We observe that removing the background fluorescence from the signal before performing this rescaling significantly reduces the intra-embryo and intra-stage variability of relative myosin fluorescence intensity (Additional file 8: Figure S8).
These differences arise due to many factors, including methodological efficiencies, life stage variability, and the use of various experimental platforms to assay expression.
However, because of individual and life-stage variability in the effectiveness of these homeostatic controls and in thyroid reserve capacities, potential sensitive subgroups must be considered in the derivation of exposure limits for perchlorate (Ginsberg et al. 2007; MA DEP 2006; Savin et al. 2003; Scinicariello et al. 2005; van den Hove et al. 1999; Zoeller 2003).
Use of defined developmental stages reduced variability of leaf texture due to ontogenetic characteristics.
In Stage 2, possible variability of ANN parameters (obtained in Stage 1) is optimized so as to create an ensemble of models with the consideration of minimum residual variance for the ensemble mean, while ensuring a maximum of the measured data to fall within the estimated prediction interval.
In Stage 2, the variability of the parameter estimates are calculated (Fig. 1 e).
At the early stage, the genetic variability of cassava was explored and that information was utilized to breed for a novel cassava line [ 3].
For another ten plants per treatment, we therefore selected defined leaf developmental stages to reduce variability of leaf texture and HCNp due to ontogeny.
Nevertheless, given the lack of morphogenetic defects at immature melanosomal stages and the variability of the macromelanosomal phenotype, it is also possible that the primary defect in ocular albinism lies in organelle motility, which secondarily leads to aberrant biogenesis.
When power is ˂ 80% at the end of stage 1, calculation of the required sample size is based on within-participant variability of stage 1 and an assumed GMR (Polli et al. 2012).
In contrast, this work proposes a more realistic TSD design where sample re-estimation relies not only on the variability of stage I, but also on the observed GMR.
