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Genetically engineered mosquitoes block development of circulating malaria strains
Distributed attention synchronization networks for advanced social media interaction analytics
Brucein D suppresses breast cancer proliferation and migration via targeting the FAK/LRG1 signaling pathway
Study on the climate impacts on the reservoir waterlevel
Therapeutic efficacy of oncolytic adenovirus YSCH-01 in osteosarcoma evaluated in PDX and CDX mouse models
Phosphorylation of PA28γ by CK2 kinase facilitates HNSCC tumor formation and progression
Low contribution of oxic methane production in shallow productive lakes
Self-assembled cellulosic superstructures with unanticipated high quantum yields
Abstract Nonconventional luminophores devoid of traditional, large π-conjugates often suffer from low solid-state fluorescence quantum yields (FLQYs). In parallel, self-assembled bowl-shaped and helical architectures at the micro- and macroscale are unusual (mostly reported at the nanoscale). Here, we report that surface-stearoylated cellulose nanocrystals and cellulose stearoyl esters co-assemble into macroscale helices (FLQY: 86%) with diameters of 32−104 μm. Meanwhile, surface-lauroylated cellulose nanocrystals and cellulose lauroyl esters co-assemble into porous bowl-shaped microparticles (FLQY: 91%) with diameters of 8−19 μm. The high FLQYs are ascribed to the synergism of the dense oxygen clusters and abundant van der Waals interactions and hydrogen bonds between side stearoyl or lauroyl groups, which can promote through-space electron delocalization, ultimately improving fluorescence performance. These results were rationalized by theoretical calculations. Such superstructures exhibit great potential for stable anti-counterfeiting materials due to the excellent regeneration ability as well as structural stability of the oxygen clusters.
Enhanced prediction and optimization of thin metal film optical properties using optimized ensemble learning models
Abstract Thin metal films are essential for expanding sensors, optoelectronic, and photovoltaic technologies. The intricate relationship between material composition, thickness, and production presents significant challenges in optimizing optical properties. The paper introduces an AI-driven framework for the simultaneous prediction and optimization of metal film optical characteristics, such as transmittance(%T), reflectance(%R), and absorptance(%A) using a dataset of 1320 experimentally measured samples across material films of gold, aluminum, nickel, tin, copper, and molybdenum over 200–2000 nm wavelength range. The input features to the model include wavelength and material type. Ensemble Models such as Random Forest, Gradient Boosting, XGBoost, and Extra Trees were trained and optimized through GridSearchCV with stratified K-fold cross-validation. A multitask learning model was also implemented to explore potential improvements from joint prediction. Among all models, the CatBoost Regressor demonstrated superior performance, achieving R 2 = 0.99928, MAE = 0.21924, and MSE = 0.28203 on average across all outputs. To enhance interpretability, the feature importance analysis was employed, revealing that Material Type had a slightly more predictive influence than Wavelength. Additionally, material-specific error analysis identified challenging prediction zones tied to spectral extremes. The best performing machine learning model was deployed via a web-based GUI, enabling real-time prediction of thin-film optical properties. Overall, the proposed framework provides a scalable, interpretable, and deployable solution for AI-assisted material design and optical characterization. These findings accelerate thin metal film optimization by offering a reliable data-driven route for quick material property identification and enhancement through machine learning.
Transformer-based deep learning enhances discovery in migraine GWAS
Enhancing media literacy to combat information fragmentation in digital short video platforms: a cross-sectional study
Entropy Governs the Structure and Reactivity of Water Dissociation Under Electric Fields
Scenario-adaptive hierarchical optimisation framework for design in hybrid energy storage systems
Deformed wing virus affects foraging success and foraging specialization of honeybee workers
Abstract Deformed wing virus (DWV) is a major driver of honeybee colony losses, yet its sublethal effects on adult foraging behaviour remain underexplored. Building on evidence that covert DWV infections impair sucrose responsiveness and associative learning, we tested whether infection changes foraging success and specialization in worker bees. Using a controlled experiment, we marked 1,000 newly emerged workers that were either inoculated with DWV lysate or injected with an RNA-interference control that suppressed viral replication. Foraging activity, success, and specialization were recorded. Our results show that DWV-infected bees began foraging earlier (“precocious foraging”) and had higher mortality, reducing their lifespan as foragers. Furthermore, infected nectar foragers were significantly less likely to return with nectar, and when they did, it contained markedly lower sugar concentrations compared to control bees. Conversely, infected bees were slightly more likely to return with pollen and showed greater specialization in pollen foraging, although pollen load weights were similar between treatments. These findings indicate that DWV disrupts multiple aspects of foraging ecology by accelerating behavioural maturation, shortening forager lifespan, reducing nectar yield and quality, and shifting resource preference towards pollen. Such changes may reflect a compensatory response to impaired nectar collection, but nonetheless could compromise colony nutrition, particularly when high-quality nectar is scarce. Our results align with previous work linking DWV to impaired foraging efficiency, and altered foraging specialization, and reduced honeybee survival, underscoring DWV’s substantial sublethal costs. By reducing nectar foraging while leaving pollen loads unaffected, DWV may limit honey stores and brood rearing during high demand periods, contributing to seasonal losses. Even moderate declines in nectar-foraging can undermine colony resilience, highlighting the need to address DWV’s role in the pollinator crisis.
SIDISH integrates single-cell and bulk transcriptomics to identify high-risk cells and guide precision therapeutics through in silico perturbation
Analytical solutions and chaotic insights into the Hirota-Maccari system
Global-scale prevalence of low nutrient use efficiency across major crops
Abstract Enhancing nitrogen (N) and phosphorus (P) use efficiency (NUE and PUE) is essential for advancing sustainable agriculture and reducing dependency on non-renewable fertilizers. However, the long-term dynamics of NUE and PUE across major crops remain poorly understood at a global scale. Here, we compile a comprehensive global database encompassing 3360 observations across 205 countries to analyze trends in NUE and PUE for major crops from 1961 to 2018. Today, PUE and NUE are still suboptimal, particularly in developing regions, emphasizing the need for context-specific strategies to improve nutrient use efficiency. Global mapping shows that NUE and PUE are highly context-dependent, with variations observed by crop type and region. For instance, rice achieves optimal NUE and PUE in tropical zones, while wheat performs best in temperate climates. Notably, maize continues to exhibit significant nutrient inefficiencies, especially in China and the United States, with considerable N and P surpluses. Taken together, this global analysis provides spatially explicit insights to guide region-specific efforts toward improving nutrient use efficiency, supporting sustainable agricultural practices and reducing global fertilizer dependence.
Synchronous motion predicts enhanced perceived human–robot teamwork
Abstract In social robotics, interaction synchrony plays a central role in creating intentional and lifelike robotic agents. However, is yet to be studied the extent to which interaction synchrony is a relevant social aspect used by external observers to make inferences about human–robot dyads. For instance, whether external observers evaluate the human–robot ability to work as a team. In one mixed-design experimental study, 34 participants were presented with two videos showing a human–robot dyad engaging in a synchronous vs. asynchronous interaction. We found evidence that the mere perception of synchronous interactions predicts external observers’ estimations of the dyad’s collective efficacy, fluency, and cohesion. Our findings also suggest that seeing asynchronous interactions after synchronous ones elicits greater differences in participants’ estimations, compared to when participants see asynchrony first. Unexpectedly, synchrony did not play a significant role in shaping participants’ affiliation intention towards the dyad. Overall, our findings speak to the importance of synchrony in shaping the way humans observe a human-robot dyad and think about their collaborative capabilities. Not only do we provide new insight into the way humans perceive social groups that include at least one robotic agent, but we also draw broader implications to the human–robot interaction field.
A bioabsorbable body-coupling-electrotherapy suture
Abstract Sutures are pivotal medical devices for postoperative incision management. The inherent capacity of sutures to regulate wound healing and promote regeneration has the potential to significantly reduce patient discomfort and conserve medical resources. Here, we developed a bioabsorbable body-coupling electrotherapy suture that modulates healing from inflammation to remodeling, enabling healthy repair. The suture combines high tensile strength, flexibility, and degradability. The conversion of body-coupled electromagnetic energy through the suture enabled electrically synergistic therapeutic capabilities. By converting body-coupled electromagnetic energy, it provides synergistic therapy: dielectric polarization accelerates antimicrobial and anti-inflammatory effects, while dielectric voltage difference enhances healing factor expression. This fully physical approach reduces reliance on silver nanoparticles. In vitro and in vivo studies confirmed stable performance, achieving more than 1.43-fold improvement in healing efficiency compared with single-function sutures and reducing postoperative infections. Offering immobilization, stage-wide modulation, sustainability, and low energy demand, this technology represents a significant advancement in surgical practice.