Browse Articles

Deep learning optimization of teaching schedules in sports dance education

Strength in numbers: how multimerization drives HpHb uptake by CD163 scavenging receptor

The quality and reliability of short videos about uraemia on BiliBili and TikTok: a cross-sectional study

Effectiveness of the 2024–2025 KP.2 COVID-19 vaccines in the United States during long-term follow-up

Abstract Up-to-date estimates of COVID-19 vaccine effectiveness (VE) are needed to inform COVID-19 vaccination strategies and recommendations. This target trial emulation study aimed to estimate the long-term vaccine effectiveness (VE) of the 2024-2025 COVID-19 vaccines targeting the KP.2 Omicron variant within the Veterans Health Administration. The study population (90.9% male, mean age 70.7 years) included 538,631 pairs of vaccinated (i.e., received the KP.2 COVID-19 vaccine) and matched unvaccinated (i.e., did not receive the KP.2 COVID-19 vaccine) persons enrolled from August 2024 to January 2025. Over a mean follow-up of 172 days (range 97-232) extending to April 12, 2025, VE was low against laboratory-diagnosed SARS-CoV-2 infection (16.60%, 95% confidence interval [CI], 11.92-21.44), SARS-CoV-2-associated emergency department/urgent care (ED/UC) visit (21.05%, 95% CI, 14.22-27.21), SARS-CoV-2-associated hospitalization (19.53%, 95% CI 6.56-30.10) and much higher against SARS-CoV-2-associated death (65.53%, 95% CI 27.79-83.37). VE declined from 60 to 90 to 120 days against infection (31.28%, 25.81%, 22.44% respectively), ED/UC visit (34.40%, 29.19%, 25.71% respectively), hospitalization (37.39%, 28.98%, 22.52% respectively) and death (75.02%, 71.02%, 63.08% respectively). In conclusion, COVID-19 vaccines targeting the KP.2 variant used in the 2024-2025 season offered high protection against death and modest protection against infection, ED/UC visits or hospitalization, and VE declined over time.

Mining-induced stress–gas permeability characteristics based on protective layer pressure-relief mining

Two-Dimensional Metastable-Phase Nickel Hexagonal Nanosheets for Highly-Performance Electrochemical Acetone Hydrogenation

Features and trends of marine heat waves and marine cold spells along the Western Iberian Coast from four decades of satellite data

Abstract Marine Heat Waves (MHWs) have been the focus of numerous studies due to the dramatic consequences they can have on coastal systems. However, Marine Cold Spells (MCSs) can also have harmful effects on the environment while play an important role in the context of global warming. Yet, there is lack of information on the physical attributes and long-term changes of MCSs. This study aims to investigate and compare the features and patterns of both MHWs and MCSs along the Western Iberian Coast (WIC) and its estuaries using satellite-derived Sea Surface Temperatures between 1982 and 2022. Overall, the WIC registered more MCSs than MHWs, although with lower intensities. The coastal region between Minho and Douro Estuaries and Cape São Vicente were favorable for MCSs development. The coastal regions of Minho through Aveiro and the south coast of Portugal registered MCSs and MHWs with the highest average and maximum intensities. No significant trends were observed for MHWs and MCSs features throughout the WIC. MHWs (MCSs) events were found to have been increasing (decreasing) offshore. However, an increase was registered near the coastline for MCS (0.06 events/year). Increased seasonal upwelling could be contributing to mask the development of MHWs and enhance MCSs.

Directional flows using capillary assembly of photo-deformable colloidal particles at water-air interfaces

Abstract Colloidal particles at liquid interfaces experience long-ranged capillary interactions, whose magnitude and directionality depend on the particle shapes. When particle shapes are determined by fabrication or synthesis, the resulting shape-mediated interactions are predefined and often lead to the formation of persistent interfacial structures. Here, we introduce polymer particles at water-air interfaces whose shape and, therefore, interactions can be altered by illumination with polarized light. Specifically, we selectively trigger capillary self-assembly by anisotropically deforming the particles at the interface. Intriguingly, further deformation of already assembled particles induces sustained interfacial flows with velocities of up to 90 μm/s. Benefitting from polarization-defined deformation directions, we create flow-patterns that do not simply follow the illumination intensity pattern, such as shear flows along a single rectangular illumination stripe. We anticipate that this interplay between photo-deformation and capillary interactions of particles will enable various forms of mixing, manipulation, and assembly of soft matter at liquid interfaces.

Focused ultrasound-mediated drug delivery of bevacizumab in treating NF2-related schwannomatosis in an animal model

Vaccine-preventable HPV burden and cervical abnormalities in women during 2022–2024 Octobre Rose campaigns in Libreville

Transverse force sensing with a uniform FBG and unpolarized light via machine learning

Proteomic signatures of smoking and their associations with risk of incident diseases and mortality in diverse populations

Abstract Smoking is the most important behavioural determinant of morbidity and mortality. Using machine learning on plasma levels of 2,917 proteins in the UK Biobank (n = 43,914), we develop a proteomic Smoking Index (pSIN) comprising 51 proteins that accurately distinguish current from never smokers (AUC = 0.95; 95% CI 0.94–0.95). Validation in the China Kadoorie Biobank (n = 3,977) shows similar accuracy (AUC = 0.91; 95% CI 0.89–0.92). pSIN is significantly associated with the risk of all-cause mortality and 18 major chronic diseases, including cardiovascular, renal, pulmonary, neurodegenerative, and cancer outcomes. Among current and former smokers, pSIN predicts death and 11 diseases independently of self-reported smoking history and lifestyle factors. Genome-wide analysis identifies 125 genes (e.g., ALPP , CST5 , IL12B ) associated with pSIN, while exposome analysis highlights maternal smoking, diet, physical activity, and air pollution as key modifiers. Notably, pSIN tracks recovery among former smokers and identifies those whose disease risks remain comparable to current smokers. These findings demonstrate that plasma proteomics effectively capture the biological imprint of smoking and predict smoking-related morbidity and mortality, offering a more nuanced, molecularly grounded assessment of individual variation in biological response to smoking.

Self-organizing complex networks with AI-driven adaptive nodes for optimized connectivity and energy efficiency

Abstract High connectivity and robustness are essential in distributed networks, ensuring resilience, efficient communication, and adaptability. Optimizing energy consumption is also crucial for sustaining energy-constrained networks and extending their operational lifespan. In this study, we introduce an Artificial Intelligence (AI)-enhanced self-organizing network model, where each adaptive node autonomously adjusts its transmission range to optimize network connectivity while lowering energy consumption. Building on our previous Hamiltonian-based methodology, which is designed to achieve globally optimized states of complete connectivity with minimal energy usage, this research integrates a Multi-Layer Perceptron (MLP)-based decision-making model at each node. By leveraging a dataset from the Hamiltonian approach, nodes independently learn and adapt transmission range based on local conditions, leading to emergent global behaviors characterized by high connectivity and resilience to structural disruptions. This distributed, AI-driven adaptability allows nodes to make context-aware range adjustments autonomously, enabling the network to maintain its optimized state over time. Simulation results show that AI-driven adaptive nodes achieve stable connectivity, robustness, and energy efficiency across different conditions, including static and mobile scenarios. This work contributes to the growing field of self-organizing networks by demonstrating AI’s potential to enhance complex network design, fostering scalable, resilient, and energy-efficient distributed systems.

Enantioselective Radical Sulfonylation of 2-Naphthols and β-Ketoamides with Sodium Sulfinates

Informationally complete distributed metrology without a shared reference frame

Estimating soil erosion utilizing geospatial method and revised universal soil loss equation (RUSLE) of Abu Ghraibat Watershed, Eastern Misan Governorate, Iraq

Abstract This study examined the synergistic and independent effects of soil properties, vegetation cover, conservation practices, and slope on the spatial distribution characteristics of soil erosion in the Abu-Ghraibat watershed in 2024. Soil samples have been collected and analyzed in the laboratory, along with high-resolution satellite imagery, meteorological data, and digital elevation model (DEM) data. The findings indicate that soil erosion in the Abu-Ghraibat watershed in 2024 was minimal, with a progressively increasing severity from north to south. In the studied area, grassland accounts for over 50% of soil erosion, with regions with vegetation coverage > 30% as the primary contributors, all of which are influenced by slope. Moreover, the enhancement of vegetation in the lower strata of the basin and in grasslands, especially on slopes ranging from 10° to 45°, along with the conversion of sloping woodlands and grasslands into terraces, has proven an effective strategy for mitigating soil erosion in the Abu-Ghraibat watershed. The present study has demonstrated that the RUSLEGIS integrated model may serve as an effective instrument for quantitatively and spatially mapping soil erosion at the watershed level in the Abu-Ghraibat, while accounting for the provision of landscape services.

Structural dynamics of mixed-subunit CaMKIIα/β heterododecamers filmed by high-speed AFM

Metabolic profiling revealed alterations associated with sedentary work in bus drivers

Local peptide signalling induces stomatal closure under drought stress

Development of a 3D-printable bioactive polycaprolactone–collagen peptides filament for biomedical applications

Abstract This study presents a scalable, nontoxic method for fabricating a 3D printable polycaprolactone (PCL)-collagen peptides composite filament via solvent-assisted blending and a customized desktop filament extrusion system. Virgin and recycled PCL feedstocks were used to study the matrix characteristics. Scanning electron microscopy, X-ray diffraction, differential scanning calorimetry, thermogravimetric analysis, and intrinsic fluorescence spectroscopy confirmed that the composite maintains PCL’s inherent crystalline and thermal properties. It also exhibits intrinsic bioactive capabilities provided by the collagen peptides. Filaments with diameters suitable for the Fused Filament Fabrication 3D printing were obtained. Tests demonstrated that the recycled matrix and the lab-scale process reduce the tensile modulus of the material. At the same time, collagen peptides enhanced tensile stiffness by creating intermolecular hydrogen bonding with the PCL. The biocompatibility of the composite has also been confirmed, while degradability studies have shown the tunability of the PCL degradation rate. Additionally, examples of 3D-printed scaffolds based on Triply Periodic Minimal Surfaces have been successfully fabricated using the PCL-collagen peptide filament. Therefore, this study demonstrates that integrating collagen peptides into a PCL matrix represents a viable, nontoxic, affordable, and promising approach for developing customized and bioactive implants, scaffolds, and other regenerative medicine applications.