Jianjiang Liu

Both SKP2 and transforming growth factor-β1 play important roles in cancer metastasis through different mechanisms: TGF-β1 via induction of epithelial-mesenchymal transition and SKP2 via downregulating p27kip1. Recent studies indicated that c-Myc and Akt1 were active players in metastasis. In this study we demonstrated a crosstalk between these pathways. Specifically, we found that TGF-β1 treatment increased SKP2 expression accompanied with increased phosphorylation of Akt1 and c-Myc protein accumulation during EMT. We demonstrated that Akt1 was required for TGF-β1-mediated SKP2 upregulation and that c-Myc transcription factor specifically bound to the promoter of SKP2 for its enhanced transcription. Analysis of 25 samples of normal human skin, nevi, and melanomas revealed a positive correlation between c-Myc and SKP2 accumulation. Furthermore, accumulation of SKP2 and c-Myc proteins was significantly higher in metastatic melanoma samples as compared with that in primary melanomas, which again was higher than that in normal skin or nevi. In summary, our results integrated TGF-β1 signals to SKP2 via Akt1 and c-Myc during EMT, and provided, to our knowledge, a previously unreported mechanistic molecular event for TGF-β1-induced metastasis in human melanoma. © 2014 The Society for Investigative Dermatology.

© 2016 AIP Publishing LLC.We present evidence for breathing modes in magnetron sputtering plasmas: periodic axial variations of plasma parameters with characteristic frequencies between 10 and 100 kHz. A set of azimuthally distributed probes shows synchronous oscillations of the floating potential. They appear most clearly when considering the intermediate current regime in which the direction of azimuthal spoke motion changes. Breathing oscillations were found to be superimposed on azimuthal spoke motion. Depending on pressure and current, one can also find a regime of chaotic fluctuations and one of stable discharges, the latter at high current. A pressure-current phase diagram for the different situations is proposed.

Group-level variance estimates of zero often arise when fitting multilevel or hierarchical linear models, especially when the number of groups is small. For situations where zero variances are implausible a priori, we propose a maximum penalized likelihood approach to avoid such boundary estimates. This approach is equivalent to estimating variance parameters by their posterior mode, given a weakly informative prior distribution. By choosing the penalty from the log-gamma family with shape parameter greater than 1, we ensure that the estimated variance will be positive. We suggest a default log-gamma penalty with λ→0, which ensures that the maximum penalized likelihood estimate is approximately one standard error from zero when the maximum likelihood estimate is zero, thus remaining consistent with the data while being nondegenerate. We also show that the maximum penalized likelihood estimator with this default penalty is a good approximation to the posterior median obtained under a noninformative prior. Our default method provides better estimates of model parameters and standard errors than the maximum likelihood or the restricted maximum likelihood estimators. The log-gamma family can also be used to convey substantive prior information. In either case-pure penalization or prior information-our recommended procedure gives nondegenerate estimates and in the limit coincides with maximum likelihood as the number of groups increases. © 2013 The Psychometric Society.