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[Since it has been shown in Frichot et al. (2014) that ADMIXTURE is slower than sNMF, we only compared PSIKO against sNMF.] In fact, as the length of the sequences grows, so too does the difference in run time between PSIKO and sNMF with that difference being significantly in favor of PSIKO.
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These differences in run time between male and female mice were offset by a significantly higher body mass in male mice (P < 0.0001) resulting in comparable levels of work in male and female mice.
The difference in run times between the C++ and Python implementations is negligible.
Of note, the difference in run-times between anemics and non-anemics appears to be the most influenced fitness parameter as compared to less operational difference for sit-ups and push-ups (Tables 3 and 4).
The difference in run time can be attributed to memory caching.
So, in this case, there is a clear difference in running time, a factor of 4.76.
Here, the differences in run times and results were substantial.
Comparisons between coalescent-based methods reveal substantial differences in running time.
Supertree methods on the simulated datasets showed some differences in running times.
To get an overview of the differences in the running times between hard and easy alignments, we sorted the instances by their running times in increasing order.
While Gifford et al. reported a reduced run-in time for NO-releasing sensors versus control sensors in rodents, we observed no significant differences in run-in time between NO-releasing sensors and controls, with all four sensor configurations requiring ∼3 6 h to achieve a steady background current.
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