deletions | additions       diff --git a/subsection_Image_analysis_Pharyngeal_pumping__.tex b/subsection_Image_analysis_Pharyngeal_pumping__.tex  index dc0d2ba..87773ed 100644  --- a/subsection_Image_analysis_Pharyngeal_pumping__.tex  +++ b/subsection_Image_analysis_Pharyngeal_pumping__.tex 
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%\end{equation}  %where $\mu$ is the mean intensity difference. During pumps, this means will be higher than during pauses, such that the time series spikes at each pumping event (Fig. \ref{fig:entropy}D).  Peaks in the entropy were identified using a standard peak detection algorithm.   %We now identify peaks in the difference image entropy, using monotonicity detection after filtering, which correspond to pumping events.   To validate the correspondence between such peaks and pumping events we compared the performance of our method to identifying pumps by visual inspection. For this purpose we generated kymograph images of a line across the center of the terminal bulb over time. The grinder appeared as a dark spot in each line and peaks in the kymograph were counted by visual inspection. The occurrence of pumping events detected manually and automatically highly correlated ($R = 0.??$, Fig. \ref{fig:longtimeseries}A. In addition, a high correlation between the two methods was observed in the median-smoothed distribution of intervals between pumps, corresponding to the instantaneous pumping rate, ($R = 0.??$, Fig. \ref{fig:longtimeseries}B and in the overall distribution (, Fig. \ref{fig:longtimeseries}C).    ****** got as far as here ************  We now identify peaks in the difference image entropy, using monotonicity detection after filtering, which correspond to pumping events.   We can compare the performance of our tracking with a manual identification of pumping events. Manual identification is achieved by tracking peaks in a kymograph. To compare the automatically tracked pumping events with the manually tracked events, we use two measures: Firstly, we show that the occurence of pumping events $\tau_i$ in the timeseries correlates well (see Fig. \ref{fig:longtimeseries}A. Besides accurately tracking peaks over long times, the distribution of intervals $\Delta$ between pumps is of interest. This corresponds to a local instantaneous pumping rate, which is used to compare our results with the literature. The interval distributions agree both locally (median-smoothed pumping rate in Fig. \ref{fig:longtimeseries}B. and in the overall distribution (Fig. \ref{fig:longtimeseries}C).    Since the animals are stably confined in a straight posture in the microfluidic devices and locomotion can only occur in the direction of the channel, we can create a kymograph of the pumping motion. The kymographs shows the characteristic triangular shape when a pump occurs. Kymographs enable easy visual inspection of the trajectories; Additionally, the image analysis frame work contains an interactive verification script: The user can manually click on pumps in the kymograph to compare the automatic tracking and the visual inspection. This method was used to assess the tracking accuracy in Fig. \ref{fig:longtimeseries}.     ****** got as far as here ************