5,262 research outputs found
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, AAS, HGS, CGA, TAS,
AGS, AGSe, GaSe, LIS, LISe, LGS, and LGSe) and 8 periodic poling crystals (LT,
LN, KTP, KN, BaTiO, MgBaF, 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
- …