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5 results for “Elasticity Maps”
Global map of elastic thickness on Venus
<p>This map is Figure 14 from Anderson, F. S., and S. E. Smrekar (2006), Global mapping of crustal and lithospheric thickness on Venus, J. Geophys. Res., 111, E08006, doi:10.1029/2004JE002395. The location labels have been removed. Estimates of elastic thickness have an error of ±10 to 15 km. Caveats in Anderson and Smrekar (2006) should be carefully understood prior to use. No value of elastic thickness was obtained in areas in white. </p>
Lattice diagrams of elastic maps
<p>This collection of figures is a supplement to the presentation by Gupta and Tape (2024) and builds upon the work of Tape and Tape (2021, 2022, 2024). The collection contains this file, 28 composite pdf files, and three additional composite pdf files. We examine 28 elastic maps, each of which is represented by a 6 x 6 symmetric matrix having 21 parameters (in general). For each map we calculate the closest elastic map in each of 8 symmetry classes, and we depict these 8 elastic maps within a lattice diagram.</p>
Data from: Empirical Bayesian elastic net for multiple quantitative trait locus mapping
In multiple quantitative trait locus (QTL) mapping, a high-dimensional sparse regression model is usually employed to account for possible multiple linked QTLs. The QTL model may include closely linked and thus highly correlated genetic markers, especially when high-density marker maps are used in QTL mapping because of the advancement in sequencing technology. Although existing algorithms, such as Lasso, empirical Bayesian Lasso (EBlasso) and elastic net (EN) are available to infer such QTL models, more powerful methods are highly desirable to detect more QTLs in the presence of correlated QTLs. We developed a novel empirical Bayesian EN (EBEN) algorithm for multiple QTL mapping that inherits the efficiency of our previously developed EBlasso algorithm. Simulation results demonstrated that EBEN provided higher power of detection and almost the same false discovery rate compared with EN and EBlasso. Particularly, EBEN can identify correlated QTLs that the other two algorithms may fail to identify. When analyzing a real dataset, EBEN detected more effects than EN and EBlasso. EBEN provides a useful tool for inferring high-dimensional sparse model in multiple QTL mapping and other applications. An R software package 'EBEN' implementing the EBEN algorithm is available on the Comprehensive R Archive Network (CRAN).
Nonlinear Elasticity Mapping of Breast Masses
ClinicalTrials.gov study NCT04863443. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Data from: Empirical Bayesian elastic net for multiple quantitative trait locus mapping
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