Browse Articles

Biogeochemical signal from marine heatwaves, cold spells, and transient warming events in a coastal upwelling system

Spatiotemporal evolution and influencing factors of the coupling coordination between Agricultural Digitalization and Agricultural New Quality Productive Forces

Mechanistic study on the influence of organic matter on soil compressive strength and nonlinear resistivity

Antimicrobial peptide LL-37 promotes proliferation of human bone marrow-derived mesenchymal stem cells

Characterizing circular and linear non-coding RNA in human aged brains and their association with dementia

Long-term, large-current operation of superconducting power transmission that carried five million passengers

A forecast model for ionospheric TEC during solar flare events based on low-latitude GPS stations data

Effects of WeChat-based palliative care intervention on cancer-related fatigue among lung cancer patients: a randomized controlled trial

New dimensions in burnt bone taphonomy through X-ray, neutron imaging and multivariate analysis

E-commerce video & content understanding via vision: a comprehensive study on lip reading and visual speech recognition for enhanced shopping experience

Microstructure-driven enhancement of cement mortar using high-entropy oxides for improved strength, durability, and corrosion resistance

Abstract This study investigates the influence of high-entropy oxide (HEO) fine powder with submicron agglomerates (Mg₀.₂Ni₀.₂Co₀.₂Cu₀.₂Zn₀.₂O) on the mechanical performance, microstructural development, and durability characteristics of cement-based mortars. HEO was synthesized via a modified sol–gel method and incorporated into mortar mixtures as an additional additive at dosages of 0–1.0 wt% relative to the cement content. The experimental results demonstrate a strong dosage-dependent behavior. The optimum performance was obtained at 0.25 wt% HEO, where compressive and flexural strengths increased by up to approximately 3–4% compared to the control sample. In contrast, higher dosages (≥ 0.75 wt%) resulted in a gradual reduction in mechanical performance, indicating a threshold beyond which HEO begins to negatively affect hydration-related processes. Microstructural analysis (XRD and SEM–EDS) revealed that HEO particles contribute to matrix densification through a filler effect and act as nucleation sites for hydration products, leading to a more compact cementitious structure at optimal content. However, excessive incorporation appears to disrupt hydration continuity due to particle agglomeration and dilution effects. Durability tests showed improved resistance to water absorption, chloride ion penetration, and corrosion current with increasing HEO content, attributed to pore refinement and reduced ion transport pathways. The most balanced performance in both mechanical and durability properties was achieved at low HEO dosage. Overall, high-entropy oxides demonstrate significant potential as nano-scale additives for cementitious systems; however, their effectiveness is highly dependent on dosage, with low replacement levels providing the most beneficial performance balance.

Bone mineral density abnormalities in end stage chronic kidney disease patients in Pakistan

Application of vision transformers to protein-ligand affinity prediction

Abstract Predicting protein-ligand binding affinity from three-dimensional (3D) structural data is a central task in structure-based drug discovery, yet it remains challenging due to limited data availability, structural complexity, and the sparse nature of 3D molecular representations. In this study, we investigate the application of vision transformers (ViTs) to the problem of affinity prediction. Unlike other neural networks used in this problem, the ViT framework can capture global, long-range interactions across the entire protein-ligand complex via self-attention, without relying on local receptive fields or predefined interaction cutoffs. We evaluate this advantage in representation of spatial information across two benchmark datasets, demonstrating competitive performance and, in some cases, surpassing state-of-the-art models. We study the model’s behavior using explainable AI (XAI) techniques, revealing that spatially proximal patches with similar attention scores cluster around biologically relevant regions, confirming the model’s ability to capture key interaction features. Furthermore, we show that data augmentation strategies can yield performance improvements, highlighting the potential for further enhancement. Despite challenges related to data sparsity and conformational variability, ViTs show strong performance and high robustness in structure-based affinity prediction tasks. Our findings underscore their effectiveness in learning spatial patterns and suggest broader applicability to related tasks, such as protein-protein or protein-nucleic acid interaction modeling.

The mediating role of moral identity in the relationship between organizational culture and emotional commitment among healthcare workers

Generation of high-producing CHO cell lines using cell picking system combined with fluorescent labeling of secreted products

Structural, electronic, magnetic, optical, and thermoelectric properties of FeCrVAl and MnCrVAl quaternary Heusler alloys for spintronics and energy harvesting applications

Computational and regression analysis of neuroprotective agents using fuzzy neural networks and topological descriptors

Physiological and biochemical responses of well-trained military personnel in a multi-day high-intensity training course

Scientific discovery in the age of AI and supercomputing

Abstract Artificial intelligence (AI) and high-performance computing (HPC) are transforming scientific capabilities and the way science is conducted. Yet their combined impact on scientific discovery remains poorly understood, as do inequalities in access to these capabilities across countries and institutions. Drawing on metadata from more than five million scientific publications (2000–2024) across 27 fields, we examine how the convergence of AI and HPC correlates with scientific breakthroughs. Our results show that this computational synergy is most pronounced at the scientific frontier: research combining AI and HPC is more likely to introduce novel ideas and achieve top-cited status than either conventional work or research using AI or HPC in isolation. We also document growing disparities in access to supercomputing resources and AI expertise, which are increasingly concentrated in a small number of regions (dominated by the United States and China, though the EU27 aggregate maintains high competitiveness in combined AI+HPC output). The future of discovery will depend not only on advances in algorithms and computing power, but also on enacting policies that democratise these capabilities across the global scientific ecosystem.

Doxorubicin-loaded gold nanoparticles for enhanced anticancer efficacy: in vitro, in vivo, and FTIR-based tissue analysis

Abstract The most challenging aspects of cancer treatment are multi-drug resistance (MDR) and damage to normal, non-malignant cells. Doxorubicin (Dox)-loaded gold nanoparticles (Dox@AuNPs) were developed and evaluated for enhanced anticancer activity. The nanocomposites exhibited spherical morphology (13 ± 3 nm) with a loading efficiency of 52%. In vitro, Dox@AuNPs reduced the IC₅₀ by approximately 50% compared to free Dox in MCF-7 cells. In vivo, treatment significantly suppressed tumor growth and increased median survival (62 days) relative to free Dox (52 days). In addition to conventional efficacy evaluation, Fourier transform infrared (FTIR) spectroscopy was applied to analyze biochemical changes in tumor tissues. Distinct alterations in protein, lipid, and nucleic acid-associated bands were observed, with treated tissues showing partial spectral shifts toward normal profiles. These findings suggest that FTIR may provide additional insight into tissue-level biochemical responses following treatment. While further validation and toxicity assessment are required, the results demonstrate that Dox@AuNPs enhance anticancer efficacy and highlight the potential utility of FTIR as a complementary tool for evaluating therapeutic response.