Illustrator cs6 cc

MATLAB (2019a or above) and Prism 8.0 were used to analyze data and plot figures. All figures were created using Adobe Illustrator CS6/CC. Data were first subjected to a normality test using the D’Agostino and Pearson normality test or the Shapiro–Wilk normality test. When homogeneity was assumed, a parametric analysis of variance test was used. The Student’s t-test was used to compare two groups. The Mann–Whitney U-test was used to determine the significance between groups. Two sample Kolmogorov–Smirnov test was used to calculate the statistical differences between the contrast of USVs and noise. Statistical data are provided in text and in the figures. In the text, values are provided as mean ± SEM. p<0.05 was considered statistically significant. The 95% confidence intervals are reported in reference to the mean. The true positive rate is computed as the ratio between true positive (hit) and real positive cases. The true negative rate is the ratio between true negative (correct rejection) and real negative cases. The false negative rate is the ratio between false negative (type I error) and real positives cases. The false positive (type II error) is the ratio between false positive and real negative cases. The false discovery rate is the ratio between false positive and the sum of false positives and real positives.