Sentence examples for models for fluorescence from inspiring English sources

This latter situation is the case in the prototype examples studied in this paper, where models for fluorescence emission in photosynthesis have been used.

The lumping makes use of efficient methods from graph-theory and ϵ-decomposition and is derived and exemplified on two published models for fluorescence emission in photosynthesis.

The method has been applied to two models for fluorescence emission in photosynthesis, and we have demonstrated how the concepts supporting the method may be used also to nonlinear problems.

Data from the assays were averaged, plotted versus time, and fit to kinetic models for fluorescence recovery (Sprague et al., 2004) using Kaleidagraph (Synergy Software, Reading, PA, USA) and MATLAB (The MathWorks Inc., Natick, MA, USA).

The MLR models for fluorescence intensities obtained at the wavelength interval of 60 nm enable the prediction of the addition of plant oils with the RMSEC and RMSECV at 1.5 and 1.7, respectively.

The phasor transformation does not assume any fitting model for fluorescence lifetime decays, but rather expresses the overall decay in each pixel in terms of the polar coordinates on a universal semi-circle [49].

Example 3. To illustrate the ideas of this section we again consider the six states model for fluorescence emission inphotosynthesis.

As a result of their studies, they develop ArcLightning, a GEVI with superior kinetics, and they posit a four-state minimal model for fluorescence changes of GEVIs.

We will now demonstrate the performance of the method based on lumping on a larger model for fluorescence emission in photosynthesis.

Fits of the full model for fluorescence recovery of Sprague et al. (Sprague et al., 2004), which accounts for all possible modes of recovery by a single binding reaction in the presence of diffusion, were performed using the inverse Laplace transform given in equation 8 of Hallen et al. (Hallen et al., 2008).

This includes a global correction for non-correlated background, batch processing when fitting multiple correlation curves measured in the same system and shared parameters across different fit models, for example, fluorescence cross-correlation spectroscopy (FCCS) experiments (Ries et al., 2010; Weidemann et al., 2002).