11,067 research outputs found
Forsythia suspensa extract has inhibitory effect on proliferation and apoptosis of A549 lung cancer cells
Purpose: To investigate the effect of Forsythia suspensa extract (FSE) on apoptosis and proliferation in A549 human lung cancer cells.
Methods: Inverted microscope was employed to observe morphological changes in A549 cells after exposure to FSE. Trypan blue staining of living cells was used to construct the cell growth curve after treatment with varying concentrations of FSE. The influence of FSE on cell proliferation, apoptosis and cell cycle was determined by 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay, while protein expressions of key apoptosis-related enzymes were evaluated by immunocytochemical method.
Results: FSE inhibited the growth of A549 lung cancer cells at a concentration range of 10 - 150 μg/mL. Flow cytometry results showed that FSE induced apoptosis in A549 cells. The proportion of cells in G0/G1-phase increased significantly (p < 0.01), while the proportion of cells in S- and G2/M-phase decreased correspondingly, indicating that the cells were in G0/G1-phase arrest. Cell cycle arrest and apoptosis-inducing effect gradually rose with increase in FSE concentration. With increasing concentrations of FSE, there was also significant increase in the expressions of caspase-3 (p < 0.05), caspase-8 (p < 0.01) and caspase-9 (p < 0.05), but significant decrease in Ki-67 (p < 0.01) and p21 ras protein (p < 0.01).
Conclusion: FSE exerts significant inhibitory effect on the proliferation of A549 lung cancer cells. Therefore, the plant can potentially be developed for the treatment of lung cancer
Manifold-based Verbalizer Space Re-embedding for Tuning-free Prompt-based Classification
Prompt-based classification adapts tasks to a cloze question format utilizing
the [MASK] token and the filled tokens are then mapped to labels through
pre-defined verbalizers. Recent studies have explored the use of verbalizer
embeddings to reduce labor in this process. However, all existing studies
require a tuning process for either the pre-trained models or additional
trainable embeddings. Meanwhile, the distance between high-dimensional
verbalizer embeddings should not be measured by Euclidean distance due to the
potential for non-linear manifolds in the representation space. In this study,
we propose a tuning-free manifold-based space re-embedding method called
Locally Linear Embedding with Intra-class Neighborhood Constraint (LLE-INC) for
verbalizer embeddings, which preserves local properties within the same class
as guidance for classification. Experimental results indicate that even without
tuning any parameters, our LLE-INC is on par with automated verbalizers with
parameter tuning. And with the parameter updating, our approach further
enhances prompt-based tuning by up to 3.2%. Furthermore, experiments with the
LLaMA-7B&13B indicate that LLE-INC is an efficient tuning-free classification
approach for the hyper-scale language models.Comment: 11 pages, 3 figure
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
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