13,463 research outputs found
Characterization of Electronic Cigarette Aerosol and Its Induction of Oxidative Stress Response in Oral Keratinocytes.
In this study, we have generated and characterized Electronic Cigarette (EC) aerosols using a combination of advanced technologies. In the gas phase, the particle number concentration (PNC) of EC aerosols was found to be positively correlated with puff duration whereas the PNC and size distribution may vary with different flavors and nicotine strength. In the liquid phase (water or cell culture media), the size of EC nanoparticles appeared to be significantly larger than those in the gas phase, which might be due to aggregation of nanoparticles in the liquid phase. By using in vitro high-throughput cytotoxicity assays, we have demonstrated that EC aerosols significantly decrease intracellular levels of glutathione in NHOKs in a dose-dependent fashion resulting in cytotoxicity. These findings suggest that EC aerosols cause cytotoxicity to oral epithelial cells in vitro, and the underlying molecular mechanisms may be or at least partially due to oxidative stress induced by toxic substances (e.g., nanoparticles and chemicals) present in EC aerosols
A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution
Realistic hyperspectral image (HSI) super-resolution (SR) techniques aim to
generate a high-resolution (HR) HSI with higher spectral and spatial fidelity
from its low-resolution (LR) counterpart. The generative adversarial network
(GAN) has proven to be an effective deep learning framework for image
super-resolution. However, the optimisation process of existing GAN-based
models frequently suffers from the problem of mode collapse, leading to the
limited capacity of spectral-spatial invariant reconstruction. This may cause
the spectral-spatial distortion on the generated HSI, especially with a large
upscaling factor. To alleviate the problem of mode collapse, this work has
proposed a novel GAN model coupled with a latent encoder (LE-GAN), which can
map the generated spectral-spatial features from the image space to the latent
space and produce a coupling component to regularise the generated samples.
Essentially, we treat an HSI as a high-dimensional manifold embedded in a
latent space. Thus, the optimisation of GAN models is converted to the problem
of learning the distributions of high-resolution HSI samples in the latent
space, making the distributions of the generated super-resolution HSIs closer
to those of their original high-resolution counterparts. We have conducted
experimental evaluations on the model performance of super-resolution and its
capability in alleviating mode collapse. The proposed approach has been tested
and validated based on two real HSI datasets with different sensors (i.e.
AVIRIS and UHD-185) for various upscaling factors and added noise levels, and
compared with the state-of-the-art super-resolution models (i.e. HyCoNet, LTTR,
BAGAN, SR- GAN, WGAN).Comment: 18 pages, 10 figure
Identification and expression profiles of sRNAs and their biogenesis and action-related genes in male and female cones of Pinus tabuliformis
Detailed description of methods. (DOCX 25Â kb
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