Zinc oxide nanoparticles cooperate with the phyllosphere to promote grain yield and nutritional quality of rice under heatwave stress.

To address rising global food demand, the development of sustainable technologies to increase productivity is urgently needed. This study revealed that foliar application of zinc oxide nanoparticles (ZnO NPs; 30 to 80 nm, 0.67 mg/d per plant, 6 d) to rice leaves under heatwave (HW) stress increased the grain yield and nutritional quality.

Interpretable Causal System Optimization Framework for the Advancement of Biological Effect Prediction and Redesign of Nanoparticles.

Nanomedicine has promising applications in disease treatment, given the remarkable safety concerns (e.g., nanotoxicity and inflammation) of nanomaterials, and realizing the trade-off between the immune response and organ burden of NPs and deeply understanding the interactions of the organism-nano systems are crucial to facilitate the biological applications of NPs.

Heat Waves Coupled with Nanoparticles Induce Yield and Nutritional Losses in Rice by Regulating Stomatal Closure.

The frequency, duration, and intensity of heat waves (HWs) within terrestrial ecosystems are increasing, posing potential risks to agricultural production. Cerium dioxide nanoparticles (CeO NPs) are garnering increasing attention in the field of agriculture because of their potential to enhance photosynthesis and improve stress tolerance. In the present study, CeO NPs decreased the grain yield, grain protein content, and amino acid content by 16.

Urbanization influences dissolved organic matter characteristics but microbes affect greenhouse gas concentrations in lakes.

Recognition and prediction of dissolved organic matter (DOM) properties and greenhouse gas (GHG) emissions is critical to understanding climate change and the fate of carbon in aquatic ecosystems, but related data is challenging to interpret due to covariance in multiple natural and anthropogenic variables with high spatial and temporal heterogeneity. Here, machine learning modeling combined with environmental analysis reveals that urbanization (e.g.

DOM Associates with Greenhouse Gas Emissions in Chinese Rivers under Diverse Land Uses.

Growing evidence indicates that rivers are hotspots of greenhouse gas (GHG) emissions and play multiple roles in the global carbon budget. However, the roles of terrestrial carbon from land use in river GHG emissions remain largely unknown. We studied the microbial composition, dissolved organic matter (DOM) properties, and GHG emission responses to different landcovers in rivers ( = 100).

Development potential of nanoenabled agriculture projected using machine learning.

The controllability and targeting of nanoparticles (NPs) offer solutions for precise and sustainable agriculture. However, the development potential of nanoenabled agriculture remains unknown. Here, we build an NP-plant database containing 1,174 datasets and predict ( higher than 0.

Microbe Regulates the Mineral Photochemical Activity and Organic Matter Compositions in Water.

Photochemical reactions that widely occur in aquatic environments play important roles in carbon fate (e.g., carbon conversion and storage from organic matter) in ecosystems.

Marine Colloids Promote the Adaptation of Diatoms to Nitrate Contamination by Directional Electron Transfer.

Nitrate contamination from human activities (, domestic pollution, livestock breeding, and fertilizer application) threatens marine ecosystems and net primary productivity. As the main component of primary productivity, diatoms can adapt to high nitrate environments, but the mechanism is unclear. We found that electron transfer from marine colloids to diatoms enhances nitrogen uptake and assimilation under visible-light irradiation, providing a new pathway for nitrogen adaptation.

Multiple factors drive imbalance in the global microbial assemblage in soil.

Soil microbial assemblages play a critical role in biogeochemical cycling processes in terrestrial ecosystems. Dynamic global information for these assemblages considering multiple factors is critical for predicting ecological safety concerns but remains unpredictable. Here, we collected microbial data from soil datasets worldwide and used a feature-explicable machine learning (FEML) approach to address this problem.

Machine learning may accelerate the recognition and control of microplastic pollution: Future prospects.

Microplastics (MPs, sizes <5 mm) have been found to be widely distributed in various environments, such as marine, freshwater, terrestrial and atmospheric systems. Machine learning provides a potential solution for evaluating the ecological risks of MPs based on big data. Compared with traditional models, data-driven machine learning can accelerate the realization of the control of hazardous MPs and reduce the impact of MPs at both local and global scales.

Multifeature superposition analysis of the effects of microplastics on microbial communities in realistic environments.

Microplastic (MP) contamination has become an increasingly serious environmental problem. However, the risks of MP contamination in complex global climatic and geographic scenarios remain unclear. We established a multifeature superposition analysis boosting (MFAB) machine learning (ML) approach to address the above knowledge gap.

Deep exploration of random forest model boosts the interpretability of machine learning studies of complicated immune responses and lung burden of nanoparticles.

The development of machine learning provides solutions for predicting the complicated immune responses and pharmacokinetics of nanoparticles (NPs) in vivo. However, highly heterogeneous data in NP studies remain challenging because of the low interpretability of machine learning. Here, we propose a tree-based random forest feature importance and feature interaction network analysis framework (TBRFA) and accurately predict the pulmonary immune responses and lung burden of NPs, with the correlation coefficient of all training sets >0.

Knowledge gaps in immune response and immunotherapy involving nanomaterials: Databases and artificial intelligence for material design.

Exploring the interactions between the immune system and nanomaterials (NMs) is critical for designing effective and safe NMs, but large knowledge gaps remain to be filled prior to clinical applications (e.g., immunotherapy).

Predicting nanotoxicity by an integrated machine learning and metabolomics approach.

Predicting the biological responses to engineered nanoparticles (ENPs) is critical to their environmental health assessment. The disturbances of metabolic pathways reflect the global profile of biological responses to ENPs but are difficult to predict due to the highly heterogeneous data from complicated biological systems and various ENP properties. Herein, integrating multiple machine learning models and metabolomics enabled accurate prediction of the disturbance of metabolic pathways induced by 33 ENPs.

Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles.

Protein corona formation is critical for the design of ideal and safe nanoparticles (NPs) for nanomedicine, biosensing, organ targeting, and other applications, but methods to quantitatively predict the formation of the protein corona, especially for functional compositions, remain unavailable. The traditional linear regression model performs poorly for the protein corona, as measured by (less than 0.40).

Untargeted Metabolic Pathway Analysis as an Effective Strategy to Connect Various Nanoparticle Properties to Nanoparticle-Induced Ecotoxicity.

Elucidation of the relationships between nanoparticle properties and ecotoxicity is a fundamental issue for environmental applications and risk assessment of nanoparticles. However, effective strategies to connect the various properties of nanoparticles with their ecotoxicity remain largely unavailable. Herein, an untargeted metabolic pathway analysis was employed to investigate the environmental risk posed by 10 typical nanoparticles (AgNPs, CuNPs, FeNPs, ZnONPs, SiONPs, TiONPs, GO, GOQDs, SWCNTs, and C) to rice (a staple food for half of the world's population).

Study of the Persistence of the Phytotoxicity Induced by Graphene Oxide Quantum Dots and of the Specific Molecular Mechanisms by Integrating Omics and Regular Analyses.

Although increasing attention has been paid to the nanotoxicity of graphene oxide quantum dots (GOQDs) due to their broad range of applications, the persistence and recoverability associated with GOQDs had been widely ignored. Interestingly, stress-response hormesis for algal growth was observed for Chlorella vulgaris as a single-celled model organism. Few physiological parameters, such as algal density, plasmolysis, and levels of reactive oxygen species, exhibited facile recovery.