Matlab r2023a

The software program NIS Elements ver. 4.30.00 (Nikon) used a median filter and local contrast image processing to preprocess all of the images, but the images of sites with extensively saturated tdTomato signals were omitted from the quantitative analysis. Accordingly, 3–6 regions were analyzed in each dish. The acquired data’s XYZ misalignment was adjusted manually. The preprocessed images were examined with Imaris ver. 8.3.1 software (Oxford Instruments, Abingdon, UK). Sixteen ROIs were created out of a single field of view. To calculate the average volume of osteoclasts (Fig. 4c), the volume of osteoclasts with a volume over 2000 µm³ at R1 and R2 was calculated to omit mononuclear cells with typical diameter of 15–22 μm [21 (link)] and cell debris. An Imaris Reader was used for projecting all of the data into MATLAB R2023a (MathWorks) for the quantitative data.

Model construction and FBA were performed using the COBRA Toolbox [66 (link)] and MATLAB R2023a (The MathWorks Inc., Massachusetts, USA) with the GNU Linear Programming Kit as a linear programming solver.

The measurements obtained with TOMATO-NOSE were processed with chemometric tools for interpretation, to identify outliers and to study the discrimination of potential groups. Exploratory data analysis was performed using PCA for visualization purposes. This type of analysis reduces the information provided by the E-nose to the minimum number of variables, called principal components. Principal components are linear combinations of the original response vectors [62 (link)]. The statistical tool used was MATLAB R2023a (The Mathworks Inc., Natick, MA, USA) with the PLS_Toolbox 9.1 (Eigenvector Research Inc., Wenatchee, WA, USA).

The intracellular solutions contained 138 mM CsCH₃SO₃, 3 mM CsCl, 2 mM MgCl₂, 0.2 mM EGTA, 10 mM HEPES, 2 mM ATP-Na₂, and 5 mM QX314. The pH was appropriately adjusted to 7.3 by CsOH, and osmolarity was adjusted to 280–290 mOsm. The electrode impedance was approximately 4–7 MΩ. When recording the evoked EPSCs (eEPSCs), the membrane potential was held at –70 mV. After eEPSC recording, the same cell was held at 0 mV to record evoked IPSCs (eIPSCs). The locations of SON and LC were identified under an IR-DIC microscope based on their location and cell density. The stimulating electrode was placed deep inside the nucleus and approximately 50 μm from the recorded cell. Membrane voltage and current were sampled at 10–25 kHz and low-pass filtered at 2–10 kHz using the patch-clamp amplifier MultiClamp 700B (Molecular Devices, LLC), digitized and sampled by Micro 1401 with Spike2 software (Cambridge Electronic Design) or by Digidata 1440A with pCLAMP 10.2 software (Molecular Devices, LLC). The evoked postsynaptic currents were analyzed using MATLAB R2023a (MathWorks, Inc, Natick, MA) and OriginPro9.1 (Originlab, Inc).

All data are expressed as the mean ± SE (standard error). MATLAB R2023a (MathWorks) was used to plot the data and perform the statistical analyses. The Lilliefors test and the Kolmogorov–Smirnov test were used to determine whether the data were normally distributed. Spearman’s rank correlation coefficients were calculated to analyze the correlations between values. The rho values were categorized as follows: 0 ≤|R|< 0.2 = negligible correlation, 0.2 ≤|R|< 0.4 = weak correlation, 0.4 ≤|R|< 0.6 = moderate correlation, and 0.6 ≤|R|< 0.8 = strong correlation. Probability (p) values < 0.05 were considered significant.

The data processing and statistical analysis were conducted with MATLAB R2023a (The MathWorks Inc., Natick, United States). As there was no relevant prior information available, the sample size was determined not by calculating based on specific endpoints but rather by convenience sampling. The Lilliefors test was used for normality testing. For normally distributed data, results were expressed as mean ± standard deviation. A two-tailed paired t-test was used to compare the changes of the EIT parameters, FVC parameters, questionnaires, and other clinical parameters. Pearson’s linear correlation was used to compare the CAT score with FEV1 and FEV1/FVC (both global and regional). A p-value < 0.05 was considered statistically significant. Significance levels were corrected for multiple comparisons using Holm’s sequential Bonferroni method.

Experimental trials were conducted across four distinct curvature levels (high, medium, and low) in addition to a curvature-free flat group for comprehensive comparative analysis. All datasets obtained from three independent replicates (N=3) and results are presented as mean values with accompanying error bars indicating standard deviation (S.D.), the number of analyzed samples (n>5) being displayed on the figures. The determination of statistically significant variations between groups was facilitated using one-way analysis of variance (ANOVA), accepting P values of less than 0.05 and a 95% confidence interval as indicators of significance in Figure 2b, Figure 3d, Figure 6 and Supplementary Figure 15c. Graphical data representation and statistical assessments were performed using Excel (Microsoft, USA), Origin Pro 2023b (OriginLab corporation, USA), SPSS 29.0 (IBM Analytics, USA), MATLAB R2023a (The MathWorks Inc, USA).