D to detect a specific range of pixel intensity within each image. The range was empirically selected from a random subset of the data such that it corresponded well with the range occupied by the ventricle. The program was set to fill gaps in detected objects, and to exclude objects less than 1000 1326631 mm2 (Figure 3A).hPTH (1-34) site ventricle taken from a midpoint of each 3D stack and analyzed with Volocity image analysis software. The radius of the ventricle in the z axis (the C radius) was computed from these volume and area measurements assuming the shape of the ventricle to be a prolate spheroid. The correlation between the C radius of the ventricle and area of these five measurements yields the relationship C = (6.861024) * A +46, where A is the area of the ventricle. This relationship allowed derivation of ventricular volume over time (see figure 3D for a detailed description).3c. Relating Heart Area to Heart VolumeFive dpf Kdrl:casper fish were euthanized in 600 mg/L MS-222 in order to arrest the heart. Z-stacks were then acquired of the entire ventricular chamber in 5 fish. The average volume of the chambers was compared to the average area of a 2D image of the3d. Automated Heart Beat AnalysisThe results of the automated detection yielded time varying measurements of ventricular area over the heart beat cycle (Figure 3B). The ventricular area versus time data was thenAutomated In Vivo Hypercholesterolemia ScreenFigure 2. Automated Hypercholesterolemia Screen. A. Images of Control, 50 mM Ezetimibe treated, and 6.5 mg/mL methanolic hawthorn extract (MHE) treated 5 pf zebrafish embryos B. Quantified results of the automated screen. Bars represent the mean of the mean fluorescence intensity of each individual well (with values of 0, no reading, excluded). Control, 50 mMEzetimibe treated, and between 3.25 mg/mL and 19.5 mg/mL MHE treated groups are show. Ezetimibe served as a positive control C. Dose response curve illustrating the relationship between hawthorn dose and fluorescent output (R2 = 0.61). For this experiment n is between 13?0 per group. doi:10.1371/journal.pone.0052409.gconverted to ventricular volume versus time as described above in the section “Relating Heart Area to Heart Volume.” MATLAB was used to TA 02 automatically extract the average change in volume of the left ventricle between systole and diastole using two different approaches. The first is solely based on the frequency-domain representation of the cardiodynamic data while the second uses both frequency- and time-domain data. The frequency-domain approach first mean subtracted each time-domain data set. The volume-time curve was then windowed using a Tukey tapered cosine window (r = 0.5) algorithm to minimize artifacts when subsequently converting to frequencydomain data using the fast Fourier transform (FFT). Proper normalization was performed on the frequency-domain to correct the amplitude for the curve length, sampling frequency, windowing, and measuring only the positive frequencies in the frequencydomain data [22]. The peak amplitude of magnitude of the normalized frequency-domain data was multiplied by two to obtain the peak-to-peak change in ventricular volume, which corresponds to the change between systole and diastole. The frequency corresponding the peak in the frequency-domain data was used as an estimate of the average heart rate during the eightsecond acquisition (Figure 4A) The frequency- and time-domain combined approach used the frequency domain data to estimate.D to detect a specific range of pixel intensity within each image. The range was empirically selected from a random subset of the data such that it corresponded well with the range occupied by the ventricle. The program was set to fill gaps in detected objects, and to exclude objects less than 1000 1326631 mm2 (Figure 3A).ventricle taken from a midpoint of each 3D stack and analyzed with Volocity image analysis software. The radius of the ventricle in the z axis (the C radius) was computed from these volume and area measurements assuming the shape of the ventricle to be a prolate spheroid. The correlation between the C radius of the ventricle and area of these five measurements yields the relationship C = (6.861024) * A +46, where A is the area of the ventricle. This relationship allowed derivation of ventricular volume over time (see figure 3D for a detailed description).3c. Relating Heart Area to Heart VolumeFive dpf Kdrl:casper fish were euthanized in 600 mg/L MS-222 in order to arrest the heart. Z-stacks were then acquired of the entire ventricular chamber in 5 fish. The average volume of the chambers was compared to the average area of a 2D image of the3d. Automated Heart Beat AnalysisThe results of the automated detection yielded time varying measurements of ventricular area over the heart beat cycle (Figure 3B). The ventricular area versus time data was thenAutomated In Vivo Hypercholesterolemia ScreenFigure 2. Automated Hypercholesterolemia Screen. A. Images of Control, 50 mM Ezetimibe treated, and 6.5 mg/mL methanolic hawthorn extract (MHE) treated 5 pf zebrafish embryos B. Quantified results of the automated screen. Bars represent the mean of the mean fluorescence intensity of each individual well (with values of 0, no reading, excluded). Control, 50 mMEzetimibe treated, and between 3.25 mg/mL and 19.5 mg/mL MHE treated groups are show. Ezetimibe served as a positive control C. Dose response curve illustrating the relationship between hawthorn dose and fluorescent output (R2 = 0.61). For this experiment n is between 13?0 per group. doi:10.1371/journal.pone.0052409.gconverted to ventricular volume versus time as described above in the section “Relating Heart Area to Heart Volume.” MATLAB was used to automatically extract the average change in volume of the left ventricle between systole and diastole using two different approaches. The first is solely based on the frequency-domain representation of the cardiodynamic data while the second uses both frequency- and time-domain data. The frequency-domain approach first mean subtracted each time-domain data set. The volume-time curve was then windowed using a Tukey tapered cosine window (r = 0.5) algorithm to minimize artifacts when subsequently converting to frequencydomain data using the fast Fourier transform (FFT). Proper normalization was performed on the frequency-domain to correct the amplitude for the curve length, sampling frequency, windowing, and measuring only the positive frequencies in the frequencydomain data [22]. The peak amplitude of magnitude of the normalized frequency-domain data was multiplied by two to obtain the peak-to-peak change in ventricular volume, which corresponds to the change between systole and diastole. The frequency corresponding the peak in the frequency-domain data was used as an estimate of the average heart rate during the eightsecond acquisition (Figure 4A) The frequency- and time-domain combined approach used the frequency domain data to estimate.

D to detect a specific range of pixel intensity within each

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