Spss statistics software 24

To calculate each cell population, and for visual display (Supplementary Table 3), each cell was classified to one of 18 groups: CK+, CD45+, FAP+, aSMA+, Thy1+, FSP-1+, aSMA+/FAP+, FAP+/Thy1+, FAP+/FSP-1+, aSMA+/Thy1+, aSMA+/FSP-1+, FSP-1+/Thy1+, aSMA+/FAP+/Thy1+, aSMA+/FAP+/FSP-1+, FAP+/FSP-1+/Thy1+, aSMA+/FSP-1+/Thy1+, aSMA+/FAP+/FSP-1+/Thy1+, or “other”. Pericytes are known to express alpha-SMA (39 (link)). To avoid misinterpretation, based on histology, we deselected the areas of blood vessels in the stroma and the selected ROIs were composed only of tumor, tumor stroma interface and stroma (excluding the blood vessels), so that pericytes are not included in the final data. We then generated 19 possible marker expression patterns based on the expression of α-SMA, Thy-1, FAP⁺, and FSP-1. In our cohort we had equal numbers of two different subsets of ILC (classic ILC vs Pleomorphic ILC). Patterns of distribution according the nearest neighbor distance were calculated for each CAF phenotype to tumor cells using the X and Y coordinates of each cell with Phenoptr script from R studio (Akoya Biosciences). We then compared the proximities of each CAF subtype to the tumor cells between the two histological subtypes of ILC using IBM SPSS statistics software 24.

Descriptive statistics (i.e., mean, SD, frequencies, and percentages) were computed to examine the participants’ demographic and behavioral characteristics. Spearman–Brown correlations were computed to examine the associations between stress levels and healthy lifestyle behaviors. We also computed a hierarchical multiple regression analysis with three sequential steps. Step 1 consisted of demographic predictors such as gender, employment status, age, annual income, friends or family tested positive for COVID-19, years in college, and race. In Step 2, we tested whether behaviors such as time spent in physical activity, sitting, sleeping, and connecting with family and friends were significant predictors of stress after controlling for the demographic variables. In Step 3, we examined whether a decrease in the time spent exercising, sleeping, or connecting with friends and family after the beginning of the COVID-19 pandemic were significant predictors of stress after controlling for the demographic and behavioral characteristics. In each step, the backward procedure was used to eliminate predictors that did not significantly add to the prediction of stress. Significance was based on a value of p ≤ 0.05. We used IBM SPSS statistics software 24 (IBM Corp., Armonk, NY, USA) for all statistical analyses.

Statistical analyses were performed using SPSS statistics software 24 (International Business Machines Corporation, USA). The numerical variables are described as mean ± standard deviation (SD) while the categorical variables are described as numbers and percentages.
Differences in mean values between young and old PACG eyes were examined using the unpaired t-test. To determine the possible factors affecting the occurrence of malignant glaucoma in young PACG patients, all the variables measured by A-scan and UBM were assessed using univariate logistic regression analysis. Variables with P < 0.1 in univariate logistic regression analysis were included in the multivariate logistic regression model. Consequently, insignificant factors were removed using a stepwise approach. P < 0.05 was considered statistically significant.

The experiment was analyzed in a series of MANOVAs in blocks of responses that were connected, i.e., bone-related gene expression (ALP, BMP-2, COL1A1, RUNX2, OPN and TGF-β), cartilage- or adipose-related gene expression (SOX9 and PPAR-γ), proliferation-related gene expression (KI67, PCNA), death-related gene expression (CASP3, P53), MSC cell numbers (adherent + supernatant cells) and viability, and monocyte cell numbers and viability. In MANOVAs related to MSCs, the model was defined by the factors LPS (with or without), material (PPi0, PPi1, PPi2, PPi3), the condition (CM, CtrM, DEM) and the interaction LPS×condition. In the MANOVA related to the monocytes, only the factors LPS and material were used. Individual factor-level effects were analyzed using estimated marginal means with Bonferroni correction. Further details on MANOVA results and associated complementary ANOVA analyses are found in the Online Resource 1 (Supplementary Table S2). Residual analyses were carried out to check the model assumptions. If the assumption of homoelasticity was doubtful, a sensitivity analysis using bootstrapping was carried out. The data were statistically evaluated in SPSS Statistics software 24.0 (IBM Corporation, Armonk, NY, USA), and a statistical significance level of 5% was used. Bar graphs represent the means ± standard error of the mean (SE).

By means of paired sample t-tests, mean search activity during winter months was compared to mean search activity during summer months for a 10-year period for each of the aforementioned diseases. Data were analyzed using IBM SPSS Statistics Software 24.0.