deletions | additions       diff --git a/section_Implementing_Burst_Variance_Analysis__.tex b/section_Implementing_Burst_Variance_Analysis__.tex  index 3fb0283..4cc11af 100644  --- a/section_Implementing_Burst_Variance_Analysis__.tex  +++ b/section_Implementing_Burst_Variance_Analysis__.tex 
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The concept of BVA is to figure out whether the observed broadness of FRET distributions are owing to the molecular dynamics, by comparison of a calculated standard deviation ($s_E$) of FRET efficiencies for sub-bursts ,comprised of \textit{n} consecutive photons within the burst, from a mean FRET efficiency for a burst to an expected standard deviation based on shot noise limited distribution~\cite{Torella_2011}.   If the observed broadness originates from different molecules having distinct FRET efficiencies without dynamics, $s_E$ of each burst is only affected by shot noise and will follow the expected standard deviation curve rationalized by a binomial distribution (see equation 4 in~\cite{Torella_2011}). However, if the observed broadness is due to dynamics of single species of biomolecules, $s_E$ of each burst has to be larger than the expected standard deviation and sits on above the expected standard deviation curve as shown in figure .  Since FRETBursts is based on open source Python packages, BVA can be easily built and implemented by FRETBursts with combination of other Python packages. packages (see notebook).