Fig. 2.

Signal processing technique and curve fitting procedure. A: the sequence of data collection, signal processing, and curve fitting. B: raw airway pressure (P_AW; solid trace, top), raw pulmonary arterial pressure (P_PA; shaded trace), and the filtered P_PA (solid trace, bottom) in the isolated perfused mouse lung being mechanically ventilated or statically inflated with the ventilator turned off. Of note is how the cyclic nature of the raw P_PA tracing approximates that in the airway and the accuracy with which the filtered P_PA signal approximates the basal P_PA signal in the presence of mechanical ventilation and the raw P_PA signal in the absence of mechanical ventilation. C: the closeness of fit of the filtered P_PA signal superimposed on the raw P_PA response (unfiltered) to stimulation with 10 μM S1P. D: the filtered P_PA response to 10 μM S1P stimulation with the location of cursor placement for data extraction for offline nonparametric curve fit analysis (shown in E). The linear portion of the response is fit to a line described by the function f(t) = P2∗t + b, where P2 represents the slope of the line (mmHg/s) and b represents the y-intercept of the line (E). Subsequently, the onset of the S1P response is fit to an equation describing the summation of exponential and linear components, f(t) = P1 + (P2∗t) + [P3∗(1 − e^{−1∗P4∗t})], where t is time, P1 represents the baseline P_PA (mmHg), P2 is the slope previously defined (mmHg/s), P3 represents the magnitude of the exponential component (mmHg), and P4 represents the time constant of the exponential phase (1/s; E).