The imbalance between adipogenic and osteogenic differentiation in bone marrow mesenchymal stem cells (BMSCs) plays a significant role in the pathogenesis of steroid-induced osteonecrosis of the femoral head (ONFH). Several microRNAs (miRNAs) are involved in regulating adipogenesis and osteogenesis. In this study, we established a steroid-induced ONFH rat model to identify the potential relevant miRNAs. We identified 9 up-regulated and 28 down-regulated miRNAs in the ONFH rat model. Of these, miR-27a was down-regulated and negatively correlated with peroxisome proliferator-activated receptor gamma (PPARγ) and gremlin 1 (GREM1) expression. Further studies confirmed that PPARγ and GREM1 were direct targets of miRNA-27a. Additionally, adipogenic differentiation was enhanced by miR-27a down-regulation, whereas miRNA-27a up-regulation attenuated adipogenesis and promoted osteogenesis in steroid-induced rat BMSCs. Moreover, miRNA-27a up-regulation had a stronger effect on adipogenic and osteogenic differentiation in steroid-induced rat BMSCs than si-PPARγ and si-GREM1. In conclusion, we identified 37 differentially expressed miRNAs in the steroid-induced ONFH model, of which miR-27a was down-regulated. Our results showed that miR-27a up-regulation could inhibit adipogenesis and promote osteogenesis by directly targeting PPARγ and GREM1. Thus, miR-27a is likely a key regulator of adipogenesis in steroid-induced BMSCs and a potential therapeutic target for ONFH treatment.

The emanations of electronic and mechanical devices have raised serious privacy concerns. It proves possible for an attacker to recover the keystrokes by acoustic signal emanations. Most existing malicious applications adopt contextbased approaches, which assume that the typed texts are potentially correlated. Those approaches often incur a high cost during the context learning stage, and can be limited by randomly typed contents (e.g., passwords). Also, context correlations can increase the risk of successive false recognition. We present a context-free and geometry-based approach to recover keystrokes. Using off-the-shelf smartphones to record acoustic emanations from keystrokes, this design estimates keystrokes' physical positions based on the Time Difference of Arrival (TDoA) method. We conduct extensive experiments and the results show that more than 72.2% of keystrokes can be successfully recovered.

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