LncRNA-000133 from secondary hair follicle of Cashmere goat: identification, regulatory network and its effects on inductive property of dermal papilla cells.

Long noncoding RNAs (lncRNAs), a class of non-protein conding RNAs > 200 nt in length, were thought to play critical roles in regulating the expression of protein-coding genes. Here, we identified and characterized a novel lncRNA-000133 from the secondary hair follicle (SHF) of cashmere goat with its ceRNA network analysis, as well as, its potential effects on inductive property of dermal papilla cells were evaluated through overexpression analysis. Expression analysis indicated that lncRNA-000133 had a significantly higher expression at anagen than that at telogen in SHF of Cashmere goat, suggesting that lncRNA-000133 might be involved in the reconstruction of SHF with the formation and growth of cashmere fiber. Taken together with methylation analysis, we showed that 5' regulatory region methylation of the lncRNA-000133 gene might be involved in its expression suppression in SHF of Cashmere goat. The ceRNA regulatory network showed that a rich and complex regulatory relationship between lncRNA-000133 and related miRNAs with their target genes. The overexpression of lncRNA-000133 led to a significant increasing in the relative expression of ET-1, SCF, ALP and LEF1 in dermal papilla cells suggesting that lncRNA-000133 appears to contribute the inductive property of dermal papilla cells.

Signal Denoising Method Using AIC-SVD and Its Application to Micro-Vibration in Reaction Wheels.

To suppress noise in signals, a denoising method called AIC-SVD is proposed on the basis of the singular value decomposition (SVD) and the Akaike information criterion (AIC). First, the Hankel matrix is chosen as the trajectory matrix of the signals, and its optimal number of rows and columns is selected according to the maximum energy of the singular values. On the basis of the improved AIC, the valid order of the optimal matrix is determined for the vibration signals mixed with Gaussian white noise and colored noise. Subsequently, the denoised signals are reconstructed by inverse operation of SVD and the averaging method. To verify the effectiveness of AIC-SVD, it is compared with wavelet threshold denoising (WTD) and empirical mode decomposition with Savitzky-Golay filter (EMD-SG). Furthermore, a comprehensive indicator of denoising (CID) is introduced to describe the denoising performance. The results show that the denoising effect of AIC-SVD is significantly better than those of WTD and EMD-SG. On applying AIC-SVD to the micro-vibration signals of reaction wheels, the weak harmonic parameters can be successfully extracted during pre-processing. The proposed method is self-adaptable and robust while avoiding the occurrence of over-denoising.