Tai Liu^1,#, Huichun Xu^1,#, Sikandar Amanullah^2,*, Ye Che¹, Ling Zhang¹, Zeyu Jiang¹, Weiyi Bi¹, Lei Zhu¹, Di Wang^1,*

Phyton-International Journal of Experimental Botany, DOI:10.32604/phyton.2025.074131

Abstract Heavy metal accumulation in agricultural soil is primarily driven by pesticides, polluted water, and industrial gas emissions, which pose threats to sustainable crop production. Chromium (Cr) stress has an adverse impact on plant development and metabolism, but approaches to reduce its toxicity and enhance plant resistance remain limited. Melatonin is a potent antioxidant involved in regulating various morpho-physiological functions of plants under different abiotic stresses. In this study, we investigated the impact of exogenous melatonin to mitigate the negative effects of potassium dichromate (PD) stress in melon plants and analyzed genetic modulation of morphological, physiological,… More >

Nadezhda Golubkina^1,*, Viktor Kharchenko¹, Ekaterina Krainyuk², Lubov Riff², Vladimir Lapchenko³, Helene Lapchenko³, Anastasia Moldovan¹, Uliana Plotnoikova⁴, Otilia Cristina Murariu⁵, Gianluca Caruso⁶

Phyton-International Journal of Experimental Botany, DOI:10.32604/phyton.2025.072328

Abstract Despite their remarkable content of biologically active compounds, highly valuable for human health, wild relatives of Umbelliferous plants show limited utilization. The aim of the present work was the evaluation of the antioxidant status of Anthriscus, Chaerophyllum, and Myrrhoides species gathered in different climatic zones (from Mediterranean to Arctic) and of their suitability to produce valuable functional food for optimizing the human Se status. Among the Crimean plants, A. sylvestris, C. bulbosis, and M. nososa showed the highest antioxidant status, while the lowest was recorded in A. cerefolium and A. caucalis, displaying a significant correlation between the antioxidant activity (AOA) and polyphenols… More >

Wei-Ting Hsueh^1,#, Kwang-Yu Chang^1,2,3,#, Chin-Chuan Tsai^4,5, Kuan-Tso Chen^5,6, Kuen-Jang Tsai⁷, Zi-Xuan Hong⁸, Chan-Chuan Liu², Jui-Mei Chu², Li-Ying Qiu², Yu-Yan Lan⁸, Chia-Hung Chien^8,*

Oncology Research, DOI:10.32604/or.2025.071258

Abstract Objectives: Glioblastoma (GBM) is a prevalent malignant brain tumor prone to drug resistance. We previously found a strong correlation between SH3 domain GRB2-like endophilin B1 (SH3GLB1) and superoxide dismutase 2 (SOD2), which converts O₂ to hydrogen peroxide (H₂O₂). Prior studies show that H₂O₂ redox signaling is vital for physiological processes and can drive tumor progression. Therefore, we aim to define how H₂O₂ signaling regulates SH3GLB1 and AKT (protein kinase B) pathways in GBM and to assess whether modulating H₂O₂ reverses temozolomide (TMZ) resistance. Methods: We used cultured cells and pharmacological inhibitors and activators to confirm the significance of… More > Graphic Abstract

Rasdianah Dahali¹, Edi Syams Zainudin^1,2,*, Mohammed Abdillah Ahmad Farid¹, Tarique Jamal³, Mohd Sapuan Salit^1,2, Muhammad Firdaus Abdul Halim²

Journal of Renewable Materials, DOI:10.32604/jrm.2025.02025-0139

Abstract This review draws attention to the innovative use of arrowroot (Maranta arundinacea) fiber as a unique and underutilized biomass source for nanocrystalline cellulose (NCC)-based nanocomposites, presenting a noteworthy alternative to extensively researched materials like wood pulp, bacterial cellulose, and chemically modified NCCs. In contrast to traditional sources, arrowroot possesses a naturally elevated cellulose and diminished lignin content, facilitating more effective NCC extraction requiring reduced chemical input and enabling environmentally friendly processing techniques. The review evaluates the performance of arrowroot-derived nanocomposites against systems documented in the literature, including NCC-based shape memory composites and nanoparticle-reinforced films, demonstrating enhanced More > Graphic Abstract

Xiaoyu Li^1,2, Jie Zhang³, Wen Shi^1,2,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.074005

Abstract The emergence of large language models (LLMs) has brought about revolutionary social value. However, concerns have arisen regarding the generation of deceptive content by LLMs and their potential for misuse. Consequently, a crucial research question arises: How can we differentiate between AI-generated and human-authored text? Existing detectors face some challenges, such as operating as black boxes, relying on supervised training, and being vulnerable to manipulation and misinformation. To tackle these challenges, we propose an innovative unsupervised white-box detection method that utilizes a “dual-driven verification mechanism” to achieve high-performance detection, even in the presence of obfuscated… More >

Danyang Zheng^*, Jinchen Xv, Xin Zhou, Zheng Shan

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073860

Abstract The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years. Nevertheless, the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity. In order to meet the needs of an increasing number of researchers, it is imperative to facilitate efficient and flexible access to computing resources in a cloud environment. In this paper, we propose a novel quantum computing paradigm, Virtual QPU (VQPU), which addresses this issue and enhances quantum cloud throughput with guaranteed circuit fidelity. The proposal introduces More >

Changjun Zhou¹, Dongfang Chen¹, Chenyang Shi¹, Taiyong Li^2,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073662

Abstract With the rapid development of smart manufacturing, intelligent safety monitoring in industrial workshops has become increasingly important. To address the challenges of complex backgrounds, target scale variation, and excessive model parameters in worker violation detection, this study proposes ADCP-YOLO, an enhanced lightweight model based on YOLOv8. Here, “ADCP” represents four key improvements: Alterable Kernel Convolution (AKConv), Dilated-Wise Residual (DWR) module, Channel Reconstruction Global Attention Mechanism (CRGAM), and Powerful-IoU loss. These components collaboratively enhance feature extraction, multi-scale perception, and localization accuracy while effectively reducing model complexity and computational cost. Experimental results show that ADCP-YOLO achieves a More >

Haiyan Long¹, Gang Chen^2,*, Hai Chen^3,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073243

Abstract The performance of deep recommendation models degrades significantly under data poisoning attacks. While adversarial training methods such as Vulnerability-Aware Training (VAT) enhance robustness by injecting perturbations into embeddings, they remain limited by coarse-grained noise and a static defense strategy, leaving models susceptible to adaptive attacks. This study proposes a novel framework, Self-Purification Data Sanitization (SPD), which integrates vulnerability-aware adversarial training with dynamic label correction. Specifically, SPD first identifies high-risk users through a fragility scoring mechanism, then applies self-purification by replacing suspicious interactions with model-predicted high-confidence labels during training. This closed-loop process continuously sanitizes the training More >

Nouman Ahmad^1,*, Changsheng Zhang¹, Uroosa Sehar^2,3,4

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073029

Abstract Although Named Entity Recognition (NER) in cybersecurity has historically concentrated on threat intelligence, vital security data can be found in a variety of sources, such as open-source intelligence and unprocessed tool outputs. When dealing with technical language, the coexistence of structured and unstructured data poses serious issues for traditional BERT-based techniques. We introduce a three-phase approach for improved NER in multi-source cybersecurity data that makes use of large language models (LLMs). To ensure thorough entity coverage, our method starts with an identification module that uses dynamic prompting techniques. To lessen hallucinations, the extraction module uses… More >

Longcang Wang, Yongbing Gao^*, Xinguang Wang, Xin Liu

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072889

Abstract Aiming at the problem of insufficient recognition of implicit variants by existing Chinese sensitive text detection methods, this paper proposes the IPKE-MoE framework, which consists of three parts, namely, a sensitive word variant extraction framework, a sensitive word variant knowledge enhancement layer and a mixture-of-experts (MoE) classification layer. First, sensitive word variants are precisely extracted through dynamic iterative prompt templates and the context-aware capabilities of Large Language Models (LLMs). Next, the extracted variants are used to construct a knowledge enhancement layer for sensitive word variants based on RoCBert models. Specifically, after locating variants via n-gram… More >