Notably, our technique allowed very reproducible bandgap tuning, as indicated by the small error bars in Figure 1a.
We observed that, compared with mechanical sieving, laser diffraction was a more efficient, reproducible (as shown by the small error bars in Figure 1 ), and less time-consuming method.
Furthermore, in contrast to Δ trx1, Δ txl1, and Δ trr1 cells, where the fold induction of gst2 +, srx1 +, and obr1 + was remarkably consistent between individual experiments (as reflected by the small error bars in Figure 4A), although consistently high, the fold increase in each mRNA in Δ trx1Δ txl1 cells varied considerably between experiments (Table S3).
While standard QSGW would be sufficient for this work, we can do a little better by exploiting our knowledge of the small errors originating from the missing vertex in Π.
This refinement improved the TRE in the deep region in the temporal bone by correcting the small error at the surface of the temporal bone.
The variability among samples was less in the CupR mice than the Cup mice as demonstrated by the smaller error bars.
Note that the small error imposed by our interpolation method, with a mean of 1.54% among all assessed sequences (see Table 2), ensures a good approximation of the synthesized body with the captured, especially at higher metric resolutions.
Finally, the smaller errors predicted by the boosted training with the better internal predictivity (Fig. 4E vs 4D) do seem to be realized in the localized errors actually observed (Fig. 4B vs 4A), even though this was not obvious in the aggregate statistics (sPRED = 0.637 and sPRED = 0.633, respectively).
The approach is typically done in an iterative manner, repeatedly passing through the data sets to produce an optimized set of rules that account for the greatest amount of variability in the training data and, by so doing, produce the smallest error in the satellite-derived estimates of AGB.
It is found that the FL-network converges more quickly to the smaller error by comparing the training process of the two networks.
