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 (CeO2 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, CeO2 NPs decreased the grain yield, grain protein content, and amino acid content by 16.2, 23.9, and 10.4%, respectively, under HW conditions. Individually, neither the CeO2 NPs nor HWs alone negatively affected rice production or triggered stomatal closure. However, under HW conditions, CeO2 NPs decreased the stomatal conductance and net photosynthetic rate by 67.6 and 33.5%, respectively. Moreover, stomatal closure in the presence of HWs and CeO2 NPs triggered reactive oxygen species (ROS) accumulation (increased by 32.3-57.1%), resulting in chloroplast distortion and reduced photosystem II activity (decreased by 9.4-36.4%). Metabolic, transcriptomic, and quantitative real-time polymerase chain reaction (qRT-PCR) analyses revealed that, under HW conditions, CeO2 NPs activated a stomatal closure pathway mediated by abscisic acid (ABA) and ROS by regulating gene expression (PP2C, NCED4, HPCA1, and RBOHD were upregulated, while CYP707A and ALMT9 were downregulated) and metabolite levels (the content of γ-aminobutyric acid (GABA) increased while that of gallic acid decreased). These findings elucidate the mechanism underlying the yield and nutritional losses induced by stomatal closure in the presence of CeO2 NPs and HWs and thus highlight the potential threat posed by CeO2 NPs to rice production during HWs.

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., population density and artificial surfaces) rather than geography determines DOM composition and properties in lakes. The structure of the bacterial community is the dominant factor determining GHG emissions from lakes. Urbanization increases DOM bioavailability and decreases the DOM degradation index (Ideg), increasing the potential for DOM conversion into inorganic carbon in lakes. The traditional fossil fuel-based path (SSP5) scenario increases carbon emission potential. Land conversion from water bodies into artificial surfaces causes organic carbon burial. It is predicted that increased urbanization will accelerate the carbon cycle in lake ecosystems in the future, which deserves attention in climate models and in the management of global warming.

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 (n = 100). The bacterial community was mainly constrained by land-use intensity, whereas the fungal community was mainly controlled by DOM chemical composition (e.g., terrestrial DOM with high photoreactivity). Anthropogenic stressors (e.g., land-use intensity, gross regional domestic product, and total population) were the main factors affecting chromophoric DOM (CDOM). DOM biodegradability exhibited a positive correlation with CDOM and contributed to microbial activity for DOM transformation. Variations in CO2 and CH4 emissions were governed by the biodegradation or photomineralization of dissolved organic carbon derived from autotrophic DOM and were indirectly affected by land use via changes in DOM properties and water chemistry. Because the GHG emissions of rivers offset some of the climatic benefits of terrestrial carbon (or ocean) sinks, intensified urban land use inevitably alters carbon cycling and changes the regional microclimate.

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 (R2 higher than 0.8 for 13 random forest models) the response and uptake/transport of various NPs by plants using a machine learning approach. Multiway feature importance analysis quantitatively shows that plant responses are driven by the total NP exposure dose and duration and plant age at exposure, as well as the NP size and zeta potential. Feature interaction and covariance analysis further improve the interpretability of the model and reveal hidden interaction factors (e.g., NP size and zeta potential). Integration of the model, laboratory, and field data suggests that Fe2O3 NP application may inhibit bean growth in Europe due to low night temperatures. In contrast, the risks of oxidative stress are low in Africa because of high night temperatures. According to the prediction, Africa is a suitable area for nanoenabled agriculture. The regional differences and temperature changes make nanoenabled agriculture complicated. In the future, the temperature increase may reduce the oxidative stress in African bean and European maize induced by NPs. This study projects the development potential of nanoenabled agriculture using machine learning, although many more field studies are needed to address the differences at the country and continental scales.

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. Aquatic microbes and natural minerals further regulate carbon fate, but the processes and mechanisms remain largely unknown. Herein, the interaction between Escherichia coli and pyrite and its influence on the fate of carbon in water were investigated at the microscopic scale and molecular level. The results showed that saccharides and phenolic compounds in microbial extracellular polymeric substances helped remove pyrite surface oxides via electron transfer. After the removal of surface oxides on pyrite, the photochemical properties under visible-light irradiation were significantly decreased, such as reactive oxygen species and electron transfer capacity. Unlike the well-accepted theory of minerals protecting organic matter in the soil, the organic matter adsorbed on minerals preferred degradation due to the enhanced photochemical reactions in water. In contrast, the minerals transformed by microbes suppressed the decomposition of organic matter due to the passivation of the chemical structure and activity. These results highlight the significance of mineral chemical activity on organic matter regulated by microbes and provide insights into organic matter conversion in water.

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

Nitrate contamination from human activities (e.g., 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. Under irradiation, marine colloids exhibit semiconductor properties (e.g., the separation of electron-hole pairs) and can trigger the generation of free electrons and singlet oxygen. They also exhibit electron acceptor and donor properties, with the former being stronger than the latter, reacting with polysaccharides in extracellular polymeric substances (EPSs) under high nitrogen stress, enhancing the elasticity and permeability of cells, and promoting nitrogen assimilation and electron transfer to marine diatom EPSs. Electron transfer promotes extracellular-to-intracellular nitrate transport by upregulating membrane nitrate transporters and nitrate reductase. The upregulation of anion transport genes and unsaturated fatty acids contributes to nitrogen assimilation. We estimate that colloids may increase the nitrate uptake efficiency of marine diatoms by 10.5-82.2%. These findings reveal a mechanism by which diatoms adapt to nitrate contamination and indicate a low-cost strategy to control marine pollution.

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. Multiple-factor and factor interaction network analysis based on FEML can be used to visualize the restrictive relationships among multiple factors (e.g., fertilizer application, land use, and changing global climate and natural environments), which are difficult to explore based on limited experimental data and traditional machine learning methods. The FEML approach predicted that areas of bacterial hotspots in South America and Africa will expand by approximately 27% and 83%, respectively, in scenario RCP8.5 in 2100. In contrast, the areas of fungal hotspots in Asia and North America will decline by approximately 34% and 62%, respectively, under RCP8.5. The unbalanced ratios of bacteria to fungi affect the soil ecosystem, and bacterial-dominated communities contribute to the reduction of easily decomposing nutrients, the growth of the bacterivore community and a high proportion of microaggregates in the soil. Therefore, mitigating climate change is critical to reduce the remarkable imbalance between soil bacteria and fungi and predict risks to soil microbial assemblages based on multiple factors.

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. However, there are some urgent issues that should be resolved. For example, lack of MP databases and incomparable literatures causing the current MP data cannot fully support big data research. Therefore, it is imperative to formulate a set of standard and universal MP collection and testing protocols. For machine learning, predictions of large-scale MP distribution and the corresponding environmental risks remain lacking. To accelerate studies of MPs in the future, the methods and theories achieved for other particle pollutants, such as nanomaterials and aerosols, can be referenced. Beyond predication alone, the improvement of causality and interpretability of machine learning deserves attention in the studies of MP risks. Overall, this perspective paper provides insights for the development of machine learning methods in research on the environmental risks of MPs.

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. MFAB-ML identified and predicted the importance, interaction networks and superposition effects of multiple features, including 34 characteristic variables (e.g., MP contamination and climatic and geographic variables), from 1354 samples distributed globally. MFAB-ML analysis achieved realistic and significant results, in some cases even opposite to those obtained using a single or a few features, revealing the importance of considering complicated scenarios. We found that the microbial diversity in East Asian seas will continually decrease due to the superposition effects of MPs with ocean warming; for example, the Chao1 index will decrease by 10.32% by 2065. The present work provides a powerful approach to identify and predict the multifeature superposition effects of pollutants on realistic environments in complicated climatic and geographic scenarios, overcoming the bias from general studies.

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 >0.9 and half of the test sets >0.75. This framework overcomes the feature importance bias brought by small datasets through a multiway importance analysis. TBRFA also builds feature interaction networks, boosts model interpretability, and reveals hidden interactional factors (e.g., various NP properties and exposure conditions). TBRFA provides guidance for the design and application of ideal NPs and discovers the feature interaction networks that contribute to complex systems with small-size data in various fields.

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). The lack of databases on interactions between the immune system and NMs affects the discovery of new NMs for immunotherapy. Complement activation and inhibition by NMs have been widely studied, but the general rules remain unclear. Biomimetic nanocoating to promote the clearance of NMs by the immune system is an alternative strategy for the immune response mediation of the biological corona. Immune response predictions based on NM properties can facilitate the design of NMs for immunotherapy, and artificial intelligences deserve much attention in the field. This review addresses the knowledge gaps regarding immune response and immunotherapy in relation to NMs, effective immunotherapy and material design without adverse immune responses.

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. Screening nine typical properties of ENPs identified type and size as the top features determining the effects on metabolic pathways. Similarity network analysis and decision tree models overcame the highly heterogeneous data sources to visualize and judge the occurrence of metabolic pathways depending on the sorting priority features. The model accuracy was verified by animal experiments and reached 75%-100%, even for the prediction of ENPs outside of databases. The models also predicted metabolic pathway-related histopathology. This work provides an approach for the quick assessment of environmental health risks induced by known and unknown 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 R2 (less than 0.40). Here, the performance with R2 over 0.75 in the prediction of the protein corona was achieved by integrating a machine learning model and meta-analysis. NPs without modification and surface modification were identified as the two most important factors determining protein corona formation. According to experimental verification, the functional protein compositions (e.g., immune proteins, complement proteins, and apolipoproteins) in complex coronas were precisely predicted with good R2 (most over 0.80). Moreover, the method successfully predicted the cellular recognition (e.g., cellular uptake by macrophages and cytokine release) mediated by functional corona proteins. This workflow provides a method to accurately and quantitatively predict the functional composition of the protein corona that determines cellular recognition and nanotoxicity to guide the synthesis and applications of a wide range of NPs by overcoming limitations and uncertainty.

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, SiO2NPs, TiO2NPs, GO, GOQDs, SWCNTs, and C60) to rice (a staple food for half of the world's population). Downregulation of carbohydrate metabolism and upregulation of amino acid metabolism were the two dominant metabolic effects induced by all tested nanoparticles. Partial least-squares regression analysis indicated that a zerovalent metal and high specific surface area positively contributed to the downregulation of carbohydrate metabolism, indicating strong abiotic stress. In contrast, the carbon type, the presence of a spherical or sheet shape, and the absence of oxygen functional groups in the nanoparticles positively contributed to the upregulation of amino acid metabolism, indicating adaptation to abiotic stress. Moreover, network relationships among five properties of nanoparticles were established for these metabolic pathways. The results of the present study will aid in the understanding and prediction of environmental risks and in the design of environmentally friendly nanoparticles.

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. In contrast, the effects on chlorophyll a levels, permeability, and starch grain accumulation exhibited persistent toxicity. In the exposure stage, the downregulation of genes related to unsaturated fatty acid biosynthesis, carotenoid biosynthesis, phenylpropanoid biosynthesis, and binding contributed to toxic effects on photosynthesis. In the recovery stage, downregulation of genes related to the cis-Golgi network, photosystem I, photosynthetic membrane, and thylakoid was linked to the persistence of toxic effects on photosynthesis. The upregulated galactose metabolism and downregulated aminoacyl-tRNA biosynthesis also indicated toxicity persistence in the recovery stage. The downregulation and upregulation of phenylalanine metabolism in the exposure and recovery stages, respectively, reflected the tolerance of the algae to GOQDs. The present study highlights the importance of studying nanotoxicity by elucidation of stress and recovery patterns with metabolomics and transcriptomics.