If the experiment conditions and mRNA amount for the reference and target samples are similar, we argue in [23] that a simple linear function is a good approximation in normalization even though the relationship between dye concentration and fluorescent intensity is nonlinear.
Nevertheless, comparing the ratio of the peak at 762 cm−1 to the one at 1700 cm−1 for both samples, we can argue that the electrochemically etched sample has more Si-C bonds and is less oxidized than the plasma-synthetized one.
Given that there were few well-preserved, clean G. ruber tests for these sites and that replicate measurements of Mn/Ca and Mg/Ca ratios were within an acceptable range, we argue that samples from runs #2 to #4 may have involved impure G. ruber tests in which barite crystallized on fossil shell surfaces.
We argue, that sampling the explanatory variables from the target population vouch for estimates adapted to the application domain rather than to a small sample of trees with unknown representation in the target population.
We argue that our sample is not representative of any particular business size or sector but rather a sampling meant to be generalised for a separate research approach such as a survey, which is not the scope of this paper.
On the contrary, we argue that unbiased sampling reflecting biological realism is necessary to effectively inform policy and planning.
First, the mortality in the three counties under study, were found to be similar to the national levels [ 27], and as the attendance was high (84 88%), we argue that this study sample is representative of Norway.
Therefore, we argue that for realistic sample sizes the bias error created by ignoring these higher-order scenarios will likely be smaller than the variance error suffered by attempting to recover them.
We argue, however, that our sample sizes, the magnitude of the differences, the diagnostic nature of our tests, and the time interval between pre and post-test (one semester) can contribute to attenuate this potential bias.
We argue, however, that our sample is enriched in recombining cells and therefore, even if we may not recover the precise transcriptional profile at the time/cells where DSBs occur, genomic regions with no evidence of transcription will be particularly informative when defining coldspots of transcription during DSB formation.
