Igor pro 9

Statistical analysis was performed for log-transformed values of relative luminescent intensities with Igor Pro 9 software (https://www.wavemetrics.com/products/igorpro) ver 9.0.2.4 (WaveMetrics).

GFETs were measured by using a homemade
electrical measurement device produced by TOSHIBA Corporation. While
drain current was measured, we set the drain voltage (Vd) at 16 or
20 mV, and back gate voltage (Vg) at 0 V. Drain voltage was chosen
with the maximum value in the Id measurable range. The real-time response
of the drain current was measured every 0.5 s. For data analysis,
we used Igor Pro 9 (WaveMetrics) and Python.

Statistical analyses were performed using R 4.1.0 (The R Project for Statistical Computing), Igor Pro 9 (Wavemetrics), and MATLAB R2021a (MathWorks). For the comparison of the intrinsic physiology between NPY^gfp and NPY^flp neurons, we used Welch’s t-test, and the significance level (α) was adjusted to account for multiple comparisons using Bonferroni correction. For the remaining experiments, effects were considered significant when p < 0.05. Data are shown as mean ± SD. Principal component analysis (PCA) was performed using the “coeff” function in MATLAB. The first component of the PCA explained 97.18% of the data. After PCA, a k-means clustering analysis was performed using the “kmeans” function in MATLAB. The “elbow method” was used to determine how many clusters to divide the data into.

SAXS measurements were performed at the DuPont-Northwestern-Dow Collaborative Access Team (DND-CAT) beamline at Argonne National Laboratory’s Advanced Photon Source (Argonne, IL, USA) with 10 keV (wavelength λ = 1.24 Å) collimated X-rays. All the samples (5 mg/mL) were analyzed in the q-range (0.001–0.5 Å⁻¹), with a sample-to-detector distance of approximately 8.5 m and an exposure time of 5 s. The beamline was calibrated using silver behenate and gold coated silicon grating with 7,200 lines/mm The momentum transfer vector q is defined as q = 4πsinθλ⁻¹, where 2θ is the scattering angle. Data reduction and buffer subtraction were performed using IRENA 2.71 package within IGOR PRO 9 software (Wavemetrics). Model fitting was completed using SasView 5.0.5 software package wherecore-shell sphere model was utilized to fit and analyze the data.

Ex vivo video imaging and analysis of colonic peristalsis were carried out as described previously (Obata et al., 2020 (link)) on age-matched male mice. Colons were dissected, flushed with sterile PBS, and pinned into an organ bath chamber (Tokai Hit, Japan) filled with Dulbecco’s Modified Eagle Medium (DMEM). DMEM was oxygenated (95% O₂ and 5% CO₂), run through the chamber using a peristaltic pump (MINIPULS 3, Gilson), and kept at 37 °C. Colons were allowed to equilibrate to the organ chamber for 20 min before video recording. Time-lapse images of colonic peristalsis were captured with a camera (MOMENT, Teledyne photometrics) using PVCAM software (500 ms time-lapse delay) and recorded for 45 min.
For analysis of colonic migrating motor complexes (CMMC), videos consisting of 5400 sequential image frames were stitched together in Fiji and read into Igor Pro 9 (WaveMetrics) to generate spatiotemporal maps using a customized algorithm developed by the Pieter Vanden Berghe lab at the University of Leuven, Belgium (Roosen et al., 2012 (link)). The generated spatiotemporal maps were used to determine the frequency and period of CMMCs. Each CMMC on the spatiotemporal map was further projected onto the axes to obtain the distance traveled (millimeters) and the time for the CMMC to travel such distance (seconds), allowing us to calculate the velocity (millimeter/second) of CMMCs.