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We also consider the problem of finding a most likely sample path that leads to an overflow control problem, and give some applications of these results in the traffic engineering of high-speed networks.
The optimal sample path X∗[ 0,t∗] is defined to be the most likely sample path that results in the observed snapshot, i.e., it solves the following optimization problem: X ∗ [ 0, t ∗ ] = arg max t, X [ 0, t ] ∈ X ( t ) Pr ( X [ 0, t ] ), (1).
Finally, the read is assigned to the most likely sample of origin.
If the mid p-value is used, this band of p-values at 1 disappears, because the probability of the most likely sample is halved and a wider range of p-values can occur.
We compute the likelihood of assignment of a read to all potential samples of origin, assign each read to the most likely sample and compute the uncertainty of the assignment.
Briefly, we compute the likelihood of a read to originate from each of the original samples, assign reads to the most likely sample of origin and compute the overall confidence in this assignment.
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Assuming that the gene paths found by GeneStitch consist of reads most likely sampled from a real gene, we can assemble genes in a metagenomic dataset by using known homologous genes as references.
However, under the CNS condition, the shear strength of the grout was found to be generally higher comparing to the CNL condition, most likely because sample dilation resulted in an increase in the normal pressure.
Most likely in sample 10 there is a small (at most 1/10 of a fully mutant crypt) clonal expansion of a mutation that by chance mapped within the RMC target.
Consequently, we can conclude that the conceptualization of European Jews as a "population isolate," which is derived from the Rhineland hypothesis, is incorrect and most likely reflects sampling bias in the lack of Caucasus non-Jewish populations in comparative analyses.
Here, these cells are found only in the right hemineuropil, which is most likely a sampling artifact, and the D5-cell bodies of the left hemineuropil are beyond the examined volume and, hence, are not reconstructed.
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most stoichiometric sample
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com