Relevant bibliographies by topics / Genetic selection methods

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Genetic selection methods'

Author: Grafiati
Published: 2 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "Genetic selection methods"

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Kang, K. S., B. H. Cheon, S. U. Han, C. S. Kim, and W. Y. Choi. "Genetic Gain and Diversity under Different Selection Methods in a Breeding Seed Orchard of Quercus serrata." Silvae Genetica 56, no. 1-6 (2007): 277–81. http://dx.doi.org/10.1515/sg-2007-0039.

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Abstract Genetic gain and diversity were estimated in a 13- year old Quercus serrata breeding seed orchard under three selection (rouging) methods. The selections were based on individual selection, family selection, and family plus within family selection. Genetic gain was for stem volume and gene diversity was estimated by status number concept. Both estimated genetic gain and gene diversity were compared to those before selection and among selection scenarios. Estimated genetic gain for tree volume ranged from 4.0% to 9.1% for three selection methods under 50% selection intensity. Individua
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Řepková, J., and J. Nedělník. "Modern methods for genetic improvement of Trifolium pratense." Czech Journal of Genetics and Plant Breeding 50, No. 2 (2014): 92–99. http://dx.doi.org/10.17221/139/2013-cjgpb.

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This review focuses on trends in genetic improvement of a significant representative forage crop, Trifolium pratense (red clover) classified taxonomically into the agronomically outstanding family Fabaceae. Red clover breeding is aimed at improving traits like persistency, resistance to biotic and abiotic factors, forage yield and quality characteristics such as protein quality and stability. Isoflavone content in forage is important for cattle reproduction. Interspecific hybridization of red clover with the related wild species T. medium was used for the introgression of useful traits into re
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Gulayeva, Nataliya, and Artem Ustilov. "Analysis of Selection Methods Used in Genetic Algorithms." NaUKMA Research Papers. Computer Science 4 (December 10, 2021): 29–43. http://dx.doi.org/10.18523/2617-3808.2021.4.29-43.

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This paper offers a comprehensive review of selection methods used in the generational genetic algorithms.Firstly, a brief description of the following selection methods is presented: fitness proportionate selection methods including roulette-wheel selection (RWS) and its modifications, stochastic remainder selection with replacement (SRSWR), remainder stochastic independent selection (RSIS), and stochastic universal selection (SUS); ranking selection methods including linear and nonlinear rankings; tournament selection methods including deterministic and stochastic tournaments as well as tour
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Huspi, Sharin Hazlin, and Chong Ke Ting. "Genetic Algorithm Ensemble Filter Methods on Kidney Disease Classification." International Journal of Innovative Computing 11, no. 2 (2021): 73–80. http://dx.doi.org/10.11113/ijic.v11n2.345.

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Kidney failure will give effect to the human body, and it can lead to a series of seriously illness and even causing death. Machine learning plays important role in disease classification with high accuracy and shorter processing time as compared to clinical lab test. There are 24 attributes in the Chronic K idney Disease (CKD) clinical dataset, which is considered as too much of attributes. To improve the performance of the classification, filter feature selection methods used to reduce the dimensions of the feature and then the ensemble algorithm is used to identify the union features that s
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Pavlidis, Pavlos, and Nikolaos Alachiotis. "A survey of methods and tools to detect recent and strong positive selection." Journal of Biological Research-Thessaloniki 24, no. 1 (2017): 7. https://doi.org/10.1186/s40709-017-0064-0.

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Positive selection occurs when an allele is favored by natural selection. The frequency of the favored allele increases in the population and due to genetic hitchhiking the neighboring linked variation diminishes, creating so-called selective sweeps. Detecting traces of positive selection in genomes is achieved by searching for signatures introduced by selective sweeps, such as regions of reduced variation, a specific shift of the site frequency spectrum, and particular LD patterns in the region. A variety of methods and tools can be used for detecting sweeps, ranging from simple implementatio
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Jameel, Noor, and Hasanen S. Abdullah. "Intelligent Feature Selection Methods: A Survey." Engineering and Technology Journal 39, no. 1B (2021): 175–83. http://dx.doi.org/10.30684/etj.v39i1b.1623.

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Consider feature selection is the main in intelligent algorithms and machine learning to select the subset of data to help acquire the optimal solution. Feature selection used an extract the relevance of the data and discarding the irrelevance of the data with increment fast to select it and to reduce the dimensional of dataset. In the past, it used traditional methods, but these methods are slow of fast and accuracy. In modern times, however, it uses the intelligent methods, Genetic algorithm and swarm optimization methods Ant colony, Bees colony, Cuckoo search, Particle optimization, fish al
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Wei, R. P., C. R. Hansen, N. K. Dhir, and F. C. Yeh. "Genetic gain with desired status number in breeding programs: a study on selection effects." Canadian Journal of Forest Research 28, no. 12 (1998): 1861–69. http://dx.doi.org/10.1139/x98-168.

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Genetic gain and average coancestry or status number was investigated for five selection methods: penalty index selection (PIS), family index selection (FIS), combined between-family and within-family selection (CBW), restricted individual selection (RIS), and combined index selection (RCS). PIS was a function of an individual's breeding value and family contributions, modelled as a stepwise procedure to select superior individuals one by one. A penalty would indicate the need to have low average coancestry or large status number. Breeding populations of unrelated families were investigated by
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Moeinizade, Saba, Aaron Kusmec, Guiping Hu, Lizhi Wang, and Patrick S. Schnable. "Multi-trait Genomic Selection Methods for Crop Improvement." Genetics 215, no. 4 (2020): 931–45. http://dx.doi.org/10.1534/genetics.120.303305.

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Plant breeders make selection decisions based on multiple traits, such as yield, plant height, flowering time, and disease resistance. A commonly used approach in multi-trait genomic selection is index selection, which assigns weights to different traits relative to their economic importance. However, classical index selection only optimizes genetic gain in the next generation, requires some experimentation to find weights that lead to desired outcomes, and has difficulty optimizing nonlinear breeding objectives. Multi-objective optimization has also been used to identify the Pareto frontier o
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Ma, Ling Yong, Bing Xin Gu, and Gong Liang Liu. "A Study on High-Rise Building Structure Selections Using Artificial Intelligence Methods." Applied Mechanics and Materials 351-352 (August 2013): 1198–201. http://dx.doi.org/10.4028/www.scientific.net/amm.351-352.1198.

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With the increasing height of building, high-rise building structure selection becomes more and more important. This paper presents the application of genetic neural network method to study high-rise building structure selection and uses the MATLAB neural network toolbox with a combination of genetic algorithm toolbox to develop a genetic neural network expert system for high-rise building structure selection to make the selection process simple.
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Jia, Yi, and Jean-Luc Jannink. "Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy." Genetics 192, no. 4 (2012): 1513–22. http://dx.doi.org/10.1534/genetics.112.144246.

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Dissertations / Theses on the topic "Genetic selection methods"

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Shringarpure, Suyash. "Statistical Methods for studying Genetic Variation in Populations." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/117.

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The study of genetic variation in populations is of great interest for the study of the evolutionary history of humans and other species. Improvement in sequencing technology has resulted in the availability of many large datasets of genetic data. Computational methods have therefore become quite important in analyzing these data. Two important problems that have been studied using genetic data are population stratification (modeling individual ancestry with respect to ancestral populations) and genetic association (finding genetic polymorphisms that affect a trait). In this thesis, we develop
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Winkleblack, Scott Kenneth swinkleb. "ReGen: Optimizing Genetic Selection Algorithms for Heterogeneous Computing." DigitalCommons@CalPoly, 2014. https://digitalcommons.calpoly.edu/theses/1236.

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GenSel is a genetic selection analysis tool used to determine which genetic markers are informational for a given trait. Performing genetic selection related analyses is a time consuming and computationally expensive task. Due to an expected increase in the number of genotyped individuals, analysis times will increase dramatically. Therefore, optimization efforts must be made to keep analysis times reasonable. This thesis focuses on optimizing one of GenSel’s underlying algorithms for heterogeneous computing. The resulting algorithm exposes task-level parallelism and data-level parallelism pre
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Cule, Erika. "Shrinkage methods for variable selection and prediction with applications to genetic data." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/12811.

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Identifying genotypes using genetic material was at first a painstaking laboratory task. In the decades since the first gene was sequenced, techniques have progressed through milestones requiring massive international collaboration. Today’s genotype sequencing facilities use high-throughput technology to sequence entire genomes within days. Despite these technological improvements, and the resultant volume of genetic data, the identification of meaningful genotype-phenotype associations has not been as straightforward as was anticipated in the pre-genome era. The genetic architecture of many c
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Pecetti, Luciano. "Genetic resources and selection methods for drought and salinity resistance in durum wheat." Thesis, Bangor University, 1994. https://research.bangor.ac.uk/portal/en/theses/genetic-resources-and-selection-methods-for-drought-and-salinity-resistance-in-durum-wheat(119af68a-9751-4451-a54e-6c16fdb941ed).html.

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The relevance of drought and salinity stress under Mediterranean conditions is reviewed and prospects for crop improvement against these constraints are discussed. Field trials under severe drought in Syria highlighted the importance of earliness to ensure satisfactory yields. Peduncle length and frost tolerance were also important attributes. Under more favourable conditions in Sicily, the yield components per se (number of spikes, number of kernels and kernel weight) had greater influence on genotype performance. At both locations of evaluation high yields were attained through different arc
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Sofer, Tamar. "Statistical Methods for High Dimensional Data in Environmental Genomics." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10403.

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In this dissertation, we propose methodology to analyze high dimensional genomics data, in which the observations have large number of outcome variables, in addition to exposure variables. In the Chapter 1, we investigate methods for genetic pathway analysis, where we have a small number of exposure variables. We propose two Canonical Correlation Analysis based methods, that select outcomes either sequentially or by screening, and show that the performance of the proposed methods depend on the correlation between the genes in the pathway. We also propose and investigate criterion for fixing th
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May, Michael. "Data analytics and methods for improved feature selection and matching." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/data-analytics-and-methods-for-improved-feature-selection-and-matching(965ded10-e3a0-4ed5-8145-2af7a8b5e35d).html.

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This work focuses on analysing and improving feature detection and matching. After creating an initial framework of study, four main areas of work are researched. These areas make up the main chapters within this thesis and focus on using the Scale Invariant Feature Transform (SIFT).The preliminary analysis of the SIFT investigates how this algorithm functions. Included is an analysis of the SIFT feature descriptor space and an investigation into the noise properties of the SIFT. It introduces a novel use of the a contrario methodology and shows the success of this method as a way of discrimin
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Rönnegård, Lars. "Selection, maternal effects and inbreeding in reindeer husbandry." Uppsala : Dept. of Animal Breeding and Genetics, Swedish Univ. of Agricultural Sciences, 2003. http://epsilon.slu.se/a370.pdf.

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Shen, Xia. "Novel Statistical Methods in Quantitative Genetics : Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation." Doctoral thesis, Uppsala universitet, Beräknings- och systembiologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-170091.

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This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models. In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL m
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Flachenecker, Christian. "Response to modified recurrent full-sib selection in two European F2 maize populations analyzed with quantitative genetic methods." [S.l. : s.n.], 2006. http://nbn-resolving.de/urn:nbn:de:bsz:100-opus-1752.

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Gadiraju, Sriphani Raju. "Modified selection mechanisms designed to help evolution strategies cope with noisy response surfaces." Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-07022003-164112.

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Books on the topic "Genetic selection methods"

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Hamilton, Arnold Frances, and Georgiou George, eds. Directed enzyme evolution: Screening and selection methods. Humana Press, 2003.

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Liu, Zhanjiang. Next generation sequencing and whole genome selection in aquaculture. Wiley-Blackwell, 2011.

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1942-, Healey Peter, Rayner Steve 1953-, and Earthscan, eds. Unnatural selection: The challenges of engineering tomorrow's people. Earthscan, 2008.

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New York State Low-Level Radioactive Waste Siting Commission. and Roy F. Weston inc, eds. Generic scope of draft environmental impact statement, site and method selection for disposal of low-level radioactive wastes. The Commission, 1988.

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Sassine, Youssef Najib, ed. Mushrooms: Agaricus bisporus. CABI, 2021. http://dx.doi.org/10.1079/9781800620414.0000.

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Abstract This book, which is selected in nature to Agaricus bisporus, presents fundamental guidelines for mushroom production together with the advances in research in this field. The first chapter presents the history of button mushroom cultivation, mushroom classification, distribution, and nutritional and medicinal value. The methods of composting for substrate preparation, their basics, application, and innovation are discussed in Chapter 2. The basic principles and methods to improve compost quality are shown in the third chapter. Moving to the fourth chapter, the genetics, breeding appro
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B, Matchar David, United States. Agency for Healthcare Research and Quality., and Duke University Evidence-based Practice Center., eds. Testing for cytochrome P450 polymorphisms in adults with non-psychotic depression treated with selective serotonin reuptake inhibitors (SSRIs). Agency for Healthcare Research and Quality, 2007.

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Fares, Mario A. Natural Selection: Methods and Applications. Taylor & Francis Group, 2014.

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Fares, Mario A. Natural Selection: Methods and Applications. Taylor & Francis Group, 2014.

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Fares, Mario A. Natural Selection: Methods and Applications. Taylor & Francis Group, 2014.

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Reymond, Jean-Louis. Enzyme Assays: High-Throughput Screening, Genetic Selection and Fingerprinting. Wiley-VCH Verlag GmbH, 2006.

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Book chapters on the topic "Genetic selection methods"

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Bos, Izak, and Peter Caligari. "Population genetic aspects of cross-fertilization." In Selection Methods in Plant Breeding. Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8432-6_2.

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Pandey, Hari Mohan, Anupriya Shukla, Ankit Chaudhary, and Deepti Mehrotra. "Evaluation of Genetic Algorithm’s Selection Methods." In Advances in Intelligent Systems and Computing. Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2752-6_72.

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Bos, Izak, and Peter Caligari. "Applications of quantitative genetic theory in plant breeding." In Selection Methods in Plant Breeding. Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8432-6_11.

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Rutkoski, Jessica E., Margaret R. Krause, and Mark E. Sorrells. "Breeding Methods: Population Improvement and Selection Methods." In Wheat Improvement. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90673-3_6.

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AbstractIn order to produce successful varieties, wheat breeding programs must develop several strategies that fall under one of the following topics: line development, population improvement, and selection methods. This chapter focuses on breeding activities related to population improvement and selection methods, while Chap. 10.1007/978-3-030-90673-3_5 discusses line development. The objective of population improvement is to enhance the entire genetic base of the breeding program, while selection methods aim to identify breeding lines with superior potential or performance. As with line development approaches, numerous population improvement and selection methods have been developed in order to enhance breeding program efficiency and achieve genetic improvement. This chapter will provide an overview of population improvement and selection methods in the context of wheat breeding, discuss their advantages and disadvantages, and summarize empirical studies that have evaluated them in order to inform breeding program design.
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Bos, Izak, and Peter Caligari. "Effects of the mode of reproduction on the genetic variance." In Selection Methods in Plant Breeding. Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8432-6_10.

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Bos, Izak, and Peter Caligari. "Population genetic effects of selection with regard to sex expression." In Selection Methods in Plant Breeding. Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8432-6_5.

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Fouilloux, Guy, and Hubert Bannerot. "Selection Methods in the Common Bean (Phaseolus vulgaris)." In Genetic Resources of Phaseolus Beans. Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-2786-5_21.

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Koropoulis, Angelos, Nikolaos Alachiotis, and Pavlos Pavlidis. "Detecting Positive Selection in Populations Using Genetic Data." In Methods in Molecular Biology. Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0199-0_5.

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Ike, Kohei, and Daisuke Umeno. "Nucleotide Kinase-Based Selection System for Genetic Switches." In Methods in Molecular Biology. Humana Press, 2014. http://dx.doi.org/10.1007/978-1-62703-755-6_10.

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Kutyna, Dariusz R., Antonio G. Cordente, and Cristian Varela. "Genetic Engineering of Industrial Saccharomyces cerevisiae Strains Using a Selection/Counter-selection Approach." In Methods in Molecular Biology. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0563-8_9.

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Conference papers on the topic "Genetic selection methods"

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Edison, F. Naranjo E., E. Marcela Mosquera, T. Berenice Arguero, and A. Julio Zambrano. "Experimental Study of Convergence and Stability of a Genetic Algorithm Using Different Selection Methods." In 2024 IEEE Eighth Ecuador Technical Chapters Meeting (ETCM). IEEE, 2024. http://dx.doi.org/10.1109/etcm63562.2024.10746169.

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Roley, M. E. "An Overview of Weatherable Finishes." In CORROSION 2005. NACE International, 2005. https://doi.org/10.5006/c2005-05008.

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Abstract Selection of a weatherable finish is an important aspect of specifying an ultraviolet-durable coating system. There are many different generic types of weatherable finishes. A review of the generic t ypes, how the various types fail by ultraviolet degradation, formulation variables, and methods of t esting weatherable finishes will be completed. This review will provide information that will aid in the selection process.
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Richter, Samuel N., Michael G. Schoen, and Daniel R. Tauritz. "Evolving mean-update selection methods for CMA-ES." In GECCO '19: Genetic and Evolutionary Computation Conference. ACM, 2019. http://dx.doi.org/10.1145/3319619.3326827.

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Liskowski, Pawel, Krzysztof Krawiec, Thomas Helmuth, and Lee Spector. "Comparison of Semantic-aware Selection Methods in Genetic Programming." In GECCO '15: Genetic and Evolutionary Computation Conference. ACM, 2015. http://dx.doi.org/10.1145/2739482.2768505.

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Richter, Samuel N., and Daniel R. Tauritz. "The automated design of probabilistic selection methods for evolutionary algorithms." In GECCO '18: Genetic and Evolutionary Computation Conference. ACM, 2018. http://dx.doi.org/10.1145/3205651.3208304.

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Yan Xu and Lin Chen. "Term-frequency Based Feature Selection Methods for Text Categorization." In 2010 Fourth International Conference on Genetic and Evolutionary Computing (ICGEC 2010). IEEE, 2010. http://dx.doi.org/10.1109/icgec.2010.76.

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Sun, Yuan, Michael Kirley, and Saman Kumara Halgamuge. "On the Selection of Decomposition Methods for Large Scale Fully Non-separable Problems." In GECCO '15: Genetic and Evolutionary Computation Conference. ACM, 2015. http://dx.doi.org/10.1145/2739482.2768483.

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Ilyasov, R. A., A. G. Nikolenko, and H. W. Kwon. "GENETIC IMPROVEMENT OF HONEY BEES FOR KEEPING IN EXTREMAL CLIMATIC CONDITIONS." In V International Scientific Conference CONCEPTUAL AND APPLIED ASPECTS OF INVERTEBRATE SCIENTIFIC RESEARCH AND BIOLOGICAL EDUCATION. Tomsk State University Press, 2020. http://dx.doi.org/10.17223/978-5-94621-931-0-2020-55.

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Genetic improvement of honey bee populations based on molecular genetics features is faster and precision in comparison with morphometry and behavior-based methods. We developed the method based on nine nuclear microsatellite loci that allow a selection of most adaptive honey bee colonies by genetically defined features. Our study the heterozygosity of the dark European bee A. m. mellifera inhabiting the extremely cold region of the Ural Mountains to provide a marker-assisted selection for revealing the high adapted to extremely cold climate honey bee population can be applied for markerassist
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SALIN, Dmitry, Vasily PODOBAEV, Ludmila GOSTINA, and Denis SIMONOV. "Methods and Approaches Used in Processing of Selection and Genetic Databases." In Current Trends of Agricultural Industry in Global Economy. SibAC, 2021. http://dx.doi.org/10.32743/agri.gl.econ.2020.211-219.

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Alzaman, Chaher. "Unlocking the Potential of Machine Learning in Portfolio Selection: A Hybrid Approach with Genetic Optimization." In CARMA 2024 - 6th International Conference on Advanced Research Methods and Analytics. Universitat Politècnica de València, 2024. http://dx.doi.org/10.4995/carma2024.2024.17554.

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In the field of financial market predictions, machine learning has been widely used to identify patterns and gain valuable insights. However, for success in portfolio selection, it is crucial to optimize factors that impact accuracy. This study focuses on combining machine learning and optimization to enhance stock selection and prediction capabilities. The work starts with hyperparameter optimization and utilizes three different machine learning algorithms: XGBoost, LSTM, and Deep RankNet. Our findings show a 40% improvement in results through the use of a genetic-based optimization technique
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Reports on the topic "Genetic selection methods"

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Weller, Joel I., Ignacy Misztal, and Micha Ron. Optimization of methodology for genomic selection of moderate and large dairy cattle populations. United States Department of Agriculture, 2015. http://dx.doi.org/10.32747/2015.7594404.bard.

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The main objectives of this research was to detect the specific polymorphisms responsible for observed quantitative trait loci and develop optimal strategies for genomic evaluations and selection for moderate (Israel) and large (US) dairy cattle populations. A joint evaluation using all phenotypic, pedigree, and genomic data is the optimal strategy. The specific objectives were: 1) to apply strategies for determination of the causative polymorphisms based on the “a posteriori granddaughter design” (APGD), 2) to develop methods to derive unbiased estimates of gene effects derived from SNP chips
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Wisniewski, Michael E., Samir Droby, John L. Norelli, Noa Sela, and Elena Levin. Genetic and transcriptomic analysis of postharvest decay resistance in Malus sieversii and the characterization of pathogenicity effectors in Penicillium expansum. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7600013.bard.

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Blue mold of apple caused by Penicilliumexpansumis a major postharvest disease. Selection for postharvest disease resistance in breeding programs has been ignored in favor of fruit quality traits such as size, color, taste, etc. The identification of postharvest disease resistance as a heritable trait would represent a significant accomplishment and has not been attempted in apple. Furthermore, insight into the biology of the pathogenicity of P. expansumin apple could provide new approaches to postharvest decay management. Hypothesis: Postharvest resistance of apple to P. expansumcan be mapped
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Weller, Joel I., Derek M. Bickhart, Micha Ron, Eyal Seroussi, George Liu, and George R. Wiggans. Determination of actual polymorphisms responsible for economic trait variation in dairy cattle. United States Department of Agriculture, 2015. http://dx.doi.org/10.32747/2015.7600017.bard.

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The project’s general objectives were to determine specific polymorphisms at the DNA level responsible for observed quantitative trait loci (QTLs) and to estimate their effects, frequencies, and selection potential in the Holstein dairy cattle breed. The specific objectives were to (1) localize the causative polymorphisms to small chromosomal segments based on analysis of 52 U.S. Holstein bulls each with at least 100 sons with high-reliability genetic evaluations using the a posteriori granddaughter design; (2) sequence the complete genomes of at least 40 of those bulls to 20 coverage; (3) de
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Hansen, Peter J., Zvi Roth, and Jeremy J. Block. Improving oocyte competence in dairy cows exposed to heat stress. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598163.bard.

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Original Objectives. The overall goal is to develop methods to increase pregnancy rate in lactating dairy cows exposed to heat stress through methods that minimize damage to the oocyte and embryo caused by heat stress. Objectives were as follows: (1) examine the protective effects of melatonin on developmental competence of oocytes exposed to elevated temperature in vitro; (2) test whether melatonin feeding can improve developmental competence of oocytes in vivo and, if so, whether effects are limited to the summer or also occur in the absence of heat stress; and (3) evaluate the effectiveness
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Crisosto, Carlos, Susan Lurie, Haya Friedman, Ebenezer Ogundiwin, Cameron Peace, and George Manganaris. Biological Systems Approach to Developing Mealiness-free Peach and Nectarine Fruit. United States Department of Agriculture, 2007. http://dx.doi.org/10.32747/2007.7592650.bard.

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Peach and nectarine production worldwide is increasing; however consumption is flat or declining because of the inconsistent eating quality experienced by consumers. The main factor for this inconsistent quality is mealiness or woolliness, a form of chilling injury that develops following shipping periods in the global fruit market today. Our research groups have devised various postharvest methods to prolong storage life, including controlled atmosphere and delayed storage; however, these treatments only delay mealiness. Mealiness texture results from disruption of the normal ripening process
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Funkenstein, Bruria, and Cunming Duan. GH-IGF Axis in Sparus aurata: Possible Applications to Genetic Selection. United States Department of Agriculture, 2000. http://dx.doi.org/10.32747/2000.7580665.bard.

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Many factors affect growth rate in fish: environmental, nutritional, genetics and endogenous (physiological) factors. Endogenous control of growth is very complex and many hormone systems are involved. Nevertheless, it is well accepted that growth hormone (GH) plays a major role in stimulating somatic growth. Although it is now clear that most, if not all, components of the GH-IGF axis exist in fish, we are still far from understanding how fish grow. In our project we used as the experimental system a marine fish, the gilthead sea bream (Sparus aurata), which inhabits lagoons along the Mediter
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Hovav, Ran, Peggy Ozias-Akins, and Scott A. Jackson. The genetics of pod-filling in peanut under water-limiting conditions. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7597923.bard.

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Pod-filling, an important yield-determining stage is strongly influenced by water stress. This is particularly true for peanut (Arachishypogaea), wherein pods are developed underground and are directly affected by the water condition. Pod-filling in peanut has a significant genetic component as well, since genotypes are considerably varied in their pod-fill (PF) and seed-fill (SF) potential. The goals of this research were to: Examine the effects of genotype, irrigation, and genotype X irrigation on PF and SF. Detect global changes in mRNA and metabolites levels that accompany PF and SF. Explo
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Norelli, John L., Moshe Flaishman, Herb Aldwinckle, and David Gidoni. Regulated expression of site-specific DNA recombination for precision genetic engineering of apple. United States Department of Agriculture, 2005. http://dx.doi.org/10.32747/2005.7587214.bard.

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Objectives: The original objectives of this project were to: 1) evaluate inducible promoters for the expression of recombinase in apple (USDA-ARS); 2) develop alternative selectable markers for use in apple to facilitate the positive selection of gene excision by recombinase (Cornell University); 3) compare the activity of three different recombinase systems (Cre/lox, FLP/FRT, and R/RS)in apple using a rapid transient assay (ARO); and 4) evaluate the use of recombinase systems in apple using the best promoters, selectable markers and recombinase systems identified in 1, 2 and 3 above (Collabor
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Barash, Itamar, and Robert E. Rhoads. Translational Mechanisms that Govern Milk Protein Levels and Composition. United States Department of Agriculture, 2004. http://dx.doi.org/10.32747/2004.7586474.bard.

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Original objectives: The long term objective of the project is to achieve higher content of protein in the milk of ruminants by modulating the translational machinery in the mammary gland. The first specific aim of the BARD proposal was to characterize responsiveness of various experimental systems to combination of lactogenic hormones and amino acids with particular emphasis on discrimination between the control of total protein synthesis and milk protein synthesis. Based on the results, we planned to proceed by characterizing the stage of protein synthesis in which the stimulation by lactoge
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David, Lior, Yaniv Palti, Moshe Kotler, Gideon Hulata, and Eric M. Hallerman. Genetic Basis of Cyprinid Herpes Virus-3 Resistance in Common Carp. United States Department of Agriculture, 2011. http://dx.doi.org/10.32747/2011.7592645.bard.

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The goal of this project was to provide scientific and technical basis for initiating the development of breeding protocols using marker assisted selection for viral disease resistance in common carp. The specific objectives were: 1) Establishing families and characterizing the phenotypic and genetic variation of viral resistance; 2) Measuring the dynamics of immune response and developing a method to measure the long term immune memory; 3) Developing markers and generating a new genetic linkage map, which will enable initial QTL mapping; and, 4) Identifying genetic linkage of markers and cand
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