Capecitabine treatment of HCT-15 colon cancer cells induces apoptosis via mitochondrial pathway

Purpose: To investigate the effect of capecitabine on apoptosis induction in HCT-15 colon carcinoma cells and investigate the underlying mechanism.Methods: Phase-contrast microscopy was used for the examination of morphological changes while flow cytometry was employed for the analysis of cell cycle distribution, induction of apoptosis, reactive oxygen species (ROS) production and expression of caspases. Western blot assay was used for the analysis of expression level of apoptosis-related and cell cycle regulatory proteins.Results: Capecitabine treatment caused changes in the morphological appearance of HCT-15 cells after 48 h. The viability of HCT-15 cells was reduced to 23 % on treatment with capecitabine (5 μM) compared to 98 % in the control cultures. Incubation with capecitabine increased the population of HCT- 15 cells in G0/G1 phase to 56.43 % compared to 41.67 % in the control. Capecitabine treatment of HCT-15 cells caused condensation of DNA and induced apoptosis in a concentration-dependent manner. At 5 μM concentration of capecitabine, apoptosis was induced in 45.74 % of the cells. Incubation of HCT-15 cells with capecitabine for 48 h led to a significant increase in the production of ROS. Translocation of Endo G and AIF from mitochondria to the nuclei increased significantly (p < 0.005) on treatment with 5 μM capecitabine. Capecitabine treatment also reduced the expression of cyclin E and Cdc25c and promoted the level of caspases, Bax, AIF, Endo G, p21, PARP and p-p53. The expression level of Bcl-2 decreased in HCT-15 cells on incubation with 5 μM concentration of capecitabine.Conclusion: Capecitabine treatment causes inhibition of colon cancer growth via the mitochondrial pathway of apoptosis. Thus, capecitabine may have therapeutic application in colon carcinoma treatment.Keywords: Capecitabine, 5-Fluorouracil, Translocation, Colon cancer, Colitis, Apoptosi

Asymmetric superradiant scattering and abnormal mode amplification induced by atomic density distortion

The superradiant Rayleigh scattering using a pump laser incident along the short axis of a Bose-Einstein condensate with a density distortion is studied, where the distortion is formed by shocking the condensate utilizing the residual magnetic force after the switching-off of the trapping potential. We find that very small variation of the atomic density distribution would induce remarkable asymmetrically populated scattering modes by the matter-wave superradiance with long time pulse. The optical field in the diluter region of the atomic cloud is more greatly amplified, which is not an ordinary mode amplification with the previous cognition. Our numerical simulations with the density envelop distortion are consistent with the experimental results. This supplies a useful method to reflect the geometric symmetries of the atomic density profile by the superradiance scattering.Comment: 7pages,4 figures, Optical Express 21,(2013)1437

Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning

Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to essential applications requiring solid robustness or vigorous security standards, such as product recommendation and user behavior modeling. Under these scenarios, exploiting GNN's vulnerabilities and further downgrading its performance become extremely incentive for adversaries. Previous attackers mainly focus on structural perturbations or node injections to the existing graphs, guided by gradients from the surrogate models. Although they deliver promising results, several limitations still exist. For the structural perturbation attack, to launch a proposed attack, adversaries need to manipulate the existing graph topology, which is impractical in most circumstances. Whereas for the node injection attack, though being more practical, current approaches require training surrogate models to simulate a white-box setting, which results in significant performance downgrade when the surrogate architecture diverges from the actual victim model. To bridge these gaps, in this paper, we study the problem of black-box node injection attack, without training a potentially misleading surrogate model. Specifically, we model the node injection attack as a Markov decision process and propose Gradient-free Graph Advantage Actor Critic, namely G2A2C, a reinforcement learning framework in the fashion of advantage actor critic. By directly querying the victim model, G2A2C learns to inject highly malicious nodes with extremely limited attacking budgets, while maintaining a similar node feature distribution. Through our comprehensive experiments over eight acknowledged benchmark datasets with different characteristics, we demonstrate the superior performance of our proposed G2A2C over the existing state-of-the-art attackers. Source code is publicly available at: https://github.com/jumxglhf/G2A2C}.Comment: AAAI 2023. v2: update acknowledgement section. arXiv admin note: substantial text overlap with arXiv:2202.0938

Algorithm Evolution Using Large Language Model

Optimization can be found in many real-life applications. Designing an effective algorithm for a specific optimization problem typically requires a tedious amount of effort from human experts with domain knowledge and algorithm design skills. In this paper, we propose a novel approach called Algorithm Evolution using Large Language Model (AEL). It utilizes a large language model (LLM) to automatically generate optimization algorithms via an evolutionary framework. AEL does algorithm-level evolution without model training. Human effort and requirements for domain knowledge can be significantly reduced. We take constructive methods for the salesman traveling problem as a test example, we show that the constructive algorithm obtained by AEL outperforms simple hand-crafted and LLM-generated heuristics. Compared with other domain deep learning model-based algorithms, these methods exhibit excellent scalability across different problem sizes. AEL is also very different from previous attempts that utilize LLMs as search operators in algorithms

CSFs for SMEs in Measuring e-Commerce Success

For the last 20 years, while many electronic business (e‐business)/electronic commerce (e‐commerce) systems have been successfully adopted in businesses across different industries, a significant number have failed, especially in small to medium enterprises (SMEs). It is therefore necessary to explore critical success factors (CSFs) for SMEs in adopting e‐commerce success. A blend of quantitative and qualitative research methods were used, consisting of literature review, focus group studies, pilot tests, and surveys. Total survey was of 11.54% (277 out of 2401). Data analysis procedures were adopted, which comprised initial reliability analysis, validity analysis, t‐testing, factor analysis, and detailed reliability analysis. As a result, a total of 15 items were identified as common CSFs for SMEs successfully adopting e‐commerce system, which could be adopted as an effective tool for assisting SMEs in effectively adopting e‐commerce systems, and as a yardstick further to develop new methods for measuring e‐commerce success