Remote-sensing-based monitoring of forest degradation/enhancement essentially involves two main approaches [18]: first, detection indicated by a change in canopy cover or proxies and second, the quantification of gain or loss in AGB.
For quantification purposes, gain and offset settings were kept constant so that the average background pixel intensity was between 0 and 10 and the fluorescent signal coming from the cells was between 60 and 220 (0-255 scale for 8 bit images).
Surrogate based uncertainty quantification has gained immense popularity in recent years due to its computational efficiency.
Corrections for randoms, dead time, and attenuation were applied but not scatter correction, since it was shown by Bahri et al. [8] that no benefit for quantification is gained by applying scatter correction for the Focus-F120.
However, the careful quantification of gain- and loss-of-function phenotypes leads to intriguing observations.
The clinical utility of quantification has gained importance for the diagnosis, prognosis and quantification of the various disorders of myocardial perfusion [ 3, 15, 24, 26– 28].
Methods using elemental loaded labels for enhancement of sensitivity and robustness of biomolecules quantification are gaining prominence and a number of key papers have been published [8,18,21,33,76 81].
Furthermore, the image-analysis method can be combined with more traditional IHC quantification techniques to gain further insight into the molecular mechanism of action of a particular therapeutic candidate.
The quantification of gene gain and loss suggests that both have been extensive in the evolutionary history of the P. syringae species complex and that the genomes of these pathovars may undergo considerable fluctuation even over relatively short evolutionary time periods.
Recently, with the application of the triple quadrupole mass spectrometer and advances in software development for multiple-reaction monitoring (MRM) analysis, a targeted quantification method has gained advantages over the above-mentioned discovery-based approach in terms of sensitivity, quantification accuracy, and reproducibility.
