Chemical genomics has been applied extensively to evaluate small molecules that modulate biological processes in Saccharomyces cerevisiae. Here, we use yeast as a surrogate system for studying compounds that are active against metazoan targets. Large-scale chemical-genetic profiling of thousands of synthetic and natural compounds from the Chinese National Compound Library identified those with high-confidence bioprocess target predictions. To discover compounds that have the potential to function like therapeutic agents with known targets, we also analyzed a reference library of approved drugs. Previously uncharacterized compounds with chemical-genetic profiles resembling existing drugs that modulate autophagy and Wnt/β-catenin signal transduction were further examined in mammalian cells, and new modulators with specific modes of action were validated. This analysis exploits yeast as a general platform for predicting compound bioactivity in mammalian cells.
Shack-Hartmann sensor is widely used in adaptive optics systems, and laser beam quality measurements. The traditional method separates measures and calculations, and the wavefront reconstruction algorithm is slow to implement on the host computer. In this paper, the embedded GPU is introduced to Shack-Hartmann sensors' wavefront phase reconstruction. A parallel calculation method is proposed to speed up the wavefront phase reconstruction process. The experiment result shows the algorithm speed improves 50× with the image size of 2592×2048 pixels.
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