    DEFF Research Database (Denmark)

    Guillot, Gilles; Leblois, Raphael; Coulon, Aurelie

    2009-01-01

    The joint analysis of spatial and genetic data is rapidly becoming the norm in population genetics. More and more studies explicitly describe and quantify the spatial organization of genetic variation and try to relate it to underlying ecological processes. As it has become increasingly difficult...... to keep abreast with the latest methodological developments, we review the statistical toolbox available to analyse population genetic data in a spatially explicit framework. We mostly focus on statistical concepts but also discuss practical aspects of the analytical methods, highlighting not only...

  16. Methods and impact of genetic selection in dairy cattle: From daughter-dam comparisons to deep learning algorithms

    Science.gov (United States)

    In the early 1900s, breed society herdbooks had been established, and milk recording programs were in their infancy. Farmers were interested in improving the productivity of dairy cattle, but the foundations of population genetics, quantitative genetics, and animal breeding had not yet been laid. Li...

  17. Machine learning in genetics and genomics

    Science.gov (United States)

    Libbrecht, Maxwell W.; Noble, William Stafford

    2016-01-01

    The field of machine learning promises to enable computers to assist humans in making sense of large, complex data sets. In this review, we outline some of the main applications of machine learning to genetic and genomic data. In the process, we identify some recurrent challenges associated with this type of analysis and provide general guidelines to assist in the practical application of machine learning to real genetic and genomic data. PMID:25948244

  18. Machine Learning in Production Systems Design Using Genetic Algorithms

    OpenAIRE

    Abu Qudeiri Jaber; Yamamoto Hidehiko Rizauddin Ramli

    2008-01-01

    To create a solution for a specific problem in machine learning, the solution is constructed from the data or by use a search method. Genetic algorithms are a model of machine learning that can be used to find nearest optimal solution. While the great advantage of genetic algorithms is the fact that they find a solution through evolution, this is also the biggest disadvantage. Evolution is inductive, in nature life does not evolve towards a good solution but it evolves aw...

  19. Genetic classification of populations using supervised learning.

    Directory of Open Access Journals (Sweden)

    Michael Bridges

    2011-05-01

    Full Text Available There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case-control studies and quality control (when participants in a study have been genotyped at different laboratories. This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available.In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines to the classification of three populations (two from Scotland and one from Bulgaria. The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.

  20. Genetic classification of populations using supervised learning.

    LENUS (Irish Health Repository)

    Bridges, Michael

    2011-01-01

    There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case-control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available.In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.

  1. Statistical methods and challenges in connectome genetics

    KAUST Repository

    Pluta, Dustin; Yu, Zhaoxia; Shen, Tong; Chen, Chuansheng; Xue, Gui; Ombao, Hernando

    2018-01-01

    The study of genetic influences on brain connectivity, known as connectome genetics, is an exciting new direction of research in imaging genetics. We here review recent results and current statistical methods in this area, and discuss some

  2. Cooperative Learning as a Democratic Learning Method

    Science.gov (United States)

    Erbil, Deniz Gökçe; Kocabas, Ayfer

    2018-01-01

    In this study, the effects of applying the cooperative learning method on the students' attitude toward democracy in an elementary 3rd-grade life studies course was examined. Over the course of 8 weeks, the cooperative learning method was applied with an experimental group, and traditional methods of teaching life studies in 2009, which was still…

  3. Machine learning methods for planning

    CERN Document Server

    Minton, Steven

    1993-01-01

    Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning.Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credi

  4. Active Learning Methods

    Science.gov (United States)

    Zayapragassarazan, Z.; Kumar, Santosh

    2012-01-01

    Present generation students are primarily active learners with varied learning experiences and lecture courses may not suit all their learning needs. Effective learning involves providing students with a sense of progress and control over their own learning. This requires creating a situation where learners have a chance to try out or test their…

  5. Qualitative methods in workplace learning

    OpenAIRE

    Fabritius, Hannele

    2015-01-01

    Methods of learning in the workplace will be introduced. The methods are connect to competence development and to the process of conducting development discussions in a dialogical way. The tools developed and applied are a fourfold table, a cycle of work identity, a plan of personal development targets, a learning meeting and a learning map. The methods introduced will aim to better learning at work.

  6. Statistical methods and challenges in connectome genetics

    KAUST Repository

    Pluta, Dustin

    2018-03-12

    The study of genetic influences on brain connectivity, known as connectome genetics, is an exciting new direction of research in imaging genetics. We here review recent results and current statistical methods in this area, and discuss some of the persistent challenges and possible directions for future work.

  7. Genetic disruptions of Drosophila Pavlovian learning leave extinction learning intact.

    Science.gov (United States)

    Qin, H; Dubnau, J

    2010-03-01

    Individuals who experience traumatic events may develop persistent posttraumatic stress disorder. Patients with this disorder are commonly treated with exposure therapy, which has had limited long-term success. In experimental neurobiology, fear extinction is a model for exposure therapy. In this behavioral paradigm, animals are repeatedly exposed in a safe environment to the fearful stimulus, which leads to greatly reduced fear. Studying animal models of extinction already has lead to better therapeutic strategies and development of new candidate drugs. Lack of a powerful genetic model of extinction, however, has limited progress in identifying underlying molecular and genetic factors. In this study, we established a robust behavioral paradigm to study the short-term effect (acquisition) of extinction in Drosophila melanogaster. We focused on the extinction of olfactory aversive 1-day memory with a task that has been the main workhorse for genetics of memory in flies. Using this paradigm, we show that extinction can inhibit each of two genetically distinct forms of consolidated memory. We then used a series of single-gene mutants with known impact on associative learning to examine the effects on extinction. We find that extinction is intact in each of these mutants, suggesting that extinction learning relies on different molecular mechanisms than does Pavlovian learning.

  8. Influence of crossover methods used by genetic algorithm-based ...

    Indian Academy of Sciences (India)

    numerical methods like Newton–Raphson, sequential homotopy calculation, Walsh ... But the paper does not touch upon the elements of crossover operators. ... if SHE problems are solved with optimization tools like GA (Schutten ..... Goldberg D E 1989 Genetic algorithms in search, optimization and machine learning.

  9. Geometrical methods in learning theory

    International Nuclear Information System (INIS)

    Burdet, G.; Combe, Ph.; Nencka, H.

    2001-01-01

    The methods of information theory provide natural approaches to learning algorithms in the case of stochastic formal neural networks. Most of the classical techniques are based on some extremization principle. A geometrical interpretation of the associated algorithms provides a powerful tool for understanding the learning process and its stability and offers a framework for discussing possible new learning rules. An illustration is given using sequential and parallel learning in the Boltzmann machine

  10. Genetic component in learning ability in bees.

    Science.gov (United States)

    Kerr, W E; Moura Duarte, F A; Oliveira, R S

    1975-10-01

    Twenty-five bees, five from each of five hives, were trained to collect food at a table. When the bee reached the table, time was recorded for 12 visits. Then a blue and yellow pan was substituted for the original metal pan, and time and correct responses were recorded for 30 trips (discrimination phase). Finally, food was taken from the pan and extinction was recorded as incorrect responses for 20 visits. Variance analysis was carried out, and genetic variance was undetected for discrimination, but was detected for extinction. It is concluded that learning is very important for bees, so that any impairment in such ability affects colony survival.

  11. The development of metacognitive-based genetic learning ...

    African Journals Online (AJOL)

    The development of metacognitive-based genetic learning Instruments at senior ... The results of the research are learning instrument product and textbook whose ... that these instruments have satisfied the criteria: very valid and very ideal.

  12. Methods to estimate the genetic risk

    International Nuclear Information System (INIS)

    Ehling, U.H.

    1989-01-01

    The estimation of the radiation-induced genetic risk to human populations is based on the extrapolation of results from animal experiments. Radiation-induced mutations are stochastic events. The probability of the event depends on the dose; the degree of the damage dose not. There are two main approaches in making genetic risk estimates. One of these, termed the direct method, expresses risk in terms of expected frequencies of genetic changes induced per unit dose. The other, referred to as the doubling dose method or the indirect method, expresses risk in relation to the observed incidence of genetic disorders now present in man. The advantage of the indirect method is that not only can Mendelian mutations be quantified, but also other types of genetic disorders. The disadvantages of the method are the uncertainties in determining the current incidence of genetic disorders in human and, in addition, the estimasion of the genetic component of congenital anomalies, anomalies expressed later and constitutional and degenerative diseases. Using the direct method we estimated that 20-50 dominant radiation-induced mutations would be expected in 19 000 offspring born to parents exposed in Hiroshima and Nagasaki, but only a small proportion of these mutants would have been detected with the techniques used for the population study. These methods were used to predict the genetic damage from the fallout of the reactor accident at Chernobyl in the vicinity of Southern Germany. The lack of knowledge for the interaction of chemicals with ionizing radiation and the discrepancy between the high safety standards for radiation protection and the low level of knowledge for the toxicological evaluation of chemical mutagens will be emphasized. (author)

  13. Genetically Modified GMDH Method with Cloning

    Czech Academy of Sciences Publication Activity Database

    Jiřina, Marcel; Jiřina jr., M.

    2007-01-01

    Roč. 28, - (2007), s. 29-37 ISSN 1870-4069. [NNAM 2007. International Conference on Neural Networks and Associative Memories /2./. Mexico City, 04.11.2007-09.11.2007] R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : GMDH neural network * genetic selection * cloning * Machine Learning Repository Subject RIV: BA - General Mathematics

  14. Visual and Verbal Learning in a Genetic Metabolic Disorder

    Science.gov (United States)

    Spilkin, Amy M.; Ballantyne, Angela O.; Trauner, Doris A.

    2009-01-01

    Visual and verbal learning in a genetic metabolic disorder (cystinosis) were examined in the following three studies. The goal of Study I was to provide a normative database and establish the reliability and validity of a new test of visual learning and memory (Visual Learning and Memory Test; VLMT) that was modeled after a widely used test of…

  15. Supervised Machine Learning for Population Genetics: A New Paradigm

    Science.gov (United States)

    Schrider, Daniel R.; Kern, Andrew D.

    2018-01-01

    As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning (ML). We review the fundamentals of ML, discuss recent applications of supervised ML to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised ML is an important and underutilized tool that has considerable potential for the world of evolutionary genomics. PMID:29331490

  16. Public health genetic counselors: activities, skills, and sources of learning.

    Science.gov (United States)

    McWalter, Kirsty M; Sdano, Mallory R; Dave, Gaurav; Powell, Karen P; Callanan, Nancy

    2015-06-01

    Specialization within genetic counseling is apparent, with 29 primary specialties listed in the National Society of Genetic Counselors' 2012 Professional Status Survey (PSS). PSS results show a steady proportion of genetic counselors primarily involved in public health, yet do not identify all those performing public health activities. Little is known about the skills needed to perform activities outside of "traditional" genetic counselor roles and the expertise needed to execute those skills. This study aimed to identify genetic counselors engaging in public health activities, the skills used, and the most influential sources of learning for those skills. Participants (N = 155) reported involvement in several public health categories: (a) Education of Public and/or Health Care Providers (n = 80, 52 %), (b) Population-Based Screening Programs (n = 70, 45 %), (c) Lobbying/Public Policy (n = 62, 40 %), (d) Public Health Related Research (n = 47, 30 %), and (e) State Chronic Disease Programs (n = 12, 8 %). Regardless of category, "on the job" was the most common primary source of learning. Genetic counseling training program was the most common secondary source of learning. Results indicate that the number of genetic counselors performing public health activities is likely higher than PSS reports, and that those who may not consider themselves "public health genetic counselors" do participate in public health activities. Genetic counselors learn a diverse skill set in their training programs; some skills are directly applicable to public health genetics, while other public health skills require additional training and/or knowledge.

  17. Reflexive Learning through Visual Methods

    DEFF Research Database (Denmark)

    Frølunde, Lisbeth

    2014-01-01

    What. This chapter concerns how visual methods and visual materials can support visually oriented, collaborative, and creative learning processes in education. The focus is on facilitation (guiding, teaching) with visual methods in learning processes that are designerly or involve design. Visual...... methods are exemplified through two university classroom cases about collaborative idea generation processes. The visual methods and materials in the cases are photo elicitation using photo cards, and modeling with LEGO Serious Play sets. Why. The goal is to encourage the reader, whether student...... or professional, to facilitate with visual methods in a critical, reflective, and experimental way. The chapter offers recommendations for facilitating with visual methods to support playful, emergent designerly processes. The chapter also has a critical, situated perspective. Where. This chapter offers case...

  18. Methods for genetic transformation of filamentous fungi.

    Science.gov (United States)

    Li, Dandan; Tang, Yu; Lin, Jun; Cai, Weiwen

    2017-10-03

    Filamentous fungi have been of great interest because of their excellent ability as cell factories to manufacture useful products for human beings. The development of genetic transformation techniques is a precondition that enables scientists to target and modify genes efficiently and may reveal the function of target genes. The method to deliver foreign nucleic acid into cells is the sticking point for fungal genome modification. Up to date, there are some general methods of genetic transformation for fungi, including protoplast-mediated transformation, Agrobacterium-mediated transformation, electroporation, biolistic method and shock-wave-mediated transformation. This article reviews basic protocols and principles of these transformation methods, as well as their advantages and disadvantages.

  19. [Application of case-based method in genetics and eugenics teaching].

    Science.gov (United States)

    Li, Ya-Xuan; Zhao, Xin; Zhang, Fei-Xiong; Hu, Ying-Kao; Yan, Yue-Ming; Cai, Min-Hua; Li, Xiao-Hui

    2012-05-01

    Genetics and Eugenics is a cross-discipline between genetics and eugenics. It is a common curriculum in many Chinese universities. In order to increase the learning interest, we introduced case teaching method and got a better teaching effect. Based on our teaching practices, we summarized some experiences about this subject. In this article, the main problem of case-based method applied in Genetics and Eugenics teaching was discussed.

  20. Decomposition methods for unsupervised learning

    DEFF Research Database (Denmark)

    Mørup, Morten

    2008-01-01

    This thesis presents the application and development of decomposition methods for Unsupervised Learning. It covers topics from classical factor analysis based decomposition and its variants such as Independent Component Analysis, Non-negative Matrix Factorization and Sparse Coding...... methods and clustering problems is derived both in terms of classical point clustering but also in terms of community detection in complex networks. A guiding principle throughout this thesis is the principle of parsimony. Hence, the goal of Unsupervised Learning is here posed as striving for simplicity...... in the decompositions. Thus, it is demonstrated how a wide range of decomposition methods explicitly or implicitly strive to attain this goal. Applications of the derived decompositions are given ranging from multi-media analysis of image and sound data, analysis of biomedical data such as electroencephalography...

  1. Methods for genetic linkage analysis using trisomies.

    OpenAIRE

    Feingold, E; Lamb, N E; Sherman, S L

    1995-01-01

    Certain genetic disorders are rare in the general population, but more common in individuals with specific trisomies. Examples of this include leukemia and duodenal atresia in trisomy 21. This paper presents a linkage analysis method for using trisomic individuals to map genes for such traits. It is based on a very general gene-specific dosage model that posits that the trait is caused by specific effects of different alleles at one or a few loci and that duplicate copies of "susceptibility" ...

  2. A Tomographic method based on genetic algorithms

    International Nuclear Information System (INIS)

    Turcanu, C.; Alecu, L.; Craciunescu, T.; Niculae, C.

    1997-01-01

    Computerized tomography being a non-destructive and non-evasive technique is frequently used in medical application to generate three dimensional images of objects. Genetic algorithms are efficient, domain independent for a large variety of problems. The proposed method produces good quality reconstructions even in case of very small number of projection angles. It requests no a priori knowledge about the solution and takes into account the statistical uncertainties. The main drawback of the method is the amount of computer memory and time needed. (author)

  3. Yeast genetics. A manual of methods

    Energy Technology Data Exchange (ETDEWEB)

    Spencer, J.F.T.; Spencer, D.M.; Bruce, I.J.

    1989-01-01

    This is a bench-top manual of methods needed both for classical genetics as related to yeasts, such as mating, sporulation, isolation of hybrids, microdissection of asci for the isolation of single-spore clones, as well as for mapping of genes and the construction of new strains by protoplast fusion. Special emphasis is on mutations in general, and on methods of isolating a number of important classes of mutants in particular. Basic techniques for the separation of chromosomes by electrophoresis, such as OFAGE, FIGE, and CHEF, are discussed, with detailed protocols for the first two. Furthermore, new methods, e.g. for the isolation of high molecular weight DNA from yeast, isolation of RNA, and techniques for transformation of yeasts, are also described in detail. (orig.) With 10 figs.

  4. Statistical learning methods: Basics, control and performance

    Energy Technology Data Exchange (ETDEWEB)

    Zimmermann, J. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: zimmerm@mppmu.mpg.de

    2006-04-01

    The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms.

  5. Statistical learning methods: Basics, control and performance

    International Nuclear Information System (INIS)

    Zimmermann, J.

    2006-01-01

    The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms

  6. Comparison of genetic algorithms with conjugate gradient methods

    Science.gov (United States)

    Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.

    1972-01-01

    Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.

  7. Determination of Selection Method in Genetic Algorithm for Land Suitability

    Directory of Open Access Journals (Sweden)

    Irfianti Asti Dwi

    2016-01-01

    Full Text Available Genetic Algoirthm is one alternative solution in the field of modeling optimization, automatic programming and machine learning. The purpose of the study was to compare some type of selection methods in Genetic Algorithm for land suitability. Contribution of this research applies the best method to develop region based horticultural commodities. This testing is done by comparing the three methods on the method of selection, the Roulette Wheel, Tournament Selection and Stochastic Universal Sampling. Parameters of the locations used in the test scenarios include Temperature = 27°C, Rainfall = 1200 mm, hummidity = 30%, Cluster fruit = 4, Crossover Probabiitiy (Pc = 0.6, Mutation Probabilty (Pm = 0.2 and Epoch = 10. The second test epoch incluides location parameters consist of Temperature = 30°C, Rainfall = 2000 mm, Humidity = 35%, Cluster fruit = 5, Crossover Probability (Pc = 0.7, Mutation Probability (Pm = 0.3 and Epoch 10. The conclusion of this study shows that the Roulette Wheel is the best method because it produces more stable and fitness value than the other two methods.

  8. [Personal identification with biometric and genetic methods].

    Science.gov (United States)

    Cabanis, Emmanuel-Alain; Le Gall, Jean-Yves; Ardaillou, Raymond

    2007-11-01

    The need for personal identification is growing in many avenues of society. To "identify" a person is to establish a link between his or her observed characteristics and those previously stored in a database. To "authenticate" is to decide whether or not someone is the person he or she claims to be. These two objectives can now be achieved by analysing biometric data and genetic prints. All biometric techniques proceed in several stages: acquisition of an image or physical parameters, encoding them with a mathematical model, comparing the results of this model with those contained in the database, and calculating the error risk. These techniques must be usable worldwide and must examine specific and permanent personal data. The most widely used are facial recognition, digital prints (flexion folds and dermatoglyphs, that offer the advantage of leaving marks), and the surface and texture of the iris. Other biometric techniques analyse behaviours such as walking, signing, typing, or speaking. Implanted radio-transmitters are another means of identification. All these systems are evaluated on the basis of the same parameters, namely the false rejection rate, the false acceptance rate, and the failure-to-enrol rate. The uses of biometrics are increasing and diversifying, and now include national and international identification systems, control of access to protected sites, criminal and victim identification, and transaction security. Genetic methods can identify individuals almost infallibly, based on short tandem repeats of 2-5 nucleotides, or microsatellites. The most recent kits analyze 11-16 independent autosomal markers. Mitochondrial DNA and Y chromosome DNA can also be analyzed. These genetic tests are currently used to identify suspected criminals or their victims from biological samples, and to establish paternity. Personal identification raises many ethical questions, however, such as when to create and how to use a database while preserving personal freedom

  9. METHOD OF CONSTRUCTION OF GENETIC DATA CLUSTERS

    Directory of Open Access Journals (Sweden)

    N. A. Novoselova

    2016-01-01

    Full Text Available The paper presents a method of construction of genetic data clusters (functional modules using the randomized matrices. To build the functional modules the selection and analysis of the eigenvalues of the gene profiles correlation matrix is performed. The principal components, corresponding to the eigenvalues, which are significantly different from those obtained for the randomly generated correlation matrix, are used for the analysis. Each selected principal component forms gene cluster. In a comparative experiment with the analogs the proposed method shows the advantage in allocating statistically significant different-sized clusters, the ability to filter non- informative genes and to extract the biologically interpretable functional modules matching the real data structure.

  10. 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…

  11. Sports genetics moving forward: lessons learned from medical research.

    Science.gov (United States)

    Mattsson, C Mikael; Wheeler, Matthew T; Waggott, Daryl; Caleshu, Colleen; Ashley, Euan A

    2016-03-01

    Sports genetics can take advantage of lessons learned from human disease genetics. By righting past mistakes and increasing scientific rigor, we can magnify the breadth and depth of knowledge in the field. We present an outline of challenges facing sports genetics in the light of experiences from medical research. Sports performance is complex, resulting from a combination of a wide variety of different traits and attributes. Improving sports genetics will foremost require analyses based on detailed phenotyping. To find widely valid, reproducible common variants associated with athletic phenotypes, study sample sizes must be dramatically increased. One paradox is that in order to confirm relevance, replications in specific populations must be undertaken. Family studies of athletes may facilitate the discovery of rare variants with large effects on athletic phenotypes. The complexity of the human genome, combined with the complexity of athletic phenotypes, will require additional metadata and biological validation to identify a comprehensive set of genes involved. Analysis of personal genetic and multiomic profiles contribute to our conceptualization of precision medicine; the same will be the case in precision sports science. In the refinement of sports genetics it is essential to evaluate similarities and differences between sexes and among ethnicities. Sports genetics to date have been hampered by small sample sizes and biased methodology, which can lead to erroneous associations and overestimation of effect sizes. Consequently, currently available genetic tests based on these inherently limited data cannot predict athletic performance with any accuracy. Copyright © 2016 the American Physiological Society.

  12. Learning a Genetic Measure for Kinship Verification Using Facial Images

    Directory of Open Access Journals (Sweden)

    Lu Kou

    2015-01-01

    Full Text Available Motivated by the key observation that children generally resemble their parents more than other persons with respect to facial appearance, distance metric (similarity learning has been the dominant choice for state-of-the-art kinship verification via facial images in the wild. Most existing learning-based approaches to kinship verification, however, are focused on learning a genetic similarity measure in a batch learning manner, leading to less scalability for practical applications with ever-growing amount of data. To address this, we propose a new kinship verification approach by learning a sparse similarity measure in an online fashion. Experimental results on the kinship datasets show that our approach is highly competitive to the state-of-the-art alternatives in terms of verification accuracy, yet it is superior in terms of scalability for practical applications.

  13. Application of active learning modalities to achieve medical genetics competencies and their learning outcome assessments

    Directory of Open Access Journals (Sweden)

    Hagiwara N

    2017-12-01

    Full Text Available Nobuko Hagiwara Division of Cardiovascular Medicine, Department of Internal Medicine, School of Medicine, University of California, Davis, CA, USA Abstract: The steadily falling costs of genome sequencing, coupled with the growing number of genetic tests with proven clinical validity, have made the use of genetic testing more common in clinical practice. This development has necessitated nongeneticist physicians, especially primary care physicians, to become more responsible for assessing genetic risks for their patients. Providing undergraduate medical students a solid foundation in genomic medicine, therefore, has become all the more important to ensure the readiness of future physicians in applying genomic medicine to their patient care. In order to further enhance the effectiveness of instructing practical skills in medical genetics, the emphasis of active learning modules in genetics curriculum at medical schools has increased in recent years. This is because of the general acceptance of a better efficacy of active learner-centered pedagogy over passive lecturer-centered pedagogy. However, an objective standard to evaluate students’ skill levels in genomic medicine achieved by active learning is currently missing. Recently, entrustable professional activities (EPAs in genomic medicine have been proposed as a framework for developing physician competencies in genomic medicine. EPAs in genomic medicine provide a convenient guideline for not only developing genomic medicine curriculum but also assessing students’ competency levels in practicing genomic medicine. In this review, the efficacy of different types of active learning modules reported for medical genetics curricula is discussed using EPAs in genomic medicine as a common evaluation standard for modules’ learning outcomes. The utility of the EPAs in genomic medicine for designing active learning modules in undergraduate medical genetics curricula is also discussed. Keywords

  14. Teaching genetics using hands-on models, problem solving, and inquiry-based methods

    Science.gov (United States)

    Hoppe, Stephanie Ann

    Teaching genetics can be challenging because of the difficulty of the content and misconceptions students might hold. This thesis focused on using hands-on model activities, problem solving, and inquiry-based teaching/learning methods in order to increase student understanding in an introductory biology class in the area of genetics. Various activities using these three methods were implemented into the classes to address any misconceptions and increase student learning of the difficult concepts. The activities that were implemented were shown to be successful based on pre-post assessment score comparison. The students were assessed on the subjects of inheritance patterns, meiosis, and protein synthesis and demonstrated growth in all of the areas. It was found that hands-on models, problem solving, and inquiry-based activities were more successful in learning concepts in genetics and the students were more engaged than tradition styles of lecture.

  15. New e-learning method using databases

    Directory of Open Access Journals (Sweden)

    Andreea IONESCU

    2012-10-01

    Full Text Available The objective of this paper is to present a new e-learning method that use databases. The solution could pe implemented for any typeof e-learning system in any domain. The article will purpose a solution to improve the learning process for virtual classes.

  16. Genetic dissection of behavioral flexibility: reversal learning in mice.

    Science.gov (United States)

    Laughlin, Rick E; Grant, Tara L; Williams, Robert W; Jentsch, J David

    2011-06-01

    Behavioral inflexibility is a feature of schizophrenia, attention-deficit/hyperactivity disorder, and behavior addictions that likely results from heritable deficits in the inhibitory control over behavior. Here, we investigate the genetic basis of individual differences in flexibility, measured using an operant reversal learning task. We quantified discrimination acquisition and subsequent reversal learning in a cohort of 51 BXD strains of mice (2-5 mice/strain, n = 176) for which we have matched data on sequence, gene expression in key central nervous system regions, and neuroreceptor levels. Strain variation in trials to criterion on acquisition and reversal was high, with moderate heritability (∼.3). Acquisition and reversal learning phenotypes did not covary at the strain level, suggesting that these traits are effectively under independent genetic control. Reversal performance did covary with dopamine D2 receptor levels in the ventral midbrain, consistent with a similar observed relationship between impulsivity and D2 receptors in humans. Reversal, but not acquisition, is linked to a locus on mouse chromosome 10 with a peak likelihood ratio statistic at 86.2 megabase (p work demonstrates the clear trait independence between, and genetic control of, discrimination acquisition and reversal and illustrates how globally coherent data sets for a single panel of highly related strains can be interrogated and integrated to uncover genetic sources and molecular and neuropharmacological candidates of complex behavioral traits relevant to human psychopathology. Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  17. Insect chromosomes preparing methods for genetic researches

    African Journals Online (AJOL)

    STORAGESEVER

    2009-01-05

    Jan 5, 2009 ... Ankara University Faculty of Science Department of Biology Tandogan Ankara Turkey. Accepted 21 ... intraspecific level; and the genetics evolution of the groups of .... Animal cytology and evolution Cambridge University. Press.

  18. Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning.

    Science.gov (United States)

    Lin, Hsuan-Ta; Lee, Po-Ming; Hsiao, Tzu-Chien

    2015-01-01

    Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students' learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction.

  19. Learning Science, Learning about Science, Doing Science: Different Goals Demand Different Learning Methods

    Science.gov (United States)

    Hodson, Derek

    2014-01-01

    This opinion piece paper urges teachers and teacher educators to draw careful distinctions among four basic learning goals: learning science, learning about science, doing science and learning to address socio-scientific issues. In elaboration, the author urges that careful attention is paid to the selection of teaching/learning methods that…

  20. Methods for control over learning individual trajectory

    Science.gov (United States)

    Mitsel, A. A.; Cherniaeva, N. V.

    2015-09-01

    The article discusses models, methods and algorithms of determining student's optimal individual educational trajectory. A new method of controlling the learning trajectory has been developed as a dynamic model of learning trajectory control, which uses score assessment to construct a sequence of studied subjects.

  1. The Guided Autobiography Method: A Learning Experience

    Science.gov (United States)

    Thornton, James E.

    2008-01-01

    This article discusses the proposition that learning is an unexplored feature of the guided autobiography method and its developmental exchange. Learning, conceptualized and explored as the embedded and embodied processes, is essential in narrative activities of the guided autobiography method leading to psychosocial development and growth in…

  2. Active teaching methods, studying responses and learning

    DEFF Research Database (Denmark)

    Christensen, Hans Peter; Vigild, Martin Etchells; Thomsen, Erik Vilain

    2010-01-01

    Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed.......Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed....

  3. Conservation Genetics of the Cheetah: Lessons Learned and New Opportunities.

    Science.gov (United States)

    O'Brien, Stephen J; Johnson, Warren E; Driscoll, Carlos A; Dobrynin, Pavel; Marker, Laurie

    2017-09-01

    The dwindling wildlife species of our planet have become a cause célèbre for conservation groups, governments, and concerned citizens throughout the world. The application of powerful new genetic technologies to surviving populations of threatened mammals has revolutionized our ability to recognize hidden perils that afflict them. We have learned new lessons of survival, adaptation, and evolution from viewing the natural history of genomes in hundreds of detailed studies. A single case history of one species, the African cheetah, Acinonyx jubatus, is here reviewed to reveal a long-term story of conservation challenges and action informed by genetic discoveries and insights. A synthesis of 3 decades of data, interpretation, and controversy, capped by whole genome sequence analysis of cheetahs, provides a compelling tale of conservation relevance and action to protect this species and other threatened wildlife. © The American Genetic Association 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  4. Genetic algorithm enhanced by machine learning in dynamic aperture optimization

    Science.gov (United States)

    Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert

    2018-05-01

    With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.

  5. Application of active learning modalities to achieve medical genetics competencies and their learning outcome assessments.

    Science.gov (United States)

    Hagiwara, Nobuko

    2017-01-01

    The steadily falling costs of genome sequencing, coupled with the growing number of genetic tests with proven clinical validity, have made the use of genetic testing more common in clinical practice. This development has necessitated nongeneticist physicians, especially primary care physicians, to become more responsible for assessing genetic risks for their patients. Providing undergraduate medical students a solid foundation in genomic medicine, therefore, has become all the more important to ensure the readiness of future physicians in applying genomic medicine to their patient care. In order to further enhance the effectiveness of instructing practical skills in medical genetics, the emphasis of active learning modules in genetics curriculum at medical schools has increased in recent years. This is because of the general acceptance of a better efficacy of active learner-centered pedagogy over passive lecturer-centered pedagogy. However, an objective standard to evaluate students' skill levels in genomic medicine achieved by active learning is currently missing. Recently, entrustable professional activities (EPAs) in genomic medicine have been proposed as a framework for developing physician competencies in genomic medicine. EPAs in genomic medicine provide a convenient guideline for not only developing genomic medicine curriculum but also assessing students' competency levels in practicing genomic medicine. In this review, the efficacy of different types of active learning modules reported for medical genetics curricula is discussed using EPAs in genomic medicine as a common evaluation standard for modules' learning outcomes. The utility of the EPAs in genomic medicine for designing active learning modules in undergraduate medical genetics curricula is also discussed.

  6. In silico machine learning methods in drug development.

    Science.gov (United States)

    Dobchev, Dimitar A; Pillai, Girinath G; Karelson, Mati

    2014-01-01

    Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, machine learning techniques such as artificial neural networks, support vector machines and genetic programming have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic targets. These methods are particularly useful for screening compound libraries of diverse chemical structures, "noisy" and high-dimensional data to complement QSAR methods, and in cases of unavailable receptor 3D structure to complement structure-based methods. A variety of studies have demonstrated the potential of machine-learning methods for predicting compounds as potential drug candidates. The present review is intended to give an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. We also regard a number of applications of the machine learning algorithms based on common classes of diseases.

  7. Learning the scientific method using GloFish.

    Science.gov (United States)

    Vick, Brianna M; Pollak, Adrianna; Welsh, Cynthia; Liang, Jennifer O

    2012-12-01

    Here we describe projects that used GloFish, brightly colored, fluorescent, transgenic zebrafish, in experiments that enabled students to carry out all steps in the scientific method. In the first project, students in an undergraduate genetics laboratory course successfully tested hypotheses about the relationships between GloFish phenotypes and genotypes using PCR, fluorescence microscopy, and test crosses. In the second and third projects, students doing independent research carried out hypothesis-driven experiments that also developed new GloFish projects for future genetics laboratory students. Brianna Vick, an undergraduate student, identified causes of the different shades of color found in orange GloFish. Adrianna Pollak, as part of a high school science fair project, characterized the fluorescence emission patterns of all of the commercially available colors of GloFish (red, orange, yellow, green, blue, and purple). The genetics laboratory students carrying out the first project found that learning new techniques and applying their knowledge of genetics were valuable. However, assessments of their learning suggest that this project was not challenging to many of the students. Thus, the independent projects will be valuable as bases to widen the scope and range of difficulty of experiments available to future genetics laboratory students.

  8. Learning Methods for Radial Basis Functions Networks

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman; Kudová, Petra

    2005-01-01

    Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005

  9. Classification and learning using genetic algorithms applications in Bioinformatics and Web Intelligence

    CERN Document Server

    Bandyopadhyay, Sanghamitra

    2007-01-01

    This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks.

  10. Methods for genetic transformation in Dendrobium.

    Science.gov (United States)

    da Silva, Jaime A Teixeira; Dobránszki, Judit; Cardoso, Jean Carlos; Chandler, Stephen F; Zeng, Songjun

    2016-03-01

    The genetic transformation of Dendrobium orchids will allow for the introduction of novel colours, altered architecture and valuable traits such as abiotic and biotic stress tolerance. The orchid genus Dendrobium contains species that have both ornamental value and medicinal importance. There is thus interest in producing cultivars that have increased resistance to pests, novel horticultural characteristics such as novel flower colours, improved productivity, longer flower spikes, or longer post-harvest shelf-life. Tissue culture is used to establish clonal plants while in vitro flowering allows for the production of flowers or floral parts within a sterile environment, expanding the selection of explants that can be used for tissue culture or genetic transformation. The latter is potentially the most effective, rapid and practical way to introduce new agronomic traits into Dendrobium. Most (69.4 %) Dendrobium genetic transformation studies have used particle bombardment (biolistics) while 64 % have employed some form of Agrobacterium-mediated transformation. A singe study has explored ovary injection, but no studies exist on floral dip transformation. While most of these studies have involved the use of selector or reporter genes, there are now a handful of studies that have introduced genes for horticulturally important traits.

  11. Kernel methods for deep learning

    OpenAIRE

    Cho, Youngmin

    2012-01-01

    We introduce a new family of positive-definite kernels that mimic the computation in large neural networks. We derive the different members of this family by considering neural networks with different activation functions. Using these kernels as building blocks, we also show how to construct other positive-definite kernels by operations such as composition, multiplication, and averaging. We explore the use of these kernels in standard models of supervised learning, such as support vector mach...

  12. The Effect of Case Teaching on Meaningful and Retentive Learning When Studying Genetic Engineering

    Science.gov (United States)

    Güccük, Ahmet; Köksal, Mustafa Serdar

    2017-01-01

    The purpose of this study is to investigate the effects of case teaching on how students learn about genetic engineering, in terms of meaningful learning and retention of learning. The study was designed as quasi-experimental research including 63 8th graders (28 boys and 35 girls). To collect data, genetic engineering achievement tests were…

  13. Reconciling genetic evolution and the associative learning account of mirror neurons through data-acquisition mechanisms.

    Science.gov (United States)

    Lotem, Arnon; Kolodny, Oren

    2014-04-01

    An associative learning account of mirror neurons should not preclude genetic evolution of its underlying mechanisms. On the contrary, an associative learning framework for cognitive development should seek heritable variation in the learning rules and in the data-acquisition mechanisms that construct associative networks, demonstrating how small genetic modifications of associative elements can give rise to the evolution of complex cognition.

  14. Extracting quantum dynamics from genetic learning algorithms through principal control analysis

    International Nuclear Information System (INIS)

    White, J L; Pearson, B J; Bucksbaum, P H

    2004-01-01

    Genetic learning algorithms are widely used to control ultrafast optical pulse shapes for photo-induced quantum control of atoms and molecules. An unresolved issue is how to use the solutions found by these algorithms to learn about the system's quantum dynamics. We propose a simple method based on covariance analysis of the control space, which can reveal the degrees of freedom in the effective control Hamiltonian. We have applied this technique to stimulated Raman scattering in liquid methanol. A simple model of two-mode stimulated Raman scattering is consistent with the results. (letter to the editor)

  15. Activating teaching methods, studying responses and learning

    OpenAIRE

    Christensen, Hans Peter; Vigild, Martin E.; Thomsen, Erik; Szabo, Peter; Horsewell, Andy

    2009-01-01

    Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed. Peer Reviewed

  16. Genetics of Alzheimer’s Disease: Lessons Learned in Two Decades

    Directory of Open Access Journals (Sweden)

    Nilüfer Ertekin Taner

    2010-03-01

    Full Text Available Alzheimer’s disease (AD is the most common type of dementia. It is estimated that more than 35 million people worldwide will suffer from dementia in 2010. Without effective therapies, this epidemic is expected to affect more than 115 million patients worldwide by 2050. Genetic studies can help us understand the disease pathophysiology, thereby providing potential therapeutic, presymptomatic predictive and preventative avenues. Since 1990, there has been evidence for a substantial genetic component underlying the risk for AD. Three genes with autosomal dominant mutations lead to early-onset familial AD, which explains less than 1% of all AD. Apolipoprotein ε4, the only widely accepted genetic risk factor for late-onset AD, accounts for only a portion of this risk. Genetic linkage and association studies have identified multiple candidate gene regions, although many resulting candidate genes suffer from lack of replication, at least partially due to underpowered studies in the setting of genetic heterogeneity and small-tomoderate effect size. Genome-wide association studies that assess hundreds of thousands of single-nucleotide polymorphisms (SNPs in thousands of subjects have been viewed as a potentially powerful approach in uncovering common risk variations for genetically complex diseases such as AD. To date, 11 independent genome-wide association studies have been completed in late-onset AD (LOAD that led to candidate regions and genes for follow-up. These studies provide evidence for novel, plausible genetic risk factors for AD, but still fail to account for all of the estimated risk. Additional genetic risk factors of even smaller effect size, rare variants and/or structural DNA polymorphisms may exist, which may escape detection by conventional methods. Alternative approaches such as nextgeneration sequencing, use of quantitative endophenotypes, copy number variation analyses, and meta-analyses may be required. This review summarizes the

  17. Genetics of Alzheimer’s Disease: Lessons Learned in Two Decades

    Directory of Open Access Journals (Sweden)

    Nilüfer Ertekin Taner

    2010-03-01

    Full Text Available Alzheimer’s disease (AD is the most common type of dementia. It is estimated that more than 35 million people worldwide will suffer from dementia in 2010. Without effective therapies, this epidemic is expected to affect more than 115 million patients worldwide by 2050. Genetic studies can help us understand the disease pathophysiology, thereby providing potential therapeutic, presymptomatic predictive and preventative avenues. Since 1990, there has been evidence for a substantial genetic component underlying the risk for AD. Three genes with autosomal dominant mutations lead to early-onset familial AD, which explains less than 1% of all AD. Apolipoprotein ε4, the only widely accepted genetic risk factor for late-onset AD, accounts for only a portion of this risk. Genetic linkage and association studies have identified multiple candidate gene regions, although many resulting candidate genes suffer from lack of replication, at least partially due to underpowered studies in the setting of genetic heterogeneity and small-tomoderate effect size. Genome-wide association studies that assess hundreds of thousands of single-nucleotide polymorphisms (SNPs in thousands of subjects have been viewed as a potentially powerful approach in uncovering common risk variations for genetically complex diseases such as AD. To date, 11 independent genome-wide association studies have been completed in late-onset AD (LOAD that led to candidate regions and genes for follow-up. These studies provide evidence for novel, plausible genetic risk factors for AD, but still fail to account for all of the estimated risk. Additional genetic risk factors of even smaller effect size, rare variants and/or structural DNA polymorphisms may exist, which may escape detection by conventional methods. Alternative approaches such as nextgeneration sequencing, use of quantitative endophenotypes, copy number variation analyses, and meta-analyses may be required. This review summarizes the

  18. "Well, good luck with that": reactions to learning of increased genetic risk for Alzheimer disease.

    Science.gov (United States)

    Zallen, Doris T

    2018-03-08

    PurposeApolipoprotein-E (APOE) genetic testing to estimate risk for developing late-onset Alzheimer disease is increasingly being offered without prior genetic counseling or preparation. Consumer interest continues to grow, raising the question of how best to conduct such testing.MethodsTwenty-six semistructured interviews were carried out to study the reactions of individuals who had already learned of their higher risk after APOE testing had been done because of a family history of Alzheimer disease, or from genetic tests done for other health-related or general-interest reasons.ResultsAdverse psychological reactions were reported by a substantial fraction of the participants, including those who had specifically sought testing, those for whom the information came as a surprise, those with a family history, and those with no known history. Still, nearly all of those interviewed said that they had benefited in the long term from lifestyle changes, often learned from online sources, that they subsequently made.ConclusionThe results show that people should be prepared prior to any genetic testing and allowed to opt out of particular tests. If testing is carried out and a higher risk is revealed, they should be actively assisted in deciding how to proceed.GENETICS in MEDICINE advance online publication, 8 March 2018; doi:10.1038/gim.2018.13.

  19. An Efficient Inductive Genetic Learning Algorithm for Fuzzy Relational Rules

    Directory of Open Access Journals (Sweden)

    Antonio

    2012-04-01

    Full Text Available Fuzzy modelling research has traditionally focused on certain types of fuzzy rules. However, the use of alternative rule models could improve the ability of fuzzy systems to represent a specific problem. In this proposal, an extended fuzzy rule model, that can include relations between variables in the antecedent of rules is presented. Furthermore, a learning algorithm based on the iterative genetic approach which is able to represent the knowledge using this model is proposed as well. On the other hand, potential relations among initial variables imply an exponential growth in the feasible rule search space. Consequently, two filters for detecting relevant potential relations are added to the learning algorithm. These filters allows to decrease the search space complexity and increase the algorithm efficiency. Finally, we also present an experimental study to demonstrate the benefits of using fuzzy relational rules.

  20. Two early studies on learning theory and genetics.

    Science.gov (United States)

    Jones, Marshall B

    2003-11-01

    The debate between Iowa and California, Spencians and Tolmanians, over the nature of learning was one of the most protracted and all-involving controversies in the history of psychology. Spencians argued that learning consisted of stimulus-response connections and grew incrementally; Tolmanians that it was perceptual or cognitive and saltatory in nature. The debate was conducted largely on the basis of experiments with rats, with each side finding evidence in its own laboratories to support its views. As the debate was winding down, two studies were carried out that called attention to a possible genetic basis of the great debate. The two schools used different strains of rat and characteristically different experimental situations. The two studies, however, were difficult to access at the time and even more so since. The present paper recalls these two studies in condensed form and discusses their relevance to the great debate and to selected current concerns.

  1. Effect of Methods of Learning and Self Regulated Learning toward Outcomes of Learning Social Studies

    Science.gov (United States)

    Tjalla, Awaluddin; Sofiah, Evi

    2015-01-01

    This research aims to reveal the influence of learning methods and self-regulated learning on students learning scores for Social Studies object. The research was done in Islamic Junior High School (MTs Manba'ul Ulum), Batuceper City Tangerang using quasi-experimental method. The research employed simple random technique to 28 students. Data were…

  2. Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies

    Directory of Open Access Journals (Sweden)

    Qiong Yang

    2012-01-01

    Full Text Available Multivariate phenotypes are frequently encountered in genetic association studies. The purpose of analyzing multivariate phenotypes usually includes discovery of novel genetic variants of pleiotropy effects, that is, affecting multiple phenotypes, and the ultimate goal of uncovering the underlying genetic mechanism. In recent years, there have been new method development and application of existing statistical methods to such phenotypes. In this paper, we provide a review of the available methods for analyzing association between a single marker and a multivariate phenotype consisting of the same type of components (e.g., all continuous or all categorical or different types of components (e.g., some are continuous and others are categorical. We also reviewed causal inference methods designed to test whether the detected association with the multivariate phenotype is truly pleiotropy or the genetic marker exerts its effects on some phenotypes through affecting the others.

  3. Machine Learning and Data Mining Methods in Diabetes Research.

    Science.gov (United States)

    Kavakiotis, Ioannis; Tsave, Olga; Salifoglou, Athanasios; Maglaveras, Nicos; Vlahavas, Ioannis; Chouvarda, Ioanna

    2017-01-01

    The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

  4. Active learning methods for interactive image retrieval.

    Science.gov (United States)

    Gosselin, Philippe Henri; Cord, Matthieu

    2008-07-01

    Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

  5. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    Energy Technology Data Exchange (ETDEWEB)

    Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.

  6. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    International Nuclear Information System (INIS)

    Bornholdt, S.

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback

  7. Deep Learning and Bayesian Methods

    Directory of Open Access Journals (Sweden)

    Prosper Harrison B.

    2017-01-01

    Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.

  8. Learning styles: The learning methods of air traffic control students

    Science.gov (United States)

    Jackson, Dontae L.

    In the world of aviation, air traffic controllers are an integral part in the overall level of safety that is provided. With a number of controllers reaching retirement age, the Air Traffic Collegiate Training Initiative (AT-CTI) was created to provide a stronger candidate pool. However, AT-CTI Instructors have found that a number of AT-CTI students are unable to memorize types of aircraft effectively. This study focused on the basic learning styles (auditory, visual, and kinesthetic) of students and created a teaching method to try to increase memorization in AT-CTI students. The participants were asked to take a questionnaire to determine their learning style. Upon knowing their learning styles, participants attended two classroom sessions. The participants were given a presentation in the first class, and divided into a control and experimental group for the second class. The control group was given the same presentation from the first classroom session while the experimental group had a group discussion and utilized Middle Tennessee State University's Air Traffic Control simulator to learn the aircraft types. Participants took a quiz and filled out a survey, which tested the new teaching method. An appropriate statistical analysis was applied to determine if there was a significant difference between the control and experimental groups. The results showed that even though the participants felt that the method increased their learning, there was no significant difference between the two groups.

  9. Pragmatics of Contemporary Teaching and Learning Methods

    Directory of Open Access Journals (Sweden)

    Ryszard Józef Panfil

    2013-09-01

    Full Text Available The dynamics of the environment in which educational institutions operate have a significant influence on the basic activity of these institutions, i.e. the process of educating, and particularly teaching and learning methods used during that process: traditional teaching, tutoring, mentoring and coaching. The identity of an educational institution and the appeal of its services depend on how flexible, diverse and adaptable is the educational process it offers as a core element of its services. Such a process is determined by how its pragmatism is displayed in the operational relativism of methods, their applicability, as well as practical dimension of achieved results and values. Based on the above premises, this publication offers a pragmatic-systemic identification of contemporary teaching and learning methods, while taking into account the differences between them and the scope of their compatibility. Secondly, using the case of sport coaches’ education, the author exemplifies the pragmatic theory of perception of contemporary teaching and learning methods.

  10. Online Learning of Genetic Network Programming and its Application to Prisoner’s Dilemma Game

    Science.gov (United States)

    Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

    A new evolutionary model with the network structure named Genetic Network Programming (GNP) has been proposed recently. GNP, that is, an expansion of GA and GP, represents solutions as a network structure and evolves it by using “offline learning (selection, mutation, crossover)”. GNP can memorize the past action sequences in the network flow, so it can deal with Partially Observable Markov Decision Process (POMDP) well. In this paper, in order to improve the ability of GNP, Q learning (an off-policy TD control algorithm) that is one of the famous online methods is introduced for online learning of GNP. Q learning is suitable for GNP because (1) in reinforcement learning, the rewards an agent will get in the future can be estimated, (2) TD control doesn’t need much memory and can learn quickly, and (3) off-policy is suitable in order to search for an optimal solution independently of the policy. Finally, in the simulations, online learning of GNP is applied to a player for “Prisoner’s dilemma game” and its ability for online adaptation is confirmed.

  11. e-Learning Business Research Methods

    Science.gov (United States)

    Cowie, Jonathan

    2004-01-01

    This paper outlines the development of a generic Business Research Methods course from a simple name in a box to a full e-Learning web based module. It highlights particular issues surrounding the nature of the discipline and the integration of a large number of cross faculty subject specific research methods courses into a single generic module.…

  12. Deep Learning and Bayesian Methods

    OpenAIRE

    Prosper Harrison B.

    2017-01-01

    A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such meth...

  13. Genetic algorithm learning in a New Keynesian macroeconomic setup.

    Science.gov (United States)

    Hommes, Cars; Makarewicz, Tomasz; Massaro, Domenico; Smits, Tom

    2017-01-01

    In order to understand heterogeneous behavior amongst agents, empirical data from Learning-to-Forecast (LtF) experiments can be used to construct learning models. This paper follows up on Assenza et al. (2013) by using a Genetic Algorithms (GA) model to replicate the results from their LtF experiment. In this GA model, individuals optimize an adaptive, a trend following and an anchor coefficient in a population of general prediction heuristics. We replicate experimental treatments in a New-Keynesian environment with increasing complexity and use Monte Carlo simulations to investigate how well the model explains the experimental data. We find that the evolutionary learning model is able to replicate the three different types of behavior, i.e. convergence to steady state, stable oscillations and dampened oscillations in the treatments using one GA model. Heterogeneous behavior can thus be explained by an adaptive, anchor and trend extrapolating component and the GA model can be used to explain heterogeneous behavior in LtF experiments with different types of complexity.

  14. Characterizing Reinforcement Learning Methods through Parameterized Learning Problems

    Science.gov (United States)

    2011-06-03

    extraneous. The agent could potentially adapt these representational aspects by applying methods from feature selection ( Kolter and Ng, 2009; Petrik et al...611–616. AAAI Press. Kolter , J. Z. and Ng, A. Y. (2009). Regularization and feature selection in least-squares temporal difference learning. In A. P

  15. Tracking by Machine Learning Methods

    CERN Document Server

    Jofrehei, Arash

    2015-01-01

    Current track reconstructing methods start with two points and then for each layer loop through all possible hits to find proper hits to add to that track. Another idea would be to use this large number of already reconstructed events and/or simulated data and train a machine on this data to find tracks given hit pixels. Training time could be long but real time tracking is really fast Simulation might not be as realistic as real data but tacking has been done for that with 100 percent efficiency while by using real data we would probably be limited to current efficiency.

  16. Comparisons of likelihood and machine learning methods of individual classification

    Science.gov (United States)

    Guinand, B.; Topchy, A.; Page, K.S.; Burnham-Curtis, M. K.; Punch, W.F.; Scribner, K.T.

    2002-01-01

    Classification methods used in machine learning (e.g., artificial neural networks, decision trees, and k-nearest neighbor clustering) are rarely used with population genetic data. We compare different nonparametric machine learning techniques with parametric likelihood estimations commonly employed in population genetics for purposes of assigning individuals to their population of origin (“assignment tests”). Classifier accuracy was compared across simulated data sets representing different levels of population differentiation (low and high FST), number of loci surveyed (5 and 10), and allelic diversity (average of three or eight alleles per locus). Empirical data for the lake trout (Salvelinus namaycush) exhibiting levels of population differentiation comparable to those used in simulations were examined to further evaluate and compare classification methods. Classification error rates associated with artificial neural networks and likelihood estimators were lower for simulated data sets compared to k-nearest neighbor and decision tree classifiers over the entire range of parameters considered. Artificial neural networks only marginally outperformed the likelihood method for simulated data (0–2.8% lower error rates). The relative performance of each machine learning classifier improved relative likelihood estimators for empirical data sets, suggesting an ability to “learn” and utilize properties of empirical genotypic arrays intrinsic to each population. Likelihood-based estimation methods provide a more accessible option for reliable assignment of individuals to the population of origin due to the intricacies in development and evaluation of artificial neural networks. In recent years, characterization of highly polymorphic molecular markers such as mini- and microsatellites and development of novel methods of analysis have enabled researchers to extend investigations of ecological and evolutionary processes below the population level to the level of

  17. Large-scale assessment of olfactory preferences and learning in Drosophila melanogaster: behavioral and genetic components

    Directory of Open Access Journals (Sweden)

    Elisabetta Versace

    2015-09-01

    Full Text Available In the Evolve and Resequence method (E&R, experimental evolution and genomics are combined to investigate evolutionary dynamics and the genotype-phenotype link. As other genomic approaches, this methods requires many replicates with large population sizes, which imposes severe restrictions on the analysis of behavioral phenotypes. Aiming to use E&R for investigating the evolution of behavior in Drosophila, we have developed a simple and effective method to assess spontaneous olfactory preferences and learning in large samples of fruit flies using a T-maze. We tested this procedure on (a a large wild-caught population and (b 11 isofemale lines of Drosophila melanogaster. Compared to previous methods, this procedure reduces the environmental noise and allows for the analysis of large population samples. Consistent with previous results, we show that flies have a preference for orange vs. apple odor. With our procedure wild-derived flies exhibit olfactory learning in the absence of previous laboratory selection. Furthermore, we find genetic differences in the olfactory learning with relatively high heritability. We propose this large-scale method as an effective tool for E&R and genome-wide association studies on olfactory preferences and learning.

  18. Method of detecting genetic deletions identified with chromosomal abnormalities

    Energy Technology Data Exchange (ETDEWEB)

    Gray, Joe W; Pinkel, Daniel; Tkachuk, Douglas

    2013-11-26

    Methods and compositions for staining based upon nucleic acid sequence that employ nucleic acid probes are provided. Said methods produce staining patterns that can be tailored for specific cytogenetic analyzes. Said probes are appropriate for in situ hybridization and stain both interphase and metaphase chromosomal material with reliable signals. The nucleic acids probes are typically of a complexity greater tha 50 kb, the complexity depending upon the cytogenetic application. Methods and reagents are provided for the detection of genetic rearrangements. Probes and test kits are provided for use in detecting genetic rearrangements, particlularly for use in tumor cytogenetics, in the detection of disease related loci, specifically cancer, such as chronic myelogenous leukemia (CML) and for biological dosimetry. Methods and reagents are described for cytogenetic research, for the differentiation of cytogenetically similar ut genetically different diseases, and for many prognostic and diagnostic applications.

  19. Learning, memory and exploratory similarities in genetically identical cloned dogs.

    Science.gov (United States)

    Shin, Chi Won; Kim, Geon A; Park, Won Jun; Park, Kwan Yong; Jeon, Jeong Min; Oh, Hyun Ju; Kim, Min Jung; Lee, Byeong Chun

    2016-12-30

    Somatic cell nuclear transfer allows generation of genetically identical animals using donor cells derived from animals with particular traits. To date, few studies have investigated whether or not these cloned dogs will show identical behavior patterns. To address this question, learning, memory and exploratory patterns were examined using six cloned dogs with identical nuclear genomes. The variance of total incorrect choice number in the Y-maze test among cloned dogs was significantly lower than that of the control dogs. There was also a significant decrease in variance in the level of exploratory activity in the open fields test compared to age-matched control dogs. These results indicate that cloned dogs show similar cognitive and exploratory patterns, suggesting that these behavioral phenotypes are related to the genotypes of the individuals.

  20. Genetic learning in rule-based and neural systems

    Science.gov (United States)

    Smith, Robert E.

    1993-01-01

    The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

  1. A Scale Development for Teacher Competencies on Cooperative Learning Method

    Science.gov (United States)

    Kocabas, Ayfer; Erbil, Deniz Gokce

    2017-01-01

    Cooperative learning method is a learning method studied both in Turkey and in the world for long years as an active learning method. Although cooperative learning method takes place in training programs, it cannot be implemented completely in the direction of its principles. The results of the researches point out that teachers have problems with…

  2. Learning Method, Facilities And Infrastructure, And Learning Resources In Basic Networking For Vocational School

    OpenAIRE

    Pamungkas, Bian Dwi

    2017-01-01

    This study aims to examine the contribution of learning methods on learning output, the contribution of facilities and infrastructure on output learning, the contribution of learning resources on learning output, and the contribution of learning methods, the facilities and infrastructure, and learning resources on learning output. The research design is descriptive causative, using a goal-oriented assessment approach in which the assessment focuses on assessing the achievement of a goal. The ...

  3. Enriching behavioral ecology with reinforcement learning methods.

    Science.gov (United States)

    Frankenhuis, Willem E; Panchanathan, Karthik; Barto, Andrew G

    2018-02-13

    This article focuses on the division of labor between evolution and development in solving sequential, state-dependent decision problems. Currently, behavioral ecologists tend to use dynamic programming methods to study such problems. These methods are successful at predicting animal behavior in a variety of contexts. However, they depend on a distinct set of assumptions. Here, we argue that behavioral ecology will benefit from drawing more than it currently does on a complementary collection of tools, called reinforcement learning methods. These methods allow for the study of behavior in highly complex environments, which conventional dynamic programming methods do not feasibly address. In addition, reinforcement learning methods are well-suited to studying how biological mechanisms solve developmental and learning problems. For instance, we can use them to study simple rules that perform well in complex environments. Or to investigate under what conditions natural selection favors fixed, non-plastic traits (which do not vary across individuals), cue-driven-switch plasticity (innate instructions for adaptive behavioral development based on experience), or developmental selection (the incremental acquisition of adaptive behavior based on experience). If natural selection favors developmental selection, which includes learning from environmental feedback, we can also make predictions about the design of reward systems. Our paper is written in an accessible manner and for a broad audience, though we believe some novel insights can be drawn from our discussion. We hope our paper will help advance the emerging bridge connecting the fields of behavioral ecology and reinforcement learning. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  4. Early Language Learning: Complexity and Mixed Methods

    Science.gov (United States)

    Enever, Janet, Ed.; Lindgren, Eva, Ed.

    2017-01-01

    This is the first collection of research studies to explore the potential for mixed methods to shed light on foreign or second language learning by young learners in instructed contexts. It brings together recent studies undertaken in Cameroon, China, Croatia, Ethiopia, France, Germany, Italy, Kenya, Mexico, Slovenia, Spain, Sweden, Tanzania and…

  5. Keystone Method: A Learning Paradigm in Mathematics

    Science.gov (United States)

    Siadat, M. Vali; Musial, Paul M.; Sagher, Yoram

    2008-01-01

    This study reports the effects of an integrated instructional program (the Keystone Method) on the students' performance in mathematics and reading, and tracks students' persistence and retention. The subject of the study was a large group of students in remedial mathematics classes at the college, willing to learn but lacking basic educational…

  6. Students' Ideas on Cooperative Learning Method

    Science.gov (United States)

    Yoruk, Abdulkadir

    2016-01-01

    Aim of this study is to investigate students' ideas on cooperative learning method. For that purpose students who are studying at elementary science education program are distributed into two groups through an experimental design. Factors threaten the internal validity are either eliminated or reduced to minimum value. Data analysis is done…

  7. Suggestology as an Effective Language Learning Method.

    Science.gov (United States)

    MaCoy, Katherine W.

    The methods used and the results obtained by means of the accelerated language learning techniques developed by Georgi Lozanov, Director of the Institute of Suggestology in Bulgaria, are discussed. The following topics are included: (1) discussion of hypermnesia, "super memory," and the reasons foreign languages were chosen for purposes…

  8. Effects of Jigsaw Learning Method on Students’ Self-Efficacy and Motivation to Learn

    OpenAIRE

    Dwi Nur Rachmah

    2017-01-01

    Jigsaw learning as a cooperative learning method, according to the results of some studies, can improve academic skills, social competence, behavior in learning, and motivation to learn. However, in some other studies, there are different findings regarding the effect of jigsaw learning method on self-efficacy. The purpose of this study is to examine the effects of jigsaw learning method on self-efficacy and motivation to learn in psychology students at the Faculty of Medicine, Universitas La...

  9. Color image definition evaluation method based on deep learning method

    Science.gov (United States)

    Liu, Di; Li, YingChun

    2018-01-01

    In order to evaluate different blurring levels of color image and improve the method of image definition evaluation, this paper proposed a method based on the depth learning framework and BP neural network classification model, and presents a non-reference color image clarity evaluation method. Firstly, using VGG16 net as the feature extractor to extract 4,096 dimensions features of the images, then the extracted features and labeled images are employed in BP neural network to train. And finally achieve the color image definition evaluation. The method in this paper are experimented by using images from the CSIQ database. The images are blurred at different levels. There are 4,000 images after the processing. Dividing the 4,000 images into three categories, each category represents a blur level. 300 out of 400 high-dimensional features are trained in VGG16 net and BP neural network, and the rest of 100 samples are tested. The experimental results show that the method can take full advantage of the learning and characterization capability of deep learning. Referring to the current shortcomings of the major existing image clarity evaluation methods, which manually design and extract features. The method in this paper can extract the images features automatically, and has got excellent image quality classification accuracy for the test data set. The accuracy rate is 96%. Moreover, the predicted quality levels of original color images are similar to the perception of the human visual system.

  10. Project Oriented Immersion Learning: Method and Results

    DEFF Research Database (Denmark)

    Icaza, José I.; Heredia, Yolanda; Borch, Ole M.

    2005-01-01

    A pedagogical approach called “project oriented immersion learning” is presented and tested on a graduate online course. The approach combines the Project Oriented Learning method with immersion learning in a virtual enterprise. Students assumed the role of authors hired by a fictitious publishing...... house that develops digital products including e-books, tutorials, web sites and so on. The students defined the problem that their product was to solve; choose the type of product and the content; and built the product following a strict project methodology. A wiki server was used as a platform to hold...

  11. The method of global learning in teaching foreign languages

    Directory of Open Access Journals (Sweden)

    Tatjana Dragovič

    2001-12-01

    Full Text Available The authors describe the method of global learning of foreign languages, which is based on the principles of neurolinguistic programming (NLP. According to this theory, the educator should use the method of the so-called periphery learning, where students learn relaxation techniques and at the same time they »incidentally « or subconsciously learn a foreign language. The method of global learning imitates successful strategies of learning in early childhood and therefore creates a relaxed attitude towards learning. Global learning is also compared with standard methods.

  12. Methods for genetic modification of megakaryocytes and platelets.

    Science.gov (United States)

    Pendaries, Caroline; Watson, Stephen P; Spalton, Jennifer C

    2007-09-01

    During recent decades there have been major advances in the fields of thrombosis and haemostasis, in part through development of powerful molecular and genetic technologies. Nevertheless, genetic modification of megakaryocytes and generation of mutant platelets in vitro remains a highly specialized area of research. Developments are hampered by the low frequency of megakaryocytes and their progenitors, a poor efficiency of transfection and a lack of understanding with regard to the mechanism by which megakaryocytes release platelets. Current methods used in the generation of genetically modified megakaryocytes and platelets include mutant mouse models, cell line studies and use of viruses to transform primary megakaryocytes or haematopoietic precursor cells. This review summarizes the advantages, limitations and technical challenges of such methods, with a particular focus on recent successes and advances in this rapidly progressing field including the potential for use in gene therapy for treatment of patients with platelet disorders.

  13. Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm.

    Science.gov (United States)

    Yan, Jingwen; Du, Lei; Kim, Sungeun; Risacher, Shannon L; Huang, Heng; Moore, Jason H; Saykin, Andrew J; Shen, Li

    2014-09-01

    Imaging genetics is an emerging field that studies the influence of genetic variation on brain structure and function. The major task is to examine the association between genetic markers such as single-nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from neuroimaging data. The complexity of these datasets has presented critical bioinformatics challenges that require new enabling tools. Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP-multi-QT associations. However, most of the existing SCCA algorithms are designed using the soft thresholding method, which assumes that the input features are independent from one another. This assumption clearly does not hold for the imaging genetic data. In this article, we propose a new knowledge-guided SCCA algorithm (KG-SCCA) to overcome this limitation as well as improve learning results by incorporating valuable prior knowledge. The proposed KG-SCCA method is able to model two types of prior knowledge: one as a group structure (e.g. linkage disequilibrium blocks among SNPs) and the other as a network structure (e.g. gene co-expression network among brain regions). The new model incorporates these prior structures by introducing new regularization terms to encourage weight similarity between grouped or connected features. A new algorithm is designed to solve the KG-SCCA model without imposing the independence constraint on the input features. We demonstrate the effectiveness of our algorithm with both synthetic and real data. For real data, using an Alzheimer's disease (AD) cohort, we examine the imaging genetic associations between all SNPs in the APOE gene (i.e. top AD gene) and amyloid deposition measures among cortical regions (i.e. a major AD hallmark). In comparison with a widely used SCCA implementation, our KG-SCCA algorithm produces not only improved cross-validation performances but also biologically meaningful

  14. Students' Understanding of Genetics Concepts: The Effect of Reasoning Ability and Learning Approaches

    Science.gov (United States)

    Kiliç, Didem; Saglam, Necdet

    2014-01-01

    Students tend to learn genetics by rote and may not realise the interrelationships in daily life. Because reasoning abilities are necessary to construct relationships between concepts and rote learning impedes the students' sound understanding, it was predicted that having high level of formal reasoning and adopting meaningful learning orientation…

  15. Simulation based virtual learning environment in medical genetics counseling

    DEFF Research Database (Denmark)

    Makransky, Guido; Bonde, Mads T.; Wulff, Julie S. G.

    2016-01-01

    BACKGROUND: Simulation based learning environments are designed to improve the quality of medical education by allowing students to interact with patients, diagnostic laboratory procedures, and patient data in a virtual environment. However, few studies have evaluated whether simulation based...... the perceived relevance of medical educational activities. The results suggest that simulations can help future generations of doctors transfer new understanding of disease mechanisms gained in virtual laboratory settings into everyday clinical practice....... learning environments increase students' knowledge, intrinsic motivation, and self-efficacy, and help them generalize from laboratory analyses to clinical practice and health decision-making. METHODS: An entire class of 300 University of Copenhagen first-year undergraduate students, most with a major...

  16. Simulation based virtual learning environment in medical genetics counseling

    DEFF Research Database (Denmark)

    Makransky, Guido; Bonde, Mads T; Wulff, Julie S G

    2016-01-01

    learning environments increase students' knowledge, intrinsic motivation, and self-efficacy, and help them generalize from laboratory analyses to clinical practice and health decision-making. METHODS: An entire class of 300 University of Copenhagen first-year undergraduate students, most with a major...... in medicine, received a 2-h training session in a simulation based learning environment. The main outcomes were pre- to post- changes in knowledge, intrinsic motivation, and self-efficacy, together with post-intervention evaluation of the effect of the simulation on student understanding of everyday clinical...... practice were demonstrated. RESULTS: Knowledge (Cohen's d = 0.73), intrinsic motivation (d = 0.24), and self-efficacy (d = 0.46) significantly increased from the pre- to post-test. Low knowledge students showed the greatest increases in knowledge (d = 3.35) and self-efficacy (d = 0.61), but a non...

  17. Effectiveness of students worksheet based on mastery learning in genetics subject

    Science.gov (United States)

    Megahati, R. R. P.; Yanti, F.; Susanti, D.

    2018-05-01

    Genetics is one of the subjects that must be followed by students in Biology education department. Generally, students do not like the genetics subject because of genetics concepts difficult to understand and the unavailability of a practical students worksheet. Consequently, the complete learning process (mastery learning) is not fulfilled and low students learning outcomes. The aim of this study develops student worksheet based on mastery learning that practical in genetics subject. This research is a research and development using 4-D models. The data analysis technique used is the descriptive analysis that describes the results of the practicalities of students worksheets based on mastery learning by students and lecturer of the genetic subject. The result is the student worksheet based on mastery learning on genetics subject are to the criteria of 80,33% and 80,14%, which means that the students worksheet practical used by lecturer and students. Student’s worksheet based on mastery learning effective because it can increase the activity and student learning outcomes.

  18. Parallelization of the ROOT Machine Learning Methods

    CERN Document Server

    Vakilipourtakalou, Pourya

    2016-01-01

    Today computation is an inseparable part of scientific research. Specially in Particle Physics when there is a classification problem like discrimination of Signals from Backgrounds originating from the collisions of particles. On the other hand, Monte Carlo simulations can be used in order to generate a known data set of Signals and Backgrounds based on theoretical physics. The aim of Machine Learning is to train some algorithms on known data set and then apply these trained algorithms to the unknown data sets. However, the most common framework for data analysis in Particle Physics is ROOT. In order to use Machine Learning methods, a Toolkit for Multivariate Data Analysis (TMVA) has been added to ROOT. The major consideration in this report is the parallelization of some TMVA methods, specially Cross-Validation and BDT.

  19. Methods in Molecular Biology Mouse Genetics: Methods and Protocols | Center for Cancer Research

    Science.gov (United States)

    Mouse Genetics: Methods and Protocols provides selected mouse genetic techniques and their application in modeling varieties of human diseases. The chapters are mainly focused on the generation of different transgenic mice to accomplish the manipulation of genes of interest, tracing cell lineages, and modeling human diseases.

  20. Machine Learning Methods for Production Cases Analysis

    Science.gov (United States)

    Mokrova, Nataliya V.; Mokrov, Alexander M.; Safonova, Alexandra V.; Vishnyakov, Igor V.

    2018-03-01

    Approach to analysis of events occurring during the production process were proposed. Described machine learning system is able to solve classification tasks related to production control and hazard identification at an early stage. Descriptors of the internal production network data were used for training and testing of applied models. k-Nearest Neighbors and Random forest methods were used to illustrate and analyze proposed solution. The quality of the developed classifiers was estimated using standard statistical metrics, such as precision, recall and accuracy.

  1. Classification of EEG signals using a genetic-based machine learning classifier.

    Science.gov (United States)

    Skinner, B T; Nguyen, H T; Liu, D K

    2007-01-01

    This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices.

  2. Decentralized indirect methods for learning automata games.

    Science.gov (United States)

    Tilak, Omkar; Martin, Ryan; Mukhopadhyay, Snehasis

    2011-10-01

    We discuss the application of indirect learning methods in zero-sum and identical payoff learning automata games. We propose a novel decentralized version of the well-known pursuit learning algorithm. Such a decentralized algorithm has significant computational advantages over its centralized counterpart. The theoretical study of such a decentralized algorithm requires the analysis to be carried out in a nonstationary environment. We use a novel bootstrapping argument to prove the convergence of the algorithm. To our knowledge, this is the first time that such analysis has been carried out for zero-sum and identical payoff games. Extensive simulation studies are reported, which demonstrate the proposed algorithm's fast and accurate convergence in a variety of game scenarios. We also introduce the framework of partial communication in the context of identical payoff games of learning automata. In such games, the automata may not communicate with each other or may communicate selectively. This comprehensive framework has the capability to model both centralized and decentralized games discussed in this paper.

  3. Implementing Collaborative Learning Methods in the Political Science Classroom

    Science.gov (United States)

    Wolfe, Angela

    2012-01-01

    Collaborative learning is one, among other, active learning methods, widely acclaimed in higher education. Consequently, instructors in fields that lack pedagogical training often implement new learning methods such as collaborative learning on the basis of trial and error. Moreover, even though the benefits in academic circles are broadly touted,…

  4. Multivariate Methods for Meta-Analysis of Genetic Association Studies.

    Science.gov (United States)

    Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

    2018-01-01

    Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

  5. Relabeling exchange method (REM) for learning in neural networks

    Science.gov (United States)

    Wu, Wen; Mammone, Richard J.

    1994-02-01

    The supervised training of neural networks require the use of output labels which are usually arbitrarily assigned. In this paper it is shown that there is a significant difference in the rms error of learning when `optimal' label assignment schemes are used. We have investigated two efficient random search algorithms to solve the relabeling problem: the simulated annealing and the genetic algorithm. However, we found them to be computationally expensive. Therefore we shall introduce a new heuristic algorithm called the Relabeling Exchange Method (REM) which is computationally more attractive and produces optimal performance. REM has been used to organize the optimal structure for multi-layered perceptrons and neural tree networks. The method is a general one and can be implemented as a modification to standard training algorithms. The motivation of the new relabeling strategy is based on the present interpretation of dyslexia as an encoding problem.

  6. Exploring the Effects of Active Learning on High School Students' Outcomes and Teachers' Perceptions of Biotechnology and Genetics Instruction

    Science.gov (United States)

    Mueller, Ashley L.; Knobloch, Neil A.; Orvis, Kathryn S.

    2015-01-01

    Active learning can engage high school students to learn science, yet there is limited understanding if active learning can help students learn challenging science concepts such as genetics and biotechnology. This quasi-experimental study explored the effects of active learning compared to passive learning regarding high school students'…

  7. DNA Cryptography and Deep Learning using Genetic Algorithm with NW algorithm for Key Generation.

    Science.gov (United States)

    Kalsi, Shruti; Kaur, Harleen; Chang, Victor

    2017-12-05

    Cryptography is not only a science of applying complex mathematics and logic to design strong methods to hide data called as encryption, but also to retrieve the original data back, called decryption. The purpose of cryptography is to transmit a message between a sender and receiver such that an eavesdropper is unable to comprehend it. To accomplish this, not only we need a strong algorithm, but a strong key and a strong concept for encryption and decryption process. We have introduced a concept of DNA Deep Learning Cryptography which is defined as a technique of concealing data in terms of DNA sequence and deep learning. In the cryptographic technique, each alphabet of a letter is converted into a different combination of the four bases, namely; Adenine (A), Cytosine (C), Guanine (G) and Thymine (T), which make up the human deoxyribonucleic acid (DNA). Actual implementations with the DNA don't exceed laboratory level and are expensive. To bring DNA computing on a digital level, easy and effective algorithms are proposed in this paper. In proposed work we have introduced firstly, a method and its implementation for key generation based on the theory of natural selection using Genetic Algorithm with Needleman-Wunsch (NW) algorithm and Secondly, a method for implementation of encryption and decryption based on DNA computing using biological operations Transcription, Translation, DNA Sequencing and Deep Learning.

  8. Improvement in PWR automatic optimization reloading methods using genetic algorithm

    International Nuclear Information System (INIS)

    Levine, S.H.; Ivanov, K.; Feltus, M.

    1996-01-01

    The objective of using automatic optimized reloading methods is to provide the Nuclear Engineer with an efficient method for reloading a nuclear reactor which results in superior core configurations that minimize fuel costs. Previous methods developed by Levine et al required a large effort to develop the initial core loading using a priority loading scheme. Subsequent modifications to this core configuration were made using expert rules to produce the final core design. Improvements in this technique have been made by using a genetic algorithm to produce improved core reload designs for PWRs more efficiently (authors)

  9. Improvement in PWR automatic optimization reloading methods using genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Levine, S H; Ivanov, K; Feltus, M [Pennsylvania State Univ., University Park, PA (United States)

    1996-12-01

    The objective of using automatic optimized reloading methods is to provide the Nuclear Engineer with an efficient method for reloading a nuclear reactor which results in superior core configurations that minimize fuel costs. Previous methods developed by Levine et al required a large effort to develop the initial core loading using a priority loading scheme. Subsequent modifications to this core configuration were made using expert rules to produce the final core design. Improvements in this technique have been made by using a genetic algorithm to produce improved core reload designs for PWRs more efficiently (authors).

  10. The 8 Learning Events Model: a Pedagogic Conceptual Tool Supporting Diversification of Learning Methods

    NARCIS (Netherlands)

    Verpoorten, Dominique; Poumay, M; Leclercq, D

    2006-01-01

    Please, cite this publication as: Verpoorten, D., Poumay, M., & Leclercq, D. (2006). The 8 Learning Events Model: a Pedagogic Conceptual Tool Supporting Diversification of Learning Methods. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence

  11. Learning Path Recommendation Based on Modified Variable Length Genetic Algorithm

    Science.gov (United States)

    Dwivedi, Pragya; Kant, Vibhor; Bharadwaj, Kamal K.

    2018-01-01

    With the rapid advancement of information and communication technologies, e-learning has gained a considerable attention in recent years. Many researchers have attempted to develop various e-learning systems with personalized learning mechanisms for assisting learners so that they can learn more efficiently. In this context, curriculum sequencing…

  12. Estimating building energy consumption using extreme learning machine method

    International Nuclear Information System (INIS)

    Naji, Sareh; Keivani, Afram; Shamshirband, Shahaboddin; Alengaram, U. Johnson; Jumaat, Mohd Zamin; Mansor, Zulkefli; Lee, Malrey

    2016-01-01

    The current energy requirements of buildings comprise a large percentage of the total energy consumed around the world. The demand of energy, as well as the construction materials used in buildings, are becoming increasingly problematic for the earth's sustainable future, and thus have led to alarming concern. The energy efficiency of buildings can be improved, and in order to do so, their operational energy usage should be estimated early in the design phase, so that buildings are as sustainable as possible. An early energy estimate can greatly help architects and engineers create sustainable structures. This study proposes a novel method to estimate building energy consumption based on the ELM (Extreme Learning Machine) method. This method is applied to building material thicknesses and their thermal insulation capability (K-value). For this purpose up to 180 simulations are carried out for different material thicknesses and insulation properties, using the EnergyPlus software application. The estimation and prediction obtained by the ELM model are compared with GP (genetic programming) and ANNs (artificial neural network) models for accuracy. The simulation results indicate that an improvement in predictive accuracy is achievable with the ELM approach in comparison with GP and ANN. - Highlights: • Buildings consume huge amounts of energy for operation. • Envelope materials and insulation influence building energy consumption. • Extreme learning machine is used to estimate energy usage of a sample building. • The key effective factors in this study are insulation thickness and K-value.

  13. Teaching learning methods of an entrepreneurship curriculum

    Directory of Open Access Journals (Sweden)

    KERAMAT ESMI

    2015-10-01

    Full Text Available Introduction: One of the most significant elements of entrepreneurship curriculum design is teaching-learning methods, which plays a key role in studies and researches related to such a curriculum. It is the teaching method, and systematic, organized and logical ways of providing lessons that should be consistent with entrepreneurship goals and contents, and should also be developed according to the learners’ needs. Therefore, the current study aimed to introduce appropriate, modern, and effective methods of teaching entrepreneurship and their validation Methods: This is a mixed method research of a sequential exploratory kind conducted through two stages: a developing teaching methods of entrepreneurship curriculum, and b validating developed framework. Data were collected through “triangulation” (study of documents, investigating theoretical basics and the literature, and semi-structured interviews with key experts. Since the literature on this topic is very rich, and views of the key experts are vast, directed and summative content analysis was used. In the second stage, qualitative credibility of research findings was obtained using qualitative validation criteria (credibility, confirmability, and transferability, and applying various techniques. Moreover, in order to make sure that the qualitative part is reliable, reliability test was used. Moreover, quantitative validation of the developed framework was conducted utilizing exploratory and confirmatory factor analysis methods and Cronbach’s alpha. The data were gathered through distributing a three-aspect questionnaire (direct presentation teaching methods, interactive, and practical-operational aspects with 29 items among 90 curriculum scholars. Target population was selected by means of purposive sampling and representative sample. Results: Results obtained from exploratory factor analysis showed that a three factor structure is an appropriate method for describing elements of

  14. Statistical learning methods in high-energy and astrophysics analysis

    Energy Technology Data Exchange (ETDEWEB)

    Zimmermann, J. [Forschungszentrum Juelich GmbH, Zentrallabor fuer Elektronik, 52425 Juelich (Germany) and Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: zimmerm@mppmu.mpg.de; Kiesling, C. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)

    2004-11-21

    We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application.

  15. Statistical learning methods in high-energy and astrophysics analysis

    International Nuclear Information System (INIS)

    Zimmermann, J.; Kiesling, C.

    2004-01-01

    We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application

  16. History of Science as an Instructional Context: Student Learning in Genetics and Nature of Science

    Science.gov (United States)

    Kim, Sun Young; Irving, Karen E.

    2010-01-01

    This study (1) explores the effectiveness of the contextualized history of science on student learning of nature of science (NOS) and genetics content knowledge (GCK), especially interrelationships among various genetics concepts, in high school biology classrooms; (2) provides an exemplar for teachers on how to utilize history of science in…

  17. Learners in dialogue. Teacher experise and learning in the context of genetic testing

    NARCIS (Netherlands)

    van der Zande, P.A.M.|info:eu-repo/dai/nl/304827363

    2011-01-01

    Learners in Dialogue; this thesis aims at the exploration of teacher expertise for teachers who want to teach genetics in the context of genetic testing and at finding ways to foster teacher learning concerning this expertise. Recent developments in the field of genomics will impact the daily

  18. Learning phacoemulsification. Results of different teaching methods.

    Directory of Open Access Journals (Sweden)

    Hennig Albrecht

    2004-01-01

    Full Text Available We report the learning curves of three eye surgeons converting from sutureless extracapsular cataract extraction to phacoemulsification using different teaching methods. Posterior capsule rupture (PCR as a per-operative complication and visual outcome of the first 100 operations were analysed. The PCR rate was 4% and 15% in supervised and unsupervised surgery respectively. Likewise, an uncorrected visual acuity of > or = 6/18 on the first postoperative day was seen in 62 (62% of patients and in 22 (22% in supervised and unsupervised surgery respectively.

  19. Subsampled Hessian Newton Methods for Supervised Learning.

    Science.gov (United States)

    Wang, Chien-Chih; Huang, Chun-Heng; Lin, Chih-Jen

    2015-08-01

    Newton methods can be applied in many supervised learning approaches. However, for large-scale data, the use of the whole Hessian matrix can be time-consuming. Recently, subsampled Newton methods have been proposed to reduce the computational time by using only a subset of data for calculating an approximation of the Hessian matrix. Unfortunately, we find that in some situations, the running speed is worse than the standard Newton method because cheaper but less accurate search directions are used. In this work, we propose some novel techniques to improve the existing subsampled Hessian Newton method. The main idea is to solve a two-dimensional subproblem per iteration to adjust the search direction to better minimize the second-order approximation of the function value. We prove the theoretical convergence of the proposed method. Experiments on logistic regression, linear SVM, maximum entropy, and deep networks indicate that our techniques significantly reduce the running time of the subsampled Hessian Newton method. The resulting algorithm becomes a compelling alternative to the standard Newton method for large-scale data classification.

  20. Teaching learning methods of an entrepreneurship curriculum.

    Science.gov (United States)

    Esmi, Keramat; Marzoughi, Rahmatallah; Torkzadeh, Jafar

    2015-10-01

    One of the most significant elements of entrepreneurship curriculum design is teaching-learning methods, which plays a key role in studies and researches related to such a curriculum. It is the teaching method, and systematic, organized and logical ways of providing lessons that should be consistent with entrepreneurship goals and contents, and should also be developed according to the learners' needs. Therefore, the current study aimed to introduce appropriate, modern, and effective methods of teaching entrepreneurship and their validation. This is a mixed method research of a sequential exploratory kind conducted through two stages: a) developing teaching methods of entrepreneurship curriculum, and b) validating developed framework. Data were collected through "triangulation" (study of documents, investigating theoretical basics and the literature, and semi-structured interviews with key experts). Since the literature on this topic is very rich, and views of the key experts are vast, directed and summative content analysis was used. In the second stage, qualitative credibility of research findings was obtained using qualitative validation criteria (credibility, confirmability, and transferability), and applying various techniques. Moreover, in order to make sure that the qualitative part is reliable, reliability test was used. Moreover, quantitative validation of the developed framework was conducted utilizing exploratory and confirmatory factor analysis methods and Cronbach's alpha. The data were gathered through distributing a three-aspect questionnaire (direct presentation teaching methods, interactive, and practical-operational aspects) with 29 items among 90 curriculum scholars. Target population was selected by means of purposive sampling and representative sample. Results obtained from exploratory factor analysis showed that a three factor structure is an appropriate method for describing elements of teaching-learning methods of entrepreneurship curriculum

  1. Influence on Learning of a Collaborative Learning Method Comprising the Jigsaw Method and Problem-based Learning (PBL).

    Science.gov (United States)

    Takeda, Kayoko; Takahashi, Kiyoshi; Masukawa, Hiroyuki; Shimamori, Yoshimitsu

    2017-01-01

    Recently, the practice of active learning has spread, increasingly recognized as an essential component of academic studies. Classes incorporating small group discussion (SGD) are conducted at many universities. At present, assessments of the effectiveness of SGD have mostly involved evaluation by questionnaires conducted by teachers, by peer assessment, and by self-evaluation of students. However, qualitative data, such as open-ended descriptions by students, have not been widely evaluated. As a result, we have been unable to analyze the processes and methods involved in how students acquire knowledge in SGD. In recent years, due to advances in information and communication technology (ICT), text mining has enabled the analysis of qualitative data. We therefore investigated whether the introduction of a learning system comprising the jigsaw method and problem-based learning (PBL) would improve student attitudes toward learning; we did this by text mining analysis of the content of student reports. We found that by applying the jigsaw method before PBL, we were able to improve student attitudes toward learning and increase the depth of their understanding of the area of study as a result of working with others. The use of text mining to analyze qualitative data also allowed us to understand the processes and methods by which students acquired knowledge in SGD and also changes in students' understanding and performance based on improvements to the class. This finding suggests that the use of text mining to analyze qualitative data could enable teachers to evaluate the effectiveness of various methods employed to improve learning.

  2. COOPERATIVE LEARNING IN DISTANCE LEARNING: A MIXED METHODS STUDY

    Directory of Open Access Journals (Sweden)

    Lori Kupczynski

    2012-07-01

    Full Text Available Distance learning has facilitated innovative means to include Cooperative Learning (CL in virtual settings. This study, conducted at a Hispanic-Serving Institution, compared the effectiveness of online CL strategies in discussion forums with traditional online forums. Quantitative and qualitative data were collected from 56 graduate student participants. Quantitative results revealed no significant difference on student success between CL and Traditional formats. The qualitative data revealed that students in the cooperative learning groups found more learning benefits than the Traditional group. The study will benefit instructors and students in distance learning to improve teaching and learning practices in a virtual classroom.

  3. Machine learning methods for metabolic pathway prediction

    Directory of Open Access Journals (Sweden)

    Karp Peter D

    2010-01-01

    Full Text Available Abstract Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations.

  4. Machine learning methods for metabolic pathway prediction

    Science.gov (United States)

    2010-01-01

    Background A key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism. Results To quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways. Conclusions ML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations. PMID:20064214

  5. Student Achievement in Basic College Mathematics: Its Relationship to Learning Style and Learning Method

    Science.gov (United States)

    Gunthorpe, Sydney

    2006-01-01

    From the assumption that matching a student's learning style with the learning method best suited for the student, it follows that developing courses that correlate learning method with learning style would be more successful for students. Albuquerque Technical Vocational Institute (TVI) in New Mexico has attempted to provide students with more…

  6. Two Undergraduate Process Modeling Courses Taught Using Inductive Learning Methods

    Science.gov (United States)

    Soroush, Masoud; Weinberger, Charles B.

    2010-01-01

    This manuscript presents a successful application of inductive learning in process modeling. It describes two process modeling courses that use inductive learning methods such as inquiry learning and problem-based learning, among others. The courses include a novel collection of multi-disciplinary complementary process modeling examples. They were…

  7. The Method of High School English Word Learning

    Institute of Scientific and Technical Information of China (English)

    吴博涵

    2016-01-01

    Most Chinese students are not interested in English learning, especially English words. In this paper, I focus on English vocabulary learning, for example, the study of high school students English word learning method, and also introduce several ways to make vocabulary memory becomes more effective. The purpose is to make high school students grasp more English word learning skills.

  8. Exergetic optimization of turbofan engine with genetic algorithm method

    Energy Technology Data Exchange (ETDEWEB)

    Turan, Onder [Anadolu University, School of Civil Aviation (Turkey)], e-mail: onderturan@anadolu.edu.tr

    2011-07-01

    With the growth of passenger numbers, emissions from the aeronautics sector are increasing and the industry is now working on improving engine efficiency to reduce fuel consumption. The aim of this study is to present the use of genetic algorithms, an optimization method based on biological principles, to optimize the exergetic performance of turbofan engines. The optimization was carried out using exergy efficiency, overall efficiency and specific thrust of the engine as evaluation criteria and playing on pressure and bypass ratio, turbine inlet temperature and flight altitude. Results showed exergy efficiency can be maximized with higher altitudes, fan pressure ratio and turbine inlet temperature; the turbine inlet temperature is the most important parameter for increased exergy efficiency. This study demonstrated that genetic algorithms are effective in optimizing complex systems in a short time.

  9. "DNA Re-EvolutioN": A Game for Learning Molecular Genetics and Evolution

    Science.gov (United States)

    Miralles, Laura; Moran, Paloma; Dopico, Eduardo; Garcia-Vazquez, Eva

    2013-01-01

    Evolution is a main concept in biology, but not many students understand how it works. In this article we introduce the game "DNA Re-EvolutioN" as an active learning tool that uses genetic concepts (DNA structure, transcription and translation, mutations, natural selection, etc.) as playing rules. Students will learn about molecular…

  10. Empirical Refinements of a Molecular Genetics Learning Progression: The Molecular Constructs

    Science.gov (United States)

    Todd, Amber; Kenyon, Lisa

    2016-01-01

    This article describes revisions to four of the eight constructs of the Duncan molecular genetics learning progression [Duncan, Rogat, & Yarden, (2009)]. As learning progressions remain hypothetical models until validated by multiple rounds of empirical studies, these revisions are an important step toward validating the progression. Our…

  11. Current perspectives on genetically modified crops and detection methods.

    Science.gov (United States)

    Kamle, Madhu; Kumar, Pradeep; Patra, Jayanta Kumar; Bajpai, Vivek K

    2017-07-01

    Genetically modified (GM) crops are the fastest adopted commodities in the agribiotech industry. This market penetration should provide a sustainable basis for ensuring food supply for growing global populations. The successful completion of two decades of commercial GM crop production (1996-2015) is underscored by the increasing rate of adoption of genetic engineering technology by farmers worldwide. With the advent of introduction of multiple traits stacked together in GM crops for combined herbicide tolerance, insect resistance, drought tolerance or disease resistance, the requirement of reliable and sensitive detection methods for tracing and labeling genetically modified organisms in the food/feed chain has become increasingly important. In addition, several countries have established threshold levels for GM content which trigger legally binding labeling schemes. The labeling of GM crops is mandatory in many countries (such as China, EU, Russia, Australia, New Zealand, Brazil, Israel, Saudi Arabia, Korea, Chile, Philippines, Indonesia, Thailand), whereas in Canada, Hong Kong, USA, South Africa, and Argentina voluntary labeling schemes operate. The rapid adoption of GM crops has increased controversies, and mitigating these issues pertaining to the implementation of effective regulatory measures for the detection of GM crops is essential. DNA-based detection methods have been successfully employed, while the whole genome sequencing using next-generation sequencing (NGS) technologies provides an advanced means for detecting genetically modified organisms and foods/feeds in GM crops. This review article describes the current status of GM crop commercialization and discusses the benefits and shortcomings of common and advanced detection systems for GMs in foods and animal feeds.

  12. Learning Genetics through an Authentic Research Simulation in Bioinformatics

    Science.gov (United States)

    Gelbart, Hadas; Yarden, Anat

    2006-01-01

    Following the rationale that learning is an active process of knowledge construction as well as enculturation into a community of experts, we developed a novel web-based learning environment in bioinformatics for high-school biology majors in Israel. The learning environment enables the learners to actively participate in a guided inquiry process…

  13. Strains and Stressors: An Analysis of Touchscreen Learning in Genetically Diverse Mouse Strains

    Science.gov (United States)

    Graybeal, Carolyn; Bachu, Munisa; Mozhui, Khyobeni; Saksida, Lisa M.; Bussey, Timothy J.; Sagalyn, Erica; Williams, Robert W.; Holmes, Andrew

    2014-01-01

    Touchscreen-based systems are growing in popularity as a tractable, translational approach for studying learning and cognition in rodents. However, while mouse strains are well known to differ in learning across various settings, performance variation between strains in touchscreen learning has not been well described. The selection of appropriate genetic strains and backgrounds is critical to the design of touchscreen-based studies and provides a basis for elucidating genetic factors moderating behavior. Here we provide a quantitative foundation for visual discrimination and reversal learning using touchscreen assays across a total of 35 genotypes. We found significant differences in operant performance and learning, including faster reversal learning in DBA/2J compared to C57BL/6J mice. We then assessed DBA/2J and C57BL/6J for differential sensitivity to an environmental insult by testing for alterations in reversal learning following exposure to repeated swim stress. Stress facilitated reversal learning (selectively during the late stage of reversal) in C57BL/6J, but did not affect learning in DBA/2J. To dissect genetic factors underlying these differences, we phenotyped a family of 27 BXD strains generated by crossing C57BL/6J and DBA/2J. There was marked variation in discrimination, reversal and extinction learning across the BXD strains, suggesting this task may be useful for identifying underlying genetic differences. Moreover, different measures of touchscreen learning were only modestly correlated in the BXD strains, indicating that these processes are comparatively independent at both genetic and phenotypic levels. Finally, we examined the behavioral structure of learning via principal component analysis of the current data, plus an archival dataset, totaling 765 mice. This revealed 5 independent factors suggestive of “reversal learning,” “motivation-related late reversal learning,” “discrimination learning,” “speed to respond,” and

  14. Lessons learned from family history in ocular genetics.

    Science.gov (United States)

    Marino, Meghan J

    2015-07-01

    Given the vast genetic and phenotypic heterogeneity seen in ocular genetic disorders, considering a patient's clinical phenotype in the context of the family history is essential. Clinicians can improve patient care by appropriately incorporating a patient's family history into their evaluation. Obtaining, reviewing, and accurately interpreting the pedigree are skills geneticists and genetic counselors possess. However, with the field of ophthalmic genetics vastly growing, it is becoming essential for ophthalmologists to understand the utility of the pedigree and develop their abilities in eliciting this information. By not considering a patient's clinical history in the context of the family history, diagnoses can be missed or inaccurate. The purpose of this review is to inform ophthalmologists on the importance of the family history and highlight how the pedigree can aid in establishing an accurate genetic diagnosis. This review also provides to ophthalmologists helpful tips on eliciting and interpreting a patient's family history.

  15. Aging and a genetic KIBRA polymorphism interactively affect feedback- and observation-based probabilistic classification learning.

    Science.gov (United States)

    Schuck, Nicolas W; Petok, Jessica R; Meeter, Martijn; Schjeide, Brit-Maren M; Schröder, Julia; Bertram, Lars; Gluck, Mark A; Li, Shu-Chen

    2018-01-01

    Probabilistic category learning involves complex interactions between the hippocampus and striatum that may depend on whether acquisition occurs via feedback or observation. Little is known about how healthy aging affects these processes. We tested whether age-related behavioral differences in probabilistic category learning from feedback or observation depend on a genetic factor known to influence individual differences in hippocampal function, the KIBRA gene (single nucleotide polymorphism rs17070145). Results showed comparable age-related performance impairments in observational as well as feedback-based learning. Moreover, genetic analyses indicated an age-related interactive effect of KIBRA on learning: among older adults, the beneficial T-allele was positively associated with learning from feedback, but negatively with learning from observation. In younger adults, no effects of KIBRA were found. Our results add behavioral genetic evidence to emerging data showing age-related differences in how neural resources relate to memory functions, namely that hippocampal and striatal contributions to probabilistic category learning may vary with age. Our findings highlight the effects genetic factors can have on differential age-related decline of different memory functions. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. Chemical Genetics — A Versatile Method to Combine Science and Higher Level Teaching in Molecular Genetics

    Directory of Open Access Journals (Sweden)

    Björn Sandrock

    2012-10-01

    Full Text Available Phosphorylation is a key event in many cellular processes like cell cycle, transformation of environmental signals to transcriptional activation or polar growth. The chemical genetics approach can be used to analyse the effect of highly specific inhibition in vivo and is a promising method to screen for kinase targets. We have used this approach to study the role of the germinal centre kinase Don3 during the cell division in the phytopathogenic fungus Ustilago maydis. Due to the easy determination of the don3 phenotype we have chosen this approach for a genetic course for M.Sc. students and for IMPRS (International Max-Planck research school students. According to the principle of “problem-based learning” the aim of this two-week course is to transfer knowledge about the broad spectrum of kinases to the students and that the students acquire the ability to design their own analog-sensitive kinase of interest. In addition to these training goals, we benefit from these annual courses the synthesis of basic constructs for genetic modification of several kinases in our model system U. maydis.

  17. A new method to estimate genetic gain in annual crops

    Directory of Open Access Journals (Sweden)

    Flávio Breseghello

    1998-12-01

    Full Text Available The genetic gain obtained by breeding programs to improve quantitative traits may be estimated by using data from regional trials. A new statistical method for this estimate is proposed and includes four steps: a joint analysis of regional trial data using a generalized linear model to obtain adjusted genotype means and covariance matrix of these means for the whole studied period; b calculation of the arithmetic mean of the adjusted genotype means, exclusively for the group of genotypes evaluated each year; c direct year comparison of the arithmetic means calculated, and d estimation of mean genetic gain by regression. Using the generalized least squares method, a weighted estimate of mean genetic gain during the period is calculated. This method permits a better cancellation of genotype x year and genotype x trial/year interactions, thus resulting in more precise estimates. This method can be applied to unbalanced data, allowing the estimation of genetic gain in series of multilocational trials.Os ganhos genéticos obtidos pelo melhoramento de caracteres quantitativos podem ser estimados utilizando resultados de ensaios regionais de avaliação de linhagens e cultivares. Um novo método estatístico para esta estimativa é proposto, o qual consiste em quatro passos: a análise conjunta da série de dados dos ensaios regionais através de um modelo linear generalizado de forma a obter as médias ajustadas dos genótipos e a matriz de covariâncias destas médias; b para o grupo de genótipos avaliados em cada ano, cálculo da média aritmética das médias ajustadas obtidas na análise conjunta; c comparação direta dos anos, conforme as médias aritméticas obtidas, e d estimativa de um ganho genético médio, por regressão. Aplicando-se o método de quadrados mínimos generalizado, é calculada uma estimativa ponderada do ganho genético médio no período. Este método permite um melhor cancelamento das interações genótipo x ano e gen

  18. Safety assessment and detection methods of genetically modified organisms.

    Science.gov (United States)

    Xu, Rong; Zheng, Zhe; Jiao, Guanglian

    2014-01-01

    Genetically modified organisms (GMOs), are gaining importance in agriculture as well as the production of food and feed. Along with the development of GMOs, health and food safety concerns have been raised. These concerns for these new GMOs make it necessary to set up strict system on food safety assessment of GMOs. The food safety assessment of GMOs, current development status of safety and precise transgenic technologies and GMOs detection have been discussed in this review. The recent patents about GMOs and their detection methods are also reviewed. This review can provide elementary introduction on how to assess and detect GMOs.

  19. Teaching and learning methods in IVET

    DEFF Research Database (Denmark)

    Aarkrog, Vibe

    The cases deals about learner centered learning in a commercial program and a technical program.......The cases deals about learner centered learning in a commercial program and a technical program....

  20. Interactive knowledge discovery from marketing questionnarie using simulated breeding and inductive learning methods

    Energy Technology Data Exchange (ETDEWEB)

    Terano, Takao [Univ. of Tsukuba, Tokyo (Japan); Ishino, Yoko [Univ. of Tokyo (Japan)

    1996-12-31

    This paper describes a novel method to acquire efficient decision rules from questionnaire data using both simulated breeding and inductive learning techniques. The basic ideas of the method are that simulated breeding is used to get the effective features from the questionnaire data and that inductive learning is used to acquire simple decision rules from the data. The simulated breeding is one of the Genetic Algorithm (GA) based techniques to subjectively or interactively evaluate the qualities of offspring generated by genetic operations. In this paper, we show a basic interactive version of the method and two variations: the one with semi-automated GA phases and the one with the relatively evaluation phase via the Analytic Hierarchy Process (AHP). The proposed method has been qualitatively and quantitatively validated by a case study on consumer product questionnaire data.

  1. Towards Transgenic Primates: What can we learn from mouse genetics?

    Institute of Scientific and Technical Information of China (English)

    KUANG Hui; WANG Phillip L.; TSIEN Joe Z.

    2009-01-01

    Considering the great physiological and behavioral similarities with humans, monkeys represent the ideal models not only for the study of complex cognitive behavior but also for the precUnical research and development of novel therapeutics for treating human diseases. Various powerful genetic tech-nologies initially developed for making mouse models are being explored for generating transgenic primate models. We review the latest genetic engineering technologies and discuss the potentials and limitations for systematic production of transgenic primates.

  2. Towards Transgenic Primates: What can we learn from mouse genetics?

    OpenAIRE

    KUANG, Hui; WANG, Phillip L.; TSIEN, Joe Z.

    2009-01-01

    Considering the great physiological and behavioral similarities with humans, monkeys represent the ideal models not only for the study of complex cognitive behavior but also for the preclinical research and development of novel therapeutics for treating human diseases. Various powerful genetic technologies initially developed for making mouse models are being explored for generating transgenic primate models. We review the latest genetic engineering technologies and discuss the potentials and...

  3. Do students’ styles of learning affect how they adapt to learning methods and to the learning environment?

    OpenAIRE

    Topal, Kenan; Sarıkaya, Özlem; Basturk, Ramazan; Buke, Akile

    2015-01-01

    Objectives: The process of development and evaluation of undergraduate medical education programs should include analysis of learners’ characteristics, needs, and perceptions about learning methods. This study aims to evaluate medical students’ perceptions about problem-based learning methods and to compare these results with their individual learning styles.Materials and Methods: The survey was conducted at Marmara University Medical School where problem-based learning was implemented in the...

  4. Deep learning versus traditional machine learning methods for aggregated energy demand prediction

    NARCIS (Netherlands)

    Paterakis, N.G.; Mocanu, E.; Gibescu, M.; Stappers, B.; van Alst, W.

    2018-01-01

    In this paper the more advanced, in comparison with traditional machine learning approaches, deep learning methods are explored with the purpose of accurately predicting the aggregated energy consumption. Despite the fact that a wide range of machine learning methods have been applied to

  5. Preparing Students for Flipped or Team-Based Learning Methods

    Science.gov (United States)

    Balan, Peter; Clark, Michele; Restall, Gregory

    2015-01-01

    Purpose: Teaching methods such as Flipped Learning and Team-Based Learning require students to pre-learn course materials before a teaching session, because classroom exercises rely on students using self-gained knowledge. This is the reverse to "traditional" teaching when course materials are presented during a lecture, and students are…

  6. Effects of Jigsaw Learning Method on Students’ Self-Efficacy and Motivation to Learn

    Directory of Open Access Journals (Sweden)

    Dwi Nur Rachmah

    2017-12-01

    Full Text Available Jigsaw learning as a cooperative learning method, according to the results of some studies, can improve academic skills, social competence, behavior in learning, and motivation to learn. However, in some other studies, there are different findings regarding the effect of jigsaw learning method on self-efficacy. The purpose of this study is to examine the effects of jigsaw learning method on self-efficacy and motivation to learn in psychology students at the Faculty of Medicine, Universitas Lambung Mangkurat. The method used in the study is the experimental method using one group pre-test and post-test design. The results of the measurements before and after the use of jigsaw learning method were compared using paired samples t-test. The results showed that there is a difference in students’ self-efficacy and motivation to learn before and after subjected to the treatments; therefore, it can be said that jigsaw learning method had significant effects on self-efficacy and motivation to learn. The application of jigsaw learning model in a classroom with large number of students was the discussion of this study.

  7. Methods for determining the genetic affinity of microorganisms and viruses

    Science.gov (United States)

    Fox, George E. (Inventor); Willson, III, Richard C. (Inventor); Zhang, Zhengdong (Inventor)

    2012-01-01

    Selecting which sub-sequences in a database of nucleic acid such as 16S rRNA are highly characteristic of particular groupings of bacteria, microorganisms, fungi, etc. on a substantially phylogenetic tree. Also applicable to viruses comprising viral genomic RNA or DNA. A catalogue of highly characteristic sequences identified by this method is assembled to establish the genetic identity of an unknown organism. The characteristic sequences are used to design nucleic acid hybridization probes that include the characteristic sequence or its complement, or are derived from one or more characteristic sequences. A plurality of these characteristic sequences is used in hybridization to determine the phylogenetic tree position of the organism(s) in a sample. Those target organisms represented in the original sequence database and sufficient characteristic sequences can identify to the species or subspecies level. Oligonucleotide arrays of many probes are especially preferred. A hybridization signal can comprise fluorescence, chemiluminescence, or isotopic labeling, etc.; or sequences in a sample can be detected by direct means, e.g. mass spectrometry. The method's characteristic sequences can also be used to design specific PCR primers. The method uniquely identifies the phylogenetic affinity of an unknown organism without requiring prior knowledge of what is present in the sample. Even if the organism has not been previously encountered, the method still provides useful information about which phylogenetic tree bifurcation nodes encompass the organism.

  8. A Swarm-Based Learning Method Inspired by Social Insects

    Science.gov (United States)

    He, Xiaoxian; Zhu, Yunlong; Hu, Kunyuan; Niu, Ben

    Inspired by cooperative transport behaviors of ants, on the basis of Q-learning, a new learning method, Neighbor-Information-Reference (NIR) learning method, is present in the paper. This is a swarm-based learning method, in which principles of swarm intelligence are strictly complied with. In NIR learning, the i-interval neighbor's information, namely its discounted reward, is referenced when an individual selects the next state, so that it can make the best decision in a computable local neighborhood. In application, different policies of NIR learning are recommended by controlling the parameters according to time-relativity of concrete tasks. NIR learning can remarkably improve individual efficiency, and make swarm more "intelligent".

  9. DMPD: The Toll-like receptors: analysis by forward genetic methods. [Dynamic Macrophage Pathway CSML Database

    Lifescience Database Archive (English)

    Full Text Available 16001129 The Toll-like receptors: analysis by forward genetic methods. Beutler B. I...mmunogenetics. 2005 Jul;57(6):385-92. (.png) (.svg) (.html) (.csml) Show The Toll-like receptors: analysis by forwar...d genetic methods. PubmedID 16001129 Title The Toll-like receptors: analysis by forward genetic meth

  10. Genetic Algorithms: A New Method for Neutron Beam Spectral Characterization

    International Nuclear Information System (INIS)

    David W. Freeman

    2000-01-01

    A revolutionary new concept for solving the neutron spectrum unfolding problem using genetic algorithms (GAs) has recently been introduced. GAs are part of a new field of evolutionary solution techniques that mimic living systems with computer-simulated chromosome solutions that mate, mutate, and evolve to create improved solutions. The original motivation for the research was to improve spectral characterization of neutron beams associated with boron neutron capture therapy (BNCT). The GA unfolding technique has been successfully applied to problems with moderate energy resolution (up to 47 energy groups). Initial research indicates that the GA unfolding technique may well be superior to popular unfolding methods in common use. Research now under way at Kansas State University is focused on optimizing the unfolding algorithm and expanding its energy resolution to unfold detailed beam spectra based on multiple foil measurements. Indications are that the final code will significantly outperform current, state-of-the-art codes in use by the scientific community

  11. Arts-based Methods and Organizational Learning

    DEFF Research Database (Denmark)

    This thematic volume explores the relationship between the arts and learning in various educational contexts and across cultures, but with a focus on higher education and organizational learning. Arts-based interventions are at the heart of this volume, which addresses how they are conceived, des...

  12. Reasons and Methods to Learn the Management

    Science.gov (United States)

    Li, Hongxin; Ding, Mengchun

    2010-01-01

    Reasons for learning the management include (1) perfecting the knowledge structure, (2) the management is the base of all organizations, (3) one person may be the manager or the managed person, (4) the management is absolutely not simple knowledge, and (5) the learning of the theoretical knowledge of the management can not be replaced by the…

  13. Microgenetic Learning Analytics Methods: Workshop Report

    Science.gov (United States)

    Aghababyan, Ani; Martin, Taylor; Janisiewicz, Philip; Close, Kevin

    2016-01-01

    Learning analytics is an emerging discipline and, as such, benefits from new tools and methodological approaches. This work reviews and summarizes our workshop on microgenetic data analysis techniques using R, held at the second annual Learning Analytics Summer Institute in Cambridge, Massachusetts, on 30 June 2014. Specifically, this paper…

  14. Situating cognitive/socio-cognitive approaches to student learning in genetics

    Science.gov (United States)

    Kindfield, Ann C. H.

    2009-03-01

    In this volume, Furberg and Arnseth report on a study of genetics learning from a socio-cultural perspective, focusing on students' meaning making as they engage in collaborative problem solving. Throughout the paper, they criticize research on student understanding and conceptual change conducted from a cognitive/socio-cognitive perspective on several reasonable grounds. However, their characterization of work undertaken from this perspective sometimes borders on caricature, failing to acknowledge the complexities of the research and the contexts within which it has been carried out. In this commentary, I expand their characterization of the cognitive/socio-cognitive perspective in general and situate my own work on genetics learning so as to provide a richer view of the enterprise. From this richer, more situated view, I conclude that research from both perspectives and collaboration between those looking at learning from different perspectives will ultimately provide a more complete picture of science learning.

  15. Genetics and Cinema: Personal Misconceptions That Constitute Obstacles to Learning

    Science.gov (United States)

    Muela, Francisco Javier; Abril, Ana María

    2014-01-01

    The primary objective of this paper is to find out whether the genetic concepts conveyed by cinema could encourage students' personal misconceptions in this area. To that end, two sources of conceptions were compared: the students' personal concepts (from a consolidated bibliography and from an experimental sample) and the concepts conveyed by…

  16. Learning about Genetic Inheritance through Technology-Enhanced Instruction

    Science.gov (United States)

    Williams, Michelle; Merritt, Joi; Opperman, Amanda; Porter, Jakob; Erlenbeck, Kyle

    2012-01-01

    Genetics is an increasingly important topic in today's society, and one that permeates people's lives on many levels. Students, teachers, and the general public alike are constantly exposed to this topic through popular television shows such as "CSI: Crime Scene Investigation," political issues like voting on stem-cell research, and the…

  17. Genetically-induced cholinergic hyper-innervation enhances taste learning

    Directory of Open Access Journals (Sweden)

    Selin eNeseliler

    2011-12-01

    Full Text Available Acute inhibition of acetylcholine (ACh has been shown to impair many forms of simple learning, and notably conditioned taste aversion (CTA. The most adhered-to theory that has emerged as a result of this work—that ACh increases a taste’s perceived novelty, and thereby its associability—would be further strengthened by evidence showing that enhanced cholinergic function improves learning above normal levels. Experimental testing of this corollary hypothesis has been limited, however, by side-effects of pharmacological ACh agonism and by the absence of a model that achieves long-term increases in cholinergic signaling. Here, we present this further test of the ACh hypothesis, making use of mice lacking the p75 pan-neurotrophin receptor gene, which show a resultant over-abundance of cholinergic neurons in subregions of the basal forebrain (BF. We first demonstrate that the p75-/- abnormality directly affects portions of the CTA circuit, locating mouse gustatory cortex (GC using a functional assay and then using immunohistochemisty to demonstrate cholinergic hyperinnervation of GC in the mutant mice—hyperinnervation that is unaccompanied by changes in cell numbers or compensatory changes in muscarinic receptor densities. We then demonstrate that both p75-/- and wild-type mice learn robust CTAs, which extinguish more slowly in the mutants. Further testing to distinguish effects on learning from alterations in memory retention demonstrate that p75-/- mice do in fact learn stronger CTAs than wild-type mice. These data provide novel evidence for the hypothesis linking ACh and taste learning.

  18. Question presentation methods for paired-associate learning

    NARCIS (Netherlands)

    Engel, F.L.; Geerings, M.P.W.

    1988-01-01

    Four different methods of question presentation, in interactive computeraided learning of Dutch-English word pairs are evaluated experimentally. These methods are: 1) the 'open-question method', 2) the 'multiple-choice method', 3) the 'sequential method' and 4) the 'true/ false method'. When

  19. A genetic algorithm based method for neutron spectrum unfolding

    International Nuclear Information System (INIS)

    Suman, Vitisha; Sarkar, P.K.

    2013-03-01

    An approach to neutron spectrum unfolding based on a stochastic evolutionary search mechanism - Genetic Algorithm (GA) is presented. It is tested to unfold a set of simulated spectra, the unfolded spectra is compared to the output of a standard code FERDOR. The method was then applied to a set of measured pulse height spectrum of neutrons from the AmBe source as well as of emitted neutrons from Li(p,n) and Ag(C,n) nuclear reactions carried out in the accelerator environment. The unfolded spectra compared to the output of FERDOR show good agreement in the case of AmBe spectra and Li(p,n) spectra. In the case of Ag(C,n) spectra GA method results in some fluctuations. Necessity of carrying out smoothening of the obtained solution is also studied, which leads to approximation of the solution yielding an appropriate solution finally. Few smoothing techniques like second difference smoothing, Monte Carlo averaging, combination of both and gaussian based smoothing methods are also studied. Unfolded results obtained after inclusion of the smoothening criteria are in close agreement with the output obtained from the FERDOR code. The present method is also tested on a set of underdetermined problems, the outputs of which is compared to the unfolded spectra obtained from the FERDOR applied to a completely determined problem, shows a good match. The distribution of the unfolded spectra is also studied. Uncertainty propagation in the unfolded spectra due to the errors present in the measurement as well as the response function is also carried out. The method appears to be promising for unfolding the completely determined as well as underdetermined problems. It also has provisions to carry out the uncertainty analysis. (author)

  20. Efficient model learning methods for actor-critic control.

    Science.gov (United States)

    Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

    2012-06-01

    We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

  1. Ensemble Machine Learning Methods and Applications

    CERN Document Server

    Ma, Yunqian

    2012-01-01

    It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face detection and are now being applied in areas as diverse as object trackingand bioinformatics.   Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for r...

  2. Learning, memory and hippocampal LTP in genetically obese rodents

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    We have found that leptin, at physiological concentrations of 10-12 mol/L, facilitates learning and memory and LTP maintenance in Wistar rats. To explore the role of leptin recepors in learning, memory and synaptic plasticity, experiments were carried out using Zucker rats (Z), db/db mice (db), and ob/ob mice(ob). The former two have defects in leptin receptors and the latter cannot produce normal leptin. Unlike the effects observed in normal rats, high or low frequency stimulation of Schaffer collateral-CA1 synapses in hippocampal slices prepared from Z, db and ob animals failed to induce the learning and memory relevant long-term potentiation or depression in CA1 neurons. However, LTP in ob CA1 synapses was facilitated by leptin at 10-12 mol/L concentration. Moreover, the paired-pulse facilitation of CA1 synaptic potentials and intracellularly recorded postsynaptic responses to the neurotransmitters AMPA, NMDA and GABA, applied electrophoretically to the apical dendrites of CA1 neurons, were approximately the same compared to the control lean animals. In addition, unlike the second messenger responses observed in Wistar rats, calmodulin kinase Ⅱ activity in the CA1 area of Z and db animals was not activated after tetanic stimulation of the Schaffer collaterals. It has been shown that all three strains, Z, db and ob display impaired spatial learning and memory when tested in the Morris water maze. The results of these experiments indicate a close relationship between spatial learning and memory, facilitation of LTP, and calmodulin kinase Ⅱ activity.

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

    Science.gov (United States)

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

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

  4. 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.

  5. Genetic Learning of Fuzzy Parameters in Predictive and Decision Support Modelling

    Directory of Open Access Journals (Sweden)

    Nebot

    2012-04-01

    Full Text Available In this research a genetic fuzzy system (GFS is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR methodology and the Linguistic Rule FIR (LR-FIR algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR models and decision support (LR-FIR models. The GFS is evaluated in an e-learning context.

  6. Adaptive e-learning methods and IMS Learning Design. An integrated approach

    NARCIS (Netherlands)

    Burgos, Daniel; Specht, Marcus

    2006-01-01

    Please, cite this publication as: Burgos, D., & Specht, M. (2006). Adaptive e-learning methods and IMS Learning Design. In Kinshuk, R. Koper, P. Kommers, P. Kirschner, D. G. Sampson & W. Didderen (Eds.), Proceedings of the 6th IEEE International Conference on Advanced Learning Technologies (pp.

  7. A Comparative Analysis of Reinforcement Learning Methods

    Science.gov (United States)

    1991-10-01

    Technology. Support for this research was provided in part by the Mazda Corporation, in part by the University Research Initiative under Office of Naval...results in an update rule (e.g. [Goldberg 89]Goldberg85), genetic algorithms which disregards all history accumulated in the current will not be addressed

  8. Horse breed discrimination using machine learning methods

    Czech Academy of Sciences Publication Activity Database

    Burócziová, Monika; Riha, J.

    2009-01-01

    Roč. 50, č. 4 (2009), s. 375-377 ISSN 1234-1983 Institutional research plan: CEZ:AV0Z50450515 Keywords : Breed discrimination * Genetics diversity * Horse breeds Subject RIV: EG - Zoology Impact factor: 1.324, year: 2009

  9. An Innovative Teaching Method To Promote Active Learning: Team-Based Learning

    Science.gov (United States)

    Balasubramanian, R.

    2007-12-01

    Traditional teaching practice based on the textbook-whiteboard- lecture-homework-test paradigm is not very effective in helping students with diverse academic backgrounds achieve higher-order critical thinking skills such as analysis, synthesis, and evaluation. Consequently, there is a critical need for developing a new pedagogical approach to create a collaborative and interactive learning environment in which students with complementary academic backgrounds and learning skills can work together to enhance their learning outcomes. In this presentation, I will discuss an innovative teaching method ('Team-Based Learning (TBL)") which I recently developed at National University of Singapore to promote active learning among students in the environmental engineering program with learning abilities. I implemented this new educational activity in a graduate course. Student feedback indicates that this pedagogical approach is appealing to most students, and promotes active & interactive learning in class. Data will be presented to show that the innovative teaching method has contributed to improved student learning and achievement.

  10. A proposed impact assessment method for genetically modified plants (AS-GMP Method)

    International Nuclear Information System (INIS)

    Jesus-Hitzschky, Katia Regina Evaristo de; Silveira, Jose Maria F.J. da

    2009-01-01

    An essential step in the development of products based on biotechnology is an assessment of their potential economic impacts and safety, including an evaluation of the potential impact of transgenic crops and practices related to their cultivation on the environment and human or animal health. The purpose of this paper is to provide an assessment method to evaluate the impact of biotechnologies that uses quantifiable parameters and allows a comparative analysis between conventional technology and technologies using GMOs. This paper introduces a method to perform an impact analysis associated with the commercial release and use of genetically modified plants, the Assessment System GMP Method. The assessment is performed through indicators that are arranged according to their dimension criterion likewise: environmental, economic, social, capability and institutional approach. To perform an accurate evaluation of the GMP specific indicators related to genetic modification are grouped in common fields: genetic insert features, GM plant features, gene flow, food/feed field, introduction of the GMP, unexpected occurrences and specific indicators. The novelty is the possibility to include specific parameters to the biotechnology under assessment. In this case by case analysis the factors of moderation and the indexes are parameterized to perform an available assessment.

  11. Deep kernel learning method for SAR image target recognition

    Science.gov (United States)

    Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

    2017-10-01

    With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

  12. A Review on Different Virtual Learning Methods in Pharmacy Education

    Directory of Open Access Journals (Sweden)

    Amin Noori

    2015-10-01

    Full Text Available Virtual learning is a type of electronic learning system based on the web. It models traditional in- person learning by providing virtual access to classes, tests, homework, feedbacks and etc. Students and teachers can interact through chat rooms or other virtual environments. Web 2.0 services are usually used for this method. Internet audio-visual tools, multimedia systems, a disco CD-ROMs, videotapes, animation, video conferencing, and interactive phones can all be used to deliver data to the students. E-learning can occur in or out of the classroom. It is time saving with lower costs compared to traditional methods. It can be self-paced, it is suitable for distance learning and it is flexible. It is a great learning style for continuing education and students can independently solve their problems but it has its disadvantages too. Thereby, blended learning (combination of conventional and virtual education is being used worldwide and has improved knowledge, skills and confidence of pharmacy students.The aim of this study is to review, discuss and introduce different methods of virtual learning for pharmacy students.Google scholar, Pubmed and Scupus databases were searched for topics related to virtual, electronic and blended learning and different styles like computer simulators, virtual practice environment technology, virtual mentor, virtual patient, 3D simulators, etc. are discussed in this article.Our review on different studies on these areas shows that the students are highly satisfied withvirtual and blended types of learning.

  13. A Comparison between the Effect of Cooperative Learning Teaching Method and Lecture Teaching Method on Students' Learning and Satisfaction Level

    Science.gov (United States)

    Mohammadjani, Farzad; Tonkaboni, Forouzan

    2015-01-01

    The aim of the present research is to investigate a comparison between the effect of cooperative learning teaching method and lecture teaching method on students' learning and satisfaction level. The research population consisted of all the fourth grade elementary school students of educational district 4 in Shiraz. The statistical population…

  14. An E-Learning Module to Improve Nongenetic Health Professionals’ Assessment of Colorectal Cancer Genetic Risk: Feasibility Study

    Science.gov (United States)

    Aalfs, Cora M; Dekker, Evelien; Tanis, Pieter J; Smets, Ellen M

    2017-01-01

    Background Nongenetic health providers may lack the relevant knowledge, experience, and communication skills to adequately detect familial colorectal cancer (CRC), despite a positive attitude toward the assessment of history of cancer in a family. Specific training may enable them to more optimally refer patients to genetic counseling. Objective The aim of this study was to develop an e-learning module for gastroenterologists and surgeons (in training) aimed at improving attitudes, knowledge, and comprehension of communication skills, and to assess the feasibility of the e-learning module for continued medical education of these specialists. Methods A focus group helped to inform the development of a training framework. The e-learning module was then developed, followed by a feasibility test among a group of surgeons-in-training (3rd- and 4th-year residents) and then among gastroenterologists, using pre- and posttest questionnaires. Results A total of 124 surgeons-in-training and 14 gastroenterologists participated. The e-learning was positively received (7.5 on a scale of 1 to 10). Between pre- and posttest, attitude increased significantly on 6 out of the 10 items. Mean test score showed that knowledge and comprehension of communication skills improved significantly from 49% to 72% correct at pretest to 67% to 87% correct at posttest. Conclusions This study shows the feasibility of a problem-based e-learning module to help surgeons-in-training and gastroenterologists in recognizing a hereditary predisposition in patients with CRC. The e-learning led to improvements in attitude toward the assessment of cancer family history, knowledge on criteria for referral to genetic counseling for CRC, and comprehension of communication skills. PMID:29254907

  15. Unsupervised process monitoring and fault diagnosis with machine learning methods

    CERN Document Server

    Aldrich, Chris

    2013-01-01

    This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data

  16. Predicting human splicing branchpoints by combining sequence-derived features and multi-label learning methods.

    Science.gov (United States)

    Zhang, Wen; Zhu, Xiaopeng; Fu, Yu; Tsuji, Junko; Weng, Zhiping

    2017-12-01

    Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints. Considering the fact that an intron may have multiple branchpoints, we transform the branchpoint prediction as the multi-label learning problem, and attempt to predict branchpoint sites from intron sequences. First, we investigate a variety of intron sequence-derived features, such as sparse profile, dinucleotide profile, position weight matrix profile, Markov motif profile and polypyrimidine tract profile. Second, we consider several multi-label learning methods: partial least squares regression, canonical correlation analysis and regularized canonical correlation analysis, and use them as the basic classification engines. Third, we propose two ensemble learning schemes which integrate different features and different classifiers to build ensemble learning systems for the branchpoint prediction. One is the genetic algorithm-based weighted average ensemble method; the other is the logistic regression-based ensemble method. In the computational experiments, two ensemble learning methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy results on the benchmark dataset.

  17. The Puzzle of Inheritance: Genetics and the Methods of Science.

    Science.gov (United States)

    Cutter, Mary Ann G.; Drexler, Edward; Friedman, B. Ellen; McCullough, Laurence B.; McInerney, Joseph D.; Murray, Jeffrey C.; Rossiter, Belinda; Zola, John

    This instructional module contains a description of the Human Genome Project (HGP). A discussion of issues in the philosophy of science and some of the ethical, legal, and social implications of research in genetics, and a survey of fundamental genetics concepts and of new, nontraditional concepts of inheritance are also included. Six…

  18. Application of machine learning methods in bioinformatics

    Science.gov (United States)

    Yang, Haoyu; An, Zheng; Zhou, Haotian; Hou, Yawen

    2018-05-01

    Faced with the development of bioinformatics, high-throughput genomic technology have enabled biology to enter the era of big data. [1] Bioinformatics is an interdisciplinary, including the acquisition, management, analysis, interpretation and application of biological information, etc. It derives from the Human Genome Project. The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets.[2]. This paper analyzes and compares various algorithms of machine learning and their applications in bioinformatics.

  19. Are students' impressions of improved learning through active learning methods reflected by improved test scores?

    Science.gov (United States)

    Everly, Marcee C

    2013-02-01

    To report the transformation from lecture to more active learning methods in a maternity nursing course and to evaluate whether student perception of improved learning through active-learning methods is supported by improved test scores. The process of transforming a course into an active-learning model of teaching is described. A voluntary mid-semester survey for student acceptance of the new teaching method was conducted. Course examination results, from both a standardized exam and a cumulative final exam, among students who received lecture in the classroom and students who had active learning activities in the classroom were compared. Active learning activities were very acceptable to students. The majority of students reported learning more from having active-learning activities in the classroom rather than lecture-only and this belief was supported by improved test scores. Students who had active learning activities in the classroom scored significantly higher on a standardized assessment test than students who received lecture only. The findings support the use of student reflection to evaluate the effectiveness of active-learning methods and help validate the use of student reflection of improved learning in other research projects. Copyright © 2011 Elsevier Ltd. All rights reserved.

  20. Undergraduates Achieve Learning Gains in Plant Genetics through Peer Teaching of Secondary Students

    Science.gov (United States)

    Chrispeels, H. E.; Klosterman, M. L.; Martin, J. B.; Lundy, S. R.; Watkins, J. M.; Gibson, C. L.

    2014-01-01

    This study tests the hypothesis that undergraduates who peer teach genetics will have greater understanding of genetic and molecular biology concepts as a result of their teaching experiences. Undergraduates enrolled in a non–majors biology course participated in a service-learning program in which they led middle school (MS) or high school (HS) students through a case study curriculum to discover the cause of a green tomato variant. The curriculum explored plant reproduction and genetic principles, highlighting variation in heirloom tomato fruits to reinforce the concept of the genetic basis of phenotypic variation. HS students were taught additional activities related to mole­cular biology techniques not included in the MS curriculum. We measured undergraduates’ learning outcomes using pre/postteaching content assessments and the course final exam. Undergraduates showed significant gains in understanding of topics related to the curriculum they taught, compared with other course content, on both types of assessments. Undergraduates who taught HS students scored higher on questions specific to the HS curriculum compared with undergraduates who taught MS students, despite identical lecture content, on both types of assessments. These results indicate the positive effect of service-learning peer-teaching experiences on undergraduates’ content knowledge, even for non–science major students. PMID:25452487

  1. An Analytical framework of social learning facilitated by participatory methods

    NARCIS (Netherlands)

    Scholz, G.; Dewulf, A.; Pahl-Wostl, C.

    2014-01-01

    Social learning among different stakeholders is often a goal in problem solving contexts such as environmental management. Participatory methods (e.g., group model-building and role playing games) are frequently assumed to stimulate social learning. Yet understanding if and why this assumption is

  2. Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model

    Directory of Open Access Journals (Sweden)

    Mojtaba Salehi

    2013-03-01

    Full Text Available In recent years, the explosion of learning materials in the web-based educational systems has caused difficulty of locating appropriate learning materials to learners. A personalized recommendation is an enabling mechanism to overcome information overload occurred in the new learning environments and deliver suitable materials to learners. Since users express their opinions based on some specific attributes of items, this paper proposes a hybrid recommender system for learning materials based on their attributes to improve the accuracy and quality of recommendation. The presented system has two main modules: explicit attribute-based recommender and implicit attribute-based recommender. In the first module, weights of implicit or latent attributes of materials for learner are considered as chromosomes in genetic algorithm then this algorithm optimizes the weights according to historical rating. Then, recommendation is generated by Nearest Neighborhood Algorithm (NNA using the optimized weight vectors implicit attributes that represent the opinions of learners. In the second, preference matrix (PM is introduced that can model the interests of learner based on explicit attributes of learning materials in a multidimensional information model. Then, a new similarity measure between PMs is introduced and recommendations are generated by NNA. The experimental results show that our proposed method outperforms current algorithms on accuracy measures and can alleviate some problems such as cold-start and sparsity.

  3. Blended learning – integrating E-learning with traditional learning methods in teaching basic medical science

    OpenAIRE

    J.G. Bagi; N.K. Hashilkar

    2014-01-01

    Background: Blended learning includes an integration of face to face classroom learning with technology enhanced online material. It provides the convenience, speed and cost effectiveness of e-learning with the personal touch of traditional learning. Objective: The objective of the present study was to assess the effectiveness of a combination of e-learning module and traditional teaching (Blended learning) as compared to traditional teaching alone to teach acid base homeostasis to Phase I MB...

  4. Statistical Methods for Studying Genetic Variation in Populations

    Science.gov (United States)

    2012-08-01

    iteration will converge to a local optimum, similar to what happens in an EM algorithm. Empirically, a near global optimal can be obtained by multiple...and E Matthysen. Genetic variability and gene flow 131 in the globally , critically-endangered Taita thrush. Conservation Genetics, 1:45–55, 2000. 4.5.2...Libioulle, Edouard Louis, Sarah Hansoul, Cynthia Sandor, Frédéric Farnir, Denis Franchi - mont, Séverine Vermeire, Olivier Dewit, Martine de Vos, Anna

  5. Learning with Admixture: Modeling, Optimization, and Applications in Population Genetics

    DEFF Research Database (Denmark)

    Cheng, Jade Yu

    2016-01-01

    the foundation for both CoalHMM and Ohana. Optimization modeling has been the main theme throughout my PhD, and it will continue to shape my work for the years to come. The algorithms and software I developed to study historical admixture and population evolution fall into a larger family of machine learning...... geneticists strive to establish working solutions to extract information from massive volumes of biological data. The steep increase in the quantity and quality of genomic data during the past decades provides a unique opportunity but also calls for new and improved algorithms and software to cope...... including population splits, effective population sizes, gene flow, etc. Since joining the CoalHMM development team in 2014, I have mainly contributed in two directions: 1) improving optimizations through heuristic-based evolutionary algorithms and 2) modeling of historical admixture events. Ohana, meaning...

  6. Machine Learning Methods to Predict Diabetes Complications.

    Science.gov (United States)

    Dagliati, Arianna; Marini, Simone; Sacchi, Lucia; Cogni, Giulia; Teliti, Marsida; Tibollo, Valentina; De Cata, Pasquale; Chiovato, Luca; Bellazzi, Riccardo

    2018-03-01

    One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.

  7. Learning in Non-Stationary Environments Methods and Applications

    CERN Document Server

    Lughofer, Edwin

    2012-01-01

    Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences.   Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dyna...

  8. The Tourette International Collaborative Genetics (TIC Genetics) study, finding the genes causing Tourette syndrome: objectives and methods.

    Science.gov (United States)

    Dietrich, Andrea; Fernandez, Thomas V; King, Robert A; State, Matthew W; Tischfield, Jay A; Hoekstra, Pieter J; Heiman, Gary A

    2015-02-01

    Tourette syndrome (TS) is a neuropsychiatric disorder characterized by recurrent motor and vocal tics, often accompanied by obsessive-compulsive disorder and/or attention-deficit/hyperactivity disorder. While the evidence for a genetic contribution is strong, its exact nature has yet to be clarified fully. There is now mounting evidence that the genetic risks for TS include both common and rare variants and may involve complex multigenic inheritance or, in rare cases, a single major gene. Based on recent progress in many other common disorders with apparently similar genetic architectures, it is clear that large patient cohorts and open-access repositories will be essential to further advance the field. To that end, the large multicenter Tourette International Collaborative Genetics (TIC Genetics) study was established. The goal of the TIC Genetics study is to undertake a comprehensive gene discovery effort, focusing both on familial genetic variants with large effects within multiply affected pedigrees and on de novo mutations ascertained through the analysis of apparently simplex parent-child trios with non-familial tics. The clinical data and biomaterials (DNA, transformed cell lines, RNA) are part of a sharing repository located within the National Institute for Mental Health Center for Collaborative Genomics Research on Mental Disorders, USA, and will be made available to the broad scientific community. This resource will ultimately facilitate better understanding of the pathophysiology of TS and related disorders and the development of novel therapies. Here, we describe the objectives and methods of the TIC Genetics study as a reference for future studies from our group and to facilitate collaboration between genetics consortia in the field of TS.

  9. Comparative Analysis of Kernel Methods for Statistical Shape Learning

    National Research Council Canada - National Science Library

    Rathi, Yogesh; Dambreville, Samuel; Tannenbaum, Allen

    2006-01-01

    .... In this work, we perform a comparative analysis of shape learning techniques such as linear PCA, kernel PCA, locally linear embedding and propose a new method, kernelized locally linear embedding...

  10. Implementing Adaptive Educational Methods with IMS Learning Design

    NARCIS (Netherlands)

    Specht, Marcus; Burgos, Daniel

    2006-01-01

    Please, cite this publication as: Specht, M. & Burgos, D. (2006). Implementing Adaptive Educational Methods with IMS Learning Design. Proceedings of Adaptive Hypermedia. June, Dublin, Ireland. Retrieved June 30th, 2006, from http://dspace.learningnetworks.org

  11. Implementation of Active Learning Method in Unit Operations II Subject

    OpenAIRE

    Ma'mun, Sholeh

    2018-01-01

    ABSTRACT: Active Learning Method which requires students to take an active role in the process of learning in the classroom has been applied in Department of Chemical Engineering, Faculty of Industrial Technology, Islamic University of Indonesia for Unit Operations II subject in the Even Semester of Academic Year 2015/2016. The purpose of implementation of the learning method is to assist students in achieving competencies associated with the Unit Operations II subject and to help in creating...

  12. Studying depression using imaging and machine learning methods

    Directory of Open Access Journals (Sweden)

    Meenal J. Patel

    2016-01-01

    Full Text Available Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1 presents a background on depression, imaging, and machine learning methodologies; (2 reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3 suggests directions for future depression-related studies.

  13. Studying depression using imaging and machine learning methods.

    Science.gov (United States)

    Patel, Meenal J; Khalaf, Alexander; Aizenstein, Howard J

    2016-01-01

    Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

  14. TMTI Task 1.6 Genetic Engineering Methods and Detection

    Energy Technology Data Exchange (ETDEWEB)

    Slezak, T; Lenhoff, R; Allen, J; Borucki, M; Vitalis, E; Gardner, S

    2009-12-04

    A large number of GE techniques can be adapted from other microorganisms to biothreat bacteria and viruses. Detection of GE in a microorganism increases in difficulty as the size of the genetic change decreases. In addition to the size of the engineered change, the consensus genomic sequence of the microorganism can impact the difficulty of detecting an engineered change in genomes that are highly variable from strain to strain. This problem will require comprehensive databases of whole genome sequences for more genetically variable biothreat bacteria and viruses. Preliminary work with microarrays for detecting synthetic elements or virulence genes and analytic bioinformatic approaches for whole genome sequence comparison to detect genetic engineering show promise for attacking this difficult problem but a large amount of future work remains.

  15. Simulating Visual Learning and Optical Illusions via a Network-Based Genetic Algorithm

    Science.gov (United States)

    Siu, Theodore; Vivar, Miguel; Shinbrot, Troy

    We present a neural network model that uses a genetic algorithm to identify spatial patterns. We show that the model both learns and reproduces common visual patterns and optical illusions. Surprisingly, we find that the illusions generated are a direct consequence of the network architecture used. We discuss the implications of our results and the insights that we gain on how humans fall for optical illusions

  16. Learning Expressive Linkage Rules for Entity Matching using Genetic Programming

    OpenAIRE

    Isele, Robert

    2013-01-01

    A central problem in data integration and data cleansing is to identify pairs of entities in data sets that describe the same real-world object. Many existing methods for matching entities rely on explicit linkage rules, which specify how two entities are compared for equivalence. Unfortunately, writing accurate linkage rules by hand is a non-trivial problem that requires detailed knowledge of the involved data sets. Another important issue is the efficient execution of link...

  17. Active teaching methods, studying responses and learning

    DEFF Research Database (Denmark)

    Christensen, Hans Peter; Vigild, Martin Etchells; Thomsen, Erik Vilain

    Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching.......Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching....

  18. Systems genetics of complex diseases using RNA-sequencing methods

    DEFF Research Database (Denmark)

    Mazzoni, Gianluca; Kogelman, Lisette; Suravajhala, Prashanth

    2015-01-01

    Next generation sequencing technologies have enabled the generation of huge quantities of biological data, and nowadays extensive datasets at different ‘omics levels have been generated. Systems genetics is a powerful approach that allows to integrate different ‘omics level and understand the bio...

  19. GMDH Method with Genetic Selection Algorithm and Cloning

    Czech Academy of Sciences Publication Activity Database

    Jiřina, Marcel; Jiřina jr., M.

    2013-01-01

    Roč. 23, č. 5 (2013), s. 451-464 ISSN 1210-0552 Institutional support: RVO:67985807 Keywords : multivariate data * GMDH * linear regression * Gauss-Markov conditions * cloning * genetic selection * classification Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.412, year: 2013

  20. Student world view as a framework for learning genetics and evolution in high school biology

    Science.gov (United States)

    McCoy, Roger Wesley

    Statement of the problem. Few studies in biology education have examined the underlying presuppositions which guide thinking and concept learning in adolescents. The purpose of this study was to describe and understand the biological world views of a variety of high school students before they take biology courses. Specifically, the study examined student world views in the domains of Classification, Relationship and Causation related to the concepts of heredity, evolution and biotechnology. The following served as guiding questions: (1) What are the personal world views of high school students entering biology classes, related to the domain of Classification, Relationship and Causality? (2) How do these student world views confound or enhance the learning of basic concepts in genetics and evolution? Methods. An interpretive method was chosen for this study. The six student participants were ninth graders and represented a wide range of world view backgrounds. A series of three interviews was conducted with each participant, with a focus group used for triangulation of data. The constant comparative method was used to categorize the data and facilitate the search for meaningful patterns. The analysis included a thick description of each student's personal views of classification, evolution and the appropriate use of biotechnology. Results. The study demonstrates that world view is the basis upon which students build knowledge in biology. The logic of their everyday thinking may not match that of scientists. The words they use are sometimes inconsistent with scientific terminology. This study provides evidence that students voice different opinions depending on the social situation, since they are strongly influenced by peers. Students classify animals based on behaviors. They largely believe that the natural world is unpredictable, and that humans are not really part of that world. Half are unlikely to accept the evolution of humans, but may accept it in other

  1. Genetic transformation of Eucalyptus camaldulensis by agrobalistic method

    Directory of Open Access Journals (Sweden)

    Evânia Galvão Mendonça

    2013-06-01

    Full Text Available Eucalyptus stands in the setting of worldwide forestry due to its adaptability, rapid growth, production of high-quality and low cost of wood pulp fibers. The eucalyptus convetional breeding is impaired mainlly by the long life cycle making the genetic transformation systems an important tool for this purpose. However, this system requires in vitro eficient protocols for plant induction, regeneration and seletion, that allow to obtain transgenic plants from the transformed cell groups. The aim of this work was to evaluate the callus formation and to optimize the leaves and callus genetic transformation protocol by using the Agrobacterium tumefaciens system. Concerning callus formation, two different culture media were evaluated: MS medium supplemented with auxin, cytokinin (M1 and the MS medium with reduced nitrogen concentration and supplemented with auxin, cytokinin coconut water (M2. To establish the leave genetic transformation, those were exposed to agrobiolistics technique (gene gun, to tissue injury, and A. tumesfasciens EHA 105 contening the vetor pCambia 3301 (35S::GUS::NOS, for gene transference and to establish the callus transformation thoses were exposed only to A. tumefasciens. For both experiments, the influence of different infection periods was evaluated. The M2 medium provided the best values for callus sizea and fresh and dry weight. The leaves genetic transformation using the agrobiolistics technique was effective, the gus gene transient expression could be observed. No significant differences were obtained in the infection periods (4, 6 and 8 minutes. The callus genetic transformation with A. tumefaciens also promotend the gus gene transient expression on the callus co-cultiveted for 15 e 30 minutes. The transformed callus was transfered to a regeneration and selection medium and transformed plants were obtained.

  2. A toolkit for incorporating genetics into mainstream medical services: Learning from service development pilots in England

    Directory of Open Access Journals (Sweden)

    Burton Hilary

    2010-05-01

    Full Text Available Abstract Background As advances in genetics are becoming increasingly relevant to mainstream healthcare, a major challenge is to ensure that these are integrated appropriately into mainstream medical services. In 2003, the Department of Health for England announced the availability of start-up funding for ten 'Mainstreaming Genetics' pilot services to develop models to achieve this. Methods Multiple methods were used to explore the pilots' experiences of incorporating genetics which might inform the development of new services in the future. A workshop with project staff, an email questionnaire, interviews and a thematic analysis of pilot final reports were carried out. Results Seven themes relating to the integration of genetics into mainstream medical services were identified: planning services to incorporate genetics; the involvement of genetics departments; the establishment of roles incorporating genetic activities; identifying and involving stakeholders; the challenges of working across specialty boundaries; working with multiple healthcare organisations; and the importance of cultural awareness of genetic conditions. Pilots found that the planning phase often included the need to raise awareness of genetic conditions and services and that early consideration of organisational issues such as clinic location was essential. The formal involvement of genetics departments was crucial to success; benefits included provision of clinical and educational support for staff in new roles. Recruitment and retention for new roles outside usual career pathways sometimes proved difficult. Differences in specialties' working practices and working with multiple healthcare organisations also brought challenges such as the 'genetic approach' of working with families, incompatible record systems and different approaches to health professionals' autonomous practice. 'Practice points' have been collated into a Toolkit which includes resources from the pilots

  3. Developing a Blended Learning-Based Method for Problem-Solving in Capability Learning

    Science.gov (United States)

    Dwiyogo, Wasis D.

    2018-01-01

    The main objectives of the study were to develop and investigate the implementation of blended learning based method for problem-solving. Three experts were involved in the study and all three had stated that the model was ready to be applied in the classroom. The implementation of the blended learning-based design for problem-solving was…

  4. SMALL GROUP LEARNING METHODS AND THEIR EFFECT ON LEARNERS’ RELATIONSHIPS

    Directory of Open Access Journals (Sweden)

    Radka Borůvková

    2016-04-01

    Full Text Available Building relationships in the classroom is an essential part of any teacher's career. Having healthy teacher-to-learner and learner-to-learner relationships is an effective way to help prevent pedagogical failure, social conflict and quarrelsome behavior. Many strategies are available that can be used to achieve good long-lasting relationships in the classroom setting. Successful teachers’ pedagogical work in the classroom requires detailed knowledge of learners’ relationships. Good understanding of the relationships is necessary, especially in the case of teenagers’ class. This sensitive period of adolescence demands attention of all teachers who should deal with the problems of their learners. Special care should be focused on children that are out of their classmates’ interest (so called isolated learners or isolates in such class and on possibilities to integrate them into the class. Natural idea how to do it is that of using some modern non-traditional teaching/learning methods, especially the methods based on work in small groups involving learners’ cooperation. Small group education (especially problem-based learning, project-based learning, cooperative learning, collaborative learning or inquire-based learning as one of these methods involves a high degree of interaction. The effectiveness of learning groups is determined by the extent to which the interaction enables members to clarify their own understanding, build upon each other's contributions, sift out meanings, ask and answer questions. An influence of this kind of methods (especially cooperative learning (CL on learners’ relationships was a subject of the further described research. Within the small group education, students work with their classmates to solve complex and authentic problems that help develop content knowledge as well as problem-solving, reasoning, communication, and self-assessment skills. The aim of the research was to answer the question: Can the

  5. Think Pair Share (TPS as Method to Improve Student’s Learning Motivation and Learning Achievement

    Directory of Open Access Journals (Sweden)

    Hetika Hetika

    2018-03-01

    Full Text Available This research aims to find out the application of Think Pair Share (TPS learning method in improving learning motivation and learning achievement in the subject of Introduction to Accounting I of the Accounting Study Program students of Politeknik Harapan Bersama. The Method of data collection in this study used observation method, test method, and documentation method. The research instruments used observation sheet, questionnaire and test question. This research used Class Action Research Design which is an action implementation oriented research, with the aim of improving quality or problem solving in a group by carefully and observing the success rate due to the action. The method of analysis used descriptive qualitative and quantitative analysis method. The results showed that the application of Think Pair Share Learning (TPS Method can improve the Learning Motivation and Achievement. Before the implementation of the action, the obtained score is 67% then in the first cycle increases to 72%, and in the second cycle increasws to 80%. In addition, based on questionnaires distributed to students, it also increases the score of Accounting Learning Motivation where the score in the first cycle of 76% increases to 79%. In addition, in the first cycle, the score of pre test and post test of the students has increased from 68.86 to 76.71 while in the second cycle the score of pre test and post test of students has increased from 79.86 to 84.86.

  6. Deep learning methods for protein torsion angle prediction.

    Science.gov (United States)

    Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

    2017-09-18

    Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

  7. Teacher's opinion about learning continuum of genetics based on student's level of competence

    Science.gov (United States)

    Juniati, Etika; Subali, Bambang

    2017-08-01

    This study focuses on designing learning continuum for developing a curriculum. The objective of this study is to get the opinion of junior and senior high school teachers about Learning Continuum based on Student's Level of Competence and Specific Pedagogical Learning Material on Aspect of Genetics Aspects. This research is a survey research involving 281 teachers from junior and senior high school teachers as respondents taken from five districts and city in Yogyakarta Special Region. The results of this study show that most of the junior high school teachers argue that sub aspects individual reproduction should be taught to students of grade VII and IX, virus reproduction at the grade X, and cell reproduction to mutation at the grade IX with level of competence to understand (C2) while most of the senior high school teachers argue that sub aspects individual, cell, and virus reproduction must be taught to students of grade X and division mechanism to mutation at the grade XII with level of competence to understand (C2), apply (C3), and analyze (C4). Based on the opinion of teachers, sub concepts in genetics can be taught from junior high school with different in the scope of materials but learning continuum that has been developed is not relevant with the students cognitive development and their grades.

  8. TEACHING METHODS IN MBA AND LIFELONG LEARNING PROGRAMMES FOR MANAGERS

    Directory of Open Access Journals (Sweden)

    Jarošová, Eva

    2017-09-01

    Full Text Available Teaching methods in MBA and Lifelong Learning Programmes (LLP for managers should be topically relevant in terms of content as well as the teaching methods used. In terms of the content, the integral part of MBA and Lifelong Learning Programmes for managers should be the development of participants’ leadership competencies and their understanding of current leadership concepts. The teaching methods in educational programmes for managers as adult learners should correspond to the strategy of learner-centred teaching that focuses on the participants’ learning process and their active involvement in class. The focus on the participants’ learning process also raises questions about whether the programme’s participants perceive the teaching methods used as useful and relevant for their development as leaders. The paper presents the results of the analysis of the responses to these questions in a sample of 54 Czech participants in the MBA programme and of lifelong learning programmes at the University of Economics, Prague. The data was acquired based on written or electronically submitted questionnaires. The data was analysed in relation to the usefulness of the teaching methods for understanding the concepts of leadership, leadership skills development as well as respondents’ personal growth. The results show that the respondents most valued the methods that enabled them to get feedback, activated them throughout the programme and got them involved in discussions with others in class. Implications for managerial education practices are discussed.

  9. FLIPPED CLASSROOM LEARNING METHOD TO IMPROVE CARING AND LEARNING OUTCOME IN FIRST YEAR NURSING STUDENT

    Directory of Open Access Journals (Sweden)

    Ni Putu Wulan Purnama Sari

    2017-08-01

    Full Text Available Background and Purpose: Caring is the essence of nursing profession. Stimulation of caring attitude should start early. Effective teaching methods needed to foster caring attitude and improve learning achievement. This study aimed to explain the effect of applying flipped classroom learning method for improving caring attitude and learning achievement of new student nurses at nursing institutions in Surabaya. Method: This is a pre-experimental study using the one group pretest posttest and posttest only design. Population was all new student nurses on nursing institutions in Surabaya. Inclusion criteria: female, 18-21 years old, majoring in nursing on their own volition and being first choice during students selection process, status were active in the even semester of 2015/2016 academic year. Sample size was 67 selected by total sampling. Variables: 1 independent: application of flipped classroom learning method; 2 dependent: caring attitude, learning achievement. Instruments: teaching plan, assignment descriptions, presence list, assignment assessment rubrics, study materials, questionnaires of caring attitude. Data analysis: paired and one sample t test. Ethical clearance was available. Results: Most respondents were 20 years old (44.8%, graduated from high school in Surabaya (38.8%, living with parents (68.7% in their homes (64.2%. All data were normally distributed. Flipped classroom learning method could improve caring attitude by 4.13%. Flipped classroom learning method was proved to be effective for improving caring attitude (p=0.021 and learning achievement (p=0.000. Conclusion and Recommendation: Flipped classroom was effective for improving caring attitude and learning achievement of new student nurse. It is recommended to use mix-method and larger sample for further study.

  10. Science Learning Cycle Method to Enhance the Conceptual Understanding and the Learning Independence on Physics Learning

    Science.gov (United States)

    Sulisworo, Dwi; Sutadi, Novitasari

    2017-01-01

    There have been many studies related to the implementation of cooperative learning. However, there are still many problems in school related to the learning outcomes on science lesson, especially in physics. The aim of this study is to observe the application of science learning cycle (SLC) model on improving scientific literacy for secondary…

  11. Finding protein sites using machine learning methods

    Directory of Open Access Journals (Sweden)

    Jaime Leonardo Bobadilla Molina

    2003-07-01

    Full Text Available The increasing amount of protein three-dimensional (3D structures determined by x-ray and NMR technologies as well as structures predicted by computational methods results in the need for automated methods to provide inital annotations. We have developed a new method for recognizing sites in three-dimensional protein structures. Our method is based on a previosly reported algorithm for creating descriptions of protein microenviroments using physical and chemical properties at multiple levels of detail. The recognition method takes three inputs: 1. A set of control nonsites that share some structural or functional role. 2. A set of control nonsites that lack this role. 3. A single query site. A support vector machine classifier is built using feature vectors where each component represents a property in a given volume. Validation against an independent test set shows that this recognition approach has high sensitivity and specificity. We also describe the results of scanning four calcium binding proteins (with the calcium removed using a three dimensional grid of probe points at 1.25 angstrom spacing. The system finds the sites in the proteins giving points at or near the blinding sites. Our results show that property based descriptions along with support vector machines can be used for recognizing protein sites in unannotated structures.

  12. Approximation methods for efficient learning of Bayesian networks

    CERN Document Server

    Riggelsen, C

    2008-01-01

    This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

  13. Experts in Teams – An experiential learning method

    DEFF Research Database (Denmark)

    Johansen, Steffen Kjær

    2017-01-01

    T becomes a learning method rather than a teaching method. Besides discussing the pedagogical characteristics of EiT, the study also gives a general introduction to EiT as it was taught at SDU fall 2016 as well as a brief review of the basic theory behind experiential learning. As such this study serves...... courses. Most of the practical courses are group work along the lines of project based learning. EiT is in a way both. It is a practical course in as much as our students get hands-on experience with interdisciplinary team work and innovation processes. EiT is a theoretical course in as much as our...... both as an introduction to e.g. new teachers of EiT but also as a starting point for a clarification of the features that makes EiT an experiential learning endeavor....

  14. The use of genetic methods to study Eurasian otters

    Czech Academy of Sciences Publication Activity Database

    Hájková, Petra; Gettová, Lenka; Sládkovičová, V.; Zemanová, Barbora

    Supp., - (2011), s. 102 ISSN 0394-1914. [International Otter Colloquium /11./. 30.08.2011-04.09.2011, Pavia] R&D Projects: GA AV ČR KJB600930804; GA MŽP SP/2D4/16/08; GA ČR GA206/03/0757 Institutional research plan: CEZ:AV0Z60930519 Keywords : Eurasian otter * genetic analyses Subject RIV: EG - Zoology http://www.internationalottercolloquium2010.eu/files/proceedings_iucn_xi_ioc_2011.pdf

  15. Genetic analysis of bulimia nervosa: methods and sample description.

    Science.gov (United States)

    Kaye, Walter H; Devlin, Bernie; Barbarich, Nicole; Bulik, Cynthia M; Thornton, Laura; Bacanu, Silviu-Alin; Fichter, Manfred M; Halmi, Katherine A; Kaplan, Allan S; Strober, Michael; Woodside, D Blake; Bergen, Andrew W; Crow, Scott; Mitchell, James; Rotondo, Alessandro; Mauri, Mauro; Cassano, Giovanni; Keel, Pamela; Plotnicov, Katherine; Pollice, Christine; Klump, Kelly L; Lilenfeld, Lisa R; Ganjei, J Kelly; Quadflieg, Norbert; Berrettini, Wade H

    2004-05-01

    Twin and family studies suggest that genetic variants contribute to the pathogenesis of bulimia nervosa (BN) and anorexia nervosa (AN). The Price Foundation has supported an international, multisite study of families with these disorders to identify these genetic variations. The current study presents the clinical characteristics of this sample as well as a description of the study methodology. All probands met modified criteria for BN or bulimia nervosa with a history of AN (BAN) as defined in the 4th ed. of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric Association, 1994). All affected relatives met DSM-IV criteria for BN, AN, BAN, or eating disorders not otherwise specified (EDNOS). Probands and affected relatives were assessed diagnostically using both trained-rater and self-report assessments. DNA samples were collected from probands, affected relatives, and available biologic parents. Assessments were obtained from 163 BN probands and 165 BAN probands. Overall, there were 365 relative pairs available for linkage analysis. Of the affected relatives of BN probands, 62 were diagnosed as BN (34.8%), 49 as BAN (27.5%), 35 as AN (19.7%), and 32 as EDNOS (18.0%). For the relatives of BAN probands, 42 were diagnosed as BN (22.5%), 67 as BAN (35.8%), 48 as AN (25.7%), and 30 as EDNOS (16.0%). This study represents the largest genetic study of eating disorders to date. Clinical data indicate that although there are a large number of individuals with BN disorders, a range of eating pathology is represented in the sample, allowing for the examination of several different phenotypes in molecular genetic analyses. Copyright 2004 by Wiley Periodicals, Inc. Int J Eat Disord 35: 556-570, 2004.

  16. Research on demand-oriented Business English learning method

    Directory of Open Access Journals (Sweden)

    Zhou Yuan

    2016-01-01

    Full Text Available Business English is integrated with visual-audio-oral English, which focuses on the application for English listening and speaking skills in common business occasions, and acquire business knowledge and improve skills through English. This paper analyzes the Business English Visual-audio-oral Course, and learning situation of higher vocational students’ learning objectives, interests, vocabulary, listening and speaking, and focuses on the research of effective methods to guide the higher vocational students to learn Business English Visual-audio-oral Course, master Business English knowledge, and improve communicative competence of Business English.

  17. Genetic dissection of memory for associative and non-associative learning in Caenorhabditis elegans.

    Science.gov (United States)

    Lau, H L; Timbers, T A; Mahmoud, R; Rankin, C H

    2013-03-01

    The distinction between non-associative and associative forms of learning has historically been based on the behavioral training paradigm. Through discovering the molecular mechanisms that mediate learning, we can develop a deeper understanding of the relationships between different forms of learning. Here, we genetically dissect short- and long-term memory for a non-associative form of learning, habituation and an associative form of learning, context conditioning for habituation, in the nematode Caenorhabditis elegans. In short-term chemosensory context conditioning for habituation, worms trained and tested in the presence of either a taste (sodium acetate) or smell (diacetyl) context cue show greater retention of habituation to tap stimuli when compared with animals trained and tested without a salient cue. Long-term memory for olfactory context conditioning was observed 24 h after a training procedure that does not normally induce 24 h memory. Like long-term habituation, this long-term memory was dependent on the transcription factor cyclic AMP-response element-binding protein. Worms with mutations in glr-1 [a non-N-methyl-d-aspartate (NMDA)-type glutamate receptor subunit] showed short-term but not long-term habituation or short- or long-term context conditioning. Worms with mutations in nmr-1 (an NMDA-receptor subunit) showed normal short- and long-term memory for habituation but did not show either short- or long-term context conditioning. Rescue of nmr-1 in the RIM interneurons rescued short- and long-term olfactory context conditioning leading to the hypothesis that these interneurons function to integrate information from chemosensory and mechanosensory systems for associative learning. © 2012 The Authors. Genes, Brain and Behavior © 2012 Blackwell Publishing Ltd and International Behavioural and Neural Genetics Society.

  18. [Which learning methods are expected for ultrasound training? Blended learning on trial].

    Science.gov (United States)

    Röhrig, S; Hempel, D; Stenger, T; Armbruster, W; Seibel, A; Walcher, F; Breitkreutz, R

    2014-10-01

    Current teaching methods in graduate and postgraduate training often include frontal presentations. Especially in ultrasound education not only knowledge but also sensomotory and visual skills need to be taught. This requires new learning methods. This study examined which types of teaching methods are preferred by participants in ultrasound training courses before, during and after the course by analyzing a blended learning concept. It also investigated how much time trainees are willing to spend on such activities. A survey was conducted at the end of a certified ultrasound training course. Participants were asked to complete a questionnaire based on a visual analogue scale (VAS) in which three categories were defined: category (1) vote for acceptance with a two thirds majority (VAS 67-100%), category (2) simple acceptance (50-67%) and category (3) rejection (learning program with interactive elements, short presentations (less than 20 min), incorporating interaction with the audience, hands-on sessions in small groups, an alternation between presentations and hands-on-sessions, live demonstrations and quizzes. For post-course learning, interactive and media-assisted approaches were preferred, such as e-learning, films of the presentations and the possibility to stay in contact with instructors in order to discuss the results. Participants also voted for maintaining a logbook for documentation of results. The results of this study indicate the need for interactive learning concepts and blended learning activities. Directors of ultrasound courses may consider these aspects and are encouraged to develop sustainable learning pathways.

  19. Research on demand-oriented Business English learning method

    OpenAIRE

    Zhou Yuan

    2016-01-01

    Business English is integrated with visual-audio-oral English, which focuses on the application for English listening and speaking skills in common business occasions, and acquire business knowledge and improve skills through English. This paper analyzes the Business English Visual-audio-oral Course, and learning situation of higher vocational students’ learning objectives, interests, vocabulary, listening and speaking, and focuses on the research of effective methods to guide the higher voca...

  20. A toolkit for incorporating genetics into mainstream medical services: Learning from service development pilots in England.

    Science.gov (United States)

    Bennett, Catherine L; Burke, Sarah E; Burton, Hilary; Farndon, Peter A

    2010-05-14

    As advances in genetics are becoming increasingly relevant to mainstream healthcare, a major challenge is to ensure that these are integrated appropriately into mainstream medical services. In 2003, the Department of Health for England announced the availability of start-up funding for ten 'Mainstreaming Genetics' pilot services to develop models to achieve this. Multiple methods were used to explore the pilots' experiences of incorporating genetics which might inform the development of new services in the future. A workshop with project staff, an email questionnaire, interviews and a thematic analysis of pilot final reports were carried out. Seven themes relating to the integration of genetics into mainstream medical services were identified: planning services to incorporate genetics; the involvement of genetics departments; the establishment of roles incorporating genetic activities; identifying and involving stakeholders; the challenges of working across specialty boundaries; working with multiple healthcare organisations; and the importance of cultural awareness of genetic conditions. Pilots found that the planning phase often included the need to raise awareness of genetic conditions and services and that early consideration of organisational issues such as clinic location was essential. The formal involvement of genetics departments was crucial to success; benefits included provision of clinical and educational support for staff in new roles. Recruitment and retention for new roles outside usual career pathways sometimes proved difficult. Differences in specialties' working practices and working with multiple healthcare organisations also brought challenges such as the 'genetic approach' of working with families, incompatible record systems and different approaches to health professionals' autonomous practice. 'Practice points' have been collated into a Toolkit which includes resources from the pilots, including job descriptions and clinical tools. These can

  1. Future Competencies and Learning Methods in Engineering Education

    DEFF Research Database (Denmark)

    Kolmos, Anette

    2002-01-01

    What are the competencies for tommorow´s enginnering education and the implications of these regarding the choice of teaching content and learning methods? The paper analyses two trends: the traditional and the techo-science approach. These two trends are based on technological innovation...... and change processes and impact on educational content and methods....

  2. Empowering and Engaging Students in Learning Research Methods

    Science.gov (United States)

    Liu, Shuang; Breit, Rhonda

    2013-01-01

    The capacity to conduct research is essential for university graduates to survive and thrive in their future career. However, research methods courses have often been considered by students as "abstract", "uninteresting", and "hard". Thus, motivating students to engage in the process of learning research methods has become a crucial challenge for…

  3. Radiochemical methods. Analytical chemistry by open learning

    Energy Technology Data Exchange (ETDEWEB)

    Geary, W.J.; James, A.M. (ed.)

    1986-01-01

    This book presents the analytical uses of radioactive isotopes within the context of radiochemistry as a whole. It is designed for scientists with relatively little background knowledge of the subject. Thus the initial emphasis is on developing the basic concepts of radioactive decay, particularly as they affect the potential usage of radioisotopes. Discussion of the properties of various types of radiation, and of factors such as half-life, is related to practical considerations such as counting and preparation methods, and handling/disposal problems. Practical aspects are then considered in more detail, and the various radioanalytical methods are outlined with particular reference to their applicability. The approach is 'user friendly' and the use of self assessment questions allows the reader to test his/her understanding of individual sections easily. For those who wish to develop their knowledge further, a reading list is provided.

  4. Uncertain Photometric Redshifts with Deep Learning Methods

    Science.gov (United States)

    D'Isanto, A.

    2017-06-01

    The need for accurate photometric redshifts estimation is a topic that has fundamental importance in Astronomy, due to the necessity of efficiently obtaining redshift information without the need of spectroscopic analysis. We propose a method for determining accurate multi-modal photo-z probability density functions (PDFs) using Mixture Density Networks (MDN) and Deep Convolutional Networks (DCN). A comparison with a Random Forest (RF) is performed.

  5. Monte Carlo methods for preference learning

    DEFF Research Database (Denmark)

    Viappiani, P.

    2012-01-01

    Utility elicitation is an important component of many applications, such as decision support systems and recommender systems. Such systems query the users about their preferences and give recommendations based on the system’s belief about the utility function. Critical to these applications is th...... is the acquisition of prior distribution about the utility parameters and the possibility of real time Bayesian inference. In this paper we consider Monte Carlo methods for these problems....

  6. A review of statistical methods for testing genetic anticipation: looking for an answer in Lynch syndrome

    DEFF Research Database (Denmark)

    Boonstra, Philip S; Gruber, Stephen B; Raymond, Victoria M

    2010-01-01

    Anticipation, manifested through decreasing age of onset or increased severity in successive generations, has been noted in several genetic diseases. Statistical methods for genetic anticipation range from a simple use of the paired t-test for age of onset restricted to affected parent-child pairs......, and this right truncation effect is more pronounced in children than in parents. In this study, we first review different statistical methods for testing genetic anticipation in affected parent-child pairs that address the issue of bias due to right truncation. Using affected parent-child pair data, we compare...... the issue of multiplex ascertainment and its effect on the different methods. We then focus on exploring genetic anticipation in Lynch syndrome and analyze new data on the age of onset in affected parent-child pairs from families seen at the University of Michigan Cancer Genetics clinic with a mutation...

  7. [An Efficient Method for Genetic Certification of Bacillus subtilis strains, Prospective Producers of Biopreparations].

    Science.gov (United States)

    Terletskiy, V P; Tyshenko, V I; Novikova, I I; Boikova, I V; Tyulebaev, S D; Shakhtamirov, I Ya

    2016-01-01

    Genetic certification of commercial strains of bacteria antagonistic to phytopathogenic microorganisms guarantees their unequivocal identification and confirmation of safety. In Russia, unlike EU countries, genetic certification of Bacillus subtilis strains is not used. Based on the previously proposed double digestion selective label (DDSL) fingerprinting, a method for genetic identification and certification of B. subtilis strains was proposed. The method was tested on several strains differing in their physiological and biochemical properties and in the composition of secondary metabolites responsible for the spectrum of antibiotic activity. High resolving power of this approach was shown. Optimal restriction endonucleases (SgsI and Eco32I) were determined and validated. A detailed protocol for genetic certification of this bacterial species was developed. DDSL is a universal method, which may be adapted for genetic identification and certification of other bacterial species.

  8. Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

    Science.gov (United States)

    Chang, P; Grinband, J; Weinberg, B D; Bardis, M; Khy, M; Cadena, G; Su, M-Y; Cha, S; Filippi, C G; Bota, D; Baldi, P; Poisson, L M; Jain, R; Chow, D

    2018-05-10

    The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 ( IDH1 ) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase ( MGMT ) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training. © 2018 by American Journal of Neuroradiology.

  9. A method for the interpretation of flow cytometry data using genetic algorithms

    Directory of Open Access Journals (Sweden)

    Cesar Angeletti

    2018-01-01

    Full Text Available Background: Flow cytometry analysis is the method of choice for the differential diagnosis of hematologic disorders. It is typically performed by a trained hematopathologist through visual examination of bidimensional plots, making the analysis time-consuming and sometimes too subjective. Here, a pilot study applying genetic algorithms to flow cytometry data from normal and acute myeloid leukemia subjects is described. Subjects and Methods: Initially, Flow Cytometry Standard files from 316 normal and 43 acute myeloid leukemia subjects were transformed into multidimensional FITS image metafiles. Training was performed through introduction of FITS metafiles from 4 normal and 4 acute myeloid leukemia in the artificial intelligence system. Results: Two mathematical algorithms termed 018330 and 025886 were generated. When tested against a cohort of 312 normal and 39 acute myeloid leukemia subjects, both algorithms combined showed high discriminatory power with a receiver operating characteristic (ROC curve of 0.912. Conclusions: The present results suggest that machine learning systems hold a great promise in the interpretation of hematological flow cytometry data.

  10. Choosing Learning Methods Suitable for Teaching and Learning in Computer Science

    Science.gov (United States)

    Taylor, Estelle; Breed, Marnus; Hauman, Ilette; Homann, Armando

    2013-01-01

    Our aim is to determine which teaching methods students in Computer Science and Information Systems prefer. There are in total 5 different paradigms (behaviorism, cognitivism, constructivism, design-based and humanism) with 32 models between them. Each model is unique and states different learning methods. Recommendations are made on methods that…

  11. Genetic Risk by Experience Interaction for Childhood Internalizing Problems: Converging Evidence across Multiple Methods

    Science.gov (United States)

    Vendlinski, Matthew K.; Lemery-Chalfant, Kathryn; Essex, Marilyn J.; Goldsmith, H. Hill

    2011-01-01

    Background: Identifying how genetic risk interacts with experience to predict psychopathology is an important step toward understanding the etiology of mental health problems. Few studies have examined genetic risk by experience interaction (GxE) in the development of childhood psychopathology. Methods: We used both co-twin and parent mental…

  12. Current Issues in the Neurology and Genetics of Learning-Related Traits and Disorders: Introduction to the Special Issue.

    Science.gov (United States)

    Gilger, Jeffrey W.

    2001-01-01

    This introductory article briefly describes each of the following eight articles in this special issue on the neurology and genetics of learning related disorders. It notes the greater appreciation of learning disability as a set of complex disorders with broad and intricate neurological bases and of the large individual differences in how these…

  13. Genetic-evolution-based optimization methods for engineering design

    Science.gov (United States)

    Rao, S. S.; Pan, T. S.; Dhingra, A. K.; Venkayya, V. B.; Kumar, V.

    1990-01-01

    This paper presents the applicability of a biological model, based on genetic evolution, for engineering design optimization. Algorithms embodying the ideas of reproduction, crossover, and mutation are developed and applied to solve different types of structural optimization problems. Both continuous and discrete variable optimization problems are solved. A two-bay truss for maximum fundamental frequency is considered to demonstrate the continuous variable case. The selection of locations of actuators in an actively controlled structure, for minimum energy dissipation, is considered to illustrate the discrete variable case.

  14. A novel structure-aware sparse learning algorithm for brain imaging genetics.

    Science.gov (United States)

    Du, Lei; Jingwen, Yan; Kim, Sungeun; Risacher, Shannon L; Huang, Heng; Inlow, Mark; Moore, Jason H; Saykin, Andrew J; Shen, Li

    2014-01-01

    Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.

  15. Machine Learning Methods for Attack Detection in the Smart Grid.

    Science.gov (United States)

    Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

    2016-08-01

    Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

  16. Learning Unknown Structure in CRFs via Adaptive Gradient Projection Method

    Directory of Open Access Journals (Sweden)

    Wei Xue

    2016-08-01

    Full Text Available We study the problem of fitting probabilistic graphical models to the given data when the structure is not known. More specifically, we focus on learning unknown structure in conditional random fields, especially learning both the structure and parameters of a conditional random field model simultaneously. To do this, we first formulate the learning problem as a convex minimization problem by adding an l_2-regularization to the node parameters and a group l_1-regularization to the edge parameters, and then a gradient-based projection method is proposed to solve it which combines an adaptive stepsize selection strategy with a nonmonotone line search. Extensive simulation experiments are presented to show the performance of our approach in solving unknown structure learning problems.

  17. DNA Re-EvolutioN: a game for learning molecular genetics and evolution.

    Science.gov (United States)

    Miralles, Laura; Moran, Paloma; Dopico, Eduardo; Garcia-Vazquez, Eva

    2013-01-01

    Evolution is a main concept in biology, but not many students understand how it works. In this article we introduce the game DNA Re-EvolutioN as an active learning tool that uses genetic concepts (DNA structure, transcription and translation, mutations, natural selection, etc.) as playing rules. Students will learn about molecular evolution while playing a game that mixes up theory and entertainment. The game can be easily adapted to different educational levels. The main goal of this play is to arrive at the end of the game with the longest protein. Students play with pawns and dices, a board containing hypothetical events (mutations, selection) that happen to molecules, "Evolution cards" with indications for DNA mutations, prototypes of a DNA and a mRNA chain with colored "nucleotides" (plasticine balls), and small pieces simulating t-RNA with aminoacids that will serve to construct a "protein" based on the DNA chain. Students will understand how changes in DNA affect the final protein product and may be subjected to positive or negative selection, using a didactic tool funnier than classical theory lectures and easier than molecular laboratory experiments: a flexible and feasible game to learn and enjoy molecular evolution at no-cost. The game was tested by majors and non-majors in genetics from 13 different countries and evaluated with pre- and post-tests obtaining very positive results. © 2013 by The International Union of Biochemistry and Molecular Biology.

  18. Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

    Science.gov (United States)

    He, Dan; Kuhn, David; Parida, Laxmi

    2016-06-15

    Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. dhe@us.ibm.com. © The Author 2016. Published by Oxford University Press.

  19. MOLECULAR GENETIC MARKERS AND METHODS OF THEIR IDENTIFICATION IN MODERN FISH-FARMING

    Directory of Open Access Journals (Sweden)

    I. Hrytsyniak

    2014-03-01

    Full Text Available Purpose. The application of molecular genetic markers has been widely used in modern experimental fish-farming in recent years. This methodology is currently presented by a differentiated approach with individual mechanisms and clearly defined possibilities. Numerous publications in the scientific literature that are dedicated to molecular genetic markers for the most part offer purely practical data. Thus, the synthesis and analysis of existing information on the general principles of action and the limits of the main methods of using molecular genetic markers is an actual problem. In particular, such a description will make it possible to plan more effectively the experiment and to obtain the desired results with high reliability. Findings. The main types of variable parts of DNA that can be used as molecular genetic markers in determining the level of stock hybridization, conducting genetic inventory of population and solving other problems in modern fish-farming are described in this paper. Also, the article provides an overview of principal modern methods that can be used to identify molecular genetic markers. Originality. This work is a generalization of modern ideas about the mechanisms of experiments with molecular genetic markers in fish-farming. Information is provided in the form of consistent presentation of the principles and purpose of each method, as well as significant advances during their practical application. Practical value. The proposed review of classic and modern literature data on molecular genetic markers can be used for planning, modernization and correction of research activity in modern fish-farming.

  20. Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm

    Science.gov (United States)

    Mundher Yaseen, Zaher; Abdulmohsin Afan, Haitham; Tran, Minh-Tung

    2018-04-01

    Scientifically evidenced that beam-column joints are a critical point in the reinforced concrete (RC) structure under the fluctuation loads effects. In this novel hybrid data-intelligence model developed to predict the joint shear behavior of exterior beam-column structure frame. The hybrid data-intelligence model is called genetic algorithm integrated with deep learning neural network model (GA-DLNN). The genetic algorithm is used as prior modelling phase for the input approximation whereas the DLNN predictive model is used for the prediction phase. To demonstrate this structural problem, experimental data is collected from the literature that defined the dimensional and specimens’ properties. The attained findings evidenced the efficitveness of the hybrid GA-DLNN in modelling beam-column joint shear problem. In addition, the accurate prediction achived with less input variables owing to the feasibility of the evolutionary phase.

  1. Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks——A Case Study for the Optimal Ordering of Tables

    Institute of Scientific and Technical Information of China (English)

    Concha Bielza; Juan A.Fernández del Pozo; Pedro Larra(n)aga

    2013-01-01

    Parameter setting for evolutionary algorithms is still an important issue in evolutionary computation.There are two main approaches to parameter setting:parameter tuning and parameter control.In this paper,we introduce self-adaptive parameter control of a genetic algorithm based on Bayesian network learning and simulation.The nodes of this Bayesian network are genetic algorithm parameters to be controlled.Its structure captures probabilistic conditional (in)dependence relationships between the parameters.They are learned from the best individuals,i.e.,the best configurations of the genetic algorithm.Individuals are evaluated by running the genetic algorithm for the respective parameter configuration.Since all these runs are time-consuming tasks,each genetic algorithm uses a small-sized population and is stopped before convergence.In this way promising individuals should not be lost.Experiments with an optimal search problem for simultaneous row and column orderings yield the same optima as state-of-the-art methods but with a sharp reduction in computational time.Moreover,our approach can cope with as yet unsolved high-dimensional problems.

  2. The double pedigree: a method for studying culturally and genetically inherited behavior in tandem.

    Directory of Open Access Journals (Sweden)

    Etienne Danchin

    Full Text Available Transgenerational sources of biological variation have been at the center of evolutionary studies ever since Darwin and Wallace identified natural selection. This is because evolution can only operate on traits whose variation is transmitted, i.e. traits that are heritable. The discovery of genetic inheritance has led to a semantic shift, resulting in the tendency to consider that only genes are inherited across generations. Today, however, concepts of heredity are being broadened again to integrate the accruing evidence of non-genetic inheritance, and many evolutionary biologists are calling for the inclusion of non-genetic inheritance into an inclusive evolutionary synthesis. Here, we focus on social heredity and its role in the inheritance of behavioral traits. We discuss quantitative genetics methods that might allow us to disentangle genetic and non-genetic transmission in natural populations with known pedigrees. We then propose an experimental design based on cross-fostering among animal cultures, environments and families that has the potential to partition inherited phenotypic variation into socially (i.e. culturally and genetically inherited components. This approach builds towards a new conceptual framework based on the use of an extended version of the animal model of quantitative genetics to integrate genetic and cultural components of behavioral inheritance.

  3. 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

  4. "Mastery Learning" Como Metodo Psicoeducativo para Ninos con Problemas Especificos de Aprendizaje. ("Mastery Learning" as a Psychoeducational Method for Children with Specific Learning Problems.)

    Science.gov (United States)

    Coya, Liliam de Barbosa; Perez-Coffie, Jorge

    1982-01-01

    "Mastery Learning" was compared with the "conventional" method of teaching reading skills to Puerto Rican children with specific learning disabilities. The "Mastery Learning" group showed significant gains in the cognitive and affective domains. Results suggested Mastery Learning is a more effective method of teaching…

  5. Comparisons and Analyses of Gifted Students' Characteristics and Learning Methods

    Science.gov (United States)

    Lu, Jiamei; Li, Daqi; Stevens, Carla; Ye, Renmin

    2017-01-01

    Using PISA 2009, an international education database, this study compares gifted and talented (GT) students in three groups with normal (non-GT) students by examining student characteristics, reading, schooling, learning methods, and use of strategies for understanding and memorizing. Results indicate that the GT and non-GT gender distributions…

  6. Identification of alternative method of teaching and learning the ...

    African Journals Online (AJOL)

    This study examines alternative method of teaching and learning of the concept of diffusion. An improvised U-shape glass tube called ionic mobility tube was used to observed and measure the rate of movement of divalent metal ions in an aqueous medium in the absence of an electric current. The study revealed that the ...

  7. Kernel Methods for Machine Learning with Life Science Applications

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie

    Kernel methods refer to a family of widely used nonlinear algorithms for machine learning tasks like classification, regression, and feature extraction. By exploiting the so-called kernel trick straightforward extensions of classical linear algorithms are enabled as long as the data only appear a...

  8. Research on Language Learning Strategies: Methods, Findings, and Instructional Issues.

    Science.gov (United States)

    Oxford, Rebecca; Crookall, David

    1989-01-01

    Surveys research on formal and informal second-language learning strategies, covering the effectiveness of research methods involving making lists, interviews and thinking aloud, note-taking, diaries, surveys, and training. Suggestions for future and improved research are presented. (131 references) (CB)

  9. Second-Order Learning Methods for a Multilayer Perceptron

    International Nuclear Information System (INIS)

    Ivanov, V.V.; Purehvdorzh, B.; Puzynin, I.V.

    1994-01-01

    First- and second-order learning methods for feed-forward multilayer neural networks are studied. Newton-type and quasi-Newton algorithms are considered and compared with commonly used back-propagation algorithm. It is shown that, although second-order algorithms require enhanced computer facilities, they provide better convergence and simplicity in usage. 13 refs., 2 figs., 2 tabs

  10. Educational integrating projects as a method of interactive learning

    Directory of Open Access Journals (Sweden)

    Иван Николаевич Куринин

    2013-12-01

    Full Text Available The article describes a method of interactive learning based on educational integrating projects. Some examples of content of such projects for the disciplines related to the study of information and Internet technologies and their application in management are presented.

  11. Learning by Designing Interview Methods in Special Education

    DEFF Research Database (Denmark)

    Jönsson, Lise Høgh

    2017-01-01

    , and people with learning disabilities worked together to develop five new visual and digital methods for interviewing in special education. Thereby not only enhancing the students’ competences, knowledge and proficiency in innovation and research, but also proposing a new teaching paradigm for university...

  12. Arabic Supervised Learning Method Using N-Gram

    Science.gov (United States)

    Sanan, Majed; Rammal, Mahmoud; Zreik, Khaldoun

    2008-01-01

    Purpose: Recently, classification of Arabic documents is a real problem for juridical centers. In this case, some of the Lebanese official journal documents are classified, and the center has to classify new documents based on these documents. This paper aims to study and explain the useful application of supervised learning method on Arabic texts…

  13. A Simulator to Enhance Teaching and Learning of Mining Methods ...

    African Journals Online (AJOL)

    Audio visual education that incorporates devices and materials which involve sight, sound, or both has become a sine qua non in recent times in the teaching and learning process. An automated physical model of mining methods aided with video instructions was designed and constructed by harnessing locally available ...

  14. The Keyimage Method of Learning Sound-Symbol Correspondences: A Case Study of Learning Written Khmer

    Directory of Open Access Journals (Sweden)

    Elizabeth Lavolette

    2009-01-01

    Full Text Available I documented my strategies for learning sound-symbol correspondences during a Khmer course. I used a mnemonic strategy that I call the keyimage method. In this method, a character evokes an image (the keyimage, which evokes the corresponding sound. For example, the keyimage for the character 2 could be a swan with its head tucked in. This evokes the sound "kaw" that a swan makes, which sounds similar to the Khmer sound corresponding to 2. The method has some similarities to the keyword method. Considering the results of keyword studies, I hypothesize that the keyimage method is more effective than rote learning and that peer-generated keyimages are more effective than researcher- or teacher-generated keyimages, which are more effective than learner-generated ones. In Dr. Andrew Cohen's plenary presentation at the Hawaii TESOL 2007 conference, he mentioned that more case studies are needed on learning strategies (LSs. One reason to study LSs is that what learners do with input to produce output is unclear, and knowing what strategies learners use may help us understand that process (Dornyei, 2005, p. 170. Hopefully, we can use that knowledge to improve language learning, perhaps by teaching learners to use the strategies that we find. With that in mind, I have examined the LSs that I used in studying Khmer as a foreign language, focusing on learning the syllabic alphabet.

  15. Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods.

    Science.gov (United States)

    Gonzalez-Navarro, Felix F; Stilianova-Stoytcheva, Margarita; Renteria-Gutierrez, Livier; Belanche-Muñoz, Lluís A; Flores-Rios, Brenda L; Ibarra-Esquer, Jorge E

    2016-10-26

    Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB) modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization.

  16. Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Felix F. Gonzalez-Navarro

    2016-10-01

    Full Text Available Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization.

  17. A Fast Optimization Method for General Binary Code Learning.

    Science.gov (United States)

    Shen, Fumin; Zhou, Xiang; Yang, Yang; Song, Jingkuan; Shen, Heng; Tao, Dacheng

    2016-09-22

    Hashing or binary code learning has been recognized to accomplish efficient near neighbor search, and has thus attracted broad interests in recent retrieval, vision and learning studies. One main challenge of learning to hash arises from the involvement of discrete variables in binary code optimization. While the widely-used continuous relaxation may achieve high learning efficiency, the pursued codes are typically less effective due to accumulated quantization error. In this work, we propose a novel binary code optimization method, dubbed Discrete Proximal Linearized Minimization (DPLM), which directly handles the discrete constraints during the learning process. Specifically, the discrete (thus nonsmooth nonconvex) problem is reformulated as minimizing the sum of a smooth loss term with a nonsmooth indicator function. The obtained problem is then efficiently solved by an iterative procedure with each iteration admitting an analytical discrete solution, which is thus shown to converge very fast. In addition, the proposed method supports a large family of empirical loss functions, which is particularly instantiated in this work by both a supervised and an unsupervised hashing losses, together with the bits uncorrelation and balance constraints. In particular, the proposed DPLM with a supervised `2 loss encodes the whole NUS-WIDE database into 64-bit binary codes within 10 seconds on a standard desktop computer. The proposed approach is extensively evaluated on several large-scale datasets and the generated binary codes are shown to achieve very promising results on both retrieval and classification tasks.

  18. Method to predict process signals to learn for SVM

    International Nuclear Information System (INIS)

    Minowa, Hirotsugu; Gofuku, Akio

    2013-01-01

    Study of diagnostic system using machine learning to reduce the incidents of the plant is in advance because an accident causes large damage about human, economic and social loss. There is a problem that 2 performances between a classification performance and generalization performance on the machine diagnostic machine is exclusive. However, multi agent diagnostic system makes it possible to use a diagnostic machine specialized either performance by multi diagnostic machines can be used. We propose method to select optimized variables to improve classification performance. The method can also be used for other supervised learning machine but Support Vector Machine. This paper reports that our method and result of evaluation experiment applied our method to output 40% of Monju. (author)

  19. Studying depression using imaging and machine learning methods

    OpenAIRE

    Patel, Meenal J.; Khalaf, Alexander; Aizenstein, Howard J.

    2015-01-01

    Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presen...

  20. E-learning as new method of medical education.

    Science.gov (United States)

    Masic, Izet

    2008-01-01

    NONE DECLARED Distance learning refers to use of technologies based on health care delivered on distance and covers areas such as electronic health, tele-health (e-health), telematics, telemedicine, tele-education, etc. For the need of e-health, telemedicine, tele-education and distance learning there are various technologies and communication systems from standard telephone lines to the system of transmission digitalized signals with modem, optical fiber, satellite links, wireless technologies, etc. Tele-education represents health education on distance, using Information Communication Technologies (ICT), as well as continuous education of a health system beneficiaries and use of electronic libraries, data bases or electronic data with data bases of knowledge. Distance learning (E-learning) as a part of tele-education has gained popularity in the past decade; however, its use is highly variable among medical schools and appears to be more common in basic medical science courses than in clinical education. Distance learning does not preclude traditional learning processes; frequently it is used in conjunction with in-person classroom or professional training procedures and practices. Tele-education has mostly been used in biomedical education as a blended learning method, which combines tele-education technology with traditional instructor-led training, where, for example, a lecture or demonstration is supplemented by an online tutorial. Distance learning is used for self-education, tests, services and for examinations in medicine i.e. in terms of self-education and individual examination services. The possibility of working in the exercise mode with image files and questions is an attractive way of self education. Automated tracking and reporting of learners' activities lessen faculty administrative burden. Moreover, e-learning can be designed to include outcomes assessment to determine whether learning has occurred. This review article evaluates the current

  1. Frank Gilbreth and health care delivery method study driven learning.

    Science.gov (United States)

    Towill, Denis R

    2009-01-01

    The purpose of this article is to look at method study, as devised by the Gilbreths at the beginning of the twentieth century, which found early application in hospital quality assurance and surgical "best practice". It has since become a core activity in all modern methods, as applied to healthcare delivery improvement programmes. The article traces the origin of what is now currently and variously called "business process re-engineering", "business process improvement" and "lean healthcare" etc., by different management gurus back to the century-old pioneering work of Frank Gilbreth. The outcome is a consistent framework involving "width", "length" and "depth" dimensions within which healthcare delivery systems can be analysed, designed and successfully implemented to achieve better and more consistent performance. Healthcare method (saving time plus saving motion) study is best practised as co-joint action learning activity "owned" by all "players" involved in the re-engineering process. However, although process mapping is a key step forward, in itself it is no guarantee of effective re-engineering. It is not even the beginning of the end of the change challenge, although it should be the end of the beginning. What is needed is innovative exploitation of method study within a healthcare organisational learning culture accelerated via the Gilbreth Knowledge Flywheel. It is shown that effective healthcare delivery pipeline improvement is anchored into a team approach involving all "players" in the system especially physicians. A comprehensive process study, constructive dialogue, proper and highly professional re-engineering plus managed implementation are essential components. Experience suggests "learning" is thereby achieved via "natural groups" actively involved in healthcare processes. The article provides a proven method for exploiting Gilbreths' outputs and their many successors in enabling more productive evidence-based healthcare delivery as summarised

  2. Statistical Genetics Methods for Localizing Multiple Breast Cancer Genes

    National Research Council Canada - National Science Library

    Ott, Jurg

    1998-01-01

    .... For a number of variables measured on a trait, a method, principal components of heritability, was developed that combines these variables in such a way that the resulting linear combination has highest heritability...

  3. Genetic Algorithms for Optimization of Machine-learning Models and their Applications in Bioinformatics

    KAUST Repository

    Magana-Mora, Arturo

    2017-04-29

    Machine-learning (ML) techniques have been widely applied to solve different problems in biology. However, biological data are large and complex, which often result in extremely intricate ML models. Frequently, these models may have a poor performance or may be computationally unfeasible. This study presents a set of novel computational methods and focuses on the application of genetic algorithms (GAs) for the simplification and optimization of ML models and their applications to biological problems. The dissertation addresses the following three challenges. The first is to develop a generalizable classification methodology able to systematically derive competitive models despite the complexity and nature of the data. Although several algorithms for the induction of classification models have been proposed, the algorithms are data dependent. Consequently, we developed OmniGA, a novel and generalizable framework that uses different classification models in a treeXlike decision structure, along with a parallel GA for the optimization of the OmniGA structure. Results show that OmniGA consistently outperformed existing commonly used classification models. The second challenge is the prediction of translation initiation sites (TIS) in plants genomic DNA. We performed a statistical analysis of the genomic DNA and proposed a new set of discriminant features for this problem. We developed a wrapper method based on GAs for selecting an optimal feature subset, which, in conjunction with a classification model, produced the most accurate framework for the recognition of TIS in plants. Finally, results demonstrate that despite the evolutionary distance between different plants, our approach successfully identified conserved genomic elements that may serve as the starting point for the development of a generic model for prediction of TIS in eukaryotic organisms. Finally, the third challenge is the accurate prediction of polyadenylation signals in human genomic DNA. To achieve

  4. Non-Gaussian Methods for Causal Structure Learning.

    Science.gov (United States)

    Shimizu, Shohei

    2018-05-22

    Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.

  5. Machine learning patterns for neuroimaging-genetic studies in the cloud.

    Science.gov (United States)

    Da Mota, Benoit; Tudoran, Radu; Costan, Alexandru; Varoquaux, Gaël; Brasche, Goetz; Conrod, Patricia; Lemaitre, Herve; Paus, Tomas; Rietschel, Marcella; Frouin, Vincent; Poline, Jean-Baptiste; Antoniu, Gabriel; Thirion, Bertrand

    2014-01-01

    Brain imaging is a natural intermediate phenotype to understand the link between genetic information and behavior or brain pathologies risk factors. Massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such data is carried out with increasingly sophisticated techniques and represents a great computational challenge. Fortunately, increasing computational power in distributed architectures can be harnessed, if new neuroinformatics infrastructures are designed and training to use these new tools is provided. Combining a MapReduce framework (TomusBLOB) with machine learning algorithms (Scikit-learn library), we design a scalable analysis tool that can deal with non-parametric statistics on high-dimensional data. End-users describe the statistical procedure to perform and can then test the model on their own computers before running the very same code in the cloud at a larger scale. We illustrate the potential of our approach on real data with an experiment showing how the functional signal in subcortical brain regions can be significantly fit with genome-wide genotypes. This experiment demonstrates the scalability and the reliability of our framework in the cloud with a 2 weeks deployment on hundreds of virtual machines.

  6. Generic Machine Learning Pattern for Neuroimaging-Genetic Studies in the Cloud

    Directory of Open Access Journals (Sweden)

    Benoit eDa Mota

    2014-04-01

    Full Text Available Brain imaging is a natural intermediate phenotype to understand the link between genetic information and behavior or brain pathologies risk factors. Massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such data is carried out with increasingly sophisticated techniques and represents a great computational challenge. Fortunately, increasing computational power in distributed architectures can be harnessed, if new neuroinformatics infrastructures are designed and training to use these new tools is provided. Combining a MapReduce framework (TomusBLOB with machine learning algorithms (Scikit-learn library, we design a scalable analysis tool that can deal with non-parametric statistics on high-dimensional data. End-users describe the statistical procedure to perform and can then test the model on their own computers before running the very same code in the cloud at a larger scale. We illustrate the potential of our approach on real data with an experiment showing how the functional signal in subcortical brain regions can be significantly fit with genome-wide genotypes. This experiment demonstrates the scalability and the reliability of our framework in the cloud with a two weeks deployment on hundreds of virtual machines.

  7. Aggregative Learning Method and Its Application for Communication Quality Evaluation

    Science.gov (United States)

    Akhmetov, Dauren F.; Kotaki, Minoru

    2007-12-01

    In this paper, so-called Aggregative Learning Method (ALM) is proposed to improve and simplify the learning and classification abilities of different data processing systems. It provides a universal basis for design and analysis of mathematical models of wide class. A procedure was elaborated for time series model reconstruction and analysis for linear and nonlinear cases. Data approximation accuracy (during learning phase) and data classification quality (during recall phase) are estimated from introduced statistic parameters. The validity and efficiency of the proposed approach have been demonstrated through its application for monitoring of wireless communication quality, namely, for Fixed Wireless Access (FWA) system. Low memory and computation resources were shown to be needed for the procedure realization, especially for data classification (recall) stage. Characterized with high computational efficiency and simple decision making procedure, the derived approaches can be useful for simple and reliable real-time surveillance and control system design.

  8. Learning and retention of quantum concepts with different teaching methods

    Science.gov (United States)

    Deslauriers, Louis; Wieman, Carl

    2011-06-01

    We measured mastery and retention of conceptual understanding of quantum mechanics in a modern physics course. This was studied for two equivalent cohorts of students taught with different pedagogical approaches using the Quantum Mechanics Conceptual Survey. We measured the impact of pedagogical approach both on the original conceptual learning and on long-term retention. The cohort of students who had a very highly rated traditional lecturer scored 19% lower than the equivalent cohort that was taught using interactive engagement methods. However, the amount of retention was very high for both cohorts, showing only a few percent decrease in scores when retested 6 and 18 months after completion of the course and with no exposure to the material in the interim period. This high level of retention is in striking contrast to the retention measured for more factual learning from university courses and argues for the value of emphasizing conceptual learning.

  9. Sunspot drawings handwritten character recognition method based on deep learning

    Science.gov (United States)

    Zheng, Sheng; Zeng, Xiangyun; Lin, Ganghua; Zhao, Cui; Feng, Yongli; Tao, Jinping; Zhu, Daoyuan; Xiong, Li

    2016-05-01

    High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate.

  10. A diagram retrieval method with multi-label learning

    Science.gov (United States)

    Fu, Songping; Lu, Xiaoqing; Liu, Lu; Qu, Jingwei; Tang, Zhi

    2015-01-01

    In recent years, the retrieval of plane geometry figures (PGFs) has attracted increasing attention in the fields of mathematics education and computer science. However, the high cost of matching complex PGF features leads to the low efficiency of most retrieval systems. This paper proposes an indirect classification method based on multi-label learning, which improves retrieval efficiency by reducing the scope of compare operation from the whole database to small candidate groups. Label correlations among PGFs are taken into account for the multi-label classification task. The primitive feature selection for multi-label learning and the feature description of visual geometric elements are conducted individually to match similar PGFs. The experiment results show the competitive performance of the proposed method compared with existing PGF retrieval methods in terms of both time consumption and retrieval quality.

  11. Learning Method and Its Influence on Nutrition Study Results Throwing the Ball

    Science.gov (United States)

    Samsudin; Nugraha, Bayu

    2015-01-01

    This study aimed to know the difference between playing and learning methods of exploratory learning methods to learning outcomes throwing the ball. In addition, this study also aimed to determine the effect of nutritional status of these two learning methods mentioned above. This research was conducted at SDN Cipinang Besar Selatan 16 Pagi East…

  12. Advanced Steel Microstructural Classification by Deep Learning Methods.

    Science.gov (United States)

    Azimi, Seyed Majid; Britz, Dominik; Engstler, Michael; Fritz, Mario; Mücklich, Frank

    2018-02-01

    The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

  13. Hybrid Method for Mobile learning Cooperative: Study of Timor Leste

    Science.gov (United States)

    da Costa Tavares, Ofelia Cizela; Suyoto; Pranowo

    2018-02-01

    In the modern world today the decision support system is very useful to help in solving a problem, so this study discusses the learning process of savings and loan cooperatives in Timor Leste. The purpose of the observation is that the people of Timor Leste are still in the process of learning the use DSS for good saving and loan cooperative process. Based on existing research on the Timor Leste community on credit cooperatives, a mobile application will be built that will help the cooperative learning process in East Timorese society. The methods used for decision making are AHP (Analytical Hierarchy Process) and SAW (simple additive Weighting) method to see the result of each criterion and the weight of the value. The result of this research is mobile leaning cooperative in decision support system by using SAW and AHP method. Originality Value: Changed the two methods of mobile application development using AHP and SAW methods to help the decision support system process of a savings and credit cooperative in Timor Leste.

  14. Hybrid Method for Mobile learning Cooperative: Study of Timor Leste

    Directory of Open Access Journals (Sweden)

    da Costa Tavares Ofelia Cizela

    2018-01-01

    Full Text Available In the modern world today the decision support system is very useful to help in solving a problem, so this study discusses the learning process of savings and loan cooperatives in Timor Leste. The purpose of the observation is that the people of Timor Leste are still in the process of learning the use DSS for good saving and loan cooperative process. Based on existing research on the Timor Leste community on credit cooperatives, a mobile application will be built that will help the cooperative learning process in East Timorese society. The methods used for decision making are AHP (Analytical Hierarchy Process and SAW (simple additive Weighting method to see the result of each criterion and the weight of the value. The result of this research is mobile leaning cooperative in decision support system by using SAW and AHP method. Originality Value: Changed the two methods of mobile application development using AHP and SAW methods to help the decision support system process of a savings and credit cooperative in Timor Leste.

  15. Machine learning methods without tears: a primer for ecologists.

    Science.gov (United States)

    Olden, Julian D; Lawler, Joshua J; Poff, N LeRoy

    2008-06-01

    Machine learning methods, a family of statistical techniques with origins in the field of artificial intelligence, are recognized as holding great promise for the advancement of understanding and prediction about ecological phenomena. These modeling techniques are flexible enough to handle complex problems with multiple interacting elements and typically outcompete traditional approaches (e.g., generalized linear models), making them ideal for modeling ecological systems. Despite their inherent advantages, a review of the literature reveals only a modest use of these approaches in ecology as compared to other disciplines. One potential explanation for this lack of interest is that machine learning techniques do not fall neatly into the class of statistical modeling approaches with which most ecologists are familiar. In this paper, we provide an introduction to three machine learning approaches that can be broadly used by ecologists: classification and regression trees, artificial neural networks, and evolutionary computation. For each approach, we provide a brief background to the methodology, give examples of its application in ecology, describe model development and implementation, discuss strengths and weaknesses, explore the availability of statistical software, and provide an illustrative example. Although the ecological application of machine learning approaches has increased, there remains considerable skepticism with respect to the role of these techniques in ecology. Our review encourages a greater understanding of machin learning approaches and promotes their future application and utilization, while also providing a basis from which ecologists can make informed decisions about whether to select or avoid these approaches in their future modeling endeavors.

  16. Genetics

    International Nuclear Information System (INIS)

    Hubitschek, H.E.

    1975-01-01

    Progress is reported on the following research projects: genetic effects of high LET radiations; genetic regulation, alteration, and repair; chromosome replication and the division cycle of Escherichia coli; effects of radioisotope decay in the DNA of microorganisms; initiation and termination of DNA replication in Bacillus subtilis; mutagenesis in mouse myeloma cells; lethal and mutagenic effects of near-uv radiation; effect of 8-methoxypsoralen on photodynamic lethality and mutagenicity in Escherichia coli; DNA repair of the lethal effects of far-uv; and near uv irradiation of bacterial cells

  17. A review of statistical methods for testing genetic anticipation: looking for an answer in Lynch syndrome

    DEFF Research Database (Denmark)

    Boonstra, Philip S; Gruber, Stephen B; Raymond, Victoria M

    2010-01-01

    the issue of multiplex ascertainment and its effect on the different methods. We then focus on exploring genetic anticipation in Lynch syndrome and analyze new data on the age of onset in affected parent-child pairs from families seen at the University of Michigan Cancer Genetics clinic with a mutation...... in one of the three main mismatch repair (MMR) genes. In contrast to the clinic-based population, we re-analyze data on a population-based Lynch syndrome cohort, derived from the Danish HNPCC-register. Both datasets indicate evidence of genetic anticipation in Lynch syndrome. We then expand our review...

  18. A screening on Specific Learning Disorders in an Italian speaking high genetic homogeneity area.

    Science.gov (United States)

    Cappa, Claudia; Giulivi, Sara; Schilirò, Antonino; Bastiani, Luca; Muzio, Carlo; Meloni, Fabrizio

    2015-01-01

    The aim of the present research is to investigate the prevalence of Specific Learning Disorders (SLD) in Ogliastra, an area of the island of Sardinia, Italy. Having experienced centuries of isolation, Ogliastra has become a high genetic homogeneity area, and is considered particularly interesting for studies on different kinds of pathologies. Here we are going to describe the results of a screening carried out throughout 2 consecutive years in 49 second grade classes (24 considered in the first year and 25 in the second year of the study) of the Ogliastra region. A total of 610 pupils (average age 7.54 years; 293 female, 317 male) corresponding to 68.69% of all pupils who were attending second grade in the area, took part in the study. The tool used for the screening was "RSR-DSA. Questionnaire for the detection of learning difficulties and disorders", which allowed the identification of 83 subjects at risk (13.61% of the whole sample involved in the study). These subjects took part in an enhancement training program of about 6 months. After the program, pupils underwent assessment for reading, writing and calculation abilities, as well as cognitive assessment. According to the results of the assessment, the prevalence of SLDs is 6.06%. For what concerns dyslexia, 4.75% of the total sample manifested this disorder either in isolation or in comorbidity with other disorders. According to the first national epidemiological investigation carried out in Italy, the prevalence of dyslexia is 3.1-3.2%, which is lower than the prevalence obtained in the present study. Given the genetic basis of SLDs, this result, together with the presence of several cases of SLD in isolation (17.14%) and with a 3:1 ratio of males to females diagnosed with a SLD, was to be expected in a sample coming from a high genetic homogeneity area. Copyright © 2015 Elsevier Ltd. All rights reserved.

  19. Evaluation of Genetic Pattern of Non-Tuberculosis Mycobacterium Using VNTR Method

    Directory of Open Access Journals (Sweden)

    Noorozi J

    2011-06-01

    Full Text Available Background and Objectives: Epidemiological studies of Non-tuberculosis Mycobacterium is important because of the drug resistance pattern and worldwide dissemination of these organisms. One of genetic fingerprinting methods for epidemiological studies is VNTR (Variable Number Tandem Repeat. In this study genetic pattern of atypical Mycobacterium was evaluated by VNTR method for epidemiologic studies. Methods: 48 pulmonary and non pulmonary specimens separated from patients with the symptoms of pulmonary tuberculosis (PTB and identified as Non-tuberculosis Mycobacteriumby phenotypic and PCR-RFLP methods were selected for this study. Clinical samples and their standard strains were evaluated according to VNTR pattern using the 7 genetic loci including ETR-B. ETR-F. ETR-C. MPTR-A. ETR-A. ETR-E. ETR-D.Results: The results of VNTR method showed that none of the 7 loci had any polymorphism in the standard strains of atypical mycobacterium. Some of these variable number tandem repeat in 42 clinical samples of non-tuberculosis Mycobacterium were polymorphic while the PCR product (for any loci was not found in the remaining 6 specimens. Conclusion: Although the used genetic loci of this study were suitable for epidemiological studies of Mycobacterium tuberculosis, these loci were not able to determine the diversity of genetics of non-tuberculosis Mycobacterium Therefore, it seems necessary that other loci be studied using VNTR method.

  20. The Effect of Using Cooperative Learning Method on Tenth Grade Students' Learning Achievement and Attitude towards Biology

    Science.gov (United States)

    Rabgay, Tshewang

    2018-01-01

    The study investigated the effect of using cooperative learning method on tenth grade students' learning achievement in biology and their attitude towards the subject in a Higher Secondary School in Bhutan. The study used a mixed method approach. The quantitative component included an experimental design where cooperative learning was the…

  1. Introduction of active learning method in learning physiology by MBBS students.

    Science.gov (United States)

    Gilkar, Suhail Ahmad; Lone, Shabiruddin; Lone, Riyaz Ahmad

    2016-01-01

    Active learning has received considerable attention over the past several years, often presented or perceived as a radical change from traditional instruction methods. Current research on learning indicates that using a variety of teaching strategies in the classroom increases student participation and learning. To introduce active learning methodology, i.e., "jigsaw technique" in undergraduate medical education and assess the student and faculty response to it. This study was carried out in the Department of Physiology in a Medical College of North India. A topic was chosen and taught using one of the active learning methods (ALMs), i.e., jigsaw technique. An instrument (questionnaire) was developed in English through an extensive review of literature and was properly validated. The students were asked to give their response on a five-point Likert scale. The feedback was kept anonymous. Faculty also provided their feedback in a separately provided feedback proforma. The data were collected, compiled, and analyzed. Of 150 students of MBBS-first year batch 2014, 142 participated in this study along with 14 faculty members of the Physiology Department. The majority of the students (>90%) did welcome the introduction of ALM and strongly recommended the use of such methods in teaching many more topics in future. 100% faculty members were of the opinion that many more topics shall be taken up using ALMs. This study establishes the fact that both the medical students and faculty want a change from the traditional way of passive, teacher-centric learning, to the more active teaching-learning techniques.

  2. The various aspects of genetic and epigenetic toxicology: testing methods and clinical applications.

    Science.gov (United States)

    Ren, Ning; Atyah, Manar; Chen, Wan-Yong; Zhou, Chen-Hao

    2017-05-22

    Genotoxicity refers to the ability of harmful substances to damage genetic information in cells. Being exposed to chemical and biological agents can result in genomic instabilities and/or epigenetic alterations, which translate into a variety of diseases, cancer included. This concise review discusses, from both a genetic and epigenetic point of view, the current detection methods of different agents' genotoxicity, along with their basic and clinical relation to human cancer, chemotherapy, germ cells and stem cells.

  3. Monitoring Species of Concern Using Noninvasive Genetic Sampling and Capture-Recapture Methods

    Science.gov (United States)

    2016-11-01

    RC-201205) Monitoring Species of Concern Using Noninvasive Genetic Sampling and Capture- Recapture Methods November 2016 This document has been...From - To) Apr 25 2012-Jan 31 2016 4. TITLE AND SUBTITLE Monitoring Species of Concern Using Noninvasive Genetic Sampling and Capture- Recapture...NGS-CR) modeling to evaluate the status of species of conservation concern . A secondary objective was to demonstrate the combination of NGS with

  4. Data Mining and Machine Learning Methods for Dementia Research.

    Science.gov (United States)

    Li, Rui

    2018-01-01

    Patient data in clinical research often includes large amounts of structured information, such as neuroimaging data, neuropsychological test results, and demographic variables. Given the various sources of information, we can develop computerized methods that can be a great help to clinicians to discover hidden patterns in the data. The computerized methods often employ data mining and machine learning algorithms, lending themselves as the computer-aided diagnosis (CAD) tool that assists clinicians in making diagnostic decisions. In this chapter, we review state-of-the-art methods used in dementia research, and briefly introduce some recently proposed algorithms subsequently.

  5. A Pharmacy Preregistration Course Using Online Teaching and Learning Methods

    Science.gov (United States)

    McDowell, Jenny; Marriott, Jennifer L.; Calandra, Angela; Duncan, Gregory

    2009-01-01

    Objective To design and evaluate a preregistration course utilizing asynchronous online learning as the primary distance education delivery method. Design Online course components including tutorials, quizzes, and moderated small-group asynchronous case-based discussions were implemented. Online delivery was supplemented with self-directed and face-to-face learning. Assessment Pharmacy graduates who had completed the course in 2004 and 2005 were surveyed. The majority felt they had benefited from all components of the course, and that online delivery provided benefits including increased peer support, shared learning, and immediate feedback on performance. A majority of the first cohort reported that the workload associated with asynchronous online discussions was too great. The course was altered in 2005 to reduce the online component. Participant satisfaction improved, and most felt that the balance of online to face-to-face delivery was appropriate. Conclusion A new pharmacy preregistration course was successfully implemented. Online teaching and learning was well accepted and appeared to deliver benefits over traditional distance education methods once workload issues were addressed. PMID:19777092

  6. Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models

    NARCIS (Netherlands)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A.; van t Veld, Aart A.

    2012-01-01

    PURPOSE: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator

  7. Genetics

    DEFF Research Database (Denmark)

    Christensen, Kaare; McGue, Matt

    2016-01-01

    The sequenced genomes of individuals aged ≥80 years, who were highly educated, self-referred volunteers and with no self-reported chronic diseases were compared to young controls. In these data, healthy ageing is a distinct phenotype from exceptional longevity and genetic factors that protect...

  8. Coherent spectroscopic methods for monitoring pathogens, genetically modified products and nanostructured materials in colloidal solution

    International Nuclear Information System (INIS)

    Moguilnaya, T.; Suminov, Y.; Botikov, A.; Ignatov, S.; Kononenko, A.; Agibalov, A.

    2017-01-01

    We developed the new automatic method that combines the method of forced luminescence and stimulated Brillouin scattering. This method is used for monitoring pathogens, genetically modified products and nanostructured materials in colloidal solution. We carried out the statistical spectral analysis of pathogens, genetically modified soy and nano-particles of silver in water from different regions in order to determine the statistical errors of the method. We studied spectral characteristics of these objects in water to perform the initial identification with 95% probability. These results were used for creation of the model of the device for monitor of pathogenic organisms and working model of the device to determine the genetically modified soy in meat.

  9. Statistical methods to detect novel genetic variants using publicly available GWAS summary data.

    Science.gov (United States)

    Guo, Bin; Wu, Baolin

    2018-03-01

    We propose statistical methods to detect novel genetic variants using only genome-wide association studies (GWAS) summary data without access to raw genotype and phenotype data. With more and more summary data being posted for public access in the post GWAS era, the proposed methods are practically very useful to identify additional interesting genetic variants and shed lights on the underlying disease mechanism. We illustrate the utility of our proposed methods with application to GWAS meta-analysis results of fasting glucose from the international MAGIC consortium. We found several novel genome-wide significant loci that are worth further study. Copyright © 2018 Elsevier Ltd. All rights reserved.

  10. About the ontological-genetic method in Philosophy

    Directory of Open Access Journals (Sweden)

    Nicolas Tertulian

    2010-10-01

    ans to show that Lukács has been the first to undertake a genealogy of the multiple activities of the conscience and their objectifications (economy, rights, politics and its institutions, art or philosophy from the dialectical tension between subjectivity and objectivity. That is to say, there is in the last thought of the Hungarian philosopher an “ontological-genetic” method, since it is attached to show the progressive stratification of the subject (for example: utilitarian activity, hedonistic activity and aesthetic activity, indicating the transitions and mediations, until the circumscription of each specificity in function of the role that it fills in social life phenomenology.

  11. Revealing barriers and facilitators to use a new genetic test: comparison of three user involvement methods.

    Science.gov (United States)

    Rhebergen, Martijn D F; Visser, Maaike J; Verberk, Maarten M; Lenderink, Annet F; van Dijk, Frank J H; Kezic, Sanja; Hulshof, Carel T J

    2012-10-01

    We compared three common user involvement methods in revealing barriers and facilitators from intended users that might influence their use of a new genetic test. The study was part of the development of a new genetic test on the susceptibility to hand eczema for nurses. Eighty student nurses participated in five focus groups (n = 33), 15 interviews (n = 15) or questionnaires (n = 32). For each method, data were collected until saturation. We compared the mean number of items and relevant remarks that could influence the use of the genetic test obtained per method, divided by the number of participants in that method. Thematic content analysis was performed using MAXQDA software. The focus groups revealed 30 unique items compared to 29 in the interviews and 21 in the questionnaires. The interviews produced more items and relevant remarks per participant (1.9 and 8.4 pp) than focus groups (0.9 and 4.8 pp) or questionnaires (0.7 and 2.3 pp). All three involvement methods revealed relevant barriers and facilitators to use a new genetic test. Focus groups and interviews revealed substantially more items than questionnaires. Furthermore, this study suggests a preference for the use of interviews because the number of items per participant was higher than for focus groups and questionnaires. This conclusion may be valid for other genetic tests as well.

  12. Genetic and Environmental Influences on Achievement Outcomes Based on Family History of Learning Disabilities Status.

    Science.gov (United States)

    Erbeli, Florina; Hart, Sara A; Taylor, Jeanette

    2018-05-01

    A risk to develop a learning disability has been shown to run in families. Having a positive family history of learning disability seems to account for mean differences in achievement outcomes (reading, math) in that children with a positive family history score significantly lower compared to their peers with no such family history. However, the role of family history status in explaining etiological (genetic and environmental) differences among these subgroups of children has yet to be established. The present study of 872 twins ( M age = 13.30, SD age = 1.40) from the Florida Twin Project on Reading, Behavior, and Environment utilized a multigroup approach to examine etiological differences on reading, spelling, and math among two subgroups defined by family history status. Results showed significant mean differences on all achievement outcomes, aside from math; however, no significant etiological differences on any achievement outcome were found among the two subgroups. Results support previous literature that the risk for developing a learning disability is transmitted through a family, but this is seemingly not manifested by differential etiology.

  13. The Learners’ Attitudes towards Using Different Learning Methods in E-Learning Portal Environment

    Directory of Open Access Journals (Sweden)

    Issham Ismail

    2011-09-01

    Full Text Available This study investigates the learners’ preference of academic, collaborative and social interaction towards interaction methods in e-learning portal. Academic interaction consists of interaction between learners and online learning resources such as online reading, online explanation, online examination and also online question answering. Collaborative interaction occurs when learners interact among themselves using online group discussion. Social interaction happens when learners and instructors participate in the session either via online text chatting or voice chatting. The study employed qualitative methodology where data were collected through questionnaire that was administered to 933 distance education students from Bachelor of Management, Bachelor of Science, Bachelor of Social Science and Bachelor of Art. The survey responses were tabulated in a 5-point Likert scale and analyzed using the Statistical Package for Social Science (SPSS Version 12.0 based on frequency and percentage distribution. The result of the study suggest that among three types of interaction, most of the student prefer academic interaction for their learning supports in e-learning portal compared to collaborative and social interaction. They wish to interact with learning content rather than interact with people. They prefer to read and learn from the resources rather than sharing knowledge among themselves and instructors via collaborative and social interaction.

  14. Realization of Chinese word segmentation based on deep learning method

    Science.gov (United States)

    Wang, Xuefei; Wang, Mingjiang; Zhang, Qiquan

    2017-08-01

    In recent years, with the rapid development of deep learning, it has been widely used in the field of natural language processing. In this paper, I use the method of deep learning to achieve Chinese word segmentation, with large-scale corpus, eliminating the need to construct additional manual characteristics. In the process of Chinese word segmentation, the first step is to deal with the corpus, use word2vec to get word embedding of the corpus, each character is 50. After the word is embedded, the word embedding feature is fed to the bidirectional LSTM, add a linear layer to the hidden layer of the output, and then add a CRF to get the model implemented in this paper. Experimental results show that the method used in the 2014 People's Daily corpus to achieve a satisfactory accuracy.

  15. A Photometric Machine-Learning Method to Infer Stellar Metallicity

    Science.gov (United States)

    Miller, Adam A.

    2015-01-01

    Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

  16. A Photometric Machine-Learning Method to Infer Stellar Metallicity

    Science.gov (United States)

    Miller, Adam A.

    2015-01-01

    Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

  17. Comparing three experiential learning methods and their effect on medical students' attitudes to learning communication skills.

    Science.gov (United States)

    Koponen, Jonna; Pyörälä, Eeva; Isotalus, Pekka

    2012-01-01

    Despite numerous studies exploring medical students' attitudes to communication skills learning (CSL), there are apparently no studies comparing different experiential learning methods and their influence on students' attitudes. We compared medical students' attitudes to learning communication skills before and after a communication course in the data as a whole, by gender and when divided into three groups using different methods. Second-year medical students (n = 129) were randomly assigned to three groups. In group A (n = 42) the theatre in education method, in group B (n = 44) simulated patients and in group C (n = 43) role-play were used. The data were gathered before and after the course using Communication Skills Attitude Scale. Students' positive attitudes to learning communication skills (PAS; positive attitude scale) increased significantly and their negative attitudes (NAS; negative attitude scale) decreased significantly between the beginning and end of the course. Female students had more positive attitudes than the male students. There were no significant differences in the three groups in the mean scores for PAS or NAS measured before or after the course. The use of experiential methods and integrating communication skills training with visits to health centres may help medical students to appreciate the importance of CSL.

  18. Housing Value Forecasting Based on Machine Learning Methods

    OpenAIRE

    Mu, Jingyi; Wu, Fang; Zhang, Aihua

    2014-01-01

    In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing...

  19. Learning with Generalization Capability by Kernel Methods of Bounded Complexity

    Czech Academy of Sciences Publication Activity Database

    Kůrková, Věra; Sanguineti, M.

    2005-01-01

    Roč. 21, č. 3 (2005), s. 350-367 ISSN 0885-064X R&D Projects: GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : supervised learning * generalization * model complexity * kernel methods * minimization of regularized empirical errors * upper bounds on rates of approximate optimization Subject RIV: BA - General Mathematics Impact factor: 1.186, year: 2005

  20. A Doctoral Seminar in Qualitative Research Methods: Lessons Learned

    Directory of Open Access Journals (Sweden)

    Suzanne Franco

    2016-09-01

    Full Text Available New qualitative research methods continue to emerge in response to factors such as renewed interest in mixed methods, better understanding of the importance of a researcher’s philosophical stance, as well as the increased use of technology in data collection and analysis, to name a few. As a result, those facilitating research methods courses must revisit content and instructional strategies in order to prepare well-informed researchers. Approaches range from paradigm to pragmatic emphasis. This descriptive case study of a doctoral seminar for novice qualitative researchers describes the intricacies of the syllabus of a pragmatic approach in a constructivist/social constructionist learning environment. The purpose was to document the delivery and faculty/student interactions and reactions. Noteworthy were the contradictions and frustrations in the delivery as well as in student experiences. In the end, student input led to seminal learning experiences. The confirmation of the effectiveness of a constructivist/social constructivist learning environment is applicable to higher education pedagogy in general.

  1. Deep Learning Methods for Underwater Target Feature Extraction and Recognition

    Directory of Open Access Journals (Sweden)

    Gang Hu

    2018-01-01

    Full Text Available The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.

  2. Genetic Learning of Fuzzy Expert Systems for Decision Support in the Automated Process of Wooden Boards Cutting

    Directory of Open Access Journals (Sweden)

    Yaroslav MATSYSHYN

    2014-03-01

    Full Text Available Sawing solid wood (lumber, wooden boards into blanks is an important technological operation, which has significant influence on the efficiency of the woodworking industry as a whole. Selecting a rational variant of lumber cutting is a complex multicriteria problem with many stochastic factors, characterized by incomplete information and fuzzy attributes. About this property by currently used automatic optimizing cross-cut saw is not always rational use of wood raw material. And since the optimization algorithms of these saw functions as a “black box”, their improvement is not possible. Therefore topical the task of developing a new approach to the optimal cross-cutting that takes into account stochastic properties of wood as a material from biological origin. Here we propose a new approach to the problem of lumber optimal cutting in the conditions of uncertainty of lumber quantity and fuzziness lengths of defect-free areas. To account for these conditions, we applied the methods of fuzzy sets theory and used a genetic algorithm to simulate the process of human learning in the implementation the technological operation. Thus, the rules of behavior with yet another defect-free area is defined in fuzzy expert system that can be configured to perform specific production tasks using genetic algorithm. The author's implementation of the genetic algorithm is used to set up the parameters of fuzzy expert system. Working capacity of the developed system verified on simulated and real-world data. Implementation of this approach will make it suitable for the control of automated or fully automatic optimizing cross cutting of solid wood.

  3. Application of machine learning methods for traffic signs recognition

    Science.gov (United States)

    Filatov, D. V.; Ignatev, K. V.; Deviatkin, A. V.; Serykh, E. V.

    2018-02-01

    This paper focuses on solving a relevant and pressing safety issue on intercity roads. Two approaches were considered for solving the problem of traffic signs recognition; the approaches involved neural networks to analyze images obtained from a camera in the real-time mode. The first approach is based on a sequential image processing. At the initial stage, with the help of color filters and morphological operations (dilatation and erosion), the area containing the traffic sign is located on the image, then the selected and scaled fragment of the image is analyzed using a feedforward neural network to determine the meaning of the found traffic sign. Learning of the neural network in this approach is carried out using a backpropagation method. The second approach involves convolution neural networks at both stages, i.e. when searching and selecting the area of the image containing the traffic sign, and when determining its meaning. Learning of the neural network in the second approach is carried out using the intersection over union function and a loss function. For neural networks to learn and the proposed algorithms to be tested, a series of videos from a dash cam were used that were shot under various weather and illumination conditions. As a result, the proposed approaches for traffic signs recognition were analyzed and compared by key indicators such as recognition rate percentage and the complexity of neural networks’ learning process.

  4. Kernel methods for interpretable machine learning of order parameters

    Science.gov (United States)

    Ponte, Pedro; Melko, Roger G.

    2017-11-01

    Machine learning is capable of discriminating phases of matter, and finding associated phase transitions, directly from large data sets of raw state configurations. In the context of condensed matter physics, most progress in the field of supervised learning has come from employing neural networks as classifiers. Although very powerful, such algorithms suffer from a lack of interpretability, which is usually desired in scientific applications in order to associate learned features with physical phenomena. In this paper, we explore support vector machines (SVMs), which are a class of supervised kernel methods that provide interpretable decision functions. We find that SVMs can learn the mathematical form of physical discriminators, such as order parameters and Hamiltonian constraints, for a set of two-dimensional spin models: the ferromagnetic Ising model, a conserved-order-parameter Ising model, and the Ising gauge theory. The ability of SVMs to provide interpretable classification highlights their potential for automating feature detection in both synthetic and experimental data sets for condensed matter and other many-body systems.

  5. A cross-benchmark comparison of 87 learning to rank methods

    NARCIS (Netherlands)

    Tax, N.; Bockting, S.; Hiemstra, D.

    2015-01-01

    Learning to rank is an increasingly important scientific field that comprises the use of machine learning for the ranking task. New learning to rank methods are generally evaluated on benchmark test collections. However, comparison of learning to rank methods based on evaluation results is hindered

  6. Characteristics and Consequences of Adult Learning Methods and Strategies. Practical Evaluation Reports, Volume 2, Number 1

    Science.gov (United States)

    Trivette, Carol M.; Dunst, Carl J.; Hamby, Deborah W.; O'Herin, Chainey E.

    2009-01-01

    The effectiveness of four adult learning methods (accelerated learning, coaching, guided design, and just-in-time training) constituted the focus of this research synthesis. Findings reported in "How People Learn" (Bransford et al., 2000) were used to operationally define six adult learning method characteristics, and to code and analyze…

  7. Introducing the Collaborative E-Learning Design Method (CoED)

    DEFF Research Database (Denmark)

    Ryberg, Thomas; Buus, Lillian; Nyvang, Tom

    2015-01-01

    In this chapter, a specific learning design method is introduced and explained, namely the Collaborative E-learning Design method (CoED), which has been developed through various projects in “e-Learning Lab – Centre for User Driven Innovation, Learning and Design” (Nyvang & Georgsen, 2007). We br...

  8. Actively Teaching Research Methods with a Process Oriented Guided Inquiry Learning Approach

    Science.gov (United States)

    Mullins, Mary H.

    2017-01-01

    Active learning approaches have shown to improve student learning outcomes and improve the experience of students in the classroom. This article compares a Process Oriented Guided Inquiry Learning style approach to a more traditional teaching method in an undergraduate research methods course. Moving from a more traditional learning environment to…

  9. Cellular, Molecular, and Genetic Substrates Underlying the Impact of Nicotine on Learning

    Science.gov (United States)

    Gould, Thomas J.; Leach, Prescott T.

    2013-01-01

    deficits in learning, and 4) the role of genetics and developmental stage (i.e., adolescence) in these effects. PMID:23973448

  10. Valenced action/inhibition learning in humans is modulated by a genetic variant linked to dopamine D2 receptor expression

    Directory of Open Access Journals (Sweden)

    Anni eRichter

    2014-08-01

    Full Text Available Motivational salience plays an important role in shaping human behavior, but recent studies demonstrate that human performance is not uniformly improved by motivation. Instead, action has been shown to dominate valence in motivated tasks, and it is particularly difficult for humans to learn the inhibition of an action to obtain a reward, but the neural mechanism behind this behavioral specificity is yet unclear. In all mammals, including humans, the monoamine neurotransmitter dopamine is particularly important in the neural manifestation of appetitively motivated behavior, and the human dopamine system is subject to considerable genetic variability. The well-studied TaqIA restriction fragment length polymorphism (rs1800497 has previously been shown to affect striatal dopamine metabolism. In this study we investigated a potential effect of this genetic variation on motivated action/inhibition learning. Two independent cohorts consisting of 87 and 95 healthy participants, respectively, were tested using the previously described valenced go/no-go learning paradigm in which participants learned the reward-associated no-go condition significantly worse than all other conditions. This effect was modulated by the TaqIA polymorphism, with carriers of the A1 allele showing a diminished learning-related performance enhancement in the rewarded no-go condition compared to the A2 homozygotes. This result highlights a modulatory role for genetic variability of the dopaminergic system in individual learning differences of action-valence interaction.

  11. Effect of Chemistry Triangle Oriented Learning Media on Cooperative, Individual and Conventional Method on Chemistry Learning Result

    Science.gov (United States)

    Latisma D, L.; Kurniawan, W.; Seprima, S.; Nirbayani, E. S.; Ellizar, E.; Hardeli, H.

    2018-04-01

    The purpose of this study was to see which method are well used with the Chemistry Triangle-oriented learning media. This quasi experimental research involves first grade of senior high school students in six schools namely each two SMA N in Solok city, in Pasaman and two SMKN in Pariaman. The sampling technique was done by Cluster Random Sampling. Data were collected by test and analyzed by one-way anova and Kruskall Wallish test. The results showed that the high school students in Solok learning taught by cooperative method is better than the results of student learning taught by conventional and Individual methods, both for students who have high initial ability and low-ability. Research in SMK showed that the overall student learning outcomes taught by conventional method is better than the student learning outcomes taught by cooperative and individual methods. Student learning outcomes that have high initial ability taught by individual method is better than student learning outcomes that are taught by cooperative method and for students who have low initial ability, there is no difference in student learning outcomes taught by cooperative, individual and conventional methods. Learning in high school in Pasaman showed no significant difference in learning outcomes of the three methods undertaken.

  12. Bioinformatics Methods for Learning Radiation-Induced Lung Inflammation from Heterogeneous Retrospective and Prospective Data

    Science.gov (United States)

    Spencer, Sarah J.; Almiron Bonnin, Damian; Deasy, Joseph O.; Bradley, Jeffrey D.; El Naqa, Issam

    2009-01-01

    Radiotherapy outcomes are determined by complex interactions between physical and biological factors, reflecting both treatment conditions and underlying genetics. Recent advances in radiotherapy and biotechnology provide new opportunities and challenges for predicting radiation-induced toxicities, particularly radiation pneumonitis (RP), in lung cancer patients. In this work, we utilize datamining methods based on machine learning to build a predictive model of lung injury by retrospective analysis of treatment planning archives. In addition, biomarkers for this model are extracted from a prospective clinical trial that collects blood serum samples at multiple time points. We utilize a 3-way proteomics methodology to screen for differentially expressed proteins that are related to RP. Our preliminary results demonstrate that kernel methods can capture nonlinear dose-volume interactions, but fail to address missing biological factors. Our proteomics strategy yielded promising protein candidates, but their role in RP as well as their interactions with dose-volume metrics remain to be determined. PMID:19704920

  13. Measuring the surgical 'learning curve': methods, variables and competency.

    Science.gov (United States)

    Khan, Nuzhath; Abboudi, Hamid; Khan, Mohammed Shamim; Dasgupta, Prokar; Ahmed, Kamran

    2014-03-01

    To describe how learning curves are measured and what procedural variables are used to establish a 'learning curve' (LC). To assess whether LCs are a valuable measure of competency. A review of the surgical literature pertaining to LCs was conducted using the Medline and OVID databases. Variables should be fully defined and when possible, patient-specific variables should be used. Trainee's prior experience and level of supervision should be quantified; the case mix and complexity should ideally be constant. Logistic regression may be used to control for confounding variables. Ideally, a learning plateau should reach a predefined/expert-derived competency level, which should be fully defined. When the group splitting method is used, smaller cohorts should be used in order to narrow the range of the LC. Simulation technology and competence-based objective assessments may be used in training and assessment in LC studies. Measuring the surgical LC has potential benefits for patient safety and surgical education. However, standardisation in the methods and variables used to measure LCs is required. Confounding variables, such as participant's prior experience, case mix, difficulty of procedures and level of supervision, should be controlled. Competency and expert performance should be fully defined. © 2013 The Authors. BJU International © 2013 BJU International.

  14. The use of different clustering methods in the evaluation of genetic diversity in upland cotton

    Directory of Open Access Journals (Sweden)

    Laíse Ferreira de Araújo

    Full Text Available The continuous development and evaluation of new genotypes through crop breeding is essential in order to obtain new cultivars. The objective of this work was to evaluate the genetic divergences between cultivars of upland cotton (Gossypium hirsutum L. using the agronomic and technological characteristics of the fibre, in order to select superior parent plants. The experiment was set up during 2010 at the Federal University of Ceará in Fortaleza, Ceará, Brazil. Eleven cultivars of upland cotton were used in an experimental design of randomised blocks with three replications. In order to evaluate the genetic diversity among cultivars, the generalised Mahalanobis distance matrix was calculated, with cluster analysis then being applied, employing various methods: single linkage, Ward, complete linkage, median, average linkage within a cluster and average linkage between clusters. Genetic variability exists among the evaluated genotypes. The most consistant clustering method was that employing average linkage between clusters. Among the characteristics assessed, mean boll weight presented the highest contribution to genetic diversity, followed by elongation at rupture. Employing the method of mean linkage between clusters, the cultivars with greater genetic divergence were BRS Acacia and LD Frego; those of greater similarity were BRS Itaúba and BRS Araripe.

  15. Application of computational methods in genetic study of inflammatory bowel disease.

    Science.gov (United States)

    Li, Jin; Wei, Zhi; Hakonarson, Hakon

    2016-01-21

    Genetic factors play an important role in the etiology of inflammatory bowel disease (IBD). The launch of genome-wide association study (GWAS) represents a landmark in the genetic study of human complex disease. Concurrently, computational methods have undergone rapid development during the past a few years, which led to the identification of numerous disease susceptibility loci. IBD is one of the successful examples of GWAS and related analyses. A total of 163 genetic loci and multiple signaling pathways have been identified to be associated with IBD. Pleiotropic effects were found for many of these loci; and risk prediction models were built based on a broad spectrum of genetic variants. Important gene-gene, gene-environment interactions and key contributions of gut microbiome are being discovered. Here we will review the different types of analyses that have been applied to IBD genetic study, discuss the computational methods for each type of analysis, and summarize the discoveries made in IBD research with the application of these methods.

  16. Genetic or pharmacological reduction of PERK enhances cortical-dependent taste learning.

    Science.gov (United States)

    Ounallah-Saad, Hadile; Sharma, Vijendra; Edry, Efrat; Rosenblum, Kobi

    2014-10-29

    Protein translation initiation is controlled by levels of eIF2α phosphorylation (p-eIF2α) on Ser51. In addition, increased p-eIF2α levels impair long-term synaptic plasticity and memory consolidation, whereas decreased levels enhance them. Levels of p-eIF2α are determined by four kinases, of which protein kinase RNA-activated (PKR), PKR-like endoplastic reticulum kinase (PERK), and general control nonderepressible 2 are extensively expressed in the mammalian mature brain. Following identification of PERK as the major kinase to determine basal levels of p-eIF2α in primary neuronal cultures, we tested its function as a physiological constraint of memory consolidation in the cortex, the brain structure suggested to store, at least in part, long-term memories in the mammalian brain. To that aim, insular cortex (IC)-dependent positive and negative forms of taste learning were used. Genetic reduction of PERK expression was accomplished by local microinfusion of a lentivirus harboring PERK Short hairpin RNA, and pharmacological inhibition was achieved by local microinfusion of a PERK-specific inhibitor (GSK2606414) to the rat IC. Both genetic reduction of PERK expression and pharmacological inhibition of its activity reduced p-eIF2α levels and enhanced novel taste learning and conditioned taste aversion, but not memory retrieval. Moreover, enhanced extinction was observed together with enhanced associative memory, suggesting increased cortical-dependent behavioral plasticity. The results suggest that, by phosphorylating eIF2α, PERK functions in the cortex as a physiological constraint of memory consolidation, and its downregulation serves as cognitive enhancement. Copyright © 2014 the authors 0270-6474/14/3314624-09$15.00/0.

  17. Application of unsupervised learning methods in high energy physics

    Energy Technology Data Exchange (ETDEWEB)

    Koevesarki, Peter; Nuncio Quiroz, Adriana Elizabeth; Brock, Ian C. [Physikalisches Institut, Universitaet Bonn, Bonn (Germany)

    2011-07-01

    High energy physics is a home for a variety of multivariate techniques, mainly due to the fundamentally probabilistic behaviour of nature. These methods generally require training based on some theory, in order to discriminate a known signal from a background. Nevertheless, new physics can show itself in ways that previously no one thought about, and in these cases conventional methods give little or no help. A possible way to discriminate between known processes (like vector bosons or top-quark production) or look for new physics is using unsupervised machine learning to extract the features of the data. A technique was developed, based on the combination of neural networks and the method of principal curves, to find a parametrisation of the non-linear correlations of the data. The feasibility of the method is shown on ATLAS data.

  18. Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

    Directory of Open Access Journals (Sweden)

    Vladimir S. Kublanov

    2017-01-01

    Full Text Available The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components.

  19. Teamwork: improved eQTL mapping using combinations of machine learning methods.

    Directory of Open Access Journals (Sweden)

    Marit Ackermann

    Full Text Available Expression quantitative trait loci (eQTL mapping is a widely used technique to uncover regulatory relationships between genes. A range of methodologies have been developed to map links between expression traits and genotypes. The DREAM (Dialogue on Reverse Engineering Assessments and Methods initiative is a community project to objectively assess the relative performance of different computational approaches for solving specific systems biology problems. The goal of one of the DREAM5 challenges was to reverse-engineer genetic interaction networks from synthetic genetic variation and gene expression data, which simulates the problem of eQTL mapping. In this framework, we proposed an approach whose originality resides in the use of a combination of existing machine learning algorithms (committee. Although it was not the best performer, this method was by far the most precise on average. After the competition, we continued in this direction by evaluating other committees using the DREAM5 data and developed a method that relies on Random Forests and LASSO. It achieved a much higher average precision than the DREAM best performer at the cost of slightly lower average sensitivity.

  20. Evolution of social versus individual learning in a subdivided population revisited: comparative analysis of three coexistence mechanisms using the inclusive-fitness method.

    Science.gov (United States)

    Kobayashi, Yutaka; Ohtsuki, Hisashi

    2014-03-01

    Learning abilities are categorized into social (learning from others) and individual learning (learning on one's own). Despite the typically higher cost of individual learning, there are mechanisms that allow stable coexistence of both learning modes in a single population. In this paper, we investigate by means of mathematical modeling how the effect of spatial structure on evolutionary outcomes of pure social and individual learning strategies depends on the mechanisms for coexistence. We model a spatially structured population based on the infinite-island framework and consider three scenarios that differ in coexistence mechanisms. Using the inclusive-fitness method, we derive the equilibrium frequency of social learners and the genetic load of social learning (defined as average fecundity reduction caused by the presence of social learning) in terms of some summary statistics, such as relatedness, for each of the three scenarios and compare the results. This comparative analysis not only reconciles previous models that made contradictory predictions as to the effect of spatial structure on the equilibrium frequency of social learners but also derives a simple mathematical rule that determines the sign of the genetic load (i.e. whether or not social learning contributes to the mean fecundity of the population). Copyright © 2013 Elsevier Inc. All rights reserved.

  1. Informing a Learning Progression in Genetics: Which Should Be Taught First, Mendelian Inheritance or the Central Dogma of Molecular Biology?

    Science.gov (United States)

    Duncan, Ravit Golan; Castro-Faix, Moraima; Choi, Jinnie

    2016-01-01

    The Framework for Science Education and the Next Generation Science Standards in the USA emphasize learning progressions (LPs) that support conceptual coherence and the gradual building of knowledge over time. In the domain of genetics there are two independently developed alternative LPs. In essence, the difference between the two progressions…

  2. Coping with the abstract and complex nature of genetics in biology education : The yo-yo learning and teaching strategy

    NARCIS (Netherlands)

    Knippels, M.C.P.J.

    2002-01-01

    This thesis describes a research project that was carried out at the Centre for Science and Mathematics Education at Utrecht University between 1998 and 2002. The study addresses problems in learning and teaching genetics in upper secondary biology education. The aim of the study is to develop a

  3. An Efficient Ensemble Learning Method for Gene Microarray Classification

    Directory of Open Access Journals (Sweden)

    Alireza Osareh

    2013-01-01

    Full Text Available The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

  4. IP-MLI: An Independency of Learning Materials from Platforms in a Mobile Learning using Intelligent Method

    Directory of Open Access Journals (Sweden)

    Mohammed Abdallh Otair

    2006-06-01

    Full Text Available Attempting to deliver a monolithic mobile learning system is too inflexible in view of the heterogeneous mixture of hardware and services available and the desirability of facility blended approaches to learning delivery, and how to build learning materials to run on all platforms[1]. This paper proposes a framework of mobile learning system using an intelligent method (IP-MLI . A fuzzy matching method is used to find suitable learning material design. It will provide a best matching for each specific platform type for each learner. The main contribution of the proposed method is to use software layer to insulate learning materials from device-specific features. Consequently, many versions of learning materials can be designed to work on many platform types.

  5. A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network

    Directory of Open Access Journals (Sweden)

    Han Kyungsook

    2010-06-01

    Full Text Available Abstract Background Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design. Results In this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI. First, a high-coverage and high-precision functional gene network (FGN is constructed by integrating protein-protein interaction (PPI, protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM, on a benchmark dataset in S. cerevisiae to validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in S. cerevisiae (with a sensitivity of 92% and specificity of 91%. Noticeably, the SSL method is more efficient than SVM, especially for

  6. From Genetics to Genetic Algorithms

    Indian Academy of Sciences (India)

    Genetic algorithms (GAs) are computational optimisation schemes with an ... The algorithms solve optimisation problems ..... Genetic Algorithms in Search, Optimisation and Machine. Learning, Addison-Wesley Publishing Company, Inc. 1989.

  7. Learning to argue as a biotechnologist: disprivileging opposition to genetically modified food

    Science.gov (United States)

    Solli, Anne; Bach, Frank; Åkerman, Björn

    2014-03-01

    In the public discussion of genetically modified (GM) food the representations of science as a social good, conducted in the public interest to solve major problems are being subjected to intense scrutiny and questioning. Scientists working in these areas have been seen to struggle for the position of science in society. However few in situ studies of how the debate about science appears in learning situations at the university level have been undertaken. In the present study an introductory course in biotechnology was observed during one semester, lectures and small group supervision concerning GM food were videotaped and student's reports on the issue were collected. The ethnographic approach to Discourse analysis was conducted by means of a set of carefully selected and representative observations of how a group of students learn to argue and appropriate views held in the Discourse they are enculturated into. While socio-scientific issues (SSIs) are often associated with achieving scientific literacy in terms of "informed decisions" involving "rational thought and Discourse" this study shows that SSI in practice, in the context studied here, is primarily concerned with using scientific language to privilege professional understandings of GMOs and discredit public worries and concerns. Scientific claims were privileged over ethical, economical and political claims which were either made irrelevant or rebutted. The students were seen to appropriate a Discourse model held in the biotechnological community that public opposition towards GMO is due to "insufficient knowledge". The present study offers insights into biotechnology students' decision making regarding socio-scientific issues, while also demonstrating the utility of Discourse analysis for understanding learning in this university context. Implications for reflection on the institutional Discourse of science and teaching of controversial issues in science are drawn and the study contributes to the

  8. Lessons learned: advantages and disadvantages of mixed method research

    DEFF Research Database (Denmark)

    Malina, Mary A.; Nørreklit, Hanne; Selto, Frank H.

    2011-01-01

    on the use and usefulness of a specialized balanced scorecard; and third, to encourage researchers to actually use multiple methods and sources of data to address the very many accounting phenomena that are not fully understood. Design/methodology/approach – This paper is an opinion piece based...... on the authors' experience conducting a series of longitudinal mixed method studies. Findings – The authors suggest that in many studies, using a mixed method approach provides the best opportunity for addressing research questions. Originality/value – This paper provides encouragement to those who may wish......Purpose – The purpose of this paper is first, to discuss the theoretical assumptions, qualities, problems and myopia of the dominating quantitative and qualitative approaches; second, to describe the methodological lessons that the authors learned while conducting a series of longitudinal studies...

  9. Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides

    Directory of Open Access Journals (Sweden)

    Stanislawski Jerzy

    2013-01-01

    Full Text Available Abstract Background Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods. Results We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%. The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile to 0.5 CPU-hours (simplified 3D profile to seconds (machine learning. Conclusions We showed that the simplified profile generation method does not introduce an error with regard to the original method, while

  10. Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides.

    Science.gov (United States)

    Stanislawski, Jerzy; Kotulska, Malgorzata; Unold, Olgierd

    2013-01-17

    Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods. We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%). The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile) to 0.5 CPU-hours (simplified 3D profile) to seconds (machine learning). We showed that the simplified profile generation method does not introduce an error with regard to the original method, while increasing the computational efficiency. Our new dataset

  11. Improving Nursing Students' Learning Outcomes in Fundamentals of Nursing Course through Combination of Traditional and e-Learning Methods.

    Science.gov (United States)

    Sheikhaboumasoudi, Rouhollah; Bagheri, Maryam; Hosseini, Sayed Abbas; Ashouri, Elaheh; Elahi, Nasrin

    2018-01-01

    Fundamentals of nursing course are prerequisite to providing comprehensive nursing care. Despite development of technology on nursing education, effectiveness of using e-learning methods in fundamentals of nursing course is unclear in clinical skills laboratory for nursing students. The aim of this study was to compare the effect of blended learning (combining e-learning with traditional learning methods) with traditional learning alone on nursing students' scores. A two-group post-test experimental study was administered from February 2014 to February 2015. Two groups of nursing students who were taking the fundamentals of nursing course in Iran were compared. Sixty nursing students were selected as control group (just traditional learning methods) and experimental group (combining e-learning with traditional learning methods) for two consecutive semesters. Both groups participated in Objective Structured Clinical Examination (OSCE) and were evaluated in the same way using a prepared checklist and questionnaire of satisfaction. Statistical analysis was conducted through SPSS software version 16. Findings of this study reflected that mean of midterm (t = 2.00, p = 0.04) and final score (t = 2.50, p = 0.01) of the intervention group (combining e-learning with traditional learning methods) were significantly higher than the control group (traditional learning methods). The satisfaction of male students in intervention group was higher than in females (t = 2.60, p = 0.01). Based on the findings, this study suggests that the use of combining traditional learning methods with e-learning methods such as applying educational website and interactive online resources for fundamentals of nursing course instruction can be an effective supplement for improving nursing students' clinical skills.

  12. CuboCube: Student creation of a cancer genetics e-textbook using open-access software for social learning.

    Directory of Open Access Journals (Sweden)

    Puya Seid-Karbasi

    2017-03-01

    Full Text Available Student creation of educational materials has the capacity both to enhance learning and to decrease costs. Three successive honors-style classes of undergraduate students in a cancer genetics class worked with a new software system, CuboCube, to create an e-textbook. CuboCube is an open-source learning materials creation system designed to facilitate e-textbook development, with an ultimate goal of improving the social learning experience for students. Equipped with crowdsourcing capabilities, CuboCube provides intuitive tools for nontechnical and technical authors alike to create content together in a structured manner. The process of e-textbook development revealed both strengths and challenges of the approach, which can inform future efforts. Both the CuboCube platform and the Cancer Genetics E-textbook are freely available to the community.

  13. Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables

    DEFF Research Database (Denmark)

    Burgess, Stephen; Thompson, Simon G; Thompson, Grahame

    2010-01-01

    Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context o...

  14. A method of genetically engineering acidophilic, heterotrophic, bacteria by electroporation and conjugation

    Energy Technology Data Exchange (ETDEWEB)

    Roberto, F.F.; Glenn, A.W.; Ward, T.E.

    1990-08-07

    A method of genetically manipulating an acidophilic bacteria is provided by two different procedures. Using electroporation, chimeric and broad-host range plasmids are introduced into Acidiphilium. Conjugation is also employed to introduce broad-host range plasmids into Acidiphilium at neutral pH.

  15. Creating IRT-Based Parallel Test Forms Using the Genetic Algorithm Method

    Science.gov (United States)

    Sun, Koun-Tem; Chen, Yu-Jen; Tsai, Shu-Yen; Cheng, Chien-Fen

    2008-01-01

    In educational measurement, the construction of parallel test forms is often a combinatorial optimization problem that involves the time-consuming selection of items to construct tests having approximately the same test information functions (TIFs) and constraints. This article proposes a novel method, genetic algorithm (GA), to construct parallel…

  16. Columbia River Stock Identification Study; Validation of Genetic Method, 1980-1981 Final Report.

    Energy Technology Data Exchange (ETDEWEB)

    Milner, George B.; Teel, David J.; Utter, Fred M. (Northwest and Alaska Fisheries Science Center, Coastal Zone and Estuarine Studies Division, Seattle, WA)

    1981-06-01

    The reliability of a method for obtaining maximum likelihood estimate of component stocks in mixed populations of salmonids through the frequency of genetic variants in a mixed population and in potentially contributing stocks was tested in 1980. A data base of 10 polymorphic loci from 14 hatchery stocks of spring chinook salmon of the Columbia River was used to estimate proportions of these stocks in four different blind'' mixtures whose true composition was only revealed subsequent to obtaining estimates. The accuracy and precision of these blind tests have validated the genetic method as a valuable means for identifying components of stock mixtures. Properties of the genetic method were further examined by simulation studies using the pooled data of the four blind tests as a mixed fishery. Replicated tests with samples sizes between 100 and 1,000 indicated that actual standard deviations on estimated contributions were consistently lower than calculated standard deviations; this difference diminished as sample size increased. It is recommended that future applications of the method be preceded by simulation studies that will identify appropriate levels of sampling required for acceptable levels of accuracy and precision. Variables in such studies include the stocks involved, the loci used, and the genetic differentiation among stocks. 8 refs., 6 figs., 4 tabs.

  17. Common genetic variants associated with cognitive performance identified using the proxy-phenotype method

    NARCIS (Netherlands)

    C.A. Rietveld (Niels); T. Esko (Tõnu); G. Davies (Gail); T.H. Pers (Tune); P. Turley (Patrick); B. Benyamin (Beben); C.F. Chabris (Christopher F.); V. Emilsson (Valur); A.D. Johnson (Andrew); J.J. Lee (James J.); C. de Leeuw (Christiaan); R.E. Marioni (Riccardo); S.E. Medland (Sarah Elizabeth); M. Miller (Mike); O. Rostapshova (Olga); S.J. van der Lee (Sven); A.A.E. Vinkhuyzen (Anna A.); N. Amin (Najaf); D. Conley (Dalton); J. Derringer; C.M. van Duijn (Cornelia); R.S.N. Fehrmann (Rudolf); L. Franke (Lude); E.L. Glaeser (Edward L.); N.K. Hansell (Narelle); C. Hayward (Caroline); W.G. Iacono (William); C.A. Ibrahim-Verbaas (Carla); V.W.V. Jaddoe (Vincent); J. Karjalainen (Juha); D. Laibson (David); P. Lichtenstein (Paul); D.C. Liewald (David C.); P.K. Magnusson (Patrik); N.G. Martin (Nicholas); M. McGue (Matt); G. Mcmahon (George); N.L. Pedersen (Nancy); S. Pinker (Steven); D.J. Porteous (David J.); D. Posthuma (Danielle); F. Rivadeneira Ramirez (Fernando); B.H. Smithk (Blair H.); J.M. Starr (John); H.W. Tiemeier (Henning); N.J. Timpsonm (Nicholas J.); M. Trzaskowskin (Maciej); A.G. Uitterlinden (André); F.C. Verhulst (Frank); M.E. Ward (Mary); M.J. Wright (Margaret); G.D. Smith; I.J. Deary (Ian J.); M. Johannesson (Magnus); R. Plomin (Robert); P.M. Visscher (Peter); D.J. Benjamin (Daniel J.); D. Cesarini (David); Ph.D. Koellinger (Philipp)

    2014-01-01

    textabstractWe identify common genetic variants associated with cognitive performance using a two-stage approach, which we call the proxyphenotype method. First, we conduct a genome-wide association study of educational attainment in a large sample (n = 106,736), which produces a set of 69

  18. Enhanced computational methods for quantifying the effect of geographic and environmental isolation on genetic differentiation

    NARCIS (Netherlands)

    Botta, Filippo; Eriksen, Casper; Fontaine, Michael Christophe; Guillot, Gilles

    2015-01-01

    In a recent paper, Bradburd et al. (2013) proposed a model to quantify the relative effect ofgeographic and environmental distance on genetic differentiation. Here, we enhance this method in several ways. 1. We modify the covariance model so as to fit better with mainstream geostatistical models and

  19. Best practices for learning physiology: combining classroom and online methods.

    Science.gov (United States)

    Anderson, Lisa C; Krichbaum, Kathleen E

    2017-09-01

    Physiology is a requisite course for many professional allied health programs and is a foundational science for learning pathophysiology, health assessment, and pharmacology. Given the demand for online learning in the health sciences, it is important to evaluate the efficacy of online and in-class teaching methods, especially as they are combined to form hybrid courses. The purpose of this study was to compare two hybrid physiology sections in which one section was offered mostly in-class (85% in-class), and the other section was offered mostly online (85% online). The two sections in 2 yr ( year 1 and year 2 ) were compared in terms of knowledge of physiology measured in exam scores and pretest-posttest improvement, and in measures of student satisfaction with teaching. In year 1 , there were some differences on individual exam scores between the two sections, but no significant differences in mean exam scores or in pretest-posttest improvements. However, in terms of student satisfaction, the mostly in-class students in year 1 rated the instructor significantly higher than did the mostly online students. Comparisons between in-class and online students in the year 2 cohort yielded data that showed that mean exam scores were not statistically different, but pre-post changes were significantly greater in the mostly online section; student satisfaction among mostly online students also improved significantly. Education researchers must investigate effective combinations of in-class and online methods for student learning outcomes, while maintaining the flexibility and convenience that online methods provide. Copyright © 2017 the American Physiological Society.

  20. MACHINE LEARNING METHODS IN DIGITAL AGRICULTURE: ALGORITHMS AND CASES

    Directory of Open Access Journals (Sweden)

    Aleksandr Vasilyevich Koshkarov

    2018-05-01

    Full Text Available Ensuring food security is a major challenge in many countries. With a growing global population, the issues of improving the efficiency of agriculture have become most relevant. Farmers are looking for new ways to increase yields, and governments of different countries are developing new programs to support agriculture. This contributes to a more active implementation of digital technologies in agriculture, helping farmers to make better decisions, increase yields and take care of the environment. The central point is the collection and analysis of data. In the industry of agriculture, data can be collected from different sources and may contain useful patterns that identify potential problems or opportunities. Data should be analyzed using machine learning algorithms to extract useful insights. Such methods of precision farming allow the farmer to monitor individual parts of the field, optimize the consumption of water and chemicals, and identify problems quickly. Purpose: to make an overview of the machine learning algorithms used for data analysis in agriculture. Methodology: an overview of the relevant literature; a survey of farmers. Results: relevant algorithms of machine learning for the analysis of data in agriculture at various levels were identified: soil analysis (soil assessment, soil classification, soil fertility predictions, weather forecast (simulation of climate change, temperature and precipitation prediction, and analysis of vegetation (weed identification, vegetation classification, plant disease identification, crop forecasting. Practical implications: agriculture, crop production.

  1. Evaluation Methods on Usability of M-Learning Environments

    Directory of Open Access Journals (Sweden)

    Teresa Magal-Royo

    2007-10-01

    Full Text Available Nowadays there are different evaluation methods focused in the assessment of the usability of telematic methods. The assessment of 3rd generation web environments evaluates the effectiveness and usability of application with regard to the user needs. Wireless usability and, specifically in mobile phones, is concentrated in the validation of the features and tools management using conventional interactive environments. There is not a specific and suitable criterion to evaluate created environments and m-learning platforms, where the restricted and sequential representation is a fundamental aspect to be considered.The present paper exposes the importance of the conventional usability methods to verify both: the employed contents in wireless formats, and the possible interfaces from the conception phases, to the validations of the platform with such characteristics.The development of usability adapted inspection could be complemented with the Remote’s techniques of usability testing, which are being carried out these days in the mobile devices field and which pointed out the need to apply common criteria in the validation of non-located learning scenarios.

  2. A Preliminary Survey of the Preferred Learning Methods for Interpretation Students

    Science.gov (United States)

    Heinz, Michael

    2013-01-01

    There are many different methods that individuals use to learn languages like reading books or writing essays. Not all methods are equally successful for second language learners but nor do all successful learners of a second language show identical preferences for learning methods. Additionally, at the highest level of language learning various…

  3. Characterizing Engineering Learners' Preferences for Active and Passive Learning Methods

    Science.gov (United States)

    Magana, Alejandra J.; Vieira, Camilo; Boutin, Mireille

    2018-01-01

    This paper studies electrical engineering learners' preferences for learning methods with various degrees of activity. Less active learning methods such as homework and peer reviews are investigated, as well as a newly introduced very active (constructive) learning method called "slectures," and some others. The results suggest that…

  4. Advanced methods in NDE using machine learning approaches

    Science.gov (United States)

    Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

    2018-04-01

    Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability

  5. Development and Evaluation of Event-Specific Quantitative PCR Method for Genetically Modified Soybean MON87701.

    Science.gov (United States)

    Tsukahara, Keita; Takabatake, Reona; Masubuchi, Tomoko; Futo, Satoshi; Minegishi, Yasutaka; Noguchi, Akio; Kondo, Kazunari; Nishimaki-Mogami, Tomoko; Kurashima, Takeyo; Mano, Junichi; Kitta, Kazumi

    2016-01-01

    A real-time PCR-based analytical method was developed for the event-specific quantification of a genetically modified (GM) soybean event, MON87701. First, a standard plasmid for MON87701 quantification was constructed. The conversion factor (C f ) required to calculate the amount of genetically modified organism (GMO) was experimentally determined for a real-time PCR instrument. The determined C f for the real-time PCR instrument was 1.24. For the evaluation of the developed method, a blind test was carried out in an inter-laboratory trial. The trueness and precision were evaluated as the bias and reproducibility of relative standard deviation (RSDr), respectively. The determined biases and the RSDr values were less than 30 and 13%, respectively, at all evaluated concentrations. The limit of quantitation of the method was 0.5%, and the developed method would thus be applicable for practical analyses for the detection and quantification of MON87701.

  6. The use of genetic algorithms with niching methods in nuclear reactor related problems

    International Nuclear Information System (INIS)

    Sacco, Wagner Figueiredo

    2000-03-01

    Genetic Algorithms (GAs) are biologically motivated adaptive systems which have been used, with good results, in function optimization. However, traditional GAs rapidly push an artificial population toward convergence. That is, all individuals in the population soon become nearly identical. Niching Methods allow genetic algorithms to maintain a population of diverse individuals. GAs that incorporate these methods are capable of locating multiple, optimal solutions within a single population. The purpose of this study is to test existing niching techniques and two methods introduced herein, bearing in mind their eventual application in nuclear reactor related problems, specially the nuclear reactor core reload one, which has multiple solutions. Tests are performed using widely known test functions and their results show that the new methods are quite promising, specially in real world problems like the nuclear reactor core reload. (author)

  7. Utilization of niching methods of genetic algorithms in nuclear reactor problems optimization

    International Nuclear Information System (INIS)

    Sacco, Wagner Figueiredo; Schirru, Roberto

    2000-01-01

    Genetic Algorithms (GAs) are biologically motivated adaptive systems which have been used, with good results, in function optimization. However, traditional GAs rapidly push an artificial population toward convergence. That is, all individuals in the population soon become nearly identical. Niching Methods allow genetic algorithms to maintain a population of diverse individuals. GAs that incorporate these methods are capable of locating multiple, optimal solutions within a single population. The purpose of this study is to test existing niching techniques and two methods introduced herein, bearing in mind their eventual application in nuclear reactor related problems, specially the nuclear reactor core reload one, which has multiple solutions. Tests are performed using widely known test functions and their results show that the new methods are quite promising, specially in real world problems like the nuclear reactor core reload. (author)

  8. 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

  9. Report of the Advisory Group Meeting on Genetic Methods of Insect Control

    International Nuclear Information System (INIS)

    1987-01-01

    Despite the availability of a range of modern pest control techniques, insects remain a major cause of production losses in agriculture and contribute significantly to diseases of man and livestock. The increasing incidence of pesticide resistance, and concerns over the environmental impact of residues, have highlighted the need for improved technologies. As a result, genetic methods of pest control, including the use of irradiation sterilized insects, have become of increasing importance. It is therefore essential that the Joint FAO/IAEA Division continues to promote the development and application of this method of pest control. The advisory group concluded that the opportunities for genetic control might be widened by the application of new techniques, particularly recombinant DNA technology. The scope for integration of genetic control methods with other control measures, and ist use as a temporary suppressive measure on an area-wide basis was also recognized. Examples are given from representative groups of insect pests to illustrate how these concepts can be applied. The advisory group regarded the Seibersdorf laboratory as a unique facility for the conduct of tactical research related to mass-rearing and release procedures for major pests such as medfly and tsetse spp. Associated research on genetic sexing of medfly, diet recycling and the development of more environmentally acceptable alternatives for pre-release suppression of medfly were considered to be important research projects. The advisory group concluded that the laboratory should continue to remain a centre of excellence for mass-rearing technologies for medfly and tsetse spp., and for training scientists and technicians from developing countries. The Joint FAO/IAEA Division currently plays a major co-ordinating and supportive role for those areas of international research which impinge on genetic control. The advisory group believes that the Joint FAO/IAEA Division should maintain its initiative

  10. Report of the Advisory Group Meeting on Genetic Methods of Insect Control

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1987-07-01

    Despite the availability of a range of modern pest control techniques, insects remain a major cause of production losses in agriculture and contribute significantly to diseases of man and livestock. The increasing incidence of pesticide resistance, and concerns over the environmental impact of residues, have highlighted the need for improved technologies. As a result, genetic methods of pest control, including the use of irradiation sterilized insects, have become of increasing importance. It is therefore essential that the Joint FAO/IAEA Division continues to promote the development and application of this method of pest control. The advisory group concluded that the opportunities for genetic control might be widened by the application of new techniques, particularly recombinant DNA technology. The scope for integration of genetic control methods with other control measures, and ist use as a temporary suppressive measure on an area-wide basis was also recognized. Examples are given from representative groups of insect pests to illustrate how these concepts can be applied. The advisory group regarded the Seibersdorf laboratory as a unique facility for the conduct of tactical research related to mass-rearing and release procedures for major pests such as medfly and tsetse spp. Associated research on genetic sexing of medfly, diet recycling and the development of more environmentally acceptable alternatives for pre-release suppression of medfly were considered to be important research projects. The advisory group concluded that the laboratory should continue to remain a centre of excellence for mass-rearing technologies for medfly and tsetse spp., and for training scientists and technicians from developing countries. The Joint FAO/IAEA Division currently plays a major co-ordinating and supportive role for those areas of international research which impinge on genetic control. The advisory group believes that the Joint FAO/IAEA Division should maintain its initiative

  11. Distance learning training in genetics and genomics testing for Italian health professionals: results of a pre and post-test evaluation

    Directory of Open Access Journals (Sweden)

    Maria Benedetta Michelazzo

    2015-09-01

    Full Text Available BackgroundProgressive advances in technologies for DNA sequencing and decreasing costs are allowing an easier diffusion of genetic and genomic tests. Physicians’ knowledge and confidence on the topic is often low and not suitable for manage this challenge. Tailored educational programs are required to reach a more and more appropriate use of genetic technologies.MethodsA distance learning course has been created by experts from different Italian medical associations with the support of the Italian Ministry of Health. The course was directed to professional figures involved in prescription and interpretation of genetic tests. A pretest-post-test study design was used to assess knowledge improvement. We analyzed the proportion of correct answers for each question pre and post-test, as well as the mean score difference stratified by gender, age, professional status and medical specialty.ResultsWe reported an improvement in the proportion of correct answers for 12 over 15 questions of the test. The overall mean score to the questions significantly increased in the post-test, from 9.44 to 12.49 (p-value < 0.0001. In the stratified analysis we reported an improvement in the knowledge of all the groups except for geneticists; the pre-course mean score of this group was already very high and did not improve significantly.ConclusionDistance learning is effective in improving the level of genetic knowledge. In the future, it will be useful to analyze which specialists have more advantage from genetic education, in order to plan more tailored education for medical professionals.

  12. Natural transformation of Vibrio parahaemolyticus: A rapid method to create genetic deletions.

    Science.gov (United States)

    Chimalapati, Suneeta; de Souza Santos, Marcela; Servage, Kelly; De Nisco, Nicole J; Dalia, Ankur B; Orth, Kim

    2018-03-19

    The Gram-negative bacterium Vibrio parahaemolyticus is an opportunistic human pathogen and the leading cause of seafood borne acute gastroenteritis worldwide. Recently, this bacterium was implicated as the etiologic agent of a severe shrimp disease with consequent devastating outcomes to shrimp farming. In both cases, acquisition of genetic material via horizontal transfer provided V. parahaemolyticus with new virulence tools to cause disease. Dissecting the molecular mechanisms of V. parahaemolyticus pathogenesis often requires manipulating its genome. Classically, genetic deletions in V. parahaemolyticus are performed using a laborious, lengthy, multi-step process. Herein, we describe a fast and efficient method to edit this bacterium's genome based on V. parahaemolyticus natural competence. Although this method is similar to one previously described, V. parahaemolyticus requires counter selection for curing of acquired plasmids due to its recalcitrant nature of retaining extrachromosomal DNA. We believe this approach will be of use to the Vibrio community. Importance Spreading of Vibrios throughout the world correlates with increased global temperatures. As they spread, they find new niches to survive, proliferate and invade. Therefore, genetic manipulation of Vibrios is of utmost importance for studying these species. Herein, we have delineated and validated a rapid method to create genetic deletions in Vibrio parahaemolyticus This study provides insightful methodology for studies with other Vibrio species. Copyright © 2018 American Society for Microbiology.

  13. Genetic methods for area-wide management of Lepidopterous pests with emphasis on F1 sterility

    International Nuclear Information System (INIS)

    Ocampo, V.R.

    1996-01-01

    Enormous losses in the production and marketing of food and fiber are caused by larvae of Lepidoptera. Currently, large quantities of insecticides are used to combat these pests. Insecticide resistance, increasing concern over pesticide pollution, and the desire to effectively manage lepidopteran pests on an area-wide basis have motivated scientists to identify and develop new pest management tactics that are compatible with current IPM. Genetic methods have emerged as a promising control strategy for lepidopteran pests. Genetic control as a practical means of pest management was first successfully implemented by Knipling and colleagues in the USA during the 1960's with the sterile insect technique (SIT) program for the screwworm fly. SIT is not a readily adapted for use against Lepidoptera as against Diptera. Radiation-induced inherited sterility (or F 1 sterility) is generally considered the most promising genetic methods for large-scale suppression of lepidopteran populations. This papers discusses four genetic control methods that have been developed and the progress that has been made in integrating sterility with other IPM tactics. (author)

  14. The experimental field work as practical learning method

    Directory of Open Access Journals (Sweden)

    Nicolás Fernández Losa

    2014-11-01

    Full Text Available This paper describes a teaching experience about experimental field work as practical learning method implemented in the subject of Organizational Behaviour. With this teaching experience we pretend to change the practical training, as well as in its evaluation process, in order to favour the development of transversal skills of students. For this purpose, the use of a practice plan, tackled through an experimental field work and carried out with the collaboration of a business organization within a work team (as organic unity of learning, arises as an alternative to the traditional method of practical teachings and allows the approach of business reality into the classroom, as well as actively promote the use of transversal skills. In particular, we develop the experience in three phases. Initially, the students, after forming a working group and define a field work project, should get the collaboration of a nearby business organization in which to obtain data on one or more functional areas of organizational behaviour. Subsequently, students carry out the field work with the realization of the scheduled visits and elaboration of a memory to establish a diagnosis of the strategy followed by the company in these functional areas in order to propose and justify alternative actions that improve existing ones. Finally, teachers assess the different field work memories and their public presentations according to evaluation rubrics, which try to objectify and unify to the maximum the evaluation criteria and serve to guide the learning process of students. The results of implementation of this teaching experience, measured through a Likert questionnaire, are very satisfactory for students.

  15. Application of blended learning in teaching statistical methods

    Directory of Open Access Journals (Sweden)

    Barbara Dębska

    2012-12-01

    Full Text Available The paper presents the application of a hybrid method (blended learning - linking traditional education with on-line education to teach selected problems of mathematical statistics. This includes the teaching of the application of mathematical statistics to evaluate laboratory experimental results. An on-line statistics course was developed to form an integral part of the module ‘methods of statistical evaluation of experimental results’. The course complies with the principles outlined in the Polish National Framework of Qualifications with respect to the scope of knowledge, skills and competencies that students should have acquired at course completion. The paper presents the structure of the course and the educational content provided through multimedia lessons made accessible on the Moodle platform. Following courses which used the traditional method of teaching and courses which used the hybrid method of teaching, students test results were compared and discussed to evaluate the effectiveness of the hybrid method of teaching when compared to the effectiveness of the traditional method of teaching.

  16. Methods of Efficient Study Habits and Physics Learning

    Science.gov (United States)

    Zettili, Nouredine

    2010-02-01

    We want to discuss the methods of efficient study habits and how they can be used by students to help them improve learning physics. In particular, we deal with the most efficient techniques needed to help students improve their study skills. We focus on topics such as the skills of how to develop long term memory, how to improve concentration power, how to take class notes, how to prepare for and take exams, how to study scientific subjects such as physics. We argue that the students who conscientiously use the methods of efficient study habits achieve higher results than those students who do not; moreover, a student equipped with the proper study skills will spend much less time to learn a subject than a student who has no good study habits. The underlying issue here is not the quantity of time allocated to the study efforts by the students, but the efficiency and quality of actions so that the student can function at peak efficiency. These ideas were developed as part of Project IMPACTSEED (IMproving Physics And Chemistry Teaching in SEcondary Education), an outreach grant funded by the Alabama Commission on Higher Education. This project is motivated by a major pressing local need: A large number of high school physics teachers teach out of field. )

  17. A Photometric Machine-Learning Method to Infer Stellar Metallicity

    Science.gov (United States)

    Miller, Adam A.

    2015-01-01

    Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' < or = 18 mag), with 4500 K < or = Teff < or = 7000 K, corresponding to those with the most reliable SSPP estimates, I find that the model predicts [Fe/H] values with a root-mean-squared-error (RMSE) of approx.0.27 dex. The RMSE from this machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

  18. Multiple instance learning tracking method with local sparse representation

    KAUST Repository

    Xie, Chengjun

    2013-10-01

    When objects undergo large pose change, illumination variation or partial occlusion, most existed visual tracking algorithms tend to drift away from targets and even fail in tracking them. To address this issue, in this study, the authors propose an online algorithm by combining multiple instance learning (MIL) and local sparse representation for tracking an object in a video system. The key idea in our method is to model the appearance of an object by local sparse codes that can be formed as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL learns the sparse codes by a classifier to discriminate the target from the background. Finally, results from the trained classifier are input into a particle filter framework to sequentially estimate the target state over time in visual tracking. In addition, to decrease the visual drift because of the accumulative errors when updating the dictionary and classifier, a two-step object tracking method combining a static MIL classifier with a dynamical MIL classifier is proposed. Experiments on some publicly available benchmarks of video sequences show that our proposed tracker is more robust and effective than others. © The Institution of Engineering and Technology 2013.

  19. Deep Learning Methods for Improved Decoding of Linear Codes

    Science.gov (United States)

    Nachmani, Eliya; Marciano, Elad; Lugosch, Loren; Gross, Warren J.; Burshtein, David; Be'ery, Yair

    2018-02-01

    The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large example space. Similar improvements are obtained for the min-sum algorithm. It is also shown that tying the parameters of the decoders across iterations, so as to form a recurrent neural network architecture, can be implemented with comparable results. The advantage is that significantly less parameters are required. We also introduce a recurrent neural decoder architecture based on the method of successive relaxation. Improvements over standard belief propagation are also observed on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the neural belief propagation decoder can be used to improve the performance, or alternatively reduce the computational complexity, of a close to optimal decoder of short BCH codes.

  20. Machine learning methods for clinical forms analysis in mental health.

    Science.gov (United States)

    Strauss, John; Peguero, Arturo Martinez; Hirst, Graeme

    2013-01-01

    In preparation for a clinical information system implementation, the Centre for Addiction and Mental Health (CAMH) Clinical Information Transformation project completed multiple preparation steps. An automated process was desired to supplement the onerous task of manual analysis of clinical forms. We used natural language processing (NLP) and machine learning (ML) methods for a series of 266 separate clinical forms. For the investigation, documents were represented by feature vectors. We used four ML algorithms for our examination of the forms: cluster analysis, k-nearest neigh-bours (kNN), decision trees and support vector machines (SVM). Parameters for each algorithm were optimized. SVM had the best performance with a precision of 64.6%. Though we did not find any method sufficiently accurate for practical use, to our knowledge this approach to forms has not been used previously in mental health.

  1. Statistical learning modeling method for space debris photometric measurement

    Science.gov (United States)

    Sun, Wenjing; Sun, Jinqiu; Zhang, Yanning; Li, Haisen

    2016-03-01

    Photometric measurement is an important way to identify the space debris, but the present methods of photometric measurement have many constraints on star image and need complex image processing. Aiming at the problems, a statistical learning modeling method for space debris photometric measurement is proposed based on the global consistency of the star image, and the statistical information of star images is used to eliminate the measurement noises. First, the known stars on the star image are divided into training stars and testing stars. Then, the training stars are selected as the least squares fitting parameters to construct the photometric measurement model, and the testing stars are used to calculate the measurement accuracy of the photometric measurement model. Experimental results show that, the accuracy of the proposed photometric measurement model is about 0.1 magnitudes.

  2. A Learning-Based Steganalytic Method against LSB Matching Steganography

    Directory of Open Access Journals (Sweden)

    Z. Xia

    2011-04-01

    Full Text Available This paper considers the detection of spatial domain least significant bit (LSB matching steganography in gray images. Natural images hold some inherent properties, such as histogram, dependence between neighboring pixels, and dependence among pixels that are not adjacent to each other. These properties are likely to be disturbed by LSB matching. Firstly, histogram will become smoother after LSB matching. Secondly, the two kinds of dependence will be weakened by the message embedding. Accordingly, three features, which are respectively based on image histogram, neighborhood degree histogram and run-length histogram, are extracted at first. Then, support vector machine is utilized to learn and discriminate the difference of features between cover and stego images. Experimental results prove that the proposed method possesses reliable detection ability and outperforms the two previous state-of-the-art methods. Further more, the conclusions are drawn by analyzing the individual performance of three features and their fused feature.

  3. Incorporating Meaningful Gamification in a Blended Learning Research Methods Class: Examining Student Learning, Engagement, and Affective Outcomes

    Science.gov (United States)

    Tan, Meng; Hew, Khe Foon

    2016-01-01

    In this study, we investigated how the use of meaningful gamification affects student learning, engagement, and affective outcomes in a short, 3-day blended learning research methods class using a combination of experimental and qualitative research methods. Twenty-two postgraduates were randomly split into two groups taught by the same…

  4. Middle school students' learning about genetic inheritance through on-line scaffolding supports

    Science.gov (United States)

    Manokore, Viola

    The main goal of school science is to enable learners to become scientifically literate through their participation in scientific discourses (McNeill & Krajcik, 2009). One of the key elements of scientific discourses is the ability to construct scientific explanations that consist of valid claims supported by appropriate evidence (e.g., McNeill & Krajcik, 2006, Sadler, 2004; Sandoval & Reiser, 2004). Curricula scaffolds may help students construct scientific explanations and achieve their learning goals. This dissertation study is part of a larger study designed to support fifth through seventh grade students' learning about genetic inheritance through curricula scaffolds. Seventh grade students in this study interacted with a Web Based Inquiry Science Environment (WISE) unit called "From Genotype to Phenotype" that had curricula scaffolds. Informed by the Scaffolded Knowledge Integration, two versions of the unit were developed around concepts on genetic inheritance. Version one of the units was explicit on explaining to students how to make a claim and support it with appropriate evidence. Although the science concepts covered were the same, Version two was not explicit on claims and evidence use. Embedded in the units were scaffolding supports in the form of prompts. This dissertation study explored students' responses to the scaffolding support prompts using a knowledge integration (KI) rubric as described by Linn and His (2000). Two teachers, each with about 150 students in five classes of about 25 each, participated in the study. Each teacher had three classes of students that received a version one and the other two classed received version two of "From Genotype to Phenotype" unit. Using the Statistical Package for Social Scientists (SPSS), I explored whether students' scores, as measured by the KI rubric, varied by the unit version the students received or by the teacher they had. The findings suggested that the two versions of the unit were equally

  5. Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework.

    Science.gov (United States)

    Oakden-Rayner, Luke; Carneiro, Gustavo; Bessen, Taryn; Nascimento, Jacinto C; Bradley, Andrew P; Palmer, Lyle J

    2017-05-10

    Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive medical tests to define the full range of phenotypic variation associated with individual health. Such knowledge is critical for improved early intervention, for better treatment decisions, and for ameliorating the steadily worsening epidemic of chronic disease. We present proof-of-concept experiments to demonstrate how routinely acquired cross-sectional CT imaging may be used to predict patient longevity as a proxy for overall individual health and disease status using computer image analysis techniques. Despite the limitations of a modest dataset and the use of off-the-shelf machine learning methods, our results are comparable to previous 'manual' clinical methods for longevity prediction. This work demonstrates that radiomics techniques can be used to extract biomarkers relevant to one of the most widely used outcomes in epidemiological and clinical research - mortality, and that deep learning with convolutional neural networks can be usefully applied to radiomics research. Computer image analysis applied to routinely collected medical images offers substantial potential to enhance precision medicine initiatives.

  6. Study on the Method of Association Rules Mining Based on Genetic Algorithm and Application in Analysis of Seawater Samples

    Directory of Open Access Journals (Sweden)

    Qiuhong Sun

    2014-04-01

    Full Text Available Based on the data mining research, the data mining based on genetic algorithm method, the genetic algorithm is briefly introduced, while the genetic algorithm based on two important theories and theoretical templates principle implicit parallelism is also discussed. Focuses on the application of genetic algorithms for association rule mining method based on association rule mining, this paper proposes a genetic algorithm fitness function structure, data encoding, such as the title of the improvement program, in particular through the early issues study, proposed the improved adaptive Pc, Pm algorithm is applied to the genetic algorithm, thereby improving efficiency of the algorithm. Finally, a genetic algorithm based association rule mining algorithm, and be applied in sea water samples database in data mining and prove its effective.

  7. A novel method to design S-box based on chaotic map and genetic algorithm

    International Nuclear Information System (INIS)

    Wang, Yong; Wong, Kwok-Wo; Li, Changbing; Li, Yang

    2012-01-01

    The substitution box (S-box) is an important component in block encryption algorithms. In this Letter, the problem of constructing S-box is transformed to a Traveling Salesman Problem and a method for designing S-box based on chaos and genetic algorithm is proposed. Since the proposed method makes full use of the traits of chaotic map and evolution process, stronger S-box is obtained. The results of performance test show that the presented S-box has good cryptographic properties, which justify that the proposed algorithm is effective in generating strong S-boxes. -- Highlights: ► The problem of constructing S-box is transformed to a Traveling Salesman Problem. ► We present a new method for designing S-box based on chaos and genetic algorithm. ► The proposed algorithm is effective in generating strong S-boxes.

  8. A novel method to design S-box based on chaotic map and genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Yong, E-mail: wangyong_cqupt@163.com [State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044 (China); Key Laboratory of Electronic Commerce and Logistics, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China); Wong, Kwok-Wo [Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong (Hong Kong); Li, Changbing [Key Laboratory of Electronic Commerce and Logistics, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China); Li, Yang [Department of Automatic Control and Systems Engineering, The University of Sheffield, Mapping Street, S1 3DJ (United Kingdom)

    2012-01-30

    The substitution box (S-box) is an important component in block encryption algorithms. In this Letter, the problem of constructing S-box is transformed to a Traveling Salesman Problem and a method for designing S-box based on chaos and genetic algorithm is proposed. Since the proposed method makes full use of the traits of chaotic map and evolution process, stronger S-box is obtained. The results of performance test show that the presented S-box has good cryptographic properties, which justify that the proposed algorithm is effective in generating strong S-boxes. -- Highlights: ► The problem of constructing S-box is transformed to a Traveling Salesman Problem. ► We present a new method for designing S-box based on chaos and genetic algorithm. ► The proposed algorithm is effective in generating strong S-boxes.

  9. "Sickle cell anemia: tracking down a mutation": an interactive learning laboratory that communicates basic principles of genetics and cellular biology.

    Science.gov (United States)

    Jarrett, Kevin; Williams, Mary; Horn, Spencer; Radford, David; Wyss, J Michael

    2016-03-01

    "Sickle cell anemia: tracking down a mutation" is a full-day, inquiry-based, biology experience for high school students enrolled in genetics or advanced biology courses. In the experience, students use restriction endonuclease digestion, cellulose acetate gel electrophoresis, and microscopy to discover which of three putative patients have the sickle cell genotype/phenotype using DNA and blood samples from wild-type and transgenic mice that carry a sickle cell mutation. The inquiry-based, problem-solving approach facilitates the students' understanding of the basic concepts of genetics and cellular and molecular biology and provides experience with contemporary tools of biotechnology. It also leads to students' appreciation of the causes and consequences of this genetic disease, which is relatively common in individuals of African descent, and increases their understanding of the first principles of genetics. This protocol provides optimal learning when led by well-trained facilitators (including the classroom teacher) and carried out in small groups (6:1 student-to-teacher ratio). This high-quality experience can be offered to a large number of students at a relatively low cost, and it is especially effective in collaboration with a local science museum and/or university. Over the past 15 yr, >12,000 students have completed this inquiry-based learning experience and demonstrated a consistent, substantial increase in their understanding of the disease and genetics in general. Copyright © 2016 The American Physiological Society.

  10. Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

    Directory of Open Access Journals (Sweden)

    Mingjie Tan

    2015-02-01

    Full Text Available The high rate of dropout is a serious problem in E-learning program. Thus it has received extensive concern from the education administrators and researchers. Predicting the potential dropout students is a workable solution to prevent dropout. Based on the analysis of related literature, this study selected student’s personal characteristic and academic performance as input attributions. Prediction models were developed using Artificial Neural Network (ANN, Decision Tree (DT and Bayesian Networks (BNs. A large sample of 62375 students was utilized in the procedures of model training and testing. The results of each model were presented in confusion matrix, and analyzed by calculating the rates of accuracy, precision, recall, and F-measure. The results suggested all of the three machine learning methods were effective in student dropout prediction, and DT presented a better performance. Finally, some suggestions were made for considerable future research.

  11. Synchronization and Arrest of the Budding Yeast Cell Cycle Using Chemical and Genetic Methods.

    Science.gov (United States)

    Rosebrock, Adam P

    2017-01-03

    The cell cycle of budding yeast can be arrested at specific positions by different genetic and chemical methods. These arrests enable study of cell cycle phase-specific phenotypes that would be missed during examination of asynchronous cultures. Some methods for arrest are reversible, with kinetics that enable release of cells back into a synchronous cycling state. Benefits of chemical and genetic methods include scalability across a large range of culture sizes from a few milliliters to many liters, ease of execution, the absence of specific equipment requirements, and synchronization and release of the entire culture. Of note, cell growth and division are decoupled during arrest and block-release experiments. Cells will continue transcription, translation, and accumulation of protein while arrested. If allowed to reenter the cell cycle, cells will do so as a population of mixed, larger-than-normal cells. Despite this important caveat, many aspects of budding yeast physiology are accessible using these simple chemical and genetic tools. Described here are methods for the block and release of cells in G 1 phase and at the M/G 1 transition using α-factor mating pheromone and the temperature-sensitive cdc15-2 allele, respectively, in addition to methods for arresting the cell cycle in early S phase and at G 2 /M by using hydroxyurea and nocodazole, respectively. © 2017 Cold Spring Harbor Laboratory Press.

  12. Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics

    Science.gov (United States)

    Safner, T.; Miller, M.P.; McRae, B.H.; Fortin, M.-J.; Manel, S.

    2011-01-01

    Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance. ?? 2011 by the authors; licensee MDPI, Basel, Switzerland.

  13. Genetic improvement of olive (Olea europaea L.) by conventional and in vitro biotechnology methods.

    Science.gov (United States)

    Rugini, E; Cristofori, V; Silvestri, C

    2016-01-01

    In olive (Olea europaea L.) traditional methods of genetic improvement have up to now produced limited results. Intensification of olive growing requires appropriate new cultivars for fully mechanized groves, but among the large number of the traditional varieties very few are suitable. High-density and super high-density hedge row orchards require genotypes with reduced size, reduced apical dominance, a semi-erect growth habit, easy to propagate, resistant to abiotic and biotic stresses, with reliably high productivity and quality of both fruits and oil. Innovative strategies supported by molecular and biotechnological techniques are required to speed up novel hybridisation methods. Among traditional approaches the Gene Pool Method seems a reasonable option, but it requires availability of widely diverse germplasm from both cultivated and wild genotypes, supported by a detailed knowledge of their genetic relationships. The practice of "gene therapy" for the most important existing cultivars, combined with conventional methods, could accelerate achievement of the main goals, but efforts to overcome some technical and ideological obstacles are needed. The present review describes the benefits that olive and its products may obtain from genetic improvement using state of the art of conventional and unconventional methods, and includes progress made in the field of in vitro techniques. The uses of both traditional and modern technologies are discussed with recommendations. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. A Simple Deep Learning Method for Neuronal Spike Sorting

    Science.gov (United States)

    Yang, Kai; Wu, Haifeng; Zeng, Yu

    2017-10-01

    Spike sorting is one of key technique to understand brain activity. With the development of modern electrophysiology technology, some recent multi-electrode technologies have been able to record the activity of thousands of neuronal spikes simultaneously. The spike sorting in this case will increase the computational complexity of conventional sorting algorithms. In this paper, we will focus spike sorting on how to reduce the complexity, and introduce a deep learning algorithm, principal component analysis network (PCANet) to spike sorting. The introduced method starts from a conventional model and establish a Toeplitz matrix. Through the column vectors in the matrix, we trains a PCANet, where some eigenvalue vectors of spikes could be extracted. Finally, support vector machine (SVM) is used to sort spikes. In experiments, we choose two groups of simulated data from public databases availably and compare this introduced method with conventional methods. The results indicate that the introduced method indeed has lower complexity with the same sorting errors as the conventional methods.

  15. PYRAMID METHOD OF DISTANCE LEARNING IN HIGER EDUCATION

    Directory of Open Access Journals (Sweden)

    Дмитрий Васильевич Сенашенко

    2017-12-01

    Full Text Available The article deals with modern methods of distance learning in the corporate sector. On the specifics of the application of the described methods is their classification and be subject to review their specific differences based on the features and applications of these techniques given the characteristics of the organization of teaching in higher education, a conclusion about their preferred sides, which can be used in distance education. Later in the article, taking into account the above factors, it is proposed an innovative method of formation of educational programs. In view of the similarity of the rendered appearance of the pyramids, this technique proposed name “pyramid”. Offered by the authors, this technique is best synthesis of the best features of the previously described in the article for the online teaching methods. In the future, we are given a detailed description and conducted a preliminary analysis of the applicability of this technique to the training process in the Russian Federation. The analysis describes the eight alleged authors of distance education problems of high school that this method can help to solve.

  16. Learning Specific Content in Technology Education: Learning Study as a Collaborative Method in Swedish Preschool Class Using Hands-On Material

    Science.gov (United States)

    Kilbrink, Nina; Bjurulf, Veronica; Blomberg, Ingela; Heidkamp, Anja; Hollsten, Ann-Christin

    2014-01-01

    This article describes the process of a learning study conducted in technology education in a Swedish preschool class. The learning study method used in this study is a collaborative method, where researchers and teachers work together as a team concerning teaching and learning about a specific learning object. The object of learning in this study…

  17. A Learning Method for Neural Networks Based on a Pseudoinverse Technique

    Directory of Open Access Journals (Sweden)

    Chinmoy Pal

    1996-01-01

    Full Text Available A theoretical formulation of a fast learning method based on a pseudoinverse technique is presented. The efficiency and robustness of the method are verified with the help of an Exclusive OR problem and a dynamic system identification of a linear single degree of freedom mass–spring problem. It is observed that, compared with the conventional backpropagation method, the proposed method has a better convergence rate and a higher degree of learning accuracy with a lower equivalent learning coefficient. It is also found that unlike the steepest descent method, the learning capability of which is dependent on the value of the learning coefficient ν, the proposed pseudoinverse based backpropagation algorithm is comparatively robust with respect to its equivalent variable learning coefficient. A combination of the pseudoinverse method and the steepest descent method is proposed for a faster, more accurate learning capability.

  18. Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

    Science.gov (United States)

    Li, Yifeng; Shi, Wenqiang; Wasserman, Wyeth W

    2018-05-31

    In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide. Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome). The predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations.

  19. Teaching biology through statistics: application of statistical methods in genetics and zoology courses.

    Science.gov (United States)

    Colon-Berlingeri, Migdalisel; Burrowes, Patricia A

    2011-01-01

    Incorporation of mathematics into biology curricula is critical to underscore for undergraduate students the relevance of mathematics to most fields of biology and the usefulness of developing quantitative process skills demanded in modern biology. At our institution, we have made significant changes to better integrate mathematics into the undergraduate biology curriculum. The curricular revision included changes in the suggested course sequence, addition of statistics and precalculus as prerequisites to core science courses, and incorporating interdisciplinary (math-biology) learning activities in genetics and zoology courses. In this article, we describe the activities developed for these two courses and the assessment tools used to measure the learning that took place with respect to biology and statistics. We distinguished the effectiveness of these learning opportunities in helping students improve their understanding of the math and statistical concepts addressed and, more importantly, their ability to apply them to solve a biological problem. We also identified areas that need emphasis in both biology and mathematics courses. In light of our observations, we recommend best practices that biology and mathematics academic departments can implement to train undergraduates for the demands of modern biology.

  20. Internet-based versus traditional teaching and learning methods.

    Science.gov (United States)

    Guarino, Salvatore; Leopardi, Eleonora; Sorrenti, Salvatore; De Antoni, Enrico; Catania, Antonio; Alagaratnam, Swethan

    2014-10-01

    The rapid and dramatic incursion of the Internet and social networks in everyday life has revolutionised the methods of exchanging data. Web 2.0 represents the evolution of the Internet as we know it. Internet users are no longer passive receivers, and actively participate in the delivery of information. Medical education cannot evade this process. Increasingly, students are using tablets and smartphones to instantly retrieve medical information on the web or are exchanging materials on their Facebook pages. Medical educators cannot ignore this continuing revolution, and therefore the traditional academic schedules and didactic schemes should be questioned. Analysing opinions collected from medical students regarding old and new teaching methods and tools has become mandatory, with a view towards renovating the process of medical education. A cross-sectional online survey was created with Google® docs and administrated to all students of our medical school. Students were asked to express their opinion on their favourite teaching methods, learning tools, Internet websites and Internet delivery devices. Data analysis was performed using spss. The online survey was completed by 368 students. Although textbooks remain a cornerstone for training, students also identified Internet websites, multimedia non-online material, such as the Encyclopaedia on CD-ROM, and other non-online computer resources as being useful. The Internet represented an important aid to support students' learning needs, but textbooks are still their resource of choice. Among the websites noted, Google and Wikipedia significantly surpassed the peer-reviewed medical databases, and access to the Internet was primarily through personal computers in preference to other Internet access devices, such as mobile phones and tablet computers. Increasingly, students are using tablets and smartphones to instantly retrieve medical information. © 2014 John Wiley & Sons Ltd.

  1. The Keyword Method of Foreign Vocabulary Learning: An Investigation of Its Generalizability. Working Paper No. 270.

    Science.gov (United States)

    Pressley, Michael; And Others

    In five experiments, college-age students of differing foreign language-learning abilities were asked to learn Latin word translations to determine the effectiveness of the keyword method of foreign language vocabulary learning. The Latin words were the types for which it has been argued that the keyword method effects would be maximized (the…

  2. Impact of virtual learning environment (VLE): A technological approach to genetics teaching on high school students' content knowledge, self-efficacy and career goal aspirations

    Science.gov (United States)

    Kandi, Kamala M.

    This study examines the effect of a technology-based instructional tool 'Geniverse' on the content knowledge gains, Science Self-Efficacy, Technology Self-Efficacy, and Career Goal Aspirations among 283 high school learners. The study was conducted in four urban high schools, two of which have achieved Adequate Yearly Progress (AYP) and two have not. Students in both types of schools were taught genetics either through Geniverse, a virtual learning environment or Dragon genetics, a paper-pencil activity embedded in traditional instructional method. Results indicated that students in all schools increased their knowledge of genetics using either type of instructional approach. Students who were taught using Geniverse demonstrated an advantage for genetics knowledge although the effect was small. These increases were more pronounced in the schools that had been meeting the AYP goal. The other significant effect for Geniverse was that students in the technology-enhanced classrooms increased in science Self-Efficacy while students in the non-technology enhanced classrooms decreased. In addition, students from Non-AYP schools showed an improvement in Science and Technology Self-Efficacy; however the effects were small. The implications of these results for the future use of technology-enriched classrooms were discussed. Keywords: Technology-based instruction, Self-Efficacy, career goals and Adequate Yearly Progress (AYP).

  3. Advances in the application of genetic manipulation methods to apicomplexan parasites.

    Science.gov (United States)

    Suarez, C E; Bishop, R P; Alzan, H F; Poole, W A; Cooke, B M

    2017-10-01

    Apicomplexan parasites such as Babesia, Theileria, Eimeria, Cryptosporidium and Toxoplasma greatly impact animal health globally, and improved, cost-effective measures to control them are urgently required. These parasites have complex multi-stage life cycles including obligate intracellular stages. Major gaps in our understanding of the biology of these relatively poorly characterised parasites and the diseases they cause severely limit options for designing novel control methods. Here we review potentially important shared aspects of the biology of these parasites, such as cell invasion, host cell modification, and asexual and sexual reproduction, and explore the potential of the application of relatively well-established or newly emerging genetic manipulation methods, such as classical transfection or gene editing, respectively, for closing important gaps in our knowledge of the function of specific genes and proteins, and the biology of these parasites. In addition, genetic manipulation methods impact the development of novel methods of control of the diseases caused by these economically important parasites. Transient and stable transfection methods, in conjunction with whole and deep genome sequencing, were initially instrumental in improving our understanding of the molecular biology of apicomplexan parasites and paved the way for the application of the more recently developed gene editing methods. The increasingly efficient and more recently developed gene editing methods, in particular those based on the CRISPR/Cas9 system and previous conceptually similar techniques, are already contributing to additional gene function discovery using reverse genetics and related approaches. However, gene editing methods are only possible due to the increasing availability of in vitro culture, transfection, and genome sequencing and analysis techniques. We envisage that rapid progress in the development of novel gene editing techniques applied to apicomplexan parasites of

  4. Statistical and Machine Learning forecasting methods: Concerns and ways forward.

    Science.gov (United States)

    Makridakis, Spyros; Spiliotis, Evangelos; Assimakopoulos, Vassilios

    2018-01-01

    Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions.

  5. Statistical and Machine Learning forecasting methods: Concerns and ways forward

    Science.gov (United States)

    Makridakis, Spyros; Assimakopoulos, Vassilios

    2018-01-01

    Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions. PMID:29584784

  6. Machine Learning-Empowered Biometric Methods for Biomedicine Applications

    Directory of Open Access Journals (Sweden)

    Qingxue Zhang

    2017-07-01

    Full Text Available Nowadays, pervasive computing technologies are paving a promising way for advanced smart health applications. However, a key impediment faced by wide deployment of these assistive smart devices, is the increasing privacy and security issue, such as how to protect access to sensitive patient data in the health record. Focusing on this challenge, biometrics are attracting intense attention in terms of effective user identification to enable confidential health applications. In this paper, we take special interest in two bio-potential-based biometric modalities, electrocardiogram (ECG and electroencephalogram (EEG, considering that they are both unique to individuals, and more reliable than token (identity card and knowledge-based (username/password methods. After extracting effective features in multiple domains from ECG/EEG signals, several advanced machine learning algorithms are introduced to perform the user identification task, including Neural Network, K-nearest Neighbor, Bagging, Random Forest and AdaBoost. Experimental results on two public ECG and EEG datasets show that ECG is a more robust biometric modality compared to EEG, leveraging a higher signal to noise ratio and also more distinguishable morphological patterns. Among different machine learning classifiers, the random forest greatly outperforms the others and owns an identification rate as high as 98%. This study is expected to demonstrate that properly selected biometric empowered by an effective machine learner owns a great potential, to enable confidential biomedicine applications in the era of smart digital health.

  7. E-learning support for Economic-mathematical methods

    Directory of Open Access Journals (Sweden)

    Pavel Kolman

    2009-01-01

    Full Text Available Article is describing process of creating and using of e-learning program for graphical solution of li­near programming problems that is used in the Economic mathematical methods course on Faculty of Business and Economics, MZLU. The program was created within FRVŠ 788/2008 grant and is intended for practicing of graphical solution of LP problems and allows better understanding of the li­near programming problems. In the article is on one hand described the way, how does the program work, it means how were the algorithms implemented, and on the other hand there is described way of use of that program. The program is constructed for working with integer and rational numbers. At the end of the article are shown basic statistics of programs use of students in the present form and the part-time form of study. It is mainly the number of programs downloads and comparison to another programs and students opinion on the e-learning support.

  8. The development and standardization of testing methods for genetically modified organisms and their derived products.

    Science.gov (United States)

    Zhang, Dabing; Guo, Jinchao

    2011-07-01

    As the worldwide commercialization of genetically modified organisms (GMOs) increases and consumers concern the safety of GMOs, many countries and regions are issuing labeling regulations on GMOs and their products. Analytical methods and their standardization for GM ingredients in foods and feed are essential for the implementation of labeling regulations. To date, the GMO testing methods are mainly based on the inserted DNA sequences and newly produced proteins in GMOs. This paper presents an overview of GMO testing methods as well as their standardization. © 2011 Institute of Botany, Chinese Academy of Sciences.

  9. Mutation-based learning to improve student autonomy and scientific inquiry skills in a large genetics laboratory course.

    Science.gov (United States)

    Wu, Jinlu

    2013-01-01

    Laboratory education can play a vital role in developing a learner's autonomy and scientific inquiry skills. In an innovative, mutation-based learning (MBL) approach, students were instructed to redesign a teacher-designed standard experimental protocol by a "mutation" method in a molecular genetics laboratory course. Students could choose to delete, add, reverse, or replace certain steps of the standard protocol to explore questions of interest to them in a given experimental scenario. They wrote experimental proposals to address their rationales and hypotheses for the "mutations"; conducted experiments in parallel, according to both standard and mutated protocols; and then compared and analyzed results to write individual lab reports. Various autonomy-supportive measures were provided in the entire experimental process. Analyses of student work and feedback suggest that students using the MBL approach 1) spend more time discussing experiments, 2) use more scientific inquiry skills, and 3) find the increased autonomy afforded by MBL more enjoyable than do students following regimented instructions in a conventional "cookbook"-style laboratory. Furthermore, the MBL approach does not incur an obvious increase in labor and financial costs, which makes it feasible for easy adaptation and implementation in a large class.

  10. Genetic contributions of the serotonin transporter to social learning of fear and economic decision making.

    Science.gov (United States)

    Crişan, Liviu G; Pana, Simona; Vulturar, Romana; Heilman, Renata M; Szekely, Raluca; Druğa, Bogdan; Dragoş, Nicolae; Miu, Andrei C

    2009-12-01

    Serotonin (5-HT) modulates emotional and cognitive functions such as fear conditioning (FC) and decision making. This study investigated the effects of a functional polymorphism in the regulatory region (5-HTTLPR) of the human 5-HT transporter (5-HTT) gene on observational FC, risk taking and susceptibility to framing in decision making under uncertainty, as well as multidimensional anxiety and autonomic control of the heart in healthy volunteers. The present results indicate that in comparison to the homozygotes for the long (l) version of 5-HTTLPR, the carriers of the short (s) version display enhanced observational FC, reduced financial risk taking and increased susceptibility to framing in economic decision making. We also found that s-carriers have increased trait anxiety due to threat in social evaluation, and ambiguous threat perception. In addition, s-carriers also show reduced autonomic control over the heart, and a pattern of reduced vagal tone and increased sympathetic activity in comparison to l-homozygotes. This is the first genetic study that identifies the association of a functional polymorphism in a key neurotransmitter-related gene with complex social-emotional and cognitive processes. The present set of results suggests an endophenotype of anxiety disorders, characterized by enhanced social learning of fear, impaired decision making and dysfunctional autonomic activity.

  11. DANN: a deep learning approach for annotating the pathogenicity of genetic variants.

    Science.gov (United States)

    Quang, Daniel; Chen, Yifei; Xie, Xiaohui

    2015-03-01

    Annotating genetic variants, especially non-coding variants, for the purpose of identifying pathogenic variants remains a challenge. Combined annotation-dependent depletion (CADD) is an algorithm designed to annotate both coding and non-coding variants, and has been shown to outperform other annotation algorithms. CADD trains a linear kernel support vector machine (SVM) to differentiate evolutionarily derived, likely benign, alleles from simulated, likely deleterious, variants. However, SVMs cannot capture non-linear relationships among the features, which can limit performance. To address this issue, we have developed DANN. DANN uses the same feature set and training data as CADD to train a deep neural network (DNN). DNNs can capture non-linear relationships among features and are better suited than SVMs for problems with a large number of samples and features. We exploit Compute Unified Device Architecture-compatible graphics processing units and deep learning techniques such as dropout and momentum training to accelerate the DNN training. DANN achieves about a 19% relative reduction in the error rate and about a 14% relative increase in the area under the curve (AUC) metric over CADD's SVM methodology. All data and source code are available at https://cbcl.ics.uci.edu/public_data/DANN/. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  12. State Generation Method for Humanoid Motion Planning Based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Xuyang Wang

    2012-05-01

    Full Text Available A new approach to generate the original motion data for humanoid motion planning is presented in this paper. And a state generator is developed based on the genetic algorithm, which enables users to generate various motion states without using any reference motion data. By specifying various types of constraints such as configuration constraints and contact constraints, the state generator can generate stable states that satisfy the constraint conditions for humanoid robots. To deal with the multiple constraints and inverse kinematics, the state generation is finally simplified as a problem of optimizing and searching. In our method, we introduce a convenient mathematic representation for the constraints involved in the state generator, and solve the optimization problem with the genetic algorithm to acquire a desired state. To demonstrate the effectiveness and advantage of the method, a number of motion states are generated according to the requirements of the motion.

  13. State Generation Method for Humanoid Motion Planning Based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Xuyang Wang

    2008-11-01

    Full Text Available A new approach to generate the original motion data for humanoid motion planning is presented in this paper. And a state generator is developed based on the genetic algorithm, which enables users to generate various motion states without using any reference motion data. By specifying various types of constraints such as configuration constraints and contact constraints, the state generator can generate stable states that satisfy the constraint conditions for humanoid robots.To deal with the multiple constraints and inverse kinematics, the state generation is finally simplified as a problem of optimizing and searching. In our method, we introduce a convenient mathematic representation for the constraints involved in the state generator, and solve the optimization problem with the genetic algorithm to acquire a desired state. To demonstrate the effectiveness and advantage of the method, a number of motion states are generated according to the requirements of the motion.

  14. An optimization method of relativistic backward wave oscillator using particle simulation and genetic algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Zaigao; Wang, Jianguo [Key Laboratory for Physical Electronics and Devices of the Ministry of Education, Xi' an Jiaotong University, Xi' an, Shaanxi 710049 (China); Northwest Institute of Nuclear Technology, P.O. Box 69-12, Xi' an, Shaanxi 710024 (China); Wang, Yue; Qiao, Hailiang; Zhang, Dianhui [Northwest Institute of Nuclear Technology, P.O. Box 69-12, Xi' an, Shaanxi 710024 (China); Guo, Weijie [Key Laboratory for Physical Electronics and Devices of the Ministry of Education, Xi' an Jiaotong University, Xi' an, Shaanxi 710049 (China)

    2013-11-15

    Optimal design method of high-power microwave source using particle simulation and parallel genetic algorithms is presented in this paper. The output power, simulated by the fully electromagnetic particle simulation code UNIPIC, of the high-power microwave device is given as the fitness function, and the float-encoding genetic algorithms are used to optimize the high-power microwave devices. Using this method, we encode the heights of non-uniform slow wave structure in the relativistic backward wave oscillators (RBWO), and optimize the parameters on massively parallel processors. Simulation results demonstrate that we can obtain the optimal parameters of non-uniform slow wave structure in the RBWO, and the output microwave power enhances 52.6% after the device is optimized.

  15. Loop-mediated isothermal amplification (LAMP) method for detection of genetically modified maize T25.

    Science.gov (United States)

    Xu, Junyi; Zheng, Qiuyue; Yu, Ling; Liu, Ran; Zhao, Xin; Wang, Gang; Wang, Qinghua; Cao, Jijuan

    2013-11-01

    The loop-mediated isothermal amplification (LAMP) assay indicates a potential and valuable means for genetically modified organism (GMO) detection especially for its rapidity, simplicity, and low cost. We developed and evaluated the specificity and sensitivity of the LAMP method for rapid detection of the genetically modified (GM) maize T25. A set of six specific primers was successfully designed to recognize six distinct sequences on the target gene, including a pair of inner primers, a pair of outer primers, and a pair of loop primers. The optimum reaction temperature and time were verified to be 65°C and 45 min, respectively. The detection limit of this LAMP assay was 5 g kg(-1) GMO component. Comparative experiments showed that the LAMP assay was a simple, rapid, accurate, and specific method for detecting the GM maize T25.

  16. A Novel Technique for Steganography Method Based on Improved Genetic Algorithm Optimization in Spatial Domain

    Directory of Open Access Journals (Sweden)

    M. Soleimanpour-moghadam

    2013-06-01

    Full Text Available This paper devotes itself to the study of secret message delivery using cover image and introduces a novel steganographic technique based on genetic algorithm to find a near-optimum structure for the pair-wise least-significant-bit (LSB matching scheme. A survey of the related literatures shows that the LSB matching method developed by Mielikainen, employs a binary function to reduce the number of changes of LSB values. This method verifiably reduces the probability of detection and also improves the visual quality of stego images. So, our proposal draws on the Mielikainen's technique to present an enhanced dual-state scoring model, structured upon genetic algorithm which assesses the performance of different orders for LSB matching and searches for a near-optimum solution among all the permutation orders. Experimental results confirm superiority of the new approach compared to the Mielikainen’s pair-wise LSB matching scheme.

  17. Approximate k-NN delta test minimization method using genetic algorithms: Application to time series

    CERN Document Server

    Mateo, F; Gadea, Rafael; Sovilj, Dusan

    2010-01-01

    In many real world problems, the existence of irrelevant input variables (features) hinders the predictive quality of the models used to estimate the output variables. In particular, time series prediction often involves building large regressors of artificial variables that can contain irrelevant or misleading information. Many techniques have arisen to confront the problem of accurate variable selection, including both local and global search strategies. This paper presents a method based on genetic algorithms that intends to find a global optimum set of input variables that minimize the Delta Test criterion. The execution speed has been enhanced by substituting the exact nearest neighbor computation by its approximate version. The problems of scaling and projection of variables have been addressed. The developed method works in conjunction with MATLAB's Genetic Algorithm and Direct Search Toolbox. The goodness of the proposed methodology has been evaluated on several popular time series examples, and also ...

  18. 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

  19. Geocoding location expressions in Twitter messages: A preference learning method

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    2014-12-01

    Full Text Available Resolving location expressions in text to the correct physical location, also known as geocoding or grounding, is complicated by the fact that so many places around the world share the same name. Correct resolution is made even more difficult when there is little context to determine which place is intended, as in a 140-character Twitter message, or when location cues from different sources conflict, as may be the case among different metadata fields of a Twitter message. We used supervised machine learning to weigh the different fields of the Twitter message and the features of a world gazetteer to create a model that will prefer the correct gazetteer candidate to resolve the extracted expression. We evaluated our model using the F1 measure and compared it to similar algorithms. Our method achieved results higher than state-of-the-art competitors.

  20. Employing Machine-Learning Methods to Study Young Stellar Objects

    Science.gov (United States)

    Moore, Nicholas

    2018-01-01

    Vast amounts of data exist in the astronomical data archives, and yet a large number of sources remain unclassified. We developed a multi-wavelength pipeline to classify infrared sources. The pipeline uses supervised machine learning methods to classify objects into the appropriate categories. The program is fed data that is already classified to train it, and is then applied to unknown catalogues. The primary use for such a pipeline is the rapid classification and cataloging of data that would take a much longer time to classify otherwise. While our primary goal is to study young stellar objects (YSOs), the applications extend beyond the scope of this project. We present preliminary results from our analysis and discuss future applications.

  1. Constructs and methods for genome editing and genetic engineering of fungi and protists

    Science.gov (United States)

    Hittinger, Christopher Todd; Alexander, William Gerald

    2018-01-30

    Provided herein are constructs for genome editing or genetic engineering in fungi or protists, methods of using the constructs and media for use in selecting cells. The construct include a polynucleotide encoding a thymidine kinase operably connected to a promoter, suitably a constitutive promoter; a polynucleotide encoding an endonuclease operably connected to an inducible promoter; and a recognition site for the endonuclease. The constructs may also include selectable markers for use in selecting recombinations.

  2. A rapid method for establishment of a reverse genetics system for canine parvovirus.

    Science.gov (United States)

    Yu, Yongle; Su, Jun; Wang, Jigui; Xi, Ji; Mao, Yaping; Hou, Qiang; Zhang, Xiaomei; Liu, Weiquan

    2017-12-01

    Canine parvovirus (CPV) is an important and highly prevalent pathogen of dogs that causes acute hemorrhagic enteritis disease. Here, we describe a rapid method for the construction and characterization of a full-length infectious clone (rCPV) of CPV. Feline kidney (F81) cells were transfected with rCPV incorporating an engineered EcoR I site that served as a genetic marker. The rescued virus was indistinguishable from that of wild-type virus in its biological properties.

  3. General Methods for Evolutionary Quantitative Genetic Inference from Generalized Mixed Models.

    Science.gov (United States)

    de Villemereuil, Pierre; Schielzeth, Holger; Nakagawa, Shinichi; Morrissey, Michael

    2016-11-01

    Methods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioral traits, have inherently nonnormal distributions. The generalized linear mixed model (GLMM) framework has become a widely used tool for estimating quantitative genetic parameters for nonnormal traits. However, whereas GLMMs provide inference on a statistically convenient latent scale, it is often desirable to express quantitative genetic parameters on the scale upon which traits are measured. The parameters of fitted GLMMs, despite being on a latent scale, fully determine all quantities of potential interest on the scale on which traits are expressed. We provide expressions for deriving each of such quantities, including population means, phenotypic (co)variances, variance components including additive genetic (co)variances, and parameters such as heritability. We demonstrate that fixed effects have a strong impact on those parameters and show how to deal with this by averaging or integrating over fixed effects. The expressions require integration of quantities determined by the link function, over distributions of latent values. In general cases, the required integrals must be solved numerically, but efficient methods are available and we provide an implementation in an R package, QGglmm. We show that known formulas for quantities such as heritability of traits with binomial and Poisson distributions are special cases of our expressions. Additionally, we show how fitted GLMM can be incorporated into existing methods for predicting evolutionary trajectories. We demonstrate the accuracy of the resulting method for evolutionary prediction by simulation and apply our approach to data from a wild pedigreed vertebrate population. Copyright © 2016 de Villemereuil et al.

  4. METHODS FOR INOCULATION WITH Fusarium guttiforme AND GENETIC RESISTANCE OF PINEAPPLE ( Ananas comosus var. comosus )

    OpenAIRE

    WANDREILLA MOREIRA GARCIA; WILLIAN KRAUSE; DEJÂNIA VIEIRA DE ARAÚJO; ISANE VERA KARSBURG; RIVANILDO DALLACORT

    2017-01-01

    The objective of this work was to evaluate Fusarium guttiforme inoculation methods and genetic resistance of pineapple accessions. Thus, three experiments were conducted: pathogen inoculation of different leaf types ( B, D and F ) of pineapple (1), pathogen inoculation of pineapple cuttings and detached D leaves (2), and identification of resistance to fusariosis in 19 pineapple accessions (3) sampled in the State of Mato Grosso, Brazil. The cultivars Pérola (susceptible...

  5. SERS-based detection methods for screening of genetically modified bacterial strains

    DEFF Research Database (Denmark)

    Morelli, Lidia

    factories vary largely, including industrial production of valuable compounds for biofuels, polymer synthesis and food, cosmetic and pharmaceutical industry. The improvement of computational and biochemical tools has revolutionized the synthesis of novel modified microbial strains, opening up new......The importance of metabolic engineering has been growing over the last decades, establishing the use of genetically modified microbial strains for overproduction of metabolites at industrial scale as an innovative, convenient and biosustainable method. Nowadays, application areas of microbial...

  6. Molecular Genetic Methods Implementation for Phytopathogen Identification in Forest Stands and Nurseries of the Russian Federation

    Directory of Open Access Journals (Sweden)

    T. S. Alimova

    2014-08-01

    Full Text Available The results of the application of molecular genetics methods for the analysis of the plant pathogens present in forest plantations and nurseries of the Russian Federation, including doughnut fungus and annosum root rot are presented. The prospects and benefits of using DNA analysis for early diagnosis of plant diseases without isolation of the pathogen in pure culture, shortening time of analysis, and the possibility of mass screening are discussed.

  7. New Learning Methods for Marine Oil Spill Response Training

    Directory of Open Access Journals (Sweden)

    Justiina Halonen

    2017-06-01

    Full Text Available In Finland the Regional Fire and Rescue Services (RFRS are responsible for near shore oil spill response and shoreline cleanup operations. In addition, they assist in other types of maritime incidents, such as search and rescue operations and fire-fighting on board. These statutory assignments require the RFRS to have capability to act both on land and at sea. As maritime incidents occur infrequently, little routine has been established. In order to improve their performance in maritime operations, the RFRS are participating in a new oil spill training programme to be launched by South-Eastern Finland University of Applied Sciences. This training programme aims to utilize new educational methods; e-learning and simulator based training. In addition to fully exploiting the existing navigational bridge simulator, radio communication simulator and crisis management simulator, an entirely new simulator is developed. This simulator is designed to model the oil recovery process; recovery method, rate and volume in various conditions with different oil types. New simulator enables creation of a comprehensive training programme covering training tasks from a distress call to the completion of an oil spill response operation. Structure of the training programme, as well as the training objectives, are based on the findings from competence and education surveys conducted in spring 2016. In these results, a need for vessel maneuvering and navigation exercises together with actual response measures training were emphasized. Also additional training for maritime radio communication, GMDSS-emergency protocols and collaboration with maritime authorities were seemed important. This paper describes new approach to the maritime operations training designed for rescue authorities, a way of learning by doing, without mobilising the vessels at sea.

  8. BEBP: An Poisoning Method Against Machine Learning Based IDSs

    OpenAIRE

    Li, Pan; Liu, Qiang; Zhao, Wentao; Wang, Dongxu; Wang, Siqi

    2018-01-01

    In big data era, machine learning is one of fundamental techniques in intrusion detection systems (IDSs). However, practical IDSs generally update their decision module by feeding new data then retraining learning models in a periodical way. Hence, some attacks that comprise the data for training or testing classifiers significantly challenge the detecting capability of machine learning-based IDSs. Poisoning attack, which is one of the most recognized security threats towards machine learning...

  9. Extremely Randomized Machine Learning Methods for Compound Activity Prediction

    Directory of Open Access Journals (Sweden)

    Wojciech M. Czarnecki

    2015-11-01

    Full Text Available Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called ‘extremely randomized methods’—Extreme Entropy Machine and Extremely Randomized Trees—for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their ‘non-extreme’ competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.

  10. Housing Value Forecasting Based on Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Jingyi Mu

    2014-01-01

    Full Text Available In the era of big data, many urgent issues to tackle in all walks of life all can be solved via big data technique. Compared with the Internet, economy, industry, and aerospace fields, the application of big data in the area of architecture is relatively few. In this paper, on the basis of the actual data, the values of Boston suburb houses are forecast by several machine learning methods. According to the predictions, the government and developers can make decisions about whether developing the real estate on corresponding regions or not. In this paper, support vector machine (SVM, least squares support vector machine (LSSVM, and partial least squares (PLS methods are used to forecast the home values. And these algorithms are compared according to the predicted results. Experiment shows that although the data set exists serious nonlinearity, the experiment result also show SVM and LSSVM methods are superior to PLS on dealing with the problem of nonlinearity. The global optimal solution can be found and best forecasting effect can be achieved by SVM because of solving a quadratic programming problem. In this paper, the different computation efficiencies of the algorithms are compared according to the computing times of relevant algorithms.

  11. Nurse practitioner preferences for distance education methods related to learning style, course content, and achievement.

    Science.gov (United States)

    Andrusyszyn, M A; Cragg, C E; Humbert, J

    2001-04-01

    The relationships among multiple distance delivery methods, preferred learning style, content, and achievement was sought for primary care nurse practitioner students. A researcher-designed questionnaire was completed by 86 (71%) participants, while 6 engaged in follow-up interviews. The results of the study included: participants preferred learning by "considering the big picture"; "setting own learning plans"; and "focusing on concrete examples." Several positive associations were found: learning on own with learning by reading, and setting own learning plans; small group with learning through discussion; large group with learning new things through hearing and with having learning plans set by others. The most preferred method was print-based material and the least preferred method was audio tape. The most suited method for content included video teleconferencing for counseling, political action, and transcultural issues; and video tape for physical assessment. Convenience, self-direction, and timing of learning were more important than delivery method or learning style. Preferred order of learning was reading, discussing, observing, doing, and reflecting. Recommended considerations when designing distance courses include a mix of delivery methods, specific content, outcomes, learner characteristics, and state of technology.

  12. Computerization of Hungarian reforestation manual with machine learning methods

    Science.gov (United States)

    Czimber, Kornél; Gálos, Borbála; Mátyás, Csaba; Bidló, András; Gribovszki, Zoltán

    2017-04-01

    Hungarian forests are highly sensitive to the changing climate, especially to the available precipitation amount. Over the past two decades several drought damages were observed for tree species which are in the lower xeric limit of their distribution. From year to year these affected forest stands become more difficult to reforest with the same native species because these are not able to adapt to the increasing probability of droughts. The climate related parameter set of the Hungarian forest stand database needs updates. Air humidity that was formerly used to define the forest climate zones is not measured anymore and its value based on climate model outputs is highly uncertain. The aim was to develop a novel computerized and objective method to describe the species-specific climate conditions that is essential for survival, growth and optimal production of the forest ecosystems. The method is expected to project the species spatial distribution until 2100 on the basis of regional climate model simulations. Until now, Hungarian forest managers have been using a carefully edited spreadsheet for reforestation purposes. Applying binding regulations this spreadsheet prescribes the stand-forming and admixed tree species and their expected growth rate for each forest site types. We are going to present a new machine learning based method to replace the former spreadsheet. We took into great consideration of various methods, such as maximum likelihood, Bayesian networks, Fuzzy logic. The method calculates distributions, setups classification, which can be validated and modified by experts if necessary. Projected climate change conditions makes necessary to include into this system an additional climate zone that does not exist in our region now, as well as new options for potential tree species. In addition to or instead of the existing ones, the influence of further limiting parameters (climatic extremes, soil water retention) are also investigated. Results will be

  13. Genetic algorithms

    Science.gov (United States)

    Wang, Lui; Bayer, Steven E.

    1991-01-01

    Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.

  14. Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

    Science.gov (United States)

    Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

    2017-03-01

    Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. DNA extraction methods for detecting genetically modified foods: A comparative study.

    Science.gov (United States)

    Elsanhoty, Rafaat M; Ramadan, Mohamed Fawzy; Jany, Klaus Dieter

    2011-06-15

    The work presented in this manuscript was achieved to compare six different methods for extracting DNA from raw maize and its derived products. The methods that gave higher yield and quality of DNA were chosen to detect the genetic modification in the samples collected from the Egyptian market. The different methods used were evaluated for extracting DNA from maize kernels (without treatment), maize flour (mechanical treatment), canned maize (sweet corn), frozen maize (sweet corn), maize starch, extruded maize, popcorn, corn flacks, maize snacks, and bread made from corn flour (mechanical and thermal treatments). The quality and quantity of the DNA extracted from the standards, containing known percentages of GMO material and from the different food products were evaluated. For qualitative detection of the GMO varieties in foods, the GMOScreen 35S/NOS test kit was used, to screen the genetic modification in the samples. The positive samples for the 35S promoter and/or the NOS terminator were identified by the standard methods adopted by EU. All of the used methods extracted yielded good DNA quality. However, we noted that the purest DNA extract were obtained using the DNA extraction kit (Roche) and this generally was the best method for extracting DNA from most of the maize-derived foods. We have noted that the yield of DNA extracted from maize-derived foods was generally lower in the processed products. The results indicated that 17 samples were positive for the presence of 35S promoter, while 34% from the samples were positive for the genetically modified maize line Bt-176. Copyright © 2010 Elsevier Ltd. All rights reserved.

  16. Unified method to integrate and blend several, potentially related, sources of information for genetic evaluation.

    Science.gov (United States)

    Vandenplas, Jérémie; Colinet, Frederic G; Gengler, Nicolas

    2014-09-30

    A condition to predict unbiased estimated breeding values by best linear unbiased prediction is to use simultaneously all available data. However, this condition is not often fully met. For example, in dairy cattle, internal (i.e. local) populations lead to evaluations based only on internal records while widely used foreign sires have been selected using internally unavailable external records. In such cases, internal genetic evaluations may be less accurate and biased. Because external records are unavailable, methods were developed to combine external information that summarizes these records, i.e. external estimated breeding values and associated reliabilities, with internal records to improve accuracy of internal genetic evaluations. Two issues of these methods concern double-counting of contributions due to relationships and due to records. These issues could be worse if external information came from several evaluations, at least partially based on the same records, and combined into a single internal evaluation. Based on a Bayesian approach, the aim of this research was to develop a unified method to integrate and blend simultaneously several sources of information into an internal genetic evaluation by avoiding double-counting of contributions due to relationships and due to records. This research resulted in equations that integrate and blend simultaneously several sources of information and avoid double-counting of contributions due to relationships and due to records. The performance of the developed equations was evaluated using simulated and real datasets. The results showed that the developed equations integrated and blended several sources of information well into a genetic evaluation. The developed equations also avoided double-counting of contributions due to relationships and due to records. Furthermore, because all available external sources of information were correctly propagated, relatives of external animals benefited from the integrated

  17. An Enhanced Genetic Approach to Composing Cooperative Learning Groups for Multiple Grouping Criteria

    Science.gov (United States)

    Hwang, Gwo-Jen; Yin, Peng-Yeng; Hwang, Chi-Wei; Tsai, Chin-Chung

    2008-01-01

    Cooperative learning is known to be an effective educational strategy in enhancing the learning performance of students. The goal of a cooperative learning group is to maximize all members' learning efficacy. This is accomplished via promoting each other's success, through assisting, sharing, mentoring, explaining, and encouragement. To achieve…

  18. Method for the production of l-serine using genetically engineered microorganisms deficient in serine degradation pathways

    DEFF Research Database (Denmark)

    2016-01-01

    The present invention generally relates to the microbiological industry, and specifically to the production of L-serine using genetically modified bacteria. The present invention provides genetically modified microorganisms, such as bacteria, wherein the expression of genes encoding for enzymes...... concentrations of serine. The present invention also provides methods for the production of L-serine or L-serine derivative using such genetically modified microorganisms....

  19. Understanding the Effects of Time on Collaborative Learning Processes in Problem Based Learning: A Mixed Methods Study

    Science.gov (United States)

    Hommes, J.; Van den Bossche, P.; de Grave, W.; Bos, G.; Schuwirth, L.; Scherpbier, A.

    2014-01-01

    Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning…

  20. Project-Based Learning Using Discussion and Lesson-Learned Methods via Social Media Model for Enhancing Problem Solving Skills

    Science.gov (United States)

    Jewpanich, Chaiwat; Piriyasurawong, Pallop

    2015-01-01

    This research aims to 1) develop the project-based learning using discussion and lesson-learned methods via social media model (PBL-DLL SoMe Model) used for enhancing problem solving skills of undergraduate in education student, and 2) evaluate the PBL-DLL SoMe Model used for enhancing problem solving skills of undergraduate in education student.…

  1. Consequences of population topology for studying gene flow using link-based landscape genetic methods.

    Science.gov (United States)

    van Strien, Maarten J

    2017-07-01

    Many landscape genetic studies aim to determine the effect of landscape on gene flow between populations. These studies frequently employ link-based methods that relate pairwise measures of historical gene flow to measures of the landscape and the geographical distance between populations. However, apart from landscape and distance, there is a third important factor that can influence historical gene flow, that is, population topology (i.e., the arrangement of populations throughout a landscape). As the population topology is determined in part by the landscape configuration, I argue that it should play a more prominent role in landscape genetics. Making use of existing literature and theoretical examples, I discuss how population topology can influence results in landscape genetic studies and how it can be taken into account to improve the accuracy of these results. In support of my arguments, I have performed a literature review of landscape genetic studies published during the first half of 2015 as well as several computer simulations of gene flow between populations. First, I argue why one should carefully consider which population pairs should be included in link-based analyses. Second, I discuss several ways in which the population topology can be incorporated in response and explanatory variables. Third, I outline why it is important to sample populations in such a way that a good representation of the population topology is obtained. Fourth, I discuss how statistical testing for link-based approaches could be influenced by the population topology. I conclude the article with six recommendations geared toward better incorporating population topology in link-based landscape genetic studies.

  2. 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)

  3. Method of fault diagnosis in nuclear power plant base on genetic algorithm and knowledge base

    International Nuclear Information System (INIS)

    Zhou Yangping; Zhao Bingquan

    2000-01-01

    Via using the knowledge base, combining Genetic Algorithm and classical probability and contraposing the characteristic of the fault diagnosis of NPP. The authors put forward a method of fault diagnosis. In the process of fault diagnosis, this method contact the state of NPP with the colony in GA and transform the colony to get the individual that adapts to the condition. On the 950MW full size simulator in Beijing NPP simulation training center, experimentation shows it has comparative adaptability to the imperfection of expert knowledge, illusive signal and other instance

  4. Transforming growth factor-β and breast cancer: Lessons learned from genetically altered mouse models

    International Nuclear Information System (INIS)

    Wakefield, Lalage M; Yang, Yu-an; Dukhanina, Oksana

    2000-01-01

    Transforming growth factor (TGF)-βs are plausible candidate tumor suppressors in the breast. They also have oncogenic activities under certain circumstances, however. Genetically altered mouse models provide powerful tools to analyze the complexities of TGF-βaction in the context of the whole animal. Overexpression of TGF-β can suppress tumorigenesis in the mammary gland, raising the possibility that use of pharmacologic agents to enhance TGF-β function locally might be an effective method for the chemoprevention of breast cancer. Conversely, loss of TGF-β response increases spontaneous and induced tumorigenesis in the mammary gland. This confirms that endogenous TGF-βs have tumor suppressor activity in the mammary gland, and suggests that the loss of TGF-β receptors seen in some human breast hyperplasias may play a causal role in tumor development

  5. Cross-organism learning method to discover new gene functionalities.

    Science.gov (United States)

    Domeniconi, Giacomo; Masseroli, Marco; Moro, Gianluca; Pinoli, Pietro

    2016-04-01

    Knowledge of gene and protein functions is paramount for the understanding of physiological and pathological biological processes, as well as in the development of new drugs and therapies. Analyses for biomedical knowledge discovery greatly benefit from the availability of gene and protein functional feature descriptions expressed through controlled terminologies and ontologies, i.e., of gene and protein biomedical controlled annotations. In the last years, several databases of such annotations have become available; yet, these valuable annotations are incomplete, include errors and only some of them represent highly reliable human curated information. Computational techniques able to reliably predict new gene or protein annotations with an associated likelihood value are thus paramount. Here, we propose a novel cross-organisms learning approach to reliably predict new functionalities for the genes of an organism based on the known controlled annotations of the genes of another, evolutionarily related and better studied, organism. We leverage a new representation of the annotation discovery problem and a random perturbation of the available controlled annotations to allow the application of supervised algorithms to predict with good accuracy unknown gene annotations. Taking advantage of the numerous gene annotations available for a well-studied organism, our cross-organisms learning method creates and trains better prediction models, which can then be applied to predict new gene annotations of a target organism. We tested and compared our method with the equivalent single organism approach on different gene annotation datasets of five evolutionarily related organisms (Homo sapiens, Mus musculus, Bos taurus, Gallus gallus and Dictyostelium discoideum). Results show both the usefulness of the perturbation method of available annotations for better prediction model training and a great improvement of the cross-organism models with respect to the single-organism ones

  6. Machine Learning Methods for Prediction of CDK-Inhibitors

    Science.gov (United States)

    Ramana, Jayashree; Gupta, Dinesh

    2010-01-01

    Progression through the cell cycle involves the coordinated activities of a suite of cyclin/cyclin-dependent kinase (CDK) complexes. The activities of the complexes are regulated by CDK inhibitors (CDKIs). Apart from its role as cell cycle regulators, CDKIs are involved in apoptosis, transcriptional regulation, cell fate determination, cell migration and cytoskeletal dynamics. As the complexes perform crucial and diverse functions, these are important drug targets for tumour and stem cell therapeutic interventions. However, CDKIs are represented by proteins with considerable sequence heterogeneity and may fail to be identified by simple similarity search methods. In this work we have evaluated and developed machine learning methods for identification of CDKIs. We used different compositional features and evolutionary information in the form of PSSMs, from CDKIs and non-CDKIs for generating SVM and ANN classifiers. In the first stage, both the ANN and SVM models were evaluated using Leave-One-Out Cross-Validation and in the second stage these were tested on independent data sets. The PSSM-based SVM model emerged as the best classifier in both the stages and is publicly available through a user-friendly web interface at http://bioinfo.icgeb.res.in/cdkipred. PMID:20967128

  7. Machine-learning methods in the classification of water bodies

    Directory of Open Access Journals (Sweden)

    Sołtysiak Marek

    2016-06-01

    Full Text Available Amphibian species have been considered as useful ecological indicators. They are used as indicators of environmental contamination, ecosystem health and habitat quality., Amphibian species are sensitive to changes in the aquatic environment and therefore, may form the basis for the classification of water bodies. Water bodies in which there are a large number of amphibian species are especially valuable even if they are located in urban areas. The automation of the classification process allows for a faster evaluation of the presence of amphibian species in the water bodies. Three machine-learning methods (artificial neural networks, decision trees and the k-nearest neighbours algorithm have been used to classify water bodies in Chorzów – one of 19 cities in the Upper Silesia Agglomeration. In this case, classification is a supervised data mining method consisting of several stages such as building the model, the testing phase and the prediction. Seven natural and anthropogenic features of water bodies (e.g. the type of water body, aquatic plants, the purpose of the water body (destination, position of the water body in relation to any possible buildings, condition of the water body, the degree of littering, the shore type and fishing activities have been taken into account in the classification. The data set used in this study involved information about 71 different water bodies and 9 amphibian species living in them. The results showed that the best average classification accuracy was obtained with the multilayer perceptron neural network.

  8. Recent Advances in Conotoxin Classification by Using Machine Learning Methods.

    Science.gov (United States)

    Dao, Fu-Ying; Yang, Hui; Su, Zhen-Dong; Yang, Wuritu; Wu, Yun; Hui, Ding; Chen, Wei; Tang, Hua; Lin, Hao

    2017-06-25

    Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.

  9. Learning a specific content in technology education : Learning Study as collaborative method in Swedish preschool class using hands-on material 

    OpenAIRE

    Kilbrink, Nina; Bjurulf, Veronica; Blomberg, Ingela; Heidkamp, Anja; Hollsten, Ann-Christin

    2014-01-01

    This article describes the process of a learning study conducted in technology education in a Swedish preschool class. The learning study method used in this study is a collaborative method, where researchers and teachers work together as a team concerning teaching and learning about a specific learning object. The object of learning in this study concerns strong constructions and framed structures. This article describes how this learning study was conducted and discusses reflections made du...

  10. Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method.

    Science.gov (United States)

    Du, Lei; Huang, Heng; Yan, Jingwen; Kim, Sungeun; Risacher, Shannon L; Inlow, Mark; Moore, Jason H; Saykin, Andrew J; Shen, Li

    2016-05-15

    Structured sparse canonical correlation analysis (SCCA) models have been used to identify imaging genetic associations. These models either use group lasso or graph-guided fused lasso to conduct feature selection and feature grouping simultaneously. The group lasso based methods require prior knowledge to define the groups, which limits the capability when prior knowledge is incomplete or unavailable. The graph-guided methods overcome this drawback by using the sample correlation to define the constraint. However, they are sensitive to the sign of the sample correlation, which could introduce undesirable bias if the sign is wrongly estimated. We introduce a novel SCCA model with a new penalty, and develop an efficient optimization algorithm. Our method has a strong upper bound for the grouping effect for both positively and negatively correlated features. We show that our method performs better than or equally to three competing SCCA models on both synthetic and real data. In particular, our method identifies stronger canonical correlations and better canonical loading patterns, showing its promise for revealing interesting imaging genetic associations. The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/angscca/ shenli@iu.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  11. Using multivariate machine learning methods and structural MRI to classify childhood onset schizophrenia and healthy controls

    Directory of Open Access Journals (Sweden)

    Deanna eGreenstein

    2012-06-01

    Full Text Available Introduction: Multivariate machine learning methods can be used to classify groups of schizophrenia patients and controls using structural magnetic resonance imaging (MRI. However, machine learning methods to date have not been extended beyond classification and contemporaneously applied in a meaningful way to clinical measures. We hypothesized that brain measures would classify groups, and that increased likelihood of being classified as a patient using regional brain measures would be positively related to illness severity, developmental delays and genetic risk. Methods: Using 74 anatomic brain MRI sub regions and Random Forest, we classified 98 COS patients and 99 age, sex, and ethnicity-matched healthy controls. We also used Random Forest to determine the likelihood of being classified as a schizophrenia patient based on MRI measures. We then explored relationships between brain-based probability of illness and symptoms, premorbid development, and presence of copy number variation associated with schizophrenia. Results: Brain regions jointly classified COS and control groups with 73.7% accuracy. Greater brain-based probability of illness was associated with worse functioning (p= 0.0004 and fewer developmental delays (p=0.02. Presence of copy number variation (CNV was associated with lower probability of being classified as schizophrenia (p=0.001. The regions that were most important in classifying groups included left temporal lobes, bilateral dorsolateral prefrontal regions, and left medial parietal lobes. Conclusions: Schizophrenia and control groups can be well classified using Random Forest and anatomic brain measures, and brain-based probability of illness has a positive relationship with illness severity and a negative relationship with developmental delays/problems and CNV-based risk.

  12. Women with learning disabilities and access to cervical screening: retrospective cohort study using case control methods

    Science.gov (United States)

    Reynolds, Fiona; Stanistreet, Debbi; Elton, Peter

    2008-01-01

    Background Several studies in the UK have suggested that women with learning disabilities may be less likely to receive cervical screening tests and a previous local study in had found that GPs considered screening unnecessary for women with learning disabilities. This study set out to ascertain whether women with learning disabilities are more likely to be ceased from a cervical screening programme than women without; and to examine the reasons given for ceasing women with learning disabilities. It was carried out in Bury, Heywood-and-Middleton and Rochdale. Methods Carried out using retrospective cohort study methods, women with learning disabilities were identified by Read code; and their cervical screening records were compared with the Call-and-Recall records of women without learning disabilities in order to examine their screening histories. Analysis was carried out using case-control methods – 1:2 (women with learning disabilities: women without learning disabilities), calculating odds ratios. Results 267 women's records were compared with the records of 534 women without learning disabilities. Women with learning disabilities had an odds ratio (OR) of 0.48 (Confidence Interval (CI) 0.38 – 0.58; X2: 72.227; p.value learning disabilities. Conclusion The reasons given for ceasing and/or not screening suggest that merely being coded as having a learning disability is not the sole reason for these actions. There are training needs among smear takers regarding appropriate reasons not to screen and providing screening for women with learning disabilities. PMID:18218106

  13. Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

    Science.gov (United States)

    Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean

    2017-12-04

    Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further

  14. The Implementation of Discovery Learning Method to Increase Learning Outcomes and Motivation of Student in Senior High School

    Directory of Open Access Journals (Sweden)

    Nanda Saridewi

    2017-11-01

    Full Text Available Based on data from the observation of high school students grade XI that daily low student test scores due to a lack of role of students in the learning process. This classroom action research aims to improve learning outcomes and student motivation through discovery learning method in colloidal material. This study uses the approach developed by Lewin consisting of planning, action, observation, and reflection. Data collection techniques used the questionnaires and ability tests end. Based on the research that results for students received a positive influence on learning by discovery learning model by increasing the average value of 74 students from the first cycle to 90.3 in the second cycle and increased student motivation in the form of two statements based competence (KD categories (sometimes on the first cycle and the first statement KD category in the second cycle. Thus the results of this study can be used to improve learning outcomes and student motivation

  15. Computer game-based and traditional learning method: a comparison regarding students’ knowledge retention

    Directory of Open Access Journals (Sweden)

    Rondon Silmara

    2013-02-01

    Full Text Available Abstract Background Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Methods Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students’ prior knowledge (i.e. before undergoing the learning method, short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method were assessed with a multiple choice questionnaire. Students’ performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Results Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. Conclusions The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students’ short and long-term knowledge retention.

  16. L2 Vocabulary Acquisition in Children: Effects of Learning Method and Cognate Status

    Science.gov (United States)

    Tonzar, Claudio; Lotto, Lorella; Job, Remo

    2009-01-01

    In this study we investigated the effects of two learning methods (picture- or word-mediated learning) and of word status (cognates vs. noncognates) on the vocabulary acquisition of two foreign languages: English and German. We examined children from fourth and eighth grades in a school setting. After a learning phase during which L2 words were…

  17. Spatial Visualization Learning in Engineering: Traditional Methods vs. a Web-Based Tool

    Science.gov (United States)

    Pedrosa, Carlos Melgosa; Barbero, Basilio Ramos; Miguel, Arturo Román

    2014-01-01

    This study compares an interactive learning manager for graphic engineering to develop spatial vision (ILMAGE_SV) to traditional methods. ILMAGE_SV is an asynchronous web-based learning tool that allows the manipulation of objects with a 3D viewer, self-evaluation, and continuous assessment. In addition, student learning may be monitored, which…

  18. Application of a Novel Collaboration Engineering Method for Learning Design: A Case Study

    Science.gov (United States)

    Cheng, Xusen; Li, Yuanyuan; Sun, Jianshan; Huang, Jianqing

    2016-01-01

    Collaborative case studies and computer-supported collaborative learning (CSCL) play an important role in the modern education environment. A number of researchers have given significant attention to learning design in order to improve the satisfaction of collaborative learning. Although collaboration engineering (CE) is a mature method widely…

  19. Strategic Management: An Evaluation of the Use of Three Learning Methods.

    Science.gov (United States)

    Jennings, David

    2002-01-01

    A study of 46 management students compared three methods for learning strategic management: cases, simulation, and action learning through consulting projects. Simulation was superior to action learning on all outcomes and equal or superior to cases on two. Simulation gave students a central role in management and greater control of the learning…

  20. Using Problem Based Learning Methods from Engineering Education in Company Based Development

    DEFF Research Database (Denmark)

    Kofoed, Lise B.; Jørgensen, Frances

    2007-01-01

    This paper discusses how Problem-Based Learning (PBL) methods were used to support a Danish company in its efforts to become more of a 'learning organisation', characterized by sharing of knowledge and experiences. One of the central barriers to organisational learning in this company involved...

  1. Investigating Learning with an Interactive Tutorial: A Mixed-Methods Strategy

    Science.gov (United States)

    de Villiers, M. R.; Becker, Daphne

    2017-01-01

    From the perspective of parallel mixed-methods research, this paper describes interactivity research that employed usability-testing technology to analyse cognitive learning processes; personal learning styles and times; and errors-and-recovery of learners using an interactive e-learning tutorial called "Relations." "Relations"…

  2. An E-Learning Module to Improve Nongenetic Health Professionals' Assessment of Colorectal Cancer Genetic Risk: Feasibility Study.

    Science.gov (United States)

    Douma, Kirsten Freya Lea; Aalfs, Cora M; Dekker, Evelien; Tanis, Pieter J; Smets, Ellen M

    2017-12-18

    Nongenetic health providers may lack the relevant knowledge, experience, and communication skills to adequately detect familial colorectal cancer (CRC), despite a positive attitude toward the assessment of history of cancer in a family. Specific training may enable them to more optimally refer patients to genetic counseling. The aim of this study was to develop an e-learning module for gastroenterologists and surgeons (in training) aimed at improving attitudes, knowledge, and comprehension of communication skills, and to assess the feasibility of the e-learning module for continued medical education of these specialists. A focus group helped to inform the development of a training framework. The e-learning module was then developed, followed by a feasibility test among a group of surgeons-in-training (3rd- and 4th-year residents) and then among gastroenterologists, using pre- and posttest questionnaires. A total of 124 surgeons-in-training and 14 gastroenterologists participated. The e-learning was positively received (7.5 on a scale of 1 to 10). Between pre- and posttest, attitude increased significantly on 6 out of the 10 items. Mean test score showed that knowledge and comprehension of communication skills improved significantly from 49% to 72% correct at pretest to 67% to 87% correct at posttest. This study shows the feasibility of a problem-based e-learning module to help surgeons-in-training and gastroenterologists in recognizing a hereditary predisposition in patients with CRC. The e-learning led to improvements in attitude toward the assessment of cancer family history, knowledge on criteria for referral to genetic counseling for CRC, and comprehension of communication skills. ©Kirsten Freya Lea Douma, Cora M Aalfs, Evelien Dekker, Pieter J Tanis, Ellen M Smets. Originally published in JMIR Medical Education (http://mededu.jmir.org), 18.12.2017.

  3. Multi-Role Project (MRP): A New Project-Based Learning Method for STEM

    Science.gov (United States)

    Warin, Bruno; Talbi, Omar; Kolski, Christophe; Hoogstoel, Frédéric

    2016-01-01

    This paper presents the "Multi-Role Project" method (MRP), a broadly applicable project-based learning method, and describes its implementation and evaluation in the context of a Science, Technology, Engineering, and Mathematics (STEM) course. The MRP method is designed around a meta-principle that considers the project learning activity…

  4. Alteration of Box-Jenkins methodology by implementing genetic algorithm method

    Science.gov (United States)

    Ismail, Zuhaimy; Maarof, Mohd Zulariffin Md; Fadzli, Mohammad

    2015-02-01

    A time series is a set of values sequentially observed through time. The Box-Jenkins methodology is a systematic method of identifying, fitting, checking and using integrated autoregressive moving average time series model for forecasting. Box-Jenkins method is an appropriate for a medium to a long length (at least 50) time series data observation. When modeling a medium to a long length (at least 50), the difficulty arose in choosing the accurate order of model identification level and to discover the right parameter estimation. This presents the development of Genetic Algorithm heuristic method in solving the identification and estimation models problems in Box-Jenkins. Data on International Tourist arrivals to Malaysia were used to illustrate the effectiveness of this proposed method. The forecast results that generated from this proposed model outperformed single traditional Box-Jenkins model.

  5. An online supervised learning method based on gradient descent for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Yang, Jing; Zhong, Shuiming

    2017-09-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. RULE-BASE METHOD FOR ANALYSIS OF QUALITY E-LEARNING IN HIGHER EDUCATION

    Directory of Open Access Journals (Sweden)

    darsih darsih darsih

    2016-04-01

    Full Text Available ABSTRACT Assessing the quality of e-learning courses to measure the success of e-learning systems in online learning is essential. The system can be used to improve education. The study analyzes the quality of e-learning course on the web site www.kulon.undip.ac.id used a questionnaire with questions based on the variables of ISO 9126. Penilaiann Likert scale was used with a web app. Rule-base reasoning method is used to subject the quality of e-learningyang assessed. A case study conducted in four e-learning courses with 133 sample / respondents as users of the e-learning course. From the obtained results of research conducted both for the value of e-learning from each subject tested. In addition, each e-learning courses have different advantages depending on certain variables. Keywords : E-Learning, Rule-Base, Questionnaire, Likert, Measuring.

  7. Approximation Methods for Inference and Learning in Belief Networks: Progress and Future Directions

    National Research Council Canada - National Science Library

    Pazzan, Michael

    1997-01-01

    .... In this research project, we have investigated methods and implemented algorithms for efficiently making certain classes of inference in belief networks, and for automatically learning certain...

  8. Multiresolution, Geometric, and Learning Methods in Statistical Image Processing, Object Recognition, and Sensor Fusion

    National Research Council Canada - National Science Library

    Willsky, Alan

    2004-01-01

    .... Our research blends methods from several fields-statistics and probability, signal and image processing, mathematical physics, scientific computing, statistical learning theory, and differential...

  9. Computer game-based and traditional learning method: a comparison regarding students' knowledge retention.

    Science.gov (United States)

    Rondon, Silmara; Sassi, Fernanda Chiarion; Furquim de Andrade, Claudia Regina

    2013-02-25

    Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students' prior knowledge (i.e. before undergoing the learning method), short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method) were assessed with a multiple choice questionnaire. Students' performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students' short and long-term knowledge retention.

  10. Learners with learning difficulties in mathematics : attitudes, curriculum and methods of teaching mathematics

    OpenAIRE

    2012-01-01

    D.Ed. The aim of this theses is to find out whether there is any relationship between learners' attitudes and learning difficulties in mathematics: To investigate whether learning difficulties in mathematics are associated with learners' gender. To establish the nature of teachers' perceptions of the learning problem areas in the mathematics curriculum. To find out about the teachers' views on the methods of teaching mathematics, resources, learning of mathematics, extra curricular activit...

  11. 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.

  12. Is preimplantation genetic diagnosis the ideal embryo selection method in aneuploidy screening?

    Directory of Open Access Journals (Sweden)

    Levent Sahin

    2014-10-01

    Full Text Available To select cytogenetically normal embryos, preimplantation genetic diagnosis (PGD aneuploidy screening (AS is used in numerous centers around the world. Chromosomal abnormalities lead to developmental problems, implantation failure, and early abortion of embryos. The usefulness of PGD in identifying single-gene diseases, human leukocyte antigen typing, X-linked diseases, and specific genetic diseases is well-known. In this review, preimplantation embryo genetics, PGD research studies, and the European Society of Human Reproduction and Embryology PGD Consortium studies and reports are examined. In addition, criteria for embryo selection, technical aspects of PGD-AS, and potential noninvasive embryo selection methods are described. Indications for PGD and possible causes of discordant PGD results between the centers are discussed. The limitations of fluorescence in situ hybridization, and the advantages of the array comparative genomic hybridization are included in this review. Although PGD-AS for patients of advanced maternal age has been shown to improve in vitro fertilization outcomes in some studies, to our knowledge, there is not sufficient evidence to use advanced maternal age as the sole indication for PGD-AS. PGD-AS might be harmful and may not increase the success rates of in vitro fertilization. At the same time PGD, is not recommended for recurrent implantation failure and unexplained recurrent pregnancy loss.

  13. A lifelong learning hyper-heuristic method for bin packing.

    Science.gov (United States)

    Sim, Kevin; Hart, Emma; Paechter, Ben

    2015-01-01

    We describe a novel hyper-heuristic system that continuously learns over time to solve a combinatorial optimisation problem. The system continuously generates new heuristics and samples problems from its environment; and representative problems and heuristics are incorporated into a self-sustaining network of interacting entities inspired by methods in artificial immune systems. The network is plastic in both its structure and content, leading to the following properties: it exploits existing knowledge captured in the network to rapidly produce solutions; it can adapt to new problems with widely differing characteristics; and it is capable of generalising over the problem space. The system is tested on a large corpus of 3,968 new instances of 1D bin-packing problems as well as on 1,370 existing problems from the literature; it shows excellent performance in terms of the quality of solutions obtained across the datasets and in adapting to dynamically changing sets of problem instances compared to previous approaches. As the network self-adapts to sustain a minimal repertoire of both problems and heuristics that form a representative map of the problem space, the system is further shown to be computationally efficient and therefore scalable.

  14. Teaching organization theory for healthcare management: three applied learning methods.

    Science.gov (United States)

    Olden, Peter C

    2006-01-01

    Organization theory (OT) provides a way of seeing, describing, analyzing, understanding, and improving organizations based on patterns of organizational design and behavior (Daft 2004). It gives managers models, principles, and methods with which to diagnose and fix organization structure, design, and process problems. Health care organizations (HCOs) face serious problems such as fatal medical errors, harmful treatment delays, misuse of scarce nurses, costly inefficiency, and service failures. Some of health care managers' most critical work involves designing and structuring their organizations so their missions, visions, and goals can be achieved-and in some cases so their organizations can survive. Thus, it is imperative that graduate healthcare management programs develop effective approaches for teaching OT to students who will manage HCOs. Guided by principles of education, three applied teaching/learning activities/assignments were created to teach OT in a graduate healthcare management program. These educationalmethods develop students' competency with OT applied to HCOs. The teaching techniques in this article may be useful to faculty teaching graduate courses in organization theory and related subjects such as leadership, quality, and operation management.

  15. Modeling Music Emotion Judgments Using Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Naresh N. Vempala

    2018-01-01

    Full Text Available Emotion judgments and five channels of physiological data were obtained from 60 participants listening to 60 music excerpts. Various machine learning (ML methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. Input for models of perceived emotion consisted of audio features extracted from the music recordings. Input for models of felt emotion consisted of physiological features extracted from the physiological recordings. Models were trained and interpreted with consideration of the classic debate in music emotion between cognitivists and emotivists. Our models supported a hybrid position wherein emotion judgments were influenced by a combination of perceived and felt emotions. In comparing the different ML approaches that were used for modeling, we conclude that neural networks were optimal, yielding models that were flexible as well as interpretable. Inspection of a committee machine, encompassing an ensemble of networks, revealed that arousal judgments were predominantly influenced by felt emotion, whereas valence judgments were predominantly influenced by perceived emotion.

  16. Genetic Algorithm (GA Method for Optimization of Multi-Reservoir Systems Operation

    Directory of Open Access Journals (Sweden)

    Shervin Momtahen

    2006-01-01

    Full Text Available A Genetic Algorithm (GA method for optimization of multi-reservoir systems operation is proposed in this paper. In this method, the parameters of operating policies are optimized using system simulation results. Hence, any operating problem with any sort of objective function, constraints and structure of operating policy can be optimized by GA. The method is applied to a 3-reservoir system and is compared with two traditional methods of Stochastic Dynamic Programming and Dynamic Programming and Regression. The results show that GA is superior both in objective function value and in computational speed. The proposed method is further improved using a mutation power updating rule and a varying period simulation method. The later is a novel procedure proposed in this paper that is believed to help in solving computational time problem in large systems. These revisions are evaluated and proved to be very useful in converging to better solutions in much less time. The final GA method is eventually evaluated as a very efficient procedure that is able to solve problems of large multi-reservoir system which is usually impossible by traditional methods. In fact, the real performance of the GA method starts where others fail to function.

  17. Matching Learning Style to Instructional Method: Effects on Comprehension

    Science.gov (United States)

    Rogowsky, Beth A.; Calhoun, Barbara M.; Tallal, Paula

    2015-01-01

    While it is hypothesized that providing instruction based on individuals' preferred learning styles improves learning (i.e., reading for visual learners and listening for auditory learners, also referred to as the "meshing hypothesis"), after a critical review of the literature Pashler, McDaniel, Rohrer, and Bjork (2008) concluded that…

  18. Newton Methods for Large Scale Problems in Machine Learning

    Science.gov (United States)

    Hansen, Samantha Leigh

    2014-01-01

    The focus of this thesis is on practical ways of designing optimization algorithms for minimizing large-scale nonlinear functions with applications in machine learning. Chapter 1 introduces the overarching ideas in the thesis. Chapters 2 and 3 are geared towards supervised machine learning applications that involve minimizing a sum of loss…

  19. Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method

    Directory of Open Access Journals (Sweden)

    Muneki Yasuda

    2018-04-01

    Full Text Available The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.

  20. WebMail versus WebApp: Comparing Problem-Based Learning Methods in a Business Research Methods Course

    Science.gov (United States)

    Williams van Rooij, Shahron

    2007-01-01

    This study examined the impact of two Problem-Based Learning (PBL) approaches on knowledge transfer, problem-solving self-efficacy, and perceived learning gains among four intact classes of adult learners engaged in a group project in an online undergraduate business research methods course. With two of the classes using a text-only PBL workbook…

  1. Learning Methods for Dynamic Topic Modeling in Automated Behavior Analysis.

    Science.gov (United States)

    Isupova, Olga; Kuzin, Danil; Mihaylova, Lyudmila

    2017-09-27

    Semisupervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators' load. In the light of the necessity to process large volumes of video data and provide autonomous decisions, this paper proposes new learning algorithms for activity analysis in video. The activities and behaviors are described by a dynamic topic model. Two novel learning algorithms based on the expectation maximization approach and variational Bayes inference are proposed. Theoretical derivations of the posterior estimates of model parameters are given. The designed learning algorithms are compared with the Gibbs sampling inference scheme introduced earlier in the literature. A detailed comparison of the learning algorithms is presented on real video data. We also propose an anomaly localization procedure, elegantly embedded in the topic modeling framework. It is shown that the developed learning algorithms can achieve 95% success rate. The proposed framework can be applied to a number of areas, including transportation systems, security, and surveillance.

  2. Establishment of an efficient genetic transformation method in Dunaliella tertiolecta mediated by Agrobacterium tumefaciens.

    Science.gov (United States)

    Norzagaray-Valenzuela, Claudia D; Germán-Báez, Lourdes J; Valdez-Flores, Marco A; Hernández-Verdugo, Sergio; Shelton, Luke M; Valdez-Ortiz, Angel

    2018-05-16

    Microalgae are photosynthetic microorganisms widely used for the production of highly valued compounds, and recently they have been shown to be promising as a system for the heterologous expression of proteins. Several transformation methods have been successfully developed, from which the Agrobacterium tumefaciens-mediated method remains the most promising. However, microalgae transformation efficiency by A. tumefaciens is shown to vary depending on several transformation conditions. The present study aimed to establish an efficient genetic transformation system in the green microalgae Dunaliella tertiolecta using the A. tumefaciens method. The parameters assessed were the infection medium, the concentration of the A. tumefaciens and co-culture time. As a preliminary screening, the expression of the gusA gene and the viability of transformed cells were evaluated and used to calculate a novel parameter called Transformation Efficiency Index (TEI). The statistical analysis of TEI values showed five treatments with the highest gusA gene expression. To ensure stable transformation, transformed colonies were cultured on selective medium using hygromycin B and the DNA of resistant colonies were extracted after five subcultures and molecularly analyzed by PCR. Results revealed that treatments which use solid infection medium, A. tumefaciens OD 600  = 0.5 and co-culture times of 72 h exhibited the highest percentage of stable gusA expression. Overall, this study established an efficient, optimized A. tumefaciens-mediated genetic transformation of D. tertiolecta, which represents a relatively easy procedure with no expensive equipment required. This simple and efficient protocol opens the possibility for further genetic manipulation of this commercially-important microalgae for biotechnological applications. Copyright © 2018 Elsevier B.V. All rights reserved.

  3. Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation

    Science.gov (United States)

    Hindriks, Koen V.; Tykhonov, Dmytro

    In automated negotiation, information gained about an opponent's preference profile by means of learning techniques may significantly improve an agent's negotiation performance. It therefore is useful to gain a better understanding of how various negotiation factors influence the quality of learning. The quality of learning techniques in negotiation are typically assessed indirectly by means of comparing the utility levels of agreed outcomes and other more global negotiation parameters. An evaluation of learning based on such general criteria, however, does not provide any insight into the influence of various aspects of negotiation on the quality of the learned model itself. The quality may depend on such aspects as the domain of negotiation, the structure of the preference profiles, the negotiation strategies used by the parties, and others. To gain a better understanding of the performance of proposed learning techniques in the context of negotiation and to be able to assess the potential to improve the performance of such techniques a more systematic assessment method is needed. In this paper we propose such a systematic method to analyse the quality of the information gained about opponent preferences by learning in single-instance negotiations. The method includes measures to assess the quality of a learned preference profile and proposes an experimental setup to analyse the influence of various negotiation aspects on the quality of learning. We apply the method to a Bayesian learning approach for learning an opponent's preference profile and discuss our findings.

  4. 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...

  5. Enhanced computational methods for quantifying the effect of geographic and environmental isolation on genetic differentiation

    DEFF Research Database (Denmark)

    Botta, Filippo; Eriksen, Casper; Fontaine, Michaël C.

    2015-01-01

    1. In a recent paper, Bradburd et al. (Evolution, 67, 2013, 3258) proposed a model to quantify the relative effect of geographic and environmental distance on genetic differentiation. Here, we enhance this method in several ways. 2. We modify the covariance model so as to fit better with mainstre...... available as an R package called sunder. It takes as input georeferenced allele counts at the individual or population level for co-dominant markers. Program homepage: http://www2.imm.dtu.dk/~gigu/Sunder/....

  6. Lesson learned - CGID based on the Method 1 and Method 2 for digital equipment

    International Nuclear Information System (INIS)

    Hwang, Wonil; Sohn, Kwang Young; Cho, Chang Hwan; Kim, Sung Jong

    2015-01-01

    The acceptance methods associated with commercial-grade dedication are the following: 1) Special tests and inspection (Method 1) 2) Commercial-grade surveys (Method 2) 3) Source verification (Method 3) 4) An acceptable item and supplier performance record (Method 4) Special tests and inspections, often referred to as Method 1, are performed by the dedicating entity after the item is received to verify selected critical characteristics. Conducting a commercial-grade survey of a supplier is often referred to as Method 2. Supplier audits to verify compliance with a nuclear QA program do not meet the intent of a commercial-grade survey. Source verification, often referred to as Method 3, entails verification of critical characteristics during manufacture and testing of the item being procured. The performance history (good or bad) of the item and supplier is a consideration when determining the use of the other acceptance methods and the rigor with which they are used on a case-by-case basis. Some digital equipment system has the delivery reference and its operating history for Nuclear Power Plant as far as surveyed. However it was found that there is difficulty in collecting this of supporting data sheet, so that supplier usually decide to conduct the CGID based on the Method-1 and Method-2 based on the initial qualification likely. It is conceived that the Method-4 might be a better approach for CGID(Commercial Grade Item Dedication) even if there are some difficulties in data package for justifying CGID from the vendor and operating organization. This paper present the lesson learned from the consulting for Method-1 and 2 for digital equipment dedication. Considering all the information above, there are a couple of issues to remind in order to perform the CGID for Method-2. In doing commercial grade survey based on Method 2, quality personnel as well as technical engineer shall be involved for integral dedication. Other than this, the review of critical

  7. Multi-objective genetic algorithm based innovative wind farm layout optimization method

    International Nuclear Information System (INIS)

    Chen, Ying; Li, Hua; He, Bang; Wang, Pengcheng; Jin, Kai

    2015-01-01

    Highlights: • Innovative optimization procedures for both regular and irregular shape wind farm. • Using real wind condition and commercial wind turbine parameters. • Using multiple-objective genetic algorithm optimization method. • Optimize the selection of different wind turbine types and their hub heights. - Abstract: Layout optimization has become one of the critical approaches to increase power output and decrease total cost of a wind farm. Previous researches have applied intelligent algorithms to optimizing the wind farm layout. However, those wind conditions used in most of previous research are simplified and not accurate enough to match the real world wind conditions. In this paper, the authors propose an innovative optimization method based on multi-objective genetic algorithm, and test it with real wind condition and commercial wind turbine parameters. Four case studies are conducted to investigate the number of wind turbines needed in the given wind farm. Different cost models are also considered in the case studies. The results clearly demonstrate that the new method is able to optimize the layout of a given wind farm with real commercial data and wind conditions in both regular and irregular shapes, and achieve a better result by selecting different type and hub height wind turbines.

  8. The Matrix Method of Representation, Analysis and Classification of Long Genetic Sequences

    Directory of Open Access Journals (Sweden)

    Ivan V. Stepanyan

    2017-01-01

    Full Text Available The article is devoted to a matrix method of comparative analysis of long nucleotide sequences by means of presenting each sequence in the form of three digital binary sequences. This method uses a set of symmetries of biochemical attributes of nucleotides. It also uses the possibility of presentation of every whole set of N-mers as one of the members of a Kronecker family of genetic matrices. With this method, a long nucleotide sequence can be visually represented as an individual fractal-like mosaic or another regular mosaic of binary type. In contrast to natural nucleotide sequences, artificial random sequences give non-regular patterns. Examples of binary mosaics of long nucleotide sequences are shown, including cases of human chromosomes and penicillins. The obtained results are then discussed.

  9. A high-resolution neutron spectra unfolding method using the Genetic Algorithm technique

    CERN Document Server

    Mukherjee, B

    2002-01-01

    The Bonner sphere spectrometers (BSS) are commonly used to determine the neutron spectra within various nuclear facilities. Sophisticated mathematical tools are used to unfold the neutron energy distribution from the output data of the BSS. This paper highlights a novel high-resolution neutron spectra-unfolding method using the Genetic Algorithm (GA) technique. The GA imitates the biological evolution process prevailing in the nature to solve complex optimisation problems. The GA method was utilised to evaluate the neutron energy distribution, average energy, fluence and equivalent dose rates at important work places of a DIDO class research reactor and a high-energy superconducting heavy ion cyclotron. The spectrometer was calibrated with a sup 2 sup 4 sup 1 Am/Be (alpha,n) neutron standard source. The results of the GA method agreed satisfactorily with the results obtained by using the well-known BUNKI neutron spectra unfolding code.

  10. Construction Method of Display Proposal for Commodities in Sales Promotion by Genetic Algorithm

    Science.gov (United States)

    Yumoto, Masaki

    In a sales promotion task, wholesaler prepares and presents the display proposal for commodities in order to negotiate with retailer's buyers what commodities they should sell. For automating the sales promotion tasks, the proposal has to be constructed according to the target retailer's buyer. However, it is difficult to construct the proposal suitable for the target retail store because of too much combination of commodities. This paper proposes a construction method by Genetic algorithm (GA). The proposed method represents initial display proposals for commodities with genes, improve ones with the evaluation value by GA, and rearrange one with the highest evaluation value according to the classification of commodity. Through practical experiment, we can confirm that display proposal by the proposed method is similar with the one constructed by a wholesaler.

  11. The Tourette International Collaborative Genetics (TIC Genetics) study, finding the genes causing Tourette syndrome : objectives and methods

    NARCIS (Netherlands)

    Dietrich, Andrea; Fernandez, Thomas V.; King, Robert A.; State, Matthew W.; Tischfield, Jay A.; Hoekstra, Pieter J.; Heiman, Gary A.

    Tourette syndrome (TS) is a neuropsychiatric disorder characterized by recurrent motor and vocal tics, often accompanied by obsessive-compulsive disorder and/or attention-deficit/hyperactivity disorder. While the evidence for a genetic contribution is strong, its exact nature has yet to be clarified

  12. Exploring an experiential learning project through Kolb's Learning Theory using a qualitative research method

    Science.gov (United States)

    Yuk Chan, Cecilia Ka

    2012-08-01

    Experiential learning pedagogy is taking a lead in the development of graduate attributes and educational aims as these are of prime importance for society. This paper shows a community service experiential project conducted in China. The project enabled students to serve the affected community in a post-earthquake area by applying their knowledge and skills. This paper documented the students' learning process from their project goals, pre-trip preparations, work progress, obstacles encountered to the final results and reflections. Using the data gathered from a focus group interview approach, the four components of Kolb's learning cycle, the concrete experience, reflection observation, abstract conceptualisation and active experimentation, have been shown to transform and internalise student's learning experience, achieving a variety of learning outcomes. The author will also explore how this community service type of experiential learning in the engineering discipline allowed students to experience deep learning and develop their graduate attributes.

  13. Are behavioral differences among wild chimpanzee communities genetic or cultural? An assessment using tool-use data and phylogenetic methods.

    Science.gov (United States)

    Lycett, Stephen J; Collard, Mark; McGrew, William C

    2010-07-01

    Over the last 30 years it has become increasingly apparent that there are many behavioral differences among wild communities of Pan troglodytes. Some researchers argue these differences are a consequence of the behaviors being socially learned, and thus may be considered cultural. Others contend that the available evidence is too weak to discount the alternative possibility that the behaviors are genetically determined. Previous phylogenetic analyses of chimpanzee behavior have not supported the predictions of the genetic hypothesis. However, the results of these studies are potentially problematic because the behavioral sample employed did not include communities from central Africa. Here, we present the results of a study designed to address this shortcoming. We carried out cladistic analyses of presence/absence data pertaining to 19 tool-use behaviors in 10 different P. troglodytes communities plus an outgroup (P. paniscus). Genetic data indicate that chimpanzee communities in West Africa are well differentiated from those in eastern and central Africa, while the latter are not reciprocally monophyletic. Thus, we predicted that if the genetic hypothesis is correct, the tool-use data should mirror the genetic data in terms of structure. The three measures of phylogenetic structure we employed (the Retention Index, the bootstrap, and the Permutation Tail Probability Test) did not support the genetic hypothesis. They were all lower when all 10 communities were included than when the three western African communities are excluded. Hence, our study refutes the genetic hypothesis and provides further evidence that patterns of behavior in chimpanzees are the product of social learning and therefore meet the main condition for culture. (c) 2010 Wiley-Liss, Inc.

  14. Robust Control Methods for On-Line Statistical Learning

    Directory of Open Access Journals (Sweden)

    Capobianco Enrico

    2001-01-01

    Full Text Available The issue of controlling that data processing in an experiment results not affected by the presence of outliers is relevant for statistical control and learning studies. Learning schemes should thus be tested for their capacity of handling outliers in the observed training set so to achieve reliable estimates with respect to the crucial bias and variance aspects. We describe possible ways of endowing neural networks with statistically robust properties by defining feasible error criteria. It is convenient to cast neural nets in state space representations and apply both Kalman filter and stochastic approximation procedures in order to suggest statistically robustified solutions for on-line learning.

  15. Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures

    OpenAIRE

    Rock, Adam J.; Coventry, William L.; Morgan, Methuen I.; Loi, Natasha M.

    2016-01-01

    Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal, Ginsburg, & Schau, 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof, Ceroni, Jeong, & Moghaddam, 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to...

  16. Valuing the benefits of genetic testing for retinitis pigmentosa: a pilot application of the contingent valuation method.

    Science.gov (United States)

    Eden, Martin; Payne, Katherine; Combs, Ryan M; Hall, Georgina; McAllister, Marion; Black, Graeme C M

    2013-08-01

    Technological advances present an opportunity for more people with, or at risk of, developing retinitis pigmentosa (RP) to be offered genetic testing. Valuation of these tests using current evaluative frameworks is problematic since benefits may be derived from diagnostic information rather than improvements in health. This pilot study aimed to explore if contingent valuation method (CVM) can be used to value the benefits of genetic testing for RP. CVM was used to elicit willingness-to-pay (WTP) values for (1) genetic counselling and (2) genetic counselling with genetic testing. Telephone and face-to-face interviews with a purposive sample of individuals with (n=25), and without (n=27), prior experience of RP were used to explore the feasibility and validity of CVM in this context. Faced with a hypothetical scenario, the majority of participants stated that they would seek genetic counselling and testing in the context of RP. Between participant groups, respondents offered similar justifications for stated WTP values. Overall stated WTP was higher for genetic counselling plus testing (median=£524.00) compared with counselling alone (median=£224.50). Between-group differences in stated WTP were statistically significant; participants with prior knowledge of the condition were willing to pay more for genetic ophthalmology services. Participants were able to attach a monetary value to the perceived potential benefit that genetic testing offered regardless of prior experience of the condition. This exploratory work represents an important step towards evaluating these services using formal cost-benefit analysis.

  17. Change Of Learning Environment Using Game Production ­Theory, Methods And Practice

    DEFF Research Database (Denmark)

    Reng, Lars; Kofoed, Lise; Schoenau-Fog, Henrik

    2018-01-01

    will focus on cases in which development of games did change the learning environments into production units where students or employees were producing games as part of the learning process. The cases indicate that the motivation as well as the learning curve became very high. The pedagogical theories......Game Based Learning has proven to have many possibilities for supporting better learning outcomes, when using educational or commercial games in the classroom. However, there is also a great potential in using game development as a motivator in other kinds of learning scenarios. This study...... and methods are based on Problem Based Learning (PBL), but are developed further by combining PBL with a production-oriented/design based approach. We illustrate the potential of using game production as a learning environment with investigation of three game productions. We can conclude that using game...

  18. Challenges in reproducibility of genetic association studies: lessons learned from the obesity field.

    Science.gov (United States)

    Li, A; Meyre, D

    2013-04-01

    A robust replication of initial genetic association findings has proved to be difficult in human complex diseases and more specifically in the obesity field. An obvious cause of non-replication in genetic association studies is the initial report of a false positive result, which can be explained by a non-heritable phenotype, insufficient sample size, improper correction for multiple testing, population stratification, technical biases, insufficient quality control or inappropriate statistical analyses. Replication may, however, be challenging even when the original study describes a true positive association. The reasons include underpowered replication samples, gene × gene, gene × environment interactions, genetic and phenotypic heterogeneity and subjective interpretation of data. In this review, we address classic pitfalls in genetic association studies and provide guidelines for proper discovery and replication genetic association studies with a specific focus on obesity.

  19. Sparse Machine Learning Methods for Understanding Large Text Corpora

    Data.gov (United States)

    National Aeronautics and Space Administration — Sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational...

  20. A blended learning approach to teaching sociolinguistic research methods

    Directory of Open Access Journals (Sweden)

    Olivier, Jako

    2014-12-01

    Full Text Available This article reports on the use of Wiktionary, an open source online dictionary, as well as generic wiki pages within a university’s e-learning environment as teaching and learning resources in an Afrikaans sociolinguistics module. In a communal constructivist manner students learnt, but also constructed learning content. From the qualitative research conducted with students it is clear that wikis provide for effective facilitation of a blended learning approach to sociolinguistic research. The use of this medium was positively received, however, some students did prefer handing in assignments in hard copy. The issues of computer literacy and access to the internet were also raised by the respondents. The use of wikis and Wiktionary prompted useful unplanned discussions around reliability and quality of public wikis. The use of a public wiki such as Wiktionary served as encouragement for students as they were able to contribute to the promotion of Afrikaans in this way.