Academic literature on the topic 'Trait estimation'
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Journal articles on the topic "Trait estimation"
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Fanourakis, Dimitrios, Filippos Kazakos, and Panayiotis A. Nektarios. "Allometric Individual Leaf Area Estimation in Chrysanthemum." Agronomy 11, no. 4 (2021): 795. http://dx.doi.org/10.3390/agronomy11040795.
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A model for estimating the area of individual leaves (LA) by employing their dimensions was developed for chrysanthemum. Further hypotheses were tested: (a) LA estimation is improved by considering blade length (Lb) rather than leaf length (L), and (b) a reasonable LA estimation can be attainable by considering L in conjunction to a shape trait, which is cultivar dependent. For the model development, six cultivars were employed (1500 leaves in total), while for model validation, an independent set of nine cultivars was utilized (1125 leaves in total). Several characteristics were digitally assessed in fully expanded leaves which included petiole length, leaf L, width (W), perimeter, shape traits (aspect ratio, circularity, roundness, solidity), together with LA. LA estimation was more accurate by considering both L and W, as compared to a single dimension. A linear model, employing the product of L by W as independent variable, provided the most accurate LA estimation (R2 = 0.84). The model validation indicated a highly significant correlation between computed and measured LA (R2 = 0.88). Replacing L by Lb reasonably predicted LA (R2 = 0.832) but at some expense of accuracy. Contrary to expectation, considering L (or W) and a cultivar-specific shape trait generally led to poor LA estimations.
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Lopez, Bryan Irvine, Ju-Hwan Son, Kangseok Seo, and Dajeong Lim. "Estimation of Genetic Parameters for Reproductive Traits in Hanwoo (Korean Cattle)." Animals 9, no. 10 (2019): 715. http://dx.doi.org/10.3390/ani9100715.
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Genetic parameters for the reproductive traits of Hanwoo cattle were estimated using data obtained from 15,355 cows in 92 herds across South Korea, which were inseminated from May 1997 to July 2016. An “average information” restricted maximum likelihood (REML) procedure that fit in single-trait and multi-trait animal models was used to estimate the variance components of age at first calving (AFC), calving interval (CI), days open (DO), and gestation length (GL). Results showed the low estimates of heritability for all reproductive traits from both single-trait and multi-trait models. Estimates of heritability for AFC were 0.08 and 0.10 with single-trait and multi-trait models, respectively, while the estimates of heritability using the same animal models ranged from 0.01 to 0.07, 0.01 to 0.09, and 0.10 to 0.16 for CI, DO, and GL, accordingly. While AFC showed positive genetic correlations of 0.52 and 0.46 with CI and DO, respectively, the estimates of genetic and phenotypic correlations of GL with AFC were close to zero. Moreover, phenotypic correlations of GL with CI and DO were also close to zero; however, the corresponding genetic correlations were 0.13 and –0.38 for CI and DO, respectively. These estimated variance components and genetic correlations for reproductive traits can be utilized for genetic improvement programs of Hanwoo cattle.
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Nagy, I., J. Farkas, I. Curik, G. Gorjanc, P. Gyovai, and Zs Szendrő. "Estimation of additive and dominance variance for litter size components in rabbits." Czech Journal of Animal Science 59, No. 4 (2014): 182–89. http://dx.doi.org/10.17221/7342-cjas.
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Additive, dominance, and permanent environmental variance components were estimated for the number of kits born alive, number of kits born dead, and total number of kits born of a synthetic rabbit line (called Pannon Ka). The data file consisted of 11 582 kindling records of 2620 does collected between the years 1996–2013. The total number of animals in the pedigree files was 4012. The examined traits were evaluated using single-trait and two-trait (number of kits born alive-dead) animal models containing all or part of the following effects: additive genetic effects, permanent environmental effects, dominance effects. Heritability estimates calculated using the basic single-trait and two-trait models were 0.094 ± 0.018 and 0.090 ± 0.016 for number of kits born alive, 0.037 ± 0.010 and 0.041 ± 0.012 for number of kits born dead, and 0.117 ± 0.018 for total number of kits born, respectively. The relative significance of permanent environmental effects was 0.069 ± 0.014 and 0.069 ± 0.012 for number of kits born alive, 0.025 ± 0.011 and 0.023 ± 0.010 for number of kits born dead, and 0.060 ± 0.013 for total number of kits born, respectively. Using the extended single-trait and two-trait models, the ratios of the dominance components compared to the phenotypic variances were 0.048 ± 0.008 and 0.046 ± 0.007 for number of kits born alive, 0.068 ± 0.006 and 0.065 ± 0.006 for number of kits born dead, and 0.005 ± 0.0073 for total number of kits born, respectively. Genetic correlation coefficients between number of kits born alive and number of kits born dead were 0.401 ± 0.171 and 0.521 ± 0.182, respectively. Spearman’s rank correlations between the breeding values of the different single-trait models were close to unity in all traits (0.992–0.990). Much lower breeding value stability was found for two-trait models (0.384–0.898), especially for number of kits born dead. Results showed that the dominance components for number of kits born alive and number of kits born dead were not zero and affected the ranking of the animals (based on the breeding values).  
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Cappa, Eduardo P., and Rodolfo JC Cantet. "Bayesian inference for normal multiple-trait individual-tree models with missing records via full conjugate Gibbs." Canadian Journal of Forest Research 36, no. 5 (2006): 1276–85. http://dx.doi.org/10.1139/x06-024.
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In forest genetics, restricted maximum likelihood (REML) estimation of (co)variance components from normal multiple-trait individual-tree models is affected by the absence of observations in any trait and individual. Missing records affect the form of the distribution of REML estimates of genetics parameters, or of functions of them, and the estimating equations are computationally involved when several traits are analysed. An alternative to REML estimation is a fully Bayesian approach through Markov chain Monte Carlo. The present research describes the use of the full conjugate Gibbs algorithm proposed by Cantet et al. (R.J.C. Cantet, A.N. Birchmeier, and J.P. Steibel. 2004. Genet. Sel. Evol. 36: 4964) to estimate (co)variance components in multiple-trait individual-tree models. This algorithm converges faster to the marginal posterior densities of the parameters than regular data augmentation from multivariate normal data with missing records. An expression to calculate the deviance information criterion for the selection of linear parameters in normal multiple-trait models is also given. The developments are illustrated by means of data from different crosses of two species of Pinus.
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Zhu, Anqi, Nana Matoba, Emma P. Wilson, et al. "MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity." PLOS Genetics 17, no. 4 (2021): e1009455. http://dx.doi.org/10.1371/journal.pgen.1009455.
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Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus’s estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.
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Thomas, Neal. "Assessing Model Sensitivity of the Imputation Methods Used in the National Assessment of Educational Progress." Journal of Educational and Behavioral Statistics 25, no. 4 (2000): 351–71. http://dx.doi.org/10.3102/10769986025004351.
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The National Assessment of Educational Progress (NAEP) uses latent trait item response models to summarize performance of students on assessments of educational proficiency in different subject areas such as mathematics and reading. Because of limited examination time and concerns about student motivation. NAEP employs sparse matrix sampling designs that assign a small number of examination items to each sampled student to measure broad curriculums. As a consequence, each sampled student’s latent trait is not accurately measured, and NAEP uses multiple imputation missing data statistical methods to account for the uncertainty about the latent traits. The sensitivity of these model-based estimation and reporting procedures to statistical and psychometric assumptions is assessed. Estimation of the mean of the latent trait train different subpopulations was very robust to the modeling assumptions. Many of the other currently reported summaries, however; may depend on the modeling assumptions underlying the estimation procedures; these assumptions, motivated primarily by analytic tractability, are unlikely to attain, raising concerns about current reporting practices. The results indicate that more conservative criteria should be considered when forming intervals about estimates, and when assessing significance. A possible expansion of the imputation model is suggested that may improve its performance.
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McINTYRE, LAUREN M., CYNTHIA J. COFFMAN, and R. W. DOERGE. "Detection and localization of a single binary trait locus in experimental populations." Genetical Research 78, no. 1 (2001): 79–92. http://dx.doi.org/10.1017/s0016672301005092.
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The advancements made in molecular technology coupled with statistical methodology have led to the successful detection and location of genomic regions (quantitative trait loci; QTL) associated with quantitative traits. Binary traits (e.g. susceptibility/resistance), while not quantitative in nature, are equally important for the purpose of detecting and locating significant associations with genomic regions. Existing interval regression methods used in binary trait analysis are adapted from quantitative trait analysis and the tests for regression coefficients are tests of effect, not detection. Additionally, estimates of recombination that fail to take into account varying penetrance perform poorly when penetrance is incomplete. In this work a complete probability model for binary trait data is developed allowing for unbiased estimation of both penetrance and recombination between a genetic marker locus and a binary trait locus for backcross and F2 experimental designs. The regression model is reparameterized allowing for tests of detection. Extensive simulations were conducted to assess the performance of estimation and testing in the proposed parameterization. The proposed parameterization was compared with interval regression via simulation. The results indicate that our parameterization shows equivalent estimation capabilities, requires less computational effort and works well with only a single marker.
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Gao, Boran, Can Yang, Jin Liu, and Xiang Zhou. "Accurate genetic and environmental covariance estimation with composite likelihood in genome-wide association studies." PLOS Genetics 17, no. 1 (2021): e1009293. http://dx.doi.org/10.1371/journal.pgen.1009293.
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Genetic and environmental covariances between pairs of complex traits are important quantitative measurements that characterize their shared genetic and environmental architectures. Accurate estimation of genetic and environmental covariances in genome-wide association studies (GWASs) can help us identify common genetic and environmental factors associated with both traits and facilitate the investigation of their causal relationship. Genetic and environmental covariances are often modeled through multivariate linear mixed models. Existing algorithms for covariance estimation include the traditional restricted maximum likelihood (REML) method and the recent method of moments (MoM). Compared to REML, MoM approaches are computationally efficient and require only GWAS summary statistics. However, MoM approaches can be statistically inefficient, often yielding inaccurate covariance estimates. In addition, existing MoM approaches have so far focused on estimating genetic covariance and have largely ignored environmental covariance estimation. Here we introduce a new computational method, GECKO, for estimating both genetic and environmental covariances, that improves the estimation accuracy of MoM while keeping computation in check. GECKO is based on composite likelihood, relies on only summary statistics for scalable computation, provides accurate genetic and environmental covariance estimates across a range of scenarios, and can accommodate SNP annotation stratified covariance estimation. We illustrate the benefits of GECKO through simulations and applications on analyzing 22 traits from five large-scale GWASs. In the real data applications, GECKO identified 50 significant genetic covariances among analyzed trait pairs, resulting in a twofold power gain compared to the previous MoM method LDSC. In addition, GECKO identified 20 significant environmental covariances. The ability of GECKO to estimate environmental covariance in addition to genetic covariance helps us reveal strong positive correlation between the genetic and environmental covariance estimates across trait pairs, suggesting that common pathways may underlie the shared genetic and environmental architectures between traits.
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Ronin, Yefim I., Abraham B. Korol, and Eviatar Nevo. "Single- and Multiple-Trait Mapping Analysis of Linked Quantitative Trait Loci: Some Asymptotic Analytical Approximations." Genetics 151, no. 1 (1999): 387–96. http://dx.doi.org/10.1093/genetics/151.1.387.
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Abstract Estimating the resolution power of mapping analysis of linked quantitative trait loci (QTL) remains a difficult problem, which has been previously addressed mainly by Monte Carlo simulations. The analytical method of evaluation of the expected LOD developed in this article spreads the “deterministic sampling approach for the case of two linked QTL for single- and two-trait analysis. Several complicated questions are addressed through this evaluation: the dependence of QTL detection power on the QTL effects, residual correlation between the traits, and the effect of epistatic interaction between the QTL for one or both traits on expected LOD (ELOD), etc. Although this method gives only an asymptotic estimation of ELOD, it allows one to get an approximate assessment of a broad spectrum of mapping situations. A good correspondence was found between the ELODs predicted by the model and LOD values averaged over Monte Carlo simulations.
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Krupová, Zuzana, Emil Krupa, Ludmila Zavadilová, Eva Kašná, and Eliska Žáková. "Current challenges for trait economic values in animal breeding." Czech Journal of Animal Science 65, No. 12 (2020): 454–62. http://dx.doi.org/10.17221/161/2020-cjas.
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Modern selection approaches are expected to bring about the cumulative and permanent improvement of animal performance and profitability of animal production. Breeding values of traits along with trait economic values (EVs) are utilised for economic selection purposes with many species all over the world. Currently, some challenges related to trait EVs in animal breeding should be considered. First, the selection response based on the higher accuracy of genomic selection may be reduced due to improper weighting of the trait breeding values of selection candidates. A comprehensive approach applied in bioeconomic models allows suitable trait EV calculations. Further challenges comprise the new breeding objectives associated with climate change, environmental mitigation and animal adaptability. The estimation of EVs for traits influencing greenhouse gas (GHG) emissions has been mostly based on including the value of CO<sub>2</sub> emission equivalent in the trait EVs, on calculating EVs for feed efficiency traits and on methane yield as a direct trait of GHG emission. Genetic improvement of production, functional, feed efficiency and methane traits through the application of multi-trait selection indices was found to be crucial for mitigation of emissions and farm profitability. Defining the non-market values of traits connected with climate protection could be a useful solution for including these traits in an economic breeding objective. While GHG emissions mostly change the costs per unit of production, animal adaptability in its complexity influences animal performance. Clear definitions of disease, fertility, mortality and other breeding objective traits allow the proper calculation of trait EVs, and an accurate estimation of trait genetic parameters could lead to sufficient economic selection response. This complex approach could be beneficial for more effective utilisation of inputs and overall economic and environmental sustainability of animal production.
Dissertations / Theses on the topic "Trait estimation"
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Martinez, de la Vega Octavio. "Quantitative trait loci estimation in plant populations." Thesis, University of Reading, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358346.
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Tzamourani, Panagiota. "Robustness, semiparametric estimation and goodness-of-fit of latent trait models." Thesis, London School of Economics and Political Science (University of London), 1999. http://etheses.lse.ac.uk/1623/.
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This thesis studies the one-factor latent trait model for binary data. In examines the sensitivity of the model when the assumptions about the model are violated, it investigates the information about the prior distribution when the model is estimated semi-parametrically and it also examines the goodness-of-fit of the model using Monte-Carlo simulations. Latent trait models are applied to data arising from psychometric tests, ability tests or attitude surveys. The data are often contaminated by guessing, cheating, unwillingness to give the true answer or gross errors. To study the sensitivity of the model when the data are contaminated we derive the Influence Function of the parameters and the posterior means, a tool developed in the frame of robust statistics theory. We study the behaviour of the Influence Function for changes in the data and also the behaviour of the parameters and the posterior means when the data are artificially contaminated. We further derive the Influence Function of the parameters and the posterior means for changes in the prior distribution and study empirically the behaviour of the model when the prior is a mixture of distributions. Semiparametric estimation involves estimation of the prior together with the item parameters. A new algorithm for fully semiparametric estimation of the model is given. The bootstrap is then used to study the information on the latent distribution than can be extracted from the data when the model is estimated semiparametrically. The use of the usual goodness-of-fit statistics has been hampered for latent trait models because of the sparseness of the tables. We propose the use of Monte-Carlo simulations to derive the empirical distribution of the goodness-of-fit statistics and also the examination of the residuals as they may pinpoint to the sources of bad fit.
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Pender, Jocelyn E. "Climatic Niche Estimation, Trait Evolution and Species Richness in North American Carex (Cyperaceae)." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34334.
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With close to 2100 species, the flowering plant genus Carex (Cyperaceae; sedges) is an example of an evolutionary radiation. Despite its potential for use as a model taxon in evolutionary studies, the diversification of sedges remains largely unexplored. This thesis realizes the potential of Carex as an evolutionary model group by using it to ask questions about species richness patterns. More specifically, it seeks to determine the relationship, if any, between rates of trait evolution and species richness. This tests the hypothesis that organisms with increased abilities to evolve new traits, speciate more rapidly. Morphological and ecological (habitat and climatic niche) traits are modelled on a nearly complete regional (North America north of Mexico) phylogeny and rates of trait evolution are compared among non-nested sister groups. However, before trait evolution is modelled, this work evaluates the sensitivity of climatic niche estimates to underlying distribution datasets. It tests the agreement of niche estimates derived from the commonly used online repository GBIF (the Global Biodiversity Information Facility) and county-level distributions via BONAP (the Biota of North America Program). Results showed that in the context of phylogenetic comparative analyses, it is not vital to obtain highly accurate climatic niche estimates. The second study found significant positive correlations between the rates of climatic niche, habitat and reproductive morphological evolution and species richness. This result supports the role of high trait lability in generating species richness and more generally, the idea that high trait disparity through evolutionary time leads to species success.
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Pita, Fabiano Veraldo da Costa. "Construction of the gametic covariance matrix for quantitative trait loci analyses in outbred populations." Universidade Federal de Viçosa, 2003. http://www.locus.ufv.br/handle/123456789/10501.
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Previous issue date: 2003-09-05<br>Conselho Nacional de Desenvolvimento Científico e Tecnológico<br>A aplicação de análises de “Quantitative Trait Loci” (QTL) em populações exogâmicas é desafiadora porque pressuposições simplificadoras não podem ser aplicadas (por exemplo, os alelos QTL não podem ser assumidos fixados em diferentes famílias, o número de alelos QTL segregantes não é conhecido a priori, não há desequilíbrio de ligação entre um dado alelo marcador e um dado alelo QTL). Quando o efeito genotípico do QTL é assumido aleatório no modelo de análise, a matriz de covariância gamética deve ser calculada para a realização das análises em populações exogâmicas. A acurácia dessa matriz é importante para a obtenção de estimativas confiáveis da posição ou efeito do QTL em análises de mapeamento, ou de valores genotípicos em avaliação genética assistida por marcadores. O objetivo do primeiro estudo foi avaliar diferente estratégias já implementadas em programas computacionais (SO- LAR, LOKI, ESIP e MATVEC) para calcular a matriz de coeficientes Idênticos por Descendência (IBD), que é necessária para o mapeamento de QTL em populações exogâmicas. SOLAR utiliza um método baseado em regressão linear, LOKI e ESIP são ambos baseados em “reverse peeling” e o amostrador implementado em MAT VEC amostra indicadores de segregação. Um pedigree com estrutura F2 típica foi simulado com uma família F2 pequena (2 indivíduos) ou grande (20 indivíduos) e marcadores flanqueadores localizados a 2 cM, 5 cM ou 10 cM de distância um do outro, com o QTL localizado no meio do intervalo. A habilidade dessas estratégias em lidar com informações de marcadores perdidas foi avaliada assumindo um dos pais da geração F2 com ou sem informação de marcador. SOLAR nao estimou os coeficientes IBD corretamente para a maior parte das situações simuladas, enquanto que LOKI apre- sentou problemas quando o tamanho da família F2 era grande. ESIP e o amostrador em MATVEC apresentaram bom desempenho em todas as situacões simuladas, com estimativas de coeficientes IBD próximas aos coeficientes verdadeiros. Portanto, ESIP e MATVEC são os softwares mais indicados quando analises genéticas são realizadas em pedigrees com estruturas complexas. O objetivo do segundo estudo foi avaliar o efeito da utilização de uma melhor aproximação da inversa da matriz de covariância gamética para a avaliação genética de grandes populações de animais domésticos. Algoritmos eficientes, baseados no rastreamento dos alelos QTL de um indivíduo em relação aos de seus avós (Probabilidade de Descendência de um QTL - PDQ), podem ser usados para construir a inversa da matriz de covariância gamética diretamente. Mas essa inversa é uma aproximação quando há informação incompleta de marcador. Também, o calculo exato de PDQºs torna-se difícil quando a informação de marcador é incompleta. Nesse estudo, a inversa da matriz de covariãncia gamética para uma pop- ulação exogãmica simulada foi calculada usando o algoritmo eficiente, mas as PDQ's foram calculadas usando um algoritmo Monte Carlo Cadeia de Markov (MCMC). Essa inversa foi utilizada para predizer o valor genético dos indivíduos através de BLUP assistido por marcadores (MABLUP). O efeito dos cálculos de PDQ usando o algoritmo MCMC sobre a acurãcia da MABLUP foi avaliado com base na resposta a seleção realizada, calculada para o pedigree simulado. Os resultados mostraram que quando as PDQ’S foram estimadas usando MCMC a perda em resposta devido ao uso da inversa aproximada pode ser reduzida em aproximadamente 20%, enquanto que em estudos anteriores essa redução foi de 50%. Ainda, quando quatro marcadores bi-alélicos foram utilizados a resposta para MABLUP foi maior e a perda em re- sposta devido a marcadores com informação perdida foi menor, quando comparadas a situação onde apenas dois marcadores bi-alélicos foram utilizados.<br>The application of Quantitative Trait Loci (QTL) analyses in outbred population is challenging because simplified assumptions do not hold for these populations (e.g., the QTL alleles cannot be assumed fixed in different families, the number of QTL alleles segregating is not known a priori, there is not gametic phase disequilibrium between a given genetic marker allele and a QTL allele). When the QTL genotypic effect is assumed random, the gametic covariance matrix must be calculated to per- form QTL analyses in outbred populations. The accuracy of this matrix is important to obtain reliable estimates of QTL position or effect when applying QTL mapping, or QTL genotypic values when applying Marker Assisted Genetic Evaluation. The objective of the first study was to evaluate the different strategies already imple- mented in softwares (SOLAR, LOKI, ESIP and MATVEC) to calculate the matrix of identical by descent (IBD) coefficients, which is required for QTL mapping anal- ysis in outbred populations. SOLAR uses a regression method, LOKI and ESIP are both based on reverse peeling, and the MAT VEC sampler samples segregation in- dicators. A typical F2 pedigree was simulated with a small (2 offspring) or a large (20 offspring) F2 family, and the flanking markers were simulated 2 CM, 5 CM, or 10 CM apart, with the QTL located in the middle. The ability of these strategies to deal with missing genetic marker information was evaluated assuming one of the F2 parents with or without marker information. SOLAR failed to estimate the correct coefficients at almost all situations simulated, while LOKI showed problems when a large family was present in the pedigree. ESIP and MATVEC sampler performed well at all situations, providing IBD coefficients closed to the true ones. Therefore, ESIP and MATVEC are more indicated when genetic analysis are carried out on complex pedigree structures. The objective of the second study was to evaluate the effect of using a better approximation of the inverse of the gametic covariance matrix on the genetic evaluation of large livestock populations. Efficient algorithms, based on trac- ing the QTL alleles of an individual to its grandmother or grandfather (probability of descent a QTL - PDQ’s), can be used to construct the inverse of the gametic covari- ance matrix directly. But this inverse is an approximation when incomplete marker information is available. Also, computing the exact PDQ’s becomes difficult when marker information is incomplete. In this study, the inverse of the gametic covariance matrix for a simulated outbred pedigree was calculated using the efficient algorithm, but the PDQ’s were calculated using a Markov chain Monte Carlo (MCMC) algo- rithm. This inverse was used to calculate the predicted genetic value of individuals through Marker Assisted Best Linear Unbiased Prediction (MABLUP). The effect of PDQ calculations using the MCMC algorithm on MABLUP accuracy was evaluated based on the realized response to selection for the simulated pedigree. The results showed that by estimating the PDQ’s by MCMC the loss in response because of using an approximate inverse could be reduced to about 20%, while in previous studies this reduction was of 50%. Further, response to MABLUP was greater when four bi-allelic markers were used, and the loss in response due to missing markers was smaller in the case with four markers compared to when only two bi-allelic markers were used.<br>Tese importada do Alexandria
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Gaasch, Jean-Christoph Verfasser], Susanne [Akademischer Betreuer] [Rässler, and Claus [Akademischer Betreuer] Carstensen. "Bayesian estimation of latent trait distributions considering hierarchical structures and partially missing covariate data / Jean-Christoph Gaasch ; Susanne Rässler, Claus Carstensen." Bamberg : Otto-Friedrich-Universität Bamberg, 2017. http://d-nb.info/1147756945/34.
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Gaasch, Jean-Christoph [Verfasser], Susanne [Akademischer Betreuer] Rässler, and Claus H. [Akademischer Betreuer] Carstensen. "Bayesian estimation of latent trait distributions considering hierarchical structures and partially missing covariate data / Jean-Christoph Gaasch ; Susanne Rässler, Claus Carstensen." Bamberg : Otto-Friedrich-Universität Bamberg, 2017. http://d-nb.info/1147756945/34.
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Li, Xiongya. "Robust multivariate mixture regression models." Diss., Kansas State University, 2017. http://hdl.handle.net/2097/38427.
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Doctor of Philosophy<br>Department of Statistics<br>Weixing Song<br>In this dissertation, we proposed a new robust estimation procedure for two multivariate mixture regression models and applied this novel method to functional mapping of dynamic traits. In the first part, a robust estimation procedure for the mixture of classical multivariate linear regression models is discussed by assuming that the error terms follow a multivariate Laplace distribution. An EM algorithm is developed based on the fact that the multivariate Laplace distribution is a scale mixture of the multivariate standard normal distribution.
The performance of the proposed algorithm is thoroughly evaluated by some simulation and comparison studies. In the second part, the similar idea is extended to the mixture of linear mixed regression models by assuming that the random effect and the regression error jointly follow a multivariate Laplace distribution. Compared with the existing robust t procedure in the literature, simulation studies indicate that the finite sample performance of the proposed estimation procedure outperforms or is at least comparable to the robust t procedure. Comparing to t procedure, there is no need to determine the degrees of freedom, so the new robust estimation procedure is computationally more efficient than the robust t procedure. The ascent property for both EM algorithms are also proved. In the third part, the proposed robust method is applied to identify quantitative trait loci (QTL) underlying a functional mapping framework with dynamic traits of agricultural or biomedical interest.
A robust multivariate Laplace mapping framework was proposed to replace the normality assumption. Simulation studies show the proposed method is comparable to the robust multivariate t-distribution developed in literature and outperforms the normal procedure.
As an illustration, the proposed method is also applied to a real data set.
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Koch, Rainer, Ulrich Julius, Werner Jaross, and Hans-Egbert Schröder. "Estimation of the Heritability of Latent Variables Which Are Included in a Structural Model for Metabolic Syndrome." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-137470.
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In a study looking for risk factors of atherosclerosis in families with combined hyperlipidemia and hypertension, clinical and biochemical data of 1,149 persons were analyzed to develop two hypothetical multivariate scores concerning the degree to which a patient is affected by the metabolic syndrome. The scores are based on a structural model for low-density cholesterol (LDL) and high-density cholesterol (HDL), triglycerides, uric acid, creatinine, glucose, insulin, systolic blood pressure and waist-to-hip ratio. Age, gender and body mass index were used for adjusting all variables. In segregation analyses of 42 pedigrees without using genotype information, estimations of the heritabilities and environmentally caused variance and covariance components were computed for the individual score values of the two latent factors. The first score shows a heritability of 42%; the environment component disappeared. The score mainly reflects the HDL, LDL and triglyceride levels. The second score shows a heritability of 16% with an environment component of 7%. It includes mainly insulin, uric acid and creatinine. In the search for genetic causes, both scores could be a basis for further phenotypic classification of the metabolic syndrome<br>Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich
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Koch, Rainer, Ulrich Julius, Werner Jaross, and Hans-Egbert Schröder. "Estimation of the Heritability of Latent Variables Which Are Included in a Structural Model for Metabolic Syndrome." Karger, 2001. https://tud.qucosa.de/id/qucosa%3A27735.
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In a study looking for risk factors of atherosclerosis in families with combined hyperlipidemia and hypertension, clinical and biochemical data of 1,149 persons were analyzed to develop two hypothetical multivariate scores concerning the degree to which a patient is affected by the metabolic syndrome. The scores are based on a structural model for low-density cholesterol (LDL) and high-density cholesterol (HDL), triglycerides, uric acid, creatinine, glucose, insulin, systolic blood pressure and waist-to-hip ratio. Age, gender and body mass index were used for adjusting all variables. In segregation analyses of 42 pedigrees without using genotype information, estimations of the heritabilities and environmentally caused variance and covariance components were computed for the individual score values of the two latent factors. The first score shows a heritability of 42%; the environment component disappeared. The score mainly reflects the HDL, LDL and triglyceride levels. The second score shows a heritability of 16% with an environment component of 7%. It includes mainly insulin, uric acid and creatinine. In the search for genetic causes, both scores could be a basis for further phenotypic classification of the metabolic syndrome.<br>Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
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IMPOLLONIA, GIORGIO. "Telerilevamento multispettrale da APR per high-throughput phenotyping dei tratti di canapa e miscanto." Doctoral thesis, Università Cattolica del Sacro Cuore, 2022. http://hdl.handle.net/10280/119451.
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Il telerilevamento basato su aeromobile a pilotaggio remoto (APR) è stato utilizzato per high-throughput phenotyping (HTP) dei tratti di canapa e miscanto. Il telerilevamento da APR, tramite l’acquisizione di immagini multispettrali ed il calcolo di diversi indici di vegetazione, permette di stimare i tratti delle colture. In questa tesi, i tratti stimati per la canapa sono stati l'indice di area fogliare e il contenuto di clorofilla fogliare, mentre per il miscanto sono stati l'intercettazione della luce, l'altezza della pianta, la biomassa delle foglie verdi, la biomassa epigea e il contenuto di umidità. La stima dei tratti è stata effettuata utilizzando algoritmi di machine learning e l’inversione del modello PROSAIL. L'HTP di canapa e miscanto è stata effettuata applicando il modello additivo generalizzato (GAM) alle serie temporali dei valori dei tratti stimati dai voli APR. Il telerilevamento da APR ha permesso di analizzare le dinamiche dei tratti durante la stagione di crescita. La combinazione di modelli di stima e modellazione GAM, applicata alle serie temporali dei valori dei tratti stimati da più immagini multispettrali acquisite da voli APR, si è rivelata un potente strumento per l'HTP.<br>Unmanned aerial vehicle (UAV) based remote sensing platform was used for high-throughput phenotyping (HTP) of hemp and miscanthus traits. UAV remote sensing, through the acquisition of multispectral images and the calculation of different vegetation indices, is able to estimate the crop traits. In this thesis, the crop traits estimated were leaf area index and leaf chlorophyll content for hemp, while light interception, plant height, green leaf biomass, standing biomass, and moisture content for miscanthus. The estimation of the traits was carried out using machine learning algorithms and the inversion of the PROSAIL model. The HTP of hemp and miscanthus was carried out by applying the generalized additive model (GAM) to the time series of traits values estimated from UAV flights. UAV remote sensing enabled to analyse of the traits' dynamics during the growing season. Combining estimation models and GAM modelling applied to time series of crop trait values estimated from multiple multispectral images of UAV flights proved to be a powerful tool for HTP.
Books on the topic "Trait estimation"
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Walsh, Bruce, and Michael Lynch. Evolution and Selection of Quantitative Traits. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.001.0001.
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Quantitative traits—be they morphological or physiological characters, aspects of behavior, or genome-level features such as the amount of RNA or protein expression for a specific gene—usually show considerable variation within and among populations. Quantitative genetics, also referred to as the genetics of complex traits, is the study of such characters and is based on mathematical models of evolution in which many genes influence the trait and in which non-genetic factors may also be important. Evolution and Selection of Quantitative Traits presents a holistic treatment of the subject, showing the interplay between theory and data with extensive discussions on statistical issues relating to the estimation of the biologically relevant parameters for these models. Quantitative genetics is viewed as the bridge between complex mathematical models of trait evolution and real-world data, and the authors have clearly framed their treatment as such. This is the second volume in a planned trilogy that summarizes the modern field of quantitative genetics, informed by empirical observations from wide-ranging fields (agriculture, evolution, ecology, and human biology) as well as population genetics, statistical theory, mathematical modeling, genetics, and genomics. Whilst volume 1 (1998) dealt with the genetics of such traits, the main focus of volume 2 is on their evolution, with a special emphasis on detecting selection (ranging from the use of genomic and historical data through to ecological field data) and examining its consequences. This extensive work of reference is suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of evolutionary biology, genetics, and genomics. It will also be of particular relevance and use to plant and animal breeders, human geneticists, and statisticians.
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Reuter, Martin, and Christian Montag. The Genetic Basis of Positive Emotionality. Edited by Turhan Canli. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199753888.013.015.
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The chapter addresses the molecular genetic basis of the personality trait positive emotionality (PE). Beginning with historical aspects of heritability estimation and personality assessment, the main portion of this chapter discusses the molecular genetics basis of PE, which is investigated far less frequently than that of negative emotionality (NE). The studies reviewed focus on individual differences in PE in healthy subjects and include only those studies that assess PE by broadly accepted personality inventories. The review is concentrated on dopaminergic and serotonergic genes because these genes show the most association with PE.
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Belitser, E. N. Minimax estimation in regression and random censorship models (CWI Tract, 127). CWI Tract, 2000.
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Makatjane, Katleho, and Roscoe van Wyk. Identifying structural changes in the exchange rates of South Africa as a regime-switching process. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/919-8.
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Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The former approach is used to reduce the dimensionality of the data using an orthogonal linear transformation by preserving the statistical variance of the data, with the proviso that a new trait is non-linearly independent, and it identifies the number of regime switches that are to be used in the Markov-switching model. The latter is used to partition the variance in each regime by allowing an estimation of multiple break transitions. The transition breakpoints estimates derived from this machine learning approach produce results that are comparable to other methods on similar system sizes. Application of these methods shows that the machine learning approach can also be employed to identify structural changes as a regime-switching process. During times of financial crisis, the growing concern over exchange rate volatility, including its adverse effects on employment and growth, broadens the debates on exchange rate policies. Our results should help the South African monetary policy committee to anticipate when exchange rates will pick up and be prepared for the effects of periods of high exchange rates.
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M. Pavlović, R. Marković, S. Radulović, et al. Estimation of apparent and true total tract digestibility of phosphorus from monocalcium phosphate in broiler diets. Verlag Eugen Ulmer, 2018. http://dx.doi.org/10.1399/eps.2018.225.
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Walsh, Richard A. “I Am Not Sure If I Should Do DaT”. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190607555.003.0008.
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Nuclear medicine-based imaging techniques can provide an estimation of nigrostriatal tract denervation based on radionucleotide uptake in the distal presynaptic terminals of dopaminergic neurons. Although unhelpful in differentiating between differing etiologies of denervation in varied neurodegenerative disorders associated with parkinsonism, this imaging is justified in situations in which parkinsonism is believed to be drug-induced or functional or in cases in which subclinical parkinsonism is suspected. The most common clinical situation in which dopamine transporter imaging is helpful is in the patient on neuroleptic therapy that cannot be stopped who has developed parkinsonism. Dopamine transporter imaging should be normal in drug-induced tremor.
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Test n° 313: Estimation des émissions issues de bois traité par un produit de préservation dans l'environnement. OECD, 2007. http://dx.doi.org/10.1787/9789264067387-fr.
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Boudreau, Joseph F., and Eric S. Swanson. Continuum dynamics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0019.
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The theory and application of a variety of methods to solve partial differential equations are introduced in this chapter. These methods rely on representing continuous quantities with discrete approximations. The resulting finite difference equations are solved using algorithms that stress different traits, such as stability or accuracy. The Crank-Nicolson method is described and extended to multidimensional partial differential equations via the technique of operator splitting. An application to the time-dependent Schrödinger equation, via scattering from a barrier, follows. Methods for solving boundary value problems are explored next. One of these is the ubiquitous fast Fourier transform which permits the accurate solution of problems with simple boundary conditions. Lastly, the finite element method that is central to modern engineering is developed. Methods for generating finite element meshes and estimating errors are also discussed.
Book chapters on the topic "Trait estimation"
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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Linear Mixed Models." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_5.
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AbstractThe linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and illustrate how genomic-enabled predictions are performed under this framework. We illustrate the use of linear mixed models by using the predictor several components such as environments, genotypes, and genotype × environment interaction. Also, the linear mixed model is illustrated under a multi-trait framework that is important in the prediction performance when the degree of correlation between traits is moderate or large. We illustrate the use of single-trait and multi-trait linear mixed models and provide the R codes for performing the analyses.
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Pfanzagl, J. "On Item Parameter Estimation in Certain Latent Trait Models." In Recent Research in Psychology. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-4308-3_19.
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Sheng, Xiaona, Weijun Ma, and Ying Zhou. "Joint Estimation of QTL Positions and Effects in Multiple-Interval Mapping for Count Trait." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45049-9_60.
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Kim, Seong Tae, and Yong Man Ro. "Facial Dynamics Interpreter Network: What Are the Important Relations Between Local Dynamics for Facial Trait Estimation?" In Computer Vision – ECCV 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01258-8_29.
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Simm, Geoff, Geoff Pollott, Raphael Mrode, Ross Houston, and Karen Marshall. "Analysing genetic variation in farm animals." In Genetic improvement of farmed animals. CABI, 2021. http://dx.doi.org/10.1079/9781789241723.0178.
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Abstract In this chapter, topics focused on how to quantify the extent to which genes affect measured traits and how to use this information in breeding programmes. Highlights include: estimating heritability; estimating non-additive parameters, correlations, and genotype by environment interactions, molecular genetics and trait variations; and calculating inbreeding using SNP markers.
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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Bayesian Genomic Linear Regression." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_6.
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AbstractThe Bayesian paradigm for parameter estimation is introduced and linked to the main problem of genomic-enabled prediction to predict the trait of interest of the non-phenotyped individuals from genotypic information, environment variables, or other information (covariates). In this situation, a convenient practice is to include the individuals to be predicted in the posterior distribution to be sampled. We explained how the Bayesian Ridge regression method is derived and exemplified with data from plant breeding genomic selection. Other Bayesian methods (Bayes A, Bayes B, Bayes C, and Bayesian Lasso) were also described and exemplified for genome-based prediction. The chapter presented several examples that were implemented in the Bayesian generalized linear regression (BGLR) library for continuous response variables. The predictor under all these Bayesian methods includes main effects (of environments and genotypes) as well as interaction terms related to genotype × environment interaction.
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Basu, Sudipta, and Steven P. C. Groot. "Seed Vigour and Invigoration." In Seed Science and Technology. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5888-5_4.
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AbstractSeed vigour is an important aspect of seed quality. It is a quantitative trait which is responsible for overall seed performance in terms of rate and uniformity of seed germination, seedling growth, emergence ability under unfavourable environments and post storage performance. Seed vigour is controlled by genetic factors, initial seed quality, production environments, harvesting and storage conditions. Seed vigour tests provide a more sensitive index of seed performance per se than the germination test. Efforts have been focused on developing novel or improving existing methods of vigour estimation in different crops. The vigour tests are tools routinely used for in-house seed quality control programs, especially for field and vegetable crops. Some treatments can improve seed vigour, although the treatment effects are more evident under sub-optimum than optimum growing conditions. This chapter deals with different aspects of seed vigour and its effects on plant growth and discusses physiological and biochemical parameters to understand underlying mechanisms.
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Anjum, Muhammad, Moizzah Asif, and Jonathan Williams. "Towards an Optimal Deep Neural Network for SOC Estimation of Electric-Vehicle Lithium-Ion Battery Cells." In Springer Proceedings in Energy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_2.
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AbstractThis paper has identified a minimal configuration of a DNN architecture and hyperparameter settings to effectively estimate SOC of EV battery cells. The results from the experimental work has shown that a minimal configuration of hidden layers and neurons can reduce the computational cost and resources required without compromising the performance. This is further supported by the number of epochs taken to train the best DNN SOC estimations model. Hence, demonstrating that, the risk of overfitting estimation models to training datasets, can also be subsided. This is further supported by the generalisation capability of the best model demonstrated through the decrease in error metrics values from test phase to those in validation phase.
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Patil, Ganapati P., Sharad D. Gore, and Charles Taillie*. "Estimating Prevalence of a Trait." In Composite Sampling. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7628-4_4.
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Oladapo, Olukunle Olaonipekun, Leonard Kofitse Amekudzi, Olatunde Micheal Oni, Abraham Adewale Aremu, and Marian Amoakowaah Osei. "Climate Change Impact on Soil Moisture Variability: Health Effects of Radon Flux Density Within Ogbomoso, Nigeria." In African Handbook of Climate Change Adaptation. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_201.
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AbstractClimate affects the quantity of soil moisture within the surface of the earth and this is obtained by affecting the amount of radon flux density escaping from the land surface. This chapter contains the evaluation of climate change conditions as it affects the variability of soil water for the purpose of estimating the health effects of radon flux density within Ogbomoso metropolis. The simulated soil moisture content around Ogbomoso was done for a period of 34 years using the hydrological model, Soil Water Assessment Tool (SWAT). The calibration and validation of the SWAT model was done using the daily observed soil moisture content. The simulated daily soil moisture within Ogbomoso showed good performance when calibrated and validated. A 20 years prediction of the daily soil moisture content was done using the SWAT model. The estimation of the radon flux density for the study area was obtained using the simulated soil temperature and soil moisture from the SWAT model. In this chapter, the UNSCEAR radon flux formula was used for the radon flux estimate. The result showed that the UNSCEAR radon flux formula performed well in estimating the radon flux density in the study area. The mean value of the radon flux density of 15.09 mBqm−2 s−1 falls below the estimated world average of 33 mBqm−2 s−1 by UNSCEAR stipulated for land surface. The results showed that Ogbomoso region is not prone to high risk of radon exposure to the public. The estimation of the radon flux density value suggested that there is no radiological health hazard such as lung cancer or any other respiratory tract diseases to the inhabitant of Ogbomoso, Nigeria.
Conference papers on the topic "Trait estimation"
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Volkov, D. P., and S. А. Zaytsev. "ESTIMATION OF COMBINING ABILITY OF CORN LINES IN DIALLELE CROSSES BY AN AMOUNT OF SEEDS FROM A COB." In 11-я Всероссийская конференция молодых учёных и специалистов «Актуальные вопросы биологии, селекции, технологии возделывания и переработки сельскохозяйственных культур». V.S. Pustovoit All-Russian Research Institute of Oil Crops, 2021. http://dx.doi.org/10.25230/conf11-2021-29-32.
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We estimated combining ability of inbred corn lines by a trait amount of seeds from a cob’. Amount of seeds from a cob is one of the important traits for plant selection with a purpose to increase their yield. Prevalence of additive effects of gens in a control of this trait is noted. We revealed lines with low and high meanings of combining ability by an amount of seeds from a cob.
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Akanno, E. C., D. M. Thekkoot, C. Zhang, C. Bierman, G. Plastow, and R. A. Kemp. "300. Multi-trait genomic estimation of genetic parameters for growth and carcass traits of Duroc pigs." In World Congress on Genetics Applied to Livestock Production. Wageningen Academic Publishers, 2022. http://dx.doi.org/10.3920/978-90-8686-940-4_300.
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O'Byrne, Patricia, Patrick Jackman, Damon Berry, Hector-Hugo Franco-Pena, Michael French, and Robert J. Ross. "Transfer Learning Performance for Remote Pastureland Trait Estimation in Real-Time Farm Monitoring." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9553222.
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Demirkus, Meltem, Doina Precup, James J. Clark, and Tal Arbel. "Soft biometric trait classification from real-world face videos conditioned on head pose estimation." In 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2012. http://dx.doi.org/10.1109/cvprw.2012.6239227.
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Zhao, Xiaowei, Guoyu Zhang, and Lin Yang. "A Strategy for Estimating State-of-Charge and State-of-Health of Li-Ion Batteries in Electric and Hybrid Electric Vehicles." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-87324.
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A task that has to be solved for the application of batteries in vehicles with an electric drive train is the determination of the actual state-of-health (SOH) and state-of-charge (SOC) of the battery cells. In this paper, an on board strategy for estimating SOC and SOH of Li-ion batteries is proposed. The equivalent circuit model is used for both SOC and SOH estimations. In SOH algorithm, the estimated value of battery capacity not only reflects the aging degree of battery pack, but also provides information for SOC estimation. Meanwhile, the extended Kaiman filtering is used in SOC estimation. Because the performance of the equivalent circuit model will be better at small currents than at high currents, extended Kaiman filtering is substituted by Ampere-Hour counting when the absolute value of current is greater than a calibration value. The Digatron battery tester was used to evaluate the proposed estimation method, and results show that the estimation method has high accuracy and efficiency at ordinary temperatures.
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Zhiwei Zhang, Tuo Fu, and Minggui Tang. "Coherent integration of quasi-random pulse train based on phased array radar." In 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF). IEEE, 2015. http://dx.doi.org/10.1109/icedif.2015.7280193.
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Meli, Enrico, Pierluca D’Adamio, Alice Innocenti, Lorenzo Marini, Luca Pugi, and Andrea Rindi. "An Innovative Procedure for High Speed Weighing in Motion of Railway Vehicles." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46315.
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In the present work the authors propose an innovative estimation algorithm for Weigh-in-motion (WIM) applications with the aim of estimating the axle or wheel loads of a generic train composition, starting from indirect track measurements. The WIM algorithm elaborates the set of experimental physical quantities chosen as track inputs, making use of estimation procedures based on least square minimization techniques. To perform an accurate estimation, the algorithm uses a flexible multibody model of the track and the vehicle. The novelty of the proposed solution is the general approach that allows to manage different kinds of measurement station and signal inputs (both experimental data and simulated ones) and the good robustness against numerical noise. The algorithm has been tested under any operating conditions through a wide simulation campaign, obtaining good results. Future developments will be based on the experimental data provided by Ansaldo STS and ECM SpA that supported the research activity.
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Ahmad, Husain, and Mehdi Ahmadian. "Train Braking Distance Estimation Under Different Operating Conditions." In ASME 2011 Rail Transportation Division Fall Technical Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/rtdf2011-67009.
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One of the critical factors in Positive Train Control (PTC) is accurately estimating the train braking distance under different conditions. Accurate estimation of the braking distance will allow the trains to be spaced closer together, with reasonable confidence that they will stop without causing a collision. This study will develop a dynamic model of a train consist that includes three railcars for performing a parametric study to evaluate how various elements will affect the train stopping distance, from an initial speed. Parameters that can be varied in the model include, initial train speed, railcar weight, wheel-rail interface condition, and dynamic braking force. Other parameters are included in the model such as aerodynamic drag forces and air brake forces. The model is based on a multibody formulation of the railcars, trucks (bogies), and suspensions. The paper will include the derivation of the mathematical model and the results of a numerical study in Matlab. The results indicate that the railcars’ weight and initial speed play a significant role in the stopping distance and the time required to stop the train. Our future work will include using this model for closed-loop control of the dynamic braking forces, such that the train braking distance and time to stop can be minimized, under various wheel-rail dynamic conditions.
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Thelen, Adam, Yu Hui Lui, Sheng Shen, Simon Laflamme, Shan Hu, and Chao Hu. "Physics-Informed Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-71407.
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Abstract State of health (SOH) estimation of lithium-ion batteries has typically been focused on estimating present cell capacity relative to initial cell capacity. While many successes have been achieved in this area, it is generally more advantageous to not only estimate cell capacity, but also the underlying degradation modes which cause capacity fade because these modes give further insight into maximizing cell usage. There have been some successes in estimating cell degradation modes, however, these methods either require long-term degradation data, are demonstrated solely on artificially constructed cells, or exhibit high error in estimating late-life degradation. To address these shortfalls and alleviate the need for long-term cycling data, we propose a method for estimating the capacity of a battery cell and diagnosing its primary degradation mechanisms using limited early-life degradation data. The proposed method uses simulation data from a physics-based half-cell model and early-life degradation data from 16 cells cycled under two temperatures and C rates to train a machine learning model. Results obtained from a four-fold cross validation study indicate that the proposed physics-informed machine learning method trained with only 60 early life data (five data from each of the 12 training cells) and 30 high-degradation simulated data can decrease estimation error by up to a total of 9.77 root mean square error % when compared to models which were trained only on the early-life experimental data.
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Goh, Choon Giap, Wee Han Lim, Justus Chua, and Indriyati Atmosukarto. "Image Analytics for Train Crowd Estimation." In 2018 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2018. http://dx.doi.org/10.1109/dicta.2018.8615794.
Full textReports on the topic "Trait estimation"
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Seroussi, Eyal, and George Liu. Genome-Wide Association Study of Copy Number Variation and QTL for Economic Traits in Holstein Cattle. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7593397.bard.
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Copy number variation (CNV) has been recently identified in human and other mammalian genomes and increasing awareness that CNV might be a major source for heritable variation in complex traits has emerged. Despite this, little has been published on CNVs in Holsteins. In order to fill this knowledge-gap, we proposed a genome-wide association study between quantitative trait loci (QTL) for economic traits and CNV in the Holstein cattle. The approved feasibility study was aimed at the genome-wide characterization of CNVs in Holstein cattle and at the demonstrating of their possible association with economic traits by performing the activities of preparation of DNA samples, Comparative Genomic Hybridization (CGH), initial association study between CNVs and production traits and characterization of CNVSNP associations. For both countries, 40 genomic DNA samples of bulls representing the extreme sub-populations for economically important traits were CGH analyzed using the same reference genome on a NimbleGen tiling array. We designed this array based on the latest build of the bovine genome (UMD3) with average probe spacing of 1150 bases (total number of probes was 2,166,672). Two CNV gene clusters, PLA2G2D on BTA2 and KIAA1683 on BTA7 revealed significant association with milk percentage and cow fertility, respectively, and were chosen for further characterization and verification in a larger sample using other methodologies including sequencing, tag SNPs and real time PCR (qPCR). Comparison between these four methods indicated that there is under estimation of the number of CNV loci in Holstein cattle and their complexity. The variation in sequence between different copies seemed to affect their functionality and thus the hybridization based methods were less informative than the methods that are based on sequencing. We thus conclude that large scale sequencing effort complemented by array CGH should be considered to better detect and characterize CNVs in order to effectively employ them in marker-assisted selection.
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Fridman, Eyal, Jianming Yu, and Rivka Elbaum. Combining diversity within Sorghum bicolor for genomic and fine mapping of intra-allelic interactions underlying heterosis. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7597925.bard.
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Heterosis, the enigmatic phenomenon in which whole genome heterozygous hybrids demonstrate superior fitness compared to their homozygous parents, is the main cornerstone of modern crop plant breeding. One explanation for this non-additive inheritance of hybrids is interaction of alleles within the same locus. This proposal aims at screening, identifying and investigating heterosis trait loci (HTL) for different yield traits by implementing a novel integrated mapping approach in Sorghum bicolor as a model for other crop plants. Originally, the general goal of this research was to perform a genetic dissection of heterosis in a diallel built from a set of Sorghum bicolor inbred lines. This was conducted by implementing a novel computational algorithm which aims at associating between specific heterozygosity found among hybrids with heterotic variation for different agronomic traits. The initial goals of the research are: (i) Perform genotype by sequencing (GBS) of the founder lines (ii) To evaluate the heterotic variation found in the diallel by performing field trails and measurements in the field (iii) To perform QTL analysis for identifying heterotic trait loci (HTL) (iv) to validate candidate HTL by testing the quantitative mode of inheritance in F2 populations, and (v) To identify candidate HTL in NAM founder lines and fine map these loci by test-cross selected RIL derived from these founders. The genetic mapping was initially achieved with app. 100 SSR markers, and later the founder lines were genotyped by sequencing. In addition to the original proposed research we have added two additional populations that were utilized to further develop the HTL mapping approach; (1) A diallel of budding yeast (Saccharomyces cerevisiae) that was tested for heterosis of doubling time, and (2) a recombinant inbred line population of Sorghum bicolor that allowed testing in the field and in more depth the contribution of heterosis to plant height, as well as to achieve novel simulation for predicting dominant and additive effects in tightly linked loci on pseudooverdominance. There are several conclusions relevant to crop plants in general and to sorghum breeding and biology in particular: (i) heterosis for reproductive (1), vegetative (2) and metabolic phenotypes is predominantly achieved via dominance complementation. (ii) most loci that seems to be inherited as overdominant are in fact achieving superior phenotype of the heterozygous due to linkage in repulsion, namely by pseudooverdominant mechanism. Our computer simulations show that such repulsion linkage could influence QTL detection and estimation of effect in segregating populations. (iii) A new height QTL (qHT7.1) was identified near the genomic region harboring the known auxin transporter Dw3 in sorghum, and its genetic dissection in RIL population demonstrated that it affects both the upper and lower parts of the plant, whereas Dw3 affects only the part below the flag leaf. (iv) HTL mapping for grain nitrogen content in sorghum grains has identified several candidate genes that regulate this trait, including several putative nitrate transporters and a transcription factor belonging to the no-apical meristem (NAC)-like large gene family. This activity was combined with another BARD-funded project in which several de-novo mutants in this gene were identified for functional analysis.
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Soller, Moshe (Morris), Hans Cheng, and Lyman Crittenden. Mapping the Chicken Genome, Including Loci Affecting Traits of Economic Importance. United States Department of Agriculture, 1994. http://dx.doi.org/10.32747/1994.7568779.bard.
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A total of 195 microsatellites were added to the chicken genome map. Mapping of fifty known genes revealed a high degree of conserved linkage order between human and chicken genomes. A new, statistically powerful mapping design, the full-sib intercross line (produced by mating two parents, and intercrossing their progeny over a number of generations), was developed for use in species with high reproductive capacity. The Jerusalem Resource Population (JRP), now at the F12 generation, was established to implement this design i chickens. The biometrical picutre in the JRP is similar to that generally found in chicken populations; inbreeding effects were not observed. The F2 and F3 generations of the JRP were genotyped with respect to twelve production traits, using a battery of 23 microsatellites markers. The number of significant effects was twice that expected on chance alone, validating the high statistical power of the JRP with respect to QTL differentiating the parental lines. Selective DNA pooling, based on estimation of marker allele frequencies in pooled DNA samples, has been proposed to reduce high genotyping costs of QTL mapping. A method to correct for overlapping shadow bands of dinucleotide microsatellite markers in pooled DNA samples was developed and validated. In a retrospective study using this procedure, previously mapped loci affecting Marek's disease were successfully identified.
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Su, Hailin, Dorian J. Garrick, Bruce Golden, and Lauren Hyde. Estimation of Genetic Parameters for Carcass Traits and Their Corresponding Ultrasound Measurements in Crossbred Beef Cattle. Iowa State University, 2016. http://dx.doi.org/10.31274/ans_air-180814-486.
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Lin, Pei-Sung. Coordinated Pre-Preemption of Traffic Signals to Enhance Railroad Grade Crossing Safety in Urban Areas and Estimation of Train Impacts to Arterial Travel Time Delay. University of South Florida, 2004. http://dx.doi.org/10.5038/cutr-nctr-rr-2014-06.
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Lers, Amnon, Majid R. Foolad, and Haya Friedman. genetic basis for postharvest chilling tolerance in tomato fruit. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7600014.bard.
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ABSTRACT Postharvest losses of fresh produce are estimated globally to be around 30%. Reducing these losses is considered a major solution to ensure global food security. Storage at low temperatures is an efficient practice to prolong postharvest performance of crops with minimal negative impact on produce quality or human health and the environment. However, many fresh produce commodities are susceptible to chilling temperatures, and the application of cold storage is limited as it would cause physiological chilling injury (CI) leading to reduced produce quality. Further, the primary CI becomes a preferred site for pathogens leading to decay and massive produce losses. Thus, chilling sensitive crops should be stored at higher minimal temperatures, which curtails their marketing life and in some cases necessitates the use of other storage strategies. Development of new knowledge about the biological basis for chilling tolerance in fruits and vegetables should allow development of both new varieties more tolerant to cold, and more efficient postharvest storage treatments and storage conditions. In order to improve the agricultural performance of modern crop varieties, including tomato, there is great potential in introgression of marker-defined genomic regions from wild species onto the background of elite breeding lines. To exploit this potential for improving tomato fruit chilling tolerance during postharvest storage, we have used in this research a recombinant inbred line (RIL) population derived from a cross between the red-fruited tomato wild species SolanumpimpinellifoliumL. accession LA2093 and an advanced Solanum lycopersicumL. tomato breeding line NCEBR-1, developed in the laboratory of the US co-PI. The original specific objectives were: 1) Screening of RIL population resulting from the cross NCEBR1 X LA2093 for fruit chilling response during postharvest storage and estimation of its heritability; 2) Perform a transcriptopmic and bioinformatics analysis for the two parental lines following exposure to chilling storage. During the course of the project, we learned that we could measure greater differences in chilling responses among specific RILs compared to that observed between the two parental lines, and thus we decided not to perform transcriptomic analysis and instead invest our efforts more on characterization of the RILs. Performing the transcriptomic analysis for several RILs, which significantly differ in their chilling tolerance/sensitivity, at a later stage could result with more significant insights. The RIL population, (172 lines), was used in field experiment in which fruits were examined for chilling sensitivity by determining CI severity. Following the field experiments, including 4 harvest days and CI measurements, two extreme tails of the response distribution, each consisting of 11 RILs exhibiting either high sensitivity or tolerance to chilling stress, were identified and were further examined for chilling response in greenhouse experiments. Across the RILs, we found significant (P < 0.01) correlation between field and greenhouse grown plants in fruit CI. Two groups of 5 RILs, whose fruits exhibited reproducible chilling tolerant/sensitive phenotypes in both field and greenhouse experiments, were selected for further analyses. Numerous genetic, physiological, biochemical and molecular variations were investigated in response to postharvest chilling stress in the selected RILs. We confirmed the differential response of the parental lines of the RIL population to chilling stress, and examined the extent of variation in the RIL population in response to chilling treatment. We determined parameters which would be useful for further characterization of chilling response in the RIL population. These included chlorophyll fluorescence Fv/Fm, water loss, total non-enzymatic potential of antioxidant activity, ascorbate and proline content, and expression of LeCBF1 gene, known to be associated with cold acclimation. These parameters could be used in continuation studies for the identification and genetic mapping of loci contributing to chilling tolerance in this population, and identifying genetic markers associated with chilling tolerance in tomato. Once genetic markers associated with chilling tolerance are identified, the trait could be transferred to different genetic background via marker-assisted selection (MAS) and breeding. The collaborative research established in this program has resulted in new information and insights in this area of research and the collaboration will be continued to obtain further insights into the genetic, molecular biology and physiology of postharvest chilling tolerance in tomato fruit. The US Co-PI, developed the RIL population that was used for screening and measurement of the relevant chilling stress responses and conducted statistical analyses of the data. Because we were not able to grow the RIL population under field conditions in two successive generations, we could not estimate heritability of response to chilling temperatures. However, we plan to continue the research, grow the RIL progeny in the field again, and determine heritability of chilling tolerance in a near future. The IS and US investigators interacted regularly and plan to continue and expand on this study, since combing the expertise of the Co-PI in genetics and breeding with that of the PI in postharvest physiology and molecular biology will have great impact on this line of research, given the significant findings of this one-year feasibility project.
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Mayfield, Colin. Capacity Development in the Water Sector: the case of Massive Open On-line Courses. United Nations University Institute for Water, Environment and Health, 2017. http://dx.doi.org/10.53328/mwud6984.
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The Sustainable Development Goal 6 targets are all dependent on capacity development as outlined in SDG 6a “Expand international cooperation and capacity-building support to developing countries in water- and sanitation related activities and programmes “. Massive Open On-line Courses (MOOCs) and distance learning in general have a significant role to play in this expansion. This report examines the role that MOOCs and similar courses could play in capacity development in the water sector. The appearance of MOOCs in 2010/11 led within 4 years to a huge increase in this type of course and in student enrollment. Some problems with student dropout rates, over-estimating the transformational and disruptive nature of MOOCs and uncertain business models remain, but less “massive” MOOCs with more engaged students are overcoming these problems. There are many existing distance learning courses and programmes in the water sector designed to train and/ or educate professionals, operators, graduate and undergraduate students and, to a lesser extent, members of communities dealing with water issues. There are few existing true MOOCs in the water sector. MOOCs could supply significant numbers of qualified practitioners for the water sector. A suite of programmes on water-related topics would allow anyone to try the courses and determine whether they were appropriate and useful. If they were, the students could officially enroll in the course or programme to gain a meaningful qualification or simply to upgrade their qualifications. To make MOOCs more relevant to education and training in the water sector an analysis of the requirements in the sector and the potential demand for such courses is required. Cooperation between institutions preparing MOOCs would be desirable given the substantial time and funding required to produce excellent quality courses. One attractive model for cooperation would be to produce modules on all aspects of water and sanitation dealing with technical, scientific, social, legal and management topics. These should be produced by recognized experts in each field and should be “stand-alone” or complete in themselves. If all modules were made freely available, users or mentors could assemble different MOOCs by linking relevant modules. Then extracts, simplified or less technical versions of the modules could then be used to produce presentations to encourage public participation and for other training purposes. Adaptive learning, where course materials are more tailored to individual students based on their test results and reactions to the material, can be an integral part of MOOCs. MOOCs efficiently provide access to quality courses at low or no cost to students around the world, they enable students to try courses at their convenience, they can be tailored to both professional and technical aspects, and they are very suitable to provide adaptive learning courses. Cooperation between institutions would provide many course modules for the water sector that collectively could provide excellent programmes to address the challenges of capacity development for SDG 6 and other issues within the water sector.