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