Integrating Non-Targeted Mass Spectrometry and Machine Learning for the Classification of Organic and Conventionally Grown Agricultural Products: A Case Study on Tomatoes.

Rising demand for organic agricultural products has made the verification of their authenticity a critical concern. Traditional classification approaches for mass spectrometry using full-scan high-resolution mass spectrometry data often emphasize feature selection, which can lead to information loss and limited model robustness. Here, an artificial intelligence-based classification framework was developed to differentiate between organically and conventionally grown agricultural products, utilizing mass spectrometry data.

Nonradical Photocatalysis for Micropollutant Degradation: Mechanism Insights and Environmental Toxicity Evaluation.

The tunable structure of covalent organic frameworks (COFs) and their sensitivity to subtle structural modifications make them highly versatile for addressing water contamination challenges in non-homogeneous phase catalysis. However, integrating solid-liquid phase interactions and enhancing the overall efficiency of non-homogeneous catalysis remain substantial challenges. In this study, we synthesized a COF material, TPCOF-2, with an optimized pore size and strong in-plane conjugation.

Nontarget Screening Analysis Combined with Computational Toxicology: A Promising Solution for Identification and Risk Assessment of Environmental Pollutants in the Big Data Era.

Synthetic chemicals are intensively utilized in modern societies, and their mixtures pose significant health and ecological threats. Nontarget screening (NTS) analysis allows for the simultaneous chemical identification and quantitative reporting of tens of thousands of chemicals in complex environmental matrices, whereas the computational toxicology (CT) serves as another high-throughput means of rapidly and comprehensively screening chemicals for toxicity. To date, there has been a lack of guidance on how to combine NTS experiments and CT for the risk assessment of chemical mixtures and the prioritization of pollutants.

Interpretable machine-learning enhanced parametrization methodology for Pluronics-water mixtures in DPD simulations.

Dissipative particle dynamics (DPD) is an incredibly powerful tool for simulating the behavior of structured fluids. However, identifying the appropriate model parameters to accurately replicate physical properties remains a challenge. This study showcases the benefits of integrating machine learning techniques into the top-down parameterization of Pluronic systems.

Nanoscale phonon dynamics in self-assembled nanoparticle lattices.

Geometry and topology endow mechanical frames with unusual properties from shape morphing to phonon wave manipulation, enabling emerging technologies. Despite important advances in macroscopic frames, the realization and phonon imaging of nanoscale mechanical metamaterials has remained challenging. Here we extend the principle of topologically engineered mechanical frames to self-assembled nanoparticle lattices, resolving phonon dynamics using liquid-phase transmission electron microscopy.

Combining a Pd Cluster and a Built-in Electric Field as a Biomimic for Stable C-Cl Bond Polarization.

Adopting the essence of enzyme catalysis, the strong binding of substrates into the active site pocket for their selective activation through multiple noncovalent interactions in the reactive site design can effectively enhance the electrocatalysis process. However, mimicking the enzyme catalytic process, particularly the introduction of reactant activation mechanisms, remains a significant challenge. Herein, we present a Pd cluster inside the FeN-FeO-based built-in electric field (BEF), denoted as Pd/FeN-FeO, to serve as an enzyme mimic to activate stable C-Cl bonds.

Development of an Interpretable Machine Learning Model for Neurotoxicity Prediction of Environmentally Related Compounds.

The rising prevalence of nervous system disorders has become a significant global health challenge, with environmental pollutants identified as key contributors. However, the large number of environmental related compounds, combined with the low efficiency of traditional methods, has resulted in substantial gaps in neurotoxicity data. In this study, we developed a robust and interpretable neurotoxicity prediction model using a high-quality data set.

From Nuclear Receptors to GPCRs: a Deep Transfer Learning Approach for Enhanced Environmental Estrogen Recognition.

Environmental estrogens (EEs), as typical endocrine-disrupting chemicals (EDCs), can bind to classic estrogen receptors (ERs) to induce genomic effects, as well as to G protein-coupled estrogen receptor (GPER) located on the membrane, thereby inducing downstream nongenomic effects rapidly. However, due to the relatively scarce ligand data, receptor-based or ligand-based screening models for GPER are challenging. Inspired by functional similarity between GPER and ER, this study constructs a deep transfer learning model named GPNET to predict potential GPER-binding ligands by using three-dimensional (3D) molecular surface electrostatic potential point clouds (SepPC) as input.

From High Resolution Tandem Mass Spectrometry to Pollutant Toxicity AI-Based Prediction: A Case Study of 7 Endocrine Disruptors Endpoints.

Based on high-resolution mass spectrometry (HRMS), nontarget analysis (NTA) can rapidly identify and characterize numerous hazardous substances in complex environmental samples. However, the intricate identification process often results in the underutilization of many mass spectrometry features. Even when chemical structures are identified, their toxicological effects and health outcomes may remain unknown.

Stability and fate of hollow mesoporous silica nanoparticles in aqueous environment: Effects of pH and electrolyte.

Hollow mesoporous silica nanoparticles (HMSNs) and their carboxyl-functionalized counterparts (C-HMSNs) are promising materials for environmental applications, but their stability in aquatic environments remains poorly understood. This study investigated the aggregation behavior of HMSNs and C-HMSNs under varying pH and electrolyte conditions. Experimental results showed that HMSNs had critical aggragation concentrations (CCC) of 35 mM in NaCl and 2 mM in CaCl, while C-HMSNs exhibited higher CCCs of 52 mM and 3 mM, respectively, indicating greater stability.

Theoretical Investigation of Cytochrome P450 Enzyme-Mediated Biotransformation Mechanism of BHPF: Unveiling the Metabolic Safety Aspects of an Alternative to BPA.

Fluorene-9-bisphenol (BHPF), emerging as an alternative to bisphenol A (BPA), is extensively utilized in industry and consumer goods. BHPF exhibits antiestrogenic effects and potential reproductive toxicity. Similar to BPA, BHPF can closely access the active site of the cytochrome P450 (CYP450) enzyme, interact with the Fe=O moiety, and potentially initiate metabolic reactions.

ChemNTP: Advanced Prediction of Neurotoxicity Targets for Environmental Chemicals Using a Siamese Neural Network.

Environmental chemicals can enter the human body through various exposure pathways, potentially leading to neurotoxic effects that pose significant health risks. Many such chemicals have been identified as neurotoxic, but the molecular mechanisms underlying their toxicity, including specific binding targets, remain unclear. To address this, we developed ChemNTP, a predictive model for identifying neurotoxicity targets of environmental chemicals.

Improving the Chemical Utilization Efficiency of Pd Hydrodechlorination Catalysts through Hydrogen-Spillover Empowered Synergy between Pd and TiNiN Support.

The sustainable and affordable environmental application of Pd catalysis needs further improvement of Pd mass activity. Besides the well-recognized importance of physical utilization efficiency─the ratio of surface atoms forming reactant-accessible reactive sites─a lesser-known fact is that the congestion of these reactive sites, which we term as the chemical utilization efficiency, also influences the mass activity. Herein, by leveraging the 100% physical utilization efficiency of a fully exposed Pd cluster (Pd) and the hydrogenation activity of TiNiN, we developed Pd/TiNiN as a high physical and chemical utilization efficiency catalyst.

Biotransformation of Tetrabromobisphenol A and Its Analogs by Selected Gut Bacteria Strains: Implications for Human Health.

Knowledge of the biotransformation of tetrabromobisphenol A (TBBPA) and its related contaminants by human gut microbiota (GM) remains unexplored. Here, TBBPA and its four analogs were incubated with mixed GM strains, and nine rhamnosylated or debrominated transformation products (TPs) were discovered. Remarkably, rhamnosylation was identified as a common and unique microbial transformation pathway for these contaminants, and six of the seven rhamnosylated TPs were reported for the first time.

New Mechanism for the Apoptosis of Human Neuroblastoma Cells by the Interaction between Fluorene-9-Bisphenol and the G Protein-Coupled Estrogen Receptor 1.

Fluorene-9-bisphenol (BHPF) is an emerging contaminant. Presently, there is no report on its interaction with G protein-coupled estrogen receptor 1 (GPER). By using an integrated toxicity research scenario that combined theoretical study with experimental methods, BHPF was found to inhibit the GPER-mediated effect via direct receptor binding.

Unravelling bioaccumulation, depletion and metabolism of organophosphate triesters in laying hens: Insight of in vivo biotransformation assisted by diester metabolites.

Organophosphate triesters (tri-OPEs) threaten human health through dietary exposure, but little is known about their feed-to-food transfer and in vivo behavior in farm animals. Herein 135 laying hens were fed with contaminated feed (control group, low-level group and high-level group) to elucidate the bioaccumulation, distribution, and metabolism of the six most commonly reported tri-OPEs. The storage (breast muscle), metabolism and mobilization (liver and blood) and non-invasive (feather) tissues were collected.

Multiscale computational simulation of pollutant behavior at water interfaces.

The investigation of pollutant behavior at water interfaces is critical to understand pollution in aquatic systems. Computational methods allow us to overcome the limitations of experimental analysis, delivering valuable insights into the chemical mechanisms and structural characteristics of pollutant behavior at interfaces across a range of scales, from microscopic to mesoscopic. Quantum mechanics, all-atom molecular dynamics simulations, coarse-grained molecular dynamics simulations, and dissipative particle dynamics simulations represent diverse molecular interaction calculation methods that can effectively model pollutant behavior at environmental interfaces from atomic to mesoscopic scales.

Dynamic Behavior and Interaction Mechanism of Soil Organic Matter in Water Systems: A Coarse-Grained Molecular Dynamics Study.

Investigating soil organic matter's (SOM) microscale assembly and functionality is challenging due to its complexity. This study constructs comparatively realistic SOM models, including diverse components such as Leonardite humic acid (LHA), lipids, peptides, carbohydrates, and lignin, to unveil their spontaneous self-assembly behavior at the mesoscopic scale through microsecond coarse-grained molecular dynamics simulations. We discovered an ordered SOM aggregation creating a layered phase from its hydrophobic core to the aqueous phase, resulting in an increasing O/C ratio and declining structural amphiphilicity.

Deciphering exogenous chemical carcinogenicity through interpretable deep learning: A novel approach for evaluating atmospheric pollutant hazards.

Cancer remains a significant global health concern, with millions of deaths attributed to it annually. Environmental pollutants play a pivotal role in cancer etiology and contribute to the growing prevalence of this disease. The carcinogenic assessment of these pollutants is crucial for chemical health evaluation and environmental risk assessments.

CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals.

Endocrine-disrupting chemicals (EDCs) pose significant environmental and health risks due to their potential to interfere with nuclear receptors (NRs), key regulators of physiological processes. Despite the evident risks, the majority of existing research narrows its focus on the interaction between compounds and the individual NR target, neglecting a comprehensive assessment across the entire NR family. In response, this study assembled a comprehensive human NR dataset, capturing 49,244 interactions between 35,467 unique compounds and 42 NRs.

Machine learning strategy on activation energy of environmental heterogeneous reactions and its application to atmospheric formation of typical montmorillonite-bound phenoxy radicals.

Heterogeneous transformation of organic pollutants into more toxic chemicals poses substantial health risks to humans. Activation energy is an important indicator that help us to understand transformation efficacy of environmental interfacial reactions. However, the determination of activation energies for large numbers of pollutants using either the experimental or high-accuracy theoretical methods is expensive and time-consuming.

Exogenous Chemicals Impact Virus Receptor Gene Transcription: Insights from Deep Learning.

Despite the fact that coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been disrupting human life and health worldwide since the outbreak in late 2019, the impact of exogenous substance exposure on the viral infection remains unclear. It is well-known that, during viral infection, organism receptors play a significant role in mediating the entry of viruses to enter host cells. A major receptor of SARS-CoV-2 is the angiotensin-converting enzyme 2 (ACE2).

Generation Mechanism of Perfluorohexanesulfonic Acid from Polyfluoroalkyl Sulfonamide Derivatives During Chloramination in Drinking Water.

Per- and polyfluoroalkyl substances (PFASs), including perfluorohexanesulfonic acid (PFHxS), as emerging persistent organic pollutants widely detected in drinking water, have drawn increasing concern. The PFHxS contamination of drinking water always results from direct and indirect sources, especially the secondary generations through environmental transformations of precursors. However, the mechanism of the transformation of precursors to PFHXS during the drinking water treatment processes remains unclear.

Insight into the formation and biological effects of natural organic matter corona on silver nanoparticles in water environment using biased cyclical electrical field-flow fractionation.

Natural organic matter (NOM) readily interacts with nanoparticles, leading to the formation of NOM corona structures on their surface. NOM corona formation is closely related to the surface coatings and bioavailability of nanoparticles. However, the mechanism underlying NOM corona formation on silver nanoparticles (AgNPs) remains largely unknown due to the lack of effective analytical methods for identifying the changes in the AgNP surface.