Super-Resolving Methodology for Noisy Unpaired Datasets.

Although it is possible to acquire high-resolution and low-resolution paired datasets, their use in directly supervised learning is impractical in real-world applications. In the present work, we focus on a practical methodology for image acquisition in real-world conditions. The main method of noise reduction involves averaging multiple noisy input images into a single image with reduced noise; we also consider unpaired datasets that contain misalignments between the high-resolution and low-resolution images. The results show that when more images are used for average denoising, better performance is achieved in the super-resolution task. Quantitatively, for a fixed noise level with a variance of 60, the proposed method of using 16 images for average denoising shows better performance than using 4 images for average denoising; it shows 0.68 and 0.0279 higher performance for the peak signal-to-noise ratio and structural similarity index map metrics, as well as 0.0071 and 1.5553 better performance for the learned perceptual image patch similarity and natural image quality evaluator metrics, respectively.

Monoamine oxidase inhibitory coumarin from Zanthoxylum schinifolium.

A methanol extract of Zanthoxylum schinifolium stems at a concentration of 250 microg/ml showed potent inhibitory activity against monoamine oxidase (MAO) in a mouse brain. Activity-guided separation and purification of the extract yielded lacinartin (1) as an active coumarin compound. Lacinartin showed significant inhibitory effects on MAO in a dose-dependent manner. The IC(50) value on MAO activity was 9.2 microM. The MAO-A (IC(50) value, 5.7 microM) sensitivity to lacinartin was greater than that of MAO-B (IC(50) value, 28.6 microM). An enzyme kinetic study revealed that lacinartin inhibited MAO activity by a non-competitive mode.