Relevant bibliographies by topics / Population structure

Contents

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

Academic literature on the topic 'Population structure'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 March 2023

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Journal articles on the topic "Population structure"

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Frean, Marcus, Paul B. Rainey, and Arne Traulsen. "The effect of population structure on the rate of evolution." Proceedings of the Royal Society B: Biological Sciences 280, no. 1762 (July 7, 2013): 20130211. http://dx.doi.org/10.1098/rspb.2013.0211.

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Ecological factors exert a range of effects on the dynamics of the evolutionary process. A particularly marked effect comes from population structure, which can affect the probability that new mutations reach fixation. Our interest is in population structures, such as those depicted by ‘star graphs’, that amplify the effects of selection by further increasing the fixation probability of advantageous mutants and decreasing the fixation probability of disadvantageous mutants. The fact that star graphs increase the fixation probability of beneficial mutations has lead to the conclusion that evolution proceeds more rapidly in star-structured populations, compared with mixed (unstructured) populations. Here, we show that the effects of population structure on the rate of evolution are more complex and subtle than previously recognized and draw attention to the importance of fixation time. By comparing population structures that amplify selection with other population structures, both analytically and numerically, we show that evolution can slow down substantially even in populations where selection is amplified.
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Toksanbaeva, Mairash, and Raisa Popova. "Labor resources as a characteristic of labor potential and their structure." Population 25, no. 4 (December 21, 2022): 151–62. http://dx.doi.org/10.19181/population.2022.25.4.13.

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One of the characteristics of labor potential is the ability to work among its carriers indiv i duals, groups and the population, by which are also studied other characteristics (demography, health, social and economic activity, professional competencies, etc.). On the basis of working capacity is determined the most general indicator of the labor potential of population, namely, labor resources. This indicator is structured according to a number of qualitative parameters. They make it possible to identify labor resources used in public production, as well as unused reserves. Their involvement in labor is becoming relevant in the context of the modern need to increase the self-sufficiency of the economy, and hence, to increase these resources. However, their growth is limited for demographic reasons. To assess the available reserves, labor resources are ranked according to the characteristics of economic activity. In descending order, the categories are distinguished according to their relation to the labor force: the real labor force (employed and unemployed), potential labor force (not employed, but willing to work) and not included in the labor force (not willing to work). Calculations for these categories showed that in 2021, the real labor force dominated in the labor force (85.6%). The potential contingent accounted for a miserable amount (slightly more than one percent), and for those who did not want to work — a little more than 10%. But among those not included in the labor force, more than two thirds of this category were those who had objective reasons for being unemployed, as well as those employed in unpaid but useful domestic work. Factors influencing the structure of labor resources are considered by regions of the Russian Federation. They showed dependence of this structure on the birth rate, aging of the population, internal migration, and, above all, on the parameters of employment and unemployment, which play a leading role among the factors for improving this structure.
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Pfaff, Carrie L., Rick A. Kittles, and Mark D. Shriver. "Adjusting for population structure in admixed populations." Genetic Epidemiology 22, no. 2 (January 10, 2002): 196–201. http://dx.doi.org/10.1002/gepi.0126.

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Mukherjee, B. N., K. C. Malhotra, M. Roy, S. banerjee, H. Walter, and R. Chakraborty. "Genetic heterogeneity and population structure in eastern India: Red cell enzyme variability in ten Assamese populations." Zeitschrift für Morphologie und Anthropologie 77, no. 3 (May 3, 1989): 287–96. http://dx.doi.org/10.1127/zma/77/1989/287.

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Skotarczak, E., P. Ćwiertnia, and T. Szwaczkowski. "Pedigree structure of American bison (Bison bison) population." Czech Journal of Animal Science 63, No. 12 (December 4, 2018): 507–17. http://dx.doi.org/10.17221/120/2017-cjas.

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An effective realization of breeding programs in zoos is strongly determined by completeness of animal pedigree information. The knowledge of pedigree structure allows to maintain optimal genetic variability of a given population. The aim of this study was to estimate the parameters describing the pedigree structure of American bison housed in zoos in the context of further management of the population. Finally, 4269 American bison were analysed (1883 males, 2217 females, and 169 with unknown sex). The registered animals were born between years 1874 and 2013. The following pedigree parameters were estimated: number of fully traced generations, number of complete generations equivalent, index of pedigree completeness, individual inbreeding coefficients, increase of inbreeding for each individual, effective population size, and genetic diversity. The maximum number of fully traced generations was 3 (the mean value is 0.693). The mean inbreeding coefficient for the population studied was 3.26%, whereas individual increase in inbreeding ranged from 0 to 25.12%. Although the pedigree parameters (including the inbreeding level) in the American bison obtained in the present study seem to be acceptable (from the perspective of other wild animal populations), they can be over/underestimated due to incomplete pedigree.
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Garbutt, K., and F. A. Bazzaz. "Population niche structure." Oecologia 72, no. 2 (May 1987): 291–96. http://dx.doi.org/10.1007/bf00379281.

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Lehoczky, István, Desiré L. Dalton, József Lanszki, Zoltán Sallai, M. Thabang Madisha, Lisa J. Nupen, and Antoinette Kotzé. "Assessment of population structure in Hungarian otter populations." Journal of Mammalogy 96, no. 6 (September 6, 2015): 1275–83. http://dx.doi.org/10.1093/jmammal/gyv136.

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Yamasaki, Masanori, and Osamu Ideta. "Population structure in Japanese rice population." Breeding Science 63, no. 1 (2013): 49–57. http://dx.doi.org/10.1270/jsbbs.63.49.

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Ragsdale, Corey S. "REGIONAL POPULATION STRUCTURE IN POSTCLASSIC MEXICO." Ancient Mesoamerica 28, no. 2 (2017): 357–69. http://dx.doi.org/10.1017/s0956536117000013.

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AbstractThe majority of our knowledge about population structure in Mexico during the Postclassic period (a.d. 900–1520) is based on archaeological data. During this time, populations were in contact with each other through extensive trade networks and via the expansion of powerful empires in central and west Mexico. Though archaeological data provides a wealth of information about these relationships, little is known about the effects of these processes on population structure and biological, morphological variation or whether these effects vary across geographic regions. In this study, dental morphological observations are used as a proxy for genetic data in order to assess the differences in regional population structures throughout Mexico. Our analyses show differences in population structure between the various cultural and geographic areas around Mexico. We further conclude that population structures are affected by economic, political, or religious processes. This study provides bioarchaeological support for archaeological interpretations of population structure in Postclassic Mexico.
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Siegenthaler, Timothy B., Kurt Lamour, and Zachariah R. Hansen. "Population structure of Phytophthora capsici in the state of Tennessee." Mycological Progress 21, no. 1 (January 2022): 159–66. http://dx.doi.org/10.1007/s11557-021-01769-7.

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AbstractThe plant pathogen Phytophthora capsici can be found all throughout the USA, and the population genetics of this organism have been studied within many of these states. Until now, no work has been done in the state of Tennessee to investigate the population structure and genetics of P. capsici found there. The population structure of P. capsici was explored using 296 isolates collected from five counties in Tennessee in 2004, 2007, 2018, and 2019. Samples were genotyped using 39 single nucleotide polymorphism (SNP) genetic markers. Multiple analyses indicate that the population structure of P. capsici in Tennessee exists in isolated clusters structured by geography. Geographically separate populations were genetically distinct, suggesting there is limited or no outcrossing among populations, but there is significant sexual reproduction occurring within populations. These findings corroborate previous studies of P. capsici throughout the midwestern and northeastern USA, where populations are generally sexually reproducing and structured by geography. This study provides the first characterization of P. capsici population structure in Tennessee.
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Dissertations / Theses on the topic "Population structure"

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Håkansson, Nina. "Population growth : analysis of an age structure population model." Thesis, Linköping University, Department of Mathematics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-4392.

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This report presents an analysis of a partial differential equation, resulting from population model with age structure. The existence and uniqueness of a solution to the equation are proved. We look at stability of the solution. The asymptotic behaviour of the solution is treated. The report also contains a section about the connection between the solution to the age structure population model and a simple model without age structure.

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Cole-Showers, Curtis Lanre. "Population structure and demographics in Nigerian populations utilizing Y-chromosome markers." University of the Western Cape, 2014. http://hdl.handle.net/11394/5326.

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Philosophiae Doctor - PhD
Nigeria is peopled by ethnically and linguistically diverse populations of which little were known until the last few millennial. The absence of major natural geographical barrier increases the possibility of the populations being affected by the same demographic events. The aim of this thesis was to ascertain the genetic variations and demographics in five major Nigerian populations using Y-markers. This was done by determining the genetic structures of the Afro-asiatic speaking Hausa (n=78) of Northern Nigeria and the Niger Congo speaking populations of Igbo (n=119), Yoruba (n=238), Bini (n=13) and Ijaw (n=15) of Southern Nigeria all spread over 22 geographical origins and four (North, South east, south west and South south) geographical regions. They were compared with more than 2000 individuals from 46 populations of 20 other African and Middle Eastern countries, in published literature. The Scientific Working Group on DNA Analysis Methods (SWGDAM) recommended Y-Short Tandem Repeats (STRs) and nine Y-Single Nucleotide Polymorphisms (SNPs) haplogroups were typed with multiplex Polymerase Chain Reaction (PCR), Restriction Fragment Length Polymorphisms (RFLP) and High Resolution Melting (HRM). Summary statistics and measures of diversity were determined. Population structure was assessed with Population Pairwise Differences, hierarchical Analysis of Molecular Variance, Multidimensional scaling and correspondence analysis plots. Mantel’s test was used to assess the correlation of genetic distances with geographic distances. Demographic inferences were assessed with lineage based Network reconstruction, Spatial autocorrelation plots, effective migrants per population and both Inter and Intra-lineages Times to the Most Recent Common Ancestor (TMRCA). The patterns of diversity of the Y-markers showed a North-South gradient and a notable sub-structure among the Hausa populations. The Niger-Congo speakers displayed rare presence of haplogroups R and E1b1b but a preponderance of E1b1a7. Overall, the Y markers showed high diversities and significant genetic sub-structure within the Hausa populations of Nigeria with stronger linguistic than geographical bias. The demographic evaluations gave credence for genetic validation of both historical records and archeological findings among these Nigerian populations. These populations showed stronger affiliations with other sub-Saharan African populations rather than with North African or Middle Eastern populations, lacking evidence for the Middle Eastern origins of the male founders of these populations. Finally, the contribution of these Nigerian dataset would greatly enhance the Africa meta-population on the YHRD with more than 274 new haplotypes of forensic estimation significance.
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Sester-Huss, Elisabeth Mariko [Verfasser], and Peter [Akademischer Betreuer] Pfaffelhuber. "Population genetic models with selection, fluctuating environments and population structure." Freiburg : Universität, 2020. http://d-nb.info/1206095830/34.

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Piskol, Robert. "Structural and population genetic determinants of RNA secondary structure evolution." Diss., lmu, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-130532.

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McVeigh, Helen Patricia. "Mitochondrial DNA and salmonid population structure." Thesis, Queen's University Belfast, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.352951.

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Watkins, Eleanor Rose. "The population structure of bacterial pathogens." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:8ab53aa1-c55f-40bc-ab4f-ec2766b37252.

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Populations of many bacterial pathogens are structured into discrete strains despite frequent genetic exchange. Although insights from several studies suggest that different selection pressures operate in different areas of the genome, few theoretical and conceptual frameworks of bacterial population structure examine the effects of distinct competitive processes acting on different loci in the genome, and fewer still have considered the metabolic genes despite their increasingly-recognised importance in virulence. Buckee et al. (2008) investigated the combined effects of ecological competition operating at metabolic genes and immunological competition at antigenic genes on strain diversity using a stochastic model. A key prediction was that prevalent strains should show stable, non-overlapping associations between alleles of antigenic and metabolic genes. In this work, the roles of ecological and immunological competition in structuring bacterial pathogen populations is explored further using a multidisciplinary approach of mathematical modelling and whole genome analysis, focusing in particular on the well-characterised pathogens Neisseria meningitidis and Streptococcus pneumoniae as detailed case studies. We find support for the hypothesis that ecological and immunological competition play roles in structuring pathogen populations - even if present at relatively low levels - including support at the whole genome level. After extending the mathematical framework to encompass virulence-associated genes, we explore the effects of strain-targeted vaccination. We find that metabolic and virulence-associated genes from vaccine-strains are observed following vaccination in association with non-vaccine strains. The final chapter demonstrates how multilocus mathematical models coupled with whole genome analysis can be used together to gain additional insight into the evolution and population structure of bacterial pathogens.
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Baric, Michelle B. "Population Structure in the Cincinnati area." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1367945142.

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Morgan, Lee W. "Allozyme Analysis of Billfish Population Structure." W&M ScholarWorks, 1992. https://scholarworks.wm.edu/etd/1539617645.

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Nussey, Daniel H. "Phenotypic plasticity and population genetic structure in a wild vertebrate population." Thesis, University of Edinburgh, 2005. http://hdl.handle.net/1842/15544.

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My thesis focuses on maternal phenotypic plasticity in two neonatal traits and population genetic structure at different spatial scales in a wild red deer (Cervus elaphus) population on the Isle of Rum, Scotland. Specifically, I present: • An analysis of offspring birth weight-spring temperature plasticity in female red deer using linear regression to measure individual reaction norms. I found evidence of variation in plasticity between females and show that early experiences of high population density reduce female plasticity. • The description of a mixed-effects linear model approach to analysing phenotypic plasticity from a reaction norm perspective, and application of this model to birth date in the Rum deer population. I use the model to examine variation in phenotypic plasticity between females and selection on plasticity at different population density levels. • An examination of population history and structure in red deer from across the Isle of Rum using mitochondria) DNA and microsatellite markers. Analysis revealed that deer in this introduced population came from geographically isolated ancestral populations, and there was genetic evidence for strongly male-biased dispersal. Recent management practices on the island may have led to spatial variation in effective male dispersal on Rum. • A comparison of fine-scale spatial genetic structure between male and female deer in the North Block study area using microsatellite markers and census data. There was evidence of structure at extremely fine spatial scales amongst females but not males, and a decline in the structure amongst females over time. • An analysis of the spatial distribution of different mtDNA haplotypes in male and female red deer across the North Block. There was evidence for spatial structuring of haplotypes in both sexes.
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Beisswanger, Steffen. "Selection and population structure in Drosophila melanogaster." Diss., [S.l.] : [s.n.], 2006. http://edoc.ub.uni-muenchen.de/archive/00006131.

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Books on the topic "Population structure"

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Kōno, Shigemi. Population structure. Tokyo, Japan: Ministry of Health and Welfare, 1993.

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White, J., ed. The Population Structure of Vegetation. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-5500-4.

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Tajuddin, Ahmad Khan Md. Social structure of migrant population. New Delhi: Rajesh Publications, 1999.

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J, White, ed. The Population structure of vegetation. Dordrecht: Dr W. Junk Publishers, 1985.

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Vietnam. Tỏ̂ng cục thó̂ng kê. and United Nations Population Fund, eds. Population structure and household composition. Hanoi: Statistical Pub. House, 1997.

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Population of Kuwait: Structure and dynamics. Kuwait: University of Kuwait, Academic Publication Council, Authorship, Translation & Publication Committee, 2010.

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Genetic structure and selection in subdivided populations. Princeton, N.J: Princeton University Press, 2004.

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Surnames and genetic structure. Cambridge [Cambridgeshire]: Cambridge University Press, 1985.

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Dharmalingam, A. Agrarian structure and population in India. Canberra: Australian National University, 1991.

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Burkina Faso. Bureau central de recensement. Etat et structure de la population. Burkina Faso: Ministère de l'économie et des finances, 2009.

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Book chapters on the topic "Population structure"

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Zheng, Gang, Yaning Yang, Xiaofeng Zhu, and Robert C. Elston. "Population Structure." In Analysis of Genetic Association Studies, 259–86. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-2245-7_9.

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Heerink, Nico. "Age structure of the population." In Population Economics, 161–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78571-9_6.

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Ermisch, John F., and Marco Francesconi. "Family structure and children’s achievements." In Population Economics, 151–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55573-2_9.

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Wu, Pengkun. "Population Structure Challenge: Serious Population Ageing." In Population Development Challenges in China, 67–109. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8010-9_4.

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Barton, N., and A. Clark. "Population Structure and Processes in Evolution." In Population Biology, 115–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-74474-7_5.

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Roberts, D. F. "Haldane and Population Structure." In Human Population Genetics, 181–88. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-2970-5_13.

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Sjödin, Per, Lucie Gattepaille, Pontus Skoglund, Carina Schlebusch, and Mattias Jakobsson. "Analysis of Population Structure." In Human Population Genomics, 47–68. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61646-5_3.

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Hill, Martha S., Wei-Jun J. Yeung, and Greg J. Duncan. "Childhood family structure and young adult behaviors." In Population Economics, 173–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55573-2_10.

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Hansen, Everett M., and Richard C. Hamelin. "Population Structure of Basidiomycetes." In Structure and Dynamics of Fungal Populations, 251–81. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4423-0_11.

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Drenth, André, and Stephen B. Goodwin. "Population Structure of Oomycetes." In Structure and Dynamics of Fungal Populations, 195–224. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4423-0_9.

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Conference papers on the topic "Population structure"

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Fernandes, Carlos M., Agostinho C. Rosa, Nuno Fachada, J. L. J. Laredo, and J. J. Merelo. "Particle swarm and population structure." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205651.3205779.

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Jacobson, Alana. "Population genetic structure ofFrankliniella fusca." In 2016 International Congress of Entomology. Entomological Society of America, 2016. http://dx.doi.org/10.1603/ice.2016.114010.

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Limpiti, T., A. Intarapanich, A. Assawamakin, P. Wangkumhang, and S. Tongsima. "Iterative PCA for population structure analysis." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946474.

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GARNIS-JONES, SYLVIA, A. V. RAVINDRAN, C. RIPLEY, and B. D. JONES. "PSYCHODERMATOLOGY CLINIC: STRUCTURE AND PATIENT POPULATION." In IX World Congress of Psychiatry. WORLD SCIENTIFIC, 1994. http://dx.doi.org/10.1142/9789814440912_0179.

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Mai, The Tien, and Pierre Alquier. "Understanding the Population Structure Correction Regression." In 4th International Conference on Statistics: Theory and Applications (ICSTA'22). Avestia Publishing, 2022. http://dx.doi.org/10.11159/icsta22.114.

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Kamidi, C. M., R. W. Waineina, C. B. Wasike, D. K. Ngeno, and E. D. Ilatsia. "707. Genetic diversity and population structure of dairy goat populations in Kenya." 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_707.

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Skakauskas, Vladas. "Large time behavior in a density-dependent population dynamics problem with age structure and child care." In Mathematical Modelling of Population Dynamics. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2003. http://dx.doi.org/10.4064/bc63-0-12.

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Hasegawa, Taku, Naoki Mori, and Keinosuke Matsumoto. "Genetic programming with multi-layered population structure." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3067695.3076048.

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Fernandes, Carlos M., Nuno Fachada, Juan L. J. Laredo, Juan Julian Merelo, Pedro A. Castillo, and Agostinho Rosa. "Revisiting Population Structure and Particle Swarm Performance." In 10th International Joint Conference on Computational Intelligence. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006959502480254.

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Hrytsenko, Yana, Noah M. Daniels, and Rachel S. Schwartz. "Determining population structure from k-mer frequencies." In BCB '22: 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3535508.3545100.

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Reports on the topic "Population structure"

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Poterba, James. Population Age Structure and Asset Returns: An Empirical Investigation. Cambridge, MA: National Bureau of Economic Research, October 1998. http://dx.doi.org/10.3386/w6774.

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Piyasatian, Napapan, and Jack C. M. Dekkers. Accuracy of Genomic Prediction when Accounting for Population Structure and Polygenic Effects. Ames (Iowa): Iowa State University, January 2013. http://dx.doi.org/10.31274/ans_air-180814-1252.

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Wolc, Anna, Jesus Arango, Petek Settar, Janet E. Fulton, Neil P. O'Sullivan, Tomasz Jankowski, Rohan L. Fernando, Jack C. M. Dekkers, and Dorian J. Garrick. Accounting for Complex Population Structure in Pedigree and Genomic Analyses of Laying Chickens. Ames (Iowa): Iowa State University, January 2015. http://dx.doi.org/10.31274/ans_air-180814-1323.

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Courbis, Sarah. Population Structure of Island-Associated Pantropical Spotted Dolphins (Stenella attenuata) in Hawaiian Waters. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.578.

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Brannon, E. L. Population Structure of Columbia River Basin Chinook Salmon and Steelhead Trout, Technical Report 2001. Office of Scientific and Technical Information (OSTI), August 2002. http://dx.doi.org/10.2172/818642.

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Waples, R. S., P. B. Aebersold, and G. A. Winans. Population genetic structure and life history variability in Oncorhynchus nerka from the Snake River basin: Final report. Office of Scientific and Technical Information (OSTI), May 1997. http://dx.doi.org/10.2172/754028.

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Morphett, Jane, Alexandra Whittaker, Amy Reichelt, and Mark Hutchinson. Perineuronal net structure as a non-cellular mechanism of affective state, a scoping review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2021. http://dx.doi.org/10.37766/inplasy2021.8.0075.

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Is the perineuronal net structure within emotional processing brain regions associated with changes in affective state? The objective of this scoping review is to bring together the literature on human and animal studies which have measured perineuronal net structure in brain regions associated with emotional processing (such as but not limited to amygdala, hippocampus and prefrontal cortex). Perineuronal nets are a specialised form of condensed extracellular matrix that enwrap and protect neurons (Suttkus et al., 2016), regulate synaptic plasticity (Celio and Blumcke, 1994) and ion homeostasis (Morawski et al., 2015). Perineuronal nets are dynamic structures that are influenced by external and internal environmental shifts – for example, increasing in intensity and number in response to stressors (Blanco and Conant, 2021) and pharmacological agents (Riga et al., 2017). This review’s objective is to generate a compilation of existing knowledge regarding the structural changes of perineuronal nets in experimental studies that manipulate affective state, including those that alter environmental stressors. The outcomes will inform future research directions by elucidating non-cellular central nervous system mechanisms that underpin positive and negative emotional states. These methods may also be targets for manipulation to manage conditions of depression or promote wellbeing. Population: human and animal Condition: affective state as determined through validated behavioural assessment methods or established biomarkers. This includes both positive and negative affective states. Context: PNN structure, measuringPNNs.
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8

Lloyd, Cynthia B. Fertility, Family Size, and Structure: Consequences for Families and Children. Population Council, 1993. http://dx.doi.org/10.31899/pgy1993.1000.

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In 1989 the Population Council began a research project on the consequences of high fertility at the family level and its implications for the next generation. Since its inception, the project has been supported by Swedish SIDA and has involved the collaboration of researchers from selected developing countries. In countries where there has been limited research on this topic, such as India, Mali, Nigeria, Pakistan, and Senegal, the Population Council provided funding for new studies or for analysis of existing data with the potential for producing insights on this topic. In instances where relevant research was already underway, the Council provided informal support through membership in the research network, which has held several meetings since the initiation of the project. The seminar held on June 9-10, 1992, was intended to convene these researchers to present and discuss the results of their research. The two-day meeting brought together 29 experts to discuss the 14 papers printed in these proceedings.
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9

Breiman, Adina, Jan Dvorak, Abraham Korol, and Eduard Akhunov. Population Genomics and Association Mapping of Disease Resistance Genes in Israeli Populations of Wild Relatives of Wheat, Triticum dicoccoides and Aegilops speltoides. United States Department of Agriculture, December 2011. http://dx.doi.org/10.32747/2011.7697121.bard.

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Wheat is the most widely grown crop on earth, together with rice it is second to maize in total global tonnage. One of the emerging threats to wheat is stripe (yellow) rust, especially in North Africa, West and Central Asia and North America. The most efficient way to control plant diseases is to introduce disease resistant genes. However, the pathogens can overcome rapidly the effectiveness of these genes when they are wildly used. Therefore, there is a constant need to find new resistance genes to replace the non-effective genes. The resistance gene pool in the cultivated wheat is depleted and there is a need to find new genes in the wild relative of wheat. Wild emmer (Triticum dicoccoides) the progenitor of the cultivated wheat can serve as valuable gene pool for breeding for disease resistance. Transferring of novel genes into elite cultivars is highly facilitated by the availability of information of their chromosomal location. Therefore, our goals in this study was to find stripe rust resistant and susceptible genotypes in Israeli T. dicoccoides population, genotype them using state of the art genotyping methods and to find association between genetic markers and stripe rust resistance. We have screened 129 accessions from our collection of wild emmer wheat for resistance to three isolates of stripe rust. About 30% of the accessions were resistant to one or more isolates, 50% susceptible, and the rest displayed intermediate response. The accessions were genotyped with Illumina'sInfinium assay which consists of 9K single nucleotide polymorphism (SNP) markers. About 13% (1179) of the SNPs were polymorphic in the wild emmer population. Cluster analysis based on SNP diversity has shown that there are two main groups in the wild population. A big cluster probably belongs to the Horanum ssp. and a small cluster of the Judaicum ssp. In order to avoid population structure bias, the Judaicum spp. was removed from the association analysis. In the remaining group of genotypes, linkage disequilibrium (LD) measured along the chromosomes decayed rapidly within one centimorgan. This is the first time when such analysis is conducted on a genome wide level in wild emmer. Such a rapid decay in LD level, quite unexpected for a selfer, was not observed in cultivated wheat collection. It indicates that wild emmer populations are highly suitable for association studies yielding a better resolution than association studies in cultivated wheat or genetic mapping in bi-parental populations. Significant association was found between an SNP marker located in the distal region of chromosome arm 1BL and resistance to one of the isolates. This region is not known in the literature to bear a stripe rust resistance gene. Therefore, there may be a new stripe rust resistance gene in this locus. With the current fast increase of wheat genome sequence data, genome wide association analysis becomes a feasible task and efficient strategy for searching novel genes in wild emmer wheat. In this study, we have shown that the wild emmer gene pool is a valuable source for new stripe rust resistance genes that can protect the cultivated wheat.
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10

Sanders, Suzanne, Jessica Kirschbaum, and Sarah Johnson. Arctic and alpine rare plant population dynamics at Isle Royale National Park: Response to changing lake levels. National Park Service, February 2022. http://dx.doi.org/10.36967/nrr-2291496.

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Arctic and alpine rare plant species populate wave-splashed rocky shorelines of Isle Royale National Park, where summer temperatures are moderated by Lake Superior. Using data from the mid-1990s and resurvey data from 1998, 2003, and 2016, we examined trajectories of change in occurrence for 25 species at 28 sites coincident with rising lake levels that followed a period of sustained low levels. We analyzed changes in site occupancy of species individually and by functional, geographic, and microhabitat groupings. We also assessed change in population structure for four focal species: Saxifraga paniculata, S. tricuspidata, Pinguicula vulgaris, and Vaccinium uliginosum. Of the 25 species, site occupancy increased for 13 and remained steady for six, declining in another six. Site occupancy did not change over time within functional, geographic, and microhabitat groupings. The four focal species showed similar dynamic and systematically changing populations, responding to similar ecological exposures. We hypothesize that the moderating influence of Lake Superior on air temperature benefits these populations despite warming temperatures and a 15-year sustained low water period. This work contributes to our understanding of the responses of at-risk species to extreme climate events.
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