Готові списки джерел за темами / Genetic theory

Добірка наукової літератури з теми "Genetic theory"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Genetic theory"

1

Farley, John D. "Evolutionary genetic theory." British Journal of Psychiatry 160, no. 6 (June 1992): 861–62. http://dx.doi.org/10.1192/bjp.160.6.861.

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2

Ahad, Md Abdul. "The Neutral Theory (Theory of Genetic Drift) and the Nearly Neutral Theory of Molecular Evolution are Opposite to Evolution." International Journal of Bio-resource and Stress Management 14, July, 7 (July 22, 2023): 1016–27. http://dx.doi.org/10.23910/1.2023.3455a.

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The Neutral theory (also known as the theory of genetic drift) means genetic drift and vice-versa. But the Nearly Neutral theory means genetic drift plus natural selection. However, genetic drift changes the gene frequency randomly and thus it is non-additive, directionless and thus valueless for evolution. Again, genetic drift works only in small populations and thus, genetic drift means small population. But small populations have to inbreed and produce homozygous organisms. Consequently, those populations suffer from various diseases and abnormalities and finally may suddenly extinct. Moreover, any homozygous organism means zero variation, mutation-genetic drift equilibrium also creates zero variation. But variation is the raw material of evolution; so, no evolution occurs by the genetic drift. So, evolutionary biologists rejected both the genetic drift and the small populations for any kind of evolution. Hence, the Neutral theory is opposite to any kind of evolution. Again, recent experiments of ecological genetics with small populations, 12 biochemical tests, and the data of the DNA sequence, fossil evidence oppose the Neutral theory. Furthermore, the rate of evolution by the Neutral theory is equal to the rate of mutation. But mutations are opposite to any kind of evolution. So, biologists rejected the Neutral theory. As the natural selection is not justified in Nearly Neutral theory; so, Neutral theory=Nearly Neutral theory. Consequently, the rejection of the Neutral theory means the rejection of the Nearly Neutral theory. Thus, both the Neutral theory and the Nearly Neutral theory are opposite to any kind of evolution.
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Hendricks, M. "Experiments Challenge Genetic Theory." Science News 134, no. 11 (September 10, 1988): 166. http://dx.doi.org/10.2307/3972733.

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4

Schmitt, Lothar M. "Theory of genetic algorithms." Theoretical Computer Science 259, no. 1-2 (May 2001): 1–61. http://dx.doi.org/10.1016/s0304-3975(00)00406-0.

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5

Bürger, Reinhard. "Multilocus population-genetic theory." Theoretical Population Biology 133 (June 2020): 40–48. http://dx.doi.org/10.1016/j.tpb.2019.09.004.

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6

O’Malley, Maureen A. "Endosymbiosis and its implications for evolutionary theory." Proceedings of the National Academy of Sciences 112, no. 33 (April 16, 2015): 10270–77. http://dx.doi.org/10.1073/pnas.1421389112.

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Historically, conceptualizations of symbiosis and endosymbiosis have been pitted against Darwinian or neo-Darwinian evolutionary theory. In more recent times, Lynn Margulis has argued vigorously along these lines. However, there are only shallow grounds for finding Darwinian concepts or population genetic theory incompatible with endosymbiosis. But is population genetics sufficiently explanatory of endosymbiosis and its role in evolution? Population genetics “follows” genes, is replication-centric, and is concerned with vertically consistent genetic lineages. It may also have explanatory limitations with regard to macroevolution. Even so, asking whether population genetics explains endosymbiosis may have the question the wrong way around. We should instead be asking how explanatory of evolution endosymbiosis is, and exactly which features of evolution it might be explaining. This paper will discuss how metabolic innovations associated with endosymbioses can drive evolution and thus provide an explanatory account of important episodes in the history of life. Metabolic explanations are both proximate and ultimate, in the same way genetic explanations are. Endosymbioses, therefore, point evolutionary biology toward an important dimension of evolutionary explanation.
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Aravindan, PP. "Host genetics and tuberculosis: Theory of genetic polymorphism and tuberculosis." Lung India 36, no. 3 (2019): 244. http://dx.doi.org/10.4103/lungindia.lungindia_146_15.

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Aravindan, PP. "Host genetics and tuberculosis : Theory of genetic polymorphism and tuberculosis." Lung India 36, no. 3 (2019): 244. http://dx.doi.org/10.4103/0970-2113.257707.

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Newmeyer, Frederick J. "Genetic dysphasia and linguistic theory." Journal of Neurolinguistics 10, no. 2-3 (April 1997): 47–73. http://dx.doi.org/10.1016/s0911-6044(97)00002-x.

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Rowe, D. E. "Errors in genetic theory equations." Theoretical and Applied Genetics 71, no. 3 (December 1985): 451–54. http://dx.doi.org/10.1007/bf00251186.

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Дисертації з теми "Genetic theory"

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Yan, Ping. "Theory of simple genetic algorithms." Thesis, University of Macau, 2000. http://umaclib3.umac.mo/record=b1446649.

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Anderson, Jon K. "Genetic algorithms applied to graph theory." Virtual Press, 1999. http://liblink.bsu.edu/uhtbin/catkey/1136714.

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This thesis proposes two new variations on the genetic algorithm. The first attempts to improve clustering problems by optimizing the structure of a genetic string dynamically during the run of the algorithm. This is done by using a permutation on the allele which is inherited by the next generation. The second is a multiple pool technique which ensures continuing convergence by maintaining unique lineages and merging pools of similar age. These variations will be tested against two well-known graph theory problems, the Traveling Salesman Problem and the Maximum Clique Problem. The results will be analyzed with respect to string rates, child improvement, pool rating resolution, and average string age.
Department of Computer Science
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3

Kang, Yong-Ho. "Adaptive control via genetic algorithms : theory and application." Thesis, University of Reading, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308023.

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4

Morr, Lindsey. "Cascade testing communication within Lynch syndrome families: An examination of communication privacy management theory." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1525765585195444.

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Popelka, Aleš. "Datamining - theory and it's application." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-164981.

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This thesis deals with the topic of the technology called data mining. First, the thesis describes the term data mining as an independent discipline and then its processing methods and the most common use. The term data mining is thereafter explained with the help of methodologies describing all parts of the process of knowledge discovery in databases -- CRISP-DM, SEMMA. The study's purpose is presenting new data mining methods and particular algorithms -- decision trees, neural networks and genetic algorithms. These facts are used as theoretical introduction, which is followed by practical application searching for causes of meningoencephalitis development of certain sample of patients. Decision trees in system Clementine, which is one of the top datamining tools, were used for the analysys.
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Hellenthal, Garrett. "Exploring rates and patterns of variability in gene conversion and crossover in the human genome /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/8961.

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Reynolds, David. "Theory of genetic algorithms with applications to heat integration networks." Thesis, Glasgow Caledonian University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296464.

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Mambrini, Andrea. "Theory grounded design of genetic programming and parallel evolutionary algorithms." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5928/.

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Evolutionary algorithms (EAs) have been successfully applied to many problems and applications. Their success comes from being general purpose, which means that the same EA can be used to solve different problems. Despite that, many factors can affect the behaviour and the performance of an EA and it has been proven that there isn't a particular EA which can solve efficiently any problem. This opens to the issue of understanding how different design choices can affect the performance of an EA and how to efficiently design and tune one. This thesis has two main objectives. On the one hand we will advance the theoretical understanding of evolutionary algorithms, particularly focusing on Genetic Programming and Parallel Evolutionary algorithms. We will do that trying to understand how different design choices affect the performance of the algorithms and providing rigorously proven bounds of the running time for different designs. This novel knowledge, built upon previous work on the theoretical foundation of EAs, will then help for the second objective of the thesis, which is to provide theory grounded design for Parallel Evolutionary Algorithms and Genetic Programming. This will consist in being inspired by the analysis of the algorithms to produce provably good algorithm designs.
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Richter, James Neal. "On mutation and crossover in the theory of evolutionary algorithms." Thesis, Montana State University, 2010. http://etd.lib.montana.edu/etd/2010/richter/RichterJ0510.pdf.

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The Evolutionary Algorithm is a population-based metaheuristic optimization algorithm. The EA employs mutation, crossover and selection operators inspired by biological evolution. It is commonly applied to find exact or approximate solutions to combinatorial search and optimization problems. This dissertation describes a series of theoretical and experimental studies on a variety of evolutionary algorithms and models of those algorithms. The effects of the crossover and mutation operators are analyzed. Multiple examples of deceptive fitness functions are given where the crossover operator is shown or proven to be detrimental to the speedy optimization of a function. While other research monographs have shown the benefits of crossover on various fitness functions, this is one of the few (or only) doing the inverse. A background literature review is given of both population genetics and evolutionary computation with a focus on results and opinions on the relative merits of crossover and mutation. Next, a family of new fitness functions is introduced and proven to be difficult for crossover to optimize. This is followed by the construction and evaluation of executable theoretical models of EAs in order to explore the effects of parameterized mutation and crossover. These models link the EA to the Metropolis-Hastings algorithm. Dynamical systems analysis is performed on models of EAs to explore their attributes and fixed points. Additional crossover deceptive functions are shown and analyzed to examine the movement of fixed points under changing parameters. Finally, a set of online adaptive parameter experiments with common fitness functions is presented.
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Krasnogor, Natalio. "Studies on the theory and design space of memetic algorithms." Thesis, University of the West of England, Bristol, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249135.

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Книги з теми "Genetic theory"

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Baldwin, James Mark. Genetic theory of reality. New Brunswick, N.J: Transaction Publishers, 2010.

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2

Baldwin, James Mark. Genetic theory of reality. New Brunswick, N.J: Transaction Publishers, 2010.

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3

Baldwin, James Mark. Genetic theory of reality. New Brunswick, N.J: Transaction Publishers, 2009.

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4

Baldwin, James Mark. Genetic theory of reality. New Brunswick, N.J: Transaction Publishers, 2010.

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5

Baldwin, James Mark. Genetic theory of reality. New Brunswick, N.J: Transaction Publishers, 2009.

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6

Baldwin, James Mark. Genetic theory of reality. New Brunswick, N.J: Transaction Publishers, 2009.

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7

Rick, Riolo, and Worzel Bill, eds. Genetic programming theory and practice. Boston: Kluwer Academic, 2003.

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8

Riolo, Rick, and Bill Worzel, eds. Genetic Programming Theory and Practice. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-8983-3.

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9

Baniasadi, Pouya, Vladimir Ejov, Jerzy A. Filar, and Michael Haythorpe. Genetic Theory for Cubic Graphs. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-19680-0.

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Riolo, Rick. Genetic Programming Theory and Practice. Boston, MA: Springer US, 2003.

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Частини книг з теми "Genetic theory"

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Arnon, Ilana, Jim Cottrill, Ed Dubinsky, Asuman Oktaç, Solange Roa Fuentes, María Trigueros, and Kirk Weller. "Genetic Decomposition." In APOS Theory, 27–55. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7966-6_4.

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2

Kramer, Oliver. "Theory." In Genetic Algorithm Essentials, 57–64. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52156-5_7.

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3

Falconer, Graham. "Genetic criticism." In Encyclopedia of Contemporary Literary Theory, edited by Irena Makaryk, 70–72. Toronto: University of Toronto Press, 1993. http://dx.doi.org/10.3138/9781442674417-021.

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4

Ferencz-Flatz, Christian. "Adorno’s Genetic Phenomenology." In Critical Theory and Phenomenology, 41–52. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27615-6_3.

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Liu, Baoding. "Genetic Algorithms." In Theory and Practice of Uncertain Programming, 9–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89484-1_2.

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Liu, Baoding. "Genetic Algorithms." In Theory and Practice of Uncertain Programming, 17–27. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1781-2_2.

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Ryan, Conor, and Miguel Nicolau. "Doing Genetic Algorithms the Genetic Programming Way." In Genetic Programming Theory and Practice, 189–204. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-8983-3_12.

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Owen, Tim. "Constructing a Genetic-Social Framework." In Criminological Theory, 63–115. London: Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137316950_3.

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Li, Xuewei, Jinpei Wu, and Xueyan Li. "Cellular Genetic Algorithms." In Theory of Practical Cellular Automaton, 131–91. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7497-4_5.

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Schmitt, Lothar M. "Theory of Coevolutionary Genetic Algorithms." In Parallel and Distributed Processing and Applications, 285–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-37619-4_29.

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Тези доповідей конференцій з теми "Genetic theory"

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Rowe, Jonathan E. "Genetic algorithm theory." In the fourteenth international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2330784.2330923.

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Poli, Riccardo, and William B. Langdon. "Genetic programming theory." In the 2007 GECCO conference companion. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1274000.1274124.

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Rowe, Jonathan E. "Genetic algorithm theory." In the 2007 GECCO conference companion. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1274000.1274125.

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Rowe, Jonathan E. "Genetic algorithm theory." In the 2008 GECCO conference companion. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1388969.1389067.

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Poli, Riccardo. "Genetic programming theory." In the 2008 GECCO conference companion. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1388969.1389068.

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Rowe, Jonathan E. "Genetic algorithm theory." In the 13th annual conference companion. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001858.2002126.

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Poli, Riccardo. "Genetic programming theory." In the 12th annual conference comp. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1830761.1830905.

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Doerr, Benjamin. "Session details: Track 15: theory." In GECCO09: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2009. http://dx.doi.org/10.1145/3257509.

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Doerr, Benjamin. "Session details: Track 15: theory." In GECCO09: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2009. http://dx.doi.org/10.1145/3257494.

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Sudholt, Dirk. "Theory of Swarm Intelligence." In GECCO '15: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2739482.2756570.

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Звіти організацій з теми "Genetic theory"

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Noviskey, Michael J., Timothy D. Ross, David A. Gadd, and Mark Axtell. Application of Genetic Algorithms to Function Decomposition in Pattern Theory. Fort Belvoir, VA: Defense Technical Information Center, January 1994. http://dx.doi.org/10.21236/ada327931.

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Goldberg, David E. From Theory to Air Force Practice: Applications and Non-Binary Extensions of Probabilistic Model-Building Genetic Algorithms. Fort Belvoir, VA: Defense Technical Information Center, May 2006. http://dx.doi.org/10.21236/ada463557.

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Parker, Paul B. Genetic algorithms and their use in Geophysical Problems. Office of Scientific and Technical Information (OSTI), April 1999. http://dx.doi.org/10.2172/8770.

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4

Cahaner, Avigdor, Susan J. Lamont, E. Dan Heller, and Jossi Hillel. Molecular Genetic Dissection of Complex Immunocompetence Traits in Broilers. United States Department of Agriculture, August 2003. http://dx.doi.org/10.32747/2003.7586461.bard.

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Objectives: (1) Evaluate Immunocompetence-OTL-containing Chromosomal Regions (ICRs), marked by microsatellites or candidate genes, for magnitude of direct effect and for contribution to relationships among multiple immunocompetence, disease-resistance, and growth traits, in order to estimate epistatic and pleiotropic effects and to predict the potential breeding applications of such markers. (2) Evaluate the interaction of the ICRs with genetic backgrounds from multiple sources and of multiple levels of genetic variation, in order to predict the general applicability of molecular genetic markers across widely varied populations. Background: Diseases cause substantial economic losses to animal producers. Emerging pathogens, vaccine failures and intense management systems increase the impact of diseases on animal production. Moreover, zoonotic pathogens are a threat to human food safety when microbiological contamination of animal products occurs. Consumers are increasingly concerned about drug residues and antibiotic- resistant pathogens derived from animal products. The project used contemporary scientific technologies to investigate the genetics of chicken resistance to infectious disease. Genetic enhancement of the innate resistance of chicken populations provides a sustainable and ecologically sound approach to reduce microbial loads in agricultural populations. In turn, animals will be produced more efficiently with less need for drug treatment and will pose less of a potential food-safety hazard. Major achievements, conclusions and implications:. The PI and co-PIs had developed a refined research plan, aiming at the original but more focused objectives, that could be well-accomplished with the reduced awarded support. The successful conduct of that research over the past four years has yielded substantial new information about the genes and genetic markers that are associated with response to two important poultry pathogens, Salmonella enteritidis (SE) and Escherichia coli (EC), about variation of immunocompetence genes in poultry, about relationships of traits of immune response and production, and about interaction of genes with environment and with other genes and genetic background. The current BARD work has generated a base of knowledge and expertise regarding the genetic variation underlying the traits of immunocompetence and disease resistance. In addition, unique genetic resource populations of chickens have been established in the course of the current project, and they are essential for continued projects. The US laboratory has made considerable progress in studies of the genetics of resistance to SE. Microsatellite-marked chromosomal regions and several specific genes were linked to SE vaccine response or bacterial burden and the important phenomenon of gene interaction was identified in this system. In total, these studies demonstrate the role of genetics in SE response, the utility of the existing resource population, and the expertise of the research group in conducting such experiments. The Israeli laboratories had showed that the lines developed by selection for high or low level of antibody (Ab) response to EC differ similarly in Ab response to several other viral and bacterial pathogens, indicating the existence of a genetic control of general capacity of Ab response in young broilers. It was also found that the 10w-Ab line has developed, possibly via compensatory "natural" selection, higher cellular immune response. At the DNA levels, markers supposedly linked to immune response were identified, as well as SNP in the MHC, a candidate gene responsible for genetic differences in immunocompetence of chickens.
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Zhang, Hongbin B., David J. Bonfil, and Shahal Abbo. Genomics Tools for Legume Agronomic Gene Mapping and Cloning, and Genome Analysis: Chickpea as a Model. United States Department of Agriculture, March 2003. http://dx.doi.org/10.32747/2003.7586464.bard.

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The goals of this project were to develop essential genomic tools for modern chickpea genetics and genomics research, map the genes and quantitative traits of importance to chickpea production and generate DNA markers that are well-suited for enhanced chickpea germplasm analysis and breeding. To achieve these research goals, we proposed the following research objectives in this period of the project: 1) Develop an ordered BAC library with an average insert size of 150 - 200 kb (USA); 2) Develop 300 simple sequence repeat (SSR) markers with an aid of the BAC library (USA); 3) Develop SSR marker tags for Ascochyta response, flowering date and grain weight (USA); 4) Develop a molecular genetic map consisting of at least 200 SSR markers (Israel and USA); 5) Map genes and QTLs most important to chickpea production in the U.S. and Israel: Ascochyta response, flowering and seed set date, grain weight, and grain yield under extreme dryland conditions (Israel); and 6) Determine the genetic correlation between the above four traits (Israel). Chickpea is the third most important pulse crop in the world and ranks the first in the Middle East. Chickpea seeds are a good source of plant protein (12.4-31.5%) and carbohydrates (52.4-70.9%). Although it has been demonstrated in other major crops that the modern genetics and genomics research is essential to enhance our capacity for crop genetic improvement and breeding, little work was pursued in these research areas for chickpea. It was absent in resources, tools and infrastructure that are essential for chickpea genomics and modern genetics research. For instance, there were no large-insert BAC and BIBAC libraries, no sufficient and user- friendly DNA markers, and no intraspecific genetic map. Grain sizes, flowering time and Ascochyta response are three main constraints to chickpea production in drylands. Combination of large seeds, early flowering time and Ascochyta blight resistance is desirable and of significance for further genetic improvement of chickpea. However, it was unknown how many genes and/or loci contribute to each of the traits and what correlations occur among them, making breeders difficult to combine these desirable traits. In this period of the project, we developed the resources, tools and infrastructure that are essential for chickpea genomics and modern genetics research. In particular, we constructed the proposed large-insert BAC library and an additional plant-transformation-competent BIBAC library from an Israeli advanced chickpea cultivar, Hadas. The BAC library contains 30,720 clones and has an average insert size of 151 kb, equivalent to 6.3 x chickpea haploid genomes. The BIBAC library contains 18,432 clones and has an average insert size of 135 kb, equivalent to 3.4 x chickpea haploid genomes. The combined libraries contain 49,152 clones, equivalent to 10.7 x chickpea haploid genomes. We identified all SSR loci-containing clones from the chickpea BAC library, generated sequences for 536 SSR loci from a part of the SSR-containing BACs and developed 310 new SSR markers. From the new SSR markers and selected existing SSR markers, we developed a SSR marker-based molecular genetic map of the chickpea genome. The BAC and BIBAC libraries, SSR markers and the molecular genetic map have provided essential resources and tools for modern genetic and genomic analyses of the chickpea genome. Using the SSR markers and genetic map, we mapped the genes and loci for flowering time and Ascochyta responses; one major QTL and a few minor QTLs have been identified for Ascochyta response and one major QTL has been identified for flowering time. The genetic correlations between flowering time, grain weight and Ascochyta response have been established. These results have provided essential tools and knowledge for effective manipulation and enhanced breeding of the traits in chickpea.
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Bertolini, Francesca, Tianfu Yang, Yanyun Huang, John C. S. Harding, Max F. Rothschild, and Graham S. Plastow. Periweaning Failure to Thrive Syndrome (PFTS): Is There a Genetic Component? Ames (Iowa): Iowa State University, January 2017. http://dx.doi.org/10.31274/ans_air-180814-342.

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7

McKenzi Norris, McKenzi Norris. How do Aedes mosquito genetics affect their habitat choice? Experiment, April 2019. http://dx.doi.org/10.18258/13395.

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8

Bobashev, Georgiy, John Holloway, Eric Solano, and Boris Gutkin. A Control Theory Model of Smoking. RTI Press, June 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.

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We present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term (“loading up” after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdrawal- and craving-like signals associated with abstinence and shows that after 3 months of abstinence, craving disappears. The model was programmed as a computer application allowing users to select simulation scenarios. The application links images of brain regions that are activated during the binge/intoxication, withdrawal, or craving with corresponding simulated states. The model was calibrated to represent smoking patterns described in peer-reviewed literature; however, it is generic enough to be adapted to other drugs, including cocaine and opioids. Although the model does not mechanistically describe specific neurobiological processes, it can be useful in prevention and treatment practices as an illustration of drug-using behaviors and expected dynamics of withdrawal and craving during abstinence.
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DeCristoforo, Richard. Development of a tool to measure applicability of the general systems theory to generic social work. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.351.

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Makarov, V. P. MOBILIZATION AND CONSERVATION OF THE GENETIC DIVERSITY OF CULTIVATED PLANTS AND THEIR WILD RELATIVES. Ljournal, 2017. http://dx.doi.org/10.18411/2227-8834-2017-2-5-15.

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