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Low-density quantitative real-time PCR (qPCR) arrays are often used to profile expression patterns of microRNAs in various biological milieus. To achieve accurate analysis of expression of miRNAs, non-biological sources of variation in data should be removed through precise normalization of data. We have systematically compared the performance of 19 normalization methods on different subsets of a real miRNA qPCR dataset that covers 40 human tissues. After robustly modeling the mean squared error (MSE) in normalized data, we demonstrate lower variability between replicates is achieved using various methods not applied to high-throughput miRNA qPCR data yet. Normalization methods that use splines or wavelets smoothing to estimate and remove Cq dependent non-linearity between pairs of samples best reduced the MSE of differences in Cq values of replicate samples. These methods also retained between-group variability in different subsets of the dataset.

Nerve tissue engineering requires suitable precursor cells as well as the necessary biochemical and physical cues to guide neurite extension and tissue development. An ideal scaffold for neural regeneration would be both fibrous and electrically conductive. We have contrasted the growth and neural differentiation of mouse embryonic stem cells on three different aligned nanofiber scaffolds composed of poly L: -lactic acid supplemented with either single- or multi-walled carbon-nanotubes. The addition of the nanotubes conferred conductivity to the nanofibers and promoted mESC neural differentiation as evidenced by an increased mature neuronal markers expression. We propose that the conductive scaffold could be a useful tool for the generation of neural tissue mimics in vitro and potentially as a scaffold for the repair of neural defects in vivo.