For each instance of a sampled parameter vector, the model is run and likelihoods are calculated for the age-dependent prevalence curves generated.
For each instance of each sampling method, we thus build the resulting surrogate aggregated contact network and rank nodes according to each centrality measure.
This arrangement, when combined with Illumina paired-end sequencing, results in each instance of a restriction site sequence being sampled many times by the first reads and the genomic DNA sequence in the nearby region being randomly sampled at a lower coverage by the second reads.
For instance, it is not uncommon for studies of occupational exposure to use previously-collected data to investigate a new research questions; for instance for studies of sampling strategies [ 11, 13, 21, 24].
Despite the precautions for avoiding contamination, we detected an instance of sample mix-up that could have led to an erroneous interpretation of multiple instabilities (see above).
It is not helpful when clear instances of sample confusion or contamination – discovered through a posteriori analysis – get subsequently defended as "real" somatic effects by unconvincing arguments.
For genetic markers, such as mitochondrial DNA (mtDNA), which are free of natural recombination, single instances of sample mix-up involving different branches of the mtDNA phylogeny would give rise to reticulate patterns and should therefore be detectable.
Of the 202 chimpanzee samples from the study site yielding usable genotypes, there were 13 instances of samples collected at the same location and on the same day as the other samples representing the same individuals; these cases were collapsed into single captures.
In addition, we detected no instances of sample switching due to aberrant barcode identification.
The bootstrap sampling is done without replacement, and subsequent instances of each out-of-sample patient (i.e. (x_{rm pre }, x_{rm post })) are removed from both (t_{rm pre }) and (t_{rm post }).
Alternatively, a sampling-based optimization approach (e.g. Monte Carlo sampling [ 6]), with the obvious caveat of being computationally intensive, probabilistically explores a possible space of enzyme activities, i.e. flux capacity distributions, and solves for an optimal intervention set for each sampled instance of flux capacities.
