deletions | additions       diff --git a/Results.md b/Results.md  index 2698d48..8223760 100644  --- a/Results.md  +++ b/Results.md 
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The method works by fitting fluorescence signal from each pixel with a transient function. Prior to fitting, candidate regions containing possible events must be detected. This is achieved by modifying a continous wavelet transform based peak detection algorithm by Du et al.,\cite{Du_2006}. We have altered the method to also yield the width of the peak in addition to the location. Specific details of the region detection algorithm are given in the Supplementary Material.  Region estimation is performed iteratively. This is done to ensure overlapping events are correctly identified. At each pass regions which have no overlaps are fitted. After this the fit result is subtracted from the original signal and region detection is performed again, followed by fitting. This is done until no more regions are detected or successfully fitted.  ### Signal fitting  The function used for fitting Ca2+ release events is shown on Figure \ref{fig:f2}. The shape of the function is described by 4 parameters: amplitude (\(A\)), rise and decay time constants (\(\tau_{rise},\tau_{decay}\)) and plateau duration (\(d\)). An additional parameter (\(t_{max}\)) determines the time when the maximum is reached.   Because it is not know which part of the signal is the event and which is the baseline the first fit also has to estimate the baseline properties.  Signal in the candidate region is fitted with an extended fit function (Figure \ref{fig:fig2}) that also depends on relaxation baseline \(B\) and baseline offset \(C\). The \(C\) parameter allows for the possibility of an elevated background before the release event. After paramater optmiziation with the extended fit function the same signal is fitted with a line. For both models the corrected Akaike Information Criterion (AICc \cite{Burnham_2004} is calculated and the region is accepted only if the AICc for the fit function is less than for the line (i.e., the goodness of the fit obtained with the fit function justifies the use of a more complicated model).  ### Clustering  ### Event characterisation