Journal: Biosensors

Article Title: An Optimization Framework for Silicon Photonic Evanescent-Field Biosensors Using Sub-Wavelength Gratings

doi: 10.3390/bios12100840

Figure Lengend Snippet: ( a ) Example of a non-split resonance peak from a spectrum measured for resonator C6, showing the Lorentzian fit and full width at half maximum ( FWHM ) calculated by the analysis script. ( b ) Example of a split resonance peak with a low R 2 (0.794) from a spectrum measured for resonator O3, showing the Lorentzian fit calculated by the analysis script ( FWHM not calculated, as this peak exhibited a R 2 value below the cutoff of 0.85). ( c ) Example of an apparent split resonance peak with a high R 2 (0.951) from a spectrum measured for resonator O3, showing the Lorentzian fit and FWHM calculated by the analysis script.

Article Snippet: On each optical spectrum, the script then performed (1) peak-finding (findpeaks() function) to identify resonance peak positions and approximate peak widths, (2) fitting of the baseline (non-peak) regions of the spectra to a third-degree polynomial function (polyfit()) and subtraction of that baseline from the optical spectra, (3) linearization of the decibel-scale baseline-subtracted data, (4) nonlinear least-squares fitting of each resonance peak to a Lorentzian function (lorentzfit() 1.7.0.0 by Jered Wells on the MATLAB File Exchange).

Techniques:

Journal: BMC Neuroscience

Article Title: PeakCaller: an automated graphical interface for the quantification of intracellular calcium obtained by high-content screening

doi: 10.1186/s12868-017-0391-y

Figure Lengend Snippet: Illustration of smoothing using the convex envelope and shows the actual graphics provided by PeakCaller . In the upper graph, for the original Ca 2+ recording (in black), the smoothing using a convex envelope is given by the dotted red curve. The lower graph shows the de-trended data series, which is simply the quotient of the original data divided by the smoothed data. Note in this case that the de-trended data “bounces off” of a minimum value of one exactly at the corners where the convex envelope touches the original profile. Peaks identified by PeakCaller are marked with a green circle, whereas peaks identified by PeakFinder are marked with a red ‘X’

Article Snippet: PeakFinder is a MATLAB script which automates the identification of “peaks” of independent variables over time, and is commonly used in the quantification of Ca 2+ signaling.

Techniques:

Journal: BMC Neuroscience

Article Title: PeakCaller: an automated graphical interface for the quantification of intracellular calcium obtained by high-content screening

doi: 10.1186/s12868-017-0391-y

Figure Lengend Snippet: Analysis of Zeiss Spinning Disk and ThermoFisher Array Scan Ca 2+ imaging data output by PeakCaller . The vertical axis represents Fluo-4 fluorescence emission intensity (arbitrary units) and the horizontal axis is time in seconds. Green circles represent PeakCaller identified peaks. Red Xs represent PeakFinder identified peaks. Parameters were chosen to best represent data generated on both platforms

Article Snippet: PeakFinder is a MATLAB script which automates the identification of “peaks” of independent variables over time, and is commonly used in the quantification of Ca 2+ signaling.

Techniques: Imaging, Fluorescence, Generated

Journal: BMC Neuroscience

Article Title: PeakCaller: an automated graphical interface for the quantification of intracellular calcium obtained by high-content screening

doi: 10.1186/s12868-017-0391-y

Figure Lengend Snippet: PeakCaller decreases the incidence of type I errors in cultured hiPSC-derived cortical neurons (* p < 0.05). a No difference in incidence of type II errors was found between PeakCaller and PeakFinder under these culture conditions. b Representative trace shows over estimation of peak incidence and frequency in PeakFinder (red X) script. c Designated ROI for the recording in this representative trace

Article Snippet: PeakFinder is a MATLAB script which automates the identification of “peaks” of independent variables over time, and is commonly used in the quantification of Ca 2+ signaling.

Techniques: Cell Culture, Derivative Assay

Journal: BMC Neuroscience

Article Title: PeakCaller: an automated graphical interface for the quantification of intracellular calcium obtained by high-content screening

doi: 10.1186/s12868-017-0391-y

Figure Lengend Snippet: a Reduction of type I errors by PeakCaller after application of PTX to induce excitation. Prior to PTX exposure both PeakFinder and PeakCaller correctly identified the same number of peaks per active cell. After PTX exposure (50 μM), PeakCaller correctly modeled the state of induced excitation and excitotoxicity in the hiPSC derived cortical neurons, while PeakFinder initially under estimated the number of peaks and then over estimated the number or Ca 2+ peaks. b Representative traces of ROI 4 are provided to show the transient increase in Ca 2+ signaling after application of PTX. c Designated ROIs across all recordings for this representative experiment

Article Snippet: PeakFinder is a MATLAB script which automates the identification of “peaks” of independent variables over time, and is commonly used in the quantification of Ca 2+ signaling.

Techniques: Derivative Assay

Journal: BMC Neuroscience

Article Title: PeakCaller: an automated graphical interface for the quantification of intracellular calcium obtained by high-content screening

doi: 10.1186/s12868-017-0391-y

Figure Lengend Snippet: a Reduction of type II error in dissociated mouse cortical neurons. PeakCaller demonstrated the ability to reduce the occurrence of type II errors ( p < 0.05) in three of four Ca 2+ recordings. No difference in incidence of type II error was found between the two scripts for the fourth recording. PeakCaller also showed a significant ( p < 0.05) reduction in type I error in recording A. b Representative traces below show PeakCaller peaks in green circles and PeakFinder peaks as red Xs. c Designated ROIs for this representative experiment

Article Snippet: PeakFinder is a MATLAB script which automates the identification of “peaks” of independent variables over time, and is commonly used in the quantification of Ca 2+ signaling.

Techniques: