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Shapley–Shubik power index, Banzhaf index, Simple game, Voting, C710, D710, D720, 91A12, 91A40, 91B12,

Induction of cardiomyocyte (CM) proliferation is a promising approach for cardiac regeneration following myocardial injury. MicroRNAs (miRs) have been reported to regulate CM proliferation. In particular, miR-449a-5p has been identified to be associated with CM proliferation in previous high throughput functional screening data. However, whether miR-449a-5p regulates CM proliferation has not been thoroughly investigated. This study aimed to explore whether miR-449a-5p modulates CM proliferation and to identify the molecular mechanism via which miR-449a-5p regulates CM proliferation. The current study demonstrated that miR-449a-5p expression levels were significantly increased during heart development. Furthermore, the results suggested that miR-449a-5p mimic inhibited CM proliferation in vitro as determined via immunofluorescence for ki67 and histone H3 phosphorylated at serine 10 (pH3), as well as the numbers of CMs. However, miR-449a-5p knockdown promoted CM proliferation. CDK6 was identified as a direct target gene of miR-449a-5p, and CDK6 mRNA and protein expression was suppressed by miR-449a-5p. Moreover, CDK6 gain-of-function increased CM proliferation. Overexpression of CDK6 also blocked the inhibitory effect of miR-449a-5p on CM proliferation, indicating that CDK6 was a functional target of miR-449a-5p in CM proliferation. In conclusion, miR-449a-5p inhibited CM proliferation by targeting CDK6, which provides a potential molecular target for preventing myocardial injury.

When ranking big data observations such as colleges in the United States, diverse consumers reveal heterogeneous preferences. The objective of this paper is to sort out a linear ordering for these observations and to recommend strategies to improve their relative positions in the ranking. A properly sorted solution could help consumers make the right choices, and governments make wise policy decisions. Previous researchers have applied exogenous weighting or multivariate regression approaches to sort big data objects, ignoring their variety and variability. By recognizing the diversity and heterogeneity among both the observations and the consumers, we instead apply endogenous weighting to these contradictory revealed preferences. The outcome is a consistent steady-state solution to the counterbalance equilibrium within these contradictions. The solution takes into consideration the spillover effects of multiple-step interactions among the observations. When information from data is efficiently revealed in preferences, the revealed preferences greatly reduce the volume of the required data in the sorting process. The employed approach can be applied in many other areas, such as sports team ranking, academic journal ranking, voting, and real effective exchange rates.