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Sample records for additive genetic models

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

    PubMed Central

    Monir, Md. Mamun; Zhu, Jun

    2017-01-01

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

  2. Product versus additive threshold models for analysis of reproduction outcomes in animal genetics.

    PubMed

    David, I; Bodin, L; Gianola, D; Legarra, A; Manfredi, E; Robert-Granié, C

    2009-08-01

    The phenotypic observation of some reproduction traits (e.g., insemination success, interval from lambing to insemination) is the result of environmental and genetic factors acting on 2 individuals: the male and female involved in a mating couple. In animal genetics, the main approach (called additive model) proposed for studying such traits assumes that the phenotype is linked to a purely additive combination, either on the observed scale for continuous traits or on some underlying scale for discrete traits, of environmental and genetic effects affecting the 2 individuals. Statistical models proposed for studying human fecundability generally consider reproduction outcomes as the product of hypothetical unobservable variables. Taking inspiration from these works, we propose a model (product threshold model) for studying a binary reproduction trait that supposes that the observed phenotype is the product of 2 unobserved phenotypes, 1 for each individual. We developed a Gibbs sampling algorithm for fitting a Bayesian product threshold model including additive genetic effects and showed by simulation that it is feasible and that it provides good estimates of the parameters. We showed that fitting an additive threshold model to data that are simulated under a product threshold model provides biased estimates, especially for individuals with high breeding values. A main advantage of the product threshold model is that, in contrast to the additive model, it provides distinct estimates of fixed effects affecting each of the 2 unobserved phenotypes.

  3. Group Sparse Additive Models

    PubMed Central

    Yin, Junming; Chen, Xi; Xing, Eric P.

    2016-01-01

    We consider the problem of sparse variable selection in nonparametric additive models, with the prior knowledge of the structure among the covariates to encourage those variables within a group to be selected jointly. Previous works either study the group sparsity in the parametric setting (e.g., group lasso), or address the problem in the nonparametric setting without exploiting the structural information (e.g., sparse additive models). In this paper, we present a new method, called group sparse additive models (GroupSpAM), which can handle group sparsity in additive models. We generalize the ℓ1/ℓ2 norm to Hilbert spaces as the sparsity-inducing penalty in GroupSpAM. Moreover, we derive a novel thresholding condition for identifying the functional sparsity at the group level, and propose an efficient block coordinate descent algorithm for constructing the estimate. We demonstrate by simulation that GroupSpAM substantially outperforms the competing methods in terms of support recovery and prediction accuracy in additive models, and also conduct a comparative experiment on a real breast cancer dataset.

  4. Functional Generalized Additive Models.

    PubMed

    McLean, Mathew W; Hooker, Giles; Staicu, Ana-Maria; Scheipl, Fabian; Ruppert, David

    2014-01-01

    We introduce the functional generalized additive model (FGAM), a novel regression model for association studies between a scalar response and a functional predictor. We model the link-transformed mean response as the integral with respect to t of F{X(t), t} where F(·,·) is an unknown regression function and X(t) is a functional covariate. Rather than having an additive model in a finite number of principal components as in Müller and Yao (2008), our model incorporates the functional predictor directly and thus our model can be viewed as the natural functional extension of generalized additive models. We estimate F(·,·) using tensor-product B-splines with roughness penalties. A pointwise quantile transformation of the functional predictor is also considered to ensure each tensor-product B-spline has observed data on its support. The methods are evaluated using simulated data and their predictive performance is compared with other competing scalar-on-function regression alternatives. We illustrate the usefulness of our approach through an application to brain tractography, where X(t) is a signal from diffusion tensor imaging at position, t, along a tract in the brain. In one example, the response is disease-status (case or control) and in a second example, it is the score on a cognitive test. R code for performing the simulations and fitting the FGAM can be found in supplemental materials available online.

  5. Fused Lasso Additive Model

    PubMed Central

    Petersen, Ashley; Witten, Daniela; Simon, Noah

    2016-01-01

    We consider the problem of predicting an outcome variable using p covariates that are measured on n independent observations, in a setting in which additive, flexible, and interpretable fits are desired. We propose the fused lasso additive model (FLAM), in which each additive function is estimated to be piecewise constant with a small number of adaptively-chosen knots. FLAM is the solution to a convex optimization problem, for which a simple algorithm with guaranteed convergence to a global optimum is provided. FLAM is shown to be consistent in high dimensions, and an unbiased estimator of its degrees of freedom is proposed. We evaluate the performance of FLAM in a simulation study and on two data sets. Supplemental materials are available online, and the R package flam is available on CRAN. PMID:28239246

  6. Additive-dominance genetic model analyses for late-maturity alpha-amylase activity in a bread wheat factorial crossing population.

    PubMed

    Rasul, Golam; Glover, Karl D; Krishnan, Padmanaban G; Wu, Jixiang; Berzonsky, William A; Ibrahim, Amir M H

    2015-12-01

    Elevated level of late maturity α-amylase activity (LMAA) can result in low falling number scores, reduced grain quality, and downgrade of wheat (Triticum aestivum L.) class. A mating population was developed by crossing parents with different levels of LMAA. The F2 and F3 hybrids and their parents were evaluated for LMAA, and data were analyzed using the R software package 'qgtools' integrated with an additive-dominance genetic model and a mixed linear model approach. Simulated results showed high testing powers for additive and additive × environment variances, and comparatively low powers for dominance and dominance × environment variances. All variance components and their proportions to the phenotypic variance for the parents and hybrids were significant except for the dominance × environment variance. The estimated narrow-sense heritability and broad-sense heritability for LMAA were 14 and 54%, respectively. High significant negative additive effects for parents suggest that spring wheat cultivars 'Lancer' and 'Chester' can serve as good general combiners, and that 'Kinsman' and 'Seri-82' had negative specific combining ability in some hybrids despite of their own significant positive additive effects, suggesting they can be used as parents to reduce LMAA levels. Seri-82 showed very good general combining ability effect when used as a male parent, indicating the importance of reciprocal effects. High significant negative dominance effects and high-parent heterosis for hybrids demonstrated that the specific hybrid combinations; Chester × Kinsman, 'Lerma52' × Lancer, Lerma52 × 'LoSprout' and 'Janz' × Seri-82 could be generated to produce cultivars with significantly reduced LMAA level.

  7. Non-additive and additive genetic effects on extraversion in 3314 Dutch adolescent twins and their parents.

    PubMed

    Rettew, David C; Rebollo-Mesa, Irene; Hudziak, James J; Willemsen, Gonneke; Boomsma, Dorret I

    2008-05-01

    The influence of non-additive genetic influences on personality traits has been increasingly reported in adult populations. Less is known, however, with respect to younger samples. In this study, we examine additive and non-additive genetic contributions to the personality trait of extraversion in 1,689 Dutch twin pairs, 1,505 mothers and 1,637 fathers of the twins. The twins were on average 15.5 years (range 12-18 years). To increase statistical power to detect non-additive genetic influences, data on extraversion were also collected in parents and simultaneously analyzed. Genetic modeling procedures incorporating age as a potential modifier of heritability showed significant influences of additive (20-23%) and non-additive genetic factors (31-33%) in addition to unshared environment (46-48%) for adolescents and for their parents. The additive genetic component was slightly and positively related to age. No significant sex differences were found for either extraversion means or for the magnitude of the genetic and environmental influences. There was no evidence of non-random mating for extraversion in the parental generation. Results show that in addition to additive genetic influences, extraversion in adolescents is influenced by non-additive genetic factors.

  8. Explaining additional genetic variation in complex traits

    PubMed Central

    Robinson, Matthew R.; Wray, Naomi R.; Visscher, Peter M.

    2015-01-01

    Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of complex traits, discovering >6000 variants associated with >500 quantitative traits and common complex diseases in humans. The associations identified so far represent only a fraction of those which influence phenotype, as there are likely to be very many variants across the entire frequency spectrum, each of which influences multiple traits, with only a small average contribution to the phenotypic variance. This presents a considerable challenge to further dissection of the remaining unexplained genetic variance within populations, which limits our ability to predict disease risk, identify new drug targets, improve and maintain food sources, and understand natural diversity. This challenge will be met within the current framework through larger sample size, better phenotyping including recording of non-genetic risk factors, focused study designs, and an integration of multiple sources of phenotypic and genetic information. The current evidence supports the application of quantitative genetic approaches, and we argue that one should retain simpler theories until simplicity can be traded for greater explanatory power. PMID:24629526

  9. Unnatural reactive amino acid genetic code additions

    SciTech Connect

    Deiters, Alexander; Cropp, T Ashton; Chin, Jason W; Anderson, J Christopher; Schultz, Peter G

    2011-02-15

    This invention provides compositions and methods for producing translational components that expand the number of genetically encoded amino acids in eukaryotic cells. The components include orthogonal tRNAs, orthogonal aminoacyl-tRNA synthetases, orthogonal pairs of tRNAs/synthetases and unnatural amino acids. Proteins and methods of producing proteins with unnatural amino acids in eukaryotic cells are also provided.

  10. Unnatural reactive amino acid genetic code additions

    SciTech Connect

    Deiters, Alexander; Cropp, Ashton T; Chin, Jason W; Anderson, Christopher J; Schultz, Peter G

    2013-05-21

    This invention provides compositions and methods for producing translational components that expand the number of genetically encoded amino acids in eukaryotic cells. The components include orthogonal tRNAs, orthogonal aminoacyl-tRNA synthetases, pairs of tRNAs/synthetases and unnatural amino acids. Proteins and methods of producing proteins with unnatural amino acids in eukaryotic cells are also provided.

  11. Unnatural reactive amino acid genetic code additions

    SciTech Connect

    Deiters, Alexander; Cropp, T. Ashton; Chin, Jason W.; Anderson, J. Christopher; Schultz, Peter G.

    2011-08-09

    This invention provides compositions and methods for producing translational components that expand the number of genetically encoded amino acids in eukaryotic cells. The components include orthogonal tRNAs, orthogonal aminoacyl-tRNAsyn-thetases, pairs of tRNAs/synthetases and unnatural amino acids. Proteins and methods of producing proteins with unnatural amino acids in eukaryotic cells are also provided.

  12. Unnatural reactive amino acid genetic code additions

    SciTech Connect

    Deiters, Alexander; Cropp, T. Ashton; Chin, Jason W.; Anderson, J. Christopher; Schultz, Peter G.

    2014-08-26

    This invention provides compositions and methods for producing translational components that expand the number of genetically encoded amino acids in eukaryotic cells. The components include orthogonal tRNAs, orthogonal aminoacyl-tRNA synthetases, orthogonal pairs of tRNAs/synthetases and unnatural amino acids. Proteins and methods of producing proteins with unnatural amino acids in eukaryotic cells are also provided.

  13. The Expression of Additive and Nonadditive Genetic Variation under Stress

    PubMed Central

    Blows, M. W.; Sokolowski, M. B.

    1995-01-01

    Experimental lines of Drosophila melanogaster derived from a natural population, which had been isolated in the laboratory for ~70 generations, were crossed to determine if the expression of additive, dominance and epistatic genetic variation in development time and viability was associated with the environment. No association was found between the level of additive genetic effects and environmental value for either trait, but nonadditive genetic effects increased at both extremes of the environmental range for development time. The expression of high levels of dominance and epistatic genetic variation at environmental extremes may be a general expectation for some traits. The disruption of the epistatic gene complexes in the parental lines resulted in hybrid breakdown toward faster development and there was some indication of hybrid breakdown toward higher viability. A combination of genetic drift and natural selection had therefore resulted in different epistatic gene complexes being selected after ~70 generations from a common genetic base. After crossing, the hybrid populations were observed for 10 generations. Epistasis contributed on average 12 hr in development time. Fluctuating asymmetry in sternopleural bristle number also evolved in the hybrid populations, decreasing by >18% in the first seven generations after hybridization. PMID:7672585

  14. Accounting for additive genetic mutations on litter size in Ripollesa sheep.

    PubMed

    Casellas, J; Caja, G; Piedrafita, J

    2010-04-01

    Little is known about mutational variability in livestock, among which only a few mutations with relatively large effects have been reported. In this manuscript, mutational variability was analyzed in 1,765 litter size records from 404 Ripollesa ewes to characterize the magnitude of this genetic source of variation and check the suitability of including mutational effects in genetic evaluations of this breed. Threshold animal models accounting for additive genetic mutations were preferred to models without mutational contributions, with an average difference in the deviance information criterion of more than 5 units. Moreover, the statistical relevance of the additive genetic mutation term was checked through a Bayes factor approach, which showed that the models with mutational variability were 8.5 to 22.7 times more probable than the others. The mutational heritability (percentage of the phenotypic variance accounted for by mutational variance) was 0.6 or 0.9%, depending on whether genetic dominance effects were accounted for by the analytical model. The inclusion of mutational effects in the genetic model for evaluating litter size in Ripollesa ewes called for some minor modifications in the genetic merit order of the individuals evaluated, which suggested that the continuous uploading of new additive mutations could be taken into account to optimize the selection scheme. This study is the first attempt to estimate mutational variances in a livestock species and thereby contribute to better characterization of the genetic background of productive traits of interest.

  15. Additive and nonadditive genetic variation in avian personality traits.

    PubMed

    van Oers, K; Drent, P J; de Jong, G; van Noordwijk, A J

    2004-11-01

    Individuals of all vertebrate species differ consistently in their reactions to mildly stressful challenges. These typical reactions, described as personalities or coping strategies, have a clear genetic basis, but the structure of their inheritance in natural populations is almost unknown. We carried out a quantitative genetic analysis of two personality traits (exploration and boldness) and the combination of these two traits (early exploratory behaviour). This study was carried out on the lines resulting from a two-directional artificial selection experiment on early exploratory behaviour (EEB) of great tits (Parus major) originating from a wild population. In analyses using the original lines, reciprocal F(1) and reciprocal first backcross generations, additive, dominance, maternal effects ands sex-dependent expression of exploration, boldness and EEB were estimated. Both additive and dominant genetic effects were important determinants of phenotypic variation in exploratory behaviour and boldness. However, no sex-dependent expression was observed in either of these personality traits. These results are discussed with respect to the maintenance of genetic variation in personality traits, and the expected genetic structure of other behavioural and life history traits in general.

  16. Additional mechanisms conferring genetic susceptibility to Alzheimer’s disease

    PubMed Central

    Calero, Miguel; Gómez-Ramos, Alberto; Calero, Olga; Soriano, Eduardo; Avila, Jesús; Medina, Miguel

    2015-01-01

    Familial Alzheimer’s disease (AD), mostly associated with early onset, is caused by mutations in three genes (APP, PSEN1, and PSEN2) involved in the production of the amyloid β peptide. In contrast, the molecular mechanisms that trigger the most common late onset sporadic AD remain largely unknown. With the implementation of an increasing number of case-control studies and the upcoming of large-scale genome-wide association studies there is a mounting list of genetic risk factors associated with common genetic variants that have been associated with sporadic AD. Besides apolipoprotein E, that presents a strong association with the disease (OR∼4), the rest of these genes have moderate or low degrees of association, with OR ranging from 0.88 to 1.23. Taking together, these genes may account only for a fraction of the attributable AD risk and therefore, rare variants and epistastic gene interactions should be taken into account in order to get the full picture of the genetic risks associated with AD. Here, we review recent whole-exome studies looking for rare variants, somatic brain mutations with a strong association to the disease, and several studies dealing with epistasis as additional mechanisms conferring genetic susceptibility to AD. Altogether, recent evidence underlines the importance of defining molecular and genetic pathways, and networks rather than the contribution of specific genes. PMID:25914626

  17. Efficient Improvement of Silage Additives by Using Genetic Algorithms

    PubMed Central

    Davies, Zoe S.; Gilbert, Richard J.; Merry, Roger J.; Kell, Douglas B.; Theodorou, Michael K.; Griffith, Gareth W.

    2000-01-01

    The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e., no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a “fitness” value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a “cost” element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage

  18. Efficient improvement of silage additives by using genetic algorithms.

    PubMed

    Davies, Z S; Gilbert, R J; Merry, R J; Kell, D B; Theodorou, M K; Griffith, G W

    2000-04-01

    The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e. , no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a "fitness" value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a "cost" element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage fermentation. We

  19. Additive genetic breeding values correlate with the load of partially deleterious mutations.

    PubMed

    Tomkins, Joseph L; Penrose, Marissa A; Greeff, Johan; LeBas, Natasha R

    2010-05-14

    The mutation-selection-balance model predicts most additive genetic variation to arise from numerous mildly deleterious mutations of small effect. Correspondingly, "good genes" models of sexual selection and recent models for the evolution of sex are built on the assumption that mutational loads and breeding values for fitness-related traits are correlated. In support of this concept, inbreeding depression was negatively genetically correlated with breeding values for traits under natural and sexual selection in the weevil Callosobruchus maculatus. The correlations were stronger in males and strongest for condition. These results confirm the role of existing, partially recessive mutations in maintaining additive genetic variation in outbred populations, reveal the nature of good genes under sexual selection, and show how sexual selection can offset the cost of sex.

  20. Common genetic variants, acting additively, are a major source of risk for autism

    PubMed Central

    2012-01-01

    Background Autism spectrum disorders (ASD) are early onset neurodevelopmental syndromes typified by impairments in reciprocal social interaction and communication, accompanied by restricted and repetitive behaviors. While rare and especially de novo genetic variation are known to affect liability, whether common genetic polymorphism plays a substantial role is an open question and the relative contribution of genes and environment is contentious. It is probable that the relative contributions of rare and common variation, as well as environment, differs between ASD families having only a single affected individual (simplex) versus multiplex families who have two or more affected individuals. Methods By using quantitative genetics techniques and the contrast of ASD subjects to controls, we estimate what portion of liability can be explained by additive genetic effects, known as narrow-sense heritability. We evaluate relatives of ASD subjects using the same methods to evaluate the assumptions of the additive model and partition families by simplex/multiplex status to determine how heritability changes with status. Results By analyzing common variation throughout the genome, we show that common genetic polymorphism exerts substantial additive genetic effects on ASD liability and that simplex/multiplex family status has an impact on the identified composition of that risk. As a fraction of the total variation in liability, the estimated narrow-sense heritability exceeds 60% for ASD individuals from multiplex families and is approximately 40% for simplex families. By analyzing parents, unaffected siblings and alleles not transmitted from parents to their affected children, we conclude that the data for simplex ASD families follow the expectation for additive models closely. The data from multiplex families deviate somewhat from an additive model, possibly due to parental assortative mating. Conclusions Our results, when viewed in the context of results from genome

  1. Computational Process Modeling for Additive Manufacturing

    NASA Technical Reports Server (NTRS)

    Bagg, Stacey; Zhang, Wei

    2014-01-01

    Computational Process and Material Modeling of Powder Bed additive manufacturing of IN 718. Optimize material build parameters with reduced time and cost through modeling. Increase understanding of build properties. Increase reliability of builds. Decrease time to adoption of process for critical hardware. Potential to decrease post-build heat treatments. Conduct single-track and coupon builds at various build parameters. Record build parameter information and QM Meltpool data. Refine Applied Optimization powder bed AM process model using data. Report thermal modeling results. Conduct metallography of build samples. Calibrate STK models using metallography findings. Run STK models using AO thermal profiles and report STK modeling results. Validate modeling with additional build. Photodiode Intensity measurements highly linear with power input. Melt Pool Intensity highly correlated to Melt Pool Size. Melt Pool size and intensity increase with power. Applied Optimization will use data to develop powder bed additive manufacturing process model.

  2. Simultaneous Estimation of Additive and Mutational Genetic Variance in an Outbred Population of Drosophila serrata

    PubMed Central

    McGuigan, Katrina; Aguirre, J. David; Blows, Mark W.

    2015-01-01

    How new mutations contribute to genetic variation is a key question in biology. Although the evolutionary fate of an allele is largely determined by its heterozygous effect, most estimates of mutational variance and mutational effects derive from highly inbred lines, where new mutations are present in homozygous form. In an attempt to overcome this limitation, middle-class neighborhood (MCN) experiments have been used to assess the fitness effect of new mutations in heterozygous form. However, because MCN populations harbor substantial standing genetic variance, estimates of mutational variance have not typically been available from such experiments. Here we employ a modification of the animal model to analyze data from 22 generations of Drosophila serrata bred in an MCN design. Mutational heritability, measured for eight cuticular hydrocarbons, 10 wing-shape traits, and wing size in this outbred genetic background, ranged from 0.0006 to 0.006 (with one exception), a similar range to that reported from studies employing inbred lines. Simultaneously partitioning the additive and mutational variance in the same outbred population allowed us to quantitatively test the ability of mutation-selection balance models to explain the observed levels of additive and mutational genetic variance. The Gaussian allelic approximation and house-of-cards models, which assume real stabilizing selection on single traits, both overestimated the genetic variance maintained at equilibrium, but the house-of-cards model was a closer fit to the data. This analytical approach has the potential to be broadly applied, expanding our understanding of the dynamics of genetic variance in natural populations. PMID:26384357

  3. Response to selection in finite locus models with non-additive effects.

    PubMed

    Esfandyari, Hadi; Henryon, Mark; Berg, Peer; Thomasen, Jorn Rind; Bijma, Piter; Sørensen, Anders Christian

    2017-01-12

    Under the finite-locus model in the absence of mutation, the additive genetic variation is expected to decrease when directional selection is acting on a population, according to quantitative-genetic theory. However, some theoretical studies of selection suggest that the level of additive variance can be sustained or even increased when non-additive genetic effects are present. We tested the hypothesis that finite-locus models with both additive and non-additive genetic effects maintain more additive genetic variance (V_A) and realize larger medium-to-long term genetic gains than models with only additive effects when the trait under selection is subject to truncation selection. Four genetic models that included additive, dominance, and additive-by-additive epistatic effects were simulated. The simulated genome for individuals consisted of 25 chromosomes, each with a length of 1M. One hundred bi-allelic QTL, four on each chromosome, were considered. In each generation, 100 sires and 100 dams were mated, producing five progeny per mating. The population was selected for a single trait (h(2)=0.1) for 100 discrete generations with selection on phenotype or BLUP-EBV. V_A decreased with directional truncation selection even in presence of non-additive genetic effects. Non-additive effects influenced long-term response to selection and among genetic models additive gene action had highest response to selection. In addition, in all genetic models, BLUP-EBV resulted in a greater fixation of favourable and unfavourable alleles and higher response than phenotypic selection. In conclusion, for the schemes we simulated, the presence of non-additive genetic effects had little effect in changes of additive variance and V_A decreased by directional selection.

  4. Genetic Assessment of Additional Endophenotypes from the Consortium on the Genetics of Schizophrenia Family Study

    PubMed Central

    Greenwood, Tiffany A.; Lazzeroni, Laura C.; Calkins, Monica E.; Freedman, Robert; Green, Michael F.; Gur, Raquel E.; Gur, Ruben C.; Light, Gregory A.; Nuechterlein, Keith H.; Olincy, Ann; Radant, Allen D.; Seidman, Larry J.; Siever, Larry J.; Silverman, Jeremy M.; Stone, William S.; Sugar, Catherine A.; Swerdlow, Neal R.; Tsuang, Debby W.; Tsuang, Ming T.; Turetsky, Bruce I.; Braff, David L.

    2015-01-01

    The Consortium on the Genetics of Schizophrenia Family Study (COGS-1) has previously reported our efforts to characterize the genetic architecture of 12 primary endophenotypes for schizophrenia. We now report the characterization of 13 additional measures derived from the same endophenotype test paradigms in the COGS-1 families. Nine of the measures were found to discriminate between schizophrenia patients and controls, were significantly heritable (31 to 62%), and were sufficiently independent of previously assessed endophenotypes, demonstrating utility as additional endophenotypes. Genotyping via a custom array of 1536 SNPs from 94 candidate genes identified associations for CTNNA2, ERBB4, GRID1, GRID2, GRIK3, GRIK4, GRIN2B, NOS1AP, NRG1, and RELN across multiple endophenotypes. An experiment-wide p value of 0.003 suggested that the associations across all SNPs and endophenotypes collectively exceeded chance. Linkage analyses performed using a genome-wide SNP array further identified significant or suggestive linkage for six of the candidate endophenotypes, with several genes of interest located beneath the linkage peaks (e.g., CSMD1, DISC1, DLGAP2, GRIK2, GRIN3A, and SLC6A3). While the partial convergence of the association and linkage likely reflects differences in density of gene coverage provided by the distinct genotyping platforms, it is also likely an indication of the differential contribution of rare and common variants for some genes and methodological differences in detection ability. Still, many of the genes implicated by COGS through endophenotypes have been identified by independent studies of common, rare, and de novo variation in schizophrenia, all converging on a functional genetic network related to glutamatergic neurotransmission that warrants further investigation. PMID:26597662

  5. Additive genetic contribution to symptom dimensions in major depressive disorder.

    PubMed

    Pearson, Rahel; Palmer, Rohan H C; Brick, Leslie A; McGeary, John E; Knopik, Valerie S; Beevers, Christopher G

    2016-05-01

    Major depressive disorder (MDD) is a phenotypically heterogeneous disorder with a complex genetic architecture. In this study, genomic-relatedness-matrix restricted maximum-likelihood analysis (GREML) was used to investigate the extent to which variance in depression symptoms/symptom dimensions can be explained by variation in common single nucleotide polymorphisms (SNPs) in a sample of individuals with MDD (N = 1,558) who participated in the National Institute of Mental Health Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. A principal components analysis of items from the Hamilton Rating Scale for Depression (HRSD) obtained prior to treatment revealed 4 depression symptom components: (a) appetite, (b) core depression symptoms (e.g., depressed mood, anhedonia), (c) insomnia, and (d) anxiety. These symptom dimensions were associated with SNP-based heritability (hSNP2) estimates of 30%, 14%, 30%, and 5%, respectively. Results indicated that the genetic contribution of common SNPs to depression symptom dimensions were not uniform. Appetite and insomnia symptoms in MDD had a relatively strong genetic contribution whereas the genetic contribution was relatively small for core depression and anxiety symptoms. While in need of replication, these results suggest that future gene discovery efforts may strongly benefit from parsing depression into its constituent parts. (PsycINFO Database Record

  6. Female and male genetic effects on offspring paternity: additive genetic (co)variances in female extra-pair reproduction and male paternity success in song sparrows (Melospiza melodia).

    PubMed

    Reid, Jane M; Arcese, Peter; Keller, Lukas F; Losdat, Sylvain

    2014-08-01

    Ongoing evolution of polyandry, and consequent extra-pair reproduction in socially monogamous systems, is hypothesized to be facilitated by indirect selection stemming from cross-sex genetic covariances with components of male fitness. Specifically, polyandry is hypothesized to create positive genetic covariance with male paternity success due to inevitable assortative reproduction, driving ongoing coevolution. However, it remains unclear whether such covariances could or do emerge within complex polyandrous systems. First, we illustrate that genetic covariances between female extra-pair reproduction and male within-pair paternity success might be constrained in socially monogamous systems where female and male additive genetic effects can have opposing impacts on the paternity of jointly reared offspring. Second, we demonstrate nonzero additive genetic variance in female liability for extra-pair reproduction and male liability for within-pair paternity success, modeled as direct and associative genetic effects on offspring paternity, respectively, in free-living song sparrows (Melospiza melodia). The posterior mean additive genetic covariance between these liabilities was slightly positive, but the credible interval was wide and overlapped zero. Therefore, although substantial total additive genetic variance exists, the hypothesis that ongoing evolution of female extra-pair reproduction is facilitated by genetic covariance with male within-pair paternity success cannot yet be definitively supported or rejected either conceptually or empirically.

  7. Widespread evidence for non-additive genetic variation in Cloninger's and Eysenck's personality dimensions using a twin plus sibling design.

    PubMed

    Keller, Matthew C; Coventry, William L; Heath, Andrew C; Martin, Nicholas G

    2005-11-01

    Studies using the classical twin design often conclude that most genetic variation underlying personality is additive in nature. However, studies analyzing only twins are very limited in their ability to detect non-additive genetic variation and are unable to detect sources of variation unique to twins, which can mask non-additive genetic variation. The current study assessed 9672 MZ and DZ twin individuals and 3241 of their siblings to investigate the environmental and genetic architecture underlying eight dimensions of personality: four from Eysenck's Personality Questionnaire and four from Cloninger's Temperament and Character Inventory. Broad-sense heritability estimates from best-fitting models were two to three times greater than the narrow-sense heritability estimates for Harm Avoidance, Novelty Seeking, Reward Dependence, Persistence, Extraversion, and Neuroticism. This genetic non-additivity could be due to dominance, additive-by-additive epistasis, or to additive genetic effects combined with higher-order epistasis. Environmental effects unique to twins were detected for both Lie and Psychoticism but accounted for little overall variation. Our results illustrate the increased sensitivity afforded by extending the classical twin design to include siblings, and may provide clues to the evolutionary origins of genetic variation underlying personality.

  8. A Spatial Statistical Model for Landscape Genetics

    PubMed Central

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

    2005-01-01

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

  9. Genetic models of focal epilepsies.

    PubMed

    Boillot, Morgane; Baulac, Stéphanie

    2016-02-15

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

  10. Additive and non-additive genetic components of the jack male life history in Chinook salmon (Oncorhynchus tshawytscha).

    PubMed

    Forest, Adriana R; Semeniuk, Christina A D; Heath, Daniel D; Pitcher, Trevor E

    2016-08-01

    Chinook salmon, Oncorhynchus tshawytscha, exhibit alternative reproductive tactics (ARTs) where males exist in two phenotypes: large "hooknose" males and smaller "jacks" that reach sexual maturity after only 1 year in seawater. The mechanisms that determine "jacking rate"-the rate at which males precociously sexually mature-are known to involve both genetics and differential growth rates, where individuals that become jacks exhibit higher growth earlier in life. The additive genetic components have been studied and it is known that jack sires produce significantly more jack offspring than hooknose sires, and vice versa. The current study was the first to investigate both additive and non-additive genetic components underlying jacking through the use of a full-factorial breeding design using all hooknose sires. The effect of dams and sires descendant from a marker-assisted broodstock program that identified "high performance" and "low performance" lines using growth- and survival-related gene markers was also studied. Finally, the relative growth of jack, hooknose, and female offspring was examined. No significant dam, sire, or interaction effects were observed in this study, and the maternal, additive, and non-additive components underlying jacking were small. Differences in jacking rates in this study were determined by dam performance line, where dams that originated from the low performance line produced significantly more jacks. Jack offspring in this study had a significantly larger body size than both hooknose males and females starting 1 year post-fertilization. This study provides novel information regarding the genetic architecture underlying ARTs in Chinook salmon that could have implications for the aquaculture industry, where jacks are not favoured due to their small body size and poor flesh quality.

  11. Advances on genetic rat models of epilepsy.

    PubMed

    Serikawa, Tadao; Mashimo, Tomoji; Kuramoro, Takashi; Voigt, Birger; Ohno, Yukihiro; Sasa, Masashi

    2015-01-01

    Considering the suitability of laboratory rats in epilepsy research, we and other groups have been developing genetic models of epilepsy in this species. After epileptic rats or seizure-susceptible rats were sporadically found in outbred stocks, the epileptic traits were usually genetically-fixed by selective breeding. So far, the absence seizure models GAERS and WAG/Rij, audiogenic seizure models GEPR-3 and GEPR-9, generalized tonic-clonic seizure models IER, NER and WER, and Canavan-disease related epileptic models TRM and SER have been established. Dissection of the genetic bases including causative genes in these epileptic rat models would be a significant step toward understanding epileptogenesis. N-ethyl-N-nitrosourea (ENU) mutagenesis provides a systematic approach which allowed us to develop two novel epileptic rat models: heat-induced seizure susceptible (Hiss) rats with an Scn1a missense mutation and autosomal dominant lateral temporal epilepsy (ADLTE) model rats with an Lgi1 missense mutation. In addition, we have established episodic ataxia type 1 (EA1) model rats with a Kcna1 missense mutation derived from the ENU-induced rat mutant stock, and identified a Cacna1a missense mutation in a N-Methyl-N-nitrosourea (MNU)-induced mutant rat strain GRY, resulting in the discovery of episodic ataxia type 2 (EA2) model rats. Thus, epileptic rat models have been established on the two paths: 'phenotype to gene' and 'gene to phenotype'. In the near future, development of novel epileptic rat models will be extensively promoted by the use of sophisticated genome editing technologies.

  12. Advances on genetic rat models of epilepsy

    PubMed Central

    Serikawa, Tadao; Mashimo, Tomoji; Kuramoto, Takashi; Voigt, Birger; Ohno, Yukihiro; Sasa, Masashi

    2014-01-01

    Considering the suitability of laboratory rats in epilepsy research, we and other groups have been developing genetic models of epilepsy in this species. After epileptic rats or seizure-susceptible rats were sporadically found in outbred stocks, the epileptic traits were usually genetically-fixed by selective breeding. So far, the absence seizure models GAERS and WAG/Rij, audiogenic seizure models GEPR-3 and GEPR-9, generalized tonic-clonic seizure models IER, NER and WER, and Canavan-disease related epileptic models TRM and SER have been established. Dissection of the genetic bases including causative genes in these epileptic rat models would be a significant step toward understanding epileptogenesis. N-ethyl-N-nitrosourea (ENU) mutagenesis provides a systematic approach which allowed us to develop two novel epileptic rat models: heat-induced seizure susceptible (Hiss) rats with an Scn1a missense mutation and autosomal dominant lateral temporal epilepsy (ADLTE) model rats with an Lgi1 missense mutation. In addition, we have established episodic ataxia type 1 (EA1) model rats with a Kcna1 missense mutation derived from the ENU-induced rat mutant stock, and identified a Cacna1a missense mutation in a N-Methyl-N-nitrosourea (MNU)-induced mutant rat strain GRY, resulting in the discovery of episodic ataxia type 2 (EA2) model rats. Thus, epileptic rat models have been established on the two paths: ‘phenotype to gene’ and ‘gene to phenotype’. In the near future, development of novel epileptic rat models will be extensively promoted by the use of sophisticated genome editing technologies. PMID:25312505

  13. Network Reconstruction Using Nonparametric Additive ODE Models

    PubMed Central

    Henderson, James; Michailidis, George

    2014-01-01

    Network representations of biological systems are widespread and reconstructing unknown networks from data is a focal problem for computational biologists. For example, the series of biochemical reactions in a metabolic pathway can be represented as a network, with nodes corresponding to metabolites and edges linking reactants to products. In a different context, regulatory relationships among genes are commonly represented as directed networks with edges pointing from influential genes to their targets. Reconstructing such networks from data is a challenging problem receiving much attention in the literature. There is a particular need for approaches tailored to time-series data and not reliant on direct intervention experiments, as the former are often more readily available. In this paper, we introduce an approach to reconstructing directed networks based on dynamic systems models. Our approach generalizes commonly used ODE models based on linear or nonlinear dynamics by extending the functional class for the functions involved from parametric to nonparametric models. Concomitantly we limit the complexity by imposing an additive structure on the estimated slope functions. Thus the submodel associated with each node is a sum of univariate functions. These univariate component functions form the basis for a novel coupling metric that we define in order to quantify the strength of proposed relationships and hence rank potential edges. We show the utility of the method by reconstructing networks using simulated data from computational models for the glycolytic pathway of Lactocaccus Lactis and a gene network regulating the pluripotency of mouse embryonic stem cells. For purposes of comparison, we also assess reconstruction performance using gene networks from the DREAM challenges. We compare our method to those that similarly rely on dynamic systems models and use the results to attempt to disentangle the distinct roles of linearity, sparsity, and derivative

  14. Computational Process Modeling for Additive Manufacturing (OSU)

    NASA Technical Reports Server (NTRS)

    Bagg, Stacey; Zhang, Wei

    2015-01-01

    Powder-Bed Additive Manufacturing (AM) through Direct Metal Laser Sintering (DMLS) or Selective Laser Melting (SLM) is being used by NASA and the Aerospace industry to "print" parts that traditionally are very complex, high cost, or long schedule lead items. The process spreads a thin layer of metal powder over a build platform, then melts the powder in a series of welds in a desired shape. The next layer of powder is applied, and the process is repeated until layer-by-layer, a very complex part can be built. This reduces cost and schedule by eliminating very complex tooling and processes traditionally used in aerospace component manufacturing. To use the process to print end-use items, NASA seeks to understand SLM material well enough to develop a method of qualifying parts for space flight operation. Traditionally, a new material process takes many years and high investment to generate statistical databases and experiential knowledge, but computational modeling can truncate the schedule and cost -many experiments can be run quickly in a model, which would take years and a high material cost to run empirically. This project seeks to optimize material build parameters with reduced time and cost through modeling.

  15. CREATION OF THE MODEL ADDITIONAL PROTOCOL

    SciTech Connect

    Houck, F.; Rosenthal, M.; Wulf, N.

    2010-05-25

    In 1991, the international nuclear nonproliferation community was dismayed to discover that the implementation of safeguards by the International Atomic Energy Agency (IAEA) under its NPT INFCIRC/153 safeguards agreement with Iraq had failed to detect Iraq's nuclear weapon program. It was now clear that ensuring that states were fulfilling their obligations under the NPT would require not just detecting diversion but also the ability to detect undeclared materials and activities. To achieve this, the IAEA initiated what would turn out to be a five-year effort to reappraise the NPT safeguards system. The effort engaged the IAEA and its Member States and led to agreement in 1997 on a new safeguards agreement, the Model Protocol Additional to the Agreement(s) between States and the International Atomic Energy Agency for the Application of Safeguards. The Model Protocol makes explicit that one IAEA goal is to provide assurance of the absence of undeclared nuclear material and activities. The Model Protocol requires an expanded declaration that identifies a State's nuclear potential, empowers the IAEA to raise questions about the correctness and completeness of the State's declaration, and, if needed, allows IAEA access to locations. The information required and the locations available for access are much broader than those provided for under INFCIRC/153. The negotiation was completed in quite a short time because it started with a relatively complete draft of an agreement prepared by the IAEA Secretariat. This paper describes how the Model Protocol was constructed and reviews key decisions that were made both during the five-year period and in the actual negotiation.

  16. The Evolution of Human Intelligence and the Coefficient of Additive Genetic Variance in Human Brain Size

    ERIC Educational Resources Information Center

    Miller, Geoffrey F.; Penke, Lars

    2007-01-01

    Most theories of human mental evolution assume that selection favored higher intelligence and larger brains, which should have reduced genetic variance in both. However, adult human intelligence remains highly heritable, and is genetically correlated with brain size. This conflict might be resolved by estimating the coefficient of additive genetic…

  17. Ontogeny of additive and maternal genetic effects: lessons from domestic mammals.

    PubMed

    Wilson, Alastair J; Reale, Denis

    2006-01-01

    Evolution of size and growth depends on heritable variation arising from additive and maternal genetic effects. Levels of heritable (and nonheritable) variation might change over ontogeny, increasing through "variance compounding" or decreasing through "compensatory growth." We test for these processes using a meta-analysis of age-specific weight traits in domestic ungulates. Generally, mean standardized variance components decrease with age, consistent with compensatory growth. Phenotypic convergence among adult sheep occurs through decreasing environmental and maternal genetic variation. Maternal variation similarly declines in cattle. Maternal genetic effects are thus reduced with age (both in absolute and relative terms). Significant trends in heritability (decreasing in cattle, increasing in sheep) result from declining maternal and environmental components rather than from changing additive variation. There was no evidence for increasing standardized variance components. Any compounding must therefore be masked by more important compensatory processes. While extrapolation of these patterns to processes in natural population is difficult, our results highlight the inadequacy of assuming constancy in genetic parameters over ontogeny. Negative covariance between direct and maternal genetic effects was common. Negative correlations with additive and maternal genetic variances indicate that antagonistic pleiotropy (between additive and maternal genetic effects) may maintain genetic variance and limit responses to selection.

  18. Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure.

    PubMed

    Crossa, José; Burgueño, Juan; Dreisigacker, Susanne; Vargas, Mateo; Herrera-Foessel, Sybil A; Lillemo, Morten; Singh, Ravi P; Trethowan, Richard; Warburton, Marilyn; Franco, Jorge; Reynolds, Matthew; Crouch, Jonathan H; Ortiz, Rodomiro

    2007-11-01

    Linkage disequilibrium can be used for identifying associations between traits of interest and genetic markers. This study used mapped diversity array technology (DArT) markers to find associations with resistance to stem rust, leaf rust, yellow rust, and powdery mildew, plus grain yield in five historical wheat international multienvironment trials from the International Maize and Wheat Improvement Center (CIMMYT). Two linear mixed models were used to assess marker-trait associations incorporating information on population structure and covariance between relatives. An integrated map containing 813 DArT markers and 831 other markers was constructed. Several linkage disequilibrium clusters bearing multiple host plant resistance genes were found. Most of the associated markers were found in genomic regions where previous reports had found genes or quantitative trait loci (QTL) influencing the same traits, providing an independent validation of this approach. In addition, many new chromosome regions for disease resistance and grain yield were identified in the wheat genome. Phenotyping across up to 60 environments and years allowed modeling of genotype x environment interaction, thereby making possible the identification of markers contributing to both additive and additive x additive interaction effects of traits.

  19. Genetic mouse models of depression.

    PubMed

    Barkus, Christopher

    2013-01-01

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

  20. Animal models for genetic neuromuscular diseases.

    PubMed

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

    2008-03-01

    , both presenting significant reduction of alpha2-laminin in the muscle and a severe phenotype. The myodystrophy mouse (Large(myd)) harbors a mutation in the glycosyltransferase Large, which leads to altered glycosylation of alpha-DG, and also a severe phenotype. Other informative models for muscle proteins include the knockout mouse for myostatin, which demonstrated that this protein is a negative regulator of muscle growth. Additionally, the stress syndrome in pigs, caused by mutations in the porcine RYR1 gene, helped to localize the gene causing malignant hypertermia and Central Core myopathy in humans. The study of animal models for genetic diseases, in spite of the existence of differences in some phenotypes, can provide important clues to the understanding of the pathogenesis of these disorders and are also very valuable for testing strategies for therapeutic approaches.

  1. Brachypodium distachyon as a Genetic Model System.

    PubMed

    Kellogg, Elizabeth A

    2015-01-01

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

  2. Genome-Enabled Estimates of Additive and Nonadditive Genetic Variances and Prediction of Apple Phenotypes Across Environments

    PubMed Central

    Kumar, Satish; Molloy, Claire; Muñoz, Patricio; Daetwyler, Hans; Chagné, David; Volz, Richard

    2015-01-01

    The nonadditive genetic effects may have an important contribution to total genetic variation of phenotypes, so estimates of both the additive and nonadditive effects are desirable for breeding and selection purposes. Our main objectives were to: estimate additive, dominance and epistatic variances of apple (Malus × domestica Borkh.) phenotypes using relationship matrices constructed from genome-wide dense single nucleotide polymorphism (SNP) markers; and compare the accuracy of genomic predictions using genomic best linear unbiased prediction models with or without including nonadditive genetic effects. A set of 247 clonally replicated individuals was assessed for six fruit quality traits at two sites, and also genotyped using an Illumina 8K SNP array. Across several fruit quality traits, the additive, dominance, and epistatic effects contributed about 30%, 16%, and 19%, respectively, to the total phenotypic variance. Models ignoring nonadditive components yielded upwardly biased estimates of additive variance (heritability) for all traits in this study. The accuracy of genomic predicted genetic values (GEGV) varied from about 0.15 to 0.35 for various traits, and these were almost identical for models with or without including nonadditive effects. However, models including nonadditive genetic effects further reduced the bias of GEGV. Between-site genotypic correlations were high (>0.85) for all traits, and genotype-site interaction accounted for <10% of the phenotypic variability. The accuracy of prediction, when the validation set was present only at one site, was generally similar for both sites, and varied from about 0.50 to 0.85. The prediction accuracies were strongly influenced by trait heritability, and genetic relatedness between the training and validation families. PMID:26497141

  3. Implementation of the Realized Genomic Relationship Matrix to Open-Pollinated White Spruce Family Testing for Disentangling Additive from Nonadditive Genetic Effects

    PubMed Central

    Gamal El-Dien, Omnia; Ratcliffe, Blaise; Klápště, Jaroslav; Porth, Ilga; Chen, Charles; El-Kassaby, Yousry A.

    2016-01-01

    The open-pollinated (OP) family testing combines the simplest known progeny evaluation and quantitative genetics analyses as candidates’ offspring are assumed to represent independent half-sib families. The accuracy of genetic parameter estimates is often questioned as the assumption of “half-sibling” in OP families may often be violated. We compared the pedigree- vs. marker-based genetic models by analysing 22-yr height and 30-yr wood density for 214 white spruce [Picea glauca (Moench) Voss] OP families represented by 1694 individuals growing on one site in Quebec, Canada. Assuming half-sibling, the pedigree-based model was limited to estimating the additive genetic variances which, in turn, were grossly overestimated as they were confounded by very minor dominance and major additive-by-additive epistatic genetic variances. In contrast, the implemented genomic pairwise realized relationship models allowed the disentanglement of additive from all nonadditive factors through genetic variance decomposition. The marker-based models produced more realistic narrow-sense heritability estimates and, for the first time, allowed estimating the dominance and epistatic genetic variances from OP testing. In addition, the genomic models showed better prediction accuracies compared to pedigree models and were able to predict individual breeding values for new individuals from untested families, which was not possible using the pedigree-based model. Clearly, the use of marker-based relationship approach is effective in estimating the quantitative genetic parameters of complex traits even under simple and shallow pedigree structure. PMID:26801647

  4. Implementation of the Realized Genomic Relationship Matrix to Open-Pollinated White Spruce Family Testing for Disentangling Additive from Nonadditive Genetic Effects.

    PubMed

    Gamal El-Dien, Omnia; Ratcliffe, Blaise; Klápště, Jaroslav; Porth, Ilga; Chen, Charles; El-Kassaby, Yousry A

    2016-01-22

    The open-pollinated (OP) family testing combines the simplest known progeny evaluation and quantitative genetics analyses as candidates' offspring are assumed to represent independent half-sib families. The accuracy of genetic parameter estimates is often questioned as the assumption of "half-sibling" in OP families may often be violated. We compared the pedigree- vs. marker-based genetic models by analysing 22-yr height and 30-yr wood density for 214 white spruce [Picea glauca (Moench) Voss] OP families represented by 1694 individuals growing on one site in Quebec, Canada. Assuming half-sibling, the pedigree-based model was limited to estimating the additive genetic variances which, in turn, were grossly overestimated as they were confounded by very minor dominance and major additive-by-additive epistatic genetic variances. In contrast, the implemented genomic pairwise realized relationship models allowed the disentanglement of additive from all nonadditive factors through genetic variance decomposition. The marker-based models produced more realistic narrow-sense heritability estimates and, for the first time, allowed estimating the dominance and epistatic genetic variances from OP testing. In addition, the genomic models showed better prediction accuracies compared to pedigree models and were able to predict individual breeding values for new individuals from untested families, which was not possible using the pedigree-based model. Clearly, the use of marker-based relationship approach is effective in estimating the quantitative genetic parameters of complex traits even under simple and shallow pedigree structure.

  5. Additive Functions in Boolean Models of Gene Regulatory Network Modules

    PubMed Central

    Darabos, Christian; Di Cunto, Ferdinando; Tomassini, Marco; Moore, Jason H.; Provero, Paolo; Giacobini, Mario

    2011-01-01

    Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity

  6. Additive functions in boolean models of gene regulatory network modules.

    PubMed

    Darabos, Christian; Di Cunto, Ferdinando; Tomassini, Marco; Moore, Jason H; Provero, Paolo; Giacobini, Mario

    2011-01-01

    Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity

  7. Estimation of Additive, Dominance, and Imprinting Genetic Variance Using Genomic Data

    PubMed Central

    Lopes, Marcos S.; Bastiaansen, John W. M.; Janss, Luc; Knol, Egbert F.; Bovenhuis, Henk

    2015-01-01

    Traditionally, exploration of genetic variance in humans, plants, and livestock species has been limited mostly to the use of additive effects estimated using pedigree data. However, with the development of dense panels of single-nucleotide polymorphisms (SNPs), the exploration of genetic variation of complex traits is moving from quantifying the resemblance between family members to the dissection of genetic variation at individual loci. With SNPs, we were able to quantify the contribution of additive, dominance, and imprinting variance to the total genetic variance by using a SNP regression method. The method was validated in simulated data and applied to three traits (number of teats, backfat, and lifetime daily gain) in three purebred pig populations. In simulated data, the estimates of additive, dominance, and imprinting variance were very close to the simulated values. In real data, dominance effects account for a substantial proportion of the total genetic variance (up to 44%) for these traits in these populations. The contribution of imprinting to the total phenotypic variance of the evaluated traits was relatively small (1–3%). Our results indicate a strong relationship between additive variance explained per chromosome and chromosome length, which has been described previously for other traits in other species. We also show that a similar linear relationship exists for dominance and imprinting variance. These novel results improve our understanding of the genetic architecture of the evaluated traits and shows promise to apply the SNP regression method to other traits and species, including human diseases. PMID:26438289

  8. The contribution of additive genetic variation to personality variation: heritability of personality.

    PubMed

    Dochtermann, Ned A; Schwab, Tori; Sih, Andrew

    2015-01-07

    Individual animals frequently exhibit repeatable differences from other members of their population, differences now commonly referred to as 'animal personality'. Personality differences can arise, for example, from differences in permanent environmental effects--including parental and epigenetic contributors--and the effect of additive genetic variation. Although several studies have evaluated the heritability of behaviour, less is known about general patterns of heritability and additive genetic variation in animal personality. As overall variation in behaviour includes both the among-individual differences that reflect different personalities and temporary environmental effects, it is possible for personality to be largely genetically influenced even when heritability of behaviour per se is quite low. The relative contribution of additive genetic variation to personality variation can be estimated whenever both repeatability and heritability are estimated for the same data. Using published estimates to address this issue, we found that approximately 52% of animal personality variation was attributable to additive genetic variation. Thus, while the heritability of behaviour is often moderate or low, the heritability of personality is much higher. Our results therefore (i) demonstrate that genetic differences are likely to be a major contributor to variation in animal personality and (ii) support the phenotypic gambit: that evolutionary inferences drawn from repeatability estimates may often be justified.

  9. Genetic algorithm-guided discovery of additive combinations that direct quantum dot assembly.

    PubMed

    Bawazer, Lukmaan A; Ihli, Johannes; Comyn, Timothy P; Critchley, Kevin; Empson, Christopher J; Meldrum, Fiona C

    2015-01-14

    The use of combinations of organic additives to control crystallization, as occurs in biomineralization, is rarely investigated due to the vast potential reaction space. It is demonstrated here that combinatorial approaches led by genetic algorithm heuristics can enable identification of active additive combinations, and four key organic molecules are rapidly identified, which generate highly fluorescent CdS quantum dot superstructures.

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

    PubMed

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

    2015-09-02

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

  11. Genetically modified pig models for human diseases.

    PubMed

    Fan, Nana; Lai, Liangxue

    2013-02-20

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

  12. Additives

    NASA Technical Reports Server (NTRS)

    Smalheer, C. V.

    1973-01-01

    The chemistry of lubricant additives is discussed to show what the additives are chemically and what functions they perform in the lubrication of various kinds of equipment. Current theories regarding the mode of action of lubricant additives are presented. The additive groups discussed include the following: (1) detergents and dispersants, (2) corrosion inhibitors, (3) antioxidants, (4) viscosity index improvers, (5) pour point depressants, and (6) antifouling agents.

  13. Do Health Professionals Need Additional Competencies for Stratified Cancer Prevention Based on Genetic Risk Profiling?

    PubMed Central

    Chowdhury, Susmita; Henneman, Lidewij; Dent, Tom; Hall, Alison; Burton, Alice; Pharoah, Paul; Pashayan, Nora; Burton, Hilary

    2015-01-01

    There is growing evidence that inclusion of genetic information about known common susceptibility variants may enable population risk-stratification and personalized prevention for common diseases including cancer. This would require the inclusion of genetic testing as an integral part of individual risk assessment of an asymptomatic individual. Front line health professionals would be expected to interact with and assist asymptomatic individuals through the risk stratification process. In that case, additional knowledge and skills may be needed. Current guidelines and frameworks for genetic competencies of non-specialist health professionals place an emphasis on rare inherited genetic diseases. For common diseases, health professionals do use risk assessment tools but such tools currently do not assess genetic susceptibility of individuals. In this article, we compare the skills and knowledge needed by non-genetic health professionals, if risk-stratified prevention is implemented, with existing competence recommendations from the UK, USA and Europe, in order to assess the gaps in current competences. We found that health professionals would benefit from understanding the contribution of common genetic variations in disease risk, the rationale for a risk-stratified prevention pathway, and the implications of using genomic information in risk-assessment and risk management of asymptomatic individuals for common disease prevention. PMID:26068647

  14. Do Health Professionals Need Additional Competencies for Stratified Cancer Prevention Based on Genetic Risk Profiling?

    PubMed

    Chowdhury, Susmita; Henneman, Lidewij; Dent, Tom; Hall, Alison; Burton, Alice; Pharoah, Paul; Pashayan, Nora; Burton, Hilary

    2015-06-09

    There is growing evidence that inclusion of genetic information about known common susceptibility variants may enable population risk-stratification and personalized prevention for common diseases including cancer. This would require the inclusion of genetic testing as an integral part of individual risk assessment of an asymptomatic individual. Front line health professionals would be expected to interact with and assist asymptomatic individuals through the risk stratification process. In that case, additional knowledge and skills may be needed. Current guidelines and frameworks for genetic competencies of non-specialist health professionals place an emphasis on rare inherited genetic diseases. For common diseases, health professionals do use risk assessment tools but such tools currently do not assess genetic susceptibility of individuals. In this article, we compare the skills and knowledge needed by non-genetic health professionals, if risk-stratified prevention is implemented, with existing competence recommendations from the UK, USA and Europe, in order to assess the gaps in current competences. We found that health professionals would benefit from understanding the contribution of common genetic variations in disease risk, the rationale for a risk-stratified prevention pathway, and the implications of using genomic information in risk-assessment and risk management of asymptomatic individuals for common disease prevention.

  15. Genetic coding and gene expression - new Quadruplet genetic coding model

    NASA Astrophysics Data System (ADS)

    Shankar Singh, Rama

    2012-07-01

    Successful demonstration of human genome project has opened the door not only for developing personalized medicine and cure for genetic diseases, but it may also answer the complex and difficult question of the origin of life. It may lead to making 21st century, a century of Biological Sciences as well. Based on the central dogma of Biology, genetic codons in conjunction with tRNA play a key role in translating the RNA bases forming sequence of amino acids leading to a synthesized protein. This is the most critical step in synthesizing the right protein needed for personalized medicine and curing genetic diseases. So far, only triplet codons involving three bases of RNA, transcribed from DNA bases, have been used. Since this approach has several inconsistencies and limitations, even the promise of personalized medicine has not been realized. The new Quadruplet genetic coding model proposed and developed here involves all four RNA bases which in conjunction with tRNA will synthesize the right protein. The transcription and translation process used will be the same, but the Quadruplet codons will help overcome most of the inconsistencies and limitations of the triplet codes. Details of this new Quadruplet genetic coding model and its subsequent potential applications including relevance to the origin of life will be presented.

  16. Genetically Engineered Pig Models for Human Diseases

    PubMed Central

    Prather, Randall S.; Lorson, Monique; Ross, Jason W.; Whyte, Jeffrey J.; Walters, Eric

    2015-01-01

    Although pigs are used widely as models of human disease, their utility as models has been enhanced by genetic engineering. Initially, transgenes were added randomly to the genome, but with the application of homologous recombination, zinc finger nucleases, and transcription activator-like effector nuclease (TALEN) technologies, now most any genetic change that can be envisioned can be completed. To date these genetic modifications have resulted in animals that have the potential to provide new insights into human diseases for which a good animal model did not exist previously. These new animal models should provide the preclinical data for treatments that are developed for diseases such as Alzheimer's disease, cystic fibrosis, retinitis pigmentosa, spinal muscular atrophy, diabetes, and organ failure. These new models will help to uncover aspects and treatments of these diseases that were otherwise unattainable. The focus of this review is to describe genetically engineered pigs that have resulted in models of human diseases. PMID:25387017

  17. Planning additional drilling campaign using two-space genetic algorithm: A game theoretical approach

    NASA Astrophysics Data System (ADS)

    Kumral, Mustafa; Ozer, Umit

    2013-03-01

    Grade and tonnage are the most important technical uncertainties in mining ventures because of the use of estimations/simulations, which are mostly generated from drill data. Open pit mines are planned and designed on the basis of the blocks representing the entire orebody. Each block has different estimation/simulation variance reflecting uncertainty to some extent. The estimation/simulation realizations are submitted to mine production scheduling process. However, the use of a block model with varying estimation/simulation variances will lead to serious risk in the scheduling. In the medium of multiple simulations, the dispersion variances of blocks can be thought to regard technical uncertainties. However, the dispersion variance cannot handle uncertainty associated with varying estimation/simulation variances of blocks. This paper proposes an approach that generates the configuration of the best additional drilling campaign to generate more homogenous estimation/simulation variances of blocks. In other words, the objective is to find the best drilling configuration in such a way as to minimize grade uncertainty under budget constraint. Uncertainty measure of the optimization process in this paper is interpolation variance, which considers data locations and grades. The problem is expressed as a minmax problem, which focuses on finding the best worst-case performance i.e., minimizing interpolation variance of the block generating maximum interpolation variance. Since the optimization model requires computing the interpolation variances of blocks being simulated/estimated in each iteration, the problem cannot be solved by standard optimization tools. This motivates to use two-space genetic algorithm (GA) approach to solve the problem. The technique has two spaces: feasible drill hole configuration with minimization of interpolation variance and drill hole simulations with maximization of interpolation variance. Two-space interacts to find a minmax solution

  18. New probabilistic graphical models for genetic regulatory networks studies.

    PubMed

    Wang, Junbai; Cheung, Leo Wang-Kit; Delabie, Jan

    2005-12-01

    This paper introduces two new probabilistic graphical models for reconstruction of genetic regulatory networks using DNA microarray data. One is an independence graph (IG) model with either a forward or a backward search algorithm and the other one is a Gaussian network (GN) model with a novel greedy search method. The performances of both models were evaluated on four MAPK pathways in yeast and three simulated data sets. Generally, an IG model provides a sparse graph but a GN model produces a dense graph where more information about gene-gene interactions may be preserved. The results of our proposed models were compared with several other commonly used models, and our models have shown to give superior performance. Additionally, we found the same common limitations in the prediction of genetic regulatory networks when using only DNA microarray data.

  19. Pattern of inbreeding depression, condition dependence, and additive genetic variance in Trinidadian guppy ejaculate traits

    PubMed Central

    Gasparini, Clelia; Devigili, Alessandro; Dosselli, Ryan; Pilastro, Andrea

    2013-01-01

    In polyandrous species, a male's reproductive success depends on his fertilization capability and traits enhancing competitive fertilization success will be under strong, directional selection. This leads to the prediction that these traits should show stronger condition dependence and larger genetic variance than other traits subject to weaker or stabilizing selection. While empirical evidence of condition dependence in postcopulatory traits is increasing, the comparison between sexually selected and ‘control’ traits is often based on untested assumption concerning the different strength of selection acting on these traits. Furthermore, information on selection in the past is essential, as both condition dependence and genetic variance of a trait are likely to be influenced by the pattern of selection acting historically on it. Using the guppy (Poecilia reticulata), a livebearing fish with high levels of multiple paternity, we performed three independent experiments on three ejaculate quality traits, sperm number, velocity, and size, which have been previously shown to be subject to strong, intermediate, and weak directional postcopulatory selection, respectively. First, we conducted an inbreeding experiment to determine the pattern of selection in the past. Second, we used a diet restriction experiment to estimate their level of condition dependence. Third, we used a half-sib/full-sib mating design to estimate the coefficients of additive genetic variance (CVA) underlying these traits. Additionally, using a simulated predator evasion test, we showed that both inbreeding and diet restriction significantly reduced condition. According to predictions, sperm number showed higher inbreeding depression, stronger condition dependence, and larger CVA than sperm velocity and sperm size. The lack of significant genetic correlation between sperm number and velocity suggests that the former may respond to selection independently one from other ejaculate quality traits

  20. Influence of dispersing additive on asphaltenes aggregation in model system

    NASA Astrophysics Data System (ADS)

    Gorshkov, A. M.; Shishmina, L. V.; Tukhvatullina, A. Z.; Ismailov, Yu R.; Ges, G. A.

    2016-09-01

    The work is devoted to investigation of the dispersing additive influence on asphaltenes aggregation in the asphaltenes-toluene-heptane model system by photon correlation spectroscopy method. The experimental relationship between the onset point of asphaltenes and their concentration in toluene has been obtained. The influence of model system composition on asphaltenes aggregation has been researched. The estimation of aggregative and sedimentation stability of asphaltenes in model system and system with addition of dispersing additive has been given.

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

    PubMed

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

    2007-01-01

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

  2. Additive genetic variation in schizophrenia risk is shared by populations of African and European descent.

    PubMed

    de Candia, Teresa R; Lee, S Hong; Yang, Jian; Browning, Brian L; Gejman, Pablo V; Levinson, Douglas F; Mowry, Bryan J; Hewitt, John K; Goddard, Michael E; O'Donovan, Michael C; Purcell, Shaun M; Posthuma, Danielle; Visscher, Peter M; Wray, Naomi R; Keller, Matthew C

    2013-09-05

    To investigate the extent to which the proportion of schizophrenia's additive genetic variation tagged by SNPs is shared by populations of European and African descent, we analyzed the largest combined African descent (AD [n = 2,142]) and European descent (ED [n = 4,990]) schizophrenia case-control genome-wide association study (GWAS) data set available, the Molecular Genetics of Schizophrenia (MGS) data set. We show how a method that uses genomic similarities at measured SNPs to estimate the additive genetic correlation (SNP correlation [SNP-rg]) between traits can be extended to estimate SNP-rg for the same trait between ethnicities. We estimated SNP-rg for schizophrenia between the MGS ED and MGS AD samples to be 0.66 (SE = 0.23), which is significantly different from 0 (p(SNP-rg = 0) = 0.0003), but not 1 (p(SNP-rg = 1) = 0.26). We re-estimated SNP-rg between an independent ED data set (n = 6,665) and the MGS AD sample to be 0.61 (SE = 0.21, p(SNP-rg = 0) = 0.0003, p(SNP-rg = 1) = 0.16). These results suggest that many schizophrenia risk alleles are shared across ethnic groups and predate African-European divergence.

  3. Predicting genetic interactions from Boolean models of biological networks.

    PubMed

    Calzone, Laurence; Barillot, Emmanuel; Zinovyev, Andrei

    2015-08-01

    Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing us to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and the phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed.

  4. Probabilistic graphical models for genetic association studies.

    PubMed

    Mourad, Raphaël; Sinoquet, Christine; Leray, Philippe

    2012-01-01

    Probabilistic graphical models have been widely recognized as a powerful formalism in the bioinformatics field, especially in gene expression studies and linkage analysis. Although less well known in association genetics, many successful methods have recently emerged to dissect the genetic architecture of complex diseases. In this review article, we cover the applications of these models to the population association studies' context, such as linkage disequilibrium modeling, fine mapping and candidate gene studies, and genome-scale association studies. Significant breakthroughs of the corresponding methods are highlighted, but emphasis is also given to their current limitations, in particular, to the issue of scalability. Finally, we give promising directions for future research in this field.

  5. Genetically modified pig models for neurodegenerative disorders.

    PubMed

    Holm, Ida E; Alstrup, Aage Kristian Olsen; Luo, Yonglun

    2016-01-01

    Increasing incidence of neurodegenerative disorders such as Alzheimer's disease and Parkinson's disease has become one of the most challenging health issues in ageing humans. One approach to combat this is to generate genetically modified animal models of neurodegenerative disorders for studying pathogenesis, prognosis, diagnosis, treatment, and prevention. Owing to the genetic, anatomic, physiologic, pathologic, and neurologic similarities between pigs and humans, genetically modified pig models of neurodegenerative disorders have been attractive large animal models to bridge the gap of preclinical investigations between rodents and humans. In this review, we provide a neuroanatomical overview in pigs and summarize and discuss the generation of genetically modified pig models of neurodegenerative disorders including Alzheimer's diseases, Huntington's disease, Parkinson's disease, amyotrophic lateral sclerosis, spinal muscular atrophy, and ataxia-telangiectasia. We also highlight how non-invasive bioimaging technologies such as positron emission tomography (PET), computer tomography (CT), and magnetic resonance imaging (MRI), and behavioural testing have been applied to characterize neurodegenerative pig models. We further propose a multiplex genome editing and preterm recloning (MAP) approach by using the rapid growth of the ground-breaking precision genome editing technology CRISPR/Cas9 and somatic cell nuclear transfer (SCNT). With this approach, we hope to shorten the temporal requirement in generating multiple transgenic pigs, increase the survival rate of founder pigs, and generate genetically modified pigs that will more closely resemble the disease-causing mutations and recapitulate pathological features of human conditions.

  6. Genetic models of homosexuality: generating testable predictions.

    PubMed

    Gavrilets, Sergey; Rice, William R

    2006-12-22

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

  7. Genetic models of homosexuality: generating testable predictions

    PubMed Central

    Gavrilets, Sergey; Rice, William R

    2006-01-01

    Homosexuality is a common occurrence in humans and other species, yet its genetic and evolutionary basis is poorly understood. Here, we formulate and study a series of simple mathematical models for the purpose of predicting empirical patterns that can be used to determine the form of selection that leads to polymorphism of genes influencing homosexuality. Specifically, we develop theory to make contrasting predictions about the genetic characteristics of genes influencing homosexuality including: (i) chromosomal location, (ii) dominance among segregating alleles and (iii) effect sizes that distinguish between the two major models for their polymorphism: the overdominance and sexual antagonism models. We conclude that the measurement of the genetic characteristics of quantitative trait loci (QTLs) found in genomic screens for genes influencing homosexuality can be highly informative in resolving the form of natural selection maintaining their polymorphism. PMID:17015344

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

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

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

  9. Additive genetic variance and developmental plasticity in growth trajectories in a wild cooperative mammal.

    PubMed

    Huchard, E; Charmantier, A; English, S; Bateman, A; Nielsen, J F; Clutton-Brock, T

    2014-09-01

    Individual variation in growth is high in cooperative breeders and may reflect plastic divergence in developmental trajectories leading to breeding vs. helping phenotypes. However, the relative importance of additive genetic variance and developmental plasticity in shaping growth trajectories is largely unknown in cooperative vertebrates. This study exploits weekly sequences of body mass from birth to adulthood to investigate sources of variance in, and covariance between, early and later growth in wild meerkats (Suricata suricatta), a cooperative mongoose. Our results indicate that (i) the correlation between early growth (prior to nutritional independence) and adult mass is positive but weak, and there are frequent changes (compensatory growth) in post-independence growth trajectories; (ii) among parameters describing growth trajectories, those describing growth rate (prior to and at nutritional independence) show undetectable heritability while associated size parameters (mass at nutritional independence and asymptotic mass) are moderately heritable (0.09 ≤ h(2) < 0.3); and (iii) additive genetic effects, rather than early environmental effects, mediate the covariance between early growth and adult mass. These results reveal that meerkat growth trajectories remain plastic throughout development, rather than showing early and irreversible divergence, and that the weak effects of early growth on adult mass, an important determinant of breeding success, are partly genetic. In contrast to most cooperative invertebrates, the acquisition of breeding status is often determined after sexual maturity and strongly impacted by chance in many cooperative vertebrates, who may therefore retain the ability to adjust their morphology to environmental changes and social opportunities arising throughout their development, rather than specializing early.

  10. Factors influencing QTL mapping accuracy under complicated genetic models by computer simulation.

    PubMed

    Su, C F; Wang, W; Gong, S L; Zuo, J H; Li, S J

    2016-12-19

    The accuracy of quantitative trait loci (QTLs) identified using different sample sizes and marker densities was evaluated in different genetic models. Model I assumed one additive QTL; Model II assumed three additive QTLs plus one pair of epistatic QTLs; and Model III assumed two additive QTLs with opposite genetic effects plus two pairs of epistatic QTLs. Recombinant inbred lines (RILs) (50-1500 samples) were simulated according to the Models to study the influence of different sample sizes under different genetic models on QTL mapping accuracy. RILs with 10-100 target chromosome markers were simulated according to Models I and II to evaluate the influence of marker density on QTL mapping accuracy. Different marker densities did not significantly influence accurate estimation of genetic effects with simple additive models, but influenced QTL mapping accuracy in the additive and epistatic models. The optimum marker density was approximately 20 markers when the recombination fraction between two adjacent markers was 0.056 in the additive and epistatic models. A sample size of 150 was sufficient for detecting simple additive QTLs. Thus, a sample size of approximately 450 is needed to detect QTLs with additive and epistatic models. Sample size must be approximately 750 to detect QTLs with additive, epistatic, and combined effects between QTLs. The sample size should be increased to >750 if the genetic models of the data set become more complicated than Model III. Our results provide a theoretical basis for marker-assisted selection breeding and molecular design breeding.

  11. Genetic demographic networks: Mathematical model and applications.

    PubMed

    Kimmel, Marek; Wojdyła, Tomasz

    2016-10-01

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

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

    PubMed

    Bijma, P

    2014-01-01

    Indirect genetic effects (IGE) occur when the genotype of an individual affects the phenotypic trait value of another conspecific individual. IGEs can have profound effects on both the magnitude and the direction of response to selection. Models of inheritance and response to selection in traits subject to IGEs have been developed within two frameworks; a trait-based framework in which IGEs are specified as a direct consequence of individual trait values, and a variance-component framework in which phenotypic variance is decomposed into a direct and an indirect additive genetic component. This work is a selective review of the quantitative genetics of traits affected by IGEs, with a focus on modelling, estimation and interpretation issues. It includes a discussion on variance-component vs trait-based models of IGEs, a review of issues related to the estimation of IGEs from field data, including the estimation of the interaction coefficient Ψ (psi), and a discussion on the relevance of IGEs for response to selection in cases where the strength of interaction varies among pairs of individuals. An investigation of the trait-based model shows that the interaction coefficient Ψ may deviate considerably from the corresponding regression coefficient when feedback occurs. The increasing research effort devoted to IGEs suggests that they are a widespread phenomenon, probably particularly in natural populations and plants. Further work in this field should considerably broaden our understanding of the quantitative genetics of inheritance and response to selection in relation to the social organisation of populations.

  13. Animal Models of Parkinson's Disease: Vertebrate Genetics

    PubMed Central

    Lee, Yunjong; Dawson, Valina L.; Dawson, Ted M.

    2012-01-01

    Parkinson's disease (PD) is a complex genetic disorder that is associated with environmental risk factors and aging. Vertebrate genetic models, especially mice, have aided the study of autosomal-dominant and autosomal-recessive PD. Mice are capable of showing a broad range of phenotypes and, coupled with their conserved genetic and anatomical structures, provide unparalleled molecular and pathological tools to model human disease. These models used in combination with aging and PD-associated toxins have expanded our understanding of PD pathogenesis. Attempts to refine PD animal models using conditional approaches have yielded in vivo nigrostriatal degeneration that is instructive in ordering pathogenic signaling and in developing therapeutic strategies to cure or halt the disease. Here, we provide an overview of the generation and characterization of transgenic and knockout mice used to study PD followed by a review of the molecular insights that have been gleaned from current PD mouse models. Finally, potential approaches to refine and improve current models are discussed. PMID:22960626

  14. Additive and subtractive scrambling in optional randomized response modeling.

    PubMed

    Hussain, Zawar; Al-Sobhi, Mashail M; Al-Zahrani, Bander

    2014-01-01

    This article considers unbiased estimation of mean, variance and sensitivity level of a sensitive variable via scrambled response modeling. In particular, we focus on estimation of the mean. The idea of using additive and subtractive scrambling has been suggested under a recent scrambled response model. Whether it is estimation of mean, variance or sensitivity level, the proposed scheme of estimation is shown relatively more efficient than that recent model. As far as the estimation of mean is concerned, the proposed estimators perform relatively better than the estimators based on recent additive scrambling models. Relative efficiency comparisons are also made in order to highlight the performance of proposed estimators under suggested scrambling technique.

  15. Complex Modelling Scheme Of An Additive Manufacturing Centre

    NASA Astrophysics Data System (ADS)

    Popescu, Liliana Georgeta

    2015-09-01

    This paper presents a modelling scheme sustaining the development of an additive manufacturing research centre model and its processes. This modelling is performed using IDEF0, the resulting model process representing the basic processes required in developing such a centre in any university. While the activities presented in this study are those recommended in general, changes may occur in specific existing situations in a research centre.

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

    PubMed

    Thomas, B

    1985-08-01

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

  17. Comprehensive European dietary exposure model (CEDEM) for food additives.

    PubMed

    Tennant, David R

    2016-05-01

    European methods for assessing dietary exposures to nutrients, additives and other substances in food are limited by the availability of detailed food consumption data for all member states. A proposed comprehensive European dietary exposure model (CEDEM) applies summary data published by the European Food Safety Authority (EFSA) in a deterministic model based on an algorithm from the EFSA intake method for food additives. The proposed approach can predict estimates of food additive exposure provided in previous EFSA scientific opinions that were based on the full European food consumption database.

  18. Genetically modified pigs to model human diseases.

    PubMed

    Flisikowska, Tatiana; Kind, Alexander; Schnieke, Angelika

    2014-02-01

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

  19. Very low levels of direct additive genetic variance in fitness and fitness components in a red squirrel population

    PubMed Central

    McFarlane, S Eryn; Gorrell, Jamieson C; Coltman, David W; Humphries, Murray M; Boutin, Stan; McAdam, Andrew G

    2014-01-01

    A trait must genetically correlate with fitness in order to evolve in response to natural selection, but theory suggests that strong directional selection should erode additive genetic variance in fitness and limit future evolutionary potential. Balancing selection has been proposed as a mechanism that could maintain genetic variance if fitness components trade off with one another and has been invoked to account for empirical observations of higher levels of additive genetic variance in fitness components than would be expected from mutation–selection balance. Here, we used a long-term study of an individually marked population of North American red squirrels (Tamiasciurus hudsonicus) to look for evidence of (1) additive genetic variance in lifetime reproductive success and (2) fitness trade-offs between fitness components, such as male and female fitness or fitness in high- and low-resource environments. “Animal model” analyses of a multigenerational pedigree revealed modest maternal effects on fitness, but very low levels of additive genetic variance in lifetime reproductive success overall as well as fitness measures within each sex and environment. It therefore appears that there are very low levels of direct genetic variance in fitness and fitness components in red squirrels to facilitate contemporary adaptation in this population. PMID:24963372

  20. A Genetic Porcine Model of Cancer

    PubMed Central

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

    2015-01-01

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

  1. Canalization, genetic assimilation and preadaptation. A quantitative genetic model.

    PubMed Central

    Eshel, I; Matessi, C

    1998-01-01

    We propose a mathematical model to analyze the evolution of canalization for a trait under stabilizing selection, where each individual in the population is randomly exposed to different environmental conditions, independently of its genotype. Without canalization, our trait (primary phenotype) is affected by both genetic variation and environmental perturbations (morphogenic environment). Selection of the trait depends on individually varying environmental conditions (selecting environment). Assuming no plasticity initially, morphogenic effects are not correlated with the direction of selection in individual environments. Under quite plausible assumptions we show that natural selection favors a system of canalization that tends to repress deviations from the phenotype that is optimal in the most common selecting environment. However, many experimental results, dating back to Waddington and others, indicate that natural canalization systems may fail under extreme environments. While this can be explained as an impossibility of the system to cope with extreme morphogenic pressure, we show that a canalization system that tends to be inactivated in extreme environments is even more advantageous than rigid canalization. Moreover, once this adaptive canalization is established, the resulting evolution of primary phenotype enables substantial preadaptation to permanent environmental changes resembling extreme niches of the previous environment. PMID:9691063

  2. Population genetics models of local ancestry.

    PubMed

    Gravel, Simon

    2012-06-01

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

  3. Modeling Errors in Daily Precipitation Measurements: Additive or Multiplicative?

    NASA Technical Reports Server (NTRS)

    Tian, Yudong; Huffman, George J.; Adler, Robert F.; Tang, Ling; Sapiano, Matthew; Maggioni, Viviana; Wu, Huan

    2013-01-01

    The definition and quantification of uncertainty depend on the error model used. For uncertainties in precipitation measurements, two types of error models have been widely adopted: the additive error model and the multiplicative error model. This leads to incompatible specifications of uncertainties and impedes intercomparison and application.In this letter, we assess the suitability of both models for satellite-based daily precipitation measurements in an effort to clarify the uncertainty representation. Three criteria were employed to evaluate the applicability of either model: (1) better separation of the systematic and random errors; (2) applicability to the large range of variability in daily precipitation; and (3) better predictive skills. It is found that the multiplicative error model is a much better choice under all three criteria. It extracted the systematic errors more cleanly, was more consistent with the large variability of precipitation measurements, and produced superior predictions of the error characteristics. The additive error model had several weaknesses, such as non constant variance resulting from systematic errors leaking into random errors, and the lack of prediction capability. Therefore, the multiplicative error model is a better choice.

  4. Electroacoustics modeling of piezoelectric welders for ultrasonic additive manufacturing processes

    NASA Astrophysics Data System (ADS)

    Hehr, Adam; Dapino, Marcelo J.

    2016-04-01

    Ultrasonic additive manufacturing (UAM) is a recent 3D metal printing technology which utilizes ultrasonic vibrations from high power piezoelectric transducers to additively weld similar and dissimilar metal foils. CNC machining is used intermittent of welding to create internal channels, embed temperature sensitive components, sensors, and materials, and for net shaping parts. Structural dynamics of the welder and work piece influence the performance of the welder and part quality. To understand the impact of structural dynamics on UAM, a linear time-invariant model is used to relate system shear force and electric current inputs to the system outputs of welder velocity and voltage. Frequency response measurements are combined with in-situ operating measurements of the welder to identify model parameters and to verify model assumptions. The proposed LTI model can enhance process consistency, performance, and guide the development of improved quality monitoring and control strategies.

  5. An Additional Symmetry in the Weinberg-Salam Model

    SciTech Connect

    Bakker, B.L.G.; Veselov, A.I.; Zubkov, M.A.

    2005-06-01

    An additional Z{sub 6} symmetry hidden in the fermion and Higgs sectors of the Standard Model has been found recently. It has a singular nature and is connected to the centers of the SU(3) and SU(2) subgroups of the gauge group. A lattice regularization of the Standard Model was constructed that possesses this symmetry. In this paper, we report our results on the numerical simulation of its electroweak sector.

  6. Modeling uranium transport in acidic contaminated groundwater with base addition.

    PubMed

    Zhang, Fan; Luo, Wensui; Parker, Jack C; Brooks, Scott C; Watson, David B; Jardine, Philip M; Gu, Baohua

    2011-06-15

    This study investigates reactive transport modeling in a column of uranium(VI)-contaminated sediments with base additions in the circulating influent. The groundwater and sediment exhibit oxic conditions with low pH, high concentrations of NO(3)(-), SO(4)(2-), U and various metal cations. Preliminary batch experiments indicate that additions of strong base induce rapid immobilization of U for this material. In the column experiment that is the focus of the present study, effluent groundwater was titrated with NaOH solution in an inflow reservoir before reinjection to gradually increase the solution pH in the column. An equilibrium hydrolysis, precipitation and ion exchange reaction model developed through simulation of the preliminary batch titration experiments predicted faster reduction of aqueous Al than observed in the column experiment. The model was therefore modified to consider reaction kinetics for the precipitation and dissolution processes which are the major mechanism for Al immobilization. The combined kinetic and equilibrium reaction model adequately described variations in pH, aqueous concentrations of metal cations (Al, Ca, Mg, Sr, Mn, Ni, Co), sulfate and U(VI). The experimental and modeling results indicate that U(VI) can be effectively sequestered with controlled base addition due to sorption by slowly precipitated Al with pH-dependent surface charge. The model may prove useful to predict field-scale U(VI) sequestration and remediation effectiveness.

  7. Generalised additive modelling approach to the fermentation process of glutamate.

    PubMed

    Liu, Chun-Bo; Li, Yun; Pan, Feng; Shi, Zhong-Ping

    2011-03-01

    In this work, generalised additive models (GAMs) were used for the first time to model the fermentation of glutamate (Glu). It was found that three fermentation parameters fermentation time (T), dissolved oxygen (DO) and oxygen uptake rate (OUR) could capture 97% variance of the production of Glu during the fermentation process through a GAM model calibrated using online data from 15 fermentation experiments. This model was applied to investigate the individual and combined effects of T, DO and OUR on the production of Glu. The conditions to optimize the fermentation process were proposed based on the simulation study from this model. Results suggested that the production of Glu can reach a high level by controlling concentration levels of DO and OUR to the proposed optimization conditions during the fermentation process. The GAM approach therefore provides an alternative way to model and optimize the fermentation process of Glu.

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

    DTIC Science & Technology

    1996-12-10

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

  9. Toxicological safety assessment of genetically modified Bacillus thuringiensis with additional N-acyl homoserine lactonase gene.

    PubMed

    Peng, Donghai; Zhou, Chenfei; Chen, Shouwen; Ruan, Lifang; Yu, Ziniu; Sun, Ming

    2008-01-01

    The aim of the present study is to evaluate the toxicology safety to mammals of a genetically modified (GM) Bacillus thuringiensis with an additional N-acyl homoserine lactones gene (aiiA), which possesses insecticidal activity together with restraint of bacterial pathogenicity and is intended for use as a multifunctional biopesticide. Safety assessments included an acute oral toxicity test and 28-d animal feeding study in Wistar rats, primary eye and dermal irritation in Zealand White rabbits, and delayed contact hypersensitivity in guinea pigs. Tests were conducted using spray-dried powder preparation. This GM product showed toxicity neither in oral acute toxicity test nor in 28-d animal feeding test at a dose of 5,000 mg/kg body weight. During the animal feeding test, there were no significant differences in growth, food and water consumption, hematology, blood biochemical indices, organ weights, and histopathology finding between rats in controls and tested groups. Tested animals in primary eye and dermal irritation and delayed contact hypersensitivity test were also devoid of any toxicity compared to controls. All the above results demonstrated that the GM based multifunctional B. thuringiensis has low toxicity and low eye and dermal irritation and would not cause hypersensitivity to laboratory mammals and therefore could be regarded as safe for use as a pesticide.

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

    PubMed Central

    Phillips, Tamara J.; Shabani, Shkelzen

    2015-01-01

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

  11. Latent spatial models and sampling design for landscape genetics

    USGS Publications Warehouse

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

    2016-01-01

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

  12. Genetic demixing and evolution in linear stepping stone models

    PubMed Central

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

    2010-01-01

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

  13. Genetic demixing and evolution in linear stepping stone models

    NASA Astrophysics Data System (ADS)

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

    2010-04-01

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

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

    PubMed

    Morton, A Jennifer; Howland, David S

    2013-01-01

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

  15. Evolutionary model with genetics, aging, and knowledge

    NASA Astrophysics Data System (ADS)

    Bustillos, Armando Ticona; de Oliveira, Paulo Murilo

    2004-02-01

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

  16. Validation of transport models using additive flux minimization technique

    NASA Astrophysics Data System (ADS)

    Pankin, A. Y.; Kruger, S. E.; Groebner, R. J.; Hakim, A.; Kritz, A. H.; Rafiq, T.

    2013-10-01

    A new additive flux minimization technique is proposed for carrying out the verification and validation (V&V) of anomalous transport models. In this approach, the plasma profiles are computed in time dependent predictive simulations in which an additional effective diffusivity is varied. The goal is to obtain an optimal match between the computed and experimental profile. This new technique has several advantages over traditional V&V methods for transport models in tokamaks and takes advantage of uncertainty quantification methods developed by the applied math community. As a demonstration of its efficiency, the technique is applied to the hypothesis that the paleoclassical density transport dominates in the plasma edge region in DIII-D tokamak discharges. A simplified version of the paleoclassical model that utilizes the Spitzer resistivity for the parallel neoclassical resistivity and neglects the trapped particle effects is tested in this paper. It is shown that a contribution to density transport, in addition to the paleoclassical density transport, is needed in order to describe the experimental profiles. It is found that more additional diffusivity is needed at the top of the H-mode pedestal, and almost no additional diffusivity is needed at the pedestal bottom. The implementation of this V&V technique uses the FACETS::Core transport solver and the DAKOTA toolkit for design optimization and uncertainty quantification. The FACETS::Core solver is used for advancing the plasma density profiles. The DAKOTA toolkit is used for the optimization of plasma profiles and the computation of the additional diffusivity that is required for the predicted density profile to match the experimental profile.

  17. Mouse Genetic Models of Human Brain Disorders

    PubMed Central

    Leung, Celeste; Jia, Zhengping

    2016-01-01

    Over the past three decades, genetic manipulations in mice have been used in neuroscience as a major approach to investigate the in vivo function of genes and their alterations. In particular, gene targeting techniques using embryonic stem cells have revolutionized the field of mammalian genetics and have been at the forefront in the generation of numerous mouse models of human brain disorders. In this review, we will first examine childhood developmental disorders such as autism, intellectual disability, Fragile X syndrome, and Williams-Beuren syndrome. We will then explore psychiatric disorders such as schizophrenia and lastly, neurodegenerative disorders including Alzheimer’s disease and Parkinson’s disease. We will outline the creation of these mouse models that range from single gene deletions, subtle point mutations to multi-gene manipulations, and discuss the key behavioral phenotypes of these mice. Ultimately, the analysis of the models outlined in this review will enhance our understanding of the in vivo role and underlying mechanisms of disease-related genes in both normal brain function and brain disorders, and provide potential therapeutic targets and strategies to prevent and treat these diseases. PMID:27047540

  18. [Questions safety and tendency of using genetically modified microorganisms in food, food additives and food derived].

    PubMed

    Khovaev, A A

    2008-01-01

    In this article analysis questions of using genetically modified microorganisms in manufacture food production, present new GMM used in manufacture -food ferments; results of medical biological appraisal/ microbiological and genetic expert examination/ of food, getting by use microorganisms or there producents with indication modern of control methods.

  19. A unified model of the standard genetic code

    PubMed Central

    Morgado, Eberto R.

    2017-01-01

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

  20. A unified model of the standard genetic code.

    PubMed

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

    2017-03-01

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

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

    PubMed

    Lewis, Linwood J

    2002-06-01

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

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

    PubMed Central

    Goltz, Heather Honoré; Bergman, Margo; Goodson, Patricia

    2016-01-01

    This exploratory qualitative study focuses on how college students conceptualize genetics and genetic risk, concepts essential for genetic literacy (GL) and genetic numeracy (GN), components of overall health literacy (HL). HL is dependent on both the background knowledge and culture of a patient, and lower HL is linked to increased morbidity and mortality for a number of chronic health conditions (e.g., diabetes and cancer). A purposive sample of 86 students from three Southwestern universities participated in eight focus groups. The sample ranged in age from 18 to 54 years, and comprised primarily of female (67.4%), single (74.4%), and non-White (57%) participants, none of whom were genetics/biology majors. A holistic-content approach revealed broad categories concerning participants’ explanatory models (EMs) of genetics and genetic risk. Participants’ EMs were grounded in highly contextualized narratives that only partially overlapped with biomedical models. While higher education levels should be associated with predominately knowledge-based EM of genetic risk, this study shows that even in well-educated populations cultural factors can dominate. Study findings reveal gaps in how this sample of young adults obtains, processes, and understands genetic/genomic concepts. Future studies should assess how individuals with low GL and GN obtain and process genetics and genetic risk information and incorporate this information into health decision making. Future work should also address the interaction of communication between health educators, providers, and genetic counselors, to increase patient understanding of genetic risk. PMID:27376052

  3. The system-resonance approach in modeling genetic structures.

    PubMed

    Petoukhov, Sergey V

    2016-01-01

    The founder of the theory of resonance in structural chemistry Linus Pauling established the importance of resonance patterns in organization of living systems. Any living organism is a great chorus of coordinated oscillatory processes. From the formal point of view, biological organism is an oscillatory system with a great number of degrees of freedom. Such systems are studied in the theory of oscillations using matrix mathematics of their resonance characteristics. This study is devoted to a new approach for modeling genetically inherited structures and processes in living organisms using mathematical tools of the theory of resonances. This approach reveals hidden relationships in a number of genetic phenomena and gives rise to a new class of bio-mathematical models, which contribute to a convergence of biology with physics and informatics. In addition some relationships of molecular-genetic ensembles with mathematics of noise-immunity coding of information in modern communications technology are shown. Perspectives of applications of the phenomena of vibrational mechanics for modeling in biology are discussed.

  4. Genetic predisposition to coronary heart disease and stroke using an additive genetic risk score: a population-based study in Greece

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Objective: To determine the extent to which the risk for incident coronary heart disease (CHD) increases in relation to a genetic risk score (GRS) that additively integrates the influence of high-risk alleles in nine documented single nucleotide polymorphisms (SNPs) for CHD, and to examine whether t...

  5. Multiscale and Multiphysics Modeling of Additive Manufacturing of Advanced Materials

    NASA Technical Reports Server (NTRS)

    Liou, Frank; Newkirk, Joseph; Fan, Zhiqiang; Sparks, Todd; Chen, Xueyang; Fletcher, Kenneth; Zhang, Jingwei; Zhang, Yunlu; Kumar, Kannan Suresh; Karnati, Sreekar

    2015-01-01

    The objective of this proposed project is to research and develop a prediction tool for advanced additive manufacturing (AAM) processes for advanced materials and develop experimental methods to provide fundamental properties and establish validation data. Aircraft structures and engines demand materials that are stronger, useable at much higher temperatures, provide less acoustic transmission, and enable more aeroelastic tailoring than those currently used. Significant improvements in properties can only be achieved by processing the materials under nonequilibrium conditions, such as AAM processes. AAM processes encompass a class of processes that use a focused heat source to create a melt pool on a substrate. Examples include Electron Beam Freeform Fabrication and Direct Metal Deposition. These types of additive processes enable fabrication of parts directly from CAD drawings. To achieve the desired material properties and geometries of the final structure, assessing the impact of process parameters and predicting optimized conditions with numerical modeling as an effective prediction tool is necessary. The targets for the processing are multiple and at different spatial scales, and the physical phenomena associated occur in multiphysics and multiscale. In this project, the research work has been developed to model AAM processes in a multiscale and multiphysics approach. A macroscale model was developed to investigate the residual stresses and distortion in AAM processes. A sequentially coupled, thermomechanical, finite element model was developed and validated experimentally. The results showed the temperature distribution, residual stress, and deformation within the formed deposits and substrates. A mesoscale model was developed to include heat transfer, phase change with mushy zone, incompressible free surface flow, solute redistribution, and surface tension. Because of excessive computing time needed, a parallel computing approach was also tested. In addition

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

    PubMed

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

    2015-01-01

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

  7. Addition Table of Colours: Additive and Subtractive Mixtures Described Using a Single Reasoning Model

    ERIC Educational Resources Information Center

    Mota, A. R.; Lopes dos Santos, J. M. B.

    2014-01-01

    Students' misconceptions concerning colour phenomena and the apparent complexity of the underlying concepts--due to the different domains of knowledge involved--make its teaching very difficult. We have developed and tested a teaching device, the addition table of colours (ATC), that encompasses additive and subtractive mixtures in a single…

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

    PubMed

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

    2015-08-21

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

  9. Sensitivity analysis of geometric errors in additive manufacturing medical models.

    PubMed

    Pinto, Jose Miguel; Arrieta, Cristobal; Andia, Marcelo E; Uribe, Sergio; Ramos-Grez, Jorge; Vargas, Alex; Irarrazaval, Pablo; Tejos, Cristian

    2015-03-01

    Additive manufacturing (AM) models are used in medical applications for surgical planning, prosthesis design and teaching. For these applications, the accuracy of the AM models is essential. Unfortunately, this accuracy is compromised due to errors introduced by each of the building steps: image acquisition, segmentation, triangulation, printing and infiltration. However, the contribution of each step to the final error remains unclear. We performed a sensitivity analysis comparing errors obtained from a reference with those obtained modifying parameters of each building step. Our analysis considered global indexes to evaluate the overall error, and local indexes to show how this error is distributed along the surface of the AM models. Our results show that the standard building process tends to overestimate the AM models, i.e. models are larger than the original structures. They also show that the triangulation resolution and the segmentation threshold are critical factors, and that the errors are concentrated at regions with high curvatures. Errors could be reduced choosing better triangulation and printing resolutions, but there is an important need for modifying some of the standard building processes, particularly the segmentation algorithms.

  10. Additive Manufacturing of Medical Models--Applications in Rhinology.

    PubMed

    Raos, Pero; Klapan, Ivica; Galeta, Tomislav

    2015-09-01

    In the paper we are introducing guidelines and suggestions for use of 3D image processing SW in head pathology diagnostic and procedures for obtaining physical medical model by additive manufacturing/rapid prototyping techniques, bearing in mind the improvement of surgery performance, its maximum security and faster postoperative recovery of patients. This approach has been verified in two case reports. In the treatment we used intelligent classifier-schemes for abnormal patterns using computer-based system for 3D-virtual and endoscopic assistance in rhinology, with appropriate visualization of anatomy and pathology within the nose, paranasal sinuses, and scull base area.

  11. The Multi-allelic Genetic Architecture of a Variance-Heterogeneity Locus for Molybdenum Concentration in Leaves Acts as a Source of Unexplained Additive Genetic Variance

    PubMed Central

    Forsberg, Simon K. G.; Andreatta, Matthew E.; Huang, Xin-Yuan; Danku, John; Salt, David E.; Carlborg, Örjan

    2015-01-01

    Genome-wide association (GWA) analyses have generally been used to detect individual loci contributing to the phenotypic diversity in a population by the effects of these loci on the trait mean. More rarely, loci have also been detected based on variance differences between genotypes. Several hypotheses have been proposed to explain the possible genetic mechanisms leading to such variance signals. However, little is known about what causes these signals, or whether this genetic variance-heterogeneity reflects mechanisms of importance in natural populations. Previously, we identified a variance-heterogeneity GWA (vGWA) signal for leaf molybdenum concentrations in Arabidopsis thaliana. Here, fine-mapping of this association reveals that the vGWA emerges from the effects of three independent genetic polymorphisms that all are in strong LD with the markers displaying the genetic variance-heterogeneity. By revealing the genetic architecture underlying this vGWA signal, we uncovered the molecular source of a significant amount of hidden additive genetic variation or “missing heritability”. Two of the three polymorphisms underlying the genetic variance-heterogeneity are promoter variants for Molybdate transporter 1 (MOT1), and the third a variant located ~25 kb downstream of this gene. A fourth independent association was also detected ~600 kb upstream of MOT1. Use of a T-DNA knockout allele highlights Copper Transporter 6; COPT6 (AT2G26975) as a strong candidate gene for this association. Our results show that an extended LD across a complex locus including multiple functional alleles can lead to a variance-heterogeneity between genotypes in natural populations. Further, they provide novel insights into the genetic regulation of ion homeostasis in A. thaliana, and empirically confirm that variance-heterogeneity based GWA methods are a valuable tool to detect novel associations of biological importance in natural populations. PMID:26599497

  12. Multiscale Modeling of Powder Bed-Based Additive Manufacturing

    NASA Astrophysics Data System (ADS)

    Markl, Matthias; Körner, Carolin

    2016-07-01

    Powder bed fusion processes are additive manufacturing technologies that are expected to induce the third industrial revolution. Components are built up layer by layer in a powder bed by selectively melting confined areas, according to sliced 3D model data. This technique allows for manufacturing of highly complex geometries hardly machinable with conventional technologies. However, the underlying physical phenomena are sparsely understood and difficult to observe during processing. Therefore, an intensive and expensive trial-and-error principle is applied to produce components with the desired dimensional accuracy, material characteristics, and mechanical properties. This review presents numerical modeling approaches on multiple length scales and timescales to describe different aspects of powder bed fusion processes. In combination with tailored experiments, the numerical results enlarge the process understanding of the underlying physical mechanisms and support the development of suitable process strategies and component topologies.

  13. Genetic Algorithm Approaches to Prebiobiotic Chemistry Modeling

    NASA Technical Reports Server (NTRS)

    Lohn, Jason; Colombano, Silvano

    1997-01-01

    We model an artificial chemistry comprised of interacting polymers by specifying two initial conditions: a distribution of polymers and a fixed set of reversible catalytic reactions. A genetic algorithm is used to find a set of reactions that exhibit a desired dynamical behavior. Such a technique is useful because it allows an investigator to determine whether a specific pattern of dynamics can be produced, and if it can, the reaction network found can be then analyzed. We present our results in the context of studying simplified chemical dynamics in theorized protocells - hypothesized precursors of the first living organisms. Our results show that given a small sample of plausible protocell reaction dynamics, catalytic reaction sets can be found. We present cases where this is not possible and also analyze the evolved reaction sets.

  14. Network growth models and genetic regulatory networks

    NASA Astrophysics Data System (ADS)

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

    2006-03-01

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

  15. Network growth models and genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Socolar, Joshua; Foster, David; Kauffman, Stuart

    2006-03-01

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

  16. Genetic models of poljes in Sicily

    NASA Astrophysics Data System (ADS)

    Di Maggio, Cipriano; Madonia, Giuliana; Vattano, Marco; De Waele, Jo

    2016-04-01

    Geomorphological and geological studies have been carried out to contribute to the recognition of controlling causes and to the definition of genetic models for poljes of Sicily. A polje is a kilometric closed depression developed mainly on karst rocks, with a conspicuously flat and alluviated bottom affected by intermittent flooding. A polje is usually characterised by relatively steep slopes enclosing an almost perfectly horizontal floor, caused by lateral solution planation related to flooding events. The origin of a polje is due to dissolution of the land surface, although geological structure generally influences its genesis. These large depressions are often elongated according to the direction of main faults, in consequence of a control due to tectonics or to differential erosion. The performed researches have shown the existence of at least seven poljes located along the north-western (chain zone) and the southern (deformed foredeep zone) areas of Sicily. These large karst depressions are developed on Mesozoic limestone/dolomitic rocks within the chain zone and on Messinian gypsum rocks within the deformed foredeep zone. They are up to 4 km in length, can reach surfaces of 3-8 km2 and are around hundred metres deep, with steep slopes and a flat bottom. Generally, they are open, occasionally active depressions and their genesis seems to be strongly controlled by structure. In particular, the studied poljes occur in two different geological/geomorphological settings: a) in graben-like tectonic depressions, where important fault slopes/scarps border the flat bottom; b) in complex depressions controlled by structure, where wide fault line slopes/scarps or large inclined degraded structural surfaces mark the poljes. Finally, landscape analysis leads to the proposition of two main genetic models in which the development of poljes is primarily due to tectonics or differential erosion followed by dissolution.

  17. WATEQ3 geochemical model: thermodynamic data for several additional solids

    SciTech Connect

    Krupka, K.M.; Jenne, E.A.

    1982-09-01

    Geochemical models such as WATEQ3 can be used to model the concentrations of water-soluble pollutants that may result from the disposal of nuclear waste and retorted oil shale. However, for a model to competently deal with these water-soluble pollutants, an adequate thermodynamic data base must be provided that includes elements identified as important in modeling these pollutants. To this end, several minerals and related solid phases were identified that were absent from the thermodynamic data base of WATEQ3. In this study, the thermodynamic data for the identified solids were compiled and selected from several published tabulations of thermodynamic data. For these solids, an accepted Gibbs free energy of formation, ..delta..G/sup 0//sub f,298/, was selected for each solid phase based on the recentness of the tabulated data and on considerations of internal consistency with respect to both the published tabulations and the existing data in WATEQ3. For those solids not included in these published tabulations, Gibbs free energies of formation were calculated from published solubility data (e.g., lepidocrocite), or were estimated (e.g., nontronite) using a free-energy summation method described by Mattigod and Sposito (1978). The accepted or estimated free energies were then combined with internally consistent, ancillary thermodynamic data to calculate equilibrium constants for the hydrolysis reactions of these minerals and related solid phases. Including these values in the WATEQ3 data base increased the competency of this geochemical model in applications associated with the disposal of nuclear waste and retorted oil shale. Additional minerals and related solid phases that need to be added to the solubility submodel will be identified as modeling applications continue in these two programs.

  18. 29 CFR 2590.702-1 - Additional requirements prohibiting discrimination based on genetic information.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... diabetes. A begins to experience excessive sweating, thirst, and fatigue. A's physician examines A and... adult onset diabetes mellitus (Type 2 diabetes). (ii) Conclusion. In this Example 1, A has been... involved. The diagnosis is not based principally on genetic information. Thus, Type 2 diabetes...

  19. 45 CFR 146.122 - Additional requirements prohibiting discrimination based on genetic information.

    Code of Federal Regulations, 2010 CFR

    2010-10-01

    ... diabetes. A begins to experience excessive sweating, thirst, and fatigue. A's physician examines A and... adult onset diabetes mellitus (Type 2 diabetes). (ii) Conclusion. In this Example 1, A has been... involved. The diagnosis is not based principally on genetic information. Thus, Type 2 diabetes...

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

    ERIC Educational Resources Information Center

    Johnson, Ronald; Kennon, Tillman

    2009-01-01

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

  1. Genetic model compensation: Theory and applications

    NASA Astrophysics Data System (ADS)

    Cruickshank, David Raymond

    1998-12-01

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

  2. A Tri-part Model for Genetics Literacy: Exploring Undergraduate Student Reasoning About Authentic Genetics Dilemmas

    NASA Astrophysics Data System (ADS)

    Shea, Nicole A.; Duncan, Ravit Golan; Stephenson, Celeste

    2015-08-01

    Genetics literacy is becoming increasingly important as advancements in our application of genetic technologies such as stem cell research, cloning, and genetic screening become more prevalent. Very few studies examine how genetics literacy is applied when reasoning about authentic genetic dilemmas. However, there is evidence that situational features of a reasoning task may influence how students apply content knowledge as they generate and support arguments. Understanding how students apply content knowledge to reason about authentic and complex issues is important for considering instructional practices that best support student thinking and reasoning. In this conceptual report, we present a tri-part model for genetics literacy that embodies the relationships between content knowledge use, argumentation quality, and the role of situational features in reasoning to support genetics literacy. Using illustrative examples from an interview study with early career undergraduate students majoring in the biological sciences and late career undergraduate students majoring in genetics, we provide insights into undergraduate student reasoning about complex genetics issues and discuss implications for teaching and learning. We further discuss the need for research about how the tri-part model of genetics literacy can be used to explore students' thinking and reasoning abilities in genetics.

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

    PubMed

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

    2004-01-01

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

  4. Mathematical Modeling of Intestinal Iron Absorption Using Genetic Programming

    PubMed Central

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

    2017-01-01

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

  5. Estimation of propensity scores using generalized additive models.

    PubMed

    Woo, Mi-Ja; Reiter, Jerome P; Karr, Alan F

    2008-08-30

    Propensity score matching is often used in observational studies to create treatment and control groups with similar distributions of observed covariates. Typically, propensity scores are estimated using logistic regressions that assume linearity between the logistic link and the predictors. We evaluate the use of generalized additive models (GAMs) for estimating propensity scores. We compare logistic regressions and GAMs in terms of balancing covariates using simulation studies with artificial and genuine data. We find that, when the distributions of covariates in the treatment and control groups overlap sufficiently, using GAMs can improve overall covariate balance, especially for higher-order moments of distributions. When the distributions in the two groups overlap insufficiently, GAM more clearly reveals this fact than logistic regression does. We also demonstrate via simulation that matching with GAMs can result in larger reductions in bias when estimating treatment effects than matching with logistic regression.

  6. [Critical of the additive model of the randomized controlled trial].

    PubMed

    Boussageon, Rémy; Gueyffier, François; Bejan-Angoulvant, Theodora; Felden-Dominiak, Géraldine

    2008-01-01

    Randomized, double-blind, placebo-controlled clinical trials are currently the best way to demonstrate the clinical effectiveness of drugs. Its methodology relies on the method of difference (John Stuart Mill), through which the observed difference between two groups (drug vs placebo) can be attributed to the pharmacological effect of the drug being tested. However, this additive model can be questioned in the event of statistical interactions between the pharmacological and the placebo effects. Evidence in different domains has shown that the placebo effect can influence the effect of the active principle. This article evaluates the methodological, clinical and epistemological consequences of this phenomenon. Topics treated include extrapolating results, accounting for heterogeneous results, demonstrating the existence of several factors in the placebo effect, the necessity to take these factors into account for given symptoms or pathologies, as well as the problem of the "specific" effect.

  7. Comprehensive Neurocognitive Endophenotyping Strategies for Mouse Models of Genetic Disorders

    PubMed Central

    Hunsaker, Michael R.

    2012-01-01

    There is a need for refinement of the current behavioral phenotyping methods for mouse models of genetic disorders. The current approach is to perform a behavioral screen using standardized tasks to define a broad phenotype of the model. This phenotype is then compared to what is known concerning the disorder being modeled. The weakness inherent in this approach is twofold: First, the tasks that make up these standard behavioral screens do not model specific behaviors associated with a given genetic mutation but rather phenotypes affected in various genetic disorders; secondly, these behavioral tasks are insufficiently sensitive to identify subtle phenotypes. An alternate phenotyping strategy is to determine the core behavioral phenotypes of the genetic disorder being studied and develop behavioral tasks to evaluate specific hypotheses concerning the behavioral consequences of the genetic mutation. This approach emphasizes direct comparisons between the mouse and human that facilitate the development of neurobehavioral biomarkers or quantitative outcome measures for studies of genetic disorders across species. PMID:22266125

  8. Marker-Based Estimates Reveal Significant Non-additive Effects in Clonally Propagated Cassava (Manihot esculenta): Implications for the Prediction of Total Genetic Value and the Selection of Varieties.

    PubMed

    Wolfe, Marnin D; Kulakow, Peter; Rabbi, Ismail Y; Jannink, Jean-Luc

    2016-08-31

    In clonally propagated crops, non-additive genetic effects can be effectively exploited by the identification of superior genetic individuals as varieties. Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop that feeds hundreds of millions. We quantified the amount and nature of non-additive genetic variation for three key traits in a breeding population of cassava from sub-Saharan Africa using additive and non-additive genome-wide marker-based relationship matrices. We then assessed the accuracy of genomic prediction for total (additive plus non-additive) genetic value. We confirmed previous findings based on diallel populations, that non-additive genetic variation is significant for key cassava traits. Specifically, we found that dominance is particularly important for root yield and epistasis contributes strongly to variation in CMD resistance. Further, we showed that total genetic value predicted observed phenotypes more accurately than additive only models for root yield but not for dry matter content, which is mostly additive or for CMD resistance, which has high narrow-sense heritability. We address the implication of these results for cassava breeding and put our work in the context of previous results in cassava, and other plant and animal species.

  9. Annual fish as a genetic model for aging.

    PubMed

    Herrera, Michael; Jagadeeswaran, Pudur

    2004-02-01

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

  10. Genetic variation at the TPH2 gene influences impulsivity in addition to eating disorders.

    PubMed

    Slof-Op't Landt, Margarita C T; Bartels, Meike; Middeldorp, Christel M; van Beijsterveldt, Catherina E M; Slagboom, P Eline; Boomsma, Dorret I; van Furth, Eric F; Meulenbelt, Ingrid

    2013-01-01

    Genes are involved in eating disorders (EDs) and self-induced vomiting (SV), a key symptom of different types of EDs. Perfectionism and impulsivity are potential risk factors for EDs. TPH2 (tryptophan hydroxylase 2) SNP rs1473473 was previously associated with anorexia nervosa and EDs characterized by SV. Could perfectionism or impulsivity be underlying the association between rs1473473 and EDs? Genetic association between TPH2 SNP rs1473473 and perfectionism or impulsivity was first evaluated in a random control group (N = 512). The associations obtained in this control group were subsequently tested in a group of patients with an ED (N = 267). The minor allele of rs1473473 (OR = 1.49) was more frequent in impulsive controls, but also in impulsive patients with an ED (OR = 1.83). The largest effect was found in the patients with an ED characterized by SV (OR = 2.51, p = 0.02). Genetic variation at the TPH2 gene appeared to affect impulsivity which, in turn, might predispose to the SV phenotype.

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

    ERIC Educational Resources Information Center

    Devor, Eric J.

    1994-01-01

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

  12. Computer Center: BASIC String Models of Genetic Information Transfer.

    ERIC Educational Resources Information Center

    Spain, James D., Ed.

    1984-01-01

    Discusses some of the major genetic information processes which may be modeled by computer program string manipulation, focusing on replication and transcription. Also discusses instructional applications of using string models. (JN)

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

    PubMed

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

    2014-12-01

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

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

    PubMed Central

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

    2014-01-01

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

  15. Additive genetic variation for tolerance to estrogen pollution in natural populations of Alpine whitefish (Coregonus sp., Salmonidae).

    PubMed

    Brazzola, Gregory; Chèvre, Nathalie; Wedekind, Claus

    2014-11-01

    The evolutionary potential of natural populations to adapt to anthropogenic threats critically depends on whether there exists additive genetic variation for tolerance to the threat. A major problem for water-dwelling organisms is chemical pollution, and among the most common pollutants is 17α-ethinylestradiol (EE2), the synthetic estrogen that is used in oral contraceptives and that can affect fish at various developmental stages, including embryogenesis. We tested whether there is variation in the tolerance to EE2 within Alpine whitefish. We sampled spawners from two species of different lakes, bred them in vitro in a full-factorial design each, and studied growth and mortality of embryos. Exposure to EE2 turned out to be toxic in all concentrations we tested (≥1 ng/L). It reduced embryo viability and slowed down embryogenesis. We found significant additive genetic variation in EE2-induced mortality in both species, that is, genotypes differed in their tolerance to estrogen pollution. We also found maternal effects on embryo development to be influenced by EE2, that is, some maternal sib groups were more susceptible to EE2 than others. In conclusion, the toxic effects of EE2 were strong, but both species demonstrated the kind of additive genetic variation that is necessary for an evolutionary response to this type of pollution.

  16. Additive genetic variation for tolerance to estrogen pollution in natural populations of Alpine whitefish (Coregonus sp., Salmonidae)

    PubMed Central

    Brazzola, Gregory; Chèvre, Nathalie; Wedekind, Claus

    2014-01-01

    The evolutionary potential of natural populations to adapt to anthropogenic threats critically depends on whether there exists additive genetic variation for tolerance to the threat. A major problem for water-dwelling organisms is chemical pollution, and among the most common pollutants is 17α-ethinylestradiol (EE2), the synthetic estrogen that is used in oral contraceptives and that can affect fish at various developmental stages, including embryogenesis. We tested whether there is variation in the tolerance to EE2 within Alpine whitefish. We sampled spawners from two species of different lakes, bred them in vitro in a full-factorial design each, and studied growth and mortality of embryos. Exposure to EE2 turned out to be toxic in all concentrations we tested (≥1 ng/L). It reduced embryo viability and slowed down embryogenesis. We found significant additive genetic variation in EE2-induced mortality in both species, that is, genotypes differed in their tolerance to estrogen pollution. We also found maternal effects on embryo development to be influenced by EE2, that is, some maternal sib groups were more susceptible to EE2 than others. In conclusion, the toxic effects of EE2 were strong, but both species demonstrated the kind of additive genetic variation that is necessary for an evolutionary response to this type of pollution. PMID:25553069

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

    PubMed

    Shugar, Andrea

    2016-11-28

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

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

    SciTech Connect

    Not Available

    2007-05-01

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

  19. Percolation model with an additional source of disorder

    NASA Astrophysics Data System (ADS)

    Kundu, Sumanta; Manna, S. S.

    2016-06-01

    The ranges of transmission of the mobiles in a mobile ad hoc network are not uniform in reality. They are affected by the temperature fluctuation in air, obstruction due to the solid objects, even the humidity difference in the environment, etc. How the varying range of transmission of the individual active elements affects the global connectivity in the network may be an important practical question to ask. Here a model of percolation phenomena, with an additional source of disorder, is introduced for a theoretical understanding of this problem. As in ordinary percolation, sites of a square lattice are occupied randomly with probability p . Each occupied site is then assigned a circular disk of random value R for its radius. A bond is defined to be occupied if and only if the radii R1 and R2 of the disks centered at the ends satisfy a certain predefined condition. In a very general formulation, one divides the R1-R2 plane into two regions by an arbitrary closed curve. One defines a point within one region as representing an occupied bond; otherwise it is a vacant bond. The study of three different rules under this general formulation indicates that the percolation threshold always varies continuously. This threshold has two limiting values, one is pc(sq) , the percolation threshold for the ordinary site percolation on the square lattice, and the other is unity. The approach of the percolation threshold to its limiting values are characterized by two exponents. In a special case, all lattice sites are occupied by disks of random radii R ∈{0 ,R0} and a percolation transition is observed with R0 as the control variable, similar to the site occupation probability.

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

    PubMed

    Thorvaldsen, Steinar

    2016-01-01

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

  1. Hyperbolic value addition and general models of animal choice.

    PubMed

    Mazur, J E

    2001-01-01

    Three mathematical models of choice--the contextual-choice model (R. Grace, 1994), delay-reduction theory (N. Squires & E. Fantino, 1971), and a new model called the hyperbolic value-added model--were compared in their ability to predict the results from a wide variety of experiments with animal subjects. When supplied with 2 or 3 free parameters, all 3 models made fairly accurate predictions for a large set of experiments that used concurrent-chain procedures. One advantage of the hyperbolic value-added model is that it is derived from a simpler model that makes accurate predictions for many experiments using discrete-trial adjusting-delay procedures. Some results favor the hyperbolic value-added model and delay-reduction theory over the contextual-choice model, but more data are needed from choice situations for which the models make distinctly different predictions.

  2. Additional studies of sheep haemopexin: genetic control, frequencies and postnatal development.

    PubMed

    Stratil, A; Bobák, P; Margetín, M; Glasnák, V

    1989-01-01

    This study presents evidence that sheep haemopexin phenotypes are genetically controlled by three alleles, HpxA, HpxB1 and HpxB2, of a single autosomal locus. Frequencies of two alleles, HpxA and HpxB (HpxB encompasses two isoalleles, HpxB1 and HpxB2), were studied in eight sheep breeds in Czechoslovakia. The frequency of the HpxA allele was highest (ranging from 0.81 in Merino to 1.0 in East Friesian sheep). Qualitative and quantitative changes in haemopexin during postnatal development were studied by starch gel electrophoresis and rocket immunoelectrophoresis respectively. In electrophoresis, 1- or 2-day-old lambs had two very weak zones corresponding in mobility to two slower zones of adult animals. Later, the third more anodic zone appeared and gradually increased in intensity. In 1-month-old lambs the patterns were practically identical with those of adult animals. Using rocket immunoelectrophoresis, the level of haemopexin shortly after birth was practically zero. It rose sharply till the sixth day of life; then the level continued to rise slowly till about 1 month of age. The mean haemopexin level in adult sheep was 64.5 +/- 18.26 (SD) mg/100ml serum, ranging from 30.5 to 116.5 mg/100ml.

  3. Genetically Engineered Humanized Mouse Models for Preclinical Antibody Studies

    PubMed Central

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

    2015-01-01

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

  4. Developing robotic behavior using a genetic programming model

    SciTech Connect

    Pryor, R.J.

    1998-01-01

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

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

    PubMed

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

    2015-10-01

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

  6. Genetically Engineered Mouse Models for Studying Inflammatory Bowel Disease

    PubMed Central

    Mizoguchi, Atsushi; Takeuchi, Takahito; Himuro, Hidetomo; Okada, Toshiyuki; Mizoguchi, Emiko

    2015-01-01

    Inflammatory bowel disease (IBD) is a chronic intestinal inflammatory condition that is mediated by very complex mechanisms controlled by genetic, immune, and environmental factors. More than 74 kinds of genetically engineered mouse strains have been established since 1993 for studying IBD. Although mouse models cannot fully reflect human IBD, they have provided significant contributions for not only understanding the mechanism, but also developing new therapeutic means for IBD. Indeed, 20 kinds of genetically engineered mouse models carry the susceptibility genes identified in human IBD, and the functions of some other IBD susceptibility genes have also been dissected out using mouse models. Cutting-edge technologies such as cell-specific and inducible knockout systems, which were recently employed to mouse IBD models, have further enhanced the ability of investigators to provide important and unexpected rationales for developing new therapeutic strategies for IBD. In this review article, we briefly introduce 74 kinds of genetically engineered mouse models that spontaneously develop intestinal inflammation. PMID:26387641

  7. An enhanced nonparametric streamflow disaggregation model with genetic algorithm

    NASA Astrophysics Data System (ADS)

    Lee, T.; Salas, J. D.; Prairie, J.

    2010-08-01

    Stochastic streamflow generation is generally utilized for planning and management of water resources systems. For this purpose, a number of parametric and nonparametric models have been suggested in literature. Among them, temporal and spatial disaggregation approaches play an important role particularly to make sure that historical variance-covariance properties are preserved at various temporal and spatial scales. In this paper, we review the underlying features of existing nonparametric disaggregation methods, identify some of their pros and cons, and propose a disaggregation algorithm that is capable of surmounting some of the shortcomings of the current models. The proposed models hinge on k-nearest neighbor resampling, the accurate adjusting procedure, and a genetic algorithm. The models have been tested and compared to an existing nonparametric disaggregation approach using data of the Colorado River system. It has been shown that the model is capable of (1) reproducing the season-to-season correlations including the correlation between the last season of the previous year and the first season of the current year, (2) minimizing or avoiding the generation of flow patterns across the year that are literally the same as those of the historical records, and (3) minimizing or avoiding the generation of negative flows. In addition, it is applicable to intermittent river regimes.

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

    PubMed

    Legarra, Andres

    2016-02-01

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

  9. Testing departure from additivity in Tukey's model using shrinkage: application to a longitudinal setting.

    PubMed

    Ko, Yi-An; Mukherjee, Bhramar; Smith, Jennifer A; Park, Sung Kyun; Kardia, Sharon L R; Allison, Matthew A; Vokonas, Pantel S; Chen, Jinbo; Diez-Roux, Ana V

    2014-12-20

    While there has been extensive research developing gene-environment interaction (GEI) methods in case-control studies, little attention has been given to sparse and efficient modeling of GEI in longitudinal studies. In a two-way table for GEI with rows and columns as categorical variables, a conventional saturated interaction model involves estimation of a specific parameter for each cell, with constraints ensuring identifiability. The estimates are unbiased but are potentially inefficient because the number of parameters to be estimated can grow quickly with increasing categories of row/column factors. On the other hand, Tukey's one-degree-of-freedom model for non-additivity treats the interaction term as a scaled product of row and column main effects. Because of the parsimonious form of interaction, the interaction estimate leads to enhanced efficiency, and the corresponding test could lead to increased power. Unfortunately, Tukey's model gives biased estimates and low power if the model is misspecified. When screening multiple GEIs where each genetic and environmental marker may exhibit a distinct interaction pattern, a robust estimator for interaction is important for GEI detection. We propose a shrinkage estimator for interaction effects that combines estimates from both Tukey's and saturated interaction models and use the corresponding Wald test for testing interaction in a longitudinal setting. The proposed estimator is robust to misspecification of interaction structure. We illustrate the proposed methods using two longitudinal studies-the Normative Aging Study and the Multi-ethnic Study of Atherosclerosis.

  10. Testing Departure from Additivity in Tukey’s Model using Shrinkage: Application to a Longitudinal Setting

    PubMed Central

    Ko, Yi-An; Mukherjee, Bhramar; Smith, Jennifer A.; Park, Sung Kyun; Kardia, Sharon L.R.; Allison, Matthew A.; Vokonas, Pantel S.; Chen, Jinbo; Diez-Roux, Ana V.

    2014-01-01

    While there has been extensive research developing gene-environment interaction (GEI) methods in case-control studies, little attention has been given to sparse and efficient modeling of GEI in longitudinal studies. In a two-way table for GEI with rows and columns as categorical variables, a conventional saturated interaction model involves estimation of a specific parameter for each cell, with constraints ensuring identifiability. The estimates are unbiased but are potentially inefficient because the number of parameters to be estimated can grow quickly with increasing categories of row/column factors. On the other hand, Tukey’s one degree of freedom (df) model for non-additivity treats the interaction term as a scaled product of row and column main effects. Due to the parsimonious form of interaction, the interaction estimate leads to enhanced efficiency and the corresponding test could lead to increased power. Unfortunately, Tukey’s model gives biased estimates and low power if the model is misspecified. When screening multiple GEIs where each genetic and environmental marker may exhibit a distinct interaction pattern, a robust estimator for interaction is important for GEI detection. We propose a shrinkage estimator for interaction effects that combines estimates from both Tukey’s and saturated interaction models and use the corresponding Wald test for testing interaction in a longitudinal setting. The proposed estimator is robust to misspecification of interaction structure. We illustrate the proposed methods using two longitudinal studies — the Normative Aging Study and the Multi-Ethnic Study of Atherosclerosis. PMID:25112650

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

    PubMed Central

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

    2012-01-01

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

  12. Genetic evidence for an additional function of phage T4 gene 32 protein: interaction with ligase.

    PubMed

    Mosig, G; Breschkin, A M

    1975-04-01

    Gene 32 of bacteriophage T4 is essential for DNA replication, recombination, and repair. In an attempt to clarify the role of the corresponding gene product, we have looked for mutations that specifically inactivate one but not all of its functions and for compensating suppressor mutations in other genes. Here we describe a gene 32 ts mutant that does not produce progeny, but in contrast to an am mutant investigated by others, is capable of some primary and secondary DNA replication and of forming "joint" recombinational intermediates after infection of Escherichia coli B at the restrictive temperature. However, parental and progeny DNA strands are not ligated to covalently linked "recombinant" molecules, and single strands of vegetative DNA do not exceed unit length. Progeny production as well as capacity for covalent linkage in this gene 32 ts mutant are partially restored by additional rII mutations. Suppression by rII depends on functioning host ligase [EC 6.5.1.2; poly(deoxyribonucleotide):poly(deoxyribonucleotide) ligase (AMP-forming, NMN-forming)]. This gene 32 ts mutation (unlike some others) in turn suppresses the characteristic plaque morphology of rII mutants. We conclude that gene 32 protein, in addition to its role in DNA replication and in the formation of "joint" recombinational intermediates, interacts with T4 ligase [EC 6.5.1.1; poly(deoxyribonucleotide):poly(deoxyribonucleotide) ligase (AMP-forming)] when recombining DNA strands are covalently linked. The protein of the mutant that we describe here is mainly defective in this interaction, thus inactivating T4 ligase in recombination. Suppressing rII mutations facilitate substitution of host ligase. There is suggestive evidence that these interactions occur at the membrane.

  13. Replication of a gene-environment interaction Via Multimodel inference: additive-genetic variance in adolescents' general cognitive ability increases with family-of-origin socioeconomic status.

    PubMed

    Kirkpatrick, Robert M; McGue, Matt; Iacono, William G

    2015-03-01

    The present study of general cognitive ability attempts to replicate and extend previous investigations of a biometric moderator, family-of-origin socioeconomic status (SES), in a sample of 2,494 pairs of adolescent twins, non-twin biological siblings, and adoptive siblings assessed with individually administered IQ tests. We hypothesized that SES would covary positively with additive-genetic variance and negatively with shared-environmental variance. Important potential confounds unaddressed in some past studies, such as twin-specific effects, assortative mating, and differential heritability by trait level, were found to be negligible. In our main analysis, we compared models by their sample-size corrected AIC, and base our statistical inference on model-averaged point estimates and standard errors. Additive-genetic variance increased with SES-an effect that was statistically significant and robust to model specification. We found no evidence that SES moderated shared-environmental influence. We attempt to explain the inconsistent replication record of these effects, and provide suggestions for future research.

  14. Replication of a Gene-Environment Interaction via Multimodel Inference: Additive-Genetic Variance in Adolescents’ General Cognitive Ability Increases with Family-of-Origin Socioeconomic Status

    PubMed Central

    Kirkpatrick, Robert M.; McGue, Matt; Iacono, William G.

    2015-01-01

    The present study of general cognitive ability attempts to replicate and extend previous investigations of a biometric moderator, family-of-origin socioeconomic status (SES), in a sample of 2,494 pairs of adolescent twins, non-twin biological siblings, and adoptive siblings assessed with individually administered IQ tests. We hypothesized that SES would covary positively with additive-genetic variance and negatively with shared-environmental variance. Important potential confounds unaddressed in some past studies, such as twin-specific effects, assortative mating, and differential heritability by trait level, were found to be negligible. In our main analysis, we compared models by their sample-size corrected AIC, and base our statistical inference on model-averaged point estimates and standard errors. Additive-genetic variance increased with SES—an effect that was statistically significant and robust to model specification. We found no evidence that SES moderated shared-environmental influence. We attempt to explain the inconsistent replication record of these effects, and provide suggestions for future research. PMID:25539975

  15. Using Generalized Additive Models to Analyze Single-Case Designs

    ERIC Educational Resources Information Center

    Shadish, William; Sullivan, Kristynn

    2013-01-01

    Many analyses for single-case designs (SCDs)--including nearly all the effect size indicators-- currently assume no trend in the data. Regression and multilevel models allow for trend, but usually test only linear trend and have no principled way of knowing if higher order trends should be represented in the model. This paper shows how Generalized…

  16. Behavioral phenotypes of genetic mouse models of autism.

    PubMed

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

    2016-01-01

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

  17. Behavioral phenotypes of genetic mouse models of autism

    PubMed Central

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

    2016-01-01

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

  18. Additive Manufacturing of Anatomical Models from Computed Tomography Scan Data.

    PubMed

    Gür, Y

    2014-12-01

    The purpose of the study presented here was to investigate the manufacturability of human anatomical models from Computed Tomography (CT) scan data via a 3D desktop printer which uses fused deposition modelling (FDM) technology. First, Digital Imaging and Communications in Medicine (DICOM) CT scan data were converted to 3D Standard Triangle Language (STL) format by using In Vaselius digital imaging program. Once this STL file is obtained, a 3D physical version of the anatomical model can be fabricated by a desktop 3D FDM printer. As a case study, a patient's skull CT scan data was considered, and a tangible version of the skull was manufactured by a 3D FDM desktop printer. During the 3D printing process, the skull was built using acrylonitrile-butadiene-styrene (ABS) co-polymer plastic. The printed model showed that the 3D FDM printing technology is able to fabricate anatomical models with high accuracy. As a result, the skull model can be used for preoperative surgical planning, medical training activities, implant design and simulation to show the potential of the FDM technology in medical field. It will also improve communication between medical stuff and patients. Current result indicates that a 3D desktop printer which uses FDM technology can be used to obtain accurate anatomical models.

  19. Non-additive model for specific heat of electrons

    NASA Astrophysics Data System (ADS)

    Anselmo, D. H. A. L.; Vasconcelos, M. S.; Silva, R.; Mello, V. D.

    2016-10-01

    By using non-additive Tsallis entropy we demonstrate numerically that one-dimensional quasicrystals, whose energy spectra are multifractal Cantor sets, are characterized by an entropic parameter, and calculate the electronic specific heat, where we consider a non-additive entropy Sq. In our method we consider an energy spectra calculated using the one-dimensional tight binding Schrödinger equation, and their bands (or levels) are scaled onto the [ 0 , 1 ] interval. The Tsallis' formalism is applied to the energy spectra of Fibonacci and double-period one-dimensional quasiperiodic lattices. We analytically obtain an expression for the specific heat that we consider to be more appropriate to calculate this quantity in those quasiperiodic structures.

  20. Modeling of additive manufacturing processes for metals: Challenges and opportunities

    DOE PAGES

    Francois, Marianne M.; Sun, Amy; King, Wayne E.; ...

    2017-01-09

    Here, with the technology being developed to manufacture metallic parts using increasingly advanced additive manufacturing processes, a new era has opened up for designing novel structural materials, from designing shapes and complex geometries to controlling the microstructure (alloy composition and morphology). The material properties used within specific structural components are also designable in order to meet specific performance requirements that are not imaginable with traditional metal forming and machining (subtractive) techniques.

  1. Additional Research Needs to Support the GENII Biosphere Models

    SciTech Connect

    Napier, Bruce A.; Snyder, Sandra F.; Arimescu, Carmen

    2013-11-30

    In the course of evaluating the current parameter needs for the GENII Version 2 code (Snyder et al. 2013), areas of possible improvement for both the data and the underlying models have been identified. As the data review was implemented, PNNL staff identified areas where the models can be improved both to accommodate the locally significant pathways identified and also to incorporate newer models. The areas are general data needs for the existing models and improved formulations for the pathway models. It is recommended that priorities be set by NRC staff to guide selection of the most useful improvements in a cost-effective manner. Suggestions are made based on relatively easy and inexpensive changes, and longer-term more costly studies. In the short term, there are several improved model formulations that could be applied to the GENII suite of codes to make them more generally useful. • Implementation of the separation of the translocation and weathering processes • Implementation of an improved model for carbon-14 from non-atmospheric sources • Implementation of radon exposure pathways models • Development of a KML processor for the output report generator module data that are calculated on a grid that could be superimposed upon digital maps for easier presentation and display • Implementation of marine mammal models (manatees, seals, walrus, whales, etc.). Data needs in the longer term require extensive (and potentially expensive) research. Before picking any one radionuclide or food type, NRC staff should perform an in-house review of current and anticipated environmental analyses to select “dominant” radionuclides of interest to allow setting of cost-effective priorities for radionuclide- and pathway-specific research. These include • soil-to-plant uptake studies for oranges and other citrus fruits, and • Development of models for evaluation of radionuclide concentration in highly-processed foods such as oils and sugars. Finally, renewed

  2. Addition of a Hydrological Cycle to the EPIC Jupiter Model

    NASA Astrophysics Data System (ADS)

    Dowling, T. E.; Palotai, C. J.

    2002-09-01

    We present a progress report on the development of the EPIC atmospheric model to include clouds, moist convection, and precipitation. Two major goals are: i) to study the influence that convective water clouds have on Jupiter's jets and vortices, such as those to the northwest of the Great Red Spot, and ii) to predict ammonia-cloud evolution for direct comparison to visual images (instead of relying on surrogates for clouds like potential vorticity). Data structures in the model are now set up to handle the vapor, liquid, and solid phases of the most common chemical species in planetary atmospheres. We have adapted the Prather conservation of second-order moments advection scheme to the model, which yields high accuracy for dealing with cloud edges. In collaboration with computer scientists H. Dietz and T. Mattox at the U. Kentucky, we have built a dedicated 40-node parallel computer that achieves 34 Gflops (double precision) at 74 cents per Mflop, and have updated the EPIC-model code to use cache-aware memory layouts and other modern optimizations. The latest test-case results of cloud evolution in the model will be presented. This research is funded by NASA's Planetary Atmospheres and EPSCoR programs.

  3. How Pupils Use a Model for Abstract Concepts in Genetics

    ERIC Educational Resources Information Center

    Venville, Grady; Donovan, Jenny

    2008-01-01

    The purpose of this research was to explore the way pupils of different age groups use a model to understand abstract concepts in genetics. Pupils from early childhood to late adolescence were taught about genes and DNA using an analogical model (the wool model) during their regular biology classes. Changing conceptual understandings of the…

  4. Modeling the genetic and environmental association between peer group deviance and cannabis use in male twins

    PubMed Central

    Gillespie, Nathan A; Neale, Michael C; Jacobson, Kristen; Kendler, Kenneth S

    2009-01-01

    Background Peer group deviance (PGD) is strongly linked to liability to drug use including cannabis. Our aim was to model the genetic and environmental association, including direction of causation, between PGD and cannabis use (CU). Method Results were based on 1753 adult males from the Mid-Atlantic Twin Registry with complete CU and PGD data measured retrospectively at three time intervals between 15 and 25 years using a life-history calendar. Results At all ages, multivariate modeling showed that familial aggregation in PGD was explained by a combination of additive genetic and shared environmental effects, Moreover the significant PGD-CU association was best explained by a CU to PGD causal model in which large portions of the additive genetic (50% to 78%) and shared environmental variance (25% to 73%) in PGD were explained by CU. Conclusions Until recently PGD was assumed to be an environmental, upstream risk factor for CU. Our data are not consistent with this hypothesis. Rather, they suggest that the liability to affiliate with deviant peers is better explained by a combination of genetic and environmental factors that are indexed by CU which sits as a “risk indicator” in the causal pathway between genetic and environmental risks and the expression of PGD. This is consistent with a process of social selection by which the genetic and environmental risks in CU largely drive the propensity to affiliate with deviant peers. PMID:19207350

  5. Generalized Additive Models, Cubic Splines and Penalized Likelihood.

    DTIC Science & Technology

    1987-05-22

    in case control studies ). All models in the table include dummy variable to account for the matching. The first 3 lines of the table indicate that OA...Ausoc. Breslow, N. and Day, N. (1980). Statistical methods in cancer research, volume 1- the analysis of case - control studies . International agency

  6. Concentration Addition, Independent Action and Generalized Concentration Addition Models for Mixture Effect Prediction of Sex Hormone Synthesis In Vitro

    PubMed Central

    Hadrup, Niels; Taxvig, Camilla; Pedersen, Mikael; Nellemann, Christine; Hass, Ulla; Vinggaard, Anne Marie

    2013-01-01

    Humans are concomitantly exposed to numerous chemicals. An infinite number of combinations and doses thereof can be imagined. For toxicological risk assessment the mathematical prediction of mixture effects, using knowledge on single chemicals, is therefore desirable. We investigated pros and cons of the concentration addition (CA), independent action (IA) and generalized concentration addition (GCA) models. First we measured effects of single chemicals and mixtures thereof on steroid synthesis in H295R cells. Then single chemical data were applied to the models; predictions of mixture effects were calculated and compared to the experimental mixture data. Mixture 1 contained environmental chemicals adjusted in ratio according to human exposure levels. Mixture 2 was a potency adjusted mixture containing five pesticides. Prediction of testosterone effects coincided with the experimental Mixture 1 data. In contrast, antagonism was observed for effects of Mixture 2 on this hormone. The mixtures contained chemicals exerting only limited maximal effects. This hampered prediction by the CA and IA models, whereas the GCA model could be used to predict a full dose response curve. Regarding effects on progesterone and estradiol, some chemicals were having stimulatory effects whereas others had inhibitory effects. The three models were not applicable in this situation and no predictions could be performed. Finally, the expected contributions of single chemicals to the mixture effects were calculated. Prochloraz was the predominant but not sole driver of the mixtures, suggesting that one chemical alone was not responsible for the mixture effects. In conclusion, the GCA model seemed to be superior to the CA and IA models for the prediction of testosterone effects. A situation with chemicals exerting opposing effects, for which the models could not be applied, was identified. In addition, the data indicate that in non-potency adjusted mixtures the effects cannot always be

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

    PubMed

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

    2017-03-01

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

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

    PubMed

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

    2016-06-01

    Tetrahymena thermophila is a ciliate model organism whose study has led to important discoveries and insights into both conserved and divergent biological processes. In this review, we describe the tools for the use of Tetrahymena as a model eukaryote, including an overview of its life cycle, orientation to its evolutionary roots, and methodological approaches to forward and reverse genetics. Recent genomic tools have expanded Tetrahymena's utility as a genetic model system. With the unique advantages that Tetrahymena provide, we argue that it will continue to be a model organism of choice.

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

    PubMed

    Detilleux, Johann C

    2005-01-01

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

  10. Genetic algorithms for modelling and optimisation

    NASA Astrophysics Data System (ADS)

    McCall, John

    2005-12-01

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

  11. Technical Work Plan for: Additional Multoscale Thermohydrologic Modeling

    SciTech Connect

    B. Kirstein

    2006-08-24

    The primary objective of Revision 04 of the MSTHM report is to provide TSPA with revised repository-wide MSTHM analyses that incorporate updated percolation flux distributions, revised hydrologic properties, updated IEDs, and information pertaining to the emplacement of transport, aging, and disposal (TAD) canisters. The updated design information is primarily related to the incorporation of TAD canisters, but also includes updates related to superseded IEDs describing emplacement drift cross-sectional geometry and layout. The intended use of the results of Revision 04 of the MSTHM report, as described in this TWP, is to predict the evolution of TH conditions (temperature, relative humidity, liquid-phase saturation, and liquid-phase flux) at specified locations within emplacement drifts and in the adjoining near-field host rock along all emplacement drifts throughout the repository. This information directly supports the TSPA for the nominal and seismic scenarios. The revised repository-wide analyses are required to incorporate updated parameters and design information and to extend those analyses out to 1,000,000 years. Note that the previous MSTHM analyses reported in Revision 03 of Multiscale Thermohydrologic Model (BSC 2005 [DIRS 173944]) only extend out to 20,000 years. The updated parameters are the percolation flux distributions, including incorporation of post-10,000-year distributions, and updated calibrated hydrologic property values for the host-rock units. The applied calibrated hydrologic properties will be an updated version of those available in Calibrated Properties Model (BSC 2004 [DIRS 169857]). These updated properties will be documented in an Appendix of Revision 03 of UZ Flow Models and Submodels (BSC 2004 [DIRS 169861]). The updated calibrated properties are applied because they represent the latest available information. The reasonableness of applying the updated calibrated' properties to the prediction of near-fieldin-drift TH conditions

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

    PubMed

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

    2005-10-01

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

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

    PubMed

    Keren, Naomi; Peled, Noam; Korngreen, Alon

    2005-12-01

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

  14. Evolution of the additive genetic variance–covariance matrix under continuous directional selection on a complex behavioural phenotype

    PubMed Central

    Careau, Vincent; Wolak, Matthew E.; Carter, Patrick A.; Garland, Theodore

    2015-01-01

    Given the pace at which human-induced environmental changes occur, a pressing challenge is to determine the speed with which selection can drive evolutionary change. A key determinant of adaptive response to multivariate phenotypic selection is the additive genetic variance–covariance matrix (G). Yet knowledge of G in a population experiencing new or altered selection is not sufficient to predict selection response because G itself evolves in ways that are poorly understood. We experimentally evaluated changes in G when closely related behavioural traits experience continuous directional selection. We applied the genetic covariance tensor approach to a large dataset (n = 17 328 individuals) from a replicated, 31-generation artificial selection experiment that bred mice for voluntary wheel running on days 5 and 6 of a 6-day test. Selection on this subset of G induced proportional changes across the matrix for all 6 days of running behaviour within the first four generations. The changes in G induced by selection resulted in a fourfold slower-than-predicted rate of response to selection. Thus, selection exacerbated constraints within G and limited future adaptive response, a phenomenon that could have profound consequences for populations facing rapid environmental change. PMID:26582016

  15. Selection for increased desiccation resistance in Drosophila melanogaster: Additive genetic control and correlated responses for other stresses

    SciTech Connect

    Hoffmann, A.A.; Parsons, P.A. )

    1989-08-01

    Previously we found that Drosophila melanogaster lines selected for increased desiccation resistance have lowered metabolic rate and behavioral activity levels, and show correlated responses for resistance to starvation and a toxic ethanol level. These results were consistent with a prediction that increased resistance to many environmental stresses may be genetically correlated because of a reduction in metabolic energy expenditure. Here we present experiments on the genetic basis of the selection response and extend the study of correlated responses to other stresses. The response to selection was not sex-specific and involved X-linked and autosomal genes acting additively. Activity differences contributed little to differences in desiccation resistance between selected and control lines. Selected lines had lower metabolic rates than controls in darkness when activity was inhibited. Adults from selected lines showed increased resistance to a heat shock, {sup 60}Co-gamma-radiation, and acute ethanol and acetic acid stress. The desiccation, ethanol and starvation resistance of isofemale lines set up from the F2s of a cross between one of the selected and one of the control lines were correlated. Selected and control lines did not differ in ether-extractable lipid content or in resistance to acetone, ether or a cold shock.

  16. Evolution of the additive genetic variance-covariance matrix under continuous directional selection on a complex behavioural phenotype.

    PubMed

    Careau, Vincent; Wolak, Matthew E; Carter, Patrick A; Garland, Theodore

    2015-11-22

    Given the pace at which human-induced environmental changes occur, a pressing challenge is to determine the speed with which selection can drive evolutionary change. A key determinant of adaptive response to multivariate phenotypic selection is the additive genetic variance-covariance matrix ( G: ). Yet knowledge of G: in a population experiencing new or altered selection is not sufficient to predict selection response because G: itself evolves in ways that are poorly understood. We experimentally evaluated changes in G: when closely related behavioural traits experience continuous directional selection. We applied the genetic covariance tensor approach to a large dataset (n = 17 328 individuals) from a replicated, 31-generation artificial selection experiment that bred mice for voluntary wheel running on days 5 and 6 of a 6-day test. Selection on this subset of G: induced proportional changes across the matrix for all 6 days of running behaviour within the first four generations. The changes in G: induced by selection resulted in a fourfold slower-than-predicted rate of response to selection. Thus, selection exacerbated constraints within G: and limited future adaptive response, a phenomenon that could have profound consequences for populations facing rapid environmental change.

  17. Software reliability: Additional investigations into modeling with replicated experiments

    NASA Technical Reports Server (NTRS)

    Nagel, P. M.; Schotz, F. M.; Skirvan, J. A.

    1984-01-01

    The effects of programmer experience level, different program usage distributions, and programming languages are explored. All these factors affect performance, and some tentative relational hypotheses are presented. An analytic framework for replicated and non-replicated (traditional) software experiments is presented. A method of obtaining an upper bound on the error rate of the next error is proposed. The method was validated empirically by comparing forecasts with actual data. In all 14 cases the bound exceeded the observed parameter, albeit somewhat conservatively. Two other forecasting methods are proposed and compared to observed results. Although demonstrated relative to this framework that stages are neither independent nor exponentially distributed, empirical estimates show that the exponential assumption is nearly valid for all but the extreme tails of the distribution. Except for the dependence in the stage probabilities, Cox's model approximates to a degree what is being observed.

  18. Additional Developments in Atmosphere Revitalization Modeling and Simulation

    NASA Technical Reports Server (NTRS)

    Coker, Robert F.; Knox, James C.; Cummings, Ramona; Brooks, Thomas; Schunk, Richard G.; Gomez, Carlos

    2013-01-01

    NASA's Advanced Exploration Systems (AES) program is developing prototype systems, demonstrating key capabilities, and validating operational concepts for future human missions beyond Earth orbit. These forays beyond the confines of earth's gravity will place unprecedented demands on launch systems. They must launch the supplies needed to sustain a crew over longer periods for exploration missions beyond earth's moon. Thus all spacecraft systems, including those for the separation of metabolic carbon dioxide and water from a crewed vehicle, must be minimized with respect to mass, power, and volume. Emphasis is also placed on system robustness both to minimize replacement parts and ensure crew safety when a quick return to earth is not possible. Current efforts are focused on improving the current state-of-the-art systems utilizing fixed beds of sorbent pellets by evaluating structured sorbents, seeking more robust pelletized sorbents, and examining alternate bed configurations to improve system efficiency and reliability. These development efforts combine testing of sub-scale systems and multi-physics computer simulations to evaluate candidate approaches, select the best performing options, and optimize the configuration of the selected approach. This paper describes the continuing development of atmosphere revitalization models and simulations in support of the Atmosphere Revitalization Recovery and Environmental Monitoring (ARREM) project within the AES program.

  19. Additional Developments in Atmosphere Revitalization Modeling and Simulation

    NASA Technical Reports Server (NTRS)

    Coker, Robert F.; Knox, James C.; Cummings, Ramona; Brooks, Thomas; Schunk, Richard G.

    2013-01-01

    NASA's Advanced Exploration Systems (AES) program is developing prototype systems, demonstrating key capabilities, and validating operational concepts for future human missions beyond Earth orbit. These forays beyond the confines of earth's gravity will place unprecedented demands on launch systems. They must launch the supplies needed to sustain a crew over longer periods for exploration missions beyond earth's moon. Thus all spacecraft systems, including those for the separation of metabolic carbon dioxide and water from a crewed vehicle, must be minimized with respect to mass, power, and volume. Emphasis is also placed on system robustness both to minimize replacement parts and ensure crew safety when a quick return to earth is not possible. Current efforts are focused on improving the current state-of-the-art systems utilizing fixed beds of sorbent pellets by evaluating structured sorbents, seeking more robust pelletized sorbents, and examining alternate bed configurations to improve system efficiency and reliability. These development efforts combine testing of sub-scale systems and multi-physics computer simulations to evaluate candidate approaches, select the best performing options, and optimize the configuration of the selected approach. This paper describes the continuing development of atmosphere revitalization models and simulations in support of the Atmosphere Revitalization Recovery and Environmental Monitoring (ARREM)

  20. Molecular basis of inherited antithrombin deficiency in Portuguese families: identification of genetic alterations and screening for additional thrombotic risk factors.

    PubMed

    David, Dezsö; Ribeiro, Sofia; Ferrão, Lénia; Gago, Teresa; Crespo, Francisco

    2004-06-01

    Antithrombin (AT), the most important coagulation serine proteases inhibitor, plays an important role in maintaining the hemostatic balance. Inherited AT deficiency, mainly characterized by predisposition to recurrent venous thromboembolism, is transmitted in an autosomal dominant manner. In this study, we analyzed the underlying genetic alterations in 12 unrelated Portuguese thrombophilic families with AT deficiency. At the same time, the modulating effect of the FV Leiden mutation, PT 20210A, PAI-1 4G, and MTHFR 677T allelic variants, on the thrombotic risk of AT deficient patients was also evaluated. Three novel frameshift alterations, a 4-bp deletion in exon 4 and two 1-bp insertions in exon 6, were identified in six unrelated type I AT deficient families. A novel missense mutation in exon 3a, which changes the highly conserved F147 residue, and a novel splice site mutation in the invariant acceptor AG dinucleotide of intron 2 were also identified in unrelated type I AT deficient families. In addition to these, two previously reported missense mutations changing the AT reactive site bond (R393-S394) and leading to type II-RS deficiency, and a previously reported cryptic splice site mutation (IVS4-14G-->A), were also identified. In these families, increased thrombotic risk associated with co-inheritance of the FV Leiden mutation and of the PAI-1 4G variant was also observed. In conclusion, we present the first data regarding the underlying genetic alterations in Portuguese thrombophilic families with AT deficiency, and confirm that the FV Leiden mutation and probably the PAI-1 4G variant represent additional thrombotic risk factors in these families.

  1. Quantitative genetic modeling and inference in the presence of nonignorable missing data.

    PubMed

    Steinsland, Ingelin; Larsen, Camilla Thorrud; Roulin, Alexandre; Jensen, Henrik

    2014-06-01

    Natural selection is typically exerted at some specific life stages. If natural selection takes place before a trait can be measured, using conventional models can cause wrong inference about population parameters. When the missing data process relates to the trait of interest, a valid inference requires explicit modeling of the missing process. We propose a joint modeling approach, a shared parameter model, to account for nonrandom missing data. It consists of an animal model for the phenotypic data and a logistic model for the missing process, linked by the additive genetic effects. A Bayesian approach is taken and inference is made using integrated nested Laplace approximations. From a simulation study we find that wrongly assuming that missing data are missing at random can result in severely biased estimates of additive genetic variance. Using real data from a wild population of Swiss barn owls Tyto alba, our model indicates that the missing individuals would display large black spots; and we conclude that genes affecting this trait are already under selection before it is expressed. Our model is a tool to correctly estimate the magnitude of both natural selection and additive genetic variance.

  2. Practical implications for genetic modeling in the genomics era

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Genetic models convert data into estimated breeding values and other information useful to breeders. The goal is to provide accurate and timely predictions of the future performance for each animal (or embryo). Modeling involves defining traits, editing raw data, removing environmental effects, incl...

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

    PubMed

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

    2016-11-10

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

  4. Hybrid mice as genetic models of high alcohol consumption.

    PubMed

    Blednov, Y A; Ozburn, A R; Walker, D; Ahmed, S; Belknap, J K; Harris, R A

    2010-01-01

    We showed that F1 hybrid genotypes may provide a broader variety of ethanol drinking phenotypes than the inbred progenitor strains used to create the hybrids (Blednov et al. in Alcohol Clin Exp Res 29:1949-1958, 2005). To extend this work, we characterized alcohol consumption as well as intake of other tastants (saccharin, quinine and sodium chloride) in five inbred strains of mice (FVB, SJL, B6, BUB, NZB) and in their reciprocal F1 hybrids with B6 (FVBxB6; B6xFVB; NZBxB6; B6xNZB; BUBxB6; B6xBUB; SJLxB6; B6xSJL). We also compared ethanol intake in these mice for several concentrations before and after two periods of abstinence. F1 hybrid mice derived from the crosses of B6 and FVB and also B6 and SJL drank higher levels of ethanol than their progenitor strains, demonstrating overdominance for two-bottle choice drinking test. The B6 and NZB hybrid showed additivity in two-bottle choice drinking, whereas the hybrid of B6 and BUB demonstrated full or complete dominance. Genealogical origin, as well as non-alcohol taste preferences (sodium chloride), predicted ethanol consumption. Mice derived from the crosses of B6 and FVB showed high sustained alcohol preference and the B6 and NZB hybrids showed reduced alcohol preference after periods of abstinence. These new genetic models offer some advantages over inbred strains because they provide high, sustained, alcohol intake, and should allow mapping of loci important for the genetic architecture of these traits.

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

    PubMed

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

    2016-01-01

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

  6. Transferability of regional permafrost disturbance susceptibility modelling using generalized linear and generalized additive models

    NASA Astrophysics Data System (ADS)

    Rudy, Ashley C. A.; Lamoureux, Scott F.; Treitz, Paul; van Ewijk, Karin Y.

    2016-07-01

    To effectively assess and mitigate risk of permafrost disturbance, disturbance-prone areas can be predicted through the application of susceptibility models. In this study we developed regional susceptibility models for permafrost disturbances using a field disturbance inventory to test the transferability of the model to a broader region in the Canadian High Arctic. Resulting maps of susceptibility were then used to explore the effect of terrain variables on the occurrence of disturbances within this region. To account for a large range of landscape characteristics, the model was calibrated using two locations: Sabine Peninsula, Melville Island, NU, and Fosheim Peninsula, Ellesmere Island, NU. Spatial patterns of disturbance were predicted with a generalized linear model (GLM) and generalized additive model (GAM), each calibrated using disturbed and randomized undisturbed locations from both locations and GIS-derived terrain predictor variables including slope, potential incoming solar radiation, wetness index, topographic position index, elevation, and distance to water. Each model was validated for the Sabine and Fosheim Peninsulas using independent data sets while the transferability of the model to an independent site was assessed at Cape Bounty, Melville Island, NU. The regional GLM and GAM validated well for both calibration sites (Sabine and Fosheim) with the area under the receiver operating curves (AUROC) > 0.79. Both models were applied directly to Cape Bounty without calibration and validated equally with AUROC's of 0.76; however, each model predicted disturbed and undisturbed samples differently. Additionally, the sensitivity of the transferred model was assessed using data sets with different sample sizes. Results indicated that models based on larger sample sizes transferred more consistently and captured the variability within the terrain attributes in the respective study areas. Terrain attributes associated with the initiation of disturbances were

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

    PubMed

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

    2015-06-01

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

  8. Modeling genetic inheritance of copy number variations

    PubMed Central

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

    2008-01-01

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

  9. A model for monitoring of Hsp90-buffered genetic variations

    NASA Astrophysics Data System (ADS)

    Kozeko, Liudmyla

    Genetic material of terrestrial organisms can be considerably injured by cosmic rays and UV-radiation in the space environment. Organisms onboard are also exposed to the entire complex of negative physical factors which can generate genetic variations and affect morphogenesis. However, species phenotypes must be robust to genetic variation, requiring "buffering" systems to ensure normal development. The molecular chaperone Hsp90 can serve as such "a buffer". It is important in the maturation and conformational regulation of a diverse set of signal transducers. The requirement of many principal regulatory proteins for Hsp90 renders entire metabolic pathways sensitive to impairment of its function. So inhibition of Hsp90 function can open cryptic genetic variations and produce morphological changes. In this paper, we present a model for monitoring of cryptic Hsp90-buffered genetic variations arising during exposure to space and spaceflight factors. This model has been developed with Arabidopsis thaliana seeds gathered in natural habitats with high anthropogenic pressure and wild type (Col-0) seeds subjected to negative influences (UV, heavy metals) experimentally. The phenotypic traits of early seedlings grown under reduction of Hsp90 activity were characterized to estimate Hsp90-buffered genetic variations. Geldanamycin was used as an inhibitor of Hsp90 function.

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

    PubMed Central

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

    2012-01-01

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

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2017-02-01

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

  13. Ghrelin and eating behavior: evidence and insights from genetically-modified mouse models

    PubMed Central

    Uchida, Aki; Zigman, Jeffrey M.; Perelló, Mario

    2013-01-01

    Ghrelin is an octanoylated peptide hormone, produced by endocrine cells of the stomach, which acts in the brain to increase food intake and body weight. Our understanding of the mechanisms underlying ghrelin's effects on eating behaviors has been greatly improved by the generation and study of several genetically manipulated mouse models. These models include mice overexpressing ghrelin and also mice with genetic deletion of ghrelin, the ghrelin receptor [the growth hormone secretagogue receptor (GHSR)] or the enzyme that post-translationally modifies ghrelin [ghrelin O-acyltransferase (GOAT)]. In addition, a GHSR-null mouse model in which GHSR transcription is globally blocked but can be cell-specifically reactivated in a Cre recombinase-mediated fashion has been generated. Here, we summarize findings obtained with these genetically manipulated mice, with the aim to highlight the significance of the ghrelin system in the regulation of both homeostatic and hedonic eating, including that occurring in the setting of chronic psychosocial stress. PMID:23882175

  14. Predicting Diabetic Nephropathy Using a Multifactorial Genetic Model

    PubMed Central

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

    2011-01-01

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

  15. Model-free Estimation of Recent Genetic Relatedness

    PubMed Central

    Conomos, Matthew P.; Reiner, Alexander P.; Weir, Bruce S.; Thornton, Timothy A.

    2016-01-01

    Genealogical inference from genetic data is essential for a variety of applications in human genetics. In genome-wide and sequencing association studies, for example, accurate inference on both recent genetic relatedness, such as family structure, and more distant genetic relatedness, such as population structure, is necessary for protection against spurious associations. Distinguishing familial relatedness from population structure with genotype data, however, is difficult because both manifest as genetic similarity through the sharing of alleles. Existing approaches for inference on recent genetic relatedness have limitations in the presence of population structure, where they either (1) make strong and simplifying assumptions about population structure, which are often untenable, or (2) require correct specification of and appropriate reference population panels for the ancestries in the sample, which might be unknown or not well defined. Here, we propose PC-Relate, a model-free approach for estimating commonly used measures of recent genetic relatedness, such as kinship coefficients and IBD sharing probabilities, in the presence of unspecified structure. PC-Relate uses principal components calculated from genome-screen data to partition genetic correlations among sampled individuals due to the sharing of recent ancestors and more distant common ancestry into two separate components, without requiring specification of the ancestral populations or reference population panels. In simulation studies with population structure, including admixture, we demonstrate that PC-Relate provides accurate estimates of genetic relatedness and improved relationship classification over widely used approaches. We further demonstrate the utility of PC-Relate in applications to three ancestrally diverse samples that vary in both size and genealogical complexity. PMID:26748516

  16. Genetics

    MedlinePlus

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

  17. ENU mutagenesis to generate genetically modified rat models.

    PubMed

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

    2010-01-01

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

  18. Reframed Genome-Scale Metabolic Model to Facilitate Genetic Design and Integration with Expression Data.

    PubMed

    Gu, Deqing; Jian, Xingxing; Zhang, Cheng; Hua, Qiang

    2016-06-08

    Genome-scale metabolic network models (GEMs) have played important roles in the design of genetically engineered strains and helped biologists to decipher metabolism. However, due to the complex gene-reaction relationships that exist in model systems, most algorithms have limited capabilities with respect to directly predicting accurate genetic design for metabolic engineering. In particular, methods that predict reaction knockout strategies leading to overproduction are often impractical in terms of gene manipulations. Recently, we proposed a method named LTM (logical transformation of model) to simplify the gene-reaction associations by introducing intermediate pseudo reactions, which makes it possible to generate genetic design. Here, we propose an alternative method to relieve researchers from deciphering complex gene-reactions by adding pseudo gene controlling reactions. In comparison to LTM, this new method introduces fewer pseudo reactions and generates a much smaller model system named as gModel. We showed that gModel allows two seldom reported applications: identification of minimal genomes and design of minimal cell factories within a modified OptKnock framework. In addition, gModel could be used to integrate expression data directly and improve the performance of the E-Fmin method for predicting fluxes. In conclusion, the model transformation procedure will facilitate genetic research based on GEMs, extending their applications.

  19. Estimation of genetic parameters and environmental factors on early growth traits for Lori breed sheep using single trait animal model.

    PubMed

    Lavvaf, A; Noshary, A

    2008-01-01

    The effects of different environmental factors and estimation of genetic parameters on early growth traits for Lori breed sheep including birth weight, weaning weight and body weight at 6 months of age using 19960 records from 35 herds of Lorestan Jahad Agriculture Organization were studied in the cities of Aleshtar, Khorramabad and Poldokhtar from 1995 to 2003. The effect of herd, sex of lambs, dam age and birth year on all traits and birth type had significant effect only on weaning weight. Different single trait animal models estimated the components of direct additive genetic variance, maternal genetic variance and maternal permanent environment variance through restricted maximum likelihood using environmental factors as a fixe effect and different random effects. The results showed that direct additive genetic effect had additionally significant effect on all traits moreover maternal additive genetic and maternal permanent environment effects. Results also revealed that the maternal permanent environment variance for all traits is higher than maternal genetic variance. Also the direct heritability for all traits was higher than maternal heritability. Estimation of the direct heritability from the birth to 6 months of age showed a reducing trend that could arise from high dependence of birth and weaning weight on maternal environment conditions as compared with the age conditions afterward. The genetic assessment of growth traits in Lori breed sheep without inclusion of maternal effect in animal model causes decreased selection accuracy and incorrect genetic assessment of the lambs.

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

    PubMed

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

    2017-01-01

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

  1. Quantitative genetics model as the unifying model for defining genomic relationship and inbreeding coefficient.

    PubMed

    Wang, Chunkao; Da, Yang

    2014-01-01

    The traditional quantitative genetics model was used as the unifying approach to derive six existing and new definitions of genomic additive and dominance relationships. The theoretical differences of these definitions were in the assumptions of equal SNP effects (equivalent to across-SNP standardization), equal SNP variances (equivalent to within-SNP standardization), and expected or sample SNP additive and dominance variances. The six definitions of genomic additive and dominance relationships on average were consistent with the pedigree relationships, but had individual genomic specificity and large variations not observed from pedigree relationships. These large variations may allow finding least related genomes even within the same family for minimizing genomic relatedness among breeding individuals. The six definitions of genomic relationships generally had similar numerical results in genomic best linear unbiased predictions of additive effects (GBLUP) and similar genomic REML (GREML) estimates of additive heritability. Predicted SNP dominance effects and GREML estimates of dominance heritability were similar within definitions assuming equal SNP effects or within definitions assuming equal SNP variance, but had differences between these two groups of definitions. We proposed a new measure of genomic inbreeding coefficient based on parental genomic co-ancestry coefficient and genomic additive correlation as a genomic approach for predicting offspring inbreeding level. This genomic inbreeding coefficient had the highest correlation with pedigree inbreeding coefficient among the four methods evaluated for calculating genomic inbreeding coefficient in a Holstein sample and a swine sample.

  2. Modeling the cardiovascular system using a nonlinear additive autoregressive model with exogenous input

    NASA Astrophysics Data System (ADS)

    Riedl, M.; Suhrbier, A.; Malberg, H.; Penzel, T.; Bretthauer, G.; Kurths, J.; Wessel, N.

    2008-07-01

    The parameters of heart rate variability and blood pressure variability have proved to be useful analytical tools in cardiovascular physics and medicine. Model-based analysis of these variabilities additionally leads to new prognostic information about mechanisms behind regulations in the cardiovascular system. In this paper, we analyze the complex interaction between heart rate, systolic blood pressure, and respiration by nonparametric fitted nonlinear additive autoregressive models with external inputs. Therefore, we consider measurements of healthy persons and patients suffering from obstructive sleep apnea syndrome (OSAS), with and without hypertension. It is shown that the proposed nonlinear models are capable of describing short-term fluctuations in heart rate as well as systolic blood pressure significantly better than similar linear ones, which confirms the assumption of nonlinear controlled heart rate and blood pressure. Furthermore, the comparison of the nonlinear and linear approaches reveals that the heart rate and blood pressure variability in healthy subjects is caused by a higher level of noise as well as nonlinearity than in patients suffering from OSAS. The residue analysis points at a further source of heart rate and blood pressure variability in healthy subjects, in addition to heart rate, systolic blood pressure, and respiration. Comparison of the nonlinear models within and among the different groups of subjects suggests the ability to discriminate the cohorts that could lead to a stratification of hypertension risk in OSAS patients.

  3. Rapid SAR target modeling through genetic inheritance mechanism

    NASA Astrophysics Data System (ADS)

    Bala, Jerzy; Pachowicz, Peter W.; Vafaie, Halleh

    1997-07-01

    The paper presents a methodology and GETP experimental system for rapid SAR target signature generation from limited initial sensory data. The methodology exploits and integrates the following four processes: (1) analysis of initial SAR image signatures and their transformation into higher-level blob representation, (2) blob modeling, (3) genetic inheritance modeling to generate new instances of a target model in blob representation, and (4) synthesis of new SAR signatures from genetically evolved blob data. The GETP system takes several SAR signatures of the target and transforms each signature into more general scattered blob graphs, where each blob represents local energy cluster. A single graph node is describe by blob relative position, confidence, and iconic data. Graph data is forwarded to the genetic modeling process while blob image is stored in a catalog. Genetic inheritance is applied to the initial population of graph data. New graph models of the target are generated and evaluated. Selected graph variations are forwarded to the synthesis process. The synthesis process restores target signature from a given graph and a catalog of blobs. The background is synthesized to complement the signature. Initial experimental results are illustrated with 64 X 32 image sections of a tank.

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

    PubMed

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

    2016-08-01

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

  5. Dissecting genetic and environmental mutation signatures with model organisms.

    PubMed

    Segovia, Romulo; Tam, Annie S; Stirling, Peter C

    2015-08-01

    Deep sequencing has impacted on cancer research by enabling routine sequencing of genomes and exomes to identify genetic changes associated with carcinogenesis. Researchers can now use the frequency, type, and context of all mutations in tumor genomes to extract mutation signatures that reflect the driving mutational processes. Identifying mutation signatures, however, may not immediately suggest a mechanism. Consequently, several recent studies have employed deep sequencing of model organisms exposed to discrete genetic or environmental perturbations. These studies exploit the simpler genomes and availability of powerful genetic tools in model organisms to analyze mutation signatures under controlled conditions, forging mechanistic links between mutational processes and signatures. We discuss the power of this approach and suggest that many such studies may be on the horizon.

  6. Evolving complex dynamics in electronic models of genetic networks

    NASA Astrophysics Data System (ADS)

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

    2004-09-01

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

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

    PubMed

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

    2015-02-01

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

  8. Autosomal and X-Linked Additive Genetic Variation for Lifespan and Aging: Comparisons Within and Between the Sexes in Drosophila melanogaster.

    PubMed

    Griffin, Robert M; Schielzeth, Holger; Friberg, Urban

    2016-12-07

    Theory makes several predictions concerning differences in genetic variation between the X chromosome and the autosomes due to male X hemizygosity. The X chromosome should: (i) typically show relatively less standing genetic variation than the autosomes, (ii) exhibit more variation in males compared to females because of dosage compensation, and (iii) potentially be enriched with sex-specific genetic variation. Here, we address each of these predictions for lifespan and aging in Drosophila melanogaster To achieve unbiased estimates of X and autosomal additive genetic variance, we use 80 chromosome substitution lines; 40 for the X chromosome and 40 combining the two major autosomes, which we assay for sex-specific and cross-sex genetic (co)variation. We find significant X and autosomal additive genetic variance for both traits in both sexes (with reservation for X-linked variation of aging in females), but no conclusive evidence for depletion of X-linked variation (measured through females). Males display more X-linked variation for lifespan than females, but it is unclear if this is due to dosage compensation since also autosomal variation is larger in males. Finally, our results suggest that the X chromosome is enriched for sex-specific genetic variation in lifespan but results were less conclusive for aging overall. Collectively, these results suggest that the X chromosome has reduced capacity to respond to sexually concordant selection on lifespan from standing genetic variation, while its ability to respond to sexually antagonistic selection may be augmented.

  9. Autosomal and X-Linked Additive Genetic Variation for Lifespan and Aging: Comparisons Within and Between the Sexes in Drosophila melanogaster

    PubMed Central

    Griffin, Robert M.; Schielzeth, Holger; Friberg, Urban

    2016-01-01

    Theory makes several predictions concerning differences in genetic variation between the X chromosome and the autosomes due to male X hemizygosity. The X chromosome should: (i) typically show relatively less standing genetic variation than the autosomes, (ii) exhibit more variation in males compared to females because of dosage compensation, and (iii) potentially be enriched with sex-specific genetic variation. Here, we address each of these predictions for lifespan and aging in Drosophila melanogaster. To achieve unbiased estimates of X and autosomal additive genetic variance, we use 80 chromosome substitution lines; 40 for the X chromosome and 40 combining the two major autosomes, which we assay for sex-specific and cross-sex genetic (co)variation. We find significant X and autosomal additive genetic variance for both traits in both sexes (with reservation for X-linked variation of aging in females), but no conclusive evidence for depletion of X-linked variation (measured through females). Males display more X-linked variation for lifespan than females, but it is unclear if this is due to dosage compensation since also autosomal variation is larger in males. Finally, our results suggest that the X chromosome is enriched for sex-specific genetic variation in lifespan but results were less conclusive for aging overall. Collectively, these results suggest that the X chromosome has reduced capacity to respond to sexually concordant selection on lifespan from standing genetic variation, while its ability to respond to sexually antagonistic selection may be augmented. PMID:27678519

  10. Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

    PubMed

    Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R

    2012-08-01

    compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.

  11. Translating therapies for Huntington's disease from genetic animal models to clinical trials.

    PubMed

    Hersch, Steven M; Ferrante, Robert J

    2004-07-01

    Genetic animal models of inherited neurological diseases provide an opportunity to test potential treatments and explore their promise for translation to humans experiencing these diseases. Therapeutic trials conducted in mouse models of Huntington's disease have identified a growing number of potential therapies that are candidates for clinical trials. Although it is very exciting to have these candidates, there has been increasing concern about the feasibility and desirability of taking all of the compounds that may work in mice and testing them in patients with HD. There is a need to begin to prioritize leads emerging from transgenic mouse studies; however, it is difficult to compare results between compounds and laboratories, and there are also many additional factors that can affect translation to humans. Among the important issues are what constitutes an informative genetic model, what principals should be followed in designing and conducting experiments using genetic animal models, how can results from different laboratories and in different models be compared, what body of evidence is desirable to fully inform clinical decision making, and what factors contribute to the equipoise in determining whether preclinical information about a therapy makes clinical study warranted. In the context of Huntington's disease, we will review the current state of genetic models and their successes in putting forward therapeutic leads, provide a guide to assessing studies in mouse models, and discuss some of the salient issues related to translation from mice to humans.

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

    PubMed

    Schulz, Angela; Kreutz, Reinhold

    2012-07-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-03-01

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

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

    PubMed Central

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

    2017-01-01

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    An extensive survey of the standing genetic variation in natural populations is among the priority steps in developing a species into a model system. In recent years, green foxtail (Setaria viridis), along with its domesticated form foxtail millet (S. italica), has rapidly become a promising new mod...

  16. Rodent Models of Genetic Contributions to Motivation to Abuse Alcohol

    PubMed Central

    Crabbe, John C.

    2016-01-01

    The distinction between alcohol use (normative) and abuse (unfortunately common) implies dysregulation of motivation directed toward the drug. Genetic contributions to abuse risk are mediated through personality differences, other predispositions to drink excessively, and differences in sensitivity to the acute and chronic consequences of the drug. How to assess motivation in laboratory animals is not straightforward but risk factors for and consequences of alcohol abuse can be modeled with reasonable fidelity in laboratory rodents. Remarkably few rodent studies focus on the genetic contributions to alcohol’s reinforcing value: almost all examine preferential drinking of unflavored alcohol over water. Such studies will likely never avoid the confounding role of taste preferences and most often yield intake levels insufficient to yield a pharmacologically significant blood alcohol level. Genotypes that avoid alcohol probably do so based on pre-ingestive sensory cues; however, post-ingestive consequences are also important. Thus, the quest for improved measures of reinforcing value continues. We have genetic differences aplenty, but still lack evidence that any genotype will readily self-administer alcohol to the devastating extent that many alcoholics will. Encouraging results that are emerging include improved behavioral methods for elevating alcohol intake and inferring alcohol reinforcement, as well as new genetic animal models. Several ingenious assays to index alcohol’s motivational effects have been used extensively. Alcoholic drinking that attempts to prevent or to alleviate withdrawal symptoms has been modeled. Another characteristic of alcoholic drinking is its persistence despite abundant evidence to the drinker of the damaging effects of the excessive drinking on work, relationships, and/or health. Modeling such persistence in rodents has been uncommon to date. New genetic animal models include lines of mice selectively bred for chronic high drinking

  17. Genetic parameters for direct and maternal calving ease in Walloon dairy cattle based on linear and threshold models.

    PubMed

    Vanderick, S; Troch, T; Gillon, A; Glorieux, G; Gengler, N

    2014-12-01

    Calving ease scores from Holstein dairy cattle in the Walloon Region of Belgium were analysed using univariate linear and threshold animal models. Variance components and derived genetic parameters were estimated from a data set including 33,155 calving records. Included in the models were season, herd and sex of calf × age of dam classes × group of calvings interaction as fixed effects, herd × year of calving, maternal permanent environment and animal direct and maternal additive genetic as random effects. Models were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Direct heritability for calving ease was approximately 8% with linear models and approximately 12% with threshold models. Maternal heritabilities were approximately 2 and 4%, respectively. Genetic correlation between direct and maternal additive effects was found to be not significantly different from zero. Models were compared in terms of goodness of fit and predictive ability. Criteria of comparison such as mean squared error, correlation between observed and predicted calving ease scores as well as between estimated breeding values were estimated from 85,118 calving records. The results provided few differences between linear and threshold models even though correlations between estimated breeding values from subsets of data for sires with progeny from linear model were 17 and 23% greater for direct and maternal genetic effects, respectively, than from threshold model. For the purpose of genetic evaluation for calving ease in Walloon Holstein dairy cattle, the linear animal model without covariance between direct and maternal additive effects was found to be the best choice.

  18. Supermultiplicative Speedups of Probabilistic Model-Building Genetic Algorithms

    DTIC Science & Technology

    2009-02-01

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

  19. Genetic code: an alternative model of translation.

    PubMed

    Damjanović, Zvonimir M; Rakocević, Miloje M

    2005-06-01

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

  20. Mining and modeling human genetics for autism therapeutics.

    PubMed

    Smith, Daniel G; Ehlers, Michael D

    2012-10-01

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

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

    PubMed

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

    2012-06-01

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

  2. Development of Genetic Occurrence Models for Geothermal Prospecting

    NASA Astrophysics Data System (ADS)

    Walker, J. D.; Sabin, A.; Unruh, J.; Monastero, F. C.; Combs, J.

    2007-12-01

    Exploration for utility-grade geothermal resources has mostly relied on identifying obvious surface manifestations of possible geothermal activity, e.g., locating and working near steaming ground or hot springs. This approach has lead to the development of over 130 resources worldwide, but geothermal exploration done in this manner is akin to locating hydrocarbon plays by searching for oil seeps. Confining exploration to areas with such features will clearly not discover a blind resource, that is, one that does not have surface expression. Blind resources, however, constitute the vast majority of hydrocarbon plays; this may be the case for geothermal resources as well. We propose a geothermal exploration strategy for finding blind systems that is based on an understanding of the geologic processes that transfer heat from the mantle to the upper crust and foster the conditions for hydrothermal circulation or enhanced geothermal exploration. The strategy employs a genetically based screening protocol to assess potential geothermal sites. The approach starts at the plate boundary scale and progressively focuses in on the scale of a producing electrical-grade field. Any active margin or hot spot is a potential location for geothermal resources. Although Quaternary igneous activity provides a clear indication of active advection of hot material into the upper crust, it is not sufficient to guarantee a potential utility-grade resource. Active faulting and/or evidence of high strain rates appear to be the critical features associated with areas of utility-grade geothermal potential. This is because deformation on its own can advect sufficient heat into the upper crust to create conditions favorable for geothermal exploitation. In addition, active deformation is required to demonstrate that open pathways for circulation of geothermal fluids are present and/or can be maintained. The last step in the screening protocol is to identify any evidence of geothermal activity

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

    PubMed

    Zhang, Yuan-Zhi; Zhong, Nanbert

    2006-11-01

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

  4. Application of hierarchical genetic models to Raven and WAIS subtests: a Dutch twin study.

    PubMed

    Rijsdijk, Frühling V; Vernon, P A; Boomsma, Dorret I

    2002-05-01

    Hierarchical models of intelligence are highly informative and widely accepted. Application of these models to twin data, however, is sparse. This paper addresses the question of how a genetic hierarchical model fits the Wechsler Adult Intelligence Scale (WAIS) subtests and the Raven Standard Progressive test score, collected in 194 18-year-old Dutch twin pairs. We investigated whether first-order group factors possess genetic and environmental variance independent of the higher-order general factor and whether the hierarchical structure is significant for all sources of variance. A hierarchical model with the 3 Cohen group-factors (verbal comprehension, perceptual organisation and freedom-from-distractibility) and a higher-order g factor showed the best fit to the phenotypic data and to additive genetic influences (A), whereas the unique environmental source of variance (E) could be modeled by a single general factor and specifics. There was no evidence for common environmental influences. The covariation among the WAIS group factors and the covariation between the group factors and the Raven is predominantly influenced by a second-order genetic factor and strongly support the notion of a biological basis of g.

  5. Regionalization of runoff models derived by genetic programming

    NASA Astrophysics Data System (ADS)

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

    2017-04-01

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

  6. Additive genetic variation in resistance of Nile tilapia (Oreochromis niloticus) to Streptococcus iniae and S. agalactiae capsular type Ib: is genetic resistance correlated?

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Streptococcus (S.) iniae and S. agalactiae are both economically important Gram positive bacterial pathogens affecting the globally farmed tilapia (Oreochromis spp.). Historically control of these bacteria in tilapia culture has included biosecurity, therapeutants and vaccination strategies. Genet...

  7. Evidence of Shared Genome-Wide Additive Genetic Effects on Interpersonal Trauma Exposure and Generalized Vulnerability to Drug Dependence in a Population of Substance Users.

    PubMed

    Palmer, Rohan H C; Nugent, Nicole R; Brick, Leslie A; Bidwell, Cinnamon L; McGeary, John E; Keller, Matthew C; Knopik, Valerie S

    2016-06-01

    Exposure to traumatic experiences is associated with an increased risk for drug dependence and poorer response to substance abuse treatment (Claus & Kindleberger, 2002; Jaycox, Ebener, Damesek, & Becker, 2004). Despite this evidence, the reasons for the observed associations of trauma and the general tendency to be dependent upon drugs of abuse remain unclear. Data (N = 2,596) from the Study of Addiction: Genetics and Environment were used to analyze (a) the degree to which commonly occurring single nucleotide polymorphisms (SNPs; minor allele frequency > 1%) in the human genome explains exposure to interpersonal traumatic experiences, and (b) the extent to which additive genetic effects on trauma are shared with additive genetic effects on drug dependence. Our results suggested moderate additive genetic influences on interpersonal trauma, h(2) SNP-Interpersonal = .47, 95% confidence interval (CI) [.10, .85], that are partially shared with additive genetic effects on generalized vulnerability to drug dependence, h(2) SNP-DD = .36, 95% CI [.11, .61]; rG-SNP = .49, 95% CI [.02, .96]. Although the design/technique does not exclude the possibility that substance abuse causally increases risk for traumatic experiences (or vice versa), these findings raise the possibility that commonly occurring SNPs influence both the general tendency towards drug dependence and interpersonal trauma.

  8. A pathway-based analysis provides additional support for an immune-related genetic susceptibility to Parkinson's disease.

    PubMed

    Holmans, Peter; Moskvina, Valentina; Jones, Lesley; Sharma, Manu; Vedernikov, Alexey; Buchel, Finja; Saad, Mohamad; Sadd, Mohamad; Bras, Jose M; Bettella, Francesco; Nicolaou, Nayia; Simón-Sánchez, Javier; Mittag, Florian; Gibbs, J Raphael; Schulte, Claudia; Durr, Alexandra; Guerreiro, Rita; Hernandez, Dena; Brice, Alexis; Stefánsson, Hreinn; Majamaa, Kari; Gasser, Thomas; Heutink, Peter; Wood, Nicholas W; Martinez, Maria; Singleton, Andrew B; Nalls, Michael A; Hardy, John; Morris, Huw R; Williams, Nigel M

    2013-03-01

    Parkinson's disease (PD) is the second most common neurodegenerative disease affecting 1-2% in people >60 and 3-4% in people >80. Genome-wide association (GWA) studies have now implicated significant evidence for association in at least 18 genomic regions. We have studied a large PD-meta analysis and identified a significant excess of SNPs (P < 1 × 10(-16)) that are associated with PD but fall short of the genome-wide significance threshold. This result was independent of variants at the 18 previously implicated regions and implies the presence of additional polygenic risk alleles. To understand how these loci increase risk of PD, we applied a pathway-based analysis, testing for biological functions that were significantly enriched for genes containing variants associated with PD. Analysing two independent GWA studies, we identified that both had a significant excess in the number of functional categories enriched for PD-associated genes (minimum P = 0.014 and P = 0.006, respectively). Moreover, 58 categories were significantly enriched for associated genes in both GWA studies (P < 0.001), implicating genes involved in the 'regulation of leucocyte/lymphocyte activity' and also 'cytokine-mediated signalling' as conferring an increased susceptibility to PD. These results were unaltered by the exclusion of all 178 genes that were present at the 18 genomic regions previously reported to be strongly associated with PD (including the HLA locus). Our findings, therefore, provide independent support to the strong association signal at the HLA locus and imply that the immune-related genetic susceptibility to PD is likely to be more widespread in the genome than previously appreciated.

  9. A Tri-Part Model for Genetics Literacy: Exploring Undergraduate Student Reasoning about Authentic Genetics Dilemmas

    ERIC Educational Resources Information Center

    Shea, Nicole A.; Duncan, Ravit Golan; Stephenson, Celeste

    2015-01-01

    Genetics literacy is becoming increasingly important as advancements in our application of genetic technologies such as stem cell research, cloning, and genetic screening become more prevalent. Very few studies examine how genetics literacy is applied when reasoning about authentic genetic dilemmas. However, there is evidence that situational…

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

    PubMed

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

    2016-02-26

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

  11. Additive Manufacturing Modeling and Simulation A Literature Review for Electron Beam Free Form Fabrication

    NASA Technical Reports Server (NTRS)

    Seufzer, William J.

    2014-01-01

    Additive manufacturing is coming into industrial use and has several desirable attributes. Control of the deposition remains a complex challenge, and so this literature review was initiated to capture current modeling efforts in the field of additive manufacturing. This paper summarizes about 10 years of modeling and simulation related to both welding and additive manufacturing. The goals were to learn who is doing what in modeling and simulation, to summarize various approaches taken to create models, and to identify research gaps. Later sections in the report summarize implications for closed-loop-control of the process, implications for local research efforts, and implications for local modeling efforts.

  12. Zebrafish: A Model System for the Study of Eye Genetics

    PubMed Central

    Fadool, James M.; Dowling, John E.

    2008-01-01

    Over the last decade, the use of the zebrafish as a genetic model has moved beyond the proof-of-concept for the analysis of vertebrate embryonic development to demonstrated utility as a mainstream model organism for the understanding of human disease. The initial identification of a variety of zebrafish mutations affecting the eye and retina, and the subsequent cloning of mutated genes have revealed cellular, molecular and physiological processes fundamental to visual system development. With the increasing development of genetic manipulations, sophisticated techniques for phenotypic characterization, behavioral approaches and screening strategies, the identification of novel genes or novel gene functions will have important implications for our understanding of human eye diseases, pathogenesis, and treatment. PMID:17962065

  13. Sleep and Development in Genetically Tractable Model Organisms.

    PubMed

    Kayser, Matthew S; Biron, David

    2016-05-01

    Sleep is widely recognized as essential, but without a clear singular function. Inadequate sleep impairs cognition, metabolism, immune function, and many other processes. Work in genetic model systems has greatly expanded our understanding of basic sleep neurobiology as well as introduced new concepts for why we sleep. Among these is an idea with its roots in human work nearly 50 years old: sleep in early life is crucial for normal brain maturation. Nearly all known species that sleep do so more while immature, and this increased sleep coincides with a period of exuberant synaptogenesis and massive neural circuit remodeling. Adequate sleep also appears critical for normal neurodevelopmental progression. This article describes recent findings regarding molecular and circuit mechanisms of sleep, with a focus on development and the insights garnered from models amenable to detailed genetic analyses.

  14. Mutational landscape of EGFR-, MYC-, and Kras-driven genetically engineered mouse models of lung adenocarcinoma

    PubMed Central

    McFadden, David G.; Politi, Katerina; Bhutkar, Arjun; Chen, Frances K.; Song, Xiaoling; Pirun, Mono; Santiago, Philip M.; Kim-Kiselak, Caroline; Platt, James T.; Lee, Emily; Hodges, Emily; Rosebrock, Adam P.; Bronson, Roderick T.; Socci, Nicholas D.; Hannon, Gregory J.; Jacks, Tyler; Varmus, Harold

    2016-01-01

    Genetically engineered mouse models (GEMMs) of cancer are increasingly being used to assess putative driver mutations identified by large-scale sequencing of human cancer genomes. To accurately interpret experiments that introduce additional mutations, an understanding of the somatic genetic profile and evolution of GEMM tumors is necessary. Here, we performed whole-exome sequencing of tumors from three GEMMs of lung adenocarcinoma driven by mutant epidermal growth factor receptor (EGFR), mutant Kirsten rat sarcoma viral oncogene homolog (Kras), or overexpression of MYC proto-oncogene. Tumors from EGFR- and Kras-driven models exhibited, respectively, 0.02 and 0.07 nonsynonymous mutations per megabase, a dramatically lower average mutational frequency than observed in human lung adenocarcinomas. Tumors from models driven by strong cancer drivers (mutant EGFR and Kras) harbored few mutations in known cancer genes, whereas tumors driven by MYC, a weaker initiating oncogene in the murine lung, acquired recurrent clonal oncogenic Kras mutations. In addition, although EGFR- and Kras-driven models both exhibited recurrent whole-chromosome DNA copy number alterations, the specific chromosomes altered by gain or loss were different in each model. These data demonstrate that GEMM tumors exhibit relatively simple somatic genotypes compared with human cancers of a similar type, making these autochthonous model systems useful for additive engineering approaches to assess the potential of novel mutations on tumorigenesis, cancer progression, and drug sensitivity. PMID:27702896

  15. Estimates of genetic parameters for stayability to consecutive calvings of Canadian Simmentals by random regression models.

    PubMed

    Jamrozik, J; McGrath, S; Kemp, R A; Miller, S P

    2013-08-01

    Stayability to consecutive calvings was selected as a measure of cow longevity in the Canadian Simmental population. Calving performance data on 188,579 cows and culling information from the Total Herd Reporting System were used to determine whether a cow stayed in a herd for her second and later (up to the eighth) calvings, given that she had calved as 2 yr old. Binary records (n = 1,164,319) were analyzed with animal linear and threshold models including fixed effects of year of birth by season of birth by parity number and age of cow at first calving by parity number and random effects of contemporary group (CG) defined as herd of birth within year by season, animal additive genetic effect, and a cow permanent environmental (PE) effect. All random effects were Legendre polynomial regressions of the same order, defined on the scale from second to the eighth calving. Bayesian methods with Gibbs sampling were used to estimate covariance components and genetic parameters for random effects of models and selected variables on the longitudinal scale. Bayes factors and analyses of mean squared error and correlation between observed and predicted observations indicated that the linear model with regressions of order 3 was most plausible for generating the current data compared with a fixed regression and other random regression (both linear and threshold) models of order up to 4. Estimates of variances for all random effects from the best fitting model changed with the calving number. Estimates of heritability decreased in time: from 0.35 (SD = 0.006) for stayability to second calving to 0.13 (SD = 0.004) for stayability to the eighth calving. Variance due to PE effect constituted the largest part of the total variance of stayability for all longitudinal points followed by genetic and CG components. Genetic effects of stayability to different calvings were relatively highly correlated, from 0.62 (SD = 0.011) to 0.99 (SD = 0.001), and correlation decreased with the time

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

    PubMed Central

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

    2012-01-01

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

  17. Multiple Magnetic Dipole Modeling Coupled with a Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Lientschnig, G.

    2012-05-01

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

  18. Additive influence of genetic predisposition and conventional risk factors in the incidence of coronary heart disease: a population-based study in Greece

    Technology Transfer Automated Retrieval System (TEKTRAN)

    An additive genetic risk score (GRS) for coronary heart disease (CHD) has previously been associated with incident CHD in the population-based Greek European Prospective Investigation into Cancer and nutrition (EPIC) cohort. In this study, we explore GRS-‘environment’ joint actions on CHD for severa...

  19. Monitoring Impact of a Pesticide Treatment on Bacterial Soil Communities by Metabolic and Genetic Fingerprinting in Addition to Conventional Testing Procedures

    PubMed Central

    Engelen, Bert; Meinken, Kristin; von Wintzingerode, Friedrich; Heuer, Holger; Malkomes, Hans-Peter; Backhaus, Horst

    1998-01-01

    Herbogil (dinoterb), a reference herbicide, the mineral oil Oleo (paraffin oil used as an additive to herbicides), and Goltix (metamitron) were taken as model compounds for the study of impacts on microbial soil communities. After the treatment of soil samples, effects on metabolic sum parameters were determined by monitoring substrate-induced respiration (SIR) and dehydrogenase activity, as well as carbon and nitrogen mineralization. These conventional ecotoxicological testing procedures are used in pesticide registration. Inhibition of biomass-related activities and stimulation of nitrogen mineralization were the most significant effects caused by the application of Herbogil. Even though Goltix and Oleo were used at a higher dosage (10 times higher), the application of Goltix resulted in smaller effects and the additive Oleo was the least-active compound, with minor stimulation of test parameters at later observation times. The results served as a background for investigation of the power of “fingerprinting” methods in microbial ecology. Changes in catabolic activities induced by treatments were analyzed by using the 95 carbon sources provided by the BIOLOG system. Variations in the complex metabolic fingerprints demonstrated inhibition of many catabolic pathways after the application of Herbogil. Again, the effects of the other compounds were expressed at much lower levels and comprised stimulations as well as inhibitions. Testing for significance by a multivariate t test indicated that the sensitivity of this method was similar to the sensitivities of the conventional testing procedures. The variation of sensitive carbon sources, as determined by factor weights at different observation times, indicated the dynamics of the community shift induced by the Herbogil treatment in more detail. DNA extractions from soil resulted in a collection of molecules representing the genetic composition of total bacterial communities. Distinct and highly reproducible

  20. Nature and nurture: environmental influences on a genetic rat model of depression.

    PubMed

    Mehta-Raghavan, N S; Wert, S L; Morley, C; Graf, E N; Redei, E E

    2016-03-29

    In this study, we sought to learn whether adverse events such as chronic restraint stress (CRS), or 'nurture' in the form of environmental enrichment (EE), could modify depression-like behavior and blood biomarker transcript levels in a genetic rat model of depression. The Wistar Kyoto More Immobile (WMI) is a genetic model of depression that aided in the identification of blood transcriptomic markers, which successfully distinguished adolescent and adult subjects with major depressive disorders from their matched no-disorder controls. Here, we followed the effects of CRS and EE in adult male WMIs and their genetically similar control strain, the Wistar Kyoto Less Immobile (WLI), that does not show depression-like behavior, by measuring the levels of these transcripts in the blood and hippocampus. In WLIs, increased depression-like behavior and transcriptomic changes were present in response to CRS, but in WMIs no behavioral or additive transcriptomic changes occurred. Environmental enrichment decreased both the inherent depression-like behavior in the WMIs and the behavioral difference between WMIs and WLIs, but did not reverse basal transcript level differences between the strains. The inverse behavioral change induced by CRS and EE in the WLIs did not result in parallel inverse expression changes of the transcriptomic markers, suggesting that these behavioral responses to the environment work via separate molecular pathways. In contrast, 'trait' transcriptomic markers with expression differences inherent and unchanging between the strains regardless of the environment suggest that in our model, environmental and genetic etiologies of depression work through independent molecular mechanisms.

  1. Asymmetrical Damage Partitioning in Bacteria: A Model for the Evolution of Stochasticity, Determinism, and Genetic Assimilation.

    PubMed

    Chao, Lin; Rang, Camilla Ulla; Proenca, Audrey Menegaz; Chao, Jasper Ubirajara

    2016-01-01

    Non-genetic phenotypic variation is common in biological organisms. The variation is potentially beneficial if the environment is changing. If the benefit is large, selection can favor the evolution of genetic assimilation, the process by which the expression of a trait is transferred from environmental to genetic control. Genetic assimilation is an important evolutionary transition, but it is poorly understood because the fitness costs and benefits of variation are often unknown. Here we show that the partitioning of damage by a mother bacterium to its two daughters can evolve through genetic assimilation. Bacterial phenotypes are also highly variable. Because gene-regulating elements can have low copy numbers, the variation is attributed to stochastic sampling. Extant Escherichia coli partition asymmetrically and deterministically more damage to the old daughter, the one receiving the mother's old pole. By modeling in silico damage partitioning in a population, we show that deterministic asymmetry is advantageous because it increases fitness variance and hence the efficiency of natural selection. However, we find that symmetrical but stochastic partitioning can be similarly beneficial. To examine why bacteria evolved deterministic asymmetry, we modeled the effect of damage anchored to the mother's old pole. While anchored damage strengthens selection for asymmetry by creating additional fitness variance, it has the opposite effect on symmetry. The difference results because anchored damage reinforces the polarization of partitioning in asymmetric bacteria. In symmetric bacteria, it dilutes the polarization. Thus, stochasticity alone may have protected early bacteria from damage, but deterministic asymmetry has evolved to be equally important in extant bacteria. We estimate that 47% of damage partitioning is deterministic in E. coli. We suggest that the evolution of deterministic asymmetry from stochasticity offers an example of Waddington's genetic assimilation

  2. An introduction to modeling longitudinal data with generalized additive models: applications to single-case designs.

    PubMed

    Sullivan, Kristynn J; Shadish, William R; Steiner, Peter M

    2015-03-01

    Single-case designs (SCDs) are short time series that assess intervention effects by measuring units repeatedly over time in both the presence and absence of treatment. This article introduces a statistical technique for analyzing SCD data that has not been much used in psychological and educational research: generalized additive models (GAMs). In parametric regression, the researcher must choose a functional form to impose on the data, for example, that trend over time is linear. GAMs reverse this process by letting the data inform the choice of functional form. In this article we review the problem that trend poses in SCDs, discuss how current SCD analytic methods approach trend, describe GAMs as a possible solution, suggest a GAM model testing procedure for examining the presence of trend in SCDs, present a small simulation to show the statistical properties of GAMs, and illustrate the procedure on 3 examples of different lengths. Results suggest that GAMs may be very useful both as a form of sensitivity analysis for checking the plausibility of assumptions about trend and as a primary data analysis strategy for testing treatment effects. We conclude with a discussion of some problems with GAMs and some future directions for research on the application of GAMs to SCDs.

  3. Modeling lactation curves and estimation of genetic parameters in Holstein cows using multiple-trait random regression models.

    PubMed

    Kheirabadi, Khabat; Rashidi, Amir; Alijani, Sadegh; Imumorin, Ikhide

    2014-11-01

    We compared the goodness of fit of three mathematical functions (including: Legendre polynomials, Lidauer-Mäntysaari function and Wilmink function) for describing the lactation curve of primiparous Iranian Holstein cows by using multiple-trait random regression models (MT-RRM). Lactational submodels provided the largest daily additive genetic (AG) and permanent environmental (PE) variance estimates at the end and at the onset of lactation, respectively, as well as low genetic correlations between peripheral test-day records. For all models, heritability estimates were highest at the end of lactation (245 to 305 days) and ranged from 0.05 to 0.26, 0.03 to 0.12 and 0.04 to 0.24 for milk, fat and protein yields, respectively. Generally, the genetic correlations between traits depend on how far apart they are or whether they are on the same day in any two traits. On average, genetic correlations between milk and fat were the lowest and those between fat and protein were intermediate, while those between milk and protein were the highest. Results from all criteria (Akaike's and Schwarz's Bayesian information criterion, and -2*logarithm of the likelihood function) suggested that a model with 2 and 5 coefficients of Legendre polynomials for AG and PE effects, respectively, was the most adequate for fitting the data.

  4. Learning from small fry: the zebrafish as a genetic model organism for aquaculture fish species.

    PubMed

    Dahm, Ralf; Geisler, Robert

    2006-01-01

    In recent years, the zebrafish has become one of the most prominent vertebrate model organisms used to study the genetics underlying development, normal body function, and disease. The growing interest in zebrafish research was paralleled by an increase in tools and methods available to study zebrafish. While zebrafish research initially centered on mutagenesis screens (forward genetics), recent years saw the establishment of reverse genetic methods (morpholino knock-down, TILLING). In addition, increasingly sophisticated protocols for generating transgenic zebrafish have been developed and microarrays are now available to characterize gene expression on a near genome-wide scale. The identification of loci underlying specific traits is aided by genetic, physical, and radiation hybrid maps of the zebrafish genome and the zebrafish genome project. As genomic resources for aquacultural species are increasingly being generated, a meaningful interaction between zebrafish and aquacultural research now appears to be possible and beneficial for both sides. In particular, research on nutrition and growth, stress, and disease resistance in the zebrafish can be expected to produce results applicable to aquacultural fish, for example, by improving husbandry and formulated feeds. Forward and reverse genetics approaches in the zebrafish, together with the known conservation of synteny between the species, offer the potential to identify and verify candidate genes for quantitative trait loci (QTLs) to be used in marker-assisted breeding. Moreover, some technologies from the zebrafish field such as TILLING may be directly transferable to aquacultural research and production.

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

    PubMed

    Watkinson-Powell, Benjamin; Alphey, Nina

    2017-01-21

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

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

    PubMed

    Bilkei-Gorzo, Andras

    2014-05-01

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

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

    PubMed Central

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

    2011-01-01

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

  8. Random regression models for the estimation of genetic and environmental covariance functions for growth traits in Santa Ines sheep.

    PubMed

    Sarmento, J L R; Torres, R A; Sousa, W H; Lôbo, R N B; Albuquerque, L G; Lopes, P S; Santos, N P S; Bignard, A B

    2016-06-20

    Polynomial functions of different orders were used to model random effects associated with weight of Santa Ines sheep from birth to 196 days. Fixed effects included in the models were contemporary groups, age of ewe at lambing, and fourth-order Legendre polynomials for age to represent the average growth curve. In the random part, functions of different orders were included to model variances associated with direct additive and maternal genetic effects and with permanent environmental effects of the animal and mother. Residual variance was fitted by a sixth-order ordinary polynomial for age. The higher the order of the functions, the better the model fit the data. According to the Akaike information criterion and likelihood ratio test, a continuous function of order, five, five, seven, and three for direct additive genetic, maternal genetic, animal permanent environmental, and maternal permanent environmental effects (k = 5573), respectively, was sufficient to model changes in (co)variances with age. However, a more parsimonious model of order three, three, five, and three (k = 3353) was suggested based on Schwarz's Bayesian information criterion for the same effects. Since it was a more flexible model, model k = 5573 provided inconsistent genetic parameter estimates when compared to the biologically expected result. Predicted breeding values obtained with models k = 3353 and k = 5573 differed, especially at young ages. Model k = 3353 adequately fit changes in variances and covariances with time, and may be used to describe changes in variances with age in the Santa Ines sheep studied.

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

    NASA Astrophysics Data System (ADS)

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

    2012-07-01

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

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

    PubMed

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

    2009-01-01

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

  11. Effective Genetic-Risk Prediction Using Mixed Models

    PubMed Central

    Golan, David; Rosset, Saharon

    2014-01-01

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

  12. Modeling of genetic gain for single traits from marker-assisted seedling selection in clonally propagated crops

    PubMed Central

    Ru, Sushan; Hardner, Craig; Carter, Patrick A; Evans, Kate; Main, Dorrie; Peace, Cameron

    2016-01-01

    Seedling selection identifies superior seedlings as candidate cultivars based on predicted genetic potential for traits of interest. Traditionally, genetic potential is determined by phenotypic evaluation. With the availability of DNA tests for some agronomically important traits, breeders have the opportunity to include DNA information in their seedling selection operations—known as marker-assisted seedling selection. A major challenge in deploying marker-assisted seedling selection in clonally propagated crops is a lack of knowledge in genetic gain achievable from alternative strategies. Existing models based on additive effects considering seed-propagated crops are not directly relevant for seedling selection of clonally propagated crops, as clonal propagation captures all genetic effects, not just additive. This study modeled genetic gain from traditional and various marker-based seedling selection strategies on a single trait basis through analytical derivation and stochastic simulation, based on a generalized seedling selection scheme of clonally propagated crops. Various trait-test scenarios with a range of broad-sense heritability and proportion of genotypic variance explained by DNA markers were simulated for two populations with different segregation patterns. Both derived and simulated results indicated that marker-based strategies tended to achieve higher genetic gain than phenotypic seedling selection for a trait where the proportion of genotypic variance explained by marker information was greater than the broad-sense heritability. Results from this study provides guidance in optimizing genetic gain from seedling selection for single traits where DNA tests providing marker information are available. PMID:27148453

  13. A genetic algorithm based global search strategy for population pharmacokinetic/pharmacodynamic model selection.

    PubMed

    Sale, Mark; Sherer, Eric A

    2015-01-01

    The current algorithm for selecting a population pharmacokinetic/pharmacodynamic model is based on the well-established forward addition/backward elimination method. A central strength of this approach is the opportunity for a modeller to continuously examine the data and postulate new hypotheses to explain observed biases. This algorithm has served the modelling community well, but the model selection process has essentially remained unchanged for the last 30 years. During this time, more robust approaches to model selection have been made feasible by new technology and dramatic increases in computation speed. We review these methods, with emphasis on genetic algorithm approaches and discuss the role these methods may play in population pharmacokinetic/pharmacodynamic model selection.

  14. Polymorphisms associated with the risk of lung cancer in a healthy Mexican Mestizo population: Application of the additive model for cancer

    PubMed Central

    Pérez-Morales, Rebeca; Méndez-Ramírez, Ignacio; Castro-Hernández, Clementina; Martínez-Ramírez, Ollin C.; Gonsebatt, María Eugenia; Rubio, Julieta

    2011-01-01

    Lung cancer is the leading cause of cancer mortality in Mexico and worldwide. In the past decade, there has been an increase in the number of lung cancer cases in young people, which suggests an important role for genetic background in the etiology of this disease. In this study, we genetically characterized 16 polymorphisms in 12 low penetrance genes (AhR, CYP1A1, CYP2E1, EPHX1, GSTM1, GSTT1, GSTPI, XRCC1, ERCC2, MGMT, CCND1 and TP53) in 382 healthy Mexican Mestizos as the first step in elucidating the genetic structure of this population and identifying high risk individuals. All of the genotypes analyzed were in Hardy-Weinberg equilibrium, but different degrees of linkage were observed for polymorphisms in the CYP1A1 and EPHX1 genes. The genetic variability of this population was distributed in six clusters that were defined based on their genetic characteristics. The use of a polygenic model to assess the additive effect of low penetrance risk alleles identified combinations of risk genotypes that could be useful in predicting a predisposition to lung cancer. Estimation of the level of genetic susceptibility showed that the individual calculated risk value (iCRV) ranged from 1 to 16, with a higher iCRV indicating a greater genetic susceptibility to lung cancer. PMID:22215955

  15. Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks.

    PubMed

    Zomorrodi, Ali R; Lafontaine Rivera, Jimmy G; Liao, James C; Maranas, Costas D

    2013-09-01

    The ensemble modeling (EM) approach has shown promise in capturing kinetic and regulatory effects in the modeling of metabolic networks. Efficacy of the EM procedure relies on the identification of model parameterizations that adequately describe all observed metabolic phenotypes upon perturbation. In this study, we propose an optimization-based algorithm for the systematic identification of genetic/enzyme perturbations to maximally reduce the number of models retained in the ensemble after each round of model screening. The key premise here is to design perturbations that will maximally scatter the predicted steady-state fluxes over the ensemble parameterizations. We demonstrate the applicability of this procedure for an Escherichia coli metabolic model of central metabolism by successively identifying single, double, and triple enzyme perturbations that cause the maximum degree of flux separation between models in the ensemble. Results revealed that optimal perturbations are not always located close to reaction(s) whose fluxes are measured, especially when multiple perturbations are considered. In addition, there appears to be a maximum number of simultaneous perturbations beyond which no appreciable increase in the divergence of flux predictions is achieved. Overall, this study provides a systematic way of optimally designing genetic perturbations for populating the ensemble of models with relevant model parameterizations.

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

    PubMed

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

    2014-10-01

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

  17. In vivo Drosophilia genetic model for calcium oxalate nephrolithiasis

    PubMed Central

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

    2012-01-01

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

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

    PubMed

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

    2012-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1999-01-01

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

  20. Effects of additional food in a delayed predator-prey model.

    PubMed

    Sahoo, Banshidhar; Poria, Swarup

    2015-03-01

    We examine the effects of supplying additional food to predator in a gestation delay induced predator-prey system with habitat complexity. Additional food works in favor of predator growth in our model. Presence of additional food reduces the predatory attack rate to prey in the model. Supplying additional food we can control predator population. Taking time delay as bifurcation parameter the stability of the coexisting equilibrium point is analyzed. Hopf bifurcation analysis is done with respect to time delay in presence of additional food. The direction of Hopf bifurcations and the stability of bifurcated periodic solutions are determined by applying the normal form theory and the center manifold theorem. The qualitative dynamical behavior of the model is simulated using experimental parameter values. It is observed that fluctuations of the population size can be controlled either by supplying additional food suitably or by increasing the degree of habitat complexity. It is pointed out that Hopf bifurcation occurs in the system when the delay crosses some critical value. This critical value of delay strongly depends on quality and quantity of supplied additional food. Therefore, the variation of predator population significantly effects the dynamics of the model. Model results are compared with experimental results and biological implications of the analytical findings are discussed in the conclusion section.

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

    PubMed Central

    Taylor, Z S; Hoffman, S M G

    2014-01-01

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

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

    PubMed Central

    2014-01-01

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

  3. Relationship between obesity phenotypes and genetic determinants in a mouse model for juvenile obesity.

    PubMed

    Brockmann, Gudrun A; Schäfer, Nadine; Hesse, Claudia; Heise, Sebastian; Neuschl, Christina; Wagener, Asja; Churchill, Gary A; Li, Renhua

    2013-09-16

    Obesity, a state of imbalance between lean mass and fat mass, is important for the etiology of diseases affected by the interplay of multiple genetic and environmental factors. Although genome-wide association studies have repeatedly associated genes with obesity and body weight, the mechanisms underlying the interaction between the muscle and adipose tissues remain unknown. Using 351 mice (at 10 wk of age) of an intercross population between Berlin Fat Mouse Inbred (BFMI) and C57BL/6NCrl (B6N) mice, we examined the causal relationships between genetic variations and multiple traits: body lean mass and fat mass, adipokines, and bone mineral density. Furthermore, evidence from structural equation modeling suggests causality among these traits. In the BFMI model, juvenile obesity affects lean mass and impairs bone mineral density via adipokines secreted from the white adipose tissues. While previous studies have indicated that lean mass has a causative effect on adiposity, in the Berlin Fat Mouse model that has been selected for juvenile obesity (at 9 wk of age) for >90 generations, however, the causality is switched from fat mass to lean mass. In addition, linkage studies and statistical modeling have indicated that quantitative trait loci on chromosomes 5 and 6 affect both lean mass and fat mass. These lines of evidence indicate that the muscle and adipose tissues interact with one another and the interaction is modulated by genetic variations that are shaped by selections. Experimental examinations are necessary to verify the biological role of the inferred causalities.

  4. Using satellite-derived backscattering coefficients in addition to chlorophyll data to constrain a simple marine biogeochemical model

    NASA Astrophysics Data System (ADS)

    Kettle, H.

    2009-08-01

    Biogeochemical models of the ocean carbon cycle are frequently validated by, or tuned to, satellite chlorophyll data. However, ocean carbon cycle models are required to accurately model the movement of carbon, not chlorophyll, and due to the high variability of the carbon to chlorophyll ratio in phytoplankton, chlorophyll is not a robust proxy for carbon. Using inherent optical property (IOP) inversion algorithms it is now possible to also derive the amount of light backscattered by the upper ocean (bb) which is related to the amount of particulate organic carbon (POC) present. Using empirical relationships between POC and bb, a 1-D marine biogeochemical model is used to simulate bb at 490 nm thereby allowing the model to be compared with both remotely-sensed chlorophyll or bb data. Here I investigate the possibility of using bb in conjunction with chlorophyll data to help constrain the parameters in a simple 1-D NPZD model. The parameters of the biogeochemical model are tuned with a genetic algorithm, so that the model is fitted to either chlorophyll data or to both chlorophyll and bb data at three sites in the Atlantic with very different characteristics. Several inherent optical property (IOP) algorithms are available for estimating bb, three of which are used here. The effect of the different bb datasets on the behaviour of the tuned model is examined to ascertain whether the uncertainty in bb is significant. The results show that the addition of bb data does not consistently alter the same model parameters at each site and in fact can lead to some parameters becoming less well constrained, implying there is still much work to be done on the mechanisms relating chlorophyll to POC and bb within the model. However, this study does indicate that including bb data has the potential to significantly effect the modelled mixed layer detritus and that uncertainties in bb due to the different IOP algorithms are not particularly significant.

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

    ERIC Educational Resources Information Center

    Castera, Jeremy; Bruguiere, Catherine; Clement, Pierre

    2008-01-01

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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  7. Testing a Gender Additive Model: The Role of Body Image in Adolescent Depression

    ERIC Educational Resources Information Center

    Bearman, Sarah Kate; Stice, Eric

    2008-01-01

    Despite consistent evidence that adolescent girls are at greater risk of developing depression than adolescent boys, risk factor models that account for this difference have been elusive. The objective of this research was to examine risk factors proposed by the "gender additive" model of depression that attempts to partially explain the increased…

  8. Genetic-based EM algorithm for learning Gaussian mixture models.

    PubMed

    Pernkopf, Franz; Bouchaffra, Djamel

    2005-08-01

    We propose a genetic-based expectation-maximization (GA-EM) algorithm for learning Gaussian mixture models from multivariate data. This algorithm is capable of selecting the number of components of the model using the minimum description length (MDL) criterion. Our approach benefits from the properties of Genetic algorithms (GA) and the EM algorithm by combination of both into a single procedure. The population-based stochastic search of the GA explores the search space more thoroughly than the EM method. Therefore, our algorithm enables escaping from local optimal solutions since the algorithm becomes less sensitive to its initialization. The GA-EM algorithm is elitist which maintains the monotonic convergence property of the EM algorithm. The experiments on simulated and real data show that the GA-EM outperforms the EM method since: 1) We have obtained a better MDL score while using exactly the same termination condition for both algorithms. 2) Our approach identifies the number of components which were used to generate the underlying data more often than the EM algorithm.

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

    PubMed

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

    2016-04-01

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

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

    PubMed

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

  12. Controversies about the genetic model of colorectal tumorigenesis.

    PubMed

    Waliszewski, P

    1995-01-01

    According to the genetic model, intestinal tumorigenesis is a result of the ordered in time inactivation of tumor suppressor genes and the activation of oncogenes. A tacit assumption is that both genes involved in the regulation of proliferation and growth factor-inducible genes, although inactivated, would not be changed during that process. The model requires that cancer cell population is homogenous, exists in a deterministic environment, and develops in a teleological manner. Meanwhile, tumorigenesis is rather a combination of both deterministic and stochastic molecular phenomena. Therefore, a novel notion of bifurcating point genes is defined as a generalization of the idea of tumor suppressor genes and oncogenes. Alternative stochastic mechanisms of tumorigenesis are discussed such as a decreased expression of intestinal-specific genes in cancer cells, most likely reflecting adaptation to survival within a heterogeneous, and non-equilibrated cellular population.

  13. An Intelligent Model for Pairs Trading Using Genetic Algorithms

    PubMed Central

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

    2015-01-01

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

  14. An original traffic additional emission model and numerical simulation on a signalized road

    NASA Astrophysics Data System (ADS)

    Zhu, Wen-Xing; Zhang, Jing-Yu

    2017-02-01

    Based on VSP (Vehicle Specific Power) model traffic real emissions were theoretically classified into two parts: basic emission and additional emission. An original additional emission model was presented to calculate the vehicle's emission due to the signal control effects. Car-following model was developed and used to describe the traffic behavior including cruising, accelerating, decelerating and idling at a signalized intersection. Simulations were conducted under two situations: single intersection and two adjacent intersections with their respective control policy. Results are in good agreement with the theoretical analysis. It is also proved that additional emission model may be used to design the signal control policy in our modern traffic system to solve the serious environmental problems.

  15. Multilevel modeling for inference of genetic regulatory networks

    NASA Astrophysics Data System (ADS)

    Ng, Shu-Kay; Wang, Kui; McLachlan, Geoffrey J.

    2005-12-01

    Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.

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

    NASA Astrophysics Data System (ADS)

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

    2011-11-01

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

  17. Estimate of influenza cases using generalized linear, additive and mixed models.

    PubMed

    Oviedo, Manuel; Domínguez, Ángela; Pilar Muñoz, M

    2015-01-01

    We investigated the relationship between reported cases of influenza in Catalonia (Spain). Covariates analyzed were: population, age, data of report of influenza, and health region during 2010-2014 using data obtained from the SISAP program (Institut Catala de la Salut - Generalitat of Catalonia). Reported cases were related with the study of covariates using a descriptive analysis. Generalized Linear Models, Generalized Additive Models and Generalized Additive Mixed Models were used to estimate the evolution of the transmission of influenza. Additive models can estimate non-linear effects of the covariates by smooth functions; and mixed models can estimate data dependence and variability in factor variables using correlations structures and random effects, respectively. The incidence rate of influenza was calculated as the incidence per 100 000 people. The mean rate was 13.75 (range 0-27.5) in the winter months (December, January, February) and 3.38 (range 0-12.57) in the remaining months. Statistical analysis showed that Generalized Additive Mixed Models were better adapted to the temporal evolution of influenza (serial correlation 0.59) than classical linear models.

  18. Modeling variation in early life mortality in the western lowland gorilla: Genetic, maternal and other effects.

    PubMed

    Ahsan, Monica H; Blomquist, Gregory E

    2015-06-01

    Uncovering sources of variation in gorilla infant mortality informs conservation and life history research efforts. The international studbook for the western lowland gorilla provides information on a sample of captive gorillas large enough for which to analyze genetic, maternal, and various other effects on early life mortality in this critically endangered species. We assess the importance of variables such as sex, maternal parity, paternal age, and hand rearing with regard to infant survival. We also quantify the proportions of variation in mortality influenced by heritable variation and maternal effects from these pedigree and survival data using variance component estimation. Markov chain Monte Carlo simulations of generalized linear mixed models produce variance component distributions in an animal model framework that employs all pedigree information. Two models, one with a maternal identity component and one with both additive genetic and maternal identity components, estimate variance components for different age classes during the first 2 years of life. This is informative of the extent to which mortality risk factors change over time during gorilla infancy. Our results indicate that gorilla mortality is moderately heritable with the strongest genetic influence just after birth. Maternal effects are most important during the first 6 months of life. Interestingly, hand-reared infants have lower mortality for the first 6 months of life. Aside from hand rearing, we found other predictors commonly used in studies of primate infant mortality to have little influence in these gorilla data.

  19. An uncertain revolution: why the rise of a genetic model of mental illness has not increased tolerance.

    PubMed

    Schnittker, Jason

    2008-11-01

    This study uses the 2006 replication of the 1996 General Social Survey Mental Health Module to explore trends in public beliefs about mental illness in the USA. Drawing on three models related to the framing of genetic arguments in popular media, the study attempts to address why tolerance of the mentally ill has not increased, despite the growing popularity of a biomedical view. The key to resolving this paradox lies in understanding how genetic arguments interact with other beliefs about mental illness, as well as the complex ideational implications of genetic frameworks. Genetic arguments have contingent relationships with tolerance. When applied to schizophrenia, genetic arguments are positively associated with fears regarding violence. Indeed, in this regard, attributing schizophrenia to genes is no different from attributing schizophrenia to bad character. However, when applied to depression, genetic arguments are positively associated with social acceptance. In addition to these contingencies, genetic explanations have discontinuous relationships with beliefs regarding treatment. Although genetic arguments are positively associated with recommending medical treatment, they are not associated with the perceived likelihood of improvement. The net result of these assorted relationships is little change in overall levels of tolerance over time. Because of the blunt nature of the forces propelling a biomedical view--including the growing popularity of psychiatric medications--altering beliefs about the etiology of mental illness is unlikely, on its own, to increase tolerance.

  20. An integrated genetic-demographic model to unravel the origin of genetic structure in European eel (Anguilla anguilla L.)

    PubMed Central

    Andrello, Marco; Bevacqua, Daniele; Maes, Gregory E; De Leo, Giulio A

    2011-01-01

    The evolutionary enlightened management of species with complex life cycles often requires the development of mathematical models integrating demographic and genetic data. The genetic structure of the endangered European eel (Anguilla anguilla L.) has been thoroughly analyzed in several studies in the past years. However, the interpretation of the key demographic and biologic processes that determine the observed spatio-temporal genetic structure has been very challenging owing to the complex life cycle of this catadromous species. Here, we present the first integrated demographic-genetic model applied to the European eel that explicitly accounts for different levels of larval and adult mixing during oceanic migrations and allows us to explore alternative hypotheses on genetic differentiation. Our analyses show that (i) very low levels of mixing occurring during larval dispersal or adult migration are sufficient to erase entirely any genetic differences among sub-populations; (ii) small-scale temporal differentiation in recruitment can arise if the spawning stock is subdivided in distinct reproductive groups; and (iii) the geographic differentiation component might be overestimated if a limited number of temporal recruits are analyzed. Our study can inspire the scientific debate on the interpretation of genetic structure in other species characterized by complex life cycle and long-range migrations. PMID:25568002

  1. Effect of Keishibukuryogan on Genetic and Dietary Obesity Models

    PubMed Central

    Gao, Fengying; Fujimoto, Makoto; Saiki, Ikuo; Hayakawa, Yoshihiro

    2015-01-01

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

  2. Genetic parameters for tunisian holsteins using a test-day random regression model.

    PubMed

    Hammami, H; Rekik, B; Soyeurt, H; Ben Gara, A; Gengler, N

    2008-05-01

    Genetic parameters of milk, fat, and protein yields were estimated in the first 3 lactations for registered Tunisian Holsteins. Data included 140,187; 97,404; and 62,221 test-day production records collected on 22,538; 15,257; and 9,722 first-, second-, and third-parity cows, respectively. Records were of cows calving from 1992 to 2004 in 96 herds. (Co)variance components were estimated by Bayesian methods and a 3-trait-3-lactation random regression model. Gibbs sampling was used to obtain posterior distributions. The model included herd x test date, age x season of calving x stage of lactation [classes of 25 days in milk (DIM)], production sector x stage of lactation (classes of 5 DIM) as fixed effects, and random regression coefficients for additive genetic, permanent environmental, and herd-year of calving effects, which were defined as modified constant, linear, and quadratic Legendre coefficients. Heritability estimates for 305-d milk, fat and protein yields were moderate (0.12 to 0.18) and in the same range of parameters estimated in management systems with low to medium production levels. Heritabilities of test-day milk and protein yields for selected DIM were higher in the middle than at the beginning or the end of lactation. Inversely, heritabilities of fat yield were high at the peripheries of lactation. Genetic correlations among 305-d yield traits ranged from 0.50 to 0.86. The largest genetic correlation was observed between the first and second lactation, potentially due to the limited expression of genetic potential of superior cows in later lactations. Results suggested a lack of adaptation under the local management and climatic conditions. Results should be useful to implement a BLUP evaluation for the Tunisian cow population; however, results also indicated that further research focused on data quality might be needed.

  3. Genetics

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  4. Genetics

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  5. Genetics of litter size in three maternal lines of rabbits: repeatability versus multiple-trait models.

    PubMed

    Piles, M; García, M L; Rafel, O; Ramon, J; Baselga, M

    2006-09-01

    Variance components were estimated in 3 lines of rabbits selected for litter size at weaning (A, Prat, and V) to test one of the assumptions of the models used for selection: that litter size data at different parities are repeated measurements of the same trait. Multiple-trait analyses were performed for the total number of kits born (TB), the number of kits born alive (BA), and the number of kits weaned (NW) per litter. Estimates were obtained by REML in multivariate analyses, including all of the information of the selection criteria, under a repeatability model or a multiple-trait model, considering litter size at the first, second, and subsequent parities as different traits. Models included the fixed effects of the physiological status of the female and the year-season of mating day, buck and doe random permanent environmental effects, and doe additive genetic effects. Results indicated that prolificacy was determined mainly by doe components and that the service sire had a very small effect. Heritabilities for the first and second parities were greater than the estimates obtained under the repeatability model (0.04 to 0.14 for the repeatability model). In the A and V lines, similar values of heritability were found at the first and second parities, but in the Prat line heritability at the second parity was always greater than at the first and greater parities (values of 0.21, 0.17, and 0.15 for TB, BA, and NW, respectively, in second parities of the Prat line). Genetic correlations between the same traits at different parities were approximately 0.8 for all traits in line A, but much lower in the other 2 lines. On average, the values were 0.64 for TB, 0.48 for BA, and 0.39 for NW between the first and second parities, and 0.65 for TB, 0.56 for BA, and 0.45 for NW between the first and third and greater parities. Genetic correlations between the second and greater parities showed the greatest values (approximately 0.8) in lines A and Prat for all traits, but

  6. [Genetically engineered mice: mouse models for cancer research].

    PubMed

    Szymańska, Hanna

    2007-10-26

    Genetically engineered mice (GEM) have been extensively used to model human cancer. Mouse models mimic the morphology, histopathology, phenotype, and genotype of the corresponding cancer in humans. GEM mice are created by random integration of a transgene into the genome, which results in gene overexpression (transgenic mice); gene deletion (knock-out mice); or targeted insertion of the transgene in a selected locus (knock-in mice). Knock-out may be constitutive, i.e. total inactivation of the gene of interest in any cell, or conditional, i.e. tissue-specific inactivation of the gene. Gene knock-down (RNAi) and humanization of the mouse are more sophisticated models of GEM mice. RNA interference (RNAi) is a mechanism in which double-stranded RNAs inhibits the respective gene expression by inducing degradation of its mRNA. Humanization is based on replacing a mouse gene by its human counterpart. The alterations in genes in GEM have to be heritable. The opportunities provided by employing GEM cancer models are: analysis of the role of specific cancer genes and modifier genes, evaluation of conventional cancer therapies and new drugs, identification of cancer markers of tumor growth, analysis of the influence of the tumor's microenvironment on tumor formation, and the definition of the pre-clinical, discrete steps of tumorigenesis. The validation of mouse models of human cancer is the task of the MMHCC (Mouse Models of Human Cancer Consortium). The GEM models of breast, pancreatic, intestinal and colon, and prostate cancer are the most actively explored. In contrast, the models of brain tumors and ovary, cervical, and skin cancer are in the early stage of investigation.

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

    NASA Astrophysics Data System (ADS)

    Nesseris, Savvas; García-Bellido, Juan

    2012-11-01

    We use Genetic Algorithms to extract information from several cosmological probes, such as the type Ia supernovae (SnIa), the Baryon Acoustic Oscillations (BAO) and the growth rate of matter perturbations. This is done by implementing a model independent and bias-free reconstruction of the various scales and distances that characterize the data, like the luminosity dL(z) and the angular diameter distance dA(z) in the SnIa and BAO data, respectively, or the dependence with redshift of the matter density Ωm(a) in the growth rate data, fσ8(z). These quantities can then be used to reconstruct the expansion history of the Universe, and the resulting Dark Energy (DE) equation of state w(z) in the context of FRW models, or the mass radial function ΩM(r) in LTB models. In this way, the reconstruction is completely independent of our prior bias. Furthermore, we use this method to test the Etherington relation, ie the well-known relation between the luminosity and the angular diameter distance, η≡dL(z)/(1+z)2dA(z), which is equal to 1 in metric theories of gravity. We find that the present data seem to suggest a 3-σ deviation from one at redshifts z ~ 0.5. Finally, we present a novel way, within the Genetic Algorithm paradigm, to analytically estimate the errors on the reconstructed quantities by calculating a Path Integral over all possible functions that may contribute to the likelihood. We show that this can be done regardless of the data being correlated or uncorrelated with each other and we also explicitly demonstrate that our approach is in good agreement with other error estimation techniques like the Fisher Matrix approach and the Bootstrap Monte Carlo.

  8. New Genetics

    MedlinePlus

    ... Home > Science Education > The New Genetics The New Genetics Living Laboratories Classroom Poster Order a Free Copy ... Piece to a Century-Old Evolutionary Puzzle Computing Genetics Model Organisms RNA Interference The New Genetics is ...

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

    PubMed

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

    2014-01-01

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

  10. Recent Enhancements to the Genetic Risk Prediction Model BRCAPRO

    PubMed Central

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

    2015-01-01

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

  11. 19 Gene × Environment Interaction Models in Psychiatric Genetics

    PubMed Central

    Karg, Katja; Sen, Srijan

    2013-01-01

    Gene-environment (G×E) interaction research is an emerging area in psychiatry, with the number of G×E studies growing rapidly in the past two decades. This article aims to give a comprehensive introduction to the field, with an emphasis on central theoretical and practical problems that are worth considering before conducting a G×E interaction study. On the theoretical side, we discuss two fundamental, but controversial questions about (1) the validity of statistical models for biological interaction and (2) the utility of G×E research for psychiatric genetics. On the practical side, we focus on study characteristics that potentially influence the outcome of G×E interaction studies and discuss strengths and pitfalls of different study designs, including recent approaches like Genome-Environment Wide Interaction Studies (GEWIS). Finally, we discuss recent developments in G×E interaction research on the most heavily investigated example in psychiatric genetics, the interaction between a serotonin transporter gene promoter variant (5-HTTLPR) and stress on depression. PMID:22241248

  12. Y genetic data support the Neolithic demic diffusion model.

    PubMed

    Chikhi, Lounes; Nichols, Richard A; Barbujani, Guido; Beaumont, Mark A

    2002-08-20

    There still is no general agreement on the origins of the European gene pool, even though Europe has been more thoroughly investigated than any other continent. In particular, there is continuing controversy about the relative contributions of European Palaeolithic hunter-gatherers and of migrant Near Eastern Neolithic farmers, who brought agriculture to Europe. Here, we apply a statistical framework that we have developed to obtain direct estimates of the contribution of these two groups at the time they met. We analyze a large dataset of 22 binary markers from the non-recombining region of the Y chromosome (NRY), by using a genealogical likelihood-based approach. The results reveal a significantly larger genetic contribution from Neolithic farmers than did previous indirect approaches based on the distribution of haplotypes selected by using post hoc criteria. We detect a significant decrease in admixture across the entire range between the Near East and Western Europe. We also argue that local hunter-gatherers contributed less than 30% in the original settlements. This finding leads us to reject a predominantly cultural transmission of agriculture. Instead, we argue that the demic diffusion model introduced by Ammerman and Cavalli-Sforza [Ammerman, A. J. & Cavalli-Sforza, L. L. (1984) The Neolithic Transition and the Genetics of Populations in Europe (Princeton Univ. Press, Princeton)] captures the major features of this dramatic episode in European prehistory.

  13. Pregnancy-associated homeostasis and dysregulation: lessons from genetically modified animal models.

    PubMed

    Ishida, Junji; Matsuoka, Toshiki; Saito-Fujita, Tomoko; Inaba, Saki; Kunita, Satoshi; Sugiyama, Fumihiro; Yagami, Ken-ichi; Fukamizu, Akiyoshi

    2011-07-01

    Physiological alterations occur in many organ systems during pregnancy. These changes are necessary for the adaptation to pregnancy-specific physiological processes in mother and fetus, and the placenta plays a critical role in the maintenance of homeostasis in pregnancy. Dysregulation of these functional feto-maternal interactions leads to severe complications. There have been many attempts to create animal models that mimic the hypertensive disorders of pregnancy, especially pre-eclampsia. In this review, we summarize the physiology of pregnancy and placental function, and discuss the placental gene expression in normal pregnancy. In addition, we assess a number of established animal models focusing on a specific pathogenic mechanism of pre-eclampsia, including genetically modified mouse models involving the renin-angiotensin system. Validation of these animal models would contribute significantly to understanding the basic principles of pregnancy-associated homeostasis and the pathogenesis of pre-eclampsia.

  14. The Triple Code Model for Pancreatic Cancer: Crosstalk Among Genetics, Epigenetics, and Nuclear Structure

    PubMed Central

    Lomberk, Gwen A; Urrutia, Raul

    2015-01-01

    Pancreatic adenocarcinoma (PDAC) is a lethal and painful disease, which has become one of the most frequent causes of death by malignant diseases around the world. Unfortunately, for the most part, this disease remains incurable. Significant advances in the field of genetics, particularly during the last two decades, has led to the proposal of a progression model, by which this cancer evolves by the accumulation of mutations and deletions in key oncogenes and tumor suppressor genes. This model has been remarkably useful for the development of tumor markers as well as elegant animal models. In spite of these strengths, this model does not take into consideration concepts and methodologies that have been derived from the field of epigenetics nor studies in the field of nuclear structure and function. Since our laboratory has been long been an advocate of these changes as critical for the pathobiology of pancreatic cancer, in this article, we describe an updated, more comprehensive model, which includes these concepts. With the widespread utilization of next generation sequencing for identifying both genetic and epigenetic changes genome-wide, we believe that the framework of this model will help to further identify and validate not only more but better markers for pancreatic cancer. In addition, as opposed to genetic changes, epigenetic alterations are amenable to pharmacological manipulations, consequently the familiarization with this model will help to better understand the potential beneficial effects of this type of therapy for this disease. Thus, we are optimistic that this new integrated paradigm will contribute to advance this field of research not only from a mechanistic point of view, but also from a translational one. PMID:26315515

  15. Plants with genetically modified events combined by conventional breeding: an assessment of the need for additional regulatory data.

    PubMed

    Pilacinski, W; Crawford, A; Downey, R; Harvey, B; Huber, S; Hunst, P; Lahman, L K; MacIntosh, S; Pohl, M; Rickard, C; Tagliani, L; Weber, N

    2011-01-01

    Crop varieties with multiple GM events combined by conventional breeding have become important in global agriculture. The regulatory requirements in different countries for such products vary considerably, placing an additional burden on regulatory agencies in countries where the submission of additional data is required and delaying the introduction of innovative products to meet agricultural needs. The process of conventional plant breeding has predictably provided safe food and feed products both historically and in the modern era of plant breeding. Thus, previously approved GM events that have been combined by conventional plant breeding and contain GM traits that are not likely to interact in a manner affecting safety should be considered to be as safe as their conventional counterparts. Such combined GM event crop varieties should require little, if any, additional regulatory data to meet regulatory requirements.

  16. Genetic structure and bio-climatic modeling support allopatric over parapatric speciation along a latitudinal gradient

    PubMed Central

    2012-01-01

    clusters corresponding to the four recognised species with the additional division of T. speciosissima into populations north and south of the Shoalhaven River valley. Unexpectedly, the northern disjunct population of T. oreades grouped with T. mongaensis and was identified as a hybrid swarm by the Bayesian assignment test implemented in NewHybrids. Present day and LGM environmental niche models differed dramatically, suggesting that distributions of all species had repeatedly expanded and contracted in response to Pleistocene climatic oscillations and confirming strongly marked historical distributional gaps among taxes. Conclusions Genetic structure and bio-climatic modeling results are more consistent with a history of allopatric speciation followed by repeated episodes of secondary contact and localised hybridisation, rather than with parapatric speciation. This study on Telopea shows that the evidence for temporal exclusion of gene flow can be found even outside obvious geographical contexts, and that it is possible to make significant progress towards excluding parapatric speciation as a contributing evolutionary process. PMID:22906180

  17. Genetic regulation of bone strength: a review of animal model studies

    PubMed Central

    Adams, Douglas J; Ackert-Bicknell, Cheryl L

    2015-01-01

    Population- and family-based studies have established that fragility fracture risk is heritable; yet, the genome-wide association studies published to date have only accounted for a small fraction of the known variation for fracture risk of either the femur or the lumbar spine. Much work has been carried out using animal models toward finding genetic loci that are associated with bone strength. Studies using animal models overcome some of the issues associated with using patient data, but caution is needed when interpreting the results. In this review, we examine the types of tests that have been used for forward genetics mapping in animal models to identify loci and/or genes that regulate bone strength and discuss the limitations of these test methods. In addition, we present a summary of the quantitative trait loci that have been mapped for bone strength in mice, rats and chickens. The majority of these loci co-map with loci for bone size and/or geometry and thus likely dictate strength via modulating bone size. Differences in bone matrix composition have been demonstrated when comparing inbred strains of mice, and these matrix differences may be associated with differences in bone strength. However, additional work is needed to identify loci that act on bone strength at the materials level. PMID:26157577

  18. Genetic variants and animal models in SNCA and Parkinson disease.

    PubMed

    Deng, Hao; Yuan, Lamei

    2014-05-01

    Parkinson disease (PD; MIM 168600) is the second most common progressive neurodegenerative disorder characterized by a variety of motor and non-motor features. To date, at least 20 loci and 15 disease-causing genes for parkinsonism have been identified. Among them, the α-synuclein (SNCA) gene was associated with PARK1/PARK4. Point mutations, duplications and triplications in the SNCA gene cause a rare dominant form of PD in familial and sporadic PD cases. The α-synuclein protein, a member of the synuclein family, is abundantly expressed in the brain. The protein is the major component of Lewy bodies and Lewy neurites in dopaminergic neurons in PD. Further understanding of its role in the pathogenesis of PD through various genetic techniques and animal models will likely provide new insights into our understanding, therapy and prevention of PD.

  19. Genetically manipulated mouse models of lung disease: potential and pitfalls

    PubMed Central

    Choi, Alexander J. S.; Owen, Caroline A.; Choi, Augustine M. K.

    2012-01-01

    Gene targeting in mice (transgenic and knockout) has provided investigators with an unparalleled armamentarium in recent decades to dissect the cellular and molecular basis of critical pathophysiological states. Fruitful information has been derived from studies using these genetically engineered mice with significant impact on our understanding, not only of specific biological processes spanning cell proliferation to cell death, but also of critical molecular events involved in the pathogenesis of human disease. This review will focus on the use of gene-targeted mice to study various models of lung disease including airways diseases such as asthma and chronic obstructive pulmonary disease, and parenchymal lung diseases including idiopathic pulmonary fibrosis, pulmonary hypertension, pneumonia, and acute lung injury. We will attempt to review the current technological approaches of generating gene-targeted mice and the enormous dataset derived from these studies, providing a template for lung investigators. PMID:22198907

  20. Efficient multiple-trait association and estimation of genetic correlation using the matrix-variate linear mixed model.

    PubMed

    Furlotte, Nicholas A; Eskin, Eleazar

    2015-05-01

    Multiple-trait association mapping, in which multiple traits are used simultaneously in the identification of genetic variants affecting those traits, has recently attracted interest. One class of approaches for this problem builds on classical variance component methodology, utilizing a multitrait version of a linear mixed model. These approaches both increase power and provide insights into the genetic architecture of multiple traits. In particular, it is possible to estimate the genetic correlation, which is a measure of the portion of the total correlation between traits that is due to additive genetic effects. Unfortunately, the practical utility of these methods is limited since they are computationally intractable for large sample sizes. In this article, we introduce a reformulation of the multiple-trait association mapping approach by defining the matrix-variate linear mixed model. Our approach reduces the computational time necessary to perform maximum-likelihood inference in a multiple-trait model by utilizing a data transformation. By utilizing a well-studied human cohort, we show that our approach provides more than a 10-fold speedup, making multiple-trait association feasible in a large population cohort on the genome-wide scale. We take advantage of the efficiency of our approach to analyze gene expression data. By decomposing gene coexpression into a genetic and environmental component, we show that our method provides fundamental insights into the nature of coexpressed genes. An implementation of this method is available at http://genetics.cs.ucla.edu/mvLMM.

  1. Integrated reservoir characterization: Improvement in heterogeneities stochastic modelling by integration of additional external constraints

    SciTech Connect

    Doligez, B.; Eschard, R.; Geffroy, F.

    1997-08-01

    The classical approach to construct reservoir models is to start with a fine scale geological model which is informed with petrophysical properties. Then scaling-up techniques allow to obtain a reservoir model which is compatible with the fluid flow simulators. Geostatistical modelling techniques are widely used to build the geological models before scaling-up. These methods provide equiprobable images of the area under investigation, which honor the well data, and which variability is the same than the variability computed from the data. At an appraisal phase, when few data are available, or when the wells are insufficient to describe all the heterogeneities and the behavior of the field, additional constraints are needed to obtain a more realistic geological model. For example, seismic data or stratigraphic models can provide average reservoir information with an excellent areal coverage, but with a poor vertical resolution. New advances in modelisation techniques allow now to integrate this type of additional external information in order to constrain the simulations. In particular, 2D or 3D seismic derived information grids, or sand-shale ratios maps coming from stratigraphic models can be used as external drifts to compute the geological image of the reservoir at the fine scale. Examples are presented to illustrate the use of these new tools, their impact on the final reservoir model, and their sensitivity to some key parameters.

  2. Analysis of error-prone survival data under additive hazards models: measurement error effects and adjustments.

    PubMed

    Yan, Ying; Yi, Grace Y

    2016-07-01

    Covariate measurement error occurs commonly in survival analysis. Under the proportional hazards model, measurement error effects have been well studied, and various inference methods have been developed to correct for error effects under such a model. In contrast, error-contaminated survival data under the additive hazards model have received relatively less attention. In this paper, we investigate this problem by exploring measurement error effects on parameter estimation and the change of the hazard function. New insights of measurement error effects are revealed, as opposed to well-documented results for the Cox proportional hazards model. We propose a class of bias correction estimators that embraces certain existing estimators as special cases. In addition, we exploit the regression calibration method to reduce measurement error effects. Theoretical results for the developed methods are established, and numerical assessments are conducted to illustrate the finite sample performance of our methods.

  3. Caenorhabditis elegans as a powerful alternative model organism to promote research in genetic toxicology and biomedicine.

    PubMed

    Honnen, Sebastian

    2017-03-15

    In view of increased life expectancy the risk for disturbed integrity of genetic information increases. This inevitably holds the implication for higher incidence of age-related diseases leading to considerable cost increase in health care systems. To develop preventive strategies it is crucial to evaluate external and internal noxae as possible threats to our DNA. Especially the interplay of DNA damage response (DDR) and DNA repair (DR) mechanisms needs further deciphering. Moreover, there is a distinct need for alternative in vivo test systems for basic research and also risk assessment in toxicology. Especially the evaluation of combinational toxicity of environmentally present genotoxins and adverse effects of clinically used DNA damaging anticancer drugs is a major challenge for modern toxicology. This review focuses on the applicability of Caenorhabditis elegans as a model organism to unravel and tackle scientific questions related to the biological consequences of genotoxin exposure and highlights methods for studying DDR and DR. In this regard large-scale in vivo screens of mixtures of chemicals and extensive parallel sequencing are highlighted as unique advantages of C. elegans. In addition, concise information regarding evolutionary conserved molecular mechanisms of the DDR and DR as well as currently available data obtained from the use of prototypical genotoxins and preferential read-outs of genotoxin testing are discussed. The use of established protocols, which are already available in the community, is encouraged to facilitate and further improve the implementation of C. elegans as a powerful genetic model system in genetic toxicology and biomedicine.

  4. Words as alleles: connecting language evolution with Bayesian learners to models of genetic drift.

    PubMed

    Reali, Florencia; Griffiths, Thomas L

    2010-02-07

    Scientists studying how languages change over time often make an analogy between biological and cultural evolution, with words or grammars behaving like traits subject to natural selection. Recent work has exploited this analogy by using models of biological evolution to explain the properties of languages and other cultural artefacts. However, the mechanisms of biological and cultural evolution are very different: biological traits are passed between generations by genes, while languages and concepts are transmitted through learning. Here we show that these different mechanisms can have the same results, demonstrating that the transmission of frequency distributions over variants of linguistic forms by Bayesian learners is equivalent to the Wright-Fisher model of genetic drift. This simple learning mechanism thus provides a justification for the use of models of genetic drift in studying language evolution. In addition to providing an explicit connection between biological and cultural evolution, this allows us to define a 'neutral' model that indicates how languages can change in the absence of selection at the level of linguistic variants. We demonstrate that this neutral model can account for three phenomena: the s-shaped curve of language change, the distribution of word frequencies, and the relationship between word frequencies and extinction rates.

  5. Nature and nurture: environmental influences on a genetic rat model of depression

    PubMed Central

    Mehta-Raghavan, N S; Wert, S L; Morley, C; Graf, E N; Redei, E E

    2016-01-01

    In this study, we sought to learn whether adverse events such as chronic restraint stress (CRS), or ‘nurture' in the form of environmental enrichment (EE), could modify depression-like behavior and blood biomarker transcript levels in a genetic rat model of depression. The Wistar Kyoto More Immobile (WMI) is a genetic model of depression that aided in the identification of blood transcriptomic markers, which successfully distinguished adolescent and adult subjects with major depressive disorders from their matched no-disorder controls. Here, we followed the effects of CRS and EE in adult male WMIs and their genetically similar control strain, the Wistar Kyoto Less Immobile (WLI), that does not show depression-like behavior, by measuring the levels of these transcripts in the blood and hippocampus. In WLIs, increased depression-like behavior and transcriptomic changes were present in response to CRS, but in WMIs no behavioral or additive transcriptomic changes occurred. Environmental enrichment decreased both the inherent depression-like behavior in the WMIs and the behavioral difference between WMIs and WLIs, but did not reverse basal transcript level differences between the strains. The inverse behavioral change induced by CRS and EE in the WLIs did not result in parallel inverse expression changes of the transcriptomic markers, suggesting that these behavioral responses to the environment work via separate molecular pathways. In contrast, ‘trait' transcriptomic markers with expression differences inherent and unchanging between the strains regardless of the environment suggest that in our model, environmental and genetic etiologies of depression work through independent molecular mechanisms. PMID:27023176

  6. Additive genetic variation in resistance traits of an exotic pine species: little evidence for constraints on evolution of resistance against native herbivores.

    PubMed

    Moreira, X; Zas, R; Sampedro, L

    2013-05-01

    The apparent failure of invasions by alien pines in Europe has been explained by the co-occurrence of native pine congeners supporting herbivores that might easily recognize the new plants as hosts. Previous studies have reported that exotic pines show reduced tolerance and capacity to induce resistance to those native herbivores. We hypothesize that limited genetic variation in resistance to native herbivores and the existence of evolutionary trade-offs between growth and resistance could represent additional potential constraints on the evolution of invasiveness of exotic pines outside their natural range. In this paper, we examined genetic variation for constitutive and induced chemical defences (measured as non-volatile resin in the stem and total phenolics in the needles) and resistance to two major native generalist herbivores of pines in cafeteria bioassays (the phloem-feeder Hylobius abietis and the defoliator Thaumetopoea pityocampa) using half-sib families drawn from a sample of the population of Pinus radiata introduced to Spain in the mid-19th century. We found (i) significant genetic variation, with moderate-to-high narrow-sense heritabilities for both the production of constitutive non-volatile resin and induced total phenolics, and for constitutive resistance against T. pityocampa in bioassays, (ii) no evolutionary trade-offs between plant resistance and growth traits or between the production of different quantitative chemical defences and (iii) a positive genetic correlation between constitutive resistance to the two studied herbivores. Overall, results of our study indicate that the exotic pine P. radiata has limited genetic constraints on the evolution of resistance against herbivores in its introduced range, suggesting that, at least in terms of interactions with these enemies, this pine species has potential to become invasive in the future.

  7. Genetic counselor perceptions of genetic counseling session goals: a validation study of the reciprocal-engagement model.

    PubMed

    Hartmann, Julianne E; Veach, Patricia McCarthy; MacFarlane, Ian M; LeRoy, Bonnie S

    2015-04-01

    Although some researchers have attempted to define genetic counseling practice goals, no study has obtained consensus about the goals from a large sample of genetic counselors. The Reciprocal-Engagement Model (REM; McCarthy Veach, Bartels & LeRoy, 2007) articulates 17 goals of genetic counseling practice. The present study investigated whether these goals could be generalized as a model of practice, as determined by a larger group of clinical genetic counselors. Accordingly, 194 genetic counselors were surveyed regarding their opinions about the importance of each goal and their perceptions of how frequently they achieve each goal. Mean importance ratings suggest they viewed every goal as important. Factor analysis of the 17 goals yielded four factors: Understanding and Appreciation, Support and Guidance, Facilitative Decision-Making, and Patient-Centered Education. Patient-Centered Education and Facilitative Decision-Making goals received the highest mean importance ratings. Mean frequency ratings were consistently lower than importance ratings, suggesting genetic counseling goals may be difficult to achieve and/or not applicable in all situations. A number of respondents provided comments about the REM goals that offer insight into factors related to implementing the goals in clinical practice. This study presents preliminary evidence concerning the validity of the goals component of the REM.

  8. Prospects for genetically modified non-human primate models, including the common marmoset.

    PubMed

    Sasaki, Erika

    2015-04-01

    Genetically modified mice have contributed much to studies in the life sciences. In some research fields, however, mouse models are insufficient for analyzing the molecular mechanisms of pathology or as disease models. Often, genetically modified non-human primate (NHP) models are desired, as they are more similar to human physiology, morphology, and anatomy. Recent progress in studies of the reproductive biology in NHPs has enabled the introduction of exogenous genes into NHP genomes or the alteration of endogenous NHP genes. This review summarizes recent progress in the production of genetically modified NHPs, including the common marmoset, and future perspectives for realizing genetically modified NHP models for use in life sciences research.

  9. Exploring the possibility of modeling a genetic counseling guideline using agile methodology.

    PubMed

    Choi, Jeeyae

    2013-01-01

    Increased demand of genetic counseling services heightened the necessity of a computerized genetic counseling decision support system. In order to develop an effective and efficient computerized system, modeling of genetic counseling guideline is an essential step. Throughout this pilot study, Agile methodology with United Modeling Language (UML) was utilized to model a guideline. 13 tasks and 14 associated elements were extracted. Successfully constructed conceptual class and activity diagrams revealed that Agile methodology with UML was a suitable tool to modeling a genetic counseling guideline.

  10. Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling.

    PubMed

    Wu, Hulin; Lu, Tao; Xue, Hongqi; Liang, Hua

    2014-04-02

    The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. High-dimensional linear ODE models have been proposed to identify GRNs, but with a limitation of the linear regulation effect assumption. In this article, we propose a sparse additive ODE (SA-ODE) model, coupled with ODE estimation methods and adaptive group LASSO techniques, to model dynamic GRNs that could flexibly deal with nonlinear regulation effects. The asymptotic properties of the proposed method are established and simulation studies are performed to validate the proposed approach. An application example for identifying the nonlinear dynamic GRN of T-cell activation is used to illustrate the usefulness of the proposed method.

  11. Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling

    PubMed Central

    Wu, Hulin; Lu, Tao; Xue, Hongqi; Liang, Hua

    2014-01-01

    Summary The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. High-dimensional linear ODE models have been proposed to identify GRNs, but with a limitation of the linear regulation effect assumption. In this article, we propose a sparse additive ODE (SA-ODE) model, coupled with ODE estimation methods and adaptive group LASSO techniques, to model dynamic GRNs that could flexibly deal with nonlinear regulation effects. The asymptotic properties of the proposed method are established and simulation studies are performed to validate the proposed approach. An application example for identifying the nonlinear dynamic GRN of T-cell activation is used to illustrate the usefulness of the proposed method. PMID:25061254

  12. Parametrically Guided Generalized Additive Models with Application to Mergers and Acquisitions Data.

    PubMed

    Fan, Jianqing; Maity, Arnab; Wang, Yihui; Wu, Yichao

    2013-01-01

    Generalized nonparametric additive models present a flexible way to evaluate the effects of several covariates on a general outcome of interest via a link function. In this modeling framework, one assumes that the effect of each of the covariates is nonparametric and additive. However, in practice, often there is prior information available about the shape of the regression functions, possibly from pilot studies or exploratory analysis. In this paper, we consider such situations and propose an estimation procedure where the prior information is used as a parametric guide to fit the additive model. Specifically, we first posit a parametric family for each of the regression functions using the prior information (parametric guides). After removing these parametric trends, we then estimate the remainder of the nonparametric functions using a nonparametric generalized additive model, and form the final estimates by adding back the parametric trend. We investigate the asymptotic properties of the estimates and show that when a good guide is chosen, the asymptotic variance of the estimates can be reduced significantly while keeping the asymptotic variance same as the unguided estimator. We observe the performance of our method via a simulation study and demonstrate our method by applying to a real data set on mergers and acquisitions.

  13. Representational Flexibility and Problem-Solving Ability in Fraction and Decimal Number Addition: A Structural Model

    ERIC Educational Resources Information Center

    Deliyianni, Eleni; Gagatsis, Athanasios; Elia, Iliada; Panaoura, Areti

    2016-01-01

    The aim of this study was to propose and validate a structural model in fraction and decimal number addition, which is founded primarily on a synthesis of major theoretical approaches in the field of representations in Mathematics and also on previous research on the learning of fractions and decimals. The study was conducted among 1,701 primary…

  14. Measuring Children's Proportional Reasoning, The "Tendency" for an Additive Strategy and The Effect of Models

    ERIC Educational Resources Information Center

    Misailadou, Christina; Williams, Julian

    2003-01-01

    We report a study of 10-14 year old children's use of additive strategies while solving ratio and proportion tasks. Rasch methodology was used to develop a diagnostic instrument that reveals children's misconceptions. Two versions of this instrument, one with "models" thought to facilitate proportional reasoning and one without were…

  15. Parametrically Guided Generalized Additive Models with Application to Mergers and Acquisitions Data

    PubMed Central

    Fan, Jianqing; Maity, Arnab; Wang, Yihui; Wu, Yichao

    2012-01-01

    Generalized nonparametric additive models present a flexible way to evaluate the effects of several covariates on a general outcome of interest via a link function. In this modeling framework, one assumes that the effect of each of the covariates is nonparametric and additive. However, in practice, often there is prior information available about the shape of the regression functions, possibly from pilot studies or exploratory analysis. In this paper, we consider such situations and propose an estimation procedure where the prior information is used as a parametric guide to fit the additive model. Specifically, we first posit a parametric family for each of the regression functions using the prior information (parametric guides). After removing these parametric trends, we then estimate the remainder of the nonparametric functions using a nonparametric generalized additive model, and form the final estimates by adding back the parametric trend. We investigate the asymptotic properties of the estimates and show that when a good guide is chosen, the asymptotic variance of the estimates can be reduced significantly while keeping the asymptotic variance same as the unguided estimator. We observe the performance of our method via a simulation study and demonstrate our method by applying to a real data set on mergers and acquisitions. PMID:23645976

  16. Present-day genetic structure of Atlantic salmon (Salmo salar) in Icelandic rivers and ice-cap retreat models.

    PubMed

    Olafsson, Kristinn; Pampoulie, Christophe; Hjorleifsdottir, Sigridur; Gudjonsson, Sigurdur; Hreggvidsson, Gudmundur O

    2014-01-01

    Due to an improved understanding of past climatological conditions, it has now become possible to study the potential concordance between former climatological models and present-day genetic structure. Genetic variability was assessed in 26 samples from different rivers of Atlantic salmon in Iceland (total of 2,352 individuals), using 15 microsatellite loci. F-statistics revealed significant differences between the majority of the populations that were sampled. Bayesian cluster analyses using both prior information and no prior information on sampling location revealed the presence of two distinguishable genetic pools - namely, the Northern (Group 1) and Southern (Group 2) regions of Iceland. Furthermore, the random permutation of different allele sizes among allelic states revealed a significant mutational component to the genetic differentiation at four microsatellite loci (SsaD144, Ssa171, SSsp2201 and SsaF3), and supported the proposition of a historical origin behind the observed variation. The estimated time of divergence, using two different ABC methods, suggested that the observed genetic pattern originated from between the Last Glacial Maximum to the Younger Dryas, which serves as additional evidence of the relative immaturity of Icelandic fish populations, on account of the re-colonisation of this young environment following the Last Glacial Maximum. Additional analyses suggested the presence of several genetic entities which were likely to originate from the original groups detected.

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

    ERIC Educational Resources Information Center

    Wendell, Douglas L.; Pickard, Dawn

    2007-01-01

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

  18. Does the model of additive effect in placebo research still hold true? A narrative review

    PubMed Central

    Berger, Bettina; Weger, Ulrich; Heusser, Peter

    2017-01-01

    Personalised and contextualised care has been turned into a major demand by people involved in healthcare suggesting to move toward person-centred medicine. The assessment of person-centred medicine can be most effectively achieved if treatments are investigated using ‘with versus without’ person-centredness or integrative study designs. However, this assumes that the components of an integrative or person-centred intervention have an additive relationship to produce the total effect. Beecher’s model of additivity assumes an additive relation between placebo and drug effects and is thus presenting an arithmetic summation. So far, no review has been carried out assessing the validity of the additive model, which is to be questioned and more closely investigated in this review. Initial searches for primary studies were undertaken in July 2016 using Pubmed and Google Scholar. In order to find matching publications of similar magnitude for the comparison part of this review, corresponding matches for all included reviews were sought. A total of 22 reviews and 3 clinical and experimental studies fulfilled the inclusion criteria. The results pointed to the following factors actively questioning the additive model: interactions of various effects, trial design, conditioning, context effects and factors, neurobiological factors, mechanism of action, statistical factors, intervention-specific factors (alcohol, caffeine), side-effects and type of intervention. All but one of the closely assessed publications was questioning the additive model. A closer examination of study design is necessary. An attempt in a more systematic approach geared towards solutions could be a suggestion for future research in this field. PMID:28321318

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

    SciTech Connect

    Rogers, Adam; Fiege, Jason D.

    2011-02-01

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

  20. General Methods for Evolutionary Quantitative Genetic Inference from Generalized Mixed Models

    PubMed Central

    de Villemereuil, Pierre; Schielzeth, Holger; Nakagawa, Shinichi; Morrissey, Michael

    2016-01-01

    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. PMID:27591750

  1. General Methods for Evolutionary Quantitative Genetic Inference from Generalized Mixed Models.

    PubMed

    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.

  2. Formation and reduction of carcinogenic furan in various model systems containing food additives.

    PubMed

    Kim, Jin-Sil; Her, Jae-Young; Lee, Kwang-Geun

    2015-12-15

    The aim of this study was to analyse and reduce furan in various model systems. Furan model systems consisting of monosaccharides (0.5M glucose and ribose), amino acids (0.5M alanine and serine) and/or 1.0M ascorbic acid were heated at 121°C for 25 min. The effects of food additives (each 0.1M) such as metal ions (iron sulphate, magnesium sulphate, zinc sulphate and calcium sulphate), antioxidants (BHT and BHA), and sodium sulphite on the formation of furan were measured. The level of furan formed in the model systems was 6.8-527.3 ng/ml. The level of furan in the model systems of glucose/serine and glucose/alanine increased 7-674% when food additives were added. In contrast, the level of furan decreased by 18-51% in the Maillard reaction model systems that included ribose and alanine/serine with food additives except zinc sulphate.

  3. Modeling Longitudinal Data with Generalized Additive Models: Applications to Single-Case Designs

    ERIC Educational Resources Information Center

    Sullivan, Kristynn J.; Shadish, William R.

    2013-01-01

    Single case designs (SCDs) are short time series that assess intervention effects by measuring units repeatedly over time both in the presence and absence of treatment. For a variety of reasons, interest in the statistical analysis and meta-analysis of these designs has been growing in recent years. This paper proposes modeling SCD data with…

  4. A study on the minimum number of loci required for genetic evaluation using a finite locus model

    PubMed Central

    Totir, Liviu R; Fernando, Rohan L; Dekkers, Jack CM; Fernández, Soledad A

    2004-01-01

    For a finite locus model, Markov chain Monte Carlo (MCMC) methods can be used to estimate the conditional mean of genotypic values given phenotypes, which is also known as the best predictor (BP). When computationally feasible, this type of genetic prediction provides an elegant solution to the problem of genetic evaluation under non-additive inheritance, especially for crossbred data. Successful application of MCMC methods for genetic evaluation using finite locus models depends, among other factors, on the number of loci assumed in the model. The effect of the assumed number of loci on evaluations obtained by BP was investigated using data simulated with about 100 loci. For several small pedigrees, genetic evaluations obtained by best linear prediction (BLP) were compared to genetic evaluations obtained by BP. For BLP evaluation, used here as the standard of comparison, only the first and second moments of the joint distribution of the genotypic and phenotypic values must be known. These moments were calculated from the gene frequencies and genotypic effects used in the simulation model. BP evaluation requires the complete distribution to be known. For each model used for BP evaluation, the gene frequencies and genotypic effects, which completely specify the required distribution, were derived such that the genotypic mean, the additive variance, and the dominance variance were the same as in the simulation model. For lowly heritable traits, evaluations obtained by BP under models with up to three loci closely matched the evaluations obtained by BLP for both purebred and crossbred data. For highly heritable traits, models with up to six loci were needed to match the evaluations obtained by BLP. PMID:15231231

  5. NB-PLC channel modelling with cyclostationary noise addition & OFDM implementation for smart grid

    NASA Astrophysics Data System (ADS)

    Thomas, Togis; Gupta, K. K.

    2016-03-01

    Power line communication (PLC) technology can be a viable solution for the future ubiquitous networks because it provides a cheaper alternative to other wired technology currently being used for communication. In smart grid Power Line Communication (PLC) is used to support communication with low rate on low voltage (LV) distribution network. In this paper, we propose the channel modelling of narrowband (NB) PLC in the frequency range 5 KHz to 500 KHz by using ABCD parameter with cyclostationary noise addition. Behaviour of the channel was studied by the addition of 11KV/230V transformer, by varying load location and load. Bit error rate (BER) Vs signal to noise ratio SNR) was plotted for the proposed model by employing OFDM. Our simulation results based on the proposed channel model show an acceptable performance in terms of bit error rate versus signal to noise ratio, which enables communication required for smart grid applications.

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

    PubMed

    Livesley, John

    2008-02-01

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

  7. Three-dimensional inverse modelling of magnetic anomaly sources based on a genetic algorithm

    NASA Astrophysics Data System (ADS)

    Montesinos, Fuensanta G.; Blanco-Montenegro, Isabel; Arnoso, José

    2016-04-01

    We present a modelling method to estimate the 3-D geometry and location of homogeneously magnetized sources from magnetic anomaly data. As input information, the procedure needs the parameters defining the magnetization vector (intensity, inclination and declination) and the Earth's magnetic field direction. When these two vectors are expected to be different in direction, we propose to estimate the magnetization direction from the magnetic map. Then, using this information, we apply an inversion approach based on a genetic algorithm which finds the geometry of the sources by seeking the optimum solution from an initial population of models in successive iterations through an evolutionary process. The evolution consists of three genetic operators (selection, crossover and mutation), which act on each generation, and a smoothing operator, which looks for the best fit to the observed data and a solution consisting of plausible compact sources. The method allows the use of non-gridded, non-planar and inaccurate anomaly data and non-regular subsurface partitions. In addition, neither constraints for the depth to the top of the sources nor an initial model are necessary, although previous models can be incorporated into the process. We show the results of a test using two complex synthetic anomalies to demonstrate the efficiency of our inversion method. The application to real data is illustrated with aeromagnetic data of the volcanic island of Gran Canaria (Canary Islands).

  8. Molecular genetics and animal models in autistic disorder.

    PubMed

    Andres, Christian

    2002-01-01

    Autistic disorder is a behavioural syndrome beginning before the age of 3 years and lasting over the whole lifetime. It is characterised by impaired communication, impaired social interactions, and repetitive interests and behaviour. The prevalence is about 7/10,000 taking a restrictive definition and more than 1/500 with a broader definition, including all the pervasive developmental disorders. The importance of genetic factors has been highlighted by epidemiological studies showing that autistic disorder is one of the most genetic neuropsychiatric diseases. The relative risk of first relatives is about 100-fold higher than the risk in the normal population and the concordance in monozygotic twin is about 60%. Different strategies have been applied on the track of susceptibility genes. The systematic search of linked loci led to contradictory results, in part due to the heterogeneity of the clinical definitions, to the differences in the DNA markers, and to the different methods of analysis used. An oversimplification of the inferred model is probably also cause of our disappointment. More work is necessary to give a clearer picture. One region emerges more frequently: the long arm of chromosome 7. Several candidate genes have been studied and some gave indications of association: the Reelin gene and the Wnt2 gene. Cytogenetical abnormalities are frequent at 15q11-13, the region of the Angelman and Prader-Willi syndrome. Imprinting plays an important role in this region, no candidate gene has been identified in autism. Biochemical abnormalities have been found in the serotonin system. Association and linkage studies gave no consistent results with some serotonin receptors and in the transporter, although it seems interesting to go further in the biochemical characterisation of the serotonin transporter activity, particularly in platelets, easily accessible. Two monogenic diseases have been associated with autistic disorder: tuberous sclerosis and fragile X. A

  9. [Approach to depressogenic genes from genetic analyses of animal models].

    PubMed

    Yoshikawa, Takeo

    2004-01-01

    Human depression or mood disorder is defined as a complex disease, making positional cloning of susceptibility genes a formidable task. We have undertaken genetic analyses of three different animal models for depression, comparing our results with advanced database resources. We first performed quantitative trait loci (QTL) analysis on two mouse models of "despair", namely, the forced swim test (FST) and tail suspension test (TST), and detected multiple chromosomal loci that control immobility time in these tests. Since one QTL detected on mouse chromosome 11 harbors the GABA A receptor subunit genes, we tested these genes for association in human mood disorder patients. We obtained significant associations of the alpha 1 and alpha 6 subunit genes with the disease, particularly in females. This result was striking, because we had previously detected an epistatic interaction between mouse chromosomes 11 and X that regulates immobility time in these animals. Next, we performed genome-wide expression analyses using a rat model of depression, learned helplessness (LH). We found that in the frontal cortex of LH rats, a disease implicated region, the LIM kinase 1 gene (Limk 1) showed greatest alteration, in this case down-regulation. By combining data from the QTL analysis of FST/TST and DNA microarray analysis of mouse frontal cortex, we identified adenylyl cyclase-associated CAP protein 1 (Cap 1) as another candidate gene for depression susceptibility. Both Limk 1 and Cap 1 are key players in the modulation of actin G-F conversion. In summary, our current study using animal models suggests disturbances of GABAergic neurotransmission and actin turnover as potential pathophysiologies for mood disorder.

  10. Genetic regulatory network models of biological clocks: evolutionary history matters.

    PubMed

    Knabe, Johannes F; Nehaniv, Chrystopher L; Schilstra, Maria J

    2008-01-01

    We study the evolvability and dynamics of artificial genetic regulatory networks (GRNs), as active control systems, realizing simple models of biological clocks that have evolved to respond to periodic environmental stimuli of various kinds with appropriate periodic behaviors. GRN models may differ in the evolvability of expressive regulatory dynamics. A new class of artificial GRNs with an evolvable number of complex cis-regulatory control sites--each involving a finite number of inhibitory and activatory binding factors--is introduced, allowing realization of complex regulatory logic. Previous work on biological clocks in nature has noted the capacity of clocks to oscillate in the absence of environmental stimuli, putting forth several candidate explanations for their observed behavior, related to anticipation of environmental conditions, compartmentation of activities in time, and robustness to perturbations of various kinds or to unselected accidents of neutral selection. Several of these hypotheses are explored by evolving GRNs with and without (Gaussian) noise and blackout periods for environmental stimulation. Robustness to certain types of perturbation appears to account for some, but not all, dynamical properties of the evolved networks. Unselected abilities, also observed for biological clocks, include the capacity to adapt to change in wavelength of environmental stimulus and to clock resetting.

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

    NASA Astrophysics Data System (ADS)

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

    2007-01-01

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

  12. Hierarchical linear modeling of longitudinal pedigree data for genetic association analysis.

    PubMed

    Tan, Qihua; B Hjelmborg, Jacob V; Thomassen, Mads; Jensen, Andreas Kryger; Christiansen, Lene; Christensen, Kaare; Zhao, Jing Hua; Kruse, Torben A

    2014-01-01

    Genetic association analysis on complex phenotypes under a longitudinal design involving pedigrees encounters the problem of correlation within pedigrees, which could affect statistical assessment of the genetic effects. Approaches have been proposed to integrate kinship correlation into the mixed-effect models to explicitly model the genetic relationship. These have proved to be an efficient way of dealing with sample clustering in pedigree data. Although current algorithms implemented in popular statistical packages are useful for adjusting relatedness in the mixed modeling of genetic effects on the mean level of a phenotype, they are not sufficiently straightforward to handle the kinship correlation on the time-dependent trajectories of a phenotype. We introduce a 2-level hierarchical linear model to separately assess the genetic associations with the mean level and the rate of change of a phenotype, integrating kinship correlation in the analysis. We apply our method to the Genetic Analysis Workshop 18 genome-wide association studies data on chromosome 3 to estimate the genetic effects on systolic blood pressure measured over time in large pedigrees. Our method identifies genetic variants associated with blood pressure with estimated inflation factors of 0.99, suggesting that our modeling of random effects efficiently handles the genetic relatedness in pedigrees. Application to simulated data captures important variants specified in the simulation. Our results show that the method is useful for genetic association studies in related samples using longitudinal design.

  13. Description and validation of a dynamical systems model of presynaptic serotonin function: genetic variation, brain activation and impulsivity.

    PubMed

    Stoltenberg, Scott F; Nag, Parthasarathi

    2010-03-01

    Despite more than a decade of empirical work on the role of genetic polymorphisms in the serotonin system on behavior, the details across levels of analysis are not well understood. We describe a mathematical model of the genetic control of presynaptic serotonergic function that is based on control theory, implemented using systems of differential equations, and focused on better characterizing pathways from genes to behavior. We present the results of model validation tests that include the comparison of simulation outcomes with empirical data on genetic effects on brain response to affective stimuli and on impulsivity. Patterns of simulated neural firing were consistent with recent findings of additive effects of serotonin transporter and tryptophan hydroxylase-2 polymorphisms on brain activation. In addition, simulated levels of cerebral spinal fluid 5-hydroxyindoleacetic acid (CSF 5-HIAA) were negatively correlated with Barratt Impulsiveness Scale (Version 11) Total scores in college students (r = -.22, p = .002, N = 187), which is consistent with the well-established negative correlation between CSF 5-HIAA and impulsivity. The results of the validation tests suggest that the model captures important aspects of the genetic control of presynaptic serotonergic function and behavior via brain activation. The proposed model can be: (1) extended to include other system components, neurotransmitter systems, behaviors and environmental influences; (2) used to generate testable hypotheses.

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

    PubMed

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

    2015-10-01

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

  15. Predicting the occurrence of wildfires with binary structured additive regression models.

    PubMed

    Ríos-Pena, Laura; Kneib, Thomas; Cadarso-Suárez, Carmen; Marey-Pérez, Manuel

    2017-02-01

    Wildfires are one of the main environmental problems facing societies today, and in the case of Galicia (north-west Spain), they are the main cause of forest destruction. This paper used binary structured additive regression (STAR) for modelling the occurrence of wildfires in Galicia. Binary STAR models are a recent contribution to the classical logistic regression and binary generalized additive models. Their main advantage lies in their flexibility for modelling non-linear effects, while simultaneously incorporating spatial and temporal variables directly, thereby making it possible to reveal possible relationships among the variables considered. The results showed that the occurrence of wildfires depends on many covariates which display variable behaviour across space and time, and which largely determine the likelihood of ignition of a fire. The joint possibility of working on spatial scales with a resolution of 1 × 1 km cells and mapping predictions in a colour range makes STAR models a useful tool for plotting and predicting wildfire occurrence. Lastly, it will facilitate the development of fire behaviour models, which can be invaluable when it comes to drawing up fire-prevention and firefighting plans.

  16. Combining hydrodynamic modelling with genetics: Can passive larval drift shape the genetic structure of Baltic Mytilus populations?

    PubMed

    Stuckas, Heiko; Knöbel, Loreen; Schade, Hanna; Breusing, Corinna; Hinrichsen, Hans-Harald; Bartel, Manuela; Langguth, Klaudia; Melzner, Frank

    2017-02-26

    While secondary contact between Mytilus edulis and M. trossulus in North America results in mosaic hybrid zone formation, both species form a hybrid swarm in the Baltic. Despite pervasive gene flow, Baltic Mytilus species maintain substantial genetic and phenotypic differentiation. Exploring mechanisms underlying the contrasting genetic composition in Baltic Mytilus species will allow insights into processes such as speciation or adaptation to extremely low salinity. Previous studies in the Baltic indicated that only weak interspecific reproductive barriers exist and discussed the putative role of adaptation to environmental conditions. Using a combination of hydrodynamic modelling and multilocus genotyping we investigate how oceanographic conditions influence passive larval dispersal and hybrid swarm formation in the Baltic. By combining our analyses with previous knowledge we show a genetic transition of Baltic Mytilus species along longitude 12°-13°E, i.e. a virtual line between Malmö (Sweden) and Stralsund (Germany). Although larval transport only occurs over short distances (10-30 km), limited larval dispersal could not explain the position of this genetic transition zone. Instead, the genetic transition zone is located at the area of maximum salinity change (15 to 10 psu). Thus, we argue that selection results in weak reproductive barriers and local adaptation. This scenario could maintain genetic and phenotypic differences between Baltic Mytilus species despite pervasive introgressive hybridization. This article is protected by copyright. All rights reserved.

  17. The effect of tailor-made additives on crystal growth of methyl paraben: Experiments and modelling

    NASA Astrophysics Data System (ADS)

    Cai, Zhihui; Liu, Yong; Song, Yang; Guan, Guoqiang; Jiang, Yanbin

    2017-03-01

    In this study, methyl paraben (MP) was selected as the model component, and acetaminophen (APAP), p-methyl acetanilide (PMAA) and acetanilide (ACET), which share the similar molecular structure as MP, were selected as the three tailor-made additives to study the effect of tailor-made additives on the crystal growth of MP. HPLC results indicated that the MP crystals induced by the three additives contained MP only. Photographs of the single crystals prepared indicated that the morphology of the MP crystals was greatly changed by the additives, but PXRD and single crystal diffraction results illustrated that the MP crystals were the same polymorph only with different crystal habits, and no new crystal form was found compared with other references. To investigate the effect of the additives on the crystal growth, the interaction between additives and facets was discussed in detail using the DFT methods and MD simulations. The results showed that APAP, PMAA and ACET would be selectively adsorbed on the growth surfaces of the crystal facets, which induced the change in MP crystal habits.

  18. Are mouse models of human mycobacterial diseases relevant? Genetics says: ‘yes!’

    PubMed Central

    Apt, Alexander S

    2011-01-01

    Relevance and accuracy of experimental mouse models of tuberculosis (TB) are the subject of constant debate. This article briefly reviews genetic aspects of this problem and provides a few examples of mycobacterial diseases with similar or identical genetic control in mice and humans. The two species display more similarities than differences regarding both genetics of susceptibility/severity of mycobacterial diseases and the networks of protective and pathological immune reactions. In the opinion of the author, refined mouse models of mycobacterial diseases are extremely useful for modelling the corresponding human conditions, if genetic diversity is taken into account. PMID:21896006

  19. Estimates of genetic parameters for total milk yield over multiple ages in Brazilian Murrah buffaloes using different models.

    PubMed

    Sesana, R C; Baldi, F; Borquis, R R A; Bignardi, A B; Hurtado-Lugo, N A; El Faro, L; Albuquerque, L G; Tonhati, H

    2014-04-14

    The objective of this study was to estimate variance components and genetic parameters for accumulated 305-day milk yield (MY305) over multiple ages, from 24 to 120 months of age, applying random regression (RRM), repeatability (REP) and multi-trait (MT) models. A total of 4472 lactation records from 1882 buffaloes of the Murrah breed were utilized. The contemporary group (herd-year-calving season) and number of milkings (two levels) were considered as fixed effects in all models. For REP and RRM, additive genetic, permanent environmental and residual effects were included as random effects. MT considered the same random effects as did REP and RRM with the exception of permanent environmental effect. Residual variances were modeled by a step function with 1, 4, and 6 classes. The heritabilities estimated with RRM increased with age, ranging from 0.19 to 0.34, and were slightly higher than that obtained with the REP model. For the MT model, heritability estimates ranged from 0.20 (37 months of age) to 0.32 (94 months of age). The genetic correlation estimates for MY305 obtained by RRM (L23.res4) and MT models were very similar, and varied from 0.77 to 0.99 and from 0.77 to 0.99, respectively. The rank correlation between breeding values for MY305 at different ages predicted by REP, MT, and RRM were high. It seems that a linear and quadratic Legendre polynomial to model the additive genetic and animal permanent environmental effects, respectively, may be sufficient to explain more parsimoniously the changes in MY305 genetic variation with age.

  20. Regulatory network reconstruction using an integral additive model with flexible kernel functions

    PubMed Central

    Novikov, Eugene; Barillot, Emmanuel

    2008-01-01

    Background Reconstruction of regulatory networks is one of the most challenging tasks of systems biology. A limited amount of experimental data and little prior knowledge make the problem difficult to solve. Although models that are currently used for inferring regulatory networks are sometimes able to make useful predictions about the structures and mechanisms of molecular interactions, there is still a strong demand to develop increasingly universal and accurate approaches for network reconstruction. Results The additive regulation model is represented by a set of differential equations and is frequently used for network inference from time series data. Here we generalize this model by converting differential equations into integral equations with adjustable kernel functions. These kernel functions can be selected based on prior knowledge or defined through iterative improvement in data analysis. This makes the integral model very flexible and thus capable of covering a broad range of biological systems more adequately and specifically than previous models. Conclusion We reconstructed network structures from artificial and real experimental data using differential and integral inference models. The artificial data were simulated using mathematical models implemented in JDesigner. The real data were publicly available yeast cell cycle microarray time series. The integral model outperformed the differential one for all cases. In the integral model, we tested the zero-degree polynomial and single exponential kernels. Further improvements could be expected if the kernel were selected more specifically depending on the system. PMID:18218091

  1. A new explained-variance based genetic risk score for predictive modeling of disease risk.

    PubMed

    Che, Ronglin; Motsinger-Reif, Alison A

    2012-09-25

    The goal of association mapping is to identify genetic variants that predict disease, and as the field of human genetics matures, the number of successful association studies is increasing. Many such studies have shown that for many diseases, risk is explained by a reasonably large number of variants that each explains a very small amount of disease risk. This is prompting the use of genetic risk scores in building predictive models, where information across several variants is combined for predictive modeling. In the current study, we compare the performance of four previously proposed genetic risk score methods and present a new method for constructing genetic risk score that incorporates explained variance information. The methods compared include: a simple count Genetic Risk Score, an odds ratio weighted Genetic Risk Score, a direct logistic regression Genetic Risk Score, a polygenic Genetic Risk Score, and the new explained variance weighted Genetic Risk Score. We compare the methods using a wide range of simulations in two steps, with a range of the number of deleterious single nucleotide polymorphisms (SNPs) explaining disease risk, genetic modes, baseline penetrances, sample sizes, relative risks (RR) and minor allele frequencies (MAF). Several measures of model performance were compared including overall power, C-statistic and Akaike's Information Criterion. Our results show the relative performance of methods differs significantly, with the new explained variance weighted GRS (EV-GRS) generally performing favorably to the other methods.

  2. A Genetically Engineered Mouse Model of Neuroblastoma Driven by Mutated ALK and MYCN

    DTIC Science & Technology

    2015-09-01

    AWARD NUMBER: W81XWH-13-1-0220 TITLE: A Genetically Engineered Mouse Model of Neuroblastoma Driven by Mutated ALK and MYCN PRINCIPAL...4. TITLE AND SUBTITLE A Genetically Engineered Mouse Model of Neuroblastoma Driven by Mutated ALK and MYCN 5a. CONTRACT NUMBER 5b. GRANT NUMBER... genetic and epigenetic changes that occur during tumorigenesis. 15. SUBJECT TERMS Anaplastic lymphoma kinase, neuroblastoma, ALK, ALKF1174L, MYCN, CDK7

  3. Placing Neuroanatomical Models of Executive Function in a Developmental Context Imaging and Imaging–Genetic Strategies

    PubMed Central

    Brocki, Karin; Fan, Jin; Fossella, John

    2009-01-01

    Children show gradual and protracted improvement in an array of behaviors involved in the conscious control of thought and emotion. Behavioral research has shown that these abilities, collectively referred to as executive functions (EF), can be dissociated into separable processes, such as inhibition and working memory. Furthermore, noninvasive neuroimaging shows that these component processes often rely on separable neural circuits involving areas of the frontal cortex and nuclei of the basal ganglia. As additional noninvasive methodologies become available, it is increasingly possible to continue to dissect and dissociate components of EF and also test predictions made by a number of theoretical neuroanatomical models. One method of late is genetics, which is noninvasive and readily used in concert with neuroimaging. The biological data obtained with neuroimaging and genetics is particularly able to inform neuroanatomical models that link specific brain systems with higher more abstract process models derived from purely behavioral work. As much progress in this area continues to occur, we seek to evaluate the age dependency and manner in which certain aspects of EF and certain anatomical circuits show changes and interactions as children develop. Some examples are taken from research on children with the developmental disability attention deficit hyperactivity disorder. A review of selected developmental research shows that current cognitive and neuroanatomical models of EF offer a great many system- and synaptic-level hypotheses that can be tested using imaging and imaging genetics in longitudinal and cross-sectional study designs. Here, we focus on age-related changes in inhibition and working memory. PMID:18591485

  4. Melanocortin MC₁ receptor in human genetics and model systems.

    PubMed

    Beaumont, Kimberley A; Wong, Shu S; Ainger, Stephen A; Liu, Yan Yan; Patel, Mira P; Millhauser, Glenn L; Smith, Jennifer J; Alewood, Paul F; Leonard, J Helen; Sturm, Richard A

    2011-06-11

    The melanocortin MC(1) receptor is a G-protein coupled receptor expressed in the melanocytes of the skin and hair and is known for its key role in the regulation of human pigmentation. Melanocortin MC(1) receptor activation after ultraviolet radiation exposure results in a switch from the red/yellow pheomelanin to the brown/black eumelanin pigment synthesis within cutaneous melanocytes; this pigment is then transferred to the surrounding keratinocytes of the skin. The increase in melanin maturation and uptake results in tanning of the skin, providing a physical protection of skin cells from ultraviolet radiation induced DNA damage. Melanocortin MC(1) receptor polymorphism is widespread within the Caucasian population and some variant alleles are associated with red hair colour, fair skin, poor tanning and increased risk of skin cancer. Here we will discuss the use of mouse coat colour models, human genetic association studies, and in vitro cell culture studies to determine the complex functions of the melanocortin MC(1) receptor and the molecular mechanisms underlying the association between melanocortin MC(1) receptor variant alleles and the red hair colour phenotype. Recent research indicates that melanocortin MC(1) receptor has many non-pigmentary functions, and that the increased risk of skin cancer conferred by melanocortin MC(1) receptor variant alleles is to some extent independent of pigmentation phenotypes. The use of new transgenic mouse models, the study of novel melanocortin MC(1) receptor response genes and the use of more advanced human skin models such as 3D skin reconstruction may provide key elements in understanding the pharmacogenetics of human melanocortin MC(1) receptor polymorphism.

  5. Test of the Additivity Principle for Current Fluctuations in a Model of Heat Conduction

    NASA Astrophysics Data System (ADS)

    Hurtado, Pablo I.; Garrido, Pedro L.

    2009-06-01

    The additivity principle allows to compute the current distribution in many one-dimensional (1D) nonequilibrium systems. Using simulations, we confirm this conjecture in the 1D Kipnis-Marchioro-Presutti model of heat conduction for a wide current interval. The current distribution shows both Gaussian and non-Gaussian regimes, and obeys the Gallavotti-Cohen fluctuation theorem. We verify the existence of a well-defined temperature profile associated to a given current fluctuation. This profile is independent of the sign of the current, and this symmetry extends to higher-order profiles and spatial correlations. We also show that finite-time joint fluctuations of the current and the profile are described by the additivity functional. These results suggest the additivity hypothesis as a general and powerful tool to compute current distributions in many nonequilibrium systems.

  6. Test of the additivity principle for current fluctuations in a model of heat conduction.

    PubMed

    Hurtado, Pablo I; Garrido, Pedro L

    2009-06-26

    The additivity principle allows to compute the current distribution in many one-dimensional (1D) nonequilibrium systems. Using simulations, we confirm this conjecture in the 1D Kipnis-Marchioro-Presutti model of heat conduction for a wide current interval. The current distribution shows both Gaussian and non-Gaussian regimes, and obeys the Gallavotti-Cohen fluctuation theorem. We verify the existence of a well-defined temperature profile associated to a given current fluctuation. This profile is independent of the sign of the current, and this symmetry extends to higher-order profiles and spatial correlations. We also show that finite-time joint fluctuations of the current and the profile are described by the additivity functional. These results suggest the additivity hypothesis as a general and powerful tool to compute current distributions in many nonequilibrium systems.

  7. Goodness-of-fit methods for additive-risk models in tumorigenicity experiments.

    PubMed

    Ghosh, Debashis

    2003-09-01

    In tumorigenicity experiments, a complication is that the time to event is generally not observed, so that the time to tumor is subject to interval censoring. One of the goals in these studies is to properly model the effect of dose on risk. Thus, it is important to have goodness of fit procedures available for assessing the model fit. While several estimation procedures have been developed for current-status data, relatively little work has been done on model-checking techniques. In this article, we propose numerical and graphical methods for the analysis of current-status data using the additive-risk model, primarily focusing on the situation where the monitoring times are dependent. The finite-sample properties of the proposed methodology are examined through numerical studies. The methods are then illustrated with data from a tumorigenicity experiment.

  8. Generalized Additive Mixed-Models for Pharmacology Using Integrated Discrete Multiple Organ Co-Culture.

    PubMed

    Ingersoll, Thomas; Cole, Stephanie; Madren-Whalley, Janna; Booker, Lamont; Dorsey, Russell; Li, Albert; Salem, Harry

    2016-01-01

    Integrated Discrete Multiple Organ Co-culture (IDMOC) is emerging as an in-vitro alternative to in-vivo animal models for pharmacology studies. IDMOC allows dose-response relationships to be investigated at the tissue and organoid levels, yet, these relationships often exhibit responses that are far more complex than the binary responses often measured in whole animals. To accommodate departure from binary endpoints, IDMOC requires an expansion of analytic techniques beyond simple linear probit and logistic models familiar in toxicology. IDMOC dose-responses may be measured at continuous scales, exhibit significant non-linearity such as local maxima or minima, and may include non-independent measures. Generalized additive mixed-modeling (GAMM) provides an alternative description of dose-response that relaxes assumptions of independence and linearity. We compared GAMMs to traditional linear models for describing dose-response in IDMOC pharmacology studies.

  9. Generalized Additive Mixed-Models for Pharmacology Using Integrated Discrete Multiple Organ Co-Culture

    PubMed Central

    Ingersoll, Thomas; Cole, Stephanie; Madren-Whalley, Janna; Booker, Lamont; Dorsey, Russell; Li, Albert; Salem, Harry

    2016-01-01

    Integrated Discrete Multiple Organ Co-culture (IDMOC) is emerging as an in-vitro alternative to in-vivo animal models for pharmacology studies. IDMOC allows dose-response relationships to be investigated at the tissue and organoid levels, yet, these relationships often exhibit responses that are far more complex than the binary responses often measured in whole animals. To accommodate departure from binary endpoints, IDMOC requires an expansion of analytic techniques beyond simple linear probit and logistic models familiar in toxicology. IDMOC dose-responses may be measured at continuous scales, exhibit significant non-linearity such as local maxima or minima, and may include non-independent measures. Generalized additive mixed-modeling (GAMM) provides an alternative description of dose-response that relaxes assumptions of independence and linearity. We compared GAMMs to traditional linear models for describing dose-response in IDMOC pharmacology studies. PMID:27110941

  10. Use of additive technologies for practical working with complex models for foundry technologies

    NASA Astrophysics Data System (ADS)

    Olkhovik, E.; Butsanets, A. A.; Ageeva, A. A.

    2016-07-01

    The article presents the results of research of additive technology (3D printing) application for developing a geometrically complex model of castings parts. Investment casting is well known and widely used technology for the production of complex parts. The work proposes the use of a 3D printing technology for manufacturing models parts, which are removed by thermal destruction. Traditional methods of equipment production for investment casting involve the use of manual labor which has problems with dimensional accuracy, and CNC technology which is less used. Such scheme is low productive and demands considerable time. We have offered an alternative method which consists in printing the main knots using a 3D printer (PLA and ABS) with a subsequent production of castings models from them. In this article, the main technological methods are considered and their problems are discussed. The dimensional accuracy of models in comparison with investment casting technology is considered as the main aspect.

  11. Evidence of thermal additivity during short laser pulses in an in vitro retinal model

    NASA Astrophysics Data System (ADS)

    Denton, Michael L.; Tijerina, Amanda J.; Dyer, Phillip N.; Oian, Chad A.; Noojin, Gary D.; Rickman, John M.; Shingledecker, Aurora D.; Clark, Clifton D.; Castellanos, Cherry C.; Thomas, Robert J.; Rockwell, Benjamin A.

    2015-03-01

    Laser damage thresholds were determined for exposure to 2.5-ms 532-nm pulses in an established in vitro retinal model. Single and multiple pulses (10, 100, 1000) were delivered to the cultured cells at three different pulse repetition frequency (PRF) values, and overt damage (membrane breach) was scored 1 hr post laser exposure. Trends in the damage data within and across the PRF range identified significant thermal additivity as PRF was increased, as evidenced by drastically reduced threshold values (< 40% of single-pulse value). Microthermography data that were collected in real time during each exposure also provided evidence of thermal additivity between successive laser pulses. Using thermal profiles simulated at high temporal resolution, damage threshold values were predicted by an in-house computational model. Our simulated ED50 value for a single 2.5-ms pulse was in very good agreement with experimental results, but ED50 predictions for multiple-pulse trains will require more refinement.

  12. Describing long-term trends in precipitation using generalized additive models

    NASA Astrophysics Data System (ADS)

    Underwood, Fiona M.

    2009-01-01

    SummaryWith the current concern over climate change, descriptions of how rainfall patterns are changing over time can be useful. Observations of daily rainfall data over the last few decades provide information on these trends. Generalized linear models are typically used to model patterns in the occurrence and intensity of rainfall. These models describe rainfall patterns for an average year but are more limited when describing long-term trends, particularly when these are potentially non-linear. Generalized additive models (GAMs) provide a framework for modelling non-linear relationships by fitting smooth functions to the data. This paper describes how GAMs can extend the flexibility of models to describe seasonal patterns and long-term trends in the occurrence and intensity of daily rainfall using data from Mauritius from 1962 to 2001. Smoothed estimates from the models provide useful graphical descriptions of changing rainfall patterns over the last 40 years at this location. GAMs are particularly helpful when exploring non-linear relationships in the data. Care is needed to ensure the choice of smooth functions is appropriate for the data and modelling objectives.

  13. Boosted structured additive regression for Escherichia coli fed-batch fermentation modeling.

    PubMed

    Melcher, Michael; Scharl, Theresa; Luchner, Markus; Striedner, Gerald; Leisch, Friedrich

    2017-02-01

    The quality of biopharmaceuticals and patients' safety are of highest priority and there are tremendous efforts to replace empirical production process designs by knowledge-based approaches. Main challenge in this context is that real-time access to process variables related to product quality and quantity is severely limited. To date comprehensive on- and offline monitoring platforms are used to generate process data sets that allow for development of mechanistic and/or data driven models for real-time prediction of these important quantities. Ultimate goal is to implement model based feed-back control loops that facilitate online control of product quality. In this contribution, we explore structured additive regression (STAR) models in combination with boosting as a variable selection tool for modeling the cell dry mass, product concentration, and optical density on the basis of online available process variables and two-dimensional fluorescence spectroscopic data. STAR models are powerful extensions of linear models allowing for inclusion of smooth effects or interactions between predictors. Boosting constructs the final model in a stepwise manner and provides a variable importance measure via predictor selection frequencies. Our results show that the cell dry mass can be modeled with a relative error of about ±3%, the optical density with ±6%, the soluble protein with ±16%, and the insoluble product with an accuracy of ±12%. Biotechnol. Bioeng. 2017;114: 321-334. © 2016 Wiley Periodicals, Inc.

  14. Physiological basis of tolerance to complete submergence in rice involves genetic factors in addition to the SUB1 gene

    PubMed Central

    Singh, Sudhanshu; Mackill, David J.; Ismail, Abdelbagi M.

    2014-01-01

    1 lines. This suggests the possibility of further improvements in submergence tolerance by incorporating additional traits present in FR13A or other similar landraces. PMID:25281725

  15. Physiological basis of tolerance to complete submergence in rice involves genetic factors in addition to the SUB1 gene.

    PubMed

    Singh, Sudhanshu; Mackill, David J; Ismail, Abdelbagi M

    2014-10-03

    1 lines. This suggests the possibility of further improvements in submergence tolerance by incorporating additional traits present in FR13A or other similar landraces.

  16. Addition of a 5/cm Spectral Resolution Band Model Option to LOWTRAN5.

    DTIC Science & Technology

    1980-10-01

    FORM I. REPORT NUMBER .GOVT ACCESSION NO. 3 . RECIPIENT’S CATALCI UMISER ARI-RR-232 -9 1 0. T Ct IIIM INNY S TYPE OF REPORT & PERIOD COVERED I ddition of...5r/TPAN (2) the addition of temperature dependent ecular absorption coefficients,’ and ( 3 ) the use of a multi-parameter, Dp 71pForentz band model for...LOWTRA.I5 and LOWTRAN5(IMOD) ..... 2-10 2.8 Comparison of LOWTRAN5 Models to Measurements 2-16 3 . MODIFICATIONS TO LOWTRAN5

  17. Model for Assembly Line Re-Balancing Considering Additional Capacity and Outsourcing to Face Demand Fluctuations

    NASA Astrophysics Data System (ADS)

    Samadhi, TMAA; Sumihartati, Atin

    2016-02-01

    The most critical stage in a garment industry is sewing process, because generally, it consists of a number of operations and a large number of sewing machines for each operation. Therefore, it requires a balancing method that can assign task to work station with balance workloads. Many studies on assembly line balancing assume a new assembly line, but in reality, due to demand fluctuation and demand increased a re-balancing is needed. To cope with those fluctuating demand changes, additional capacity can be carried out by investing in spare sewing machine and paying for sewing service through outsourcing. This study develops an assembly line balancing (ALB) model on existing line to cope with fluctuating demand change. Capacity redesign is decided if the fluctuation demand exceeds the available capacity through a combination of making investment on new machines and outsourcing while considering for minimizing the cost of idle capacity in the future. The objective of the model is to minimize the total cost of the line assembly that consists of operating costs, machine cost, adding capacity cost, losses cost due to idle capacity and outsourcing costs. The model develop is based on an integer programming model. The model is tested for a set of data of one year demand with the existing number of sewing machines of 41 units. The result shows that additional maximum capacity up to 76 units of machine required when there is an increase of 60% of the average demand, at the equal cost parameters..

  18. Patient-specific in vitro models for hemodynamic analysis of congenital heart disease - Additive manufacturing approach.

    PubMed

    Medero, Rafael; García-Rodríguez, Sylvana; François, Christopher J; Roldán-Alzate, Alejandro

    2017-03-21

    Non-invasive hemodynamic assessment of total cavopulmonary connection (TCPC) is challenging due to the complex anatomy. Additive manufacturing (AM) is a suitable alternative for creating patient-specific in vitro models for flow measurements using four-dimensional (4D) Flow MRI. These in vitro systems have the potential to serve as validation for computational fluid dynamics (CFD), simulating different physiological conditions. This study investigated three different AM technologies, stereolithography (SLA), selective laser sintering (SLS) and fused deposition modeling (FDM), to determine differences in hemodynamics when measuring flow using 4D Flow MRI. The models were created using patient-specific MRI data from an extracardiac TCPC. These models were connected to a perfusion pump circulating water at three different flow rates. Data was processed for visualization and quantification of velocity, flow distribution, vorticity and kinetic energy. These results were compared between each model. In addition, the flow distribution obtained in vitro was compared to in vivo. The results showed significant difference in velocities measured at the outlets of the models that required internal support material when printing. Furthermore, an ultrasound flow sensor was used to validate flow measurements at the inlets and outlets of the in vitro models. These results were highly correlated to those measured with 4D Flow MRI. This study showed that commercially available AM technologies can be used to create patient-specific vascular models for in vitro hemodynamic studies at reasonable costs. However, technologies that do not require internal supports during manufacturing allow smoother internal surfaces, which makes them better suited for flow analyses.

  19. Genetic Model Fitting in IQ, Assortative Mating & Components of IQ Variance.

    ERIC Educational Resources Information Center

    Capron, Christiane; Vetta, Adrian R.; Vetta, Atam

    1998-01-01

    The biometrical school of scientists who fit models to IQ data traces their intellectual ancestry to R. Fisher (1918), but their genetic models have no predictive value. Fisher himself was critical of the concept of heritability, because assortative mating, such as for IQ, introduces complexities into the study of a genetic trait. (SLD)

  20. Achieving World-Class Schools: Mastering School Improvement Using a Genetic Model.

    ERIC Educational Resources Information Center

    Kimmelman, Paul L.; Kroeze, David J.

    In providing its program for education reform, this book uses, as an analogy, the genetic model taken from the Human Genome project. In the first part, "Theoretical Underpinnings," the book explains why a genetic model can be used to improve school systems; describes the critical components of a world-class school system; and details the…

  1. Genetic analysis of calving traits by the multi-trait individual animal model.

    PubMed

    Weller, J I; Ezra, E

    2016-01-01

    Five alternative models were applied for analysis of dystocia and stillbirth in first and second parities. Models 1 and 2 were included only to estimate the parameters required for model 4, and models 3 and 5 are included only as comparisons to the model 4 estimates. Variance components were estimated by multi-trait REML, including cows with valid calving records for both parities. For the effects of sire of calf on first and second parities, variance components were estimated including only calvings with the same sire of calf for both parities. All heritabilities for the cow effect were quite low, but higher for dystocia than for stillbirth and higher in first parity. The sire-of-calf heritabilities were higher than the cow effect heritabilities, except for stillbirth in parity 2. Unlike the effect of cow correlations, all sire of calf correlations were >0.6, and the correlations for the same trait in parities 1 and 2 were >0.9. Thus, a multi-trait analysis should yield a significant gain in accuracy with respect to the sire of calf effects for bulls not mated to virgin heifers. A multi-trait individual animal model algorithm was developed for joint analysis of dystocia and stillbirth in first and second parities. Relationships matrices were included both for the effects of cow and sire of calf. In addition, random herd-year-season and fixed sex of calf effects were included in the model. Records were preadjusted for calving month and age. A total of 899,223 Israeli Holstein cows with first calvings since 1985 were included in the complete analysis. Approximate reliabilities were computed for both sire of cow and sire of calf effects. Correlations between these reliabilities and reliabilities obtained by direct inversion of the coefficient matrix for a sire of cow-sire of calf model were all close to 0.99. Phenotypic trends for cows born from 1983 through 2007 were economically unfavorable for dystocia and favorable for stillbirth in both parities. Genetic trends

  2. Estimating Modifying Effect of Age on Genetic and Environmental Variance Components in Twin Models

    PubMed Central

    He, Liang; Sillanpää, Mikko J.; Silventoinen, Karri; Kaprio, Jaakko; Pitkäniemi, Janne

    2016-01-01

    Twin studies have been adopted for decades to disentangle the relative genetic and environmental contributions for a wide range of traits. However, heritability estimation based on the classical twin models does not take into account dynamic behavior of the variance components over age. Varying variance of the genetic component over age can imply the existence of gene–environment (G × E) interactions that general genome-wide association studies (GWAS) fail to capture, which may lead to the inconsistency of heritability estimates between twin design and GWAS. Existing parametric G × E interaction models for twin studies are limited by assuming a linear or quadratic form of the variance curves with respect to a moderator that can, however, be overly restricted in reality. Here we propose spline-based approaches to explore the variance curves of the genetic and environmental components. We choose the additive genetic, common, and unique environmental variance components (ACE) model as the starting point. We treat the component variances as variance functions with respect to age modeled by B-splines or P-splines. We develop an empirical Bayes method to estimate the variance curves together with their confidence bands and provide an R package for public use. Our simulations demonstrate that the proposed methods accurately capture dynamic behavior of the component variances in terms of mean square errors with a data set of >10,000 twin pairs. Using the proposed methods as an alternative and major extension to the classical twin models, our analyses with a large-scale Finnish twin data set (19,510 MZ twins and 27,312 DZ same-sex twins) discover that the variances of the A, C, and E components for body mass index (BMI) change substantially across life span in different patterns and the heritability of BMI drops to ∼50% after middle age. The results further indicate that the decline of heritability is due to increasing unique environmental variance, which provides

  3. Use of anatomical and kinetic models in the evaluation of human food additive safety.

    PubMed

    Roth, William L

    2005-09-22

    Toxicological testing in animals is relied upon as a surrogate for clinical testing of most food additives. Both animal and human clinical test results are generally available for direct additives when high levels of exposure are expected. Limited animal studies or in vitro test results may be the only sources of toxicological data available when low levels of exposure (microg/person/day) are expected and where no effects of the additive on the food itself are desired. Safety assessment of such materials for humans requires mathematical extrapolation from any effects observed in test animals to arrive at acceptable daily intakes (ADIs) for humans. Models of anatomy may be used to estimate tissue and organ weights where that information is missing and necessary for evaluation of a data set. The effect of growth on target tissue exposure during critical phases of organ development can be more accurately assessed when models of growth and known physiological changes are combined with pharmacokinetic results for test species. Kinetic models, when combined with limited chemical property, kinetic, and distribution data, can often be used to predict steady-state plasma and tissue levels of a test material over the range of doses employed in chronic studies to aid in interpretation of effects that are often nonlinear with respect to delivered dose. A better understanding of the reasons for nonlinearity of effects in animals improves our confidence in extrapolation to humans.

  4. Rain water transport and storage in a model sandy soil with hydrogel particle additives.

    PubMed

    Wei, Y; Durian, D J

    2014-10-01

    We study rain water infiltration and drainage in a dry model sandy soil with superabsorbent hydrogel particle additives by measuring the mass of retained water for non-ponding rainfall using a self-built 3D laboratory set-up. In the pure model sandy soil, the retained water curve measurements indicate that instead of a stable horizontal wetting front that grows downward uniformly, a narrow fingered flow forms under the top layer of water-saturated soil. This rain water channelization phenomenon not only further reduces the available rain water in the plant root zone, but also affects the efficiency of soil additives, such as superabsorbent hydrogel particles. Our studies show that the shape of the retained water curve for a soil packing with hydrogel particle additives strongly depends on the location and the concentration of the hydrogel particles in the model sandy soil. By carefully choosing the particle size and distribution methods, we may use the swollen hydrogel particles to modify the soil pore structure, to clog or extend the water channels in sandy soils, or to build water reservoirs in the plant root zone.

  5. Genetic analysis of body weights of individually fed beef bulls in South Africa using random regression models.

    PubMed

    Selapa, N W; Nephawe, K A; Maiwashe, A; Norris, D

    2012-02-08

    The aim of this study was to estimate genetic parameters for body weights of individually fed beef bulls measured at centralized testing stations in South Africa using random regression models. Weekly body weights of Bonsmara bulls (N = 2919) tested between 1999 and 2003 were available for the analyses. The model included a fixed regression of the body weights on fourth-order orthogonal Legendre polynomials of the actual days on test (7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, and 84) for starting age and contemporary group effects. Random regressions on fourth-order orthogonal Legendre polynomials of the actual days on test were included for additive genetic effects and additional uncorrelated random effects of the weaning-herd-year and the permanent environment of the animal. Residual effects were assumed to be independently distributed with heterogeneous variance for each test day. Variance ratios for additive genetic, permanent environment and weaning-herd-year for weekly body weights at different test days ranged from 0.26 to 0.29, 0.37 to 0.44 and 0.26 to 0.34, respectively. The weaning-herd-year was found to have a significant effect on the variation of body weights of bulls despite a 28-day adjustment period. Genetic correlations amongst body weights at different test days were high, ranging from 0.89 to 1.00. Heritability estimates were comparable to literature using multivariate models. Therefore, random regression model could be applied in the genetic evaluation of body weight of individually fed beef bulls in South Africa.

  6. Can ligand addition to soil enhance Cd phytoextraction? A mechanistic model study.

    PubMed

    Lin, Zhongbing; Schneider, André; Nguyen, Christophe; Sterckeman, Thibault

    2014-11-01

    Phytoextraction is a potential method for cleaning Cd-polluted soils. Ligand addition to soil is expected to enhance Cd phytoextraction. However, experimental results show that this addition has contradictory effects on plant Cd uptake. A mechanistic model simulating the reaction kinetics (adsorption on solid phase, complexation in solution), transport (convection, diffusion) and root absorption (symplastic, apoplastic) of Cd and its complexes in soil was developed. This was used to calculate plant Cd uptake with and without ligand addition in a great number of combinations of soil, ligand and plant characteristics, varying the parameters within defined domains. Ligand addition generally strongly reduced hydrated Cd (Cd(2+)) concentration in soil solution through Cd complexation. Dissociation of Cd complex ([Formula: see text]) could not compensate for this reduction, which greatly lowered Cd(2+) symplastic uptake by roots. The apoplastic uptake of [Formula: see text] was not sufficient to compensate for the decrease in symplastic uptake. This explained why in the majority of the cases, ligand addition resulted in the reduction of the simulated Cd phytoextraction. A few results showed an enhanced phytoextraction in very particular conditions (strong plant transpiration with high apoplastic Cd uptake capacity), but this enhancement was very limited, making chelant-enhanced phytoextraction poorly efficient for Cd.

  7. Spectral prediction model for color prints on paper with fluorescent additives.

    PubMed

    Hersch, Roger David

    2008-12-20

    I propose a model for predicting the total reflectance of color halftones printed on paper incorporating fluorescent brighteners. The total reflectance is modeled as the additive superposition of the relative fluorescent emission and the pure reflectance of the color print. The fluorescent emission prediction model accounts for both the attenuation of light by the halftone within the excitation wavelength range and for the attenuation of the fluorescent emission by the same halftone within the emission wavelength range. The model's calibration relies on reflectance measurements of the optically brightened paper and of the solid colorant patches with two illuminants, one including and one excluding the UV components. The part of the model predicting the pure reflectance relies on an ink-spreading extended Clapper-Yule model. On uniformly distributed surface coverages of cyan, magenta, and yellow halftone patches, the proposed model predicts the relative fluorescent emission with a high accuracy (mean DeltaE(94)=0.42 under a D65 standard illuminant). For optically brightened paper exhibiting a moderate fluorescence, the total reflectance prediction improves the spectral reflectance prediction mainly for highlight color halftones, comprising a proportion of paper white above 12%. Applications include the creation of improved printer characterization tables for color management purposes and the prediction of color gamuts for new combinations of optically brightened papers and inks.

  8. Generalized linear and generalized additive models in studies of species distributions: Setting the scene

    USGS Publications Warehouse

    Guisan, A.; Edwards, T.C.; Hastie, T.

    2002-01-01

    An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001. We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling. ?? 2002 Elsevier Science B.V. All rights reserved.

  9. Rodent models of genetic contributions to motivation to abuse alcohol.

    PubMed

    Crabbe, John C

    2014-01-01

    In summary, there are remarkably few studies focused on the genetic contributions to alcohol's reinforcing values. Almost all such studies examine the two-bottle preference test. Despite the deficiencies I have raised in its interpretation, a rodent genotype's willingness to drink ethanol when water is freely available offers a reasonable aggregate estimate of alcohol's reinforcing value relative to other genotypes (Green and Grahame 2008). As indicated above, however, preference drinking studies will likely never avoid the confounding role of taste preferences and most often yield intake levels not sufficient to yield a pharmacologically significant BAL. Thus, the quest for improved measures of reinforcing value continues. Of the potential motivational factors considered by McClearn in his seminal review in this series, we can safely conclude that rodent alcohol drinking is not primarily directed at obtaining calories. The role of taste (and odor) remains a challenge. McClearn appears to have been correct that especially those genotypes that avoid alcohol are probably doing so based on preingestive sensory cues; however, postingestive consequences are also important. Cunningham's intragastric model shows the role of both preingestional and postingestional modulating factors for the best known examples, the usually nearly absolutely alcohol-avoiding DBA/2J and HAP-2 mice. Much subsequent data reinforce McClearn's earlier conclusion that C57BL/6J mice, at least, do not regulate their intake around a given self-administered dose of alcohol by adjusting their intake. This leaves us with the puzzle of why nearly all genotypes, even those directionally selectively bred for high voluntary intake for many generations, fail to self-administer intoxicating amounts of alcohol. Since McClearn's review, many ingenious assays to index alcohol's motivational effects have been used extensively, and new methods for inducing dependence have supplanted the older ones prevalent in

  10. Genetic evaluation of egg production curve in Thai native chickens by random regression and spline models.

    PubMed

    Mookprom, S; Boonkum, W; Kunhareang, S; Siripanya, S; Duangjinda, M

    2017-02-01

    The objective of this research is to investigate appropriate random regression models with various covariance functions, for the genetic evaluation of test-day egg production. Data included 7,884 monthly egg production records from 657 Thai native chickens (Pradu Hang Dam) that were obtained during the first to sixth generation and were born during 2007 to 2014 at the Research and Development Network Center for Animal Breeding (Native Chickens), Khon Kaen University. Average annual and monthly egg productions were 117 ± 41 and 10.20 ± 6.40 eggs, respectively. Nine random regression models were analyzed using the Wilmink function (WM), Koops and Grossman function (KG), Legendre polynomials functions with second, third, and fourth orders (LG2, LG3, LG4), and spline functions with 4, 5, 6, and 8 knots (SP4, SP5, SP6, and SP8). All covariance functions were nested within the same additive genetic and permanent environmental random effects, and the variance components were estimated by Restricted Maximum Likelihood (REML). In model comparisons, mean square error (MSE) and the coefficient of detemination (R(2)) calculated the goodness of fit; and the correlation between observed and predicted values [Formula: see text] was used to calculate the cross-validated predictive abilities. We found that the covariance functions of SP5, SP6, and SP8 proved appropriate for the genetic evaluation of the egg production curves for Thai native chickens. The estimated heritability of monthly egg production ranged from 0.07 to 0.39, and the highest heritability was found during the first to third months of egg production. In conclusion, the spline functions within monthly egg production can be applied to breeding programs for the improvement of both egg number and persistence of egg production.

  11. Resources allocation in healthcare for cancer: a case study using generalised additive mixed models.

    PubMed

    Musio, Monica; Sauleau, Erik A; Augustin, Nicole H

    2012-11-01

    Our aim is to develop a method for helping resources re-allocation in healthcare linked to cancer, in order to replan the allocation of providers. Ageing of the population has a considerable impact on the use of health resources because aged people require more specialised medical care due notably to cancer. We propose a method useful to monitor changes of cancer incidence in space and time taking into account two age categories, according to healthcar general organisation. We use generalised additive mixed models with a Poisson response, according to the methodology presented in Wood, Generalised additive models: an introduction with R. Chapman and Hall/CRC, 2006. Besides one-dimensional smooth functions accounting for non-linear effects of covariates, the space-time interaction can be modelled using scale invariant smoothers. Incidence data collected by a general cancer registry between 1992 and 2007 in a specific area of France is studied. Our best model exhibits a strong increase of the incidence of cancer along time and an obvious spatial pattern for people more than 70 years with a higher incidence in the central band of the region. This is a strong argument for re-allocating resources for old people cancer care in this sub-region.

  12. Short communication: Validation of two animal models for estimation of genetic trends for female fertility in Norwegian dairy cattle.

    PubMed

    Andersen-Ranberg, I M; Klemetsdal, G; Heringstad, B

    2003-12-01

    Two animal models were compared with respect to potential bias in genetic trend estimates for female fertility and for their predictive ability. In addition to either a fixed effect for month of first insemination or for month-year of first insemination, the models had fixed effects of age and double insemination and random effects of herd-year and animal. The model with a fixed effect of month of first insemination had a larger positive genetic trend for 56-d nonreturn rate in virgin heifers (0.16% yr), smaller downward bias, and somewhat higher predictive ability. These results demonstrate the importance of verifying models to be used in the calculation of breeding values.

  13. Quantum-chemical model evaluations of thermodynamics and kinetics of oxygen atom additions to narrow nanotubes.

    PubMed

    Slanina, Zdenĕk; Stobinski, Leszek; Tomasik, Piotr; Lin, Hong-Ming; Adamowicz, Ludwik

    2003-01-01

    This paper reports a computational study of oxygen additions to narrow nanotubes, a problem frequently studied with fullerenes. In fact, fullerene oxides were the first observed fullerene derivatives, and they have naturally attracted the attention of both experiment and theory. C60O had represented a long-standing case of experiment-theory disagreement, and there has been a similar problem with C60O2. The disagreement has been explained by kinetic rather than thermodynamic control. In this paper a similar computational approach is applied to narrow nanotubes. Recently, very narrow nanotubes have been observed with a diameter of 5 A and even with a diameter of 4 A. It has been supposed that the narrow nanotubes are closed by fragments of small fullerenes like C36 or C20. In this report we perform calculations for oxygen additions to such model nanotubes capped by fragments of D2d C36, D4d C32, and Ih C20 fullerenic cages (though the computational models have to be rather short). The three models have the following carbon contents: C84, C80, and C80. Both thermodynamic enthalpy changes and kinetic activation barriers for oxygen addition to six selected bonds are computed and analyzed. The lowest isomer (thermodynamically the most stable) is never of the 6/6 type, that is, the enthalpically favored structures are produced by oxygen additions to the nanotube tips. Interestingly enough, the lowest energy isomer has, for the D2d C36 and D4d C32 cases, the lowest kinetic activation barrier as well.

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

    PubMed Central

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

    2014-01-01

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

  15. DNA looping increases the range of bistability in a stochastic model of the lac genetic switch

    NASA Astrophysics Data System (ADS)

    Earnest, Tyler M.; Roberts, Elijah; Assaf, Michael; Dahmen, Karin; Luthey-Schulten, Zaida

    2013-04-01

    Conditions and parameters affecting the range of bistability of the lac genetic switch in Escherichia coli are examined for a model which includes DNA looping interactions with the lac repressor and a lactose analogue. This stochastic gene-mRNA-protein model of the lac switch describes DNA looping using a third transcriptional state. We exploit the fast bursting dynamics of mRNA by combining a novel geometric burst extension with the finite state projection method. This limits the number of protein/mRNA states, allowing for an accelerated search of the model's parameter space. We evaluate how the addition of the third state changes the bistability properties of the model and find a critical region of parameter space where the phenotypic switching occurs in a range seen in single molecule fluorescence studies. Stochastic simulations show induction in the looping model is preceded by a rare complete dissociation of the loop followed by an immediate burst of mRNA rather than a slower build up of mRNA as in the two-state model. The overall effect of the looped state is to allow for faster switching times while at the same time further differentiating the uninduced and induced phenotypes. Furthermore, the kinetic parameters are consistent with free energies derived from thermodynamic studies suggesting that this minimal model of DNA looping could have a broader range of application.

  16. DNA looping increases the range of bistability in a stochastic model of the lac genetic switch.

    PubMed

    Earnest, Tyler M; Roberts, Elijah; Assaf, Michael; Dahmen, Karin; Luthey-Schulten, Zaida

    2013-04-01

    Conditions and parameters affecting the range of bistability of the lac genetic switch in Escherichia coli are examined for a model which includes DNA looping interactions with the lac repressor and a lactose analogue. This stochastic gene-mRNA-protein model of the lac switch describes DNA looping using a third transcriptional state. We exploit the fast bursting dynamics of mRNA by combining a novel geometric burst extension with the finite state projection method. This limits the number of protein/mRNA states, allowing for an accelerated search of the model's parameter space. We evaluate how the addition of the third state changes the bistability properties of the model and find a critical region of parameter space where the phenotypic switching occurs in a range seen in single molecule fluorescence studies. Stochastic simulations show induction in the looping model is preceded by a rare complete dissociation of the loop followed by an immediate burst of mRNA rather than a slower build up of mRNA as in the two-state model. The overall effect of the looped state is to allow for faster switching times while at the same time further differentiating the uninduced and induced phenotypes. Furthermore, the kinetic parameters are consistent with free energies derived from thermodynamic studies suggesting that this minimal model of DNA looping could have a broader range of application.

  17. Broad bandwidth or high fidelity? Evidence from the structure of genetic and environmental effects on the facets of the five factor model.

    PubMed

    Briley, Daniel A; Tucker-Drob, Elliot M

    2012-09-01

    The Five Factor Model of personality is well-established at the phenotypic level, but much less is known about the coherence of the genetic and environmental influences within each personality domain. Univariate behavioral genetic analyses have consistently found the influence of additive genes and nonshared environment on multiple personality facets, but the extent to which genetic and environmental influences on specific facets reflect more general influences on higher order factors is less clear. We applied a multivariate quantitative-genetic approach to scores on the CPI-Big Five facets for 490 monozygotic and 317 dizygotic twins who took part in the National Merit Twin Study. Our results revealed a complex genetic structure for facets composing all five factors, with both domain-general and facet-specific genetic and environmental influences. For three of the Big Five domains, models that required common genetic and environmental influences on each facet to occur by way of effects on a higher order trait did not fit as well as models allowing for common genetic and environmental effects to act directly on the facets. These results add to the growing body of literature indicating that important variation in personality occurs at the facet level which may be overshadowed by aggregating to the trait level. Research at the facet level, rather than the factor level, is likely to have pragmatic advantages in future research on the genetics of personality.

  18. Broad Bandwidth or High Fidelity? Evidence from the Structure of Genetic and Environmental Effects on the Facets of the Five Factor Model

    PubMed Central

    Briley, Daniel A.; Tucker-Drob, Elliot M.

    2017-01-01

    The Five Factor Model (FFM) of personality is well-established at the phenotypic level, but much less is known about the coherence of the genetic and environmental influences within each personality domain. Univariate behavioral genetic analyses have consistently found the influence of additive genes and nonshared environment on multiple personality facets, but the extent to which genetic and environmental influences on specific facets reflect more general influences on higher order factors is less clear. We applied a multivariate quantitative-genetic approach to scores on the CPI-Big Five facets for 490 monozygotic and 317 dizygotic twins who took part in the National Merit Twin Study. Our results revealed a complex genetic structure for facets composing all five factors, with both domain-general and facet-specific genetic and environmental influences. Models that required common genetic and environmental influences on each facet to occur by way of effects on a higher order trait did not fit as well as models allowing for common genetic and environmental effects to act directly on the facets for three of the Big Five domains. These results add to the growing body of literature indicating that important variation in personality occurs at the facet level which may be overshadowed by aggregating to the trait level. Research at the facet level, rather than the factor level, is likely to have pragmatic advantages in future research on the genetics of personality. PMID:22695681

  19. [The discussion of the infiltrative model of mathematical knowledge to genetics teaching].

    PubMed

    Liu, Jun; Luo, Pei-Gao

    2011-11-01

    Genetics, the core course of biological field, is an importance major-basic course in curriculum of many majors related with biology. Due to strong theoretical and practical as well as abstract of genetics, it is too difficult to study on genetics for many students. At the same time, mathematics is one of the basic courses in curriculum of the major related natural science, which has close relationship with the establishment, development and modification of genetics. In this paper, to establish the intrinsic logistic relationship and construct the integral knowledge network and to help students improving the analytic, comprehensive and logistic abilities, we applied some mathematical infiltrative model genetic knowledge in genetics teaching, which could help students more deeply learn and understand genetic knowledge.

  20. Nephron Deficiency and Predisposition to Renal Injury in a Novel One-Kidney Genetic Model.

    PubMed

    Wang, Xuexiang; Johnson, Ashley C; Williams, Jan M; White, Tiffani; Chade, Alejandro R; Zhang, Jie; Liu, Ruisheng; Roman, Richard J; Lee, Jonathan W; Kyle, Patrick B; Solberg-Woods, Leah; Garrett, Michael R

    2015-07-01

    Some studies have reported up to 40% of patients born with a single kidney develop hypertension, proteinuria, and in some cases renal failure. The increased susceptibility to renal injury may be due, in part, to reduced nephron numbers. Notably, children who undergo nephrectomy or adults who serve as kidney donors exhibit little difference in renal function compared with persons who have two kidneys. However, the difference in risk between being born with a single kidney versus being born with two kidneys and then undergoing nephrectomy are unclear. Animal models used previously to investigate this question are not ideal because they require invasive methods to model congenital solitary kidney. In this study, we describe a new genetic animal model, the heterogeneous stock-derived model of unilateral renal agenesis (HSRA) rat, which demonstrates 50%-75% spontaneous incidence of a single kidney. The HSRA model is characterized by reduced nephron number (more than would be expected by loss of one kidney), early kidney/glomerular hypertrophy, and progressive renal injury, which culminates in reduced renal function. Long-term studies of temporal relationships among BP, renal hemodynamics, and renal function demonstrate that spontaneous single-kidney HSRA rats are more likely than uninephrectomized normal littermates to exhibit renal impairment because of the combination of reduced nephron numbers and prolonged exposure to renal compensatory mechanisms (i.e., hyperfiltration). Future studies with this novel animal model may provide additional insight into the genetic contributions to kidney development and agenesis and the factors influencing susceptibility to renal injury in individuals with congenital solitary kidney.

  1. Testing the genetic predictions of a biogeographical model in a dominant endemic Eastern Pacific coral (Porites panamensis) using a genetic seascape approach

    PubMed Central

    Saavedra-Sotelo, Nancy C; Calderon-Aguilera, Luis E; Reyes-Bonilla, Héctor; Paz-García, David A; López-Pérez, Ramón A; Cupul-Magaña, Amilcar; Cruz-Barraza, José A; Rocha-Olivares, Axayácatl

    2013-01-01

    The coral fauna of the Eastern Tropical Pacific (ETP) is depauperate and peripheral; hence, it has drawn attention to the factors allowing its survival. Here, we use a genetic seascape approach and ecological niche modeling to unravel the environmental factors correlating with the genetic variation of Porites panamensis, a hermatypic coral endemic to the ETP. Specifically, we test if levels of diversity and connectivity are higher among abundant than among depauperate populations, as expected by a geographically relaxed version of the Abundant Center Hypothesis (rel-ACH). Unlike the original ACH, referring to a geographical center of distribution of maximal abundance, the rel-ACH refers only to a center of maximum abundance, irrespective of its geographic position. The patterns of relative abundance of P. panamensis in the Mexican Pacific revealed that northern populations from Baja California represent its center of abundance; and southern depauperate populations along the continental margin are peripheral relative to it. Genetic patterns of diversity and structure of nuclear DNA sequences (ribosomal DNA and a single copy open reading frame) and five alloenzymatic loci partially agreed with rel-ACH predictions. We found higher diversity levels in peninsular populations and significant differentiation between peninsular and continental colonies. In addition, continental populations showed higher levels of differentiation and lower connectivity than peninsular populations in the absence of isolation by distance in each region. Some discrepancies with model expectations may relate to the influence of significant habitat discontinuities in the face of limited dispersal potential. Environmental data analyses and niche modeling allowed us to identify temperature, water clarity, and substrate availability as the main factors correlating with patterns of abundance, genetic diversity, and structure, which may hold the key to the survival of P. panamensis in the face of

  2. Reduction of carcinogenic 4(5)-methylimidazole in a caramel model system: influence of food additives.

    PubMed

    Seo, Seulgi; Ka, Mi-Hyun; Lee, Kwang-Geun

    2014-07-09

    The effect of various food additives on the formation of carcinogenic 4(5)-methylimidazole (4-MI) in a caramel model system was investigated. The relationship between the levels of 4-MI and various pyrazines was studied. When glucose and ammonium hydroxide were heated, the amount of 4-MI was 556 ± 1.3 μg/mL, which increased to 583 ± 2.6 μg/mL by the addition of 0.1 M of sodium sulfite. When various food additives, such as 0.1 M of iron sulfate, magnesium sulfate, zinc sulfate, tryptophan, and cysteine were added, the amount of 4-MI was reduced to 110 ± 0.7, 483 ± 2.0, 460 ± 2.0, 409 ± 4.4, and 397 ± 1.7 μg/mL, respectively. The greatest reduction, 80%, occurred with the addition of iron sulfate. Among the 12 pyrazines, 2-ethyl-6-methylpyrazine with 4-MI showed the highest correlation (r = -0.8239).

  3. Plasmodium falciparum genetic crosses in a humanized mouse model

    PubMed Central

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

    2015-01-01

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

  4. Marginal regression approach for additive hazards models with clustered current status data.

    PubMed

    Su, Pei-Fang; Chi, Yunchan

    2014-01-15

    Current status data arise naturally from tumorigenicity experiments, epidemiology studies, biomedicine, econometrics and demographic and sociology studies. Moreover, clustered current status data may occur with animals from the same litter in tumorigenicity experiments or with subjects from the same family in epidemiology studies. Because the only information extracted from current status data is whether the survival times are before or after the monitoring or censoring times, the nonparametric maximum likelihood estimator of survival function converges at a rate of n(1/3) to a complicated limiting distribution. Hence, semiparametric regression models such as the additive hazards model have been extended for independent current status data to derive the test statistics, whose distributions converge at a rate of n(1/2) , for testing the regression parameters. However, a straightforward application of these statistical methods to clustered current status data is not appropriate because intracluster correlation needs to be taken into account. Therefore, this paper proposes two estimating functions for estimating the parameters in the additive hazards model for clustered current status data. The comparative results from simulation studies are presented, and the application of the proposed estimating functions to one real data set is illustrated.

  5. Review of Pathological Hallmarks of Schizophrenia: Comparison of Genetic Models With Patients and Nongenetic Models

    PubMed Central

    Jaaro-Peled, Hanna; Ayhan, Yavuz; Pletnikov, Mikhail V.; Sawa, Akira

    2010-01-01

    Schizophrenia is a condition that impairs higher brain functions, some of which are specific to humans. After identification of susceptibility genes for schizophrenia, many efforts have been made to generate genetics-based models for the disease. It is under debate whether behavioral deficits observed in rodents are sufficient to characterize these models. Alternatively, anatomical and neuropathological changes identified in brains of patients with schizophrenia may be utilized as translatable characteristics between humans and rodents, which are important for validation of the models. Here, we overview such anatomical and neuropathological changes in humans: enlarged ventricles, dendritic changes in the pyramidal neurons, and alteration of specific subtypes of interneurons. In this review, we will overview such morphological changes in brains from patients with schizophrenia. Then, we will describe that some of these alterations are already recapitulated even in classic nongenetic models for schizophrenia. Finally, in comparison with the changes in patients and nongenetic models, we will discuss the anatomical and neuropathological manifestation in genetic models for schizophrenia. PMID:19903746

  6. Identification of Treatment Targets in a Genetic Mouse Model of Voluntary Methamphetamine Drinking.

    PubMed

    Phillips, T J; Mootz, J R K; Reed, C

    2016-01-01

    Methamphetamine has powerful stimulant and euphoric effects that are experienced as rewarding and encourage use. Methamphetamine addiction is associated with debilitating illnesses, destroyed relationships, child neglect, violence, and crime; but after many years of research, broadly effective medications have not been identified. Individual differences that may impact not only risk for developing a methamphetamine use disorder but also affect treatment response have not been fully considered. Human studies have identified candidate genes that may be relevant, but lack of control over drug history, the common use or coabuse of multiple addictive drugs, and restrictions on the types of data that can be collected in humans are barriers to progress. To overcome some of these issues, a genetic animal model comprised of lines of mice selectively bred for high and low voluntary methamphetamine intake was developed to identify risk and protective alleles for methamphetamine consumption, and identify therapeutic targets. The mu opioid receptor gene was supported as a target for genes within a top-ranked transcription factor network associated with level of methamphetamine intake. In addition, mice that consume high levels of methamphetamine were found to possess a nonfunctional form of the trace amine-associated receptor 1 (TAAR1). The Taar1 gene is within a mouse chromosome 10 quantitative trait locus for methamphetamine consumption, and TAAR1 function determines sensitivity to aversive effects of methamphetamine that may curb intake. The genes, gene interaction partners, and protein products identified in this genetic mouse model represent treatment target candidates for methamphetamine addiction.

  7. Genetic basis of hindlimb loss in a naturally occurring vertebrate model

    PubMed Central

    Don, Emily K.; de Jong-Curtain, Tanya A.; Doggett, Karen; Hall, Thomas E.; Heng, Benjamin; Badrock, Andrew P.; Winnick, Claire; Nicholson, Garth A.; Guillemin, Gilles J.; Currie, Peter D.; Hesselson, Daniel; Heath, Joan K.; Cole, Nicholas J.

    2016-01-01

    ABSTRACT Here we genetically characterise pelvic finless, a naturally occurring model of hindlimb loss in zebrafish that lacks pelvic fin structures, which are homologous to tetrapod hindlimbs, but displays no other abnormalities. Using a hybrid positional cloning and next generation sequencing approach, we identified mutations in the nuclear localisation signal (NLS) of T-box transcription factor 4 (Tbx4) that impair nuclear localisation of the protein, resulting in altered gene expression patterns during pelvic fin development and the failure of pelvic fin development. Using a TALEN-induced tbx4 knockout allele we confirm that mutations within the Tbx4 NLS (A78V; G79A) are sufficient to disrupt pelvic fin development. By combining histological, genetic, and cellular approaches we show that the hindlimb initiation gene tbx4 has an evolutionarily conserved, essential role in pelvic fin development. In addition, our novel viable model of hindlimb deficiency is likely to facilitate the elucidation of the detailed molecular mechanisms through which Tbx4 functions during pelvic fin and hindlimb development. PMID:26892237

  8. Analysis of Time to Event Outcomes in Randomized Controlled Trials by Generalized Additive Models

    PubMed Central

    Argyropoulos, Christos; Unruh, Mark L.

    2015-01-01

    Background Randomized Controlled Trials almost invariably utilize the hazard ratio calculated with a Cox proportional hazard model as a treatment efficacy measure. Despite the widespread adoption of HRs, these provide a limited understanding of the treatment effect and may even provide a biased estimate when the assumption of proportional hazards in the Cox model is not verified by the trial data. Additional treatment effect measures on the survival probability or the time scale may be used to supplement HRs but a framework for the simultaneous generation of these measures is lacking. Methods By splitting follow-up time at the nodes of a Gauss Lobatto numerical quadrature rule, techniques for Poisson Generalized Additive Models (PGAM) can be adopted for flexible hazard modeling. Straightforward simulation post-estimation transforms PGAM estimates for the log hazard into estimates of the survival function. These in turn were used to calculate relative and absolute risks or even differences in restricted mean survival time between treatment arms. We illustrate our approach with extensive simulations and in two trials: IPASS (in which the proportionality of hazards was violated) and HEMO a long duration study conducted under evolving standards of care on a heterogeneous patient population. Findings PGAM can generate estimates of the survival function and the hazard ratio that are essentially identical to those obtained by Kaplan Meier curve analysis and the Cox model. PGAMs can simultaneously provide multiple measures of treatment efficacy after a single data pass. Furthermore, supported unadjusted (overall treatment effect) but also subgroup and adjusted analyses, while incorporating multiple time scales and accounting for non-proportional hazards in survival data. Conclusions By augmenting the HR conventionally reported, PGAMs have the potential to support the inferential goals of multiple stakeholders involved in the evaluation and appraisal of clinical trial

  9. Random regression test day models to estimate genetic parameters for milk yield and milk components in Philippine dairy buffaloes.

    PubMed

    Flores, E B; van der Werf, J

    2015-08-01

    Heritabilities and genetic correlations for milk production traits were estimated from first-parity test day records on 1022 Philippine dairy buffalo cows. Traits analysed included milk (MY), fat (FY) and protein (PY) yields, and fat (Fat%) and protein (Prot%) concentrations. Varying orders of Legendre polynomials (Leg(m)) as well as the Wilmink function (Wil) were used in random regression models. These various models were compared based on log likelihood, Akaike's information criterion, Bayesian information criterion and genetic variance estimates. Six residual variance classes were sufficient for MY, FY, PY and Fat%, while seven residual classes for Prot%. Multivariate analysis gave higher estimates of genetic variance and heritability compared with univariate analysis for all traits. Heritability estimates ranged from 0.25 to 0.44, 0.13 to 0.31 and 0.21 to 0.36 for MY, FY and PY, respectively. Wilmink's function was the better fitting function for additive genetic effects for all traits. It was also the preferred function for permanent environment effects for Fat% and Prot%, but for MY, FY and PY, the Legm was the appropriate function. Genetic correlations of MY with FY and PY were high and they were moderately negative with Fat% and Prot%. To prevent deterioration in Fat% and Prot% and improve milk quality, more weight should be applied to milk component traits.

  10. Topsoil organic carbon content of Europe, a new map based on a generalised additive model

    NASA Astrophysics Data System (ADS)

    de Brogniez, Delphine; Ballabio, Cristiano; Stevens, Antoine; Jones, Robert J. A.; Montanarella, Luca; van Wesemael, Bas

    2014-05-01

    There is an increasing demand for up-to-date spatially continuous organic carbon (OC) data for global environment and climatic modeling. Whilst the current map of topsoil organic carbon content for Europe (Jones et al., 2005) was produced by applying expert-knowledge based pedo-transfer rules on large soil mapping units, the aim of this study was to replace it by applying digital soil mapping techniques on the first European harmonised geo-referenced topsoil (0-20 cm) database, which arises from the LUCAS (land use/cover area frame statistical survey) survey. A generalized additive model (GAM) was calibrated on 85% of the dataset (ca. 17 000 soil samples) and a backward stepwise approach selected slope, land cover, temperature, net primary productivity, latitude and longitude as environmental covariates (500 m resolution). The validation of the model (applied on 15% of the dataset), gave an R2 of 0.27. We observed that most organic soils were under-predicted by the model and that soils of Scandinavia were also poorly predicted. The model showed an RMSE of 42 g kg-1 for mineral soils and of 287 g kg-1 for organic soils. The map of predicted OC content showed the lowest values in Mediterranean countries and in croplands across Europe, whereas highest OC content were predicted in wetlands, woodlands and in mountainous areas. The map of standard error of the OC model predictions showed high values in northern latitudes, wetlands, moors and heathlands, whereas low uncertainty was mostly found in croplands. A comparison of our results with the map of Jones et al. (2005) showed a general agreement on the prediction of mineral soils' OC content, most probably because the models use some common covariates, namely land cover and temperature. Our model however failed to predict values of OC content greater than 200 g kg-1, which we explain by the imposed unimodal distribution of our model, whose mean is tilted towards the majority of soils, which are mineral. Finally, average

  11. Genetic and genomic analysis of RNases in model cyanobacteria.

    PubMed

    Cameron, Jeffrey C; Gordon, Gina C; Pfleger, Brian F

    2015-10-01

    Cyanobacteria are diverse photosynthetic microbes with the ability to convert CO2 into useful products. However, metabolic engineering of cyanobacteria remains challenging because of the limited resources for modifying the expression of endogenous and exogenous biochemical pathways. Fine-tuned control of protein production will be critical to optimize the biological conversion of CO2 into desirable molecules. Messenger RNAs (mRNAs) are labile intermediates that play critical roles in determining the translation rate and steady-state protein concentrations in the cell. The majority of studies on mRNA turnover have focused on the model heterotrophic bacteria Escherichia coli and Bacillus subtilis. These studies have elucidated many RNA modifying and processing enzymes and have highlighted the differences between these Gram-negative and Gram-positive bacteria, respectively. In contrast, much less is known about mRNA turnover in cyanobacteria. We generated a compendium of the major ribonucleases (RNases) and provide an in-depth analysis of RNase III-like enzymes in commonly studied and diverse cyanobacteria. Furthermore, using targeted gene deletion, we genetically dissected the RNases in Synechococcus sp. PCC 7002, one of the fastest growing and industrially attractive cyanobacterial strains. We found that all three cyanobacterial homologs of RNase III and a member of the RNase II/R family are not essential under standard laboratory conditions, while homologs of RNase E/G, RNase J1/J2, PNPase, and a different member of the RNase II/R family appear to be essential for growth. This work will enhance our understanding of native control of gene expression and will facilitate the development of an RNA-based toolkit for metabolic engineering in cyanobacteria.

  12. Motivational Interviewing in the Reciprocal Engagement Model of Genetic Counseling: a Method Overview and Case Illustration.

    PubMed

    Ash, Erin

    2016-12-28

    Motivational Interviewing is a well-described counseling method that has been applied to a broad range of health behavior encounters. Genetic counseling is an emerging area of utilization for the method of Motivational Interviewing. The relational and technical elements of the MI method are described within the context of genetic counseling encounters. Case excerpts will be used to illustrate incorporation of MI methods into the Reciprocal Engagement Model of the genetic counseling encounter.

  13. [Biochemical genetics in St. Petersburg university: from the gene-enzyme model to medical biotechnology].

    PubMed

    Padkina, M V; Sambuk, E V

    2007-10-01

    The history of biochemical genetic research in St. Petersburg (Leningrad) State University is described. The main research projects and achievements of the Laboratory of Biochemical Genetics in studies on the mechanisms of gene expression control, coordinated regulation of metabolism, and the relationship of the physiological state of yeast cells with the maintenance of genetic stability are discussed. The fundamental importance of studies on the acid phosphatase model for the formation and development of medical biotechnology in St. Petersburg University is demonstrated.

  14. Improving the predictive accuracy of hurricane power outage forecasts using generalized additive models.

    PubMed

    Han, Seung-Ryong; Guikema, Seth D; Quiring, Steven M

    2009-10-01

    Electric power is a critical infrastructure service after hurricanes, and rapid restoration of electric power is important in order to minimize losses in the impacted areas. However, rapid restoration of electric power after a hurricane depends on obtaining the necessary resources, primarily repair crews and materials, before the hurricane makes landfall and then appropriately deploying these resources as soon as possible after the hurricane. This, in turn, depends on having sound estimates of both the overall severity of the storm and the relative risk of power outages in different areas. Past studies have developed statistical, regression-based approaches for estimating the number of power outages in advance of an approaching hurricane. However, these approaches have either not been applicable for future events or have had lower predictive accuracy than desired. This article shows that a different type of regression model, a generalized additive model (GAM), can outperform the types of models used previously. This is done by developing and validating a GAM based on power outage data during past hurricanes in the Gulf Coast region and comparing the results from this model to the previously used generalized linear models.

  15. Predicting the Survival Time for Bladder Cancer Using an Additive Hazards Model in Microarray Data

    PubMed Central

    TAPAK, Leili; MAHJUB, Hossein; SADEGHIFAR, Majid; SAIDIJAM, Massoud; POOROLAJAL, Jalal

    2016-01-01

    Background: One substantial part of microarray studies is to predict patients’ survival based on their gene expression profile. Variable selection techniques are powerful tools to handle high dimensionality in analysis of microarray data. However, these techniques have not been investigated in competing risks setting. This study aimed to investigate the performance of four sparse variable selection methods in estimating the survival time. Methods: The data included 1381 gene expression measurements and clinical information from 301 patients with bladder cancer operated in the years 1987 to 2000 in hospitals in Denmark, Sweden, Spain, France, and England. Four methods of the least absolute shrinkage and selection operator, smoothly clipped absolute deviation, the smooth integration of counting and absolute deviation and elastic net were utilized for simultaneous variable selection and estimation under an additive hazards model. The criteria of area under ROC curve, Brier score and c-index were used to compare the methods. Results: The median follow-up time for all patients was 47 months. The elastic net approach was indicated to outperform other methods. The elastic net had the lowest integrated Brier score (0.137±0.07) and the greatest median of the over-time AUC and C-index (0.803±0.06 and 0.779±0.13, respectively). Five out of 19 selected genes by the elastic net were significant (P<0.05) under an additive hazards model. It was indicated that the expression of RTN4, SON, IGF1R and CDC20 decrease the survival time, while the expression of SMARCAD1 increase it. Conclusion: The elastic net had higher capability than the other methods for the prediction of survival time in patients with bladder cancer in the presence of competing risks base on additive hazards model. PMID:27114989

  16. Comparison of prosthetic models produced by traditional and additive manufacturing methods

    PubMed Central

    Park, Jin-Young; Kim, Hae-Young; Kim, Ji-Hwan; Kim, Jae-Hong

    2015-01-01

    PURPOSE The purpose of this study was to verify the clinical-feasibility of additive manufacturing by comparing the accuracy of four different manufacturing methods for metal coping: the conventional lost wax technique (CLWT); subtractive methods with wax blank milling (WBM); and two additive methods, multi jet modeling (MJM), and micro-stereolithography (Micro-SLA). MATERIALS AND METHODS Thirty study models were created using an acrylic model with the maxillary upper right canine, first premolar, and first molar teeth. Based on the scan files from a non-contact blue light scanner (Identica; Medit Co. Ltd., Seoul, Korea), thirty cores were produced using the WBM, MJM, and Micro-SLA methods, respectively, and another thirty frameworks were produced using the CLWT method. To measure the marginal and internal gap, the silicone replica method was adopted, and the silicone images obtained were evaluated using a digital microscope (KH-7700; Hirox, Tokyo, Japan) at 140X magnification. Analyses were performed using two-way analysis of variance (ANOVA) and Tukey post hoc test (α=.05). RESULTS The mean marginal gaps and internal gaps showed significant differences according to tooth type (P<.001 and P<.001, respectively) and manufacturing method (P<.037 and P<.001, respectively). Micro-SLA did not show any significant difference from CLWT regarding mean marginal gap compared to the WBM and MJM methods. CONCLUSION The mean values of gaps resulting from the four different manufacturing methods were within a clinically allowable range, and, thus, the clinical use of additive manufacturing methods is acceptable as an alternative to the traditional lost wax-technique and subtractive manufacturing. PMID:26330976

  17. Female mate choice predicts paternity success in the absence of additive genetic variance for other female paternity bias mechanisms in Drosophila serrata.

    PubMed

    Collet, J M; Blows, M W

    2014-11-01

    After choosing a first mate, polyandrous females have access to a range of opportunities to bias paternity, such as repeating matings with the preferred male, facilitating fertilization from the best sperm or differentially investing in offspring according to their sire. Female ability to bias paternity after a first mating has been demonstrated in a few species, but unambiguous evidence remains limited by the access to complex behaviours, sperm storage organs and fertilization processes within females. Even when found at the phenotypic level, the potential evolution of any mechanism allowing females to bias paternity other than mate choice remains little explored. Using a large population of pedigreed females, we developed a simple test to determine whether there is additive genetic variation in female ability to bias paternity after a first, chosen, mating. We applied this method in the highly polyandrous Drosophila serrata, giving females the opportunity to successively mate with two males ad libitum. We found that despite high levels of polyandry (females mated more than once per day), the first mate choice was a significant predictor of male total reproductive success. Importantly, there was no detectable genetic variance in female ability to bias paternity beyond mate choice. Therefore, whether or not females can bias paternity before or after copulation, their role on the evolution of sexual male traits is likely to be limited to their first mate choice in D. serrata.

  18. Assessing a landscape barrier using genetic simulation modelling: implications for raccoon rabies management.

    PubMed

    Rees, Erin E; Pond, Bruce A; Cullingham, Catherine I; Tinline, Rowland; Ball, David; Kyle, Christopher J; White, Bradley N

    2008-08-15

    Landscape barriers influence movement patterns of animals, which in turn, affect spatio-temporal spread of infectious wildlife disease. We compare genetic data from computer simulations to those acquired from field samples to measure the effect of a landscape barrier on raccoon (Procyon lotor) movement, enabling risk assessment of raccoon rabies disease spread across the Niagara River from New York State into Ontario, an area currently uninfected by rabies. An individual-based spatially explicit model is used to simulate the expansion of a raccoon population to cross the Niagara River, for different permeabilities of the river to raccoon crossings. Since the model records individual raccoon genetics, the genetic population structure of neutral mitochondrial DNA haplotypes are characterised in the expanding population, every 25 years, using a genetic distance measure, phi ST, Mantel tests and a gene diversity measure. The river barrier effect is assessed by comparing genetic measures computed from model outputs to those calculated from 166 raccoons recently sampled from the same landscape. The "best fit" between modelled scenarios and field data indicate the river prevents 50% of attempts to cross the river. Founder effects dominated the colonizing genetic population structure, and, as the river barrier effect increased, its genetic diversity decreased. Using gene flow to calibrate the effect of the river as a barrier to movement provides an estimate of the effect of a river in reducing the likelihood of cross-river infection. Including individual genetic markers in simulation modelling benefits investigations of disease spread and control.

  19. Thermodynamic network model for predicting effects of substrate addition and other perturbations on subsurface microbial communities

    SciTech Connect

    Jack Istok; Melora Park; James McKinley; Chongxuan Liu; Lee Krumholz; Anne Spain; Aaron Peacock; Brett Baldwin

    2007-04-19

    The overall goal of this project is to develop and test a thermodynamic network model for predicting the effects of substrate additions and environmental perturbations on microbial growth, community composition and system geochemistry. The hypothesis is that a thermodynamic analysis of the energy-yielding growth reactions performed by defined groups of microorganisms can be used to make quantitative and testable predictions of the change in microbial community composition that will occur when a substrate is added to the subsurface or when environmental conditions change.

  20. Generalized additive models and Lucilia sericata growth: assessing confidence intervals and error rates in forensic entomology.

    PubMed

    Tarone, Aaron M; Foran, David R

    2008-07-01

    Forensic entomologists use blow fly development to estimate a postmortem interval. Although accurate, fly age estimates can be imprecise for older developmental stages and no standard means of assigning confidence intervals exists. Presented here is a method for modeling growth of the forensically important blow fly Lucilia sericata, using generalized additive models (GAMs). Eighteen GAMs were created to predict the extent of juvenile fly development, encompassing developmental stage, length, weight, strain, and temperature data, collected from 2559 individuals. All measures were informative, explaining up to 92.6% of the deviance in the data, though strain and temperature exerted negligible influences. Predictions made with an independent data set allowed for a subsequent examination of error. Estimates using length and developmental stage were within 5% of true development percent during the feeding portion of the larval life cycle, while predictions for postfeeding third instars were less precise, but within expected error.

  1. Phase-Field Modeling of Microstructure Evolution in Electron Beam Additive Manufacturing

    NASA Astrophysics Data System (ADS)

    Gong, Xibing; Chou, Kevin

    2015-05-01

    In this study, the microstructure evolution in the powder-bed electron beam additive manufacturing (EBAM) process is studied using phase-field modeling. In essence, EBAM involves a rapid solidification process and the properties of a build partly depend on the solidification behavior as well as the microstructure of the build material. Thus, the prediction of microstructure evolution in EBAM is of importance for its process optimization. Phase-field modeling was applied to study the microstructure evolution and solute concentration of the Ti-6Al-4V alloy in the EBAM process. The effect of undercooling was investigated through the simulations; the greater the undercooling, the faster the dendrite grows. The microstructure simulations show multiple columnar-grain growths, comparable with experimental results for the tested range.

  2. Robust estimation of mean and dispersion functions in extended generalized additive models.

    PubMed

    Croux, Christophe; Gijbels, Irène; Prosdocimi, Ilaria

    2012-03-01

    Generalized linear models are a widely used method to obtain parametric estimates for the mean function. They have been further extended to allow the relationship between the mean function and the covariates to be more flexible via generalized additive models. However, the fixed variance structure can in many cases be too restrictive. The extended quasilikelihood (EQL) framework allows for estimation of both the mean and the dispersion/variance as functions of covariates. As for other maximum likelihood methods though, EQL estimates are not resistant to outliers: we need methods to obtain robust estimates for both the mean and the dispersion function. In this article, we obtain functional estimates for the mean and the dispersion that are both robust and smooth. The performance of the proposed method is illustrated via a simulation study and some real data examples.

  3. Observations and model calculations of an additional layer in the topside ionosphere above Fortaleza, Brazil

    NASA Astrophysics Data System (ADS)

    Jenkins, B.; Bailey, G. J.; Abdu, M. A.; Batista, I. S.; Balan, N.

    1997-06-01

    Calculations using the Sheffield University plasmasphere ionosphere model have shown that under certain conditions an additional layer can form in the low latitude topside ionosphere. This layer (the F3 layer) has subsequently been observed in ionograms recorded at Fortaleza in Brazil. It has not been observed in ionograms recorded at the neighbouring station São Luis. Model calculations have shown that the F3 layer is most likely to form in summer at Fortaleza due to a combination of the neutral wind and the E×B drift acting to raise the plasma. At the location of São Luis, almost on the geomagnetic equator, the neutral wind has a smaller vertical component so the F3 layer does not form.

  4. Guarana provides additional stimulation over caffeine alone in the planarian model.

    PubMed

    Moustakas, Dimitrios; Mezzio, Michael; Rodriguez, Branden R; Constable, Mic Andre; Mulligan, Margaret E; Voura, Evelyn B

    2015-01-01

    The stimulant effect of energy drinks is primarily attributed to the caffeine they contain. Many energy drinks also contain other ingredients that might enhance the tonic effects of these caffeinated beverages. One of these additives is guarana. Guarana is a climbing plant native to the Amazon whose seeds contain approximately four times the amount of caffeine found in coffee beans. The mix of other natural chemicals contained in guarana seeds is thought to heighten the stimulant effects of guarana over caffeine alone. Yet, despite the growing use of guarana as an additive in energy drinks, and a burgeoning market for it as a nutritional supplement, the science examining guarana and how it affects other dietary ingredients is lacking. To appreciate the stimulant effects of guarana and other natural products, a straightforward model to investigate their physiological properties is needed. The planarian provides such a system. The locomotor activity and convulsive response of planarians with substance exposure has been shown to provide an excellent system to measure the effects of drug stimulation, addiction and withdrawal. To gauge the stimulant effects of guarana we studied how it altered the locomotor activity of the planarian species Dugesia tigrina. We report evidence that guarana seeds provide additional stimulation over caffeine alone, and document the changes to this stimulation in the context of both caffeine and glucose.

  5. Guarana Provides Additional Stimulation over Caffeine Alone in the Planarian Model

    PubMed Central

    Moustakas, Dimitrios; Mezzio, Michael; Rodriguez, Branden R.; Constable, Mic Andre; Mulligan, Margaret E.; Voura, Evelyn B.

    2015-01-01

    The stimulant effect of energy drinks is primarily attributed to the caffeine they contain. Many energy drinks also contain other ingredients that might enhance the tonic effects of these caffeinated beverages. One of these additives is guarana. Guarana is a climbing plant native to the Amazon whose seeds contain approximately four times the amount of caffeine found in coffee beans. The mix of other natural chemicals contained in guarana seeds is thought to heighten the stimulant effects of guarana over caffeine alone. Yet, despite the growing use of guarana as an additive in energy drinks, and a burgeoning market for it as a nutritional supplement, the science examining guarana and how it affects other dietary ingredients is lacking. To appreciate the stimulant effects of guarana and other natural products, a straightforward model to investigate their physiological properties is needed. The planarian provides such a system. The locomotor activity and convulsive response of planarians with substance exposure has been shown to provide an excellent system to measure the effects of drug stimulation, addiction and withdrawal. To gauge the stimulant effects of guarana we studied how it altered the locomotor activity of the planarian species Dugesia tigrina. We report evidence that guarana seeds provide additional stimulation over caffeine alone, and document the changes to this stimulation in the context of both caffeine and glucose. PMID:25880065

  6. Analysis and Modeling of soil hydrology under different soil additives in artificial runoff plots

    NASA Astrophysics Data System (ADS)

    Ruidisch, M.; Arnhold, S.; Kettering, J.; Huwe, B.; Kuzyakov, Y.; Ok, Y.; Tenhunen, J. D.

    2009-12-01

    The impact of monsoon events during June and July in the Korean project region Haean Basin, which is located in the northeastern part of South Korea plays a key role for erosion, leaching and groundwater pollution risk by agrochemicals. Therefore, the project investigates the main hydrological processes in agricultural soils under field and laboratory conditions on different scales (plot, hillslope and catchment). Soil hydrological parameters were analysed depending on different soil additives, which are known for prevention of soil erosion and nutrient loss as well as increasing of water infiltration, aggregate stability and soil fertility. Hence, synthetic water-soluble Polyacrylamides (PAM), Biochar (Black Carbon mixed with organic fertilizer), both PAM and Biochar were applied in runoff plots at three agricultural field sites. Additionally, as control a subplot was set up without any additives. The field sites were selected in areas with similar hillslope gradients and with emphasis on the dominant land management form of dryland farming in Haean, which is characterised by row planting and row covering by foil. Hydrological parameters like satured water conductivity, matrix potential and water content were analysed by infiltration experiments, continuous tensiometer measurements, time domain reflectometry as well as pressure plates to indentify characteristic water retention curves of each horizon. Weather data were observed by three weather stations next to the runoff plots. Measured data also provide the input data for modeling water transport in the unsatured zone in runoff plots with HYDRUS 1D/2D/3D and SWAT (Soil & Water Assessment Tool).

  7. “Skill of Generalized Additive Model to Detect PM2.5 Health ...

    EPA Pesticide Factsheets

    Summary. Measures of health outcomes are collinear with meteorology and air quality, making analysis of connections between human health and air quality difficult. The purpose of this analysis was to determine time scales and periods shared by the variables of interest (and by implication scales and periods that are not shared). Hospital admissions, meteorology (temperature and relative humidity), and air quality (PM2.5 and daily maximum ozone) for New York City during the period 2000-2006 were decomposed into temporal scales ranging from 2 days to greater than two years using a complex wavelet transform. Health effects were modeled as functions of the wavelet components of meteorology and air quality using the generalized additive model (GAM) framework. This simulation study showed that GAM is extremely successful at extracting and estimating a health effect embedded in a dataset. It also shows that, if the objective in mind is to estimate the health signal but not to fully explain this signal, a simple GAM model with a single confounder (calendar time) whose smooth representation includes a sufficient number of constraints is as good as a more complex model.Introduction. In the context of wavelet regression, confounding occurs when two or more independent variables interact with the dependent variable at the same frequency. Confounding also acts on a variety of time scales, changing the PM2.5 coefficient (magnitude and sign) and its significance ac

  8. Genetic hotels for the standard genetic code: evolutionary analysis based upon novel three-dimensional algebraic models.

    PubMed

    José, Marco V; Morgado, Eberto R; Govezensky, Tzipe

    2011-07-01

    Herein, we rigorously develop novel 3-dimensional algebraic models called Genetic Hotels of the Standard Genetic Code (SGC). We start by considering the primeval RNA genetic code which consists of the 16 codons of type RNY (purine-any base-pyrimidine). Using simple algebraic operations, we show how the RNA code could have evolved toward the current SGC via two different intermediate evolutionary stages called Extended RNA code type I and II. By rotations or translations of the subset RNY, we arrive at the SGC via the former (type I) or via the latter (type II), respectively. Biologically, the Extended RNA code type I, consists of all codons of the type RNY plus codons obtained by considering the RNA code but in the second (NYR type) and third (YRN type) reading frames. The Extended RNA code type II, comprises all codons of the type RNY plus codons that arise from transversions of the RNA code in the first (YNY type) and third (RNR) nucleotide bases. Since the dimensions of remarkable subsets of the Genetic Hotels are not necessarily integer numbers, we also introduce the concept of algebraic fractal dimension. A general decoding function which maps each codon to its corresponding amino acid or the stop signals is also derived. The Phenotypic Hotel of amino acids is also illustrated. The proposed evolutionary paths are discussed in terms of the existing theories of the evolution of the SGC. The adoption of 3-dimensional models of the Genetic and Phenotypic Hotels will facilitate the understanding of the biological properties of the SGC.

  9. Computation of octanol-water partition coefficients by guiding an additive model with knowledge.

    PubMed

    Cheng, Tiejun; Zhao, Yuan; Li, Xun; Lin, Fu; Xu, Yong; Zhang, Xinglong; Li, Yan; Wang, Renxiao; Lai, Luhua

    2007-01-01

    We have developed a new method, i.e., XLOGP3, for logP computation. XLOGP3 predicts the logP value of a query compound by using the known logP value of a reference compound as a starting point. The difference in the logP values of the query compound and the reference compound is then estimated by an additive model. The additive model implemented in XLOGP3 uses a total of 87 atom/group types and two correction factors as descriptors. It is calibrated on a training set of 8199 organic compounds with reliable logP data through a multivariate linear regression analysis. For a given query compound, the compound showing the highest structural similarity in the training set will be selected as the reference compound. Structural similarity is quantified based on topological torsion descriptors. XLOGP3 has been tested along with its predecessor, i.e., XLOGP2, as well as several popular logP methods on two independent test sets: one contains 406 small-molecule drugs approved by the FDA and the other contains 219 oligopeptides. On both test sets, XLOGP3 produces more accurate predictions than most of the other methods with average unsigned errors of 0.24-0.51 units. Compared to conventional additive methods, XLOGP3 does not rely on an extensive classification of fragments and correction factors in order to improve accuracy. It is also able to utilize the ever-increasing experimentally measured logP data more effectively.

  10. Genetic evaluation of growth in a multibreed beef cattle population using random regression-linear spline models.

    PubMed

    Sánchez, J P; Misztal, I; Aguilar, I; Bertrand, J K

    2008-02-01

    The objective of this study was to examine the feasibility of using random regression-spline (RR-spline) models for fitting growth traits in a multibreed beef cattle population. To meet the objective, the results from the RR-spline model were compared with the widely used multitrait (MT) model when both were fit to a data set (1.8 million records and 1.1 million animals) provided by the American Gelbvieh Association. The effect of prior information on the EBV of sires was also investigated. In both RR-spline and MT models, the following effects were considered: individual direct and maternal additive genetic effects, contemporary group, age of the animal at measurement, direct and maternal heterosis, and direct and maternal additive genetic mean effect of the breed. Additionally, the RR-spline model included an individual direct permanent environmental effect. When both MT and RR-spline models were applied to a data set containing records for weaning weight (WWT) and yearling weight (YWT) within specified age ranges, the rankings of bulls' direct EBV (as measured via Pearson correlations) provided by both models were comparable, with slightly greater differences in the reranking of bulls observed for YWT evaluations (>or=0.99 for BWT and WWT and >or=0.98 for YWT); also, some bulls dropped from the top 100 list when these lists were compared across methods. For maternal effects, the estimated correlations were slightly smaller, particularly for YWT; again, some drops from the top 100 animals were observed. As in regular MT multibreed genetic evaluations, the heterosis effects and the additive genetic effects of the breed could not be estimated from field data, because there were not enough contemporary groups with the proper composition of purebred and crossbred animals; thus, prior information based on literature values had to be included. The inclusion of prior information had a negligible effect in the overall ranking for bulls with greater than 20 birth weight

  11. The psoriasis genetics as a model of complex disease.

    PubMed

    Giardina, Emiliano; Sinibaldi, Cecilia; Novelli, Giuseppe

    2004-06-01

    Psoriasis [OMIM*177900] is a common, chronic and papulosquamous inflammatory skin disease affecting approximately 2% of Caucasian. However, this disorder is rare among Japanese, Eskimos, West Africans and North American blacks and very uncommon in North American and South American natives. The causes for these variations are likely to be both genetic and environmental. Population-based studies and twin studies indicate that psoriasis is a heritable disease with a polygenic mode of inheritance with variable penetrance. Independent genome-wide scans have suggested the involvement of a large number of chromosomal regions (loci), and many candidate genes have been proposed. We discuss genetic approaches to the disease, results and interpretations of relevant studies, as well as future perspectives. Understanding the genetic basis of psoriasis will represent a major advance in our understanding of the disease and will reveal novel disease-specific biologic pathways.

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

    NASA Astrophysics Data System (ADS)

    Windarto

    2016-03-01

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

  13. Efficacy of targeted AKT inhibition in genetically engineered mouse models of PTEN-deficient prostate cancer.

    PubMed

    De Velasco, Marco A; Kura, Yurie; Yoshikawa, Kazuhiro; Nishio, Kazuto; Davies, Barry R; Uemura, Hirotsugu

    2016-03-29

    The PI3K/AKT pathway is frequently altered in advanced human prostate cancer mainly through the loss of functional PTEN, and presents as potential target for personalized therapy. Our aim was to determine the therapeutic potential of the pan-AKT inhibitor, AZD5363, in PTEN-deficient prostate cancer. Here we used a genetically engineered mouse (GEM) model of PTEN-deficient prostate cancer to evaluate the in vivo pharmacodynamic and antitumor activity of AZD5363 in castration-naïve and castration-resistant prostate cancer. An additional GEM model, based on the concomitant inactivation of PTEN and Trp53 (P53), was established as an aggressive model of advanced prostate cancer and was used to further evaluate clinically relevant endpoints after treatment with AZD5363. In vivo pharmacodynamic studies demonstrated that AZD5363 effectively inhibited downstream targets of AKT. AZD5363 monotherapy significantly reduced growth of tumors in castration-naïve and castration-resistant models of PTEN-deficient prostate cancer. More importantly, AZD5363 significantly delayed tumor growth and improved overall survival and progression-free survival in PTEN/P53 double knockout mice. Our findings demonstrate that AZD5363 is effective against GEM models of PTEN-deficient prostate cancer and provide lines of evidence to support further investigation into the development of treatment strategies targeting AKT for the treatment of PTEN-deficient prostate cancer.

  14. Genome editing revolutionize the creation of genetically modified pigs for modeling human diseases.

    PubMed

    Yao, Jing; Huang, Jiaojiao; Zhao, Jianguo

    2016-09-01

    Pigs have anatomical, physiological and genomic characteristics that make them highly suitable for modeling human diseases. Genetically modified (GM) pig models of human diseases are critical for studying pathogenesis, treatment, and prevention. The emergence of nuclease-mediated genome editing technology has been successfully employed for engineering of the pig genome, which has revolutionize the creation of GM pig models with highly complex pathophysiologies and comorbidities. In this review, we summarize the progress of recently developed genome editing technologies, including zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and the clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9), which enable highly efficient and precise introduction of genome modifications into pigs, and tailored disease models that have been generated in various disciplines via genome editing technology. We also summarize the GM pig models that have been generated by conventional transgenic strategies. Additionally, perspectives regarding the application of GM pigs in biomedical research are discussed.

  15. Interstitial Calcinosis in Renal Papillae of Genetically Engineered Mouse Models: Relation to Randall’s Plaques

    PubMed Central

    Wu, Xue-Ru

    2014-01-01

    Genetically engineered mouse models (GEMMs) have been highly instrumental in elucidating gene functions and molecular pathogenesis of human diseases, although their use in studying kidney stone formation or nephrolithiasis remains relatively limited. This review intends to provide an overview of several knockout mouse models that develop interstitial calcinosis in the renal papillae. Included herein are mice deficient for Tamm-Horsfall protein (THP; also named uromodulin), osteopontin (OPN), both THP and OPN, Na+-phosphate cotransporter Type II (Npt2a) and Na+/H+ exchanger regulatory factor (NHERF-1). The baseline information of each protein is summarized, along with key morphological features of the interstitial calcium deposits in mice lacking these proteins. Attempts are made to correlate the papillary interstitial deposits found in GEMMs with Randall’s plaques, the latter considered precursors of idiopathic calcium stones in patients. The pathophysiology that underlies the renal calcinosis in the knockout mice are also discussed wherever information is available. Not all the knockout models are allocated equal space because some are more extensively characterized than others. Despite the inroads already made, the exact physiological underpinning, origin, evolution and fate of the papillary interstitial calcinosis in the GEMMs remain incompletely defined. Greater investigative efforts are warranted in order to pin down the precise role of the papillary interstitial calcinosis in nephrolithiasis using the existing models. Additionally, more sophisticated, second-generation GEMMs that allow gene inactivation in a time-controlled manner and “compound mice” that bear several genetic alterations are urgently needed, in light of mounting evidence that nephrolithiasis is a multifactorial, multi-stage and polygenic disease. PMID:25096800

  16. Developing Novel Therapeutic Approaches in Small Cell Lung Carcinoma Using Genetically Engineered Mouse Models and Human Circulating Tumor Cells

    DTIC Science & Technology

    2015-10-01

    Using Genetically Engineered Mouse Models and Human Circulating Tumor Cells PRINCIPAL INVESTIGATOR: Jeffrey Engelman MD PhD CONTRACTING...SUBTITLE Developiing Novel Therapeutic Approaches in Small Cell Lung 5a. CONTRACT NUMBER Carcinoma Using Genetically Engineered Mouse Models and 5b...biomarkers. 15. SUBJECT TERMS Small cell lung cancer (SCLC), Genetically engineered mouse model (GEMM), BH3 mimetic, TORC inhibitor, Apoptosis

  17. The biobehavioral family model: testing social support as an additional exogenous variable.

    PubMed

    Woods, Sarah B; Priest, Jacob B; Roush, Tara

    2014-12-01

    This study tests the inclusion of social support as a distinct exogenous variable in the Biobehavioral Family Model (BBFM). The BBFM is a biopsychosocial approach to health that proposes that biobehavioral reactivity (anxiety and depression) mediates the relationship between family emotional climate and disease activity. Data for this study included married, English-speaking adult participants (n = 1,321; 55% female; M age = 45.2 years) from the National Comorbidity Survey Replication, a nationally representative epidemiological study of the frequency of mental disorders in the United States. Participants reported their demographics, marital functioning, social support from friends and relatives, anxiety and depression (biobehavioral reactivity), number of chronic health conditions, and number of prescription medications. Confirmatory factor analyses supported the items used in the measures of negative marital interactions, social support, and biobehavioral reactivity, as well as the use of negative marital interactions, friends' social support, and relatives' social support as distinct factors in the model. Structural equation modeling indicated a good fit of the data to the hypothesized model (χ(2)  = 846.04, p = .000, SRMR = .039, CFI = .924, TLI = .914, RMSEA = .043). Negative marital interactions predicted biobehavioral reactivity (β = .38, p < .001), as did relatives' social support, inversely (β = -.16, p < .001). Biobehavioral reactivity predicted disease activity (β = .40, p < .001) and was demonstrated to be a significant mediator through tests of indirect effects. Findings are consistent with previous tests of the BBFM with adult samples, and suggest the important addition of family social support as a predicting factor in the model.

  18. A habitat suitability model for Chinese sturgeon determined using the generalized additive method

    NASA Astrophysics Data System (ADS)

    Yi, Yujun; Sun, Jie; Zhang, Shanghong

    2016-03-01

    The Chinese sturgeon is a type of large anadromous fish that migrates between the ocean and rivers. Because of the construction of dams, this sturgeon's migration path has been cut off, and this species currently is on the verge of extinction. Simulating suitable environmental conditions for spawning followed by repairing or rebuilding its spawning grounds are effective ways to protect this species. Various habitat suitability models based on expert knowledge have been used to evaluate the suitability of spawning habitat. In this study, a two-dimensional hydraulic simulation is used to inform a habitat suitability model based on the generalized additive method (GAM). The GAM is based on real data. The values of water depth and velocity are calculated first via the hydrodynamic model and later applied in the GAM. The final habitat suitability model is validated using the catch per unit effort (CPUEd) data of 1999 and 2003. The model results show that a velocity of 1.06-1.56 m/s and a depth of 13.33-20.33 m are highly suitable ranges for the Chinese sturgeon to spawn. The hydraulic habitat suitability indexes (HHSI) for seven discharges (4000; 9000; 12,000; 16,000; 20,000; 30,000; and 40,000 m3/s) are calculated to evaluate integrated habitat suitability. The results show that the integrated habitat suitability reaches its highest value at a discharge of 16,000 m3/s. This study is the first to apply a GAM to evaluate the suitability of spawning grounds for the Chinese sturgeon. The study provides a reference for the identification of potential spawning grounds in the entire basin.

  19. Modeling particulate matter concentrations measured through mobile monitoring in a deletion/substitution/addition approach

    NASA Astrophysics Data System (ADS)

    Su, Jason G.; Hopke, Philip K.; Tian, Yilin; Baldwin, Nichole; Thurston, Sally W.; Evans, Kristin; Rich, David Q.

    2015-12-01

    Land use regression modeling (LUR) through local scale circular modeling domains has been used to predict traffic-related air pollution such as nitrogen oxides (NOX). LUR modeling for fine particulate matters (PM), which generally have smaller spatial gradients than NOX, has been typically applied for studies involving multiple study regions. To increase the spatial coverage for fine PM and key constituent concentrations, we designed a mobile monitoring network in Monroe County, New York to measure pollutant concentrations of black carbon (BC, wavelength at 880 nm), ultraviolet black carbon (UVBC, wavelength at 3700 nm) and Delta-C (the difference between the UVBC and BC concentrations) using the Clarkson University Mobile Air Pollution Monitoring Laboratory (MAPL). A Deletion/Substitution/Addition (D/S/A) algorithm was conducted, which used circular buffers as a basis for statistics. The algorithm maximizes the prediction accuracy for locations without measurements using the V-fold cross-validation technique, and it reduces overfitting compared to other approaches. We found that the D/S/A LUR modeling approach could achieve good results, with prediction powers of 60%, 63%, and 61%, respectively, for BC, UVBC, and Delta-C. The advantage of mobile monitoring is that it can monitor pollutant concentrations at hundreds of spatial points in a region, rather than the typical less than 100 points from a fixed site saturation monitoring network. This research indicates that a mobile saturation sampling network, when combined with proper modeling techniques, can uncover small area variations (e.g., 10 m) in particulate matter concentrations.

  20. Revisiting automated G-protein coupled receptor modeling: the benefit of additional template structures for a neurokinin-1 receptor model.

    PubMed

    Kneissl, Benny; Leonhardt, Bettina; Hildebrandt, Andreas; Tautermann, Christofer S

    2009-05-28

    The feasibility of automated procedures for the modeling of G-protein coupled receptors (GPCR) is investigated on the example of the human neurokinin-1 (NK1) receptor. We use a combined method of homology modeling and molecular docking and analyze the information content of the resulting docking complexes regarding the binding mode for further refinements. Moreover, we explore the impact of different template structures, the bovine rhodopsin structure, the human beta(2) adrenergic receptor, and in particular a combination of both templates to include backbone flexibility in the target conformational space. Our results for NK1 modeling demonstrate that model selection from a set of decoys can in general not solely rely on docking experiments but still requires additional mutagenesis data. However, an enrichment factor of 2.6 in a nearly fully automated approach indicates that reasonable models can be created automatically if both available templates are used for model construction. Thus, the recently resolved GPCR structures open new ways to improve the model building fundamentally.

  1. Generalized Additive Models Used to Predict Species Abundance in the Gulf of Mexico: An Ecosystem Modeling Tool

    PubMed Central

    Drexler, Michael; Ainsworth, Cameron H.

    2013-01-01

    Spatially explicit ecosystem models of all types require an initial allocation of biomass, often in areas where fisheries independent abundance estimates do not exist. A generalized additive modelling (GAM) approach is used to describe the abundance of 40 species groups (i.e. functional groups) across the Gulf of Mexico (GoM) using a large fisheries independent data set (SEAMAP) and climate scale oceanographic conditions. Predictor variables included in the model are chlorophyll a, sediment type, dissolved oxygen, temperature, and depth. Despite the presence of a large number of zeros in the data, a single GAM using a negative binomial distribution was suitable to make predictions of abundance for multiple functional groups. We present an example case study using pink shrimp (Farfantepenaeus duroarum) and compare the results to known distributions. The model successfully predicts the known areas of high abundance in the GoM, including those areas where no data was inputted into the model fitting. Overall, the model reliably captures areas of high and low abundance for the large majority of functional groups observed in SEAMAP. The result of this method allows for the objective setting of spatial distributions for numerous functional groups across a modeling domain, even where abundance data may not exist. PMID:23691223

  2. Impact of an additional chronic BDNF reduction on learning performance in an Alzheimer mouse model

    PubMed Central

    Psotta, Laura; Rockahr, Carolin; Gruss, Michael; Kirches, Elmar; Braun, Katharina; Lessmann, Volkmar; Bock, Jörg; Endres, Thomas

    2015-01-01

    There is increasing evidence that brain-derived neurotrophic factor (BDNF) plays a crucial role in Alzheimer’s disease (AD) pathology. A number of studies demonstrated that AD patients exhibit reduced BDNF levels in the brain and the blood serum, and in addition, several animal-based studies indicated a potential protective effect of BDNF against Aβ-induced neurotoxicity. In order to further investigate the role of BDNF in the etiology of AD, we created a novel mouse model by crossing a well-established AD mouse model (APP/PS1) with a mouse exhibiting a chronic BDNF deficiency (BDNF+/−). This new triple transgenic mouse model enabled us to further analyze the role of BDNF in AD in vivo. We reasoned that in case BDNF has a protective effect against AD pathology, an AD-like phenotype in our new mouse model should occur earlier and/or in more severity than in the APP/PS1-mice. Indeed, the behavioral analysis revealed that the APP/PS1-BDNF+/−-mice show an earlier onset of learning impairments in a two-way active avoidance task in comparison to APP/PS1- and BDNF+/−-mice. However in the Morris water maze (MWM) test, we could not observe an overall aggrevated impairment in spatial learning and also short-term memory in an object recognition task remained intact in all tested mouse lines. In addition to the behavioral experiments, we analyzed the amyloid plaque pathology in the APP/PS1 and APP/PS1-BDNF+/−-mice and observed a comparable plaque density in the two genotypes. Moreover, our results revealed a higher plaque density in prefrontal cortical compared to hippocampal brain regions. Our data reveal that higher cognitive tasks requiring the recruitment of cortical networks appear to be more severely affected in our new mouse model than learning tasks requiring mainly sub-cortical networks. Furthermore, our observations of an accelerated impairment in active avoidance learning in APP/PS1-BDNF+/−-mice further supports the hypothesis that BDNF deficiency

  3. Spectral models of additive and modulation noise in speech and phonatory excitation signals

    NASA Astrophysics Data System (ADS)

    Schoentgen, Jean

    2003-01-01

    The article presents spectral models of additive and modulation noise in speech. The purpose is to learn about the causes of noise in the spectra of normal and disordered voices and to gauge whether the spectral properties of the perturbations of the phonatory excitation signal can be inferred from the spectral properties of the speech signal. The approach to modeling consists of deducing the Fourier series of the perturbed speech, assuming that the Fourier series of the noise and of the clean monocycle-periodic excitation are known. The models explain published data, take into account the effects of supraglottal tremor, demonstrate the modulation distortion owing to vocal tract filtering, establish conditions under which noise cues of different speech signals may be compared, and predict the impossibility of inferring the spectral properties of the frequency modulating noise from the spectral properties of the frequency modulation noise (e.g., phonatory jitter and frequency tremor). The general conclusion is that only phonatory frequency modulation noise is spectrally relevant. Other types of noise in speech are either epiphenomenal, or their spectral effects are masked by the spectral effects of frequency modulation noise.

  4. Mental self-government: development of the additional democratic learning style scale using Rasch measurement models.

    PubMed

    Nielsen, Tine; Kreiner, Svend; Styles, Irene

    2007-01-01

    This paper describes the development and validation of a democratic learning style scale intended to fill a gap in Sternberg's theory of mental self-government and the associated learning style inventory (Sternberg, 1988, 1997). The scale was constructed as an 8-item scale with a 7-category response scale. The scale was developed following an adapted version of DeVellis' (2003) guidelines for scale development. The validity of the Democratic Learning Style Scale was assessed by items analysis using graphical loglinear Rasch models (Kreiner and Christensen, 2002, 2004, 2006) The item analysis confirmed that the full 8-item revised Democratic Learning Style Scale fitted a graphical loglinear Rasch model with no differential item functioning but weak to moderate uniform local dependence between two items. In addition, a reduced 6-item version of the scale fitted the pure Rasch model with a rating scale parameterization. The revised Democratic Learning Style Scale can therefore be regarded as a sound measurement scale meeting requirements of both construct validity and objectivity.

  5. A Bayesian additive model for understanding public transport usage in special events.

    PubMed

    Rodrigues, Filipe; Borysov, Stanislav; Ribeiro, Bernardete; Pereira, Francisco

    2016-12-02

    Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise. Although these are usually planned well in advance, their impact is difficult to predict, even when organisers and transportation operators coordinate. The problem highly increases when several events happen concurrently. To solve these problems, costly processes, heavily reliant on manual search and personal experience, are usual practice in large cities like Singapore, London or Tokyo. This paper presents a Bayesian additive model with Gaussian process components that combines smart card records from public transport with context information about events that is continuously mined from the Web. We develop an efficient approximate inference algorithm using expectation propagation, which allows us to predict the total number of public transportation trips to the special event areas, thereby contributing to a more adaptive transportation system. Furthermore, for multiple concurrent event scenarios, the proposed algorithm is able to disaggregate gross trip counts into their most likely components related to specific events and routine behavior. Using real data from Singapore, we show that the presented model outperforms the best baseline model by up to 26% in R2 and also has explanatory power for its individual components.

  6. The case for developing a lacewing genetic model organism

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  7. Linguini Models of Molecular Genetic Mapping and Fingerprinting.

    ERIC Educational Resources Information Center

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

    1997-01-01

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

  8. A Tailored Approach to Family-Centered Genetic Counseling for Cystic Fibrosis Newborn Screening: The Wisconsin Model

    PubMed Central

    Tluczek, Audrey; Zaleski, Christina; Stachiw-Hietpas, Dania; Modaff, Peggy; Adamski, Craig R.; Nelson, Megan R.; Reiser, Catherine A.; Ghate, Sumedha; Josephson, Kevin D.

    2010-01-01

    Objective Develop a tailored family-centered approach to genetic counseling following abnormal newborn screening (NBS) for cystic fibrosis (CF). Method A genetic counseling consortium reviewed research literature, selected theoretical frameworks, and incorporated counseling psychology micro skills. Results This innovative intervention integrated theories and empirically validated techniques. Pilot testing and parent feedback confirmed satisfaction with and feasibility of the approach designed to (a) minimize parents’ distress, (b) facilitate parents’ understanding, (c) increase parents’ capacities to use genetic information, and (d) enhance parents’ experiences with genetic counseling. Counselors engage in a highly interactive process of evaluating parents’ needs and tailoring assessments and interventions that include a therapeutic environment, the family’s emotional needs, parents’ informational needs, and a follow-up plan. Conclusion This promising new model is the first to establish a theory-driven, evidence-based standard for genetic counseling in the context of NBS for CF. Additional research will evaluate the model’s efficacy in clinical practice. PMID:20936425

  9. Exploring the population genetic consequences of the colonization process with spatio-temporally explicit models: insights from coupled ecological, demographic and genetic models in montane grasshoppers.

    PubMed

    Knowles, L Lacey; Alvarado-Serrano, Diego F

    2010-09-01

    Understanding the genetic consequences of shifting species distributions is critical for evaluating the impact of climate-induced distributional changes. However, the demographic expansion associated with the colonization process typically takes place across a heterogeneous environment, with population sizes and migration rates varying across the landscape. Here we describe an approach for coupling ecological-niche models (ENMs) with demographic and genetic models to explore the genetic consequences of distributional shifts across a heterogeneous landscape. Analyses of a flightless grasshopper from the sky islands of the Rocky Mountains of North America are used to show how biologically informed predictions can be generated about the genetic consequences of a colonization process across a spatially and temporally heterogeneous landscape (i.e. the suitability of habitats for the montane species differs across the landscape and is itself not static, with the displacement of contemporary populations into glacial refugia). By using (i) ENMs for current climatic conditions and the last glacial maximum to (ii) parameterize a demographic model of the colonization process, which then (iii) informs coalescent simulations, a set of models can be generated that capture different processes associated with distributional shifts. We discuss how the proposed approach for model generation can be integrated into a statistical framework for estimating key demographic parameters and testing hypotheses about the conditions for which distributional shifts may (or may not) enhance species divergence, including the importance of habitat stability, past gene-flow among currently isolated populations, and maintenance of refugial populations in multiple geographic regions.

  10. Design and tuning of standard additive model based fuzzy PID controllers for multivariable process systems.

    PubMed

    Harinath, Eranda; Mann, George K I

    2008-06-01

    This paper describes a design and two-level tuning method for fuzzy proportional-integral derivative (FPID) controllers for a multivariable process where the fuzzy inference uses the inference of standard additive model. The proposed method can be used for any n x n multi-input-multi-output process and guarantees closed-loop stability. In the two-level tuning scheme, the tuning follows two steps: low-level tuning followed by high-level tuning. The low-level tuning adjusts apparent linear gains, whereas the high-level tuning changes the nonlinearity in the normalized fuzzy output. In this paper, two types of FPID configurations are considered, and their performances are evaluated by using a real-time multizone temperature control problem having a 3 x 3 process system.

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

    EPA Science Inventory

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

  12. Genetic LRRK2 Models of Parkinson’s Disease: Dissecting Pathogenic pathway and Exploring Clinical Application

    PubMed Central

    Yue, Zhenyu; Lachenmayer, M. Lenard

    2011-01-01

    Dominantly inherited mutations in leucine-rich repeat kinase 2 (LRRK2) are the most common cause of familial Parkinson’s disease (PD). Understanding LRRK2 biology and pathophysiology is central to the elucidation of PD etiology and development of disease intervention. Recently a number of genetic mouse models of LRRK2 have been reported utilizing different genetic approaches. Some similarities in PD-related pathology emerge in these genetic models, despite lack of substantial neuropathology and clinical syndromes of PD. The systematic characterization of these models has begun to shed light on LRRK2 biology and pathophysiology and is expected to offer the identification and validation of drug targets. In this review, we summarize the progress of genetic LRRK2 mouse models and discuss their utility in understanding much needed knowledge regarding early stage (pre-symptomatic) disease progression, identifying drug targets, and exploring the potential in aiding compound screening focused on inhibitors of kinase activity of LRRK2. PMID:21538530

  13. A QTL model to map the common genetic basis for correlative phenotypic plasticity.

    PubMed

    Zhou, Tao; Lyu, Yafei; Xu, Fang; Bo, Wenhao; Zhai, Yi; Zhang, Jian; Pang, Xiaoming; Zheng, Bingsong; Wu, Rongling

    2015-01-01

    As an important mechanism for adaptation to heterogeneous environment, plastic responses of correlated traits to environmental alteration may also be genetically correlated, but less is known about the underlying genetic basis. We describe a statistical model for mapping specific quantitative trait loci (QTLs) that control the interrelationship of phenotypic plasticity between different traits. The model is constructed by a bivariate mixture setting, implemented with the EM algorithm to estimate the genetic effects of QTLs on correlative plastic response. We provide a series of procedure that test (1) how a QTL controls the phenotypic plasticity of a single trait; and (2) how the QTL determines the correlation of environment-induced changes of different traits. The model is readily extended to test how epistatic interactions among QTLs play a part in the correlations of different plastic traits. The model was validated through computer simulation and used to analyse multi-environment data of genetic mapping in winter wheat, showing its utilization in practice.

  14. Modeling the flux of metabolites in the juvenile hormone biosynthesis pathway using generalized additive models and ordinary differential equations.

    PubMed

    Martínez-Rincón, Raúl O; Rivera-Pérez, Crisalejandra; Diambra, Luis; Noriega, Fernando G

    2017-01-01

    Juvenile hormone (JH) regulates development and reproductive maturation in insects. The corpora allata (CA) from female adult mosquitoes synthesize fluctuating levels of JH, which have been linked to the ovarian development and are influenced by nutritional signals. The rate of JH biosynthesis is controlled by the rate of flux of isoprenoids in the pathway, which is the outcome of a complex interplay of changes in precursor pools and enzyme levels. A comprehensive study of the changes in enzymatic activities and precursor pool sizes have been previously reported for the mosquito Aedes aegypti JH biosynthesis pathway. In the present studies, we used two different quantitative approaches to describe and predict how changes in the individual metabolic reactions in the pathway affect JH synthesis. First, we constructed generalized additive models (GAMs) that described the association between changes in specific metabolite concentrations with changes in enzymatic activities and substrate concentrations. Changes in substrate concentrations explained 50% or more of the model deviances in 7 of the 13 metabolic steps analyzed. Addition of information on enzymatic activities almost always improved the fitness of GAMs built solely based on substrate concentrations. GAMs were validated using experimental data that were not included when the model was built. In addition, a system of ordinary differential equations (ODE) was developed to describe the instantaneous changes in metabolites as a function of the levels of enzymatic catalytic activities. The results demonstrated the ability of the models to predict changes in the flux of metabolites in the JH pathway, and can be used in the future to design and validate experimental manipulations of JH synthesis.

  15. Modeling the flux of metabolites in the juvenile hormone biosynthesis pathway using generalized additive models and ordinary differential equations

    PubMed Central

    Martínez-Rincón, Raúl O.; Rivera-Pérez, Crisalejandra; Diambra, Luis; Noriega, Fernando G.

    2017-01-01

    Juvenile hormone (JH) regulates development and reproductive maturation in insects. The corpora allata (CA) from female adult mosquitoes synthesize fluctuating levels of JH, which have been linked to the ovarian development and are influenced by nutritional signals. The rate of JH biosynthesis is controlled by the rate of flux of isoprenoids in the pathway, which is the outcome of a complex interplay of changes in precursor pools and enzyme levels. A comprehensive study of the changes in enzymatic activities and precursor pool sizes have been previously reported for the mosquito Aedes aegypti JH biosynthesis pathway. In the present studies, we used two different quantitative approaches to describe and predict how changes in the individual metabolic reactions in the pathway affect JH synthesis. First, we constructed generalized additive models (GAMs) that described the association between changes in specific metabolite concentrations with changes in enzymatic activities and substrate concentrations. Changes in substrate concentrations explained 50% or more of the model deviances in 7 of the 13 metabolic steps analyzed. Addition of information on enzymatic activities almost always improved the fitness of GAMs built solely based on substrate concentrations. GAMs were validated using experimental data that were not included when the model was built. In addition, a system of ordinary differential equations (ODE) was developed to describe the instantaneous changes in metabolites as a function of the levels of enzymatic catalytic activities. The results demonstrated the ability of the models to predict changes in the flux of metabolites in the JH pathway, and can be used in the future to design and validate experimental manipulations of JH synthesis. PMID:28158248

  16. Use of generalised additive models to categorise continuous variables in clinical prediction

    PubMed Central

    2013-01-01

    Background In medical practice many, essentially continuous, clinical parameters tend to be categorised by physicians for ease of decision-making. Indeed, categorisation is a common practice both in medical research and in the development of clinical prediction rules, particularly where the ensuing models are to be applied in daily clinical practice to support clinicians in the decision-making process. Since the number of categories into which a continuous predictor must be categorised depends partly on the relationship between the predictor and the outcome, the need for more than two categories must be borne in mind. Methods We propose a categorisation methodology for clinical-prediction models, using Generalised Additive Models (GAMs) with P-spline smoothers to determine the relationship between the continuous predictor and the outcome. The proposed method consists of creating at least one average-risk category along with high- and low-risk categories based on the GAM smooth function. We applied this methodology to a prospective cohort of patients with exacerbated chronic obstructive pulmonary disease. The predictors selected were respiratory rate and partial pressure of carbon dioxide in the blood (PCO2), and the response variable was poor evolution. An additive logistic regression model was used to show the relationship between the covariates and the dichotomous response variable. The proposed categorisation was compared to the continuous predictor as the best option, using the AIC and AUC evaluation parameters. The sample was divided into a derivation (60%) and validation (40%) samples. The first was used to obtain the cut points while the second was used to validate the proposed methodology. Results The three-category proposal for the respiratory rate was ≤ 20;(20,24];> 24, for which the following values were obtained: AIC=314.5 and AUC=0.638. The respective values for the continuous predictor were AIC=317.1 and AUC=0.634, with no statistically

  17. Nonlinear software sensor for monitoring genetic regulation processes with noise and modeling errors

    NASA Astrophysics Data System (ADS)

    Ibarra-Junquera, V.; Torres, L. A.; Rosu, H. C.; Argüello, G.; Collado-Vides, J.

    2005-07-01

    Nonlinear control techniques by means of a software sensor that are commonly used in chemical engineering could be also applied to genetic regulation processes. We provide here a realistic formulation of this procedure by introducing an additive white Gaussian noise, which is usually found in experimental data. Besides, we include model errors, meaning that we assume we do not know the nonlinear regulation function of the process. In order to illustrate this procedure, we employ the Goodwin dynamics of the concentrations [B. C. Goodwin, Temporal Oscillations in Cells (Academic, New York, 1963)] in the simple form recently applied to single gene systems and some operon cases [H. De Jong, J. Comput. Biol. 9, 67 (2002)], which involves the dynamics of the mRNA, given protein and metabolite concentrations. Further, we present results for a three gene case in coregulated sets of transcription units as they occur in prokaryotes. However, instead of considering their full dynamics, we use only the data of the metabolites and a designed software sensor. We also show, more generally, that it is possible to rebuild the complete set of nonmeasured concentrations despite the uncertainties in the regulation function or, even more, in the case of not knowing the mRNA dynamics. In addition, the rebuilding of concentrations is not affected by the perturbation due to the additive white Gaussian noise and also we managed to filter the noisy output of the biological system.

  18. Nonlinear software sensor for monitoring genetic regulation processes with noise and modeling errors.

    PubMed

    Ibarra-Junquera, V; Torres, L A; Rosu, H C; Argüello, G; Collado-Vides, J

    2005-07-01

    Nonlinear control techniques by means of a software sensor that are commonly used in chemical engineering could be also applied to genetic regulation processes. We provide here a realistic formulation of this procedure by introducing an additive white Gaussian noise, which is usually found in experimental data. Besides, we include model errors, meaning that we assume we do not know the nonlinear regulation function of the process. In order to illustrate this procedure, we employ the Goodwin dynamics of the concentrations [B. C. Goodwin, (Academic, New York, 1963)] in the simple form recently applied to single gene systems and some operon cases [H. De Jong, J. Comput. Biol. 9, 67 (2002)], which involves the dynamics of the mRNA, given protein and metabolite concentrations. Further, we present results for a three gene case in coregulated sets of transcription units as they occur in prokaryotes. However, instead of considering their full dynamics, we use only the data of the metabolites and a designed software sensor. We also show, more generally, that it is possible to rebuild the complete set of nonmeasured concentrations despite the uncertainties in the regulation function or, even more, in the case of not knowing the mRNA dynamics. In addition, the rebuilding of concentrations is not affected by the perturbation due to the additive white Gaussian noise and also we managed to filter the noisy output of the biological system.

  19. Application of random regression model to estimate genetic parameters for average daily gains in Lori-Bakhtiari sheep breed of Iran.

    PubMed

    Farhangfar, H; Naeemipour, H; Zinvand, B

    2007-07-15

    A random regression model was applied to estimate (co) variances, heritabilities and additive genetic correlations among average daily gains. The data was a total of 10876 records belonging to 1828 lambs (progenies of 123 sires and 743 dams) born between 1995 and 2001 in a single large size flock of Lori-Bakhtiari sheep breed in Iran. In the model, fixed environmental effects of year-season of birth, sex, birth type, age of dam and random effects of direct and maternal additive genetic and permanent environment were included. Orthogonal polynomial regression (on the Legendre scale) of third order (cubic) was utilized to model the genetic and permanent environmental (co) variance structure throughout the growth trajectory. Direct and maternal heritability estimates of average daily gains ranged from 0.011 to 0.131 and 0.008 to 0.181, respectively in which pre-weaning average daily gain (0-3 in months) had the lowest direct and highest maternal heritability estimates among the other age groups. The highest and lowest positive direct additive genetic correlations were found to be 0.993 and 0.118 between ADG (0-9) and ADG (0-12) and between ADG (0-3) and ADG (0-12), respectively. The direct additive genetic correlations between adjacent age groups were more closely than between remote age groups.

  20. Connecting the dots between genes, biochemistry, and disease susceptibility: systems biology modeling in human genetics.

    PubMed

    Moore, Jason H; Boczko, Erik M; Summar, Marshall L

    2005-02-01

    Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two or more DNA sequence variations. We review here this approach and then discuss how it can be used to model biochemical and metabolic data in the context of genetic studies of human disease susceptibility.

  1. A Genetically Engineered Mouse Model of Neuroblastoma Driven by Mutated ALK and MYCN

    DTIC Science & Technology

    2014-09-01

    AWARD NUMBER: W81XWH-13-1-0220 TITLE: A Genetically Engineered Mouse Model of Neuroblastoma ...CONTRACT NUMBER A Genetically Engineered Mouse Model of Neuroblastoma Driven by Mutated ALK and MYCN 5b. GRANT NUMBER W81XWH-13-1-0220 5c...common ALK mutations in neuroblastoma , F1174L and R1275Q. We have determined that in tumors cells expressing mutated ALK, different downstream

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

    PubMed Central

    Martin, Ciara A.; Krantz, David E.

    2014-01-01

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

  3. On the underlying assumptions of threshold Boolean networks as a model for genetic regulatory network behavior

    PubMed Central

    Tran, Van; McCall, Matthew N.; McMurray, Helene R.; Almudevar, Anthony

    2013-01-01

    Boolean networks (BoN) are relatively simple and interpretable models of gene regulatory networks. Specifying these models with fewer parameters while retaining their ability to describe complex regulatory relationships is an ongoing methodological challenge. Additionally, extending these models to incorporate variable gene decay rates, asynchronous gene response, and synergistic regulation while maintaining their Markovian nature increases the applicability of these models to genetic regulatory networks (GRN). We explore a previously-proposed class of BoNs characterized by linear threshold functions, which we refer to as threshold Boolean networks (TBN). Compared to traditional BoNs with unconstrained transition functions, these models require far fewer parameters and offer a more direct interpretation. However, the functional form of a TBN does result in a reduction in the regulatory relationships which can be modeled. We show that TBNs can be readily extended to permit self-degradation, with explicitly modeled degradation rates. We note that the introduction of variable degradation compromises the Markovian property fundamental to BoN models but show that a simple state augmentation procedure restores their Markovian nature. Next, we study the effect of assumptions regarding self-degradation on the set of possible steady states. Our findings are captured in two theorems relating self-degradation and regulatory feedback to the steady state behavior of a TBN. Finally, we explore assumptions of synchronous gene response and asynergistic regulation and show that TBNs can be easily extended to relax these assumptions. Applying our methods to the budding yeast cell-cycle network revealed that although the network is complex, its steady state is simplified by the presence of self-degradation and lack of purely positive regulatory cycles. PMID:24376454

  4. Spin-probe ESR and molecular modeling studies on calcium carbonate dispersions in overbased detergent additives.

    PubMed

    Montanari, Luciano; Frigerio, Francesco

    2010-08-15

    Oil-soluble calcium carbonate colloids are used as detergent additives in lubricating oils. They are colloidal dispersions of calcium carbonate particles stabilized by different surfactants; in this study alkyl-aryl-sulfonates and sulfurized alkyl-phenates, widely used in the synthesis of these additives, are considered. The physical properties of surfactant layers surrounding the surfaces of calcium carbonate particles were analyzed by using some nitroxide spin-probes (stable free radicals) and observing the corresponding ESR spectra. The spin-probe molecules contain polar groups which tend to tether them to the carbonate particle polar surface. They can reach these surfaces only if the surfactant layers are not very compact, hence the relative amounts of spin-probe molecules accessing carbonate surfaces are an index of the compactness of surfactant core. ESR signals of spin-probe molecules dissolved in oil or "locked" near the carbonate surfaces are different because of the different molecular mobility. Through deconvolution of the ESR spectra, the fraction of spin-probes penetrating surfactant shells have been calculated, and differences were observed according to the surfactant molecular structures. Moreover, by using specially labeled spin-probes based on stearic acids, functionalized at different separations from the carboxylic acid group, it was possible to interrogate the molecular physical behavior of surfactant shells at different distances from carbonate surfaces. Molecular modeling was applied to generate some three-dimensional micellar models of the colloidal stabilizations of the stabilized carbonate particles with different molecular structures of the surfactant. The diffusion of spin-probe molecules into the surfactant shells were studied by applying a starting force to push the molecules towards the carbonate surfaces and then observing the ensuing behavior. The simulations are in accordance with the ESR data and show that the geometrical

  5. Modeling external carbon addition in biological nutrient removal processes with an extension of the international water association activated sludge model.

    PubMed

    Swinarski, M; Makinia, J; Stensel, H D; Czerwionka, K; Drewnowski, J

    2012-08-01

    The aim of this study was to expand the International Water Association Activated Sludge Model No. 2d (ASM2d) to account for a newly defined readily biodegradable substrate that can be consumed by polyphosphate-accumulating organisms (PAOs) under anoxic and aerobic conditions, but not under anaerobic conditions. The model change was to add a new substrate component and process terms for its use by PAOs and other heterotrophic bacteria under anoxic and aerobic conditions. The Gdansk (Poland) wastewater treatment plant (WWTP), which has a modified University of Cape Town (MUCT) process for nutrient removal, provided field data and mixed liquor for batch tests for model evaluation. The original ASM2d was first calibrated under dynamic conditions with the results of batch tests with settled wastewater and mixed liquor, in which nitrate-uptake rates, phosphorus-release rates, and anoxic phosphorus uptake rates were followed. Model validation was conducted with data from a 96-hour measurement campaign in the full-scale WWTP. The results of similar batch tests with ethanol and fusel oil as the external carbon sources were used to adjust kinetic and stoichiometric coefficients in the expanded ASM2d. Both models were compared based on their predictions of the effect of adding supplemental carbon to the anoxic zone of an MUCT process. In comparison with the ASM2d, the new model better predicted the anoxic behaviors of carbonaceous oxygen demand, nitrate-nitrogen (NO3-N), and phosphorous (PO4-P) in batch experiments with ethanol and fusel oil. However, when simulating ethanol addition to the anoxic zone of a full-scale biological nutrient removal facility, both models predicted similar effluent NO3-N concentrations (6.6 to 6.9 g N/m3). For the particular application, effective enhanced biological phosphorus removal was predicted by both models with external carbon addition but, for the new model, the effluent PO4-P concentration was approximately one-half of that found from

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

    NASA Astrophysics Data System (ADS)

    Bányai, T.

    2009-04-01

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

  7. Rate of evolutionary change in cranial morphology of the marsupial genus Monodelphis is constrained by the availability of additive genetic variation.

    PubMed

    Porto, A; Sebastião, H; Pavan, S E; VandeBerg, J L; Marroig, G; Cheverud, J M

    2015-04-01

    We tested the hypothesis that the rate of marsupial cranial evolution is dependent on the distribution of genetic variation in multivariate space. To do so, we carried out a genetic analysis of cranial morphological variation in laboratory strains of Monodelphis domestica and used estimates of genetic covariation to analyse the morphological diversification of the Monodelphis brevicaudata species group. We found that within-species genetic variation is concentrated in only a few axes of the morphospace and that this strong genetic covariation influenced the rate of morphological diversification of the brevicaudata group, with between-species divergence occurring fastest when occurring along the genetic line of least resistance. Accounting for the geometric distribution of genetic variation also increased our ability to detect the selective regimen underlying species diversification, with several instances of selection only being detected when genetic covariances were taken into account. Therefore, this work directly links patterns of genetic covariation among traits to macroevolutionary patterns of morphological divergence. Our findings also suggest that the limited distribution of Monodelphis species in morphospace is the result of a complex interplay between the limited dimensionality of available genetic variation and strong stabilizing selection along two major axes of genetic variation.

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

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

  9. Additive surface complexation modeling of uranium(VI) adsorption onto quartz-sand dominated sediments.

    PubMed

    Dong, Wenming; Wan, Jiamin

    2014-06-17

    Many aquifers contaminated by U(VI)-containing acidic plumes are composed predominantly of quartz-sand sediments. The F-Area of the Savannah River Site (SRS) in South Carolina (USA) is an example. To predict U(VI) mobility and natural attenuation, we conducted U(VI) adsorption experiments using the F-Area plume sediments and reference quartz, goethite, and kaolinite. The sediments are composed of ∼96% quartz-sand and 3-4% fine fractions of kaolinite and goethite. We developed a new humic acid adsorption method for determining the relative surface area abundances of goethite and kaolinite in the fine fractions. This method is expected to be applicable to many other binary mineral pairs, and allows successful application of the component additivity (CA) approach based surface complexation modeling (SCM) at the SRS F-Area and other similar aquifers. Our experimental results indicate that quartz has stronger U(VI) adsorption ability per unit surface area than goethite and kaolinite at pH ≤ 4.0. Our modeling results indicate that the binary (goethite/kaolinite) CA-SCM under-predicts U(VI) adsorption to the quartz-sand dominated sediments at pH ≤ 4.0. The new ternary (quartz/goethite/kaolinite) CA-SCM provides excellent predictions. The contributions of quartz-sand, kaolinite, and goethite to U(VI) adsorption and the potential influences of dissolved Al, Si, and Fe are also discussed.

  10. Modeling and additive manufacturing of bio-inspired composites with tunable fracture mechanical properties.

    PubMed

    Dimas, Leon S; Buehler, Markus J

    2014-07-07

    Flaws, imperfections and cracks are ubiquitous in material systems and are commonly the catalysts of catastrophic material failure. As stresses and strains tend to concentrate around cracks and imperfections, structures tend to fail far before large regions of material have ever been subjected to significant loading. Therefore, a major challenge in material design is to engineer systems that perform on par with pristine structures despite the presence of imperfections. In this work we integrate knowledge of biological systems with computational modeling and state of the art additive manufacturing to synthesize advanced composites with tunable fracture mechanical properties. Supported by extensive mesoscale computer simulations, we demonstrate the design and manufacturing of composites that exhibit deformation mechanisms characteristic of pristine systems, featuring flaw-tolerant properties. We analyze the results by directly comparing strain fields for the synthesized composites, obtained through digital image correlation (DIC), and the computationally tested composites. Moreover, we plot Ashby diagrams for the range of simulated and experimental composites. Our findings show good agreement between simulation and experiment, confirming that the proposed mechanisms have a significant potential for vastly improving the fracture response of composite materials. We elucidate the role of stiffness ratio variations of composite constituents as an important feature in determining the composite properties. Moreover, our work validates the predictive ability of our models, presenting them as useful tools for guiding further material design. This work enables the tailored design and manufacturing of composites assembled from inferior building blocks, that obtain optimal combinations of stiffness and toughness.

  11. Evaluation of the performance of smoothing functions in generalized additive models for spatial variation in disease.

    PubMed

    Siangphoe, Umaporn; Wheeler, David C

    2015-01-01

    Generalized additive models (GAMs) with bivariate smoothing functions have been applied to estimate spatial variation in risk for many types of cancers. Only a handful of studies have evaluated the performance of smoothing functions applied in GAMs with regard to different geographical areas of elevated risk and different risk levels. This study evaluates the ability of different smoothing functions to detect overall spatial variation of risk and elevated risk in diverse geographical areas at various risk levels using a simulation study. We created five scenarios with different true risk area shapes (circle, triangle, linear) in a square study region. We applied four different smoothing functions in the GAMs, including two types of thin plate regression splines (TPRS) and two versions of locally weighted scatterplot smoothing (loess). We tested the null hypothesis of constant risk and detected areas of elevated risk using analysis of deviance with permutation methods and assessed the performance of the smoothing methods based on the spatial detection rate, sensitivity, accuracy, precision, power, and false-positive rate. The results showed that all methods had a higher sensitivity and a consistently moderate-to-high accuracy rate when the true disease risk was higher. The models generally performed better in detecting elevated risk areas than detecting overall spatial variation. One of the loess methods had the highest precision in detecting overall spatial variation across scenarios and outperformed the other methods in detecting a linear elevated risk area. The TPRS methods outperformed loess in detecting elevated risk in two circular areas.

  12. Evaluation of the Performance of Smoothing Functions in Generalized Additive Models for Spatial Variation in Disease

    PubMed Central

    Siangphoe, Umaporn; Wheeler, David C.

    2015-01-01

    Generalized additive models (GAMs) with bivariate smoothing functions have been applied to estimate spatial variation in risk for many types of cancers. Only a handful of studies have evaluated the performance of smoothing functions applied in GAMs with regard to different geographical areas of elevated risk and different risk levels. This study evaluates the ability of different smoothing functions to detect overall spatial variation of risk and elevated risk in diverse geographical areas at various risk levels using a simulation study. We created five scenarios with different true risk area shapes (circle, triangle, linear) in a square study region. We applied four different smoothing functions in the GAMs, including two types of thin plate regression splines (TPRS) and two versions of locally weighted scatterplot smoothing (loess). We tested the null hypothesis of constant risk and detected areas of elevated risk using analysis of deviance with permutation methods and assessed the performance of the smoothing methods based on the spatial detection rate, sensitivity, accuracy, precision, power, and false-positive rate. The results showed that all methods had a higher sensitivity and a consistently moderate-to-high accuracy rate when the true disease risk was higher. The models generally performed better in detecting elevated risk areas than detecting overall spatial variation. One of the loess methods had the highest precision in detecting overall spatial variation across scenarios and outperformed the other methods in detecting a linear elevated risk area. The TPRS methods outperformed loess in detecting elevated risk in two circular areas. PMID:25983545

  13. Generalized additive models reveal the intrinsic complexity of wood formation dynamics.

    PubMed

    Cuny, Henri E; Rathgeber, Cyrille B K; Kiessé, Tristan Senga; Hartmann, Felix P; Barbeito, Ignacio; Fournier, Meriem

    2013-04-01

    The intra-annual dynamics of wood formation, which involves the passage of newly produced cells through three successive differentiation phases (division, enlargement, and wall thickening) to reach the final functional mature state, has traditionally been described in conifers as three delayed bell-shaped curves followed by an S-shaped curve. Here the classical view represented by the 'Gompertz function (GF) approach' was challenged using two novel approaches based on parametric generalized linear models (GLMs) and 'data-driven' generalized additive models (GAMs). These three approaches (GFs, GLMs, and GAMs) were used to describe seasonal changes in cell numbers in each of the xylem differentiation phases and to calculate the timing of cell development in three conifer species [Picea abies (L.), Pinus sylvestris L., and Abies alba Mill.]. GAMs outperformed GFs and GLMs in describing intra-annual wood formation dynamics, showing two left-skewed bell-shaped curves for division and enlargement, and a right-skewed bimodal curve for thickening. Cell residence times progressively decreased through the season for enlargement, whilst increasing late but rapidly for thickening. These patterns match changes in cell anatomical features within a tree ring, which allows the separation of earlywood and latewood into two distinct cell populations. A novel statistical approach is presented which renews our understanding of xylogenesis, a dynamic biological process in which the rate of cell production interplays with cell residence times in each developmental phase to create complex seasonal patterns.

  14. The host acts as a genetic bottleneck during serial infections: an insect-fungal model system.

    PubMed

    Scully, Lisa R; Bidochka, Michael J

    2006-11-01

    The genetic variation of a pathogen population is a pivotal component of pathogen evolution, having important implications for emerging diseases, nosocomial infections, and laboratory subculturing practices. Furthermore, it is undoubtedly altered during infection of a host. We address this issue using an insect-fungal model system to examine the influence of serial host passage on the genetic variation of a pathogen population. Using amplified fragment length polymorphism, a strain of the opportunistic fungus, Aspergillus flavus, showing initially 98% genetic similarity, was assessed for changes in genetic diversity during repeated passage through Galleria mellonella larvae and compared to that of a parallel population serially subcultured on artificial media. In two independent trials, the genetic diversity of the population passed through the insect dropped significantly, while the genetic variation of the population subcultured on media increased or remained unchanged. However, there were no changes in virulence or the production of protease or aflatoxin, indicating an apparent lack of selection. We suggest that the insect acted as a genetic bottleneck, reducing the genetic diversity of the A. flavus population. The ability of a host to produce a genetic bottleneck in a pathogen population impacts our understanding of emerging diseases, nosocomial infections, and laboratory subculturing practices.

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

    PubMed

    Bellucci, Michael A; Coker, David F

    2011-07-28

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

  16. Using genetically engineered animal models in the postgenomic era to understand gene function in alcoholism.

    PubMed

    Reilly, Matthew T; Harris, R Adron; Noronha, Antonio

    2012-01-01

    Over the last 50 years, researchers have made substantial progress in identifying genetic variations that underlie the complex phenotype of alcoholism. Not much is known, however, about how this genetic variation translates into altered biological function. Genetic animal models recapitulating specific characteristics of the human condition have helped elucidate gene function and the genetic basis of disease. In particular, major advances have come from the ability to manipulate genes through a variety of genetic technologies that provide an unprecedented capacity to determine gene function in the living organism and in alcohol-related behaviors. Even newer genetic-engineering technologies have given researchers the ability to control when and where a specific gene or mutation is activated or deleted, allowing investigators to narrow the role of the gene's function to circumscribed neural pathways and across development. These technologies are important for all areas of neuroscience, and several public and private initiatives are making a new generation of genetic-engineering tools available to the scientific community at large. Finally, high-throughput "next-generation sequencing" technologies are set to rapidly increase knowledge of the genome, epigenome, and transcriptome, which, combined with genetically engineered mouse mutants, will enhance insight into biological function. All of these resources will provide deeper insight into the genetic basis of alcoholism.

  17. Multinomial additive hazard model to assess the disability burden using cross-sectional data.

    PubMed

    Yokota, Renata T C; Van Oyen, Herman; Looman, Caspar W N; Nusselder, Wilma J; Otava, Martin; Kifle, Yimer Wasihun; Molenberghs, Geert

    2017-03-23

    Population aging is accompanied by the burden of chronic diseases and disability. Chronic diseases are among the main causes of disability, which is associated with poor quality of life and high health care costs in the elderly. The identification of which chronic diseases contribute most to the disability prevalence is important to reduce the burden. Although longitudinal studies can be considered the gold standard to assess the causes of disability, they are costly and often with restricted sample size. Thus, the use of cross-sectional data under certain assumptions has become a popular alternative. Among the existing methods based on cross-sectional data, the attribution method, which was originally developed for binary disability outcomes, is an attractive option, as it enables the partition of disability into the additive contribution of chronic diseases, taking into account multimorbidity and that disability can be present even in the absence of disease. In this paper, we propose an extension of the attribution method to multinomial responses, since disability is often measured as a multicategory variable in most surveys, representing different severity levels. The R function constrOptim is used to maximize the multinomial log-likelihood function subject to a linear inequality constraint. Our simulation study indicates overall good performance of the model, without convergence problems. However, the model must be used with care for populations with low marginal disability probabilities and with high sum of conditional probabilities, especially with small sample size. For illustration, we apply the model to the data of the Belgian Health Interview Surveys.

  18. A Comparative Kirkwood-Buff Study of Aqueous Methanol Solutions Modeled by the CHARMM Additive and Drude Polarizable Force Fields

    PubMed Central

    Lin, Bin; He, Xibing; MacKerell, Alexander D.

    2013-01-01

    A comparative study on aqueous methanol solutions modeled by the CHARMM additive and Drude polarizable force fields was carried out by employing Kirkwood-Buff analysis. It was shown that both models reproduced the experimental Kirkwood-Buff integrals and excess coordination numbers adequately well over the entire concentration range. The Drude model showed significant improvement over the additive model in solution densities, partial molar volumes, excess molar volumes, concentration-dependent diffusion constants, and dielectric constants. However, the additive model performed somewhat better than the Drude model in reproducing the activity derivative, excess molar Gibbs energy and excess molar enthalpy of mixing. This is due to the additive achieving a better balance among solute-solute, solute-solvent, and solvent-solvent interactions, indicating the potential for improvements in the Drude polarizable alcohol model. PMID:23947568

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

    USGS Publications Warehouse

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

    2003-01-01

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

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

    ERIC Educational Resources Information Center

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

    2015-01-01

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

  1. A mathematical ecogenetic predator-prey model where both populations are genetically distinguishable

    NASA Astrophysics Data System (ADS)

    Castellino, Luisa; Peretti, Sabrina; Rivoira, Stella; Venturino, Ezio

    2016-10-01

    A mathematical ecogenetic predator-prey model with both populations genetically distinguishable is introduced. Equilibria are investigated for feasibility and stability and are numerically found to be related via a transcritical bifurcation. These results are in line with parallel studies on related models. A sensitivity analysis in terms of pairs of model parameters is performed.

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

    NASA Astrophysics Data System (ADS)

    Sastry, Kumara Narasimha

    2007-03-01

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

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

    PubMed

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

    2014-10-01

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

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

    PubMed Central

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

    2015-01-01

    The major type of human liver cancer is hepatocellular carcinoma (HCC), and there are currently many risk factors that contribute to this deadly disease. The majority of HCC occurrences are associated with chronic hepatitis viral infection, and hepatitis B viral (HBV) infection is currently a major health problem in Eastern Asia. Elucidating the genetic mechanisms associated with HBV-induced HCC has been difficult due to the heterogeneity and genetic complexity associated with this disease. A repertoire of animal models has been broadly used to study the pathophysiology and to develop potential treatment regimens for HBV-associated HCC. The use of these animal models has provided valuable genetic information and has been an important contributor to uncovering the factors involved in liver malignant transformation, invasion and metastasis. Recently, transposon-based