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Olfactory dysfunction significantly impedes the life quality of patients. Neuropeptide Y (NPY) is not only a neurotrophic factor in the rodent olfactory system but also an orexigenic peptide that regulates feeding behavior. NPY increases the olfactory receptor neurons (ORNs) responsivity during starvation; however, whether NPY can promote differentiation of human ORNs remains unexplored. This study investigates the effect of NPY on the differentiation of human olfactory neuroepithelial cells in vitro. Human olfactory neuroepithelium explants were cultured on tissue culture polystyrene dishes for 21 days. Then, cells were cultured with or without NPY at the concentration of 0.5 ng/mℓ for 7 days. The effects of treatment were assessed by phase contrast microscopy, immunocytochemistry and western blot analysis. The further mechanism was evaluated with NPY Y1 receptor-selected antagonist BIBP3226. NPY-treated olfactory neuroepithelial cells exhibited thin bipolar shape, low circularity, low spread area, and long processes. The expression levels of Ascl1, βIII tubulin, GAP43 and OMP were significantly higher in NPY-treated cells than in controls (p < 0.05). NPY-treated olfactory neuroepithelial cells expressed more components of signal transduction apparatuses, G olf and ADCY3, than those without NPY treatment. Western blot analysis also further confirmed these findings (p < 0.05). Additionally, the expression levels of Ascl1, βIII2 tubulin, GAP43, OMP, ADCY3, and Golf in BIBP3226 + NPY and controls were comparable (p > 0.05). NPY not only increases expressions of protein markers of human olfactory neuronal progenitor cells, but also promotes differentiation of ORN and enhances formation of components of olfactory-specific signal transduction pathway through Y1 receptors., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Because of the limited reflected energy and incoming illumination in an individual band, the reflected energy captured by a hyperspectral sensor might be low and there is inevitable noise that significantly decreases the performance of the subsequent analysis. Denoising is therefore of first importance in hyperspectral image (HSI) analysis and interpretation. However, most HSI denoising methods remove noise with the important spectral information being severely distorted. This paper presents an HSI denoising method using trainable spectral difference learning with spatial initialization (called HDnTSDL) aimed at preserving the spectral information. In the proposed HDnTSDL model, a key band is automatically selected and denoised. The denoised key band acts as a starting point to reconstruct the rest of the non-key bands. Meanwhile, a deep convolutional neural network (CNN) with trainable non-linearity functions is proposed to learn the spectral difference mapping. Then, the rest of the non-key bands are denoised under the guidance of the learned spectral difference with the key band as a starting point. Experiments have been conducted on five databases with both indoor and outdoor scenes. Comparative analyses validate that the proposed method: (i) presents superior performance in spatial recovery and spectral preservation, and (ii) requires less computational time than state-of-the-art methods., (Copyright © 2018 Elsevier Ltd. All rights reserved.)