Symptoms of coronavirus disease of 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), range from asymptomatic to life-threatening, affecting the respiratory system and many other organs, and involved in SARS-CoV-2 pathogenesis, particularly its viral entry, is the enzyme transmembrane protease serine 2 (TMPRSS2). In response, in this study, polymorphisms in the TMPRSS2 gene (rs2070788 and rs12329760) were examined for their association with COVID-19 susceptibility and severity in Jordanians. The study participants (n = 861) included 427 individuals who had never contracted COVID-19 (control group) and 434 who had experienced the disease (case group). The case group was subsequently divided into two subgroups: severe (n = 166) and non-severe (n = 268) COVID-19. The results show that rs2070788 is associated with disease susceptibility in all inheritance models (P < 0.05), as well as an association between rs12329760 and COVID-19 disease severity in both the dominant and over-dominant models (P < 0.05). Further, a higher body mass index (BMI) and older age were associated with disease severity (p < 0.05), though females were less likely to develop severe COVID-19 and to be hospitalized. In conclusion, polymorphisms in the TMPRSS2 gene may be considered a potential predictor of COVID-19 outcomes in the Jordanian population. The study findings could be used to manage similar viral infections in Jordan in the future.

Plasma bubbles in the ionosphere over equatorial and low-latitude regions are a common nighttime plasma density depletion structure that can cause severe scintillation interference on high-frequency communications, making them an important target of space weather monitoring. Traditional detection methods rely on statistical thresholds derived from temporal fluctuations in ion density; however, plasma bubbles exhibit significant variability in spatiotemporal scales (ranging from kilometers to hundreds of kilometers), and their occurrence frequency is strongly modulated by solar activity and seasonal factors. Consequently, fixed-threshold methods struggle to accommodate this diversity, with miss rates reaching up to 40% during periods of low solar activity. Although deep learning has demonstrated powerful capabilities in automatically extracting and recognizing complex waveform features in fields such as medical image segmentation and signal peak detection, its systematic application to ionospheric plasma bubble detection—and thus addressing the adaptability deficiencies of traditional threshold methods—remains underexplored.

With growing concerns over the impact threat of near-Earth small bodies and the utilization of space resources, the orbital evolution of binary asteroid systems has become an important subject in space science. When describing the full two-body dynamics of such systems, the gravitational potential of irregular bodies is determined by both their positions and attitudes, leading to coupling between translational and rotational motions, and the resulting model complexity significantly exceeds that of the classical two-body problem. Although existing numerical methods, such as explicit Runge–Kutta integrators, are widely used, they fail to preserve the symplectic structure and Lie group geometric properties of the system. In long-term simulations, key conserved quantities such as total energy and angular momentum are prone to numerical drift, severely undermining the long-term credibility of orbital predictions. While Lie group variational integrators can preserve geometric structure to a certain extent, their implicit equations are formulated on Lie group elements, which incurs substantial computational overhead. Therefore, how to construct a high-performance integration method that simultaneously achieves numerical accuracy, structure preservation, and computational efficiency has become a core challenge in the long-term evolutionary simulation of binary asteroid systems.

Using MODIS observations from 2003 to 2024, researchers evaluated candidate regions and seasonal windows for marine cloud brightening (MCB) worldwide by examining how key cloud properties varied with aerosol optical depth (AOD). Among five representative ocean regions, the North Pacific during boreal autumn (SON) showed a favorable coordinated pattern: cloud fraction and cloud optical thickness were positively sensitive to AOD, whereas cloud droplet effective radius was negatively sensitive. The study provides an observational basis for further evaluation of MCB candidate regions and seasonal windows. The findings were published in Science China Earth Sciences.

In a paper published in SCIENCE CHINA Earth Sciences, a team of researchers conducted a series of CESM2 ensemble simulations under SSP2-4.5 to examine the impact of marine cloud brightening (MCB) on terrestrial drought. By injecting sea salt aerosols over certain subtropical ocean regions, they found that MCB could produce about 0.9°C global cooling and mitigate global land drying trend via reduced potential evapotranspiration by 2100 relative to that of SSP2-4.5. However, regional differences in drought response to MCB are evident, indicating the underlying complexity of terrestrial hydrological responses to artificial climate intervention. It sheds new light on the potential and risks of solar geoengineering for drought mitigation.

A new study published in National Science Review reveals the dual effects of urban greenery on compound heat-ozone pollution risks across Northern Hemisphere cities. While urban greening is widely promoted as a nature-based method to cool overheated cities, complex interactions between urban meteorology and atmospheric chemistry can create important trade-offs for ozone control. The findings call for integrated urban greening strategies that jointly enhance climate resilience, air quality, and public health.

A Nankai University team designed azaphenanthrene-fused non-fullerene acceptors and tuned their central cores with fluorine, chlorine, or bromine. Increasing halogen size progressively enhanced crystallinity and packing order. The brominated acceptor CHNBr achieved a fill factor of 78.84%, enabling binary organic solar cells to exceed 20% power conversion efficiency.

Solar interfacial evaporation has undergone rapid development in recent years, yet its overall performance has reached a plateau due to limited advances in solar-thermal materials. Herein, we propose a synergistic nano-confinement and physical-field–modulation strategy that enables concurrent acceleration of solar-driven evaporation of water and on-site remediation of organic pollutants. Implemented in hollow mesoporous carbon nanocages integrated with Fe–N₄ catalytic sites and inner wall plasmonic Au nanoparticles, the system couples mesoporous confinement with localized thermal and pressure perturbations to transform bulk water into thermodynamically activated intermediate states and substantially reduce the effective vaporization enthalpy. This integrated framework delivers high evaporation rates of 2.56 kg m⁻² h⁻¹ in planar devices and 6.84 kg m⁻² h⁻¹ in 3D architectures under one-sun irradiation, with a kinetic enhanced Hertz–Knudsen–Schrage–derived evaporation coefficient. Simultaneously, the Fe–N₄ sites enable non-radical peroxymonosulfate activation for ultrafast degradation of bisphenol A, achieving a rate of 182.5 L g⁻¹ min⁻¹. This work establishes an ingenious strategy for coupling water-state regulation and catalytic pollutant degradation to break the performance bottleneck of solar-thermal purification.

A new study reveals that oncogenic signaling can reshape cancer cells not only by changing gene activity, but also by changing how cellular machinery reads the genetic code.