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We tested the possibility that sampling bias resulted in the observed pattern by simulating a temporal data set under a constant population size model.
In order to test the possibility of sampling effects within our data set we used the software Bayesian Serial Simcoal (BayeSSC) [79] to simulate our temporal data under a constant population size model (fNe ranging from 30,000 to 50,000).
Although we have shown elsewhere how such modifications can potentially benefit the application of the TjD test to P. falciparum genes [15], here we retain a conservative approach by testing TjD values under a constant population size model, as our primary aim is to compare among different types of standard tests with minimal modifications.
In the sub-Saharan African sample, both the MAF and the DAF spectra showed a highly significant increase in the proportion of singletons with respect to the proportion expected under a constant population size model (χ2 P = 3×10−8 and χ2 P = 9×10−5, respectively).
Then, based on the results obtained in the previous clock-model analyses, continuous-time models were calibrated for the ORF Ash and ORF Bsh datasets under a constant population size tree prior and an exponential relaxed molecular clock.
The expectation of both these statistics, assuming an isochronous sampling, is zero under a constant population size model, but becomes negative under a population expansion model which also produces a star-like genealogy.
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In this first set of simulations 100 independent data sets were generated and each was analyzed in three different ways: (a) with the EBSP, (b) under the assumption of a constant size coalescent model, and (c) under the assumption of a constant population size with the genealogy (i.e. coalescent times) fixed to the simulated values.
To detect departures from a constant population size under the neutral model, Tajima's D [ 47], and Fu's and Li's D* and F* [ 48] statistics were calculated using Arlequin 3.5.1.2 [ 49].
In order to detect departures from a constant population size under the neutral model, Tajima's D [ 61], Ramos-Onsins & Rozas' R 2 [ 62] and Fu's Fs [ 63] tests were applied to both mtDNA and nDNA datasets.
This scenario underscores the importance of considering the possibility of a variable virus population size; population genetic models that assume a constant population size may not be appropriate under certain epidemiological conditions.
The approach calculates branch support under a standard Wright Fisher coalescent model with panmictic mating, nonoverlapping generations and a constant population size (Waddell et al. 2001).
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under a constant load
under a constant stress
under a constant summer
under a constant injection
under a constant value
under a constant oxygen
under a constant fear
under a constant volume
under a constant flow
under a constant bit
under a constant contact
under a large population
under a constant potential
under a low population
under a constant displacement
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