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Journal articles on the topic 'Fine-scale mapping'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Whittaker, J. C., A. P. Morris, and D. J. Balding. "Fine-scale mapping of disease loci." GeneScreen 1, no. 2 (2000): 101–2. http://dx.doi.org/10.1046/j.1466-9218.2000.00030.x.

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2

Smith, Lucian P., and Mary K. Kuhner. "The limits of fine-scale mapping." Genetic Epidemiology 33, no. 4 (2009): 344–56. http://dx.doi.org/10.1002/gepi.20387.

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3

JIANG, R., J. DONG, D. WANG, and F. Z. SUN. "Fine-scale mapping using Hardy-Weinberg disequilibrium." Annals of Human Genetics 65, no. 2 (2001): 207–19. http://dx.doi.org/10.1046/j.1469-1809.2001.6520207.x.

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4

Bleazard, Thomas, Young Seok Ju, Joohon Sung, and Jeong-Sun Seo. "Fine-scale mapping of meiotic recombination in Asians." BMC Genetics 14, no. 1 (2013): 19. http://dx.doi.org/10.1186/1471-2156-14-19.

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5

CLIFTON, K. E., J. W. BRADBURY, and S. L. VEHRENCAMP. "The fine-scale mapping of grassland protein densities." Grass and Forage Science 49, no. 1 (1994): 1–8. http://dx.doi.org/10.1111/j.1365-2494.1994.tb01970.x.

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6

Lin, Yi, Juha Hyyppa, and Anttoni Jaakkola. "Mini-UAV-Borne LIDAR for Fine-Scale Mapping." IEEE Geoscience and Remote Sensing Letters 8, no. 3 (2011): 426–30. http://dx.doi.org/10.1109/lgrs.2010.2079913.

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7

Dawy, Z., M. Sarkis, J. Hagenauer, and J. C. Mueller. "Fine-Scale Genetic Mapping Using Independent Component Analysis." IEEE/ACM Transactions on Computational Biology and Bioinformatics 5, no. 3 (2008): 448–60. http://dx.doi.org/10.1109/tcbb.2007.1072.

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8

MATSUBAYASHI, Kenichi, Yoshiyuki HIOKI, Jyunko IMAKIRE-HOSHINO, and Tohru UMEHARA. "A case study of ecotpe mapping in fine scale." Bulletion of the International Association for Landscape Ecology-Japan 5, no. 1 (2000): 4–9. http://dx.doi.org/10.5738/jale.5.4.

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9

Guo, Sun-Wei. "Linkage Disequilibrium Measures for Fine-Scale Mapping: A Comparison." Human Heredity 47, no. 6 (1997): 301–14. http://dx.doi.org/10.1159/000154430.

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10

Udler, Miriam S., Shahana Ahmed, Catherine S. Healey, et al. "Fine scale mapping of the breast cancer 16q12 locus." Human Molecular Genetics 19, no. 12 (2010): 2507–15. http://dx.doi.org/10.1093/hmg/ddq122.

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11

Chugg, Andrew M., Jonathan Ward, James McIntosh, et al. "Improved Fine-Scale Laser Mapping of Component SEE Sensitivity." IEEE Transactions on Nuclear Science 59, no. 4 (2012): 1007–14. http://dx.doi.org/10.1109/tns.2012.2189582.

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12

Xiong, Momiao, and Sun-Wei Guo. "Fine-Scale Mapping of Quantitative Trait Loci Using Historical Recombinations." Genetics 145, no. 4 (1997): 1201–18. http://dx.doi.org/10.1093/genetics/145.4.1201.

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With increasing popularity of QTL mapping in economically important animals and experimental species, the need for statistical methodology for fine-scale QTL mapping becomes increasingly urgent. The ability to disentangle several linked QTL depends on the number of recombination events. An obvious approach to increase the recombination events is to increase sample size, but this approach is often constrained by resources. Moreover, increasing the sample size beyond a certain point will not further reduce the length of confidence interval for QTL map locations. The alternative approach is to us

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13

Wiltshire, Steven, Andrew P. Morris, and Eleftheria Zeggini. "Examining the statistical properties of fine-scale mapping in large-scale association studies." Genetic Epidemiology 32, no. 3 (2008): 204–14. http://dx.doi.org/10.1002/gepi.20295.

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14

Lazzeroni, L. C. "A chronology of fine-scale gene mapping by linkage disequilibrium." Statistical Methods in Medical Research 10, no. 1 (2001): 57–76. http://dx.doi.org/10.1191/096228001666442347.

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15

Ellis, E. C., and H. Wang. "Estimating area errors for fine‐scale feature‐based ecological mapping." International Journal of Remote Sensing 27, no. 21 (2006): 4731–49. http://dx.doi.org/10.1080/01431160600735632.

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16

MORRIS, A. P., and J. C. WHITTAKER. "Fine scale association mapping of disease loci using simplex families." Annals of Human Genetics 64, no. 3 (2000): 223–37. http://dx.doi.org/10.1046/j.1469-1809.2000.6430223.x.

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17

Lazzeroni, Laura C. "A chronology of fine-scale gene mapping by linkage disequilibrium." Statistical Methods in Medical Research 10, no. 1 (2001): 57–76. http://dx.doi.org/10.1177/096228020101000104.

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18

Boucher, Alexandre, and Phaedon C. Kyriakidis. "Integrating Fine Scale Information in Super-resolution Land-cover Mapping." Photogrammetric Engineering & Remote Sensing 73, no. 8 (2007): 913–21. http://dx.doi.org/10.14358/pers.73.8.913.

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19

Xiang, Yang, Yumei Li, Zaiming Liu, and Zhenqiu Sun. "An Entropy-based Index for Fine-scale Mapping of QTL." Journal of Genetics and Genomics 34, no. 4 (2007): 373–80. http://dx.doi.org/10.1016/s1673-8527(07)60040-x.

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20

Graham, Jinko, and Elizabeth A. Thompson. "Disequilibrium Likelihoods for Fine-Scale Mapping of a Rare Allele." American Journal of Human Genetics 63, no. 5 (1998): 1517–30. http://dx.doi.org/10.1086/302102.

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21

Mailund, T., M. H. Schierup, C. N. S. Pedersen, J. N. Madsen, J. Hein, and L. Schauser. "GeneRecon--a coalescent based tool for fine-scale association mapping." Bioinformatics 22, no. 18 (2006): 2317–18. http://dx.doi.org/10.1093/bioinformatics/btl153.

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22

DEVLIN, B., and NEIL RISCH. "A Comparison of Linkage Disequilibrium Measures for Fine-Scale Mapping." Genomics 29, no. 2 (1995): 311–22. http://dx.doi.org/10.1006/geno.1995.9003.

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23

Fang, Ming. "A fast expectation-maximum algorithm for fine-scale QTL mapping." Theoretical and Applied Genetics 125, no. 8 (2012): 1727–34. http://dx.doi.org/10.1007/s00122-012-1949-9.

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24

Talbot, Christopher J., Richard A. Radcliffe, Jan Fullerton, Robert Hitzemann, Jeanne M. Wehner, and Jonathan Flint. "Fine scale mapping of a genetic locus for conditioned fear." Mammalian Genome 14, no. 4 (2003): 223–30. http://dx.doi.org/10.1007/s00335-002-3059-5.

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25

Gilfrich, N. L., D. E. Leyden, and E. A. Erslev. "XRF Macroprobe Analysis of Geologic Materials." Advances in X-ray Analysis 33 (1989): 593–601. http://dx.doi.org/10.1154/s0376030800020061.

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An x-ray fluorescence macroprobe was built for intermediate-scale compositional mapping to bridge the gap in spatial resolution between bulk x-ray fluorescence and electron beam methods. The macroprobe was optimized for quantitative whole rock mapping on a millimeter scale to evaluate changes in bulk composition of fine-grained mineral aggregates.

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26

Bhatt, Parth, Ann Maclean, Yvette Dickinson, and Chandan Kumar. "Fine-Scale Mapping of Natural Ecological Communities Using Machine Learning Approaches." Remote Sensing 14, no. 3 (2022): 563. http://dx.doi.org/10.3390/rs14030563.

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Remote sensing technology has been used widely in mapping forest and wetland communities, primarily with moderate spatial resolution imagery and traditional classification techniques. The success of these mapping efforts varies widely. The natural communities of the Laurentian Mixed Forest are an important component of Upper Great Lakes ecosystems. Mapping and monitoring these communities using high spatial resolution imagery benefits resource management, conservation and restoration efforts. This study developed a robust classification approach to delineate natural habitat communities utilizi

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27

Crowther-Swanepoel, Dalemari, Peter Broderick, Yussanne Ma, et al. "Fine-scale mapping of the 6p25.3 chronic lymphocytic leukaemia susceptibility locus." Human Molecular Genetics 19, no. 9 (2010): 1840–45. http://dx.doi.org/10.1093/hmg/ddq044.

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28

Zhan, Liang, Bin Liu, Xuejia Sang, and Linfu Xue. "Accelerate fine-scale geological mapping with UAV and convolutional neural networks." IOP Conference Series: Materials Science and Engineering 768 (March 31, 2020): 072082. http://dx.doi.org/10.1088/1757-899x/768/7/072082.

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29

Turissini, David A., and Daniel R. Matute. "Fine scale mapping of genomic introgressions within the Drosophila yakuba clade." PLOS Genetics 13, no. 9 (2017): e1006971. http://dx.doi.org/10.1371/journal.pgen.1006971.

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30

Li, Yumei, Yang Xiang, Hongwen Deng, and Zhenqiu Sun. "An Entropy-based Index for Fine-scale Mapping of Disease Genes." Journal of Genetics and Genomics 34, no. 7 (2007): 661–68. http://dx.doi.org/10.1016/s1673-8527(07)60075-7.

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31

Morris, A. P., J. C. Whittaker, and D. J. Balding. "Bayesian Fine-Scale Mapping of Disease Loci, by Hidden Markov Models." American Journal of Human Genetics 67, no. 1 (2000): 155–69. http://dx.doi.org/10.1086/302956.

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32

Xiong, Momiao, and Sun-Wei Guo. "Fine-Scale Genetic Mapping Based on Linkage Disequilibrium: Theory and Applications." American Journal of Human Genetics 60, no. 6 (1997): 1513–31. http://dx.doi.org/10.1086/515475.

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33

Kirst, Christoph, Sophie Skriabine, Alba Vieites-Prado, et al. "Mapping the Fine-Scale Organization and Plasticity of the Brain Vasculature." Cell 180, no. 4 (2020): 780–95. http://dx.doi.org/10.1016/j.cell.2020.01.028.

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34

Choi, Sungkyoung, and Sungho Won. "Fine-scale mapping of disease susceptibility locus with Bayesian partition model." Genes & Genomics 34, no. 4 (2012): 401–7. http://dx.doi.org/10.1007/s13258-011-0220-0.

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35

Ma, Xiao, Guang Zheng, Xu Chi, et al. "Mapping fine-scale building heights in urban agglomeration with spaceborne lidar." Remote Sensing of Environment 285 (February 2023): 113392. http://dx.doi.org/10.1016/j.rse.2022.113392.

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36

Correll, Maureen D., Wouter Hantson, Thomas P. Hodgman, et al. "Fine-Scale Mapping of Coastal Plant Communities in the Northeastern USA." Wetlands 39, no. 1 (2018): 17–28. http://dx.doi.org/10.1007/s13157-018-1028-3.

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37

De Iorio, Maria, and Claudio J. Verzilli. "A spatial probit model for fine-scale mapping of disease genes." Genetic Epidemiology 31, no. 3 (2007): 252–60. http://dx.doi.org/10.1002/gepi.20206.

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38

Turissini, D.A., and D.R. Matute. "Fine scale mapping of genomic introgressions within the Drosophila yakuba clade." PLoS Gen., 13(9 e1006971) (June 5, 2017): 1–40. https://doi.org/10.1371/journal.pgen.1006971.

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39

Maurer, E. P., D. L. Ficklin, and W. Wang. "Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies." Hydrology and Earth System Sciences 20, no. 2 (2016): 685–96. http://dx.doi.org/10.5194/hess-20-685-2016.

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Abstract. Statistical downscaling is a commonly used technique for translating large-scale climate model output to a scale appropriate for assessing impacts. To ensure downscaled meteorology can be used in climate impact studies, downscaling must correct biases in the large-scale signal. A simple and generally effective method for accommodating systematic biases in large-scale model output is quantile mapping, which has been applied to many variables and shown to reduce biases on average, even in the presence of non-stationarity. Quantile-mapping bias correction has been applied at spatial sca

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40

Maurer, E. P., D. L. Ficklin, and W. Wang. "Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies." Hydrology and Earth System Sciences Discussions 12, no. 10 (2015): 10893–920. http://dx.doi.org/10.5194/hessd-12-10893-2015.

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Abstract. Statistical downscaling is a commonly used technique for translating large-scale climate model output to a scale appropriate for assessing impacts. To ensure downscaled meteorology can be used in climate impact studies, downscaling must correct biases in the large-scale signal. A simple and generally effective method for accommodating systematic biases in large-scale model output is quantile mapping, which has been applied to many variables and shown to reduce biases on average, even in the presence of non-stationarity. Quantile mapping bias correction has been applied at spatial sca

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41

Deith, Mairin C. M., and Jedediah F. Brodie. "Predicting defaunation: accurately mapping bushmeat hunting pressure over large areas." Proceedings of the Royal Society B: Biological Sciences 287, no. 1922 (2020): 20192677. http://dx.doi.org/10.1098/rspb.2019.2677.

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Unsustainable hunting is emptying forests of large animals around the world, but current understanding of how human foraging spreads across landscapes has been stymied by data deficiencies and cryptic hunter behaviour. Unlike other global threats to biodiversity like deforestation, climate change and overfishing, maps of wild meat hunters' movements—often based on forest accessibility—typically cover small scales and are rarely validated with real-world observations. Using camera trapping data from rainforests across Malaysian Borneo, we show that while hunter movements are strongly correlated

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42

Romualdo Cardoso, Shirleny, Andrea Gillespie, Syed Haider, and Olivia Fletcher. "Functional annotation of breast cancer risk loci: current progress and future directions." British Journal of Cancer 126, no. 7 (2021): 981–93. http://dx.doi.org/10.1038/s41416-021-01612-6.

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AbstractGenome-wide association studies coupled with large-scale replication and fine-scale mapping studies have identified more than 150 genomic regions that are associated with breast cancer risk. Here, we review efforts to translate these findings into a greater understanding of disease mechanism. Our review comes in the context of a recently published fine-scale mapping analysis of these regions, which reported 352 independent signals and a total of 13,367 credible causal variants. The vast majority of credible causal variants map to noncoding DNA, implicating regulation of gene expression

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43

Steenvoorden, Jasper, Nina Leestemaker, Daniël Kooij, et al. "Towards standardised large-scale monitoring of peatland habitats through fine-scale drone-derived vegetation mapping." Ecological Indicators 166 (September 2024): 112265. http://dx.doi.org/10.1016/j.ecolind.2024.112265.

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44

Jia, Ge, Chen, Li, Heuvelink, and Ling. "Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network." Remote Sensing 11, no. 15 (2019): 1815. http://dx.doi.org/10.3390/rs11151815.

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Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using prior geographic information are conventional approaches used to derive fine-scale land cover maps. As the convolutional neural network (CNN) has been shown to be effective in capturing the spatial characteristics of geographic objects and extrapolating calibrated methods to other study areas, it may be a useful approach to overcome limitations of current SRM methods. In this paper, a new SRM method based on the CNN (SRMCNN) is proposed and

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45

CHUNG, I.-HSIN, CHE-RUNG LEE, JIAZHENG ZHOU, and YEH-CHING CHUNG. "HIERARCHICAL MAPPING FOR HPC APPLICATIONS." Parallel Processing Letters 21, no. 03 (2011): 279–99. http://dx.doi.org/10.1142/s0129626411000229.

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As the high performance computing systems scale up, mapping the tasks of a parallel application onto physical processors to allow efficient communication becomes one of the critical performance issues. Existing algorithms were usually designed to map applications with regular communication patterns. Their mapping criterion usually overlooks the size of communicated messages, which is the primary factor of communication time. In addition, most of their time complexities are too high to process large scale problems. In this paper, we present a hierarchical mapping algorithm (HMA), which is capab

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46

Zhang, Wenge, Xuan Yang, Zhanliang Yuan, Zhengchao Chen, and Yue Xu. "A Framework for Fine-Grained Land-Cover Classification." Remote Sensing 16, no. 2 (2024): 390. http://dx.doi.org/10.3390/rs16020390.

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Land-cover mapping plays a crucial role in resource detection, ecological environmental protection, and sustainable development planning. The existing large-scale land-cover products with coarse spatial resolution have a wide range of categories, but they suffer from low mapping accuracy. Conversely, land-cover products with fine spatial resolution tend to lack diversity in the types of land cover they encompass. Currently, there is a lack of large-scale land-cover products simultaneously possessing fine-grained classifications and high accuracy. Therefore, we propose a mapping framework for f

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47

Moran, Christopher J., Valentijn Hoff, Russell A. Parsons, Lloyd P. Queen, and Carl A. Seielstad. "Mapping Fine-Scale Crown Scorch in 3D with Remotely Piloted Aircraft Systems." Fire 5, no. 3 (2022): 59. http://dx.doi.org/10.3390/fire5030059.

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Remotely piloted aircraft systems (RPAS) are providing fresh perspectives for the remote sensing of fire. One opportunity is mapping tree crown scorch following fires, which can support science and management. This proof-of-concept shows that crown scorch is distinguishable from uninjured canopy in point clouds derived from low-cost RGB and calibrated RGB-NIR cameras at fine resolutions (centimeter level). The Normalized Difference Vegetation Index (NDVI) provided the most discriminatory spectral data, but a low-cost RGB camera provided useful data as well. Scorch heights from the point cloud

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48

Hammond, Thomas M., David G. Rehard, Bryant C. Harris, and Patrick K. T. Shiu. "Fine-scale mapping in Neurospora crassa by using genome-wide knockout strains." Mycologia 104, no. 1 (2012): 321–23. http://dx.doi.org/10.3852/11-062.

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49

Milling, Charlotte R., Janet L. Rachlow, Peter J. Olsoy, et al. "Habitat structure modifies microclimate: An approach for mapping fine‐scale thermal refuge." Methods in Ecology and Evolution 9, no. 6 (2018): 1648–57. http://dx.doi.org/10.1111/2041-210x.13008.

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50

Berndt, Sonja I., Joshua Sampson, Meredith Yeager, et al. "Large-scale fine mapping of the HNF1B locus and prostate cancer risk." Human Molecular Genetics 20, no. 16 (2011): 3322–29. http://dx.doi.org/10.1093/hmg/ddr213.

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