Wide field small aperture telescopes are widely used for optical transient observations. Detection and classification of astronomical targets in observed images are the most important and basic step. In this paper, we propose an astronomical targets detection and classification framework based on deep neural networks. Our framework adopts the concept of the Faster R-CNN and uses a modified Resnet-50 as backbone network and a Feature Pyramid Network to extract features from images of different astronomical targets. To increase the generalization ability of our framework, we use both simulated and real observation images to train the neural network. After training, the neural network could detect and classify astronomical targets automatically. We test the performance of our framework with simulated data and find that our framework has almost the same detection ability as that of the traditional method for bright and isolated sources and our framework has 2 times better detection ability for dim targets, albeit all celestial objects detected by the traditional method can be classified correctly. We also use our framework to process real observation data and find that our framework can improve 25% detection ability than that of the traditional method when the threshold of our framework is 0.6. Rapid discovery of transient targets is quite important and we further propose to install our framework in embedded devices such as the Nvidia Jetson Xavier to achieve real-time astronomical targets detection and classification abilities.

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BackgroundConstipation is one of the most common gastrointestinal complaints with a highly prevalent and often chronic functional gastrointestinal disorder affecting health-related quality of life. The aim of the present study was to evaluate the effects of Salecan on fecal output and small intestinal transit in normal and two models of drug-induced constipation mice.MethodsICR mice were administrated intragastrically (i.g.) by gavage with 100, 200 and 300 mg/kg body weight (BW) of Salecan while the control mice were received saline. The constipated mice were induced by two types of drugs, loperamide (5 mg/kg BW, i.g.) and clonidine (200 μg/kg BW, i.g.), after Salecan treatment while the control mice were received saline. Number, weight and water content of feces were subsequently measured. Small intestinal transit was monitored by phenol red marker meal.ResultsSalecan (300 mg/kg BW) significantly increased the number and weight of feces in normal mice. In two models of drug-induced constipation, Salecan dose-dependently restored the fecal number and fecal weight. The water content of feces was markedly affected by loperamide, but not by clonidine. Treatment with Salecan significantly raised the fecal water content in loperamide-induced constipation mice. Moreover, Salecan markedly stimulated the small intestinal transit in both loperamide- and clonidine-induced constipation model mice.ConclusionsThese results suggest that Salecan has a potential to be used as a hydrophilic laxative for constipation.

Ground based optical telescopes are seriously affected by atmospheric turbulence induced aberrations. Understanding properties of these aberrations is important both for instruments design and image restoration methods development. Because the point spread function can reflect performance of the whole optic system, it is appropriate to use the point spread function to describe atmospheric turbulence induced aberrations. Assuming point spread functions induced by the atmospheric turbulence with the same profile belong to the same manifold space, we propose a non-parametric point spread function -PSF-NET. The PSF-NET has a cycle convolutional neural network structure and is a statistical representation of the manifold space of PSFs induced by the atmospheric turbulence with the same profile. Testing the PSF-NET with simulated and real observation data, we find that a well trained PSF-NET can restore any short exposure images blurred by atmospheric turbulence with the same profile. Besides, we further use the impulse response of the PSF-NET, which can be viewed as the statistical mean PSF, to analyze interpretation properties of the PSF-NET. We find that variations of statistical mean PSFs are caused by variations of the atmospheric turbulence profile: as the difference of the atmospheric turbulence profile increases, the difference between statistical mean PSFs also increases. The PSF-NET proposed in this paper provides a new way to analyze atmospheric turbulence induced aberrations, which would be benefit to develop new observation methods for ground based optical telescopes.