Matlab s statistics and machine learning toolbox

Standard replication of measurements were performed for this study. The reported findings were reproduced across animals. All quantifications were conducted at the single-neuron level. Sample sizes (the numbers of animals, sessions, and neurons) were estimated according to previous studies^{45 –47 (link)} and confirmed to be adequate by power analyses (power = 0.9; alpha error = 0.05). We used the following statistical methods: KS test, Mann–Whitney test, one-sample signed-rank test, Wilcoxon signed-rank test, χ² test with post hoc residual analysis, and Kruskal–Wallis test with post hoc Steel–Dwass test. All tests were two-sided unless otherwise stated. These statistical tests were conducted with MATLAB’s Statistics and Machine Learning Toolbox (MathWorks). Differences were considered statistically significant when p < 0.05 (see Results for details). Blinding and randomization were not performed.

BP and HR rates of SHR and normotensive animals were monitored daily in real-time over 10 s every minute for 3 days prior to anesthetizing the animals for IgFUS treatment and after the treatment for up to 30-day period using the telemeter (DSI, St Paul, MN) according to manufacturer’s recommendations.
All statistical analyses were performed using MATLAB’s Statistics and Machine Learning Toolbox (Mathworks, Natick, MA). Daily MAP measurements are presented with box plots, which display the summary statistics of median, interquartile range and extremes. The normality of the data was tested with the Kolmogorov-Smirnov test. For comparing the post-treatment MAP measurements between the different groups, one-way ANOVA was performed on the MAP measurements from selected treatment groups. Post-hoc analysis using the Tukey HSD test was applied to the pairwise comparisons. P < 0.05 was considered to indicate a statistically significant difference.

According to our previous study [17 ], the Gaussian process regression (GPR) has the best performance among several tested algorithms in DA assessments. Therefore, we chose GPR for the efficacy comparison in the present study. The MATLAB’s Statistics and Machine Learning Toolbox (2021a release; MathWorks, Inc., Natick, MA, USA) was used for model developments. For the model training, the TDS of each tooth was transformed into numerical order. Values of 1 to 8 represented stages A to H, and absent tooth buds were registered as 0. The patient’s sex was another input parameter during the model training. To develop population standards, the training dataset retrieved from the initial 80% healthy samples was randomly divided for repeated training and validation by the principle of the five-fold cross-validation. Such a training sequence was repeated twenty times, and the model of the best performance was adopted for the following tests.