A new study published in Big Earth Data develops explainable Random Forest models to predict stream total nitrogen (TN) and total phosphorus (TP) concentrations using upstream catchment characteristics derived from openly accessible environmental datasets. The results show that the models provide robust predictions for TN, while reduced and spatial covariates help mitigate overfitting and improve model interpretability, demonstrating the potential of explainable machine learning for regional nutrient monitoring.

A recent study published in National Science Review has revealed global inequalities in lake water quality through the development of GlakeWQ, the first satellite–machine learning framework enabling consistent assessment across countries. Mapping 16,409 lakes in 145 countries, the framework shows that only about two-thirds of lakes meet sustainable water quality standards, leaving approximately 1.15 billion people exposed to potential water quality-related health risks and highlighting the urgent need for equitable freshwater management.

How much load can a crumpled sheet bear, and does size matter? A new study of two-dimensional macromolecules, a class of ultrathin sheet-like materials, shows that at equal packing density smaller sheets carry higher compressive loads than larger ones. Coarse-grained simulations and compression tests on paper, aluminum foil, PDMS, and silicone rubber reveal a universal “smaller is stronger” rule rooted in denser internal ridge networks, offering a new design principle for lightweight, high-strength structural metamaterials.

CuAu alloys with tailored compositions were integrated onto copper foam by a simple one-step electrodeposition method. The optimized Cu_0.25Au_0.75 catalyst exhibited rather excellent performance for the electrocatalytic oxidation of 5-hydroxymethylfurfural (HMF) via a one-electron dehydrogenation pathway. The improved catalytic activity could be attributed to the alloying-induced charge redistribution that modulates the d-band centers, thereby steering surface adsorption behaviors to balance HMF adsorption and HMFCA desorption, ensuring active site availability for the selective production of 5-hydroxymethyl-2-furancarboxylic acid (HMFCA) and simultaneous anodic hydrogen evolution.

With the growing demand for marine exploration and environmental monitoring, underwater sensing technology faces severe challenges due to high humidity and the need for simultaneous detection of multiple physical parameters, such as temperature and pressure. Traditional underwater sensors typically rely on combinations of various functional materials to achieve dual-mode temperature–pressure detection, often requiring additional waterproof encapsulation, which complicates system integration. Achieving dual-mode temperature–pressure detection within a single material underwater remains particularly challenging. Herein, a waterproof dual-mode sensor based on Bi₂Se₃/carbon paper (CP) composite films is developed by electrochemically depositing pyramid-interface-structured Bi₂Se₃ layers on both sides of a flexible CP substrate. The film exhibits enhanced thermoelectric properties, with a power factor of 106.0 μW m⁻¹ K⁻² (9 times that of CP). Sensors constructed from vertically stacked pyramid-interface-structured films enable high-precision detection of temperature and pressure underwater, featuring a temperature-sensing response time of 0.9 s and a pressure sensitivity of 0.94% kPa⁻¹, while allowing synchronous decoupling of the two signals. Moreover, the sensor exhibits excellent hydrophobicity, with a contact angle of 143.7°, along with robust stability, overcoming key limitations for underwater applications. This advance in flexible underwater sensing technology offers a reliable strategy for underwater human–machine interaction and monitoring.

A new Perspective in Science Bulletin argues that AI tools for intracranial aneurysm detection should be evaluated beyond test-set accuracy. The authors propose a four-stage pathway covering lesion-level validation, reader performance, prospective implementation and post-deployment surveillance to assess whether AI can deliver real clinical benefit.

Monitoring COVID-19 NAbs dynamics remains dissolved as the lack of analytical strategies deployable in most practice, as stably remaining protein activity seems particularly difficult. We engineer selenium nanoparticles for LFIA that preserves protein activity and enhances stability. NAbs levels decrease with age and over time after infection, and they drop sharply with neurological and hematological diseases according to 1,328 samples. Our work provides a reliable and universally platform for next-generation point-of-care immunity monitoring and vaccine evaluation.

Anti-angiogenic therapies targeting VEGF signaling have limited clinical efficacy due to inevitable resistance. Researchers constructed a pancancer single-cell atlas of 1.24 million cells across 13 cancer types, revealing that pericytes (PCs) and PC-derived PGF/ANGPT2 are more prominent pro-angiogenic regulators than the classical VEGFA-centric model. Tumor-educated MCAM+ immature PCs (imPCs) were identified as the primary source of PGF/ANGPT2, driving alternative angiogenesis and anti-VEGFR resistance. Combining anti-VEGFR with MCAM-targeting antibody-drug conjugates achieved superior tumor control through dual EC/PC inhibition.

This review summarizes recent advances in wearable uric acid sensors, covering electrochemical and optical detection mechanisms, performance-enhancement strategies, device integration, and emerging applications. It highlights progress in sensitivity, selectivity, anti-fouling performance, self-calibration, and user comfort, while identifying persistent challenges in long-term stability, interference resistance, multimodal monitoring, clinical validation, data security, and commercialization. Future directions emphasize intelligent, sustainable, and personalized sensing platforms.