How to interpret results from multivariate empirical Bayes (MEBA) time-series analysis?

The MEBA approach is designed to compare the short-course time-course profiles with regard to different experimental conditions. It is based on the timecourse method described by YC Tai. et al.

The result is a list of variables that are ranked by their difference in temporal profiles across different biological conditions. The Hotelling-T2 is used to rank the variables with different temporal profiles between two biological conditions under study; And the MB-statistics is used for more than two biological conditions. Higher statistical value indicates the time-course profiles are more different across the biological conditions under study. An example output from the top ranked feature is given below
3.06411042_7_dpi72

Please note, MEBA allows prioritize the features based on their temporal profiles. No associated p-values are computed in the implementation.