Aspirin Has Antitumor Effects via Expression of Calpain Gene in Cervical Cancer Cells

Aspirin and other nonsteroidal anti-inflammatory drugs show efficacy in the prevention of cancers. It is known that they can inhibit cyclooxygenases, and some studies have shown that they can induce apoptosis. Our objective in this study was to investigate the mechanism by which aspirin exerts its apoptosis effects in human cervical cancer HeLa cells. The effect of aspirin on the gene expression was studied by differential mRNA display RT-PCR. Among the isolated genes, mu-type calpain gene was upregulated by aspirin treatment. To examine whether calpain mediates the antitumor effects, HeLa cells were stably transfected with the mammalian expression vector pCR3.1 containing mu-type calpain cDNA (pCRCAL/HeLa), and tumor formations were measured in nude mice. When tumor burden was measured by day 49, HeLa cells and pCR/HeLa cells (vector control) produced tumors of 2126 mm3 and 1638 mm3, respectively, while pCRCAL/HeLa cells produced markedly smaller tumor of 434 mm3 in volume. The caspase-3 activity was markedly elevated in pCRCAL/HeLa cells. The increased activity levels of caspase-3 in pCRCAL/HeLa cells, in parallel with the decreased tumor formation, suggest a correlation between caspase-3 activity and calpain protein. Therefore, we conclude that aspirin-induced calpain mediates an antitumor effect via caspase-3 in cervical cancer cells

Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning

The development of sixth-generation (6G)/Beyond Fifth-Generation (B5G) wireless networks, which have requirements that go beyond current 5G networks, is gaining interest from academia and industry. However, to increase 6G/B5G network quality, conventional cellular networks that rely on terrestrial base stations are constrained geographically and economically. Meanwhile, Non-Orthogonal Multiple Access (NOMA) allows multiple users to share the same resources, which improves the spectral efficiency of the system and has the advantage of supporting a larger number of users. Additionally, by intelligently manipulating the phase and amplitude of both the reflected and transmitted signals, Simultaneously Transmitting and Reflecting RISs (STAR-RISs)can achieve improved coverage, increased spectral efficiency,and enhanced communication reliability. However, STAR-RISsmust simultaneously optimize the amplitude and phase shiftcorresponding to reflection and transmission, which makes theexisting terrestrial networks more complicated and is considereda major challenging issue. Motivated by the above, we studythe joint user pairing for NOMA and beamforming design ofMulti-STAR-RISs in an indoor environment. Then, we formulatethe optimization problem with the objective of maximizing thetotal throughput of mobile users (MUs) by jointly optimizingthe decoding order, user pairing, active beamforming, andpassive beamforming. However, the formulated problem is amixed-integer non-linear programming (MINLP). To addressthis challenge, we first introduce the decoding order for NOMAnetworks. Next, we decompose the original problem into twosubproblems, namely: 1) MU pairing and 2) Beamformingoptimization under the optimal decoding order. For the firstsubproblem, we employ correlation-based K-means clusteringto solve the user pairing problem. Then, to jointly deal withbeamforming vector optimizations, we propose Multi-AgentProximal Policy Optimization (MAPPO), which can make quickdecisions in the given environment owing to its low complexity.Finally, simulation results prove that our proposed MAPPOalgorithm is superior to Proximal Policy Optimization (PPO)and Advanced Actor-Critic (A2C) by a maximum of 1% and6%, respectively.<br/

Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning

The development of 6G/B5G wireless networks, which have requirements that go beyond current 5G networks, is gaining interest from academia and industry. However, to increase 6G/B5G network quality, conventional cellular networks that rely on terrestrial base stations are constrained geographically and economically. Meanwhile, NOMA allows multiple users to share the same resources, which improves the spectral efficiency of the system and has the advantage of supporting a larger number of users. Additionally, by intelligently manipulating the phase and amplitude of both the reflected and transmitted signals, STAR-RISs can achieve improved coverage, increased spectral efficiency, and enhanced communication reliability. However, STAR-RISs must simultaneously optimize the amplitude and phase shift corresponding to reflection and transmission, which makes the existing terrestrial networks more complicated and is considered a major challenging issue. Motivated by the above, we study the joint user pairing for NOMA and beamforming design of Multi-STAR-RISs in an indoor environment. Then, we formulate the optimization problem with the objective of maximizing the total throughput of MUs by jointly optimizing the decoding order, user pairing, active beamforming, and passive beamforming. However, the formulated problem is a MINLP. To address this challenge, we first introduce the decoding order for NOMA networks. Next, we decompose the original problem into two subproblems, namely: 1) MU pairing and 2) Beamforming optimization under the optimal decoding order. For the first subproblem, we employ correlation-based K-means clustering to solve the user pairing problem. Then, to jointly deal with beamforming vector optimizations, we propose MAPPO, which can make quick decisions in the given environment owing to its low complexity.Comment: 8 pages, 9 figures, IEEE/IFIP Network Operations and Management Symposium (NOMS) 2024 submitte

Validity and reliability of the Korean version of the gender-friendliness barriers in nursing programs scale

IntroductionThe gender-friendliness barriers in nursing programs (GFB-NP) were used to measure perceived gender affinity among male nursing students in nursing education programs. Originally developed in Taiwan, this scale has not been used in Korea. The purpose of this study is to confirm the reliability and validity of the GFB-NP scale for Korean male nursing students.MethodsA convenience sample of male nursing students enrolled in the 1st to 4th year of nursing departments at five four-year universities located in three cities in Korea was used in the study. To confirm the validity and factor structure of the scale, both exploratory factor analysis and confirmatory factor analysis were employed.ResultsThe results support a four-factor structure: Professional acquisition opportunity, peer interaction, sociocultural prejudice, and gender role attitude. We confirmed that the Korean version of the GFB-NP is an appropriate tool for measuring the gender-friendliness educational environment perceived by male nursing students in nursing education.DiscussionThe GFB-NP will serve as a framework for developing counseling and management strategies to help male nursing students successfully adapt to school life within the nursing education curriculum. Research with a longitudinal study design is recommended to investigate the progression of school adaptation through undergraduate program courses

Trajectory Optimization and Phase-Shift Design in IRS Assisted UAV Network for High Speed Trains

The recent trend towards the high-speed transportation system has spurred the development of high-speed trains (HSTs). However, enabling HST users with seamless wireless connectivity using the roadside units (RSUs) is extremely challenging, mostly due to the lack of line of sight link. To address this issue, we propose a novel framework that uses intelligent reflecting surfaces (IRS)-enabled unmanned aerial vehicles (UAVs) to provide line of sight communication to HST users. First, we formulate the optimization problem where the objective is to maximize the minimum achievable rate of HSTs by jointly optimizing the trajectory of UAV and the phase-shift of IRS. Due to the non-convex nature of the formulated problem, it is decomposed into two subproblems: IRS phase-shift problem and UAV trajectory optimization problem. Next, a Binary Integer Linear Programming (BILP) and a Soft Actor-Critic (SAC) are constructed in order to solve our decomposed problems. Finally, comprehensive numerical results are provided in order to show the effectiveness of our proposed framework.Comment: This paper has been submitted to IEEE Wireless Communications Letter

Federated Learning with Intermediate Representation Regularization

In contrast to centralized model training that involves data collection, federated learning (FL) enables remote clients to collaboratively train a model without exposing their private data. However, model performance usually degrades in FL due to the heterogeneous data generated by clients of diverse characteristics. One promising strategy to maintain good performance is by limiting the local training from drifting far away from the global model. Previous studies accomplish this by regularizing the distance between the representations learned by the local and global models. However, they only consider representations from the early layers of a model or the layer preceding the output layer. In this study, we introduce FedIntR, which provides a more fine-grained regularization by integrating the representations of intermediate layers into the local training process. Specifically, FedIntR computes a regularization term that encourages the closeness between the intermediate layer representations of the local and global models. Additionally, FedIntR automatically determines the contribution of each layer's representation to the regularization term based on the similarity between local and global representations. We conduct extensive experiments on various datasets to show that FedIntR can achieve equivalent or higher performance compared to the state-of-the-art approaches. Our code is available at https://github.com/YLTun/FedIntR.Comment: IEEE BigComp 202

When sex doesn’t sell to men: Mortality salience, disgust and the appeal of products and advertisements featuring sexualized women

Although men typically hold favorable views of advertisements featuring female sexuality, from a Terror Management Theory perspective, this should be less the case when thoughts of human mortality are salient. Two experiments conducted in South Korea supported this hypothesis across a variety of products (e.g., perfume and vodka). Men became more negative towards advertisements featuring female sexuality, and had reduced purchase intentions for those products, after thinking about their own mortality. Study 2 found that these effects were mediated by heightened disgust. Mortality thoughts did not impact women in either study. These findings uniquely demonstrate that thoughts of death interact with female sex-appeal to influence men’s consumer choices, and that disgust mediates these processes. Implications for the role of emotion, and cultural differences, in terror management, for attitudes toward female sexuality, and for marketing strategies are discussed

Attribution of the 2015 record high sea surface temperatures over the central equatorial Pacific and tropical Indian Ocean

This study assessed the anthropogenic contribution to the 2015 record-breaking high sea surface temperatures (SSTs) observed in the central equatorial Pacific and tropical Indian Ocean. Considering a close link between extreme warm events in these regions, we conducted a joint attribution analysis using a fraction of attributable risk approach. Probability of occurrence of such extreme anomalies and long-term trends for the two oceanic regions were compared between CMIP5 multi-model simulations with and without anthropogenic forcing. Results show that the excessive warming in both regions is well beyond the range of natural variability and robustly attributable to human activities due to greenhouse gas increase. We further explored associated mechanisms including the Bjerknes feedback and background anthropogenic warming. It is concluded that background warming was the main contribution to the 2015 extreme SST event over the central equatorial Pacific Ocean on a developing El Niño condition, which in turn induced the extreme SST event over the tropical Indian Ocean through the atmospheric bridge effect.113Ysciescopu

Joint Trajectory and Resource Optimization of MEC-Assisted UAVs in Sub-THz Networks: A Resources-based Multi-Agent Proximal Policy Optimization DRL with Attention Mechanism

THz band communication technology will be used in the 6G networks to enable high-speed and high-capacity data service demands. However, THz-communication losses arise owing to limitations, i.e., molecular absorption, rain attenuation, and coverage range. Furthermore, to maintain steady THz-communications and overcome coverage distances in rural and suburban regions, the required number of BSs is very high. Consequently, a new communication platform that enables aerial communication services is required. Furthermore, the airborne platform supports LoS communications rather than NLoS communications, which helps overcome these losses. Therefore, in this work, we investigate the deployment and resource optimization for MEC-enabled UAVs, which can provide THz-based communications in remote regions. To this end, we formulate an optimization problem to minimize the sum of the energy consumption of both MEC-UAV and MUs and the delay incurred by MUs under the given task information. The formulated problem is a MINLP problem, which is NP-hard. We decompose the main problem into two subproblems to address the formulated problem. We solve the first subproblem with a standard optimization solver, i.e., CVXPY, due to its convex nature. To solve the second subproblem, we design a RMAPPO DRL algorithm with an attention mechanism. The considered attention mechanism is utilized for encoding a diverse number of observations. This is designed by the network coordinator to provide a differentiated fit reward to each agent in the network. The simulation results show that the proposed algorithm outperforms the benchmark and yields a network utility which is $2.22\%$, $15.55\%$, and $17.77\%$ more than the benchmarks.Comment: 13 pages, 12 figure