Dose-dependent effect of ghrelin on gastric emptying in rats and the related mechanism of action

AbstractThe aim of this study was to investigate the dose-dependent effect of ghrelin on gastric emptying in rats and the related mechanism of action. Sixty Wistar rats were randomized into control and test groups, which respectively received intraperitoneal injection of normal saline and ghrelin at different doses (0.5 nmol/kg, 1.0 nmol/kg, 1.5 nmol/kg, 2.0 nmol/kg, and 2.5 nmol/kg). After 45 minutes, all rats were gavaged with semisolid paste. The gastric emptying rate was determined 30 minutes later, and the plasma cholecystokinin level was tested by radioimmunoassay. The mean gastric emptying rate in the test groups was significantly higher than in the control group (38.24 ± 7.15% and 27.18 ± 2.37%, respectively, p < 0.05). Medium and high doses of ghrelin (1.0 nmol/kg, 1.5 nmol/kg, 2.0 nmol/kg, and 2.5 nmol/kg), but not low dose (0.5 nmol/kg), accelerated the gastric emptying. In addition, the plasma cholecystokinin level in the test groups was significantly higher than in the control group (p < 0.01). The gastric emptying rate was positively correlated with the plasma cholecystokinin level (p < 0.01). Intraperitoneal injection of ghrelin at medium and high doses significantly accelerated gastric emptying in rats

Mid-infrared spectrally-pure single-photon states generation from 22 nonlinear optical crystals

We theoretically investigate the preparation of pure-state single-photon source from 14 birefringent crystals (CMTC, THI, LiIO$_3$, AAS, HGS, CGA, TAS, AGS, AGSe, GaSe, LIS, LISe, LGS, and LGSe) and 8 periodic poling crystals (LT, LN, KTP, KN, BaTiO$_3$, MgBaF$_4$, PMN-0.38PT, and OP-ZnSe) in a wavelength range from 1224 nm to 11650 nm. The three kinds of group-velocity-matching (GVM) conditions, the phase matching conditions, the spectral purity, and the Hong-Ou-Mandel interference are calculated for each crystal. This study may provide high-quality single-photon sources for quantum sensing, quantum imaging, and quantum communication applications at the mid-infrared wavelength range.Comment: 11 pages, 5 figure

Online Manufacturer Referral to Heterogeneous Retailers

Since the development of the Internet, thousands of manufacturers have been referring consumers visiting their websites to some or all of their retailers. Through a model with one manufacturer and two heterogeneous retailers, we investigate whether it is an equilibrium for the manufacturer to refer consumers exclusively to a retailer or nonexclusively to both retailers. Our analysis indicates that nonexclusive referral is the manufacturer\u27s equilibrium choice if the referral segment market size is sufficiently large; otherwise, exclusive referral is the equilibrium choice. In exclusive referral, the manufacturer would refer consumers to the more cost-efficient and smaller retailer. In the presence of infomediary referral, it is less likely for both exclusive and nonexclusive referrals to be an equilibrium, as the infomediary referral segment grows. We also show our qualitative results are robust even if there were price discrimination among consumers, referral position disparity, local consumers, and asymmetric referral market sizes

SketchBodyNet: A Sketch-Driven Multi-faceted Decoder Network for 3D Human Reconstruction

Reconstructing 3D human shapes from 2D images has received increasing attention recently due to its fundamental support for many high-level 3D applications. Compared with natural images, freehand sketches are much more flexible to depict various shapes, providing a high potential and valuable way for 3D human reconstruction. However, such a task is highly challenging. The sparse abstract characteristics of sketches add severe difficulties, such as arbitrariness, inaccuracy, and lacking image details, to the already badly ill-posed problem of 2D-to-3D reconstruction. Although current methods have achieved great success in reconstructing 3D human bodies from a single-view image, they do not work well on freehand sketches. In this paper, we propose a novel sketch-driven multi-faceted decoder network termed SketchBodyNet to address this task. Specifically, the network consists of a backbone and three separate attention decoder branches, where a multi-head self-attention module is exploited in each decoder to obtain enhanced features, followed by a multi-layer perceptron. The multi-faceted decoders aim to predict the camera, shape, and pose parameters, respectively, which are then associated with the SMPL model to reconstruct the corresponding 3D human mesh. In learning, existing 3D meshes are projected via the camera parameters into 2D synthetic sketches with joints, which are combined with the freehand sketches to optimize the model. To verify our method, we collect a large-scale dataset of about 26k freehand sketches and their corresponding 3D meshes containing various poses of human bodies from 14 different angles. Extensive experimental results demonstrate our SketchBodyNet achieves superior performance in reconstructing 3D human meshes from freehand sketches.Comment: 9 pages, to appear in Pacific Graphics 202

Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation

In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising. Compared with traditional RGB image denoising, performing this task on direct camera sensor readings presents new challenges such as how to effectively handle various Bayer patterns from different data sources, and subsequently how to perform valid data augmentation with raw images. To address the first problem, we propose a Bayer pattern unification (BayerUnify) method to unify different Bayer patterns. This allows us to fully utilize a heterogeneous dataset to train a single denoising model instead of training one model for each pattern. Furthermore, while it is essential to augment the dataset to improve model generalization and performance, we discovered that it is error-prone to modify raw images by adapting augmentation methods designed for RGB images. Towards this end, we present a Bayer preserving augmentation (BayerAug) method as an effective approach for raw image augmentation. Combining these data processing technqiues with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969 in NTIRE 2019 Real Image Denoising Challenge, demonstrating the state-of-the-art performance. Our code is available at https://github.com/Jiaming-Liu/BayerUnifyAug.Comment: Accepted by CVPRW 201