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https://www.selleckchem.com/products/srpin340.html The prediction accuracy of GS and phenotypes was similar to each other regardless of the amount (0, 50, 100%) of stage-1 data incorporated in the GS model. Ranking of stage-1 lines by GS predictions that used no stage-1 phenotypic data had marginally lower correspondence to stage-2 phenotypic rankings than rankings of stage-1 lines based on phenotypes. Stage-1 lines ranked high by GS had slightly inferior phenotypes in stage-2 trials than lines ranked high by phenotypes. Cost analysis indicated that replacing stage-1 phenotyping with GS would allow nearly three times more stage-1 candidates to be assessed and provide 0.84-2.23 times greater gain from selection. We conclude that GS can complement or replace phenotyping in early stages of phenotyping.A computational methodology to simulate the diffusion of ions from point sources (e.g., ion channels) is described. The outlined approach computes the ion concentration from a cluster of many ion channels at pre-specified locations as a function of time using the theory of propagation integrals. How the channels' open/closed states evolve in time does not need to be known at the start of the simulation, but can be updated on-the-fly as the simulation goes along. The technique uses analytic formulas for the solutions of the diffusion equation for three common geometries (1) ions diffusing from a membrane (planar symmetry); (2) ions diffusing into a narrow cleft for effective two-dimensional diffusion (cylindrical symmetry); and (3) ions diffusing into open space like the cytosol (spherical symmetry). Because these formulas are exact solutions valid for arbitrarily long timesteps, no spatial or time discretizations are necessary. The only discrete locations are where the ion concentration is computed, and the only discrete timesteps are when the channels' open/closed states are updated. Beyond pure diffusion, the technique is generalized to the Excess Buffer Approximation o

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