Academic literature on the topic 'Trait estimation'
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Journal articles on the topic "Trait estimation"
Fanourakis, Dimitrios, Filippos Kazakos, and Panayiotis A. Nektarios. "Allometric Individual Leaf Area Estimation in Chrysanthemum." Agronomy 11, no. 4 (April 18, 2021): 795. http://dx.doi.org/10.3390/agronomy11040795.
Full textLopez, 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 (September 24, 2019): 715. http://dx.doi.org/10.3390/ani9100715.
Full textNagy, 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 (April 15, 2014): 182–89. http://dx.doi.org/10.17221/7342-cjas.
Full textCappa, 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 (May 1, 2006): 1276–85. http://dx.doi.org/10.1139/x06-024.
Full textZhu, Anqi, Nana Matoba, Emma P. Wilson, Amanda L. Tapia, Yun Li, Joseph G. Ibrahim, Jason L. Stein, and Michael I. Love. "MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity." PLOS Genetics 17, no. 4 (April 19, 2021): e1009455. http://dx.doi.org/10.1371/journal.pgen.1009455.
Full textThomas, 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 (December 2000): 351–71. http://dx.doi.org/10.3102/10769986025004351.
Full textMcINTYRE, 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 (August 2001): 79–92. http://dx.doi.org/10.1017/s0016672301005092.
Full textGao, 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 (January 4, 2021): e1009293. http://dx.doi.org/10.1371/journal.pgen.1009293.
Full textRonin, 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 (January 1, 1999): 387–96. http://dx.doi.org/10.1093/genetics/151.1.387.
Full textKrupová, 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 (December 21, 2020): 454–62. http://dx.doi.org/10.17221/161/2020-cjas.
Full textDissertations / Theses on the topic "Trait estimation"
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.
Full textTzamourani, 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/.
Full textPender, 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.
Full textPita, 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.
Full textMade available in DSpace on 2017-06-02T16:19:15Z (GMT). No. of bitstreams: 1 texto completo.pdf: 390406 bytes, checksum: ccedce081a84047d48e29f58c45f1176 (MD5) Previous issue date: 2003-09-05
Conselho Nacional de Desenvolvimento Científico e Tecnológico
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.
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.
Tese importada do Alexandria
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.
Full textGaasch, 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.
Full textLi, Xiongya. "Robust multivariate mixture regression models." Diss., Kansas State University, 2017. http://hdl.handle.net/2097/38427.
Full textDepartment of Statistics
Weixing Song
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.
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.
Full textDieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich
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.
Full textDieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
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.
Full textUnmanned 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"
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.
Full textReuter, 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.
Full textBelitser, E. N. Minimax estimation in regression and random censorship models (CWI Tract, 127). CWI Tract, 2000.
Find full textMakatjane, 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.
Full textM. Pavlović, R. Marković, S. Radulović, B. Petrujkić, D. Jovanović, M. Ž. Baltić, and D. Šefer. 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.
Full textWalsh, 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.
Full textTest 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.
Full textBoudreau, Joseph F., and Eric S. Swanson. Continuum dynamics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0019.
Full textBook chapters on the topic "Trait estimation"
Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Linear Mixed Models." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 141–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_5.
Full textPfanzagl, J. "On Item Parameter Estimation in Certain Latent Trait Models." In Recent Research in Psychology, 249–63. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-4308-3_19.
Full textSheng, 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, 374–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45049-9_60.
Full textKim, 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, 475–91. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01258-8_29.
Full textSimm, Geoff, Geoff Pollott, Raphael Mrode, Ross Houston, and Karen Marshall. "Analysing genetic variation in farm animals." In Genetic improvement of farmed animals, 178–92. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789241723.0178.
Full textMontesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Bayesian Genomic Linear Regression." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 171–208. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_6.
Full textBasu, Sudipta, and Steven P. C. Groot. "Seed Vigour and Invigoration." In Seed Science and Technology, 67–89. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5888-5_4.
Full textAnjum, 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, 11–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_2.
Full textPatil, Ganapati P., Sharad D. Gore, and Charles Taillie*. "Estimating Prevalence of a Trait." In Composite Sampling, 81–86. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7628-4_4.
Full textOladapo, 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, 437–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_201.
Full textConference papers on the topic "Trait estimation"
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.
Full textAkanno, 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. The Netherlands: Wageningen Academic Publishers, 2022. http://dx.doi.org/10.3920/978-90-8686-940-4_300.
Full textO'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.
Full textDemirkus, 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.
Full textZhao, 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.
Full textZhiwei 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.
Full textMeli, 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.
Full textAhmad, 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.
Full textThelen, 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.
Full textGoh, 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"
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, September 2010. http://dx.doi.org/10.32747/2010.7593397.bard.
Full textFridman, 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, January 2012. http://dx.doi.org/10.32747/2012.7597925.bard.
Full textSoller, Moshe (Morris), Hans Cheng, and Lyman Crittenden. Mapping the Chicken Genome, Including Loci Affecting Traits of Economic Importance. United States Department of Agriculture, September 1994. http://dx.doi.org/10.32747/1994.7568779.bard.
Full textSu, 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. Ames (Iowa): Iowa State University, January 2016. http://dx.doi.org/10.31274/ans_air-180814-486.
Full textLin, 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. Tampa, FL: University of South Florida, January 2004. http://dx.doi.org/10.5038/cutr-nctr-rr-2014-06.
Full textLers, Amnon, Majid R. Foolad, and Haya Friedman. genetic basis for postharvest chilling tolerance in tomato fruit. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7600014.bard.
Full textMayfield, Colin. Capacity Development in the Water Sector: the case of Massive Open On-line Courses. United Nations University Institute for Water, Environment and Health, January 2017. http://dx.doi.org/10.53328/mwud6984.
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